0:00:19 The following is a conversation with Bernie Sanders, Senator from Vermont and two time presidential candidate, both times as the underdog who against the long odds captivated the support and excitement of millions of people both on the left and the right.
0:00:39 And now, a quick few second mention of each sponsor. Check them out in the description. It’s the best way to support this podcast. We got Aidsley for Naps, Saley for Esim, Magic, Ground News for nonpartisan presentation of the truth, AG1 for vitamins and element for electrolytes.
0:00:47 Choose wisely my friends. Also, if you want to get in touch with me for a multitude of reasons, go to lexfreedman.com/contact.
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0:01:17 This episode is brought to you by Aidsleep and it’s pod for ultra. I have some travel coming up and one of, if not the biggest thing I will miss is not sleeping on a bed that has the ability to cool itself on both sides.
0:01:35 It really is a magical reminder of what technology has been able to achieve since the industrial revolution. Yes, there’s endless ways in which suffering has been alleviated across the world and it continues to and the quality of life continues to improve.
0:02:04 But this little inkling, this little reminder is a beautiful one to be able to lay down on a cold surface using an app control the temperature of that surface, put on a warm blanket and I am forever lost in the creation of the millions of engineers who came before us and the millions of engineers who will come after who are doing everything they can to build a better world.
0:02:16 And that’s what I dream about when I’m taking a nap is that better world. Go to Aidsleep.com/Lex and use code Lex to get $350 off the pod for ultra.
0:02:28 This episode is also brought to you by Saley, a brand new eSIM service app that offers several affordable data plans in over 150 countries.
0:02:48 That for me has been the biggest pain when traveling is to make sure when I get to the airport when I leave the airport, I’m able to communicate with the world or just communicate with a map that can make sure I don’t get completely lost in this new country where I don’t speak the language and all the other complexities of travel.
0:03:03 Everything is made easier when you have an internet connection. And again, just a reminder how lucky we are in the 21st century, billions of people are able to be seamlessly connected with each other.
0:03:20 So anyway, I’ve had countless and countless experiences where I have a terrible internet and the SIM situation is complicated. Do I buy a new SIM at the airport? It is just a mess. And so Saley takes on this problem, make sure it’s affordable, but also make sure it’s super easy.
0:03:35 If you’re traveling and you want to make sure you stay connected, try them out. Go to Saley.com/lex and get 15% off any eSIM data plan. That’s Saley.com/lex to get 15% off.
0:03:52 This episode is also brought to you by Ground News, a nonpartisan news aggregator that I use to compare media coverage from across the political spectrum in these days and weeks, especially a site like this is just priceless.
0:04:18 Reporting on the different things going on in the world, whether it’s local, whether it’s national, whether it’s international, it’s really nice to be able to get multiple perspectives and for that perspective to be presented with a clear indication of the best kind of estimate from which side of the political spectrum this reporting is coming from.
0:04:34 Anyway, Ground News is doing an amazing thing that I always thought should exist and I’m glad they’re building it, basically creating a tech solution to the problem of the division of the completely biased subjective reporting,
0:04:48 presenting itself as if it’s objective. So breaking through all of that and they’re doing a lot of really nice other things like the blind spot feed shows discrepancies in media coverage on the left and the right. There’s just a lot of really nice features.
0:05:05 Go check them out. It’s groundnews.com/lex and you’ll get 40% off the Ground News Vantage plan, giving you access to all of their features. Trust me, it’s worth it. That’s Ground G-R-O-U-N-D News.com/lex.
0:05:18 This episode is also brought to you by AG1 and all in one daily drink to support better health and peak performance. I drink it twice a day, usually after training.
0:05:33 That training could be a long run or jiu-jitsu, grappling, which I really love and I’ve loved the fact that I’ve been injury free, not even really minor injuries for quite a long time.
0:05:47 I’ve been able to knock on wood. I’ve been able to train really hard and enjoy doing it. And of course, go through the whole roller coaster of training, which is when you go against people that are really good.
0:06:00 You sometimes get humbled and that humbling could be emotionally challenging. But then from that you grow and it’s the beautiful journey of the sport, especially as you get older.
0:06:09 Somebody has better technique than you. Somebody has better timing than you on that particular day. And together you figure out what works and what doesn’t.
0:06:32 And through that process of humbling, you chip away at the ego that most human beings have. I think it just makes you a better person when you realize that you’re somewhere in the food chain, nowhere close to the top, and you’re mortal, and you kind of suck at most things.
0:06:44 And the only way to get better is by working really hard. All of those truths hit you really hard when you’re doing a combat sport, because you can’t pretend you didn’t just get your ass kicked.
0:06:56 So it’s a beautiful process of humbling. And when you should try AG1, they’ll give you one month supply of fish oil when you sign up at drinkag1.com/lex.
0:07:04 This episode is also brought to you by Element, my daily zero sugar and delicious electrolyte mix.
0:07:15 Whenever you see me drinking something that looks like water, during the podcast, it’s almost always water mixed with watermelon salt flavored element.
0:07:29 I take one of them Powerade bottles that are 28 ounces. I fill it with water, shake up one packet of element, put in the fridge, and after like 30 minutes it’s ready, and it’s delicious.
0:07:39 I’m surprised how many problems in the mind in the body are solved by making sure you get enough water, getting enough sodium, potassium, magnesium.
0:07:52 To me, probably sodium is the most important, then magnesium, then potassium, but electrolytes, it’s crazy how I’m just feeling tired or having a headache or any of those kinds of feelings can go away.
0:08:09 If you, as the meme goes, just drink water. If you’re thirsty, just drink water. It’s true, like water and electrolytes and a nap and a shower, those four things can like transform your mind entirely in a matter of minutes.
0:08:25 It’s crazy. Humans are such fragile creatures, at once resilient and at once fragile. Anyway, get a sample pack for free with any purchase. Try it at drinkelement.com/lex.
0:08:35 This is the Lex Freeman podcast. To support it, please check out our sponsors in the description. And now, dear friends, here’s Bernie Sanders.
0:08:50 [Music]
0:08:53 Growing up, did you ever think you’d be a politician?
0:08:54 Nope.
0:08:56 A million years.
0:09:01 Yeah, I know that you hate talking about yourself, which is rare for a politician, I would say.
0:09:06 What’s your philosophy behind that? You like talking about the issues you’re talking about.
0:09:20 Yeah, I do. I mean, you know, everybody talks about themselves. It’s not about me. You know, nice guy, not a nice guy. What’s the, what, you know, politics should be about is the issues facing the people of our country, the people of the world and how we’re going to address it. That’s what it should be.
0:09:41 That said, there’s interesting aspects to your life story. For example, in 1963, you were very active in the Civil Rights Movement, got arrested even for protesting segregation in Chicago, and you attended the famous March on Washington, where MLK gave his “I Have a Dream” speech. What was that like?
0:09:56 It’s extraordinary. I took a bus ride down with fellow students from the University of Chicago, and it was a zillion people there. I’m not sure if it was the first time I’d ever been in Washington in my life, but it was, you know, it was a very impressive moment.
0:10:10 And what he was talking about with people very often forget about that. It was not only racial justice, it was jobs. Jobs and justice, that was the name of that rally. And so, it’s something I’ve never forgotten.
0:10:17 What influence did he have on you? What would you learn about the way he enacted change in the world?
0:10:43 King was a very impressive guy. More impressive, I think, than people think that he was. And what he did is he created his movement from the bottom on up. In some ways, he developed real organization, grassroots organization, which put pressure on communities and officials to end segregation, to open up voting patterns.
0:10:54 And I think what has to also be remembered about King, which is really quite extraordinary, is, you know, he won the Nobel Peace Prize and never said, “Oh, you’re great, you’re wonderful.”
0:11:07 But then, to the end of his life, he took on Lyndon Johnson on the war in Vietnam. And as soon as he did that, suddenly the editorial pages throughout America established from papers, no longer thought he was so great.
0:11:23 In fact, the message sent out, “You’re black, deal with civil rights, don’t worry about foreign policy, we’ll take care of that.” But he said, you know, “If I talk about peace and nonviolence, I can’t sit back and allow what’s going on in Vietnam to continue without speaking out.”
0:11:41 Incredible courage to do that. And, by the way, when he was assassinated at a fighting for the rights of AFSCME workers, garbage, guys with a little bit of garbage, who were treated terribly, low wages, bad working conditions, he went out to support their right to form a union.
0:11:43 That’s when he got killed.
0:12:00 So, on the war front, one of the things that people don’t often talk about, your work in politics, you gave what I think is a truly brave speech on the Iraq war in 2002, I believe.
0:12:12 You voted no on the Iraq resolution, you voted no on the Patriot Act, and you basically predicted very accurately what would happen if we go into Iraq.
0:12:19 What was your thinking at the time, behind those speeches, behind voting no on the Patriot Act on the Iraq resolution?
0:12:30 It maybe, ironically, came out of maybe the war in Vietnam and the ease and lies that people told. We went into Vietnam under a lie.
0:12:38 We lost close to 60,000 Americans, millions of people in Vietnam. Cambodia died as a result of that.
0:12:50 So, you think twice about it. And then the war in Iraq, you had people like Dick Cheney and others telling us, “Oh, they have nuclear weapons and all that stuff. It’s the only way we can resolve the issue.”
0:12:57 I didn’t believe it. I didn’t agree with it. It turns out historically, I was right.
0:13:04 What’s the way to fight this thing that Martin Luther King tried to fight, which is the military industrial complex?
0:13:17 It’s huge. I mean, it gets to the broader issue where we are as a nation. And what I almost uniquely in Congress talk about is the fact that we are moving lex to an oligarchic form of society.
0:13:27 Not a lot of people are familiar with that term, but what it means, we talk about oligarchy in Russia. “Oh, I’m putting this around by the oligarchs.” Well, guess what? What do you think is happening in the United States?
0:13:44 So, what you have right now is an economy with more concentration of ownership than we’ve ever had. That means whether it’s agriculture, transportation, healthcare, whatever it may be, fewer and fewer massively large corporations control what’s produced and the prices we pay.
0:13:56 And then you look at our political system, and we don’t talk about it. What is the reality of the political system today? And that is that billionaires are spending huge amounts of money to buy this election.
0:14:06 In Trump’s campaign, he got three multi-billionaires spending over $200 million. Three people. Democrats have their billionaires. It’s not quite as concentrated.
0:14:17 But at the end of the day, billionaires play an enormous role in terms of electing politicians and in Washington in determining what legislation gets seen and not seen.
0:14:21 But it’s not just single billionaires. It’s companies with lobbyists.
0:14:29 You got it. Let me give you one example. Lobbyists. We pay in the United States by far the highest prices in the world for prescription drugs.
0:14:38 This is an issue I’ve been working hard on with some success. Take a wild and crazy guess. How many lobbyists are there from the drug companies in Washington, DC?
0:14:40 Over a thousand.
0:14:56 Over a thousand? There are 100 members of the Senate, 435 members of the House, 535 members of Congress. There are 1,800 well-paid lobbyists representing the drug companies, including former leaders of the Republican and Democratic Party.
0:15:04 That is why, one of the reasons why we pay the highest prices in the world for prescription drugs, military industrial complex. You got a revolving door.
0:15:11 People go from the military into the general dynamics, into Lockheed Martin and the other large companies.
0:15:19 And what we see there is an institution in the Pentagon. We spend a trillion dollars a year on the Pentagon.
0:15:25 It is the only federal agency that is not able to submit to an independent audit.
0:15:32 So if you think there’s not massive fraud and waste and cost overruns in the Pentagon, you would be sort of mistaken.
0:15:40 Do you think most politicians are corrupt in accepting the money or is the system corrupt or is it a bit of both?
0:15:51 If the corrupt means that, hey, here’s $10,000 vote this way, it doesn’t work like that. Very, very rare. Occasionally, very, very rare. That’s corruption.
0:16:03 What happens is that if you are in a campaign and right now the amount of money that people have to raise, you’re running for a Senate in Ohio, you’re talking about $50, $60 million.
0:16:06 Where the hell are you going to get that money? It’s not going to be $10 donations.
0:16:13 You’re going to be surrounding yourself with people who have the money. You’re going to go $5,000 a plate, dinners, et cetera.
0:16:21 So you surround yourself with those people who say, oh, these are my problems. This is what I need. I need a tax break for billionaires, blah, blah, blah, blah.
0:16:29 So you become, you live in that world. They are your financial support. They are, in a sense, your political base.
0:16:43 So you’re very cognizant of what you do in terms of not upsetting them. So it’s not corruption in the sense of people taking envelopes with huge amounts of money to vote a certain way.
0:16:51 That very, very rarely, if ever happens. It is the power of big money to make politicians dependent on those folks.
0:17:02 And that’s why, you know, when I ran for president, what I probably may be most proud of is the fact that we received millions and millions of campaign contributions averaging $27 a piece, I think, in 2016.
0:17:06 Have companies, lobbyists ever tried to buy you, tried to influence you?
0:17:15 We don’t welcome them into our office. I do deal with these guys, but it’s usually on a confrontational tone, though, so they don’t come into my office very often telling me their problems.
0:17:18 So how do we fix the system? How do we get money out of politics?
0:17:26 This is not, you know, like many other issues, we don’t have to reinvent the wheel here. It exists in other countries.
0:17:37 If you go to, you know, every country has their own election system, but nobody has a system where billionaires can spend unlimited sums of money through super PACs to elect the candidates of their choice.
0:17:48 So first thing you got to do, you know, one of the things, Lex, I found that the more important the issue, the less discussion there is, the less important the issue, the more discussion there is.
0:17:58 A number of years ago, the United States Supreme Court in one of its more pathetic decisions passed the Citizens United decision.
0:18:09 And what Citizens United decision said is you’re a multi-billionaire. You want the freedom. You’re a free person in a free country. You want the freedom to buy the government.
0:18:16 And how terrible it would be to deny you the freedom to spend hundreds of millions of dollars on a campaign to elect the candidates.
0:18:21 And they said, that’s your freedom. And that’s what Citizens United is about.
0:18:27 We’ve got to end that. In my view, we move to public funding of elections. That means you want to run for governing.
0:18:36 You want to run for Senate. Show that you have some support, get, you know, $5 contributions from X number of people to show you, you know, you’re not a flake.
0:18:43 You have some support and government will pay a certain amount more and there will be a limited amount of money that can be spent.
0:18:49 So it’ll be a real, you can run against me and I’m not going to outspend you 10 to one. That’s what we should be moving toward in my view.
0:18:58 How do we make that happen when there’s so much money in the system and the politicians owe to the people who paid for their election?
0:19:05 Does it have to come from the very top, essentially sort of a really strong popular populist president?
0:19:13 You’re right. You raised exactly the question. If I am getting a huge amount of money from billionaires, do you think I’m going to go out and announce?
0:19:18 I think billionaires should not be involved in buying elections. I doubt that very much.
0:19:26 So what you’re going to need, and you tell me if I’m missing something but I pay attention, you don’t hear either of the major candidates talking about that issue, do you?
0:19:32 I think what happens is when an individual politician speaks out about it, they get punished.
0:19:49 But I think this is a popular idea. So if a lot of them speak out, that’s why if it came from the top, if a president was using a very large platform to basically speak out, it provides a safety blanket for the other politicians to get it out of the system.
0:19:51 But there has to be kind of a mass movement.
0:20:01 Yes, it does. I mean, and every place I go, I always speak about the issue and it always, people understand that you’re a Republican, you’re a Democrat, you’re progressive, you’re conservative.
0:20:10 Who really believes that we are democracy when billionaires can spend, you know, tens and tens of millions of dollars to buy elections.
0:20:15 So it is a very popular issue. It’s important. You’re right. We need political leaders to be speaking out on that.
0:20:22 But we need a grassroots movement to say, when somebody is at a town meeting, you’re running for the Senate, you’re running for the House, what’s your view on Citizens United?
0:20:29 Are you prepared to vote to overturn that decision and move to public funding of elections? Extraordinarily important.
0:20:33 So many of your policy proposals are quite radical.
0:20:35 No, they’re not. I beg to differ.
0:20:36 Okay, great.
0:20:37 Go to.
0:20:46 Well, they’re popular. So what I mean is relative to what the way other politicians speak, it’s usually a little bit more moderate.
0:20:56 So from everything you’ve learned from politics, is it better to go sort of radical, maybe you can come up with a different word versus a more moderate, safe, ambiguous kind of policy?
0:21:01 Okay, let’s talk about it. Fair enough. We talked about one issue, very important, money in politics.
0:21:02 Money, yes.
0:21:05 Getting money out of, big money out of politics. Do you think that’s a radical idea?
0:21:18 Well, I mean, yeah, it’s a popular idea. It’s an idea that makes sense. But in order to implement it and actually make it happen, it requires, I mean, to flip the system upside down, right?
0:21:19 In that sense, it’s radical.
0:21:27 In that sense, it’s radical. But if you go to walk down the street here and you say, do you think billionaires should be able to spend as much money as they want to buy politicians?
0:21:31 I would say nine out of 10 people say, that’s crazy. That’s not what Americans are supposed to be about.
0:21:39 So in that sense, it’s certainly not radical. Let’s talk about health care. Go out on the street. Do it. Do a poll. I’ve done the polling.
0:21:46 Is health care a human right? Should every American be able to go to a doctor when they need regardless of their income?
0:21:51 You know what people say? I would say about 85, 90% of the people say, of course.
0:22:03 The idea that health care is a human right available to all exists, Lex, in every major country on earth except the United States.
0:22:16 So you’re here with me in Burlington, Vermont, right? If you got a car, go 50 miles north to Canada. Walk a little Canadian, you know, walk into Canada and ask people, when you go to the hospital, how much does it cost you?
0:22:25 Which kind of bill? What are you talking about? Doesn’t cost us anything. Doesn’t cost us a nickel. That’s the case in virtually every country in Europe.
0:22:41 So the idea that health care should be available to all, that there should be no out-of-pocket expense because it’s a human right, is widespread around the world and very much agreed to in this country.
0:22:54 Bottom line is that because of our corrupt political system, we have a health care system designed not to provide health care to all people to make huge profits for the drug companies and the insurance companies.
0:23:01 And that is what’s happening. And we got to change that system. So I’m a strong advocate and I’ve led the effort on Medicare for All.
0:23:12 Okay, let’s talk about Medicare for All. If you could snap your fingers today and implement the best possible health care system for the United States of America, what would that look like?
0:23:23 Well, we have a pretty good system, not great, but a pretty good system in Medicare. So it’s there for the elderly and Lyndon Johnson passed that in the 1960s, a huge step forward.
0:23:36 It is being chopped away by the private insurance companies through Medicare Advantage. But if you strengthen Medicare and you do away with the kind of deductibles that seniors now have to pay, you do away with other stuff.
0:23:46 And you say, basically, right now, you’re a senior in America. You go to any doctor you want. You know, when you’re in the hospital, Medicare will pay the entire bill.
0:23:52 If you expand Medicare to cover dental, hearing and vision, which it doesn’t now cover, you do all of those things.
0:24:05 And then the next thing you do is say, okay, to be eligible for Medicare, now you have to be 65. First year, we’re going to lower it to 55, then we’ll lower it to 45, then we’ll lower it to 35, then we’ll have everybody in the system.
0:24:16 So I think in a four or five year period, you can strengthen Medicare and have everybody in the system. And when you do that, and this is not just me talking, number of studies have pointed this out.
0:24:26 When you take the profit motive out of it from the insurance companies and the drug companies, you can end up providing quality care to all people and no more than we’re spending right now.
0:24:34 Because right now, we are spending twice as much per person in healthcare as the people of any other nation. Incredibly wasteful system.
0:24:42 So the way to pay for the system is to increase taxes. But you’re saying if you cut that cost and increase the taxes, you’re saying it’s going to cost a lot.
0:24:54 That’s the story. And I’ve gotten my share of 30 second ads attacking me on this. Bernie Sanders wants to raise your taxes on health care in a progressive way.
0:24:56 But right now, do you have health insurance?
0:24:57 Yes.
0:25:04 Okay. Somebody’s paying for your health insurance. It depends if you are working, most people get their health insurance through their jobs.
0:25:13 Okay. So if you’re working for a large company, your employer is paying your health insurance. And by the way, that comes out of your wages. Health care costs in America are very high.
0:25:20 And your employer will tell you, honestly, look, I can’t give you more than a 3% wage increase because I got a 10% increase in your health care costs.
0:25:26 You want that? Or if you’re a union negotiating, you know what they will say, hey, you want decent wages, we’re going to have to cut back on your health care.
0:25:30 That’s what every union has to deal with, you know, every negotiating session.
0:25:40 So we’re paying for it through employers out of pocket. We pay through it through Medicare, Medicaid, Veterans Administration, etc.
0:25:54 What I am proposing is really not radical. It’s what exists in Canada and other countries. It is publicly funded, like the police departments and like libraries are like public education is publicly funded in a progressive way.
0:26:12 So right now, rather than paying out of your own pocket, if you are a family, let’s just say you’re self-employed right now and you are, you know, you want to have a couple of kids and a wife, it could cost you $15,000, $20,000 a year in insurance costs.
0:26:22 Well, that’s all eliminated. Will you have to pay more in taxes? Of course you will. Maybe it depends on your income level, but it could be that you’d be paying $12,000 more in taxes,
0:26:34 but not $20,000 more in premiums, copayments and deductibles. You save money. So it’s paying taxes rather than paying money to the insurance company. You got a better deal through the tax system.
0:26:46 So the most painful thing in today’s system is these surprise bills, the No. 1 cause of bankruptcy and the psychological pain that comes from that, just worrying, stressed in debt.
0:26:55 You got it. And just basically afraid constantly of getting sick because you don’t know if insurance is going to cover it and if you’re not insured, you don’t know how much it’s going to cost.
0:27:05 So you’re not going to go to the hospital, even if there’s something wrong with you, if there’s pain and all that. So you just live in a state of fear, psychological fear. That’s the No. 1 problem. It’s just not just financial, psychological.
0:27:19 Look, and I think you said it very well. I’m chairman of the committee that deals with this stuff. So I talk to a lot of doctors. And doctors in Vermont and all over this country tell me that they are astounded.
0:27:30 People walk into their offices much sicker than they should have been. And the doctor said, “Why didn’t you come here six months ago when you first felt your symptoms?”
0:27:37 And they said, “Well, you know, I have a high deductible of a $10,000 deductible. I don’t have any money to pay. I’m uninsured.”
0:27:50 Some of those people don’t make it. Other people, and this is what is totally crazy, they end up in the hospital at huge expense to the system rather than getting the care they need when they needed it.
0:28:01 So that is how– I’ll give you another example of it. We pay the highest prices in the world for prescription drugs. One out of four Americans can’t afford the drugs their doctors prescribed.
0:28:07 So you walk into the doctor’s office and they say, “Okay, Lex, you got this, that, and the other thing. Here’s your prescription. You can’t afford to fill it.”
0:28:14 What happens? You get sicker. You end up in the emergency room, which is an extremely expensive proposition. Okay?
0:28:24 Or you end up in the hospital rather than dealing with the problem when it occurs. And what is not talked about– I mentioned earlier how we don’t talk about some of the major issues.
0:28:34 The estimate is that some 60,000 people in America die every single year unnecessarily because they can’t get to a doctor when they need because of financial reasons.
0:28:43 And you want to hear it even crazier. One out of four people who get cancer treatment in this country either go bankrupt or deplete their financial resources or their family.
0:28:51 So your point is right. You know, if somebody diagnosed you with cancer, you’re scared to death. You’re worried about how you’re going to live. You’re going to die. What’s going to happen?
0:28:57 And then on top of that, you’ve got to worry about whether your family goes bankrupt. How insane and cruel is that?
0:29:08 So to me, you know, I think health care is what unites us all. Everybody has family. They get sick. We’ll get born. We all die. We all want care.
0:29:14 And we all have got to come together to create a system that works for all of us, not just the drug companies or the insurance companies.
0:29:20 There’s just so many stories and not even the horrific stories. It’s countless horrific stories, but just basic stories of cost.
0:29:34 Like my friend, a doctor, Peter Atia, has this story where he happens to be wealthy so he can afford it, but he had to take his son to the emergency room and the son was dehydrated and the bill was $6,000.
0:29:42 They just did a basic test and gave him an IV, a basic thing. And he has really good insurance and the insurance covered $4,000 of it.
0:29:53 So he had to, at the end, pay $2,000 for a basic emergency room visit. And there’s a lot of families for whom that one visit for such a simple thing would be just financially devastating.
0:30:02 And you know what? People know that. And you know what they say, you know, I don’t feel well today. Something’s wrong. I ain’t going to go to that emergency room.
0:30:08 Because I don’t want a $6,000 bill. And what happens if he had insurance that paid two-thirds of it, right?
0:30:14 So what happens if he didn’t? What happens if he didn’t have money? He’d be handed by bill collectors for the rest of his life.
0:30:27 So it is a disgusting system. It is an inhumane system. But you know, the insurance companies and the drug companies are very powerful and they make a lot of campaign contributions, have a lot of lobbyists and we are where we are.
0:30:32 But you know, I think, you know, the American people want fundamental changes there.
0:30:39 So that’s another good example, a really popular idea that is not implemented because of the money in politics.
0:30:47 You got it. That’s a wonder. And I’ll tell you that not only that, not only is it not implemented because of money, it’s not even discussed.
0:30:57 Alright, so I’m saying here, and no one disputes me, we are spending twice as much per person on healthcare, right?
0:31:05 And yet 85 million Americans are uninsured or underinsured and our life expectancy is lower than virtually every other major country.
0:31:09 Do you think that might be an issue that we’d be discussing?
0:31:20 Again, if a single politician discusses it, they get punished for it. So there needs to be a mass movement and probably, I mean, from my perspective, it has to come from the very top.
0:31:27 It has to come from the president and the president has to be a populist president where they don’t care about the parties with the rich people.
0:31:32 They just speak out because they know it’s a popular message and they know it’s the right thing.
0:31:40 So speaking of that, you had a historic campaign run for president in 2016.
0:31:52 And in the eyes of many people, mine included, you were screwed over by the DNC as the, especially the WikiLeaks emails showed.
0:31:57 What’s your just looking back feelings about that? Are you angry? Are you upset?
0:32:11 Yeah, of course, I’m angry and of course, I’m upset. But, you know, when you take on, in this case, the democratic establishment who have controlled their party forever,
0:32:20 money to interest in the democratic party, you know, you’re taking on corporate America when you’re taking on the corporate media.
0:32:31 And when you’re calling for a political revolution that creates the government that works for all and not just the few, you know, the opposition is going to be extraordinary.
0:32:41 But what I am extremely proud of from that campaign in 2020 as well is that we tip con the anointed candidate of the establishment.
0:32:50 And we showed, you know, we showed, despite the fact, the entire establishment I had in the Senate, I had one supporter.
0:32:57 There were 50 Democrats. I had one supporter. I had no governor supporting me, I think, maybe a few people in the house.
0:33:10 But we took on the whole political establishment and we did, you know, got millions of votes and the ideas that we brought forth were ideas that they had to eventually deal with in one way or another.
0:33:20 If you look at the American rescue plan, which I’m proud to have helped write during the midst of COVID, a lot of the ideas that we fought for were implemented in that bill.
0:33:22 And I want to make them obviously permanent.
0:33:28 And you almost won. And a lot of people thought that you would win against Donald Trump.
0:33:32 I think we would have. I think we would have.
0:33:38 You know, I think Trump is a very, you know, I think he’s a little bit crazy between you and me, but he is a smart politician.
0:33:44 And he’s appealing to a lot of the anger that working class people feel.
0:33:58 And you know what, working class people should feel angry, but they should make sure that their anger is directed in the right direction and not against people who are even worse off than they are, which is what demagogues like Trump always do.
0:34:07 So, you know, I think we had as I went around the country then and now we have a lot of support from working class people who understand that there is something wrong.
0:34:10 And this is an incredible fact that no one talks about.
0:34:14 All right, I’m going to ask you a question. Are you ready for this? Let’s go. Here we go.
0:34:23 Over the last 50 years, there’s been a massive increase in worker productivity as a result of technology, right? Everyone agrees that.
0:34:30 And it’s, I don’t know exactly what it is, but the worker today is producing a lot more than the worker 50 years ago doing something similar.
0:34:37 Is the worker today in real inflation account for dollars making more money than that worker 50 years ago?
0:34:41 Well, there’s a lot of close arguments there, but your point is well taken.
0:34:45 It’s either the same or a little bit higher or a little bit lower, depending on the statistics.
0:34:50 It has not increased significantly and the wealth inequality has increased significantly.
0:34:51 That is the point.
0:35:00 So you would think that if a worker is producing a lot more, that worker would be better off, would be working lesser hours, etc.
0:35:02 That hasn’t been the case.
0:35:15 And what has happened in that 50 years is according to the Rand Corporation, there has been a 50 trillion trillion with the T redistribution of wealth from the bottom 90% to the top 1%.
0:35:19 You got CEOs today making 300 times more than their workers.
0:35:24 You got three people on top owning more wealth than the bottom half of American society.
0:35:32 So that’s why people are angry and they’re worried that their kids may have a lower standard of living than they in the wealthiest country in the history of the world.
0:35:34 So there’s a lot of anger out there.
0:35:43 And I think we tap some of that anger in a constructive way, essentially saying, you know what, we don’t need so few to have so much in wealth and power.
0:35:46 Let’s distribute it more fairly in America.
0:35:51 I got to get back to 2016 because it’s such a historic moment.
0:36:01 So there’s a lot of fans of yours that wanted you to keep fighting because you forgave in the end the establishment and join them in support.
0:36:08 And your fans wanted to keep fighting for a takeover for progressive takeover the Democratic Party.
0:36:13 If you just look back and had to do it all over again, what would you do different?
0:36:17 Well, by the way, in terms of a takeover the Democratic Party, we did try.
0:36:23 We ran, you know, Keith Ellison is Keith is now the attorney general of the state of Minnesota.
0:36:27 He’s doing a great job really, one of the outstanding attorney generals in the country.
0:36:39 And Keith was then a member of Congress and we ran Keith to become the head of the DNC and the establishment from the president of the United States on down when crazy.
0:36:42 And they beat him by a few votes, not a whole lot.
0:36:50 So it’s look you’re faced and you know, that’s the exact same position that many of us are in right today.
0:36:53 So people say, well, why did you support Hillary Clinton?
0:36:55 Yeah, what’s the alternative Donald Trump?
0:37:01 I think Donald Trump is an extremely dangerous person trying to undermine American democracy.
0:37:03 So I can’t support him.
0:37:07 You know, Hillary Clinton obviously views are very, very different than mine.
0:37:12 But that in that moment, you know, that’s where politics becomes really tricky.
0:37:14 And it ain’t easy.
0:37:20 And, you know, sometimes you have to do things that you’re not really all that excited about.
0:37:27 But I think it was right to try to do what I could to prevent Trump from getting elected.
0:37:32 And in 2020, I did the same with Biden and we had more success with Biden than we had with Clinton.
0:37:41 Well, there’s this interesting story about a long time coming meeting between you and Obama in 2018, I believe.
0:37:50 So Ari Ravenhaft, who was a former deputy campaign manager wrote a great book, I would say about you called The Fighting Soul on the Road with Bernie Sanders.
0:37:56 And he tells many great stories, but one of them is your meeting with Obama.
0:38:02 And he says that Obama told you, Bernie, I wish I could do a good Obama impression.
0:38:07 Bernie, you’re an Old Testament prophet, a moral voice for our party giving us guidance.
0:38:11 Here’s the thing, though, prophets don’t get to be king.
0:38:13 Kings have to make choices, prophets don’t.
0:38:16 Are you willing to make those choices?
0:38:23 Basically, Obama’s making the case that you have to sort of moderate your approach in order to win.
0:38:25 So was Obama right?
0:38:31 Look, and again, that’s why politics is very, very fascinating.
0:38:35 You know, sometimes you can run and lose and you really win.
0:38:41 If your goal is not just individual power, but transforming society.
0:38:45 One of my heroes, you mentioned Dr. Martin Luther King, Jr., who is one of my heroes?
0:38:48 Another one of my heroes is Eugene Victor Debs.
0:38:49 That ring a bell?
0:38:50 Yeah, yes.
0:38:51 Okay.
0:38:52 For many reasons, yes.
0:38:53 All right.
0:38:58 Debs, many listeners may not know who Debs was.
0:39:16 Debs was a union organizer in the early 1900s, helped form the American Railway Union, ran for president, I think, five times, ran the last time while he was in a jail cell because of his opposition to World War I and got a million votes doing that.
0:39:20 Debs lost badly in every race that he ran in.
0:39:29 In 1932, Franklin Delano Roosevelt ran for president and much of what Roosevelt ended up doing was at least some of what Debs had talked about.
0:39:32 Debs helped lay the groundwork for ideas.
0:39:37 So sometimes you can lose and win if you’re into transforming society.
0:39:46 What my view is where I disagree with Obama is I think you have got to raise consciousness among ordinary people.
0:39:53 And when people know what’s going on and are prepared in an organized way to fight for change, they can make incredible changes.
0:39:55 And we’ve seen that in recent years.
0:40:02 You know, today we take for granted, well, we have a woman running for president of the United States, so I’m supporting.
0:40:04 We have had other women running for president.
0:40:11 We have women governors and senators not so many years ago in the United States Senate, there were 98 men, two women.
0:40:15 Even before that, 1920, it was when women got the right to vote.
0:40:17 How did that change?
0:40:19 How did women’s role in society change?
0:40:22 It changed because women and their male allies stood up and fought.
0:40:28 Gay rights, you know, old enough to remember that anybody I knew who was gay, you think they would talk about it?
0:40:29 Come out about it?
0:40:30 No, they wouldn’t.
0:40:32 That’s changed.
0:40:36 We have seen, you know, in terms of civil rights, massive changes.
0:40:41 Change happens when people at the grassroots level demand that we talked about healthcare a moment ago.
0:40:47 We will get universal Medicare for all when millions of people make it clear that’s what they want.
0:40:52 So I believe politics starts at the grassroots level, and that’s how you got to bring about change.
0:41:01 So just to go back to Obama, though, in many ways, he too is a singular historic figure in American politics who has brought about a lot of change.
0:41:05 He’s a symbol I think they’ll be remembered for a long time.
0:41:09 What do you, what do you admire most about Obama?
0:41:14 Well, you know, I know him, we’re not best friends, but I know him well and we chat every once in a while.
0:41:24 First of all, don’t underestimate what it was in 2008 to be the first black president in the history of this country.
0:41:37 And I think few would deny that he’s an extraordinarily intelligent guy, very, very articulate, one of the best speakers that there is in America.
0:41:41 And that he and his family, and again, it’s a lot harder than it looks.
0:41:57 He and his family for eight years, that’s his wife, Michelle and his kids, really held that office in a way that earned, I think, the respect of the American people, even if people disagreed with him politically.
0:42:03 So he deserves, and again, don’t ever, don’t underestimate.
0:42:11 I think, you know, years ago, there were people who said a black president in our lifetimes, never going to happen, can’t happen, too racist the country, he did it.
0:42:14 And that is a huge accomplishment.
0:42:21 And I think, you know, he has had some significant achievements in his presidential tenure.
0:42:27 He and I, you know, did disagree on a number of issues.
0:42:35 I think he would tell you, I think his public stance is that, yeah, if you had to start all over again, he would do Medicare for all single payer.
0:42:38 But where we are right now, the best he could do is the Affordable Care Act.
0:42:41 Well, we disagree on that, and we disagree on other things.
0:42:47 But, you know, I think he deserves an enormous amount of credit for what he has accomplished.
0:42:53 And he, like you, also gave a damn good speech opposing the Iraq War before running for president.
0:42:55 And that takes courage.
0:42:56 Yes, it does.
0:43:04 But then it also shows that once you get into office, it’s not so easy to oppose or to work against the military industrial complex.
0:43:06 It is very hard.
0:43:18 People do not fully appreciate how powerful the establishment is, whether it is the healthcare industry, whether it’s the military industrial complex, whether it’s the fossil fuel industry.
0:43:27 These people have unlimited amounts of money. They have very smart lobbyists in Washington, D.C., and they are very, very greedy people. They want it all.
0:43:30 I have to ask you about capitalism, the pros and cons.
0:43:34 So you wrote a book. It’s okay to be angry about capitalism.
0:43:40 That is a thorough, rigorous criticism of, I would say, hyper-capitalism.
0:43:41 Yes, that’s right.
0:43:47 A certain kind of capitalism that you argue that we are existing in today in the United States.
0:44:02 A lot of people would attribute to capitalism all the amazing technological innovations over the past 70 plus years that have contributed to increase in quality of life in GDP,
0:44:11 in decrease in poverty, decrease in infant mortality, increase in expected life, life expectancy.
0:44:19 So what are the sort of, how do you see the tension, the pros of capitalism and the cons of capitalism?
0:44:24 You know, some of my European friends, they say, you know, Bernie, in the United States, you’re considered to be very radical.
0:44:33 If you were here in, you know, France or Denmark or someplace, you’d be kind of mainstream left guy. Not all that radical.
0:44:40 So this is what I think. I mean, I think the best that we could do right now, where we are right now, is to create,
0:44:55 a society which does two things. It encourages innovation, but at the same time, it makes sure that all people in a wealthy nation have a decent standard of living.
0:45:00 In some countries, if you look at Scandinavia, and this shocks people because we don’t talk about this at all.
0:45:09 So in Scandinavia, it has been the case, you know, Denmark, Finland, Norway, for years that people have health care.
0:45:13 That’s not a big deal. You end up in a hospital, so what? They don’t pay a bill.
0:45:21 You have, and this shocks people, in America right now, we have people who get one week, two weeks off paid vacation.
0:45:25 Sometimes we look at nothing, you know that? There are people out there with no vacation rule.
0:45:33 You know, in Germany, you get six weeks paid vacation, and other holidays as well. People are shocked by that.
0:45:38 In America, we don’t have paid family and medical leave. The only major country not to do it.
0:45:44 You know, other countries, you know, your wife gets sick, you stay home with her. Your kids get sick, not a big deal.
0:45:50 You get a certain amount of paid family and medical leave. Cost of prescription drugs are far more affordable.
0:45:56 So what you want to do is create what’s called a social safety net. That means, I don’t care what your income is.
0:46:01 Of course, you’re going to have health care as a human right. Of course, you’re going to have housing that is affordable.
0:46:08 Of course, your kids are going to have great quality education from child care to university without much cost.
0:46:13 You know, every country has a little bit different, but there are countries in the world right now, I think in Germany.
0:46:23 I think college is now tuition free, as I recall. For obvious reasons, they want to have the best educated workforce they can.
0:46:36 So in terms of government playing a role in a civilized democratic society, are providing all basic needs, health care, education, housing, retirement benefits.
0:46:42 Yes, that is what we’ve got to do. Now, does that mean then that the government is going to run every mom and pop store in the corner?
0:46:49 Of course not. You want innovation. You want, you know, you want to go out and start a business, produce a product. Good luck to you, make money.
0:46:56 But on the other hand, in terms of even making money, we want you to be able to do that, come up with good products, good services.
0:47:01 But do I think you should end up with a hundred billion dollars? I don’t.
0:47:09 And you know what’s funny? I had, I did an interview with Bill Gates, who was, I think, the third wealthiest guy in the country.
0:47:14 Uh, struggling behind, uh, Musk and Bezos, I think.
0:47:18 And he’s only worth a hundred plus a billion, but he gets by.
0:47:22 And I said to him, Bill, he was supposed to ask me questions. I asked him the question. I said, Bill, tell me something.
0:47:26 You know, you’re an innovator with Microsoft and all that stuff.
0:47:31 Did you know that you’d become a multi-billionaire? And was that motivated you? That would motivate you?
0:47:38 And he said, no. And I believe he was honest. I love doing what I love programming. I was a kid. He started doing it. He loved it. He was motivated by it.
0:47:45 Do you think that there are scientists out there who work in day and night trying to develop drugs to deal with Alzheimer’s or cancer?
0:47:49 That they motivate, oh boy, if I come up with this drug, I’m going to become a billionaire?
0:47:55 So I think, you know, we want to reward success. Fine. But you don’t need a billion dollars.
0:48:03 We want people to get satisfaction from what they accomplished, the work they’re doing, whether it’s cleaning the street or developing a new, you know, drug.
0:48:11 So I think we have gone a little bit too far. And you’re right in talking about the book was an attack on, you call it hypercapitalism or ubercapitalism.
0:48:22 But right now, and this is not an American issue. This is a global issue. You know, it’s not an accident that Musk is over there in Saudi Arabia talking to the, you know, trillionaire families in the mid-east.
0:48:35 These guys that were Putin and his friends, you got a probably not more than, you know, five, 10,000 extraordinarily wealthy families who have unbelievable economic power over 7 billion people on this planet.
0:48:40 Well, Elon Musk is actually an interesting case because he’s investing all the money back into the businesses.
0:48:53 So I think there is a balance to be struck. And you just spoke to it, which is we can still celebrate even big companies that are bringing wealth to the world that are building cool stuff that are improving quality of life.
0:49:03 But we can question of why is it that the working class does not have a living wage in many cases and sort of trying to find that balance.
0:49:10 Right. That is the story. Look, I am no great fan of Elon Musk, especially in the role that he’s playing right now in Trump’s campaign.
0:49:21 But is he a brilliant guy? Of course he is. Does he work like a dog? Of course he does. Does he come up with these incredible innovations in companies? Yes, he does. Does he deserve credit for that? Yeah, he does.
0:49:33 But, you know, even in terms of encouraging innovation, I would hope that we are focusing on the important issues. I would love to see great innovators figure out how we build the affordable housing that we need.
0:49:41 Come up with the great drugs that we need to solve, many of the terrible illnesses that plague people. Climate change for God’s sakes.
0:49:51 All right, do we need innovation? You know, we’re making good some progress in this country. Should we do more? What kind of technologies out there can really cut back on carbon emissions?
0:50:04 So, you know, I hope we focus on some of the most important issues that impact humanity. But, you know, reward innovators, I don’t have a problem with that, but I do have a problem when three people end up owning more wealth at the bottom half of American society.
0:50:16 Maybe you can briefly speak to something you tweeted recently about Donald Trump going to McDonald’s and the minimum wage, I believe, of seven and a half dollars. Can you just speak to that tweet?
0:50:27 Look, nothing new. Trump didn’t invent it. That’s of course a photo opportunity. I’ve done one or two in my life, too. So you go to a place he puts on an apron. He got old Donald Trump, just another McDonald’s worker.
0:50:38 But anyhow, he was sublime. He did his photo op. That’s fine. Kamala Harris was in North Carolina handing out food to people who were victims of the hurricane. Fine. That’s what politicians do.
0:50:55 But some reporter asked them. They said, you know, Mr. Trump, are you for raising the minimum wage? And that was a fair question because she got, I don’t know how many, but many, many thousands of McDonald’s workers and millions of other American workers right now
0:51:06 are trying to get by on nine, 10, 11 bucks an hour. Federal minimum wage is seven and a quarter. You have people working in McDonald’s right now for sure who are working with 12, 13 bucks an hour.
0:51:14 So the reporter said, how, you know, what do you think about raising the federal minimum wage? And he’s, oh, these are great workers. I love McDonald’s. So he didn’t answer the question.
0:51:21 Well, I think that in the richest country in the history of the world, if you work 40 hours a week, you should not be living in poverty.
0:51:30 And that means we should have a federal minimum wage, not absurdly seven and a quarter an hour. But in my view, $17 an hour.
0:51:36 Will that solve all the economic problems for working class people? No, it won’t. It’ll help. It’ll help.
0:51:46 Since running for president, you’ve often been attacked, especially from the right, about being worth, I believe, $2 million and owning three houses.
0:51:55 So from my perspective, the answer to that is most of your wealth has been earned from writing books and selling those books.
0:52:06 And you are one of the most famous politicians in the world. And so your wealth and the context in comparison to other people of that fame level and other politicians is actually quite modest.
0:52:13 So what’s your response usually to those attacks? Do I own three residences? Yeah, I do.
0:52:20 I live here in Burlington, Vermont. We live in a middle class neighborhood. Nice house.
0:52:28 Guess what? I’m a United States senator and I own a home in Washington, D.C. as the most senators. You know, you live there year after year.
0:52:34 When I first went there, actually when I was in Congress for 16 years, I rented all the time. But I got elected. Okay, you got a six year term.
0:52:41 You know what? Let’s buy a house. So we bought a house. And guess what? Like many thousands of people in the state of Vermont, I have a summer camp.
0:52:52 It’s a nice one on Lake Champlain. That’s it. Now, how did I get the money? You’re right. I wrote two bestselling books, including this book on capitalism.
0:53:01 It was The York Times bestseller for a while. And also another book was a youth book. And that’s what I make, I don’t know, 175,000 a year.
0:53:06 And that’s more or less how I became the zillionaire that I am.
0:53:13 I should also mention that sometimes the word mansion is used, and I think your residences are quite modest, at least from my perspective.
0:53:18 Yeah, my home house is in Brooklyn. You know, middle class house is a very nice house.
0:53:22 So when you started in politics, I read you were worth $1,100.
0:53:23 Not much?
0:53:30 Yeah, that much. That’s right. Has the increase in wealth changed your ability to relate to the working class?
0:53:43 Well, that’s a good question. And obviously growing up in a working class family has been maybe the most significantly significant aspect of my politics.
0:53:49 It’s, you know, I grew up without money in a family that lived in a rent control department in Brooklyn, New York.
0:53:57 So that has impacted me. I’ll tell you, I don’t really give a damn about money. I drive a car that’s 11 years old, you know, it’s an old car.
0:54:06 And money, here’s my jewelry, it’s the Soul of Watch, and my wedding ring, that’s about it. I don’t have a Rolex watch, would not be interested in it.
0:54:12 But I’ll tell you what has impacted me. My wife, who also grew up in a working class family, will tell you the same.
0:54:19 We don’t worry. You raise that issue, you know, if we have to go to the doctor, if our kids have to go to the doctor, we go to the doctor.
0:54:25 I don’t spend nights worrying. I used to, there was a time I have to worry about how to pay my electric bill. I don’t worry about that anymore.
0:54:34 So what has happened, that stress, that economic stress of not worrying about a financial disaster, that’s gone. And that is enormous.
0:54:45 I, you know, maybe as much or more than any other member of the Senate work hard, not only for, but with working class people and chairman of the committee of deals with labor issues.
0:54:51 We have been involved probably in dozens of strikes all over this country. I’ve been on picket lines.
0:55:00 So, you know, I do my best. It’s a very easy trap to fall into. You can get separate, separated from ordinary people and their struggles, not hard to do.
0:55:03 I try as hard as I can not to do that.
0:55:20 So sometimes people say, can money buy happiness? I think I agree with you that worry, sort of being able to fill up your car and not worry about how much it’s going to cost or be able to get food for dinner and not worry about how much it’s going to cost.
0:55:33 Or even, you know, I’ve been very, I’ve been poor most of my life, but I’ve been very fortunate recently to have enough wealth to not worry about health care, to have insurance and be able to afford an emergency room visit.
0:55:37 And that worry is just such a giant lift off your shoulders.
0:55:43 Lexi, I think you said it very well. I remember even to, and I saw this change in myself.
0:55:52 When I used to go out and I do the grocery shop, my wife does a lot of the cooking. I do the grocery shop. I used to look at the prices of everything. I do that less now.
0:55:57 You know, I said, what the hell, so what? It costs 50 cents more for this can of stuff. So what?
0:56:02 But that’s a luxury you have when you don’t have to worry about that. And I don’t have to worry about that.
0:56:16 But your point is, again, to me, I don’t like big, fancy cars or big, fancy homes don’t go on. My wife will tell you, we’ve not been on a real vacation for God knows how long because I work pretty hard.
0:56:21 But the major thing about having money, which is enormously important is just what you said.
0:56:30 I don’t have to worry if somebody in my family gets sick. I don’t have to worry about it. I don’t have to worry about putting food on the table or paying the mortgage.
0:56:32 So that’s what money has done.
0:56:46 Okay. Let me ask you about the future of the Democratic Party. So one of the biggest impacts you’ve had is you’ve been the fuel, the catalyst for the increase of the progressive caucus, the progressive movement within the Democratic Party.
0:56:53 Do you think that is the future? The progressives, even Democratic socialist leaders will take over the party?
0:56:58 That is the most important question regarding, to my mind, American politics.
0:57:13 One of the successes that we’ve had, and I’m proud to have played a role in this, is that if you go to the House of Representatives right now, you will see almost a hundred members of the progressive caucus led very well by a woman from Washington,
0:57:28 Jayapal does a great job. You know, it’s people like Alexandra Acasio-Cortez in Ilhan Omar and many others. Many of them are young, often women, people of color, and many of them come from working class backgrounds.
0:57:38 So what we have been able to do in recent years, elect a number of strong progressives who represent working families very, very effectively.
0:57:45 The struggle in the Democratic Party is between the corporate wing and the progressive wing.
0:58:04 The corporate wing takes a whole lot of money, sees its salvation in getting a whole lot of money from wealthy individuals and large corporations, and is not very vigorous in my view in representing the needs of working class people.
0:58:13 If they were, we would have health care for all. We would have a minimum wage that was a living wage. We would not have a housing crisis.
0:58:21 We would not have a tax system in which billionaires pay an effective tax rate that is lower than a truck driver or a nurse.
0:58:37 So I think one of the reasons that Trump has had political success is he is not so much his ideas. Most working class people don’t think we should give tax breaks to billionaires or worry about the size of, you know, Arnold Palmer’s genitalia.
0:58:49 But they are angry. People are angry. And the Democrats have not responded effectively to that anger. So the struggle that we are waging right now is the future of the Democratic Party.
0:59:00 Will it be a party of the working class and represent working class issues, whether you’re black or white or Latino or Asian or whatever you may be? Or will it be a corporately dominated party? That’s the struggle we’re in right now.
0:59:11 Did you consider running in 2024? From my perspective, I would have loved it if you ran. I think you would have had a great chance of winning, not just the primary, but the presidency.
0:59:34 I gave about five minutes thought to it. And the reason was we have a slogan in the progressive movement. It’s not about me. It’s about us. And, you know, to have taken on Biden, who in my view, on domestic issues has been quite strong, would have really split the Democratic Party and laid the groundwork for an easy Trump victory.
0:59:45 And that I did not want to see. So sometimes in life, and I know that a lot of younger people don’t agree with me, but, you know, you got to make choices which are painful.
1:00:01 So I strongly supported Biden because I liked his domestic record. He’s done some good things against a lot of opposition. And I’m supporting Kamala right now. But I’m doing my best to see that a dangerous guy like Donald Trump does not become president.
1:00:06 And the hope for you is that there will be future candidates that are populist, that are progressive.
1:00:07 Yes, absolutely.
1:00:22 Let me ask you about AOC. She’s become one of the most influential voices for the progressive cause in the United States. You two had a great conversation on your podcast. And in general, you worked together. So what’s to use most impressive about her?
1:00:39 I really like Alexandra a whole lot. She is a young woman who comes from a working class background. She helped her mother clean houses. She was a bartender in the Bronx, New York.
1:00:57 And I’m very proud that my campaign for president inspired her to run. And she ran on a progressive working class program. And she took on one of the more powerful guys, a guy named Joe Crowley, who was pretty high up in the Democratic Party.
1:01:08 And she knocked on doors. She had no money. She did a very strong grassroots effort. And I appreciate that. So that’s number one. I like what she stands for. She’s incredibly smart.
1:01:22 And she has that certain charisma that, you know, maybe you’re born with it, maybe you develop it. I don’t know. She, a couple of years ago, she came up here to Vermont, this was the time she and her partner Riley came up.
1:01:34 And we were out in the street and people saw her and they said, “Oh, Congresswoman!” And she just smiled. And she had an approach to people, which was beautiful. I mean, it wasn’t phony. It was real.
1:01:41 But to be a politician, you got to know how to, you know, you could be a great intellectual, but you can’t relate to people. She relates well to people.
1:01:53 And so I think both from a personality perspective, from an intellect perspective, from an ideological perspective, she helped create the Green New Deal concept.
1:02:03 The need to create jobs as we transform our energy system away from fossil fuel. Strong advocate for Medicare for all workers rights. So I’m a big fan of Alexandria.
1:02:11 What do you think is the most powerful and enduring impact you’ve had on American politics? Looking back, you’ve been in it for quite a bit.
1:02:23 Well, you know, I don’t know, I can give you a singular answer. You know, I was mayor of the city and proud of what we accomplished here. Proud of my accomplishments as a U.S. senator, you know, when COVID was devastating this country.
1:02:31 And we had a massive economic downturn. As chairman of the Budget Committee, I helped write the American Rescue Plan, which put a lot of money into people’s pockets.
1:02:41 We got childhood poverty by 40% by providing a child tax credit. We kept hospitals going. We kept colleges going. Kept people from getting evicted.
1:02:54 I helped get public health out of people getting the vaccines. You know, I’m proud of that. But at the end of the day, I think what I have shown is that the ideas gets back to the early part of this conversation.
1:03:01 The ideas that I am talking about are ideas that are widely supported, you know.
1:03:16 So, you know, Donald Trump says, “Oh, Bernie Sanders is a far left!” You know, it’s like I’m some kind of extremist coming up with ideas that nobody supports. Everything that I talk about, raising them in ways healthcare for all, a tax system which demands the billionaires pay their fair share.
1:03:24 Those are all popular ideas, but people didn’t know. You got to run for president and have 20,000 people come out to your rallies and win 23 states.
1:03:37 They say, “Hmm, well, maybe those ideas are not so crazy after all.” And we got to entertain them. The establishment doesn’t like that. They really don’t. They want to tell you, and this is their main, this is how they succeed.
1:03:49 What they say, Lex, is the world is the way it is. It always will be this way. We got the wealth. We got the power. And don’t think of anything else. This is the way it is. You have no power. Give up.
1:04:05 They don’t say it quite that way, but that’s really what the intent is. And what we showed is, guess what? You know, running an outsider campaign, we took on the democratic establishment, we came close to winning it, and we did win 23 states.
1:04:10 And the ideas that we’re talking about are the ideas that working class people, young people believe in.
1:04:16 Yeah, you showed that it’s possible to win. And that’s an idea that will resonate for decades to come.
1:04:24 And out of that came dozens of candidates now in the House of Representatives, people on city council, people on state legislature, who did win.
1:04:41 So we mentioned about the worry of getting sick, the worry of life that many people in the working class are suffering from, but there’s also the worry that we all experience of the finiteness of life. Do you ponder your own mortality? Are you afraid of it?
1:04:47 Well, when you’re 83, it does come across. Yeah, of course I do.
1:04:48 Are you afraid of it?
1:05:01 No, I’m not afraid of death. What I am afraid of, I think, is infirmity. I have been knocked on wood, this is wood, I think. Reasonably healthy, with an exception, I had a heart attack five years ago.
1:05:12 And what blew me away was that my body failed me for the very first time in my life. That was stunning to me, that suddenly I was in a hospital bed.
1:05:31 I have a great deal of compassion for people as we speak who are in nursing homes, having a hard time walking, maybe your mental agility is slipping a little bit. That’s tough. That’s what worries me. We’re all going to die, and that’s that.
1:05:38 So I’m not afraid of that, but that aspect of getting older, and that does concern me.
1:05:53 That said, your mind is as sharp as any politician that I’ve ever heard, and also just off-mic, I should say, just the warmth that you radiate, and I deeply, deeply appreciate that as a human being.
1:06:08 So you still got it, after all that, after all those speeches, after all those houses, after all of it, there’s still the humility and just the sharpness, the wit is all there.
1:06:16 So Bernie, yeah, like I said, I wish you would have ran this year, but I also wish that there’s future candidates.
1:06:30 And there will be, Lex. I absolutely do. And I think, you know, you asked about my legacy and the idea that they’re all wonderful, really, really wonderful people who are now got involved in the political process and are fighting for justice. That’s a great legacy.
1:06:34 What gives you hope about the future of this country, about the future of the world?
1:06:48 You know, sometimes one can become very cynical. You look at the terrible wars that are going on right now. You look at the dizziness in this country, the ugliness, the poverty. You look at climate change.
1:06:58 You know, you get depressed from all of that, but I am lucky in this sense, and that I’ve had the opportunity. People often have, what inspires you? How do you keep going?
1:07:09 And I remember, it actually was in California where it really crystallized. There was a rally in the agricultural area of California, and we did a rally. It was sunset.
1:07:26 Thousands of people were out, and you looked around the crowd, and there were young people, black and white and Latino and Asian American, huge cross-section. They’re older people, and they all wanted to make America a very much better country.
1:07:40 And it really moved me. I mean, I see that time and time. I’ve just been on the campaign trail, and you see great people, really beautiful people who are not interested in becoming billionaires. They want to improve life for other people in this country.
1:07:48 So, you know, I am grateful that I, you know, it sounds like a platitude. You know, it’s what every politician says, blah, blah, blah, blah, blah.
1:08:05 But when you go out around the country, you know, you go to Native American reservations, and you go to factories and everything, and you see so many wonderful people. You know, I have been able to see things that many others have not been to every state in the country, and that inspires me.
1:08:13 I share their optimism. I share your optimism. Bernie, I’ve been a fan for a long time. It’s a great honor to speak to you today. Thank you so much.
1:08:16 Well, thank you very much for what you’re doing. Let me just say a word about what you’re doing.
1:08:17 Okay, let’s go.
1:08:30 The compliments here. You know, I think there is a growing dissatisfaction with corporate media, and not because it’s fake news or the report is live all the time. That’s nonsense. They don’t.
1:08:45 But I think people want to hear folks really talk about in a calm manner about some of the very important issues which are not discussed in corporate media, and I think that’s what you and some others are doing. So, I thank you very much. It’s a very important service to the country.
1:08:53 And thank you from a mayor perspective of creating a wonderful town, and I look forward to looking at the fall leaves walking around tonight.
1:08:57 I think Mike rate the leaves. I did create some other things.
1:08:59 Okay, thank you so much, Bernie.
1:09:00 Thank you, Alex.
1:09:11 Thanks for listening to this conversation with Bernie Sanders. To support this podcast, please check out our sponsors in the description. And now, let me leave you with some words from Aristotle.
1:09:30 The real difference between democracy and oligarchy is poverty and wealth. Wherever men rule by reason of their wealth, whether they be few or many, that is an oligarchy. And where the poor rule, that is democracy.
1:09:34 Thank you for listening and hope to see you next time.
1:09:49 (music)

0:00:18 The following is a conversation with Graham Hancock, a journalist and author who for over 30 years has explored the controversial possibility that there existed a law civilization during the last Ice Age and that it was destroyed in a global cataclysm some 12,000 years ago.
0:00:36 He is the presenter of the Netflix documentary series, Ancient Apocalypse, the second season of which has just been released and it’s focused on the distant past of the Americas, a topic I recently discussed with the archaeologist Ed Barnhart.
0:00:49 Let me say that Ed represents the kind of archaeologist, scholar I love talking to on the podcast, extremely knowledgeable, humble, open-minded and respectful in disagreement.
0:00:54 I’ll do many more podcasts on history, including ancient history.
0:01:08 Our distant past is full of mysteries and I find it truly exciting to explore those mysteries with people both on the inside and the outside of the mainstream in the various disciplines involved.
0:01:28 And now a quick few second-mention of each sponsor. Check them out in the description. It’s the best way to support this podcast. We’ve got Notion for note-taking, Riverside for making amazing looking podcasts online, Element for hydration, Shopify for selling stuff online, and BetterHelp for your mind.
0:01:45 Choose wise and my friends. Also, if you want to get in touch with me for whatever reason, go to LexReven.com/contact and now onto the full adaries. As always, no ads in the middle. I try to make these interesting, but if you skip them, please do check out our sponsors. I enjoy their stuff. Maybe you will too.
0:02:05 This episode is brought to you by Notion, a note-taking and team collaboration tool. To me, Notion is hands down the best integration of LLMs into the note-taking process when there’s a lot of documents, a lot of different kinds of documents, and a lot of different kinds of people creating the documents.
0:02:19 In the same way that, in the recent episode I talked about with the cursor, can query the code base, Notion generalizes that, and can query the document base.
0:02:26 So all the wikis and projects and all the notes that you take, all of that can be query. You can ask questions about it. You can find stuff.
0:02:34 You can summarize, especially when there’s multiple people on the team, you can summarize all the progress made in a particular project, all that kind of stuff.
0:02:43 You show up at the beginning of the day and you want to know, okay, what happened yesterday? Where can I help? Those kinds of questions can be answered with Notion.
0:02:52 Try Notion AI for free when you go to Notion.com/lex. That’s all lowercase Notion.com/lex to try the power of Notion AI today.
0:03:03 This episode is also brought to you by Riverside. It really is just an incredible platform for recording podcasts online. A lot of people are doing podcasts.
0:03:08 And the natural question that people ask me and people ask on the internet is how to do it easy.
0:03:17 I think Riverside is the place to go to achieve easy, professional level quality on both the audio and the video.
0:03:24 I’ve used it a bunch over the years to record remote podcasts. In fact, I need to be doing more remote podcasts.
0:03:35 The point is the technology is super easy because you have double-ended recording, so you have extremely high-quality recording on both ends. All you do is just log in on the browser. It just works.
0:03:49 I’m so glad this exists. It just works. And of course, they have a bunch of nice features that are leveraging AI, for example. You have a text-based editor for both audio and video, which is just incredible.
0:04:01 The syncing of the multiple guests. Obviously, simple, seeming thing, but hard to do seamlessly and flawlessly. And they do just that.
0:04:09 I mean, it’s just incredible. They do. They pulled it off. It’s not easy to pull off. And they make it look easy, which is wonderful.
0:04:15 So it’s a product that I recommend to a lot of people who are interested in doing a podcast of any kind.
0:04:27 Like I said, I record my remote interviews with Riverside. Give it a try at riverside.fm and use code LEX for 30% off. That’s riverside.fm and use code LEX.
0:04:37 This episode is brought to you by Element, my daily zero sugar, delicious electrolyte mix. I drink it throughout the day. I’m drinking it now.
0:04:46 And I’m actually pretty low on water and electrolytes at the moment because I did a really hard training session.
0:04:58 The training session was about an hour and a half, and I think I only took one round off. It was just hard training round after round after round.
0:05:16 And by the end of it, I was just sort of both in the zone, technique wise, but also kind of psychologically accepting whatever happens in each particular puzzle that is jiu-jitsu.
0:05:27 So I trained against some really strong dudes today. It was wrestlers intense. The technique was there too. So it’s like it’s a battle for everything.
0:05:35 Lots of just attacking for submissions over and over and over everything in the transitions. There’s no stalling in a particular position.
0:05:43 It’s just movement and movement and movement and constant attacks. Yeah, it was exhausting. Plus the heat, just all of that sweating.
0:05:53 And I usually don’t drink during training. So by the time I’m done, I’m just like, no water in me. And that’s when the element really helps out.
0:06:02 I go from feeling really shitty to feeling really good. So get a sample pack for free with any purchase. Try to drink element.com/lex.
0:06:11 This episode is also brought to you by Shopify, a platform designed for anyone to sell anywhere with a great looking online store.
0:06:32 I set up a store on lexfumi.com/store. And I need to add shirts there, especially shirts that don’t have my face on them. I keep getting all kinds of ideas, but just haven’t gotten around to it, even though it’s super easy.
0:06:50 No, here’s an interesting thing. I’ve been getting more and more into programming languages that I haven’t used before because I’m doing interviews with more and more programmers coming up, planning on it, thinking about it, excited for it.
0:07:14 Programming makes me happy. Anyway, I found out that Shopify, you know, the product of service, the website was originally maybe still built with Rails, Ruby on Rails. So Ruby on Rails is this technology that’s super sexy, super popular, or was for a long time and I never just got around to using it.
0:07:29 So one of the things I would like to do is to get better at that so I can get a greater understanding of what it takes to program for the web so that I can talk to people who excel at that, who are experts at that.
0:07:48 Anyway, all that said, you can build incredible stuff with Ruby on Rails, which is Shopify, and you can sell stuff. Whatever it is you want to sell, you can sell it with Shopify. Sign up for a dollar per month trial period at Shopify.com/Lex. That’s all lowercase. Go to Shopify.com/Lex to take your business to the next level today.
0:08:11 This episode was also brought to you by BetterHelp, spelled H-E-L-P Help. They figure out what you need and match you with a licensed therapist in under 48 hours. I think at the end of the podcast, Graham called death a leap into the next great adventure. Something like that.
0:08:32 And I remember that made me smile, a kind of smile that just warms my heart and the warmth stays there for a time. It’s the kind of joyful acceptance of the transitory nature of life, those words and the way he said them.
0:09:02 And just as he said, death indeed is one of the great concerns for us humans, whether we’re acknowledged or not. It is the darkness beneath the surface waves of our daily concerns. At least I personally believe that there is a fear there, a great fear, that must be confronted and dealt with and integrated into our conception of what it means to be a human being.
0:09:05 And how to survive the waves.
0:09:18 Anyway, I’m a big believer that talking is one of the tools that should be used to understand your mind and to figure out what strategies can be used to navigate life.
0:09:31 And yeah, that’s what talk therapy can do. And I recommend the easiest way to do that is BetterHelp. Check them out at betterhelp.com/Lex and save in your first month. That’s betterhelp.com/Lex.
0:09:42 This is a Lex Friedman podcast. To support it, please check out our sponsors in the description. And now, dear friends, here’s Graham Hancock.
0:10:21 Let’s start with a big foundational idea that you have about human history, that there was an advanced ice age civilization that came before and perhaps seeded what people now call the six cradles of civilization, Mesopotamia, Egypt, India, China, Andes, and Mesoamerica. So let’s talk about this idea that you have. Can you at the highest possible level describe it?
0:10:43 It would be better to describe it as a foundational sense of puzzlement and incompleteness in the story that we are taught about our past, which envisages more or less, there have been a few ups and downs, but more or less straightforward evolutionary progress.
0:10:56 We start out as hunter foragers. Then we become agriculturalists. The hunter for the forager phase could go back hundreds of thousands of years.
0:11:08 I mean, this is where it’s also is also important to mention that anatomically modern humans and we’re not the only humans. We had Neanderthals from, I don’t know, 400,000 years ago to about 40,000 years ago.
0:11:16 They were certainly human because anatomically modern humans interbred with them and we carry Neanderthal genes.
0:11:32 They were the Denisovans, maybe 300,000 to perhaps even as recently as 30,000 years ago. And again, interbreeding took place. They’re obviously a human species. So, you know, we’ve got this background of humans who didn’t look quite like us.
0:11:46 And then we have anatomically modern humans. And I think the earliest anatomically modern human skeletal remains are from Jebeler Hood in Morocco and date to about 310,000 years ago.
0:12:00 So the question is what were our ancestors doing after that? And I think we can include the Neanderthals and the Denisovans in that in that general picture. And why did it take so long? This is one of the puzzles, one of the questions that bother me.
0:12:15 Why did it take so long when we have creatures who are physically identical to us? We cannot actually weigh and measure their brains. But from the work that’s been done on the crania, it looks like they had the same brains that we do with the same wiring.
0:12:34 So if we’ve been around for 300,000 plus years at least, and if ultimately in our future was the process to create civilization or civilizations, why didn’t it happen sooner? Why did it take so long? Why was it such a long time?
0:12:53 Even the story of anatomically modern humans has kept on changing. I remember a time when it was said that there hadn’t been anatomically modern humans before 50,000 years ago. And then it became 196,000 years ago with the findings in Ethiopia and then 310,000 years ago.
0:13:19 There’s a lot of missing pieces in the puzzle there. But the big question for me in that timeline is why didn’t we do it sooner? Why did it take so long? Why did we wait until after 12,000 years ago, really after 10,000 years ago, to start seeing the beginnings, what are selected as the beginnings of civilization in places like Turkey, for example?
0:13:30 And then there’s a relatively slow process of adopting agriculture. And by 6,000 years ago, we see ancient Sumer emerging as a civilization.
0:13:45 And we’re then in the pre-dynastic period in ancient Egypt as well, 6,000 years ago, beginning to see definite signs of what will become the dynastic civilization of Egypt about 5,000 years ago.
0:14:04 And interestingly, around about the same time, you have the Indus Valley civilization popping up out of nowhere. And by the way, the Indus Valley civilization was a lost civilization until the 1920s, when railway workers accidentally stumbled across some ruins.
0:14:17 I’ve been to Harappa and Mohenjo-Daro, and these are extraordinarily beautifully centrally planned cities. Clearly, they’re the work of an already sophisticated civilization.
0:14:27 One of the things that strikes me about the Indus Valley civilization is that we find a steatite seal of an individual seated in a recognizable yoga posture.
0:14:37 And that seal is 5,000 years old. And the yoga posture is Mula Bandhasana, which involves a real contortion of the ankles and twisting the feet back.
0:14:48 It’s an advanced yoga posture. So there it is 5,000 years ago. And that raises the question, well, how long did yoga take to get to that place when it was already so advanced 5,000 years ago?
0:14:58 What’s the background to this? China, the Yellow River civilization. Again, it’s around about the same period. 5,000 to 6,000 years ago, you get these first signs of something happening.
0:15:14 So it’s very odd that all around the world, we have this sudden upsurge of civilization about 6,000 years ago preceded by what seems like a natural evolutionary process that would lead to a civilization.
0:15:23 And yet certain ideas being being carried down and manifested and expressed in many of these in many of these different civilizations.
0:15:40 I just find that that whole idea very puzzling and very and very disturbing, especially when I look at this radical break that takes place in not just the human story, but the story of all life on earth,
0:15:48 which was the last great cataclysm that the earth went through, which was the Younger Dryas event. It was an extinction level event.
0:16:01 That’s when all the great megafauna of the Ice Age went extinct. It’s after that, it’s after that event that we start seeing this, what are taken to be the beginnings of the first gradual steps towards civilization.
0:16:08 We come out of the upper Paleolithic as it’s defined the old end of the old Stone Age and into the Neolithic.
0:16:13 And that’s when the wheels are supposedly set in motion to start civilization rolling.
0:16:17 But but what happened before that? And why did that? Why did that suddenly happen then?
0:16:24 And I can’t help feeling and I felt this for a very long while that there are major missing pieces in our story.
0:16:34 It’s often said that I’m claiming to have proved that there was an advanced lost civilization in the Ice Age, and I am not claiming to have proved that.
0:16:43 That is a hypothesis that I am putting forward to answer some of the questions that I have about about prehistory.
0:16:56 And and I think it’s worthwhile to inquire into those possibilities because the Younger Dryas event was a massive global cataclysm, whatever caused it.
0:17:03 And and it’s strange that just after it, we start seeing these these first signs.
0:17:16 So the current understanding in mainstream archaeology is that after the Younger Dryas is when the civilizations popped up in different places of the globe with a lot of similarities.
0:17:26 But they popped up independently independently and and by coincidence and by coincidence, those those big civilizations that we all remember as the first civilizations.
0:17:32 Sumer, Egypt, the Indus Valley civilization, China, they all pop up at the pretty much the same time.
0:17:35 That is that is the mainstream view.
0:17:36 And then I’ll just pop up.
0:17:39 They kind of build up gradually first.
0:17:40 There’s some settlements.
0:17:40 Oh, definitely.
0:17:41 Yes.
0:17:45 And then there’s different dynamics of how they build up and the role of the role of agriculture.
0:17:54 And that is also not obvious, but it’s just there’s a first kind of settlement, a stabilization of where the people are living.
0:17:55 Then they start using agriculture.
0:17:58 Then they start getting urban centers and that kind of stuff.
0:17:59 It seems like an entirely reasonable argument.
0:18:01 Everything everything about that makes sense.
0:18:13 There is no doubt that you’re seeing evolutionary progress, social evolution taking taking place in those thousands of years before Sumer emerges.
0:18:22 But what’s happening now really, I spent much of the 90s and the late 1980s investigating this issue of a lost civilization.
0:18:24 I wrote a series of books about it.
0:18:34 But by 2002, when I published a book called Underworld, which was the most massive and most heavy book that I’ve ever written because I was writing very defensively at the time.
0:18:46 By the time I finished that book, my wife, Santa, and I spent seven years scuba diving all around the world looking for structures under water, often led by local fishermen or local divers to anomalies that they’d seen underwater.
0:18:50 By the time that book was finished, I thought, actually, I’ve done this story.
0:18:51 I’ve walked the walk.
0:18:53 I really don’t have much more to say about it.
0:19:02 And I turned in another direction and I wrote a book called Supernatural Meetings with the Ancient Teachers of Mankind recently retitled Visionary.
0:19:10 And that was about the role of fundamentally about the role of psychedelics in the evolution of human human culture.
0:19:14 And I didn’t think that I would go back to the lost civilization issue.
0:19:26 But Gobekli Tepe in Turkey kept on forcing itself upon me the more and more discoveries there, the eleven thousand six hundred year date from enclosure D, which is the two largest megalithic pillars.
0:19:30 And I reached a point where I realized I have to get back in.
0:19:33 I have to get back in the water and I have to investigate this again.
0:19:36 And Gobekli Tepe was a game changer.
0:19:45 But I think it’s a game changer for everything because Gobekli Tepe, the extraordinary nature of it, we’re looking at a major megalithic side.
0:19:56 Which is at least five and a half thousand years older than, say, Gigantia in Malta, which was previously considered to be the oldest megalithic site in the world.
0:20:09 And this led, of course, to a huge amount of interest and attention, both from the Turkish government, who see the potential tourism potential of having the world’s oldest megalithic site and from archaeologists.
0:20:14 And this in turn has led to exploration and excavation throughout the region.
0:20:34 And what they’re finding throughout that whole region around Gobekli Tepe and going down into Syria and further down into the Jordan Valley, as far as Jericho and even across a bit of the Mediterranean into Cyprus is what Turkish archaeologists are now calling the Tass-Tepeler civilization.
0:20:51 They’re calling it a civilization, the Stone Hills civilization, with very definite identifying characteristics, semi-subterranean circular structures, the use of T-shaped megalithic pillars, sometimes not anywhere near as big as those at Gobekli Tepe.
0:20:55 It’s clear that Gobekli Tepe now was not the beginning of this process.
0:20:57 It was actually in a way the end of this process.
0:21:03 It was the summation of everything that that Stone Hills civilization had achieved.
0:21:09 But what is becoming clear is that this is a period before the foundation of Gobekli Tepe.
0:21:16 As far as we know, that date of 11,600 years ago is the oldest date for Gobekli Tepe.
0:21:18 But of course, there’s a lot of Gobekli Tepe still underground.
0:21:25 So we can’t say for sure that that’s the oldest, but it’s the oldest so far excavated.
0:21:36 What we’re seeing is that in that whole region around there, there was something was in motion and it began to go into motion round about the beginning of the Younger Dryas.
0:21:39 And this is where these two dates are really important.
0:21:49 The Younger Dryas around the figures off begins around 12,800 years ago and it ends around 11,600 years ago.
0:21:57 So Gobekli Tepe’s construction date, if it is 11,600 years ago, if they don’t find older materials, marks the end of the Younger Dryas.
0:22:07 But the beginning of the Younger Dryas, we’re already seeing the stirrings of the kind of culture that manifests in full form at Gobekli Tepe.
0:22:17 And after the construction of Gobekli Tepe, in fact, even during the construction of Gobekli Tepe, we see agriculture beginning to be adopted.
0:22:23 The people who created Gobekli Tepe were all hunter foragers at the beginning.
0:22:42 But by the time Gobekli Tepe was finished and it was definitely deliberately finished, closed off, closed down, deliberately buried, covered with earth, covered with rubble, and then topped off with a hill, which is why Gobekli Tepe is called what it is.
0:22:46 Gobekli Tepe means potbellied hill or the hill of the naval.
0:22:53 For a long time, Gobekli Tepe was thought to be just a hill that looked a bit like a potbelly.
0:22:55 Can you say how it was discovered?
0:22:58 I think this is one of the most fascinating things on earth, period.
0:23:01 So maybe can you say what it is and how it was discovered?
0:23:11 Well, Gobekli Tepe is, first of all, the oldest fully elaborated megalithic site that we know of anywhere in the world.
0:23:16 It doesn’t mean that older ones won’t be found, but it is the oldest so far found.
0:23:25 The part of the site that’s been excavated, which is a tiny percentage of the whole site, we do know, my first visit to Gobekli Tepe was in 2013, and Dr.
0:23:27 Klaus Schmidt, the late Dr.
0:23:34 Klaus Schmidt, who died a year later, was very generous to me and showed me around the site for over a period of three days.
0:23:39 And he explained to me that they’ve already used ground penetrating radar on the site.
0:23:42 And they know that there’s much more Gobekli Tepe still underground.
0:23:48 So anything is, anything is possible in terms of the, in terms of the dating of Gobekli Tepe.
0:23:58 But what we have at the moment is a series of almost circular, but not quite circular enclosures, which are, which are walled with relatively small stones.
0:24:02 And then inside them, you have pairs of megalithic pillars.
0:24:18 And the archetypal part of that site is enclosure D, which contains the two largest upright megaliths, about 18 feet tall and reckoned to weigh somewhere in the range of 20 tons, if I have my memory correct.
0:24:20 They’re substantial, hefty pieces of stone.
0:24:29 It isn’t, it isn’t some kind of extraordinary feat to create a 20 foot tall or 20 ton megalith, nor is it an extraordinary feat to move it.
0:24:33 There’s nothing, there’s nothing magical or, or really weird about that.
0:24:40 Human beings can do that and always have, besides the quarry for the megaliths is right there.
0:24:44 It’s within 200 meters of the, of the main enclosure.
0:24:54 So that’s not a mystery, but the mystery is, the mystery is why suddenly this, this new form of architecture, this massive, massive megalithic pillars appear.
0:25:00 And the pillars, what one of the things that interests me about the pillars is their alignment.
0:25:07 And there is good work that’s been done, which suggests that enclosure D aligns to the rising of the star Sirius.
0:25:15 And the rising points of the star Sirius appear to be mapped by the other enclosures, which are all oriented in slightly different directions.
0:25:28 It was the work entirely of hunter foragers, but by the time Gobekli Tepe was completed, agriculture was being introduced and was, was, was taking place there.
0:25:31 Now you asked how Gobekli Tepe was found.
0:25:40 The answer to that is that there was a survey of that potbellied hill in the 1960s by some American archaeologists.
0:25:47 And they were looking, absolutely looking for stone age material for material from the Paleolithic.
0:25:52 And they had found some Paleolithic flints, upper Paleolithic flints around there.
0:25:53 So it looked like a good place to look.
0:26:02 But then they noticed sticking out of the side of the hill, some very finely cut stone bits of very large and very finely cut stone.
0:26:10 And looking at that, the workmanship was so good that those archaeologists were confident that it had nothing to do with the stone age.
0:26:15 And they thought they were looking at perhaps some Byzantine remains.
0:26:18 And they abandoned the site and never looked at it further.
0:26:20 And it wasn’t until the German Archaeological Institute got involved.
0:26:34 And particularly Klaus Schmidt, who I think was a genius, had real insight into this and started to dig at Gobekli Tepe that they realized what they’d found that they’d found potentially the oldest megalithic site in the world.
0:26:45 And they’d found it at a place where agriculture, according to the established historical timeline, that’s where agriculture at any rate in Europe and Western Asia begins.
0:26:51 It begins in Anatolia, in Turkey, and then it gradually disseminates westward from there.
0:26:56 And yet the understanding is it was created by hunter-gatherers.
0:26:58 It was created by hunter-gatherers, yeah.
0:27:13 There was no agriculture 11,600 years ago in Gobekli Tepe, but by the time Gobekli Tepe was decommissioned, and I use that word deliberately, was closed down and buried, agriculture was all around it.
0:27:18 And this was agriculture of people who knew how to cultivate plants.
0:27:27 Do we have an understanding when it was turned into, if I could say, a time capsule, so protected by forming a mound around it?
0:27:29 Is it around that similar time?
0:27:38 It stood from roughly 11,600 years ago to about 10,400 years ago to about 8,400 BC.
0:27:44 So around 1,200 years, it was there and it continued to be elaborated as a site.
0:27:48 And while it was being elaborated as a site, we see agriculture.
0:27:51 I’m going to use the word being introduced.
0:27:55 It had not, there’d been no sign of it before and suddenly it’s there.
0:27:57 And to me, that’s another of the mysteries about Gobekli Tepe.
0:28:06 And then with the new work that’s being done, we realize that it’s part of a much wider phenomenon, which spreads across an enormous distance.
0:28:12 And the puzzling thing is that after Gobekli Tepe, there almost seems to be a decline.
0:28:14 Things fall down again.
0:28:26 And then we enter this long, slow process of the Neolithic thousands of years, gradual developments until we come to ancient Sumer and Mesopotamia.
0:28:30 But agriculture has taken a firm, a firm route by then.
0:28:37 Actually, one of the things I’ll just say this in passing, when I talk about a lost civilization, introducing ideas to people.
0:28:42 I’m often accused of stealing credit from the indigenous people who had those ideas in the first place.
0:28:52 So I do find it slightly hypocritical that archaeology is fully accepts that the idea of agriculture was introduced to Western Europe from Turkey.
0:28:57 And that Western Europeans didn’t invent agriculture.
0:29:01 It was absolutely introduced by Anatolian farmers who traveled west.
0:29:07 So the notion of dissemination of ideas perhaps shouldn’t be so annoying to archaeologists as it is.
0:29:18 And perhaps we should also state, if you look at the entirety of history of hominids, humans or hominids have been explorers.
0:29:23 I didn’t even know this when I was preparing for this, looking at Homo erectus.
0:29:29 Yeah, 1.9 million years ago, almost right away, they spread out through the whole world.
0:29:33 And we, Homo sapiens evolved from them.
0:29:43 And we should also mention, since we’re talking about a sort of controversial debates going on, as I understand, there’s still debates about the dynamics of all that was going on there.
0:29:52 Like we mentioned in Africa that it’s, you know, I think the current understanding, we didn’t come from one particular point of Africa that there’s multiple locations.
0:29:55 This is the out of Africa theory.
0:29:58 I think it’s more than a theory, it’s really strongly evidenced.
0:30:03 Why? Because we’re part of the great ape family and it’s an African family.
0:30:08 There’s no doubt that human beings, our deep origins are in Africa.
0:30:19 But then there, as you rightly say, there were these very early migrations out of Africa by species that are likely ancestral to anatomically modern humans,
0:30:25 including definitely Homo erectus and the astonishingly distant travels that they undertook.
0:30:30 Yes, I think there is an urge to explore in all of humanity.
0:30:36 I think there is an urge to find out what’s around the next corner, what’s over the brow of the next hill.
0:30:40 And I think that goes very deep into human character.
0:30:47 And I think it was being manifested in those early adventures of people who left Africa and traveled all around the world.
0:30:57 And then settling in different parts of the world, I think a lot of anatomically modern human evolution took place outside Africa as well, not only in Africa.
0:31:16 So I guess the general puzzlement that you’re filled with is, given that these creatures explore and spread and try out different environments, why did it take hundreds of thousands of years for them to develop complicated society settlements?
0:31:17 That’s the first big question.
0:31:18 Why did it take so long?
0:31:22 And that raises in my mind a hypothesis, a possibility.
0:31:24 Maybe it didn’t take so long.
0:31:31 Maybe things were happening that we haven’t yet got hold of in the archaeological record, which await to be discovered.
0:31:38 And of course, there are huge parts of the world that have not been studied at all by archaeology.
0:31:48 But the fact that the fact that huge parts of the world have not been studied at all by archaeology is not on its own enough to suggest that we’re missing a chapter in the human story.
0:31:54 The reason that I come to that isn’t only puzzlement about that 300,000 year gap.
0:32:07 It’s also to do with the fact that there’s common iconography, there’s common myths and traditions, and there’s common spiritual ideas that are found all around the world.
0:32:17 And they’re found amongst cultures that are geographically distant from one another and that are also distant from one another in time.
0:32:20 They don’t necessarily occur at the same time.
0:32:35 And this is where I think that archaeology is perhaps desperately needing a history of ideas, as well as just a history of things, because an idea can manifest again and again throughout the human story.
0:32:45 So there are particular issues, for example, the notion of the afterlife destiny of the soul, what happens to us when we die.
0:32:52 And believe me, when you reach my age, that’s something you do think about, what happens.
0:32:58 I used to feel immortal when I was in my forties, but now that I’m 74, I definitely know that I’m not.
0:33:03 Well, it would be natural for human beings all around the world to have that same feeling, that same idea.
0:33:14 But why would they all decide that what happens to the soul after death is that it makes a leap to the heavens, to the Milky Way, that it makes a journey along the Milky Way,
0:33:26 that there it is confronted by challenges, by monsters, by closed gates, the course of the life that that person has lived will determine their destiny in that afterlife journey.
0:33:36 And this idea, the path of souls, the Milky Way is called the path of souls, it’s very strongly found in the Americas, right from South America, through Mexico, through into North America.
0:33:43 But it’s also found in ancient Egypt, in ancient India, in ancient Mesopotamia, the same the same idea.
0:33:46 And I don’t feel that that can be a coincidence.
0:34:00 I feel I feel that what we’re looking at is an inheritance of an idea, a legacy that’s been passed down from a remote common source to cultures all around the world and that and then has taken on a life of its own within those cultures.
0:34:07 So the remote common source would explain both the similarities and the differences in the expression of these ideas.
0:34:18 The other thing, very puzzling thing, is this sequence of numbers that are a result of the precession of the equinoxes.
0:34:21 At least I think that’s the best theory to explain them.
0:34:31 Here, I think it’s important to pay tribute to the work of Giorgio de Santillana and Hirthavondeshend.
0:34:38 Giorgio de Santillana was Professor of History of Science, actually at MIT, where you’re based back in the sixties.
0:34:43 And Hirthavondeshend was Professor of the History of Science at Frankfurt University.
0:35:00 And they wrote an immense book in the 1960s called Hamlet’s Mill, and Hamlet’s Mill differs very strongly from established opinion on the issue of the phenomenon of precession.
0:35:02 And I’ll explain what precession is in a moment.
0:35:09 Generally, it’s held that it was the Greeks who discovered the precession.
0:35:15 And the dating on that is put back not very far, maybe 2,300 years ago or so.
0:35:22 Santillana and Vundeshend are pointing out that knowledge of precession is much, much older than that, thousands of years older than that.
0:35:30 And they do actually trace it, I think I’m quoting them pretty much correctly, to some almost unbelievable ancestor civilization.
0:35:36 Reading that book was one of the several reasons that I got into this mystery in the first place.
0:35:49 OK, now, the precession of the equinoxes, to give it its full name, is results from the fact that our planet is the viewing platform from which we observe the stars.
0:35:55 And our planet, of course, is rotating on its own axis at roughly 1,000 miles an hour at the equator.
0:35:59 But what’s less obvious is that it’s also wobbling on its axis.
0:36:10 And that, so if you imagine the extended North Pole of the Earth pointing up at the sky, in our time, it’s pointing at the star Polaris, and that is our pole star.
0:36:16 But Polaris has not always been the pole star precisely because of this wobble on the axis of the Earth.
0:36:22 Other stars have occupied the pole position, and sometimes the extended North Pole of the Earth points at empty space.
0:36:24 There is no pole star.
0:36:28 That’s one of the obvious results of the wobble on the Earth’s axis.
0:36:39 The other one is that there are 12 well-known constellations in our time, the 12 constellations of the zodiac, that lie along what is referred to as the path of the sun.
0:36:48 The Earth is orbiting the sun, and we are seeing what’s in direct line with the sun in our view.
0:36:52 And the zodiacal constellations all lie along the path of the sun.
0:36:57 So at different times of the year, the sun will rise against the background of a particular zodiacal constellation.
0:37:07 Today, we live in the age of Pisces, and it’s definitely not an accident that the early Christians used the fish as their symbol.
0:37:10 This is another area where I differ from archaeology.
0:37:16 I think the constellations of the zodiac were recognized as such much earlier than we suppose.
0:37:24 Anyway, to get to the point, the key marker of the year, certainly in the northern hemisphere, was the spring equinox.
0:37:30 This was the question was, what constellation is rising behind the sun?
0:37:34 What constellation is housing the sun at dawn on the spring equinox?
0:37:36 Right now, it’s Pisces.
0:37:39 In another 150 years or so, it’ll be Aquarius.
0:37:42 We do live in the dawning of the age of Aquarius.
0:37:51 Back in the time of the late ancient Egyptians, it was Ares, going back to the time of Ramesses or before.
0:37:54 Before that, it was Taurus, and so on and so forth.
0:38:04 It’s backwards through the zodiac until 12,500 years ago, you come to the age of Leo, when the constellation of Leo houses the sun on the spring equinox.
0:38:09 Now, this process unfolds very, very, very, very slowly.
0:38:19 The whole cycle, and it is a cycle, it repeats itself roughly every 26,000 years, put a more exact figure on it, 25,920 years.
0:38:21 That may be a convention.
0:38:25 Some scholars would say it was a bit less than that, a bit more, but you’re talking fractions.
0:38:29 It’s in that area, 25,920 years.
0:38:40 And to observe it, you really need more than one human lifetime, because it unfolds very, very slowly at a rate of one degree every 72 years.
0:38:45 And the parallel that I often give is, hold your finger up to the horizon, the distant horizon.
0:38:51 The movement in one lifetime in a period of 72 years is about the width of your finger.
0:38:55 It’s not impossible to notice in a lifetime, but it’s difficult.
0:38:56 You’ve got to pass it on.
0:39:05 And what seems to have happened is that some ancient culture, the culture that Santigana and Vondeshan call some almost unbelievable ancestor culture,
0:39:17 worked out the entire process of procession and selected the key numbers of procession, of which the most important number, the governing number, is the number 72.
0:39:23 But we also have numbers related to the number 72.
0:39:29 72 plus 36 is 108. 108 divided by 2 is 54.
0:39:35 These numbers are also found in mythology all around the world.
0:39:47 There were 72 conspirators who were involved in killing the goddess Cyrus in ancient Egypt and nailing him up in a wooden coffer and dumping him in the Nile.
0:39:52 There are 432,000 in the Rig Vesa.
0:39:56 432,000 is a multiple of 72.
0:40:04 And at Angkor in Cambodia, for example, you have the bridge to Angkor Tom.
0:40:13 And on that bridge, you have figures on both sides, sculpted figures, which are holding the body of a serpent.
0:40:19 That serpent is Vazuki. And what they’re doing is they’re churning the milky ocean.
0:40:26 It’s the same metaphor of churning and turning that’s defined in the story of Hamlet’s Mill, of Amladi’s Mill.
0:40:31 There are 54 on each side. 54 plus 54 is 108.
0:40:33 108 is 72 plus 36.
0:40:38 It’s a pre-sessional number, according to the work that Santigana and Vondeshan did.
0:41:01 And the fascination with this number system and its discovery all around the world is one of the puzzles that intrigue me and suggest to me that we are looking at ancestral knowledge that was passed down and probably was passed down from a specific single common source at one time, but then was spread out very, very widely around the world.
0:41:19 So one of the defining ways that you approach the study of human history that I think contrasts with mainstream archaeology is you take this sort of astronomical symbolism and the relationship between humans and the stars very seriously.
0:41:21 I do, as I believe the ancients did.
0:41:28 I think it’s important to sort of consider what humans would have thought about back then.
0:41:43 Now we have a lot of distractions, we have social media, we can watch videos on YouTube and whatever, but back then, especially before sort of electricity, the stars is like the sexiest thing to talk about.
0:41:44 There’s no light pollution.
0:41:45 There’s no light pollution.
0:41:57 So there’s that in your space, every single night, you’re spending looking up at the stars and you can imagine there’s a lot of sort of status value to be the guy who’s very good at studying the stars.
0:42:06 And sort of the scientists of the day, and I’m sure there’s going to be these geniuses that emerge, they’re able to do two things.
0:42:16 One, tell stories about the gods or whatever based on the stars, and then also as we’ll probably talk about, use the stars practically for navigation, for example.
0:42:31 So it makes sense that the stars had primal importance for the ideas of the times, for the status, for religious explorations.
0:42:37 It was an ever-present reality, and it was bright, and it was brilliant, and it was full of lights.
0:42:44 It’s inconceivable that the ancients would not have paid attention to it, it was an overwhelming presence.
0:42:55 And that’s one of the reasons why I’m really confident that the constellations that we now recognize as the constellations of the zodiac were recognized much earlier, because it’s hard to miss when you pay attention to the sky.
0:43:02 That the sun over the course of the solar year is month by month rising against the background of different constellations.
0:43:08 And then there’s a much longer process, the process of procession, which takes that journey backwards.
0:43:15 And where we have a period of 2,160 years for each sign of the zodiac, I think it would have been hard for the ancients to have missed that.
0:43:20 They might not have identified the constellations in exactly the same way we do today.
0:43:28 That may well be a Babylonian or Greek convention, but that the constellations were there, I think was very clear.
0:43:35 And that there were special constellations, unlike other ones higher up in the sky, which were not on the path of the sun, that people paid attention to.
0:43:42 Well, but detecting the procession of the equinox is hard, because especially they don’t have any writing systems.
0:43:44 They don’t have any mathematical systems.
0:43:46 So everything is told through words.
0:43:50 Yeah, well, they have let’s not underestimate oral traditions.
0:43:55 The oral traditions, that’s something we’ve lost in our culture today.
0:44:01 One of the things that happens with the written word is that you gradually lose your memory.
0:44:12 Actually, there’s a nice story from from ancient Egypt about the God Thoth, the God of Wisdom, who is very proud of himself, because he has invented writing.
0:44:17 Look at this gift, he says to a mythical pharaoh of that time.
0:44:20 Look at the gift that I am giving humanity writing.
0:44:21 This is a wonderful thing.
0:44:25 It’ll enable you to preserve so much that you would otherwise lose.
0:44:32 And the pharaoh in this story replies to him, “No, you have not given us a wonderful gift.
0:44:34 You have destroyed the art of memory.
0:44:36 We will forget everything.
0:44:43 Words will roam free around the world, not accompanied by any wise advice to set them into context.”
0:44:46 And actually, that’s a very interesting point.
0:44:52 And we do know that cultures that still do have oral traditions are able to preserve information for very long periods of time.
0:44:58 One thing I think is clear in any time, in any period of history, is human beings love stories.
0:44:59 We love great stories.
0:45:06 And one way to preserve information is to encode it, embed it in a great story.
0:45:15 And so carefully done that actually it doesn’t matter whether the storyteller knows that they’re passing on that information or not.
0:45:23 The story itself is the vehicle and as long as it’s repeated faithfully, the information contained within it will be will be passed on.
0:45:27 And I do think this is this is part of the story of the preservation of knowledge.
0:45:31 So that’s one of the reasons that you take myths seriously?
0:45:35 I take them very seriously and the other many reasons.
0:45:48 But I can’t help being deeply impressed and deeply puzzled by the worldwide tradition of a global cataclysm within human memory.
0:45:55 I mean, we know that we know scientifically that there have been many, many cataclysms in the past going back millions of years.
0:46:01 I mean, the best known one, of course, is the K-PG event, as it’s now called, that made the dinosaurs extinct.
0:46:06 65 million or 66 million years ago.
0:46:11 But has there been such a cataclysm in the lifetime of the human species?
0:46:17 Yeah, the Mount Toba eruption about 70,000 years ago was pretty bad.
0:46:28 But a global cataclysm, the Younger Dryas really ticks all the boxes as a worldwide disaster, which definitely involved sea level rise,
0:46:32 both at the beginning and at the end of the Younger Dryas.
0:46:36 It definitely involved the swallowing up of lands that previously had been above water.
0:46:47 And I think it’s an excellent candidate for this worldwide tradition of a global cataclysm, of which one of, but not the only distinguishing characteristics was a flood,
0:46:54 an enormous flood and the submergence of lands that had previously been above water, underwater.
0:47:05 The fact that this story is found all around the world suggests to me that the archaeological explanation is, look, people suffer local floods all the time.
0:47:14 I mean, as we’re talking, there’s flooding in Florida, but I don’t think anybody in Florida is going to make the mistake of believing that that’s a global flood.
0:47:20 They know it’s local, but that’s the argument largely of archaeology dealing with the flood myths.
0:47:29 So that some local population experienced a nasty local flooding event, and they decided to say that it was that it affected the whole world.
0:47:44 I’m not persuaded by that, particularly since we know there was a nasty epoch, the Younger Dryas, when flooding did occur and when the earth was subjected to events cataclysmic enough to extinguish entirely the megafauna of the Ice Age.
0:47:55 So there is the Younger Dryas Impact Hypothesis that provides an explanation of what happened during this period that resulted in such rapid environmental change.
0:47:57 So can you explain this hypothesis?
0:48:10 Yes, the Younger Dryas Impact Hypothesis, YDIH for short, is not a lunatic fringe theory as its opponents often attempt to write it off.
0:48:22 It’s the work of more than 60 major scientists working across many different disciplines, including archaeology and including oceanography as well.
0:48:37 And they are collectively puzzled by the sudden onset of the Younger Dryas and by the fact that it is accompanied 12,800 years ago by a distinct layer in the earth.
0:48:44 You can see it most clearly at Murray Springs in Arizona, for example.
0:48:54 You can see it’s about the width of a human hand, and there’s a draw there that’s been cut by flash flooding at some time, and that draw has revealed the sides of the draw.
0:49:03 And you can see the cross-section, and in the cross-section is this distinct dark layer that runs through the earth, and it contains evidence of wildfires.
0:49:05 There’s a lot of soot in it.
0:49:08 There are also nanodiamonds in it.
0:49:11 There is shocked quartz in it.
0:49:16 There is quartz that’s been melted at temperatures in excess of 2,200 degrees centigrade.
0:49:19 There are carbon microspherules.
0:49:23 All of these are proxies for some kind of cosmic impact.
0:49:26 I talked a moment ago about the extinction of the dinosaurs.
0:49:36 Lewis and Walter Alvarez, who made that incredible discovery, initially, their discovery was based entirely on impact proxies just as the Younger Dryas is.
0:49:49 There was no crater, and for a long time they were disbelieved because they couldn’t produce a crater, but when they finally did produce that deeply buried chicks-glued crater, that’s when people started to say, “Yeah, they have to be right.”
0:49:51 But they weren’t relying on the crater.
0:49:59 They were relying on the impact proxies, and they’re the same impact proxies that we find in what’s called the Younger Dryas boundary layer all around the world.
0:50:12 So it’s the fact that at the moment when the earth tips into a radical climate shift, it’s been warming up for at least 2,000 years before 12,800 years ago.
0:50:15 People at the time must have been feeling a great sense of relief.
0:50:19 You know, we’ve been living through this really cold time, but it’s getting better.
0:50:34 Things are getting better, and then suddenly around 12,800 years ago, some might say 12,860 years ago, there’s a massive global plunge in global temperatures, and the world suddenly gets as cold as it was at the peak of the ice age.
0:50:37 And it’s almost literally overnight.
0:50:39 It’s very, very, very rapid.
0:50:49 Normally, in an epoch, when the earth is going into a freeze, you would not expect sea levels to rise, but there is a sea level rise, a sudden one, right at the beginning of the Younger Dryas.
0:51:14 And then you have this long frozen period from 12,800 to 11,600 years ago, and then equally dramatically and equally suddenly, the Younger Dryas comes to an end, and the world very rapidly warms up, and you have a recognized pulse of meltwater at that time as the last of the glaciers collapse into the sea, called Meltwater Pulse 1B, around about 11,600 years ago.
0:51:37 So this is a period which is very tightly defined, it’s a period when we know that human populations were grievously disturbed, that’s when the so-called Clovis culture of North America vanished entirely from the record during the Younger Dryas, and it’s the time when the mammoths and the saber-toothed tigers vanished from the record as well.
0:51:48 Is there a good understanding of what happened geologically, whether there was an impact or not? Like, what explains this huge dip in temperature and then rise in temperature?
0:52:17 The abrupt cessation of the global meridional overturning circulation, of which the Gulf Stream is the best known part. The main theory that’s been put forward up to now, and I don’t dispute that theory at all, is that the sudden freeze was caused by the cutting off of the Gulf Stream, basically, which is part of the central heating system of our planet, so no wonder it became cold.
0:52:31 But what’s not really been addressed before is why that happened, why the Gulf Stream was cut, why a sudden pulse of meltwater went into the world ocean, and it was so much of it, and it was so cold that actually stopped the Gulf Stream in its tracks.
0:52:38 And that’s where the Younger Dryas Impact Hypothesis offers a very elegant and very satisfactory solution to the problem.
0:52:49 Now, the hypothesis, of course, is broader than that. Amongst the scientists working on it are, for example, Bill Napier, an astrophysicist and astronomer.
0:53:07 They have assembled a great deal of evidence, which suggests that the culprit in the Younger Dryas Impact event, or events, was what we now call the Torrid Meteor Stream, which the Earth still passes through twice a year.
0:53:18 It’s now about 30 million kilometers wide. It takes the Earth a couple of days to pass through it on its orbit. It passes through it in June, and it passes through it at the end of October.
0:53:39 The suggestion is that the Torrid Meteor Stream is the end product of a very large comet that entered the solar system around about 20,000 years ago, came in from the Oort cloud, got trapped by the gravity of the sun, and went into orbit around the sun, an orbit that crossed the orbit of the Earth.
0:54:00 However, when it was one object, the likelihood of a collision with the Earth was extremely small. But as it started to do what all comets do, which was to break up into multiple fragments, because these are chunks of rock held together by ice, and as they warm up, they split and disintegrate and break into pieces,
0:54:22 as it passed through that, its debris stream became larger and larger and wider and wider. And the theory is that 12,800 years ago, the Earth passed through a particularly dense part of the Torrid Meteor Stream and was hit by multiple impacts all around the planet, certainly from the west of North America, as far east as Syria.
0:54:45 And that we are by and large not talking about impacts that would have caused craters, although there certainly were some. We’re talking about air bursts. When an object is 100 or 150 meters in diameter, and it’s coming in very fast into the Earth’s atmosphere, it is very unlikely to reach the Earth.
0:55:03 It’s going to blow up in the sky. And the best known recent example of that is the Tunguska event in Siberia, which took place on the 30th of June, 1908. The Tunguska event was nobody disputes. It was definitely an air burst of a cometary fragment.
0:55:18 And the date is interesting, because the 30th of June is the height of the beta torrid. It’s one of the two times when the Earth is going through the Torrid Meteor Stream. Well, luckily, that part of Siberia wasn’t inhabited, but 2,000 square miles of forest were destroyed.
0:55:28 If that had happened over a major city, we would all be thinking very hard about objects out of the Torrid Meteor Stream and about the risk of cosmic impact.
0:55:44 So the suggestion is that it wasn’t one impact. It wasn’t two impacts. It wasn’t three impacts. It was hundreds of air bursts all around the planet, coupled with a number of bigger objects, which the scientists working on this think hit the North American ice cap largely.
0:55:58 Some of the may also have hit the Northern European ice cap, resulting in that sudden, otherwise unexplained, flooded meltwater that went into the world ocean and caused the cooling that then took place.
0:56:13 But this was a disaster for life all over the planet. And it’s interesting that one of the sites where they find the Younger Dryas boundary and where they find overwhelming evidence of an air burst and where they find all the shocked quartz,
0:56:23 the carbon microspherials, the nanodiamonds, the Trinitite and so on and so forth. All of those impact proxies are found at Apoherera.
0:56:31 That was a settlement within 150 miles of Gobekli Tepe. And it was hit 12,800 years ago and it was obliterated.
0:56:40 Interestingly, it was re-inhabited by human beings within probably five years, but it was completely obliterated at that time.
0:56:55 And it’s difficult to imagine that the people who lived in that area would not have been very impressed by what they saw happening by these massive explosions in the sky and the obliteration of Apoherera.
0:57:03 Now, this is a theory. The Younger Dryas impact, it’s a hypothesis actually. It’s not even a theory. A theory is, I think, considered a higher level than a hypothesis.
0:57:10 That’s why it’s the Younger Dryas impact hypothesis. And of course, it has many opponents and there are many who disagree with it.
0:57:19 And there have been a series of peer-reviewed papers that have been published supposedly debunking the Younger Dryas impact hypothesis.
0:57:37 One, I think, was in 2011. It was called a Requiem for the Younger Dryas impact hypothesis. And there’s one just been published a few months ago or a year ago called a complete refutation of the Younger Dryas impact hypothesis, something like that, some lengthy title.
0:57:48 So it’s a hypothesis that has its opponents. And even within those of us who are looking at the alternative side of history, there are different points of view.
0:57:59 Robert Shock from Boston University, the geologist who demonstrated that the erosion on the Sphinx may well have been caused by exposure to a long period of very heavy rainfall.
0:58:09 He doesn’t go for the Younger Dryas impact hypothesis. He fully accepts that the Younger Dryas was a global cataclysm and that the extinctions took place.
0:58:20 But he thinks it was caused by some kind of massive solar outburst. So what everybody’s agreed on is the Younger Dryas was bad, but there is dispute about what caused it.
0:58:29 I personally have found the Younger Dryas impact hypothesis to be the most persuasive, which most effectively explains all the evidence.
0:58:45 How important is the impact hypothesis to your understanding of the Ice Age advanced civilizations? So is it possible to have another explanation for environmental factors that could have erased most of an advanced civilization during this period?
0:58:57 In a sense, it’s not the impact hypothesis that is central to what I’m saying. It’s the Younger Dryas that’s central to what I’m saying. And the Younger Dryas required a trigger. Something caused it.
0:59:11 I think the Younger Dryas impact hypothesis, the notion that we’re looking at a debris stream of a fragment in Comet, and we can still see that debris stream because it’s still up there and we still pass through it twice a year, is the best explanation.
0:59:19 But I don’t mind other explanations. It’s good that there are other explanations. The Younger Dryas is a big mystery, and it’s not a mystery that’s been solved yet.
0:59:40 And that word advanced civilization, this is another word that is easily misunderstood. And I’ve tried to make clear many, many times that when we consider the possibility of something like a civilization in the past, we shouldn’t imagine that it’s us, that it’s something like us.
1:00:05 We should expect it to be completely different from us, but that it would have achieved certain things. So amongst the clues that intrigue me are those precessional numbers that are found all around the world, and are a category of ancient maps called Portolanos, which suddenly started to appear just after the crusade that entered Constantinople and sacked Constantinople.
1:00:18 The Portolanos suddenly start to appear, and they’re extremely accurate maps. The most of the ones that have survived are extremely accurate maps of the Mediterranean alone, but some of them show much wider areas.
1:00:34 For example, on these Portolanos style maps, you do find a depiction of Antarctica again and again. And another thing that these maps have in common is that many of the map makers state that they based their maps on multiple older source maps, which have not survived.
1:00:40 These maps are intriguing because they have very accurate relative longitudes.
1:00:50 Our civilization did not crack the longitude problem until the mid 18th century with Harrison’s chronometer, which was able to keep accurate time at sea.
1:00:58 So you could have the time in London, and you could have the local time at sea at the same time, and then you could work out your longitude.
1:01:07 There might be other ways of working out longitude as well, but there it is. The fact is these Portolanos have extremely accurate relative longitudes.
1:01:23 Secondly, some of them show the world to my eye as it looked during the Ice Age. They show a much extended Indonesia and Malaysian Peninsula, and the series of islands that make up Indonesia today are all grouped together into one land mass.
1:01:41 And that was the case during the Ice Age. That was the Sunda shelf. And the presence of Antarctica on some of these maps also puzzles and intrigues me and is not satisfactorily explained in my view by archaeology, which says, oh, those map makers, they felt that the world needed something underneath it to balance it.
1:01:52 So they put a fictional land mass there. I don’t think that makes sense. I think somebody was mapping the world during the last Ice Age, but that doesn’t mean that they had our kind of tech.
1:02:09 It means that they were following that exploration instinct, that they knew how to navigate. They’d been watching the stars for thousands of years before. They knew how to navigate and they knew how to build sea-going ships, and they explored the world and they mapped the world.
1:02:28 Those maps were made a very, very long time ago. Some of them, I believe, were likely preserved in the library of Alexandria. I think even then they were being copied and recopied. We don’t know exactly what happened to the library of Alexandria, except that it was destroyed.
1:02:47 I suggest it’s likely this was during the period of the Roman Empire. I suggest it’s likely that some of those maps were taken out of the library and taken to Constantinople, and that’s where they were liberated during the Crusade and entered world culture again and started to be copied and recopied.
1:03:02 From this perspective, when we talk about advanced Ice Age civilization, it could have been a relatively small group of people with the technology of their scholars of the stars and their expert seafaring navigators.
1:03:15 Yes. That’s about as far as I would take it. When I say that, as I have said on a number of occasions, that it had technology equivalent to ours in the 18th century, I’m referring specifically to the ability to calculate longitude.
1:03:20 I’m not saying that they were building steam engines. I don’t see any evidence for that.
1:03:26 And perhaps some building tricks and skills of how to–
1:03:49 Well, definitely. And this again is where you come to a series of mysteries which are perhaps best expressed on the Giza Plateau in Egypt with the Three Great Pyramids and the extraordinary megalithic temples that many people don’t pay much attention to on the Giza Plateau and the Great Sphinx itself.
1:03:55 This is an area of particular importance in understanding this issue.
1:04:01 Well, can you actually describe the Sphinx and the Great Pyramids and what you find most mysterious and interesting about them?
1:04:21 Well, first of all, the astronomy. And here I must pay tribute to two individuals, actually three individuals in particular. One of them is John Anthony West, passed away in 2018. He was the first person in our era to begin to wonder if the Sphinx was much older than it had been.
1:04:34 Actually, he got that idea from a philosopher called Shwala Dilubix who’d noticed what he thought was water erosion on the body of the Sphinx. John West picked that up and he was a great amateur Egyptologist himself.
1:04:50 He spent most of his life in Egypt and he was hugely versed in ancient Egypt. And when he looked at the Sphinx and at the strange scalloped erosion patterns and the vertical fishes, particularly in the trench around the Sphinx, he began to think maybe Shwala was right.
1:05:13 Maybe there was some kind of flooding here. And that’s when he brought Robert Shock, second person I’d like to recognize, geologist at Boston University. He brought Shock to Giza and Shock was the first geologist to stick his neck out, risk the ryer, the ire of Egyptologists and say, well, it looks to me like the Sphinx was exposed to at least a thousand years of heavy rainfall.
1:05:29 And as Shock’s calculations have continued, as he’s continued to be immersed in this mystery, he’s continuously pushed that back. And he’s now, again, looking at the date of around 12,000, 12 and a half thousand years ago during the younger dryers for the creation of the great Sphinx.
1:05:42 And then, of course, this is the period of the wet Sahara, the humid Sahara. The Sahara was a completely different place during the Ice Age. There were rivers in it. There were lakes in it. It was fertile.
1:05:52 It was possibly densely populated. And there was a lot of rain. There’s not no rain in Giza today, but there’s relatively little rain.
1:06:01 The next person, not enough rain to cause that erosion damage on the Sphinx. The next person who needs to be mentioned in this context is Robert Boval.
1:06:19 Robert and I have co-authored a number of books together. Unfortunately, Robert has been very ill for the last seven years. He’s got a very bad chest infection. And I think also that Robert became very demoralized by the attacks of Egyptologists on his work.
1:06:33 But Robert is the genius. And it does take a genius some time to make these connections because nobody noticed it before that the three pyramids of Giza are laid out on the ground in the pattern of the three stars of Orion’s belt.
1:06:49 And skeptics will say, well, you can find any buildings and line them up with any stars you want. But Orion actually isn’t any old constellation. Orion was the God of Cyrus in the sky. He was the ancient Egyptians called the Orion constellation Sahu.
1:07:00 And they recognize it as the celestial image of the God of Cyrus. So what’s being copied on the ground is the belt of a deity, of a celestial deity. It’s not just a random constellation.
1:07:07 And then when we take precession into account, you find something else very intriguing happening.
1:07:19 First of all, you find that the exact orientation of the pyramids as it is today and pretty much as it was when they’re supposed to have been built 4,500 years ago.
1:07:28 It’s not precisely related to how Orion’s belt looked at that time. There’s a bit of a twist is they’re not quite right.
1:07:45 But as you precess the stars backwards, as you go back and back and back and you come to around 10,500 BC, 12 and a half thousand years ago, in the younger dryers, you find that suddenly they lock perfectly, they match perfectly with the three pyramids on the ground.
1:07:54 And that’s the same moment that the great Sphinx, an equinoctial monument, aligned perfectly to the rising sun on the spring equinox.
1:08:04 Anybody can test this for themselves. Just go to Giza on the 21st of March, be there before dawn, stand behind the Sphinx, and you will see the sun rising directly in line with the gaze of the Sphinx.
1:08:15 But the question is, what constellation was behind the Sphinx? And 12 and a half thousand years ago, it was the constellation of Leo. And actually, the constellation of Leo has a very Sphinx-like look.
1:08:33 And I and my colleagues are pretty sure that the Sphinx was originally a lion entirely, and that it, over the thousands of years, it became damaged, it became eroded, particularly the part of it that sticks out the head.
1:08:38 There were periods when the Sphinx was completely covered in sand, but still the head stuck out.
1:08:51 By the time you come to the fourth dynasty, when the great pyramids are supposedly built, by the time you come to the fourth dynasty, the head of the lion, the original lion head, would have been a complete mess.
1:09:01 And we suggest that it was then re-carved into a pharaonic head. Egyptologists think it was the pharaoh kafre, but there’s no real strong resemblance.
1:09:06 But it’s definitely wearing the nemesis headdress of an ancient Egyptian pharaoh.
1:09:13 And we think that that’s the result of a re-carving of what was originally not only a lion-bodied, but also a lion-headed monument.
1:09:21 It wouldn’t make sense if you create an equinoctial marker in the time of kafre 4,500 years ago.
1:09:28 And the Sphinx is an equinoctial marker. I mean, it’s 270 feet long and 70 feet high, and it’s looking directly at the rising sun on the equinox.
1:09:40 If you create it then, you’d be more likely to create it in the shape of a bull, because that was the age of Taurus, when the constellation of Taurus housed the sun on the spring equinox.
1:09:56 So why is it a lion? And again, we think that’s because of that observation of the skies and putting on the ground as above, so below, putting on the ground an image of the sky at a particular time.
1:10:04 Now, the fact that the Giza plateau, it’s a fact, of course, that Egyptologists completely dispute.
1:10:18 But the fact that the principal monuments of the Giza plateau, the three great pyramids and the great Sphinx all lock astronomically on the date of around 10,500 BC, to me is most unlikely to be an accident.
1:10:24 And actually, if you look at computer software at the sky at that time, you’ll see that the Milky Way is very prominent.
1:10:28 And seems to be mirrored on the ground by the River Nile.
1:10:36 I suggest that maybe one of the reasons amongst many why Giza was chosen as the site for this very special place.
1:10:50 So the point I want to make is that an astronomical design on the ground, which memorializes a very ancient date, does not have to have been done 12,500 years ago.
1:11:02 If from the ancient Egyptian point of view, you’re there 4,500 years ago, and there’s a time 8,000 years before that, which is very, very, very important to you.
1:11:13 You could use astronomical language and megalithic architecture to memorialize that date on the Giza plateau, which is what we think we’re looking at, except for one thing.
1:11:22 And that’s the erosion patterns on the Sphinx. And we’re pretty sure that the Sphinx at least does date back to 12,500 years ago.
1:11:34 And with it, the megalithic temples, the so-called Vali temple, which stands just to the east and just to the south of the Sphinx.
1:11:40 And the Sphinx temple, which stands directly in front of the Sphinx. The Sphinx temple has largely been destroyed.
1:11:53 But the Vali temple attributed to Kafe on no good grounds whatsoever is a huge megalithic construction with blocks of limestone that weigh up to 100 tons each.
1:11:59 And yet it has been remodeled, refaced with granite.
1:12:04 There are granite blocks that are placed on top of the core limestone blocks.
1:12:10 And those core limestone blocks were already eroded when the granite granite blocks were put there. Why?
1:12:20 Because the granite blocks have actually been purposefully and deliberately cut to fit into the erosion marks on the, we believe, much older megalithic blocks there.
1:12:23 So I think the Giza is a very complicated site.
1:12:36 I would never seek to divorce the dynastic ancient Egyptians from the Great Pyramids. They were closely involved in the construction of the Great Pyramids as we see them today.
1:12:56 But what I do suggest is that there were very low platforms on the Giza plateau that are much older and that the, when we look at the three Great Pyramids, we’re looking at a renovation and a restoration and an enhancement of much older structures that had existed on the Giza plateau for a much longer period before that.
1:13:08 Actually, the Great Pyramid is built around a natural hill. And that natural hill might have been seen as the original primeval mound to the ancient Egyptians.
1:13:19 So the idea is that this thing was there long before the pyramids and the pyramids were built by the Egyptian to celebrate further an already holy place.
1:13:31 Yeah. And there were platforms in place where the pyramids stand, not the pyramids as we see them today, but the bases, the base of those pyramids was already in place at that time.
1:13:40 So what’s the case, what’s the evidence that the Egyptologists used to make the attributions that they do for the dating of the pyramids and the Sphinx?
1:13:55 Well, the three Great Pyramids of Giza are different from later pyramids. This is another problem that I have with the whole thing, is the story of pyramid building. When did it really begin?
1:14:12 The timeline that we get from Egyptology is the first pyramid, is the pyramid of the pharaoh Zoser, the steppe pyramid at Sakara, about 100 years or so before the Giza pyramids are built.
1:14:30 And then we have this explosion in the fourth dynasty of true pyramids. We have three of them attributed to a single pharaoh, Sneferu, who built supposedly the pyramid at Maidum and the two pyramids at Dashur, the Benton, the Red Pyramid.
1:14:38 And then within that same 100 year span, we have the Giza pyramids being built. This is according to the Orthodox chronology.
1:14:45 And then suddenly, once the Giza project is finished, pyramid building goes into a massive slump in ancient Egypt.
1:14:56 And the pyramids of the fifth dynasty are, frankly speaking, a mess outside. They’re very inferior constructions. You can hardly recognize them as pyramids at all.
1:15:12 But what happens when you go inside them, is you find that they’re extensively covered in hieroglyphs and imagery, repeating the name of the king who was supposedly buried in that place, whereas the Giza pyramids have no internal inscriptions whatsoever.
1:15:28 What we do have is one piece of graffiti about which there is some controversy, basic statistics. It’s a six million ton structure. Each side is about 750 feet long.
1:15:40 It’s aligned almost perfectly to true north, south, east and west within three sixtieths of a single degree. It’s sixtieths because degrees are divided into sixties.
1:15:54 And it’s the precision of the orientation and the absolute massive size of the thing. Plus, it’s very complicated internal passageways that are involved in it.
1:16:11 You know, in the ninth century, the Great Pyramid still had its facing stones in place. But there was an Arab, an Arab caliph, Caliph al-Mamun, who had already realized that other pyramids did have their entrances in the north face.
1:16:18 Nobody knew where the entrance to the Great Pyramid was. But he figured, there’s an entrance to this thing. It’s going to be in the north face somewhere.
1:16:26 So he put together a team of workers and they went in with sledgehammers and they started smashing where he thought would be the entrance.
1:16:32 And they cut their way into the Great Pyramid for a distance of maybe 100 feet.
1:16:44 And then the hammering that they did dislodged something. They heard a little bit further away, something big falling. And they realized there was a cavity there. And they started heading in that direction.
1:16:52 And then they joined the internal passageway of the Great Pyramid, the descending and the ascending corridors that go up.
1:17:01 When you go up the ascending corridor, every one of the internal passageways in the Great Pyramid that people can walk in slopes at an angle of 26 degrees.
1:17:06 That’s interesting because the angle of slope of the exterior of the Great Pyramid is 52 degrees.
1:17:13 So we know mathematicians were at work as well as geometers in the creation of the Great Pyramid.
1:17:22 If you go up the Grand Gallery, which is at the end of the so-called ascending corridor and it’s above the so-called Queen’s Chamber,
1:17:28 you go up the Grand Gallery, you’re eventually going to come to what is known as the King’s Chamber, in which there is a sarcophagus.
1:17:34 And that sarcophagus is a little bit too big to have been got in through the narrow entrance passageway.
1:17:40 It’s almost as though the so-called King’s Chamber was built around the sarcophagus already in place.
1:17:46 Above the King’s Chamber are five other chambers. These are known as relieving chambers.
1:17:52 The theory was that they were built to relieve the pressure on the King’s Chamber of the weight of the monument.
1:17:58 But I think what makes that theory dubious is the fact that even lower down where more weight was involved,
1:18:02 you have the Queen’s Chamber and there are no such relieving chambers above that.
1:18:09 In the top of these five chambers, a British adventurer and vandal called Howard Weiss,
1:18:13 who dynamited his way into those chambers in the first place.
1:18:23 Allegedly, well, he claims he found a piece of graffiti left by a work gang naming the Pharaoh Khufu.
1:18:28 And it’s true, I’ve been in that chamber and there is the cartouche of Khufu there, quite recognizable.
1:18:35 But the dispute around it is whether that is a genuine piece of graffiti dating from the Old Kingdom,
1:18:42 or whether Howard Weiss actually put it there himself because he was in desperate need of money at the time.
1:18:45 I’m not sure what the answer to that question is.
1:18:53 Another reason why, but it’s one of the reasons that Egyptologists feel confident in saying that the pyramid is the work of Khufu.
1:19:01 Another is what is called the Wadi al-Jaf Papiri, where on the Red Sea a diary,
1:19:09 the diary of an individual comrade was found and he talks about bringing highly polished limestone to the Great Pyramid.
1:19:13 And it’s clear that what he’s talking about is the facing stones of the Great Pyramid.
1:19:15 He’s not talking about the body of the Great Pyramid.
1:19:19 He’s talking about the facing stones of the Great Pyramid during the reign of Khufu.
1:19:23 So that’s another reason why the Great Pyramid is attributed to Khufu.
1:19:30 But I think that Khufu was undoubtedly involved in the Great Pyramid and in a big way,
1:19:33 but I think he was building upon and elaborating a much older structure.
1:19:42 And I think the heart of that structure is the subterranean chamber, which is a 100 feet vertically beneath the base of the Great Pyramid.
1:19:46 Anybody who suffers from claustrophobia will not enjoy being down there.
1:19:54 You’ve got to go down a 26 degree sloping corridor until a distance of about 300 feet.
1:19:59 It’s 100 feet vertically, but the slope means you’re going to walk a distance of about, not walk, you’re going to eight walk.
1:20:01 You’re almost going to have to crawl.
1:20:06 I’ve learned from long experience that the best way to go down these corridors is actually backwards.
1:20:10 If you go forward, you keep bumping your head on them because they’re only three feet, five inches high.
1:20:17 You get down to the bottom, you have a short horizontal passage, and then you get into the subterranean chamber.
1:20:23 The theory of Egyptology is that this was supposed to be the burial place of Khufu.
1:20:32 But after cutting out that 300 foot long, 26 degree sloping passage, a lot of which passes through bedrock.
1:20:39 And having cut the subterranean chamber out of bedrock, gone to all that trouble, they decided they wouldn’t bury him there.
1:20:44 And they built what’s now known as the Queen’s Chamber as his burial chamber.
1:20:46 But then they decided that wouldn’t do either.
1:20:50 So they then built the King’s Chamber and that’s where the Pharaoh is supposed to have been buried.
1:20:55 Those Arab raiders under Khalif Mahmoun didn’t find anything in the Great Pyramid at all.
1:21:03 So your idea is that this finks and maybe some aspects of the pyramid were much earlier.
1:21:14 And why that’s important is in that case, it would be evidence of some transfer of technology from a much older civilization.
1:21:25 The idea is that during the Younger Dryas, most of that civilization was either destroyed or damaged and they desperately scattered across the globe.
1:21:26 Seeking refuge.
1:21:33 Seeking refuge and telling stories of maybe one, the importance of the stars.
1:21:39 Their knowledge about the stars and their knowledge about building and knowledge about navigation.
1:21:42 That’s roughly the idea.
1:21:54 So it’s interesting that the ancient Egyptians have a notion of an epoch that they call Zeptepi, which is the first time.
1:21:56 It means the first time.
1:21:58 This is when the gods walked the earth.
1:22:04 This is when seven sages brought wisdom to ancient Egypt.
1:22:07 And that is seen as the origin of ancient Egyptian civilization.
1:22:10 There are king lists by the ancient Egyptians themselves.
1:22:21 There are king lists that go back way beyond the first dynasty, go back 30,000 years into the past in ancient Egypt, considered to be entirely mythical by Egyptologists.
1:22:26 But nevertheless, it’s interesting that there’s that reference to remote time.
1:22:32 Now, what you also have in Egypt are what might almost be described as secret societies.
1:22:42 The followers of Horus are one of those specifically tasked with bringing forward the knowledge from the first time into later periods.
1:22:53 The souls of Pei and Nekhen are another one of these mysterious secret society groups who are possessors of knowledge that they transmit to the future.
1:23:04 And what I’m broadly suggesting is that those survivors of the Younger Darius Cataclysm who settled in Giza may have been relatively small in number.
1:23:12 It’s interesting that they are referred to in the Edfu building text as seven sages, because that repeats again and again.
1:23:15 It’s also in Mesopotamia.
1:23:26 It’s seven sages, seven Apkalu who come out of the waters of the Persian Gulf and teach people all the skills of agriculture and of architecture and of astronomy.
1:23:41 It’s found all around the world that there was a relatively small number of people who took refuge in Giza, who benefited from the survival skills of the hunter foragers who lived at Giza at that time and who also passed on their knowledge to those hunter foragers.
1:23:46 But it was not knowledge that was ready to be put into shape at that time.
1:23:55 And that knowledge was then preserved and kept and handled within very secretive groups that passed it down over thousands of years.
1:24:02 And finally it burst into full form in the fourth dynasty in ancient Egypt.
1:24:11 And the notion that knowledge might be transferred over thousands of years shouldn’t be observed.
1:24:20 We know, for example, in the case of ancient Israel, it goes back to the time of Abraham, which is pretty much I think around 2000 BC.
1:24:24 And knowledge has been preserved from that time right up to the present day.
1:24:29 So if you can preserve knowledge for 4,000 years, you can probably preserve it for eight.
1:24:37 Now, of course, the air bars on us are quite large, but if an advanced ice age civilization existed, where do you think it was?
1:24:40 Where do you think we might find it one day if it existed?
1:24:43 And how big do you think it might have been?
1:24:47 Well, this is where I’m often accused of presenting a God of the gaps argument.
1:24:52 And I think there was a lot of civilization because there’s lots of the earth that archaeologists have never looked at.
1:24:54 Of course, I’m not thinking that.
1:24:57 These are very special gaps that I’m interested in.
1:25:02 And I’m interested in them because of all the curiosities and the puzzlement that I’ve expressed to you before.
1:25:05 It’s not just because there are gaps in the archaeological record.
1:25:12 It’s because those gaps involve places that were very interesting places to live during the ice age.
1:25:24 And they specifically include the Sahara Desert, which was not a desert during the ice age and went through this warm wet period when it was very, very fertile.
1:25:28 Certainly, some archaeology has been done in the Sahara, but it’s fractional.
1:25:30 It’s tiny.
1:25:39 And I think if we want to get into the origins, true origins of ancient Egyptian civilization, of the peoples of ancient Egypt, we need to be looking in the Sahara for that.
1:25:46 And the Amazon rainforest is another example of this.
1:25:49 I think the Sahara is about nine million square kilometers.
1:25:58 The Amazon that’s left under dense canopy rainforest is about five million square kilometers, maybe closer to six.
1:26:05 And then you have the continental shelves that were submerged by sea level rise at the end of the ice age.
1:26:12 Now, it’s well established that sea level rose by 400 feet, but it didn’t rise by 400 feet overnight.
1:26:18 It came in dribs and drabs. There were periods of very rapid, quite significant sea level rise.
1:26:22 And there were periods when the sea level was rising much more slowly.
1:26:27 So that 400 foot sea level rise is spread out over a period of about 10,000 years.
1:26:36 But there are episodes within it like Meltwater Pulse 1B, like Meltwater Pulse 1A, when the flooding was really immense.
1:26:42 How big do you think it might have been? And do you think it was spread across the globe?
1:26:47 So if there were expert navigators, do you think they spread across the globe?
1:26:54 Well, the reason that I’m talking about the gaps is I don’t know where this civilization started or where it was based.
1:27:03 All I’m seeing are clues and mysteries and puzzles that intrigue me and which suggest to me that something is missing from our past.
1:27:09 And I’m not inclined to look for that missing something in, for example, Northern Europe.
1:27:13 Because Northern Europe was not a very nice place to live during the Ice Age.
1:27:19 I mean, nobody smart would build a civilization in Northern Europe 12,000 years ago.
1:27:22 It was hideous, frozen wasteland.
1:27:27 The places to look are places that were hospitable and welcoming to human beings during the Ice Age.
1:27:31 And that, of course, includes the coastlines that are now under water.
1:27:36 Of course, it includes the Sahara Desert, and of course, it includes the Amazon rainforest as well.
1:27:42 All of these places, I think, are candidates for “my lost civilization”.
1:27:50 And because I think, largely from those ancient maps, that it was a navigating seafaring civilization,
1:27:55 I suspect that it wasn’t only in one place, it was probably in a number of places.
1:27:57 And then I can only speculate.
1:28:10 Maybe there was a cultural value where it was felt that it was not appropriate to interfere with the lives of hunter foragers at that time.
1:28:14 Maybe it was felt that they should keep their distance from them.
1:28:23 Just as even today, there is a feeling that we shouldn’t be interfering too much with the uncontacted tribes in the Amazon rainforest.
1:28:28 Although, interestingly, some of those tribes are now using cell phones.
1:28:32 That possibility may have been there in the past.
1:28:41 And only when we come to a global cataclysm, does it become essential to have outreach and actually to take refuge amongst those hunter forager populations.
1:28:44 That is the hypothesis that I’m putting forward.
1:28:48 I’m not claiming that it’s a fact, but for me, it helps to explain the evidence.
1:29:01 So that speaks to one of the challenges that archaeologists provide to this idea is that there is a lot of evidence of humans in the Ice Age, and they appear to be all hunter-gatherers.
1:29:10 But like you said, only a small percent of areas where humans have lived have been studied by archaeologists.
1:29:11 That’s right.
1:29:12 Very tiny percent.
1:29:16 Even a tiny percent of every archaeological site has been studied by archaeologists.
1:29:20 Typically, one to five percent of any archaeological site is excavated.
1:29:28 I mean, that’s why, go back a type that fills my mind with imagination, especially seeing as a time capsule.
1:29:39 It’s almost certain that there is places on Earth we haven’t discovered that once we do, even if it’s after the Ice Age, will change our view of human history.
1:29:41 Do you think there is going to be a place?
1:29:45 What will be your dream thing to discover?
1:29:53 Like Gobekli Tepe, that says a definitive perturbation to our understanding of Ice Age history.
1:29:56 Some kind of archive, some kind of Hall of Records.
1:30:04 There’s both mystical associations with the Hall of Records at Giza from people like the Edike Casey organization.
1:30:13 There’s also ancient Egyptian traditions which suggest that something was concealed beneath the Sphinx.
1:30:16 This is not an idea that is alien to ancient Egypt.
1:30:18 It’s quite present in ancient Egypt.
1:30:23 So far, as far as I know, nobody has dug down beneath the Sphinx.
1:30:25 And of course, there’s very good reasons for that.
1:30:28 You don’t want to damage the place too much.
1:30:30 But let’s call it the Hall of Records.
1:30:33 I’d love to find that.
1:30:37 But I think in a way, that’s what Gobekli Tepe is.
1:30:39 Gobekli Tepe is a Hall of Records.
1:30:56 You know, it’s interesting that just as I’ve tried to outline, I hope reasonably clearly that the three great pyramids of Giza match Orion’s belt in 10,500 BC, just as the Sphinx matches Leo in 10,500 BC, 12,500 years ago or so.
1:31:05 Pillar 43 in Enclosure D at Gobekli Tepe contains what a number of researchers, myself included, regard as an astronomical diagram.
1:31:10 Martin Sweatman of Edinburgh University is brought forward the best work in this field.
1:31:23 But it was initially started by a gentleman called Paul Burley who noticed that one of the figures on Pillar 43 is a scorpion, very much like we represent the constellation of Scorpio today.
1:31:32 And that above it is a vulture with outstretched wings, which is in a posture very similar to the constellation that we call Sagittarius.
1:31:37 And on that outstretched wing is a circular object.
1:31:48 And the suggestion is that it’s marking the time when the sun was at the center of the dark rift in the Milky Way at the summer solstice, 12,500 years ago.
1:31:50 That’s what it’s marking.
1:31:56 And it’s interesting that the same date can be deduced from Pillar 43.
1:31:57 Of course, it’s controversial.
1:32:06 Martin Sweatman’s ideas by no means accepted by archaeology, but he’s done very, very thorough detailed statistical work on this and I’m personally convinced.
1:32:16 So we have a time capsule at Gobekli Tepe, which is memorializing a date that is at least 1,200 years before Gobekli Tepe was built.
1:32:23 If that dating of 11,600 years ago proves to be absolutely the oldest date as it is at present.
1:32:33 The date memorialized on Pillar 43 is 12,800 years ago, the beginning of the Young Adryus, the beginning of the impact event.
1:32:37 And then Giza does the same thing, but in much larger scale.
1:32:55 It draws, it uses massive megalithic architecture, which is very difficult to destroy and a profound knowledge of astronomy to encode a date in a language that any culture which is sufficiently literate in astronomy will be able to decode.
1:33:01 We don’t have to have a script that we can’t read like we do with the Indus Valley civilization or with the Easter Island script.
1:33:03 We don’t have to have a script that can’t be interpreted.
1:33:12 If you use astronomical language, then any astronomical literate civilization will be able to give you a date.
1:33:15 The Hoover Dam has a star map built into it.
1:33:23 And that star map is part of an exhibition that was put there at the founding of the Hoover Dam.
1:33:30 And what it does is it freezes the sky above the Hoover Dam at the moment of its completion.
1:33:41 And Oscar Hansen, the artist who created that piece, said so specifically that this would be so that any future culture would be able to know the time of the dam’s construction.
1:33:46 So you can use astronomy and architecture to memorialize a particular date.
1:33:47 Quick pause.
1:33:48 Bath and break?
1:33:49 Sounds good.
1:34:03 So to me, the story that we’ve been talking about, it is both exciting if the mainstream archaeology narrative is correct and the one you’re constructing is correct.
1:34:15 Both are super interesting because the mainstream archaeology perspective means that there is something about the human mind from which the pyramids, these ideas spring naturally.
1:34:19 You place humans anywhere, you place them on Mars, it’s going to come out that way.
1:34:29 So that’s an interesting story of human psychology that then becomes even more interesting when you evolve out of Africa with Homo sapiens, how they think about the world.
1:34:31 That’s super interesting.
1:34:48 And then if there’s an ancient civilization, advanced civilization that explains why there’s so many similar types of ideas that spread, that means that there’s so much undiscovered still about the sort of the spring of these ideas of civilization that come.
1:34:50 So to me, they’re both fascinating.
1:34:53 So I don’t know why there’s so much sort of infighting.
1:34:58 But I think it’s partly territorial.
1:35:06 I don’t speak of all archaeologists, but some archaeologists feel very territorial about their profession.
1:35:17 And they do not feel happy about outsiders entering their realm, especially if those outsiders have a large platform.
1:35:28 And that’s, I found that the attacks on me by archaeologists have increased step by step with the increase of my exposure.
1:35:36 I wasn’t very interesting to them when I just had one minor bestseller in 1992 with a book called The Sign and the Seal.
1:35:45 But when Fingerprints of the Gods was published in 1995 and became a global bestseller, then I started to attract their attention.
1:35:50 And appear to have been regarded as a threat to them.
1:35:52 And that is the case today.
1:35:59 That is why Ancient Apocalypse Season 1 was defined as the most dangerous show on Netflix.
1:36:08 It’s why the Society for American Archaeology wrote an open letter to Netflix asking Netflix to reclassify the series of science fiction.
1:36:20 It’s why they accused the series of anti-Semitism, misogyny, white supremacism, and a whole bunch of other things.
1:36:24 There’s nothing to do with anything that’s in the series.
1:36:29 It was like, “We must shut this down. This is so dangerous to us.”
1:36:37 Certainly not a danger. There are many more dangerous things in the world than a television series going on right now.
1:36:50 But maybe it was seen as a danger to archaeology, that this non-archaeologist was in archaeological terrain and being viewed and seen by large, and read by large numbers of people.
1:36:52 Maybe that was part of the problem.
1:37:06 And human nature being what it is, I noticed that two of my principal critics, John Hoops from the University of Kansas and Flint Dibble, who’s now teaching at the University of Cardiff in Wales in the UK,
1:37:09 are both people who like to have media exposure.
1:37:19 And John Hoops had just recently started his YouTube channel. Flint Dibble has had one for quite a while, a pretty small number of followers.
1:37:27 I think that they feel that they should be the ones who are getting the global attention and that it’s not right that I am.
1:37:34 And that the best way to stop that is to stop me, to shut me down, to get me cancelled.
1:37:46 And basically requiring Netflix to relabel my series from a documentary to science fiction, which is what they actually had the temerity to suggest to Netflix.
1:37:54 If that had gone through, if Netflix had listened to them, that would have effectively been the cancellation of my documentary series.
1:37:56 It would no longer have been ranked under documentary.
1:38:05 So it was a deliberate attempt to shut me down, and I see that going on again and again, and it’s so unfortunate and so unnecessary.
1:38:07 I’ve become very defensive towards archaeology.
1:38:13 I hit back after 30 years of these attacks on my work.
1:38:17 I’m tired of it, and I do defend myself.
1:38:20 And sometimes I’m perhaps over vigorous in that defence.
1:38:26 Maybe I was a little bit too strong in my critique of archaeology in the first season of ancient apocalypse.
1:38:28 Maybe I should have been a bit gentler and a bit kinder.
1:38:40 And I’ve tried to reflect that in the second season and to bring also many more indigenous voices into the second season, as well as the voices of many more archaeologists.
1:39:03 Yeah, in general, I got a chance to get a glimpse of the archaeology community, and in archaeology, in science in general, I don’t have much patience for this kind of arrogance or snark or dismissal of general human curiosity that I think your work inspires in people.
1:39:12 And so that’s why people like Ed Barnhart, who I recently had a conversation with, you know, he radiates sort of kindness and curiosity as well.
1:39:21 And it’s like that kind of approach to ideas, especially about human history, it inspires people, inspires millions of people to ask questions.
1:39:25 I mean, that’s why you had Keanu Reeves on the new season.
1:39:29 He’s basically coming to the show from that same perspective with curiosity.
1:39:34 Keanu is genuinely curious about the past and very, very interested in it.
1:39:37 And he’s bringing to it questions that everybody brings to the past.
1:39:41 He’s speaking for every man in the series.
1:39:53 So given that, can you maybe steal man the case that archaeologists make about this period that we’ve been talking about?
1:39:58 Can you make the case that that is indeed what happened is it was hunter-gathered for a long time.
1:40:12 And then there was a cataclysm, a very difficult period in the human history with the young Andreas, and that changed the environment and then led to the springing up of civilizations at a different place on Earth.
1:40:14 Can you sort of make the case for that?
1:40:20 No, I completely understand why that is the position of archaeology, because that’s what they’ve found.
1:40:33 Archaeology is very much wishing to define itself as a science, and it uses the techniques of weighing and measuring and counting are very key to what archaeology does.
1:40:41 And in what they’ve found and what they’ve studied around the world, they don’t see any traces of a lost civilization.
1:41:00 Besides, we live in a very politically correct world today, and the idea that some kind of lost civilization brought knowledge to other cultures around the world is seen as almost racist or colonialist in some way.
1:41:03 It triggers that aspect as well.
1:41:08 But basically, I think majority of archaeologists are in complete good faith on this.
1:41:12 I don’t think that anybody is really seeking to frame me.
1:41:25 I think that what we’re hearing from most archaeologists, some much more vicious than others, but what we’re hearing from most archaeologists is this is what we found, and we don’t see evidence for a lost civilization in it.
1:41:31 And to that, I must reply, please look at the myths.
1:41:35 Please consider the implications of the younger dryers.
1:41:37 Please look at the ancient astronomy.
1:41:41 Please look at those ancient maps and don’t just dismiss them and stare at them.
1:41:52 And for God’s sake, please look more deeply at the parts of the world that were immensely habitable and attractive during the Ice Age and that have hardly been studied by archaeology at all.
1:41:57 Before you tell us that your theory is the only one that can possibly be correct.
1:42:04 In fact, it’s a very arrogant and silly position of archaeology because archaeological theories are always being overthrown.
1:42:05 It can take years.
1:42:06 It can take decades.
1:42:12 It took decades in the case of the Clovis first hypothesis for the settlement of the Americas.
1:42:21 But sooner or later, a bad idea will be kicked out by a preponderance of evidence that that idea does not explain.
1:42:29 If we can just look back at your debate with Flint Dibble on Joe Rogan’s experience, what are some takeaways from that?
1:42:31 What have you learned?
1:42:34 Maybe what are some things you like about Flint?
1:42:42 You said that he’s one of your big critics, but what do you like about his ideas and what would you maybe bother by?
1:42:47 First of all, just very recently, and it can be found on my YouTube channel and it’s signaled on my website.
1:42:58 I have made a video, runs about an hour, which looks at a series of statements that Flint made during the debate, which I was not prepared to answer.
1:43:05 And it turns out that some of those statements are not correct.
1:43:18 The notion, for example, that there were three million shipwrecks that have been mapped, Flint actually uses the word mapped, three million shipwrecks that have been mapped at one point in the debate.
1:43:22 And I’ve put that clip into the video that I brought out.
1:43:23 That is not a fact.
1:43:26 That is an estimate, a UNESCO estimate.
1:43:36 And actually in the small print on one of the slides that he has on the screen, you can see the word estimate, but he never expresses that word out loud.
1:43:40 So those who are listening to the podcast rather than watching it wouldn’t even have a chance to see that.
1:43:43 And I sitting there in the studio didn’t see that word estimate either.
1:43:44 And I didn’t know that.
1:43:47 I thought, my God, Flint has a point here.
1:43:58 If there have been three million shipwrecks found and mapped, if that’s the case, the absence of any shipwreck from a lost civilization of the Ice Age is a problem.
1:44:01 But then I discovered that it isn’t three million shipwrecks that have been mapped.
1:44:03 It’s much, much less than that.
1:44:10 And maybe it’s 250,000, still a large number, but most of them from the last thousand years.
1:44:26 And unfortunately, what Flint didn’t go into, and perhaps he should have shared with the audience, and again, I go into this in the video, is that there is indisputable evidence that human beings were seafarers as much as 50 or 60,000 years ago.
1:44:34 The people of Australia involved a relatively short 90 kilometers, 100 kilometer ocean voyage, but nevertheless, it was an ocean voyage.
1:44:42 And it must have involved a large enough people, a large enough number of people to create a permanent population that wouldn’t go extinct.
1:44:44 The settlement of Cyprus is the same thing.
1:44:47 It was always an island even during the Ice Age.
1:44:55 And no ships have survived that speak to the settlement of Australia, and no ships have survived that speak to the settlement of Cyprus either.
1:44:57 But that doesn’t mean that that thing didn’t happen.
1:45:01 I should just link around this, because for me, it was the shipwrecks thing was convincing.
1:45:09 And then looking back, first of all, watching your video, but also just realizing the people in Australia part.
1:45:12 That’s mind boggling 50,000 years ago.
1:45:28 Just imagine being the person standing on the shore, looking out into the ocean, standing on the shore of a harsh environment, looking out to the ocean of a harsh environment, and deciding that, you know what, I’m going to go towards near certain death and explore.
1:45:30 I don’t know what’s on the other side of that water.
1:45:31 You can’t see 90 kilometers.
1:45:32 And humans did it.
1:45:33 Yeah, yeah.
1:45:35 And again, it’s that urge to explore.
1:45:41 And I suggest that it probably began with a few pioneers who made the journey there and back.
1:45:42 They ventured into the water.
1:45:44 They definitely had boats.
1:45:50 And lo and behold, after a two or three day voyage, they ended up on a coastline.
1:45:51 You’re an individual.
1:45:58 You’ve got my relatively straightforward island hopping with where each island is within sight of each other as far as Timor.
1:46:02 And when you get to Timor, suddenly you can’t island hop anymore.
1:46:05 There’s an expansive ocean that you can’t see across.
1:46:19 But that urge to explore, that curiosity that is central to the human condition would undoubtedly have led some adventurous individuals to want to find out more and even be willing to risk their lives.
1:46:31 And that first reconnoitering of what lay beyond that strait would have undoubtedly been undertaken by very few individuals, not enough to create a permanent population in Australia.
1:46:35 When they came back with the good news that there’s a whole land there.
1:46:49 That’s the land that geographers call Sahul, which in just as Sunda was the Ice Age, Indonesian and Malaysian Peninsula all joined together into one land mass.
1:46:53 So Sahul was New Guinea joined to Australia.
1:46:56 So they would have made landfall in New Guinea.
1:47:01 And then they think, well, here is this vast, open, incredible land.
1:47:03 We need to bring more people here.
1:47:08 And that would have involved larger craft.
1:47:19 You need to bring people with resources and you need to bring enough of them, both men and women, in order to produce a population that will not rapidly become extinct.
1:47:34 It’s the same in Cyprus. There, the detailed work that’s been done suggests very strongly that we’re looking at planned migrations of groups of people in excess of a thousand at a time bringing animals with them.
1:47:39 And this certainly would have involved multiple boats and boats of a significant size.
1:47:42 And there’s no archaeological evidence of those boats.
1:47:43 None whatsoever.
1:47:50 The oldest boat that’s ever been found in the world is the Doko Shipwreck off Greece, which is around 5,000 years old, if I recall correctly.
1:47:54 So everything that makes a boat is lost to time?
1:47:57 Yes, boats can be preserved under certain circumstances.
1:48:00 There’s a wreck at the bottom of the Black Sea, almost two miles deep.
1:48:02 I didn’t know the Black Sea was that deep.
1:48:11 But there’s a wreck and there’s no oxygen down there that is more than 2,000 years old and is still in pretty much perfect condition.
1:48:16 But in other conditions, the structure of the ship evaporates.
1:48:19 Sometimes what you’re left with is the cargo of the ship.
1:48:24 And you could say there was a ship that sank here, but the ship itself has gone.
1:48:34 The fact is, we know that our ancestors were seafarers as much as 50,000 years ago and no ship has survived to testify to that, yet we accept that they were.
1:48:40 Do you think one day we’ll find a ship that’s 10, 20, 30, 40, 50,000 years old?
1:48:42 It’s not impossible.
1:48:53 I think it’s quite unlikely, given the very thin survival of ships the further back you go in time with the oldest, as I say, being about 6,000 years old now.
1:49:07 And then the other thing to take into account is the Younger Dryas event itself and the cataclysmic circumstances of that event and the roiling of the seas that would have taken place then.
1:49:15 How much would have survived in a boat accident at that time would have survived for thousands of years afterwards, I’m not sure.
1:49:18 But I don’t give up hope, it’s possible.
1:49:22 So okay, so that’s back to the 3 million shipwrecks.
1:49:23 Yeah.
1:49:25 So what’s your takeaway from that debate?
1:49:33 Well, my takeaway from that debate is that I should have been better prepared and I should have been less angry.
1:49:48 I have to say that Flint had really disturbed me with these constant snide, not quite exact references to racism and white supremacism in my work.
1:49:54 I detest such things and to have those labels stuck on me.
1:49:58 He’s always avoided taking direct responsibility, pretty much always avoided.
1:50:04 There’s one example that I include in the video I’ve made where he really hasn’t successfully avoided it.
1:50:12 But in most cases, he’s trying to say that I rely on sources that were racist, but that he’s not saying that I myself am a racist.
1:50:22 But the end result of those statements is that people all around the world came to the conclusion that Graham Hancock is a racist and a white supremacist.
1:50:31 And that really got under my skin and it really upset me and I felt angry about it and I felt that I was there to defend ancient apocalypse season one.
1:50:44 Whereas in fact, what I was there to do was to listen to a series of lectures where an archaeologist tells me what archaeologists have found and that somehow I’m to deduce that from what they have found, they’re not going to find anything else.
1:50:47 At least not anything to do with the lost civilization.
1:50:55 Listen, I feel you. I’ve seen the intensity of the attacks and the whole racism label is the one that can get under your skin.
1:51:07 And it’s a toolbox that’s been prevalent over the past, let’s say a decade, maybe a little bit more as a method of cancellation when a person is the opposite of racist very often.
1:51:22 It’s kind of hilarious to watch, but it can get under your skin, especially when you have certain dynamics that happen on the internet where it seeps into a Wikipedia page and then other people read that Wikipedia page and you get to hear it from like friends.
1:51:36 Oh, I didn’t know you’re at whatever and you realize that Wikipedia description of who you are is actually has a lot of power, not by people that know you well but by people that just kind of are learning about you for the first time.
1:51:37 Definitely.
1:51:48 And they can really start to annoy you and get under your skin when the people are kind of indirectly injecting, they’re writing articles about you, they can then be cited by Wikipedia.
1:51:54 It can really bother a person who is actually trying to do good science or just trying to inspire people with different ideas.
1:52:05 I felt that my work was being deliberately misrepresented and I felt that I as a human being was being insulted and wronged in ways that are deeply hurtful.
1:52:15 My wife and I have six children between us and we have nine grandchildren and of those nine grandchildren, seven are of mixed race.
1:52:25 And this is my family and these are kids who are going to grow up and read Wikipedia and learn from reading Wikipedia that grandpa was some kind of racist.
1:52:35 You know, this is a personal issue for me and I’m afraid I carried that personal anger into the debate and it made me less effective than I should have been.
1:52:37 But ultimately I do want to pay tribute to Flint.
1:52:38 He is an excellent debater.
1:52:40 He’s got a very sharp mind.
1:52:44 He’s a very clever man and he’s very fast on his feet.
1:52:50 And I recognize that I was definitely up against a superior debater in that debate.
1:52:56 I’m not sure that I have those debating skills and I certainly didn’t have them on that particular day.
1:53:01 I also admire about Flint, something else, which is that he was willing to be there.
1:53:03 Most archaeologists don’t want to talk to me at all.
1:53:05 They want to insult me from the sidelines.
1:53:13 They want to make sure that Wikipedia keeps on calling me a pseudoarchaeologist or a purveyor of pseudoarchaeological theories.
1:53:18 They want to make sure that the hints of racism are there, but they actually don’t want to sit down and confront me.
1:53:21 At least Flint was willing to do that and I’m grateful to him for that.
1:53:32 And I think in that sense it is an important encounter between people with, let’s say, an alternative view of history and those with the very much mainstream view of history that archaeology gives us.
1:53:36 And he’s also a very determined character. He doesn’t give up.
1:53:42 So all of those things about him I admire and respect.
1:53:50 But I think he fought dirty during the debate and I’ve said exactly why in this video that I now have up on YouTube.
1:53:57 To say a positive thing that I enjoyed, I think towards the end in him speaking about agriculture was pretty interesting.
1:54:11 So the techniques of archaeology are pretty interesting like where you can get some insights through the fog of time about what people were doing, how they were living.
1:54:12 That’s pretty interesting.
1:54:20 It’s very interesting. It’s a very important discipline and I’ve said many times before publicly, I couldn’t do any of my work without the way that archaeologists do.
1:54:26 I emphasize very strongly in this video that I don’t study what archaeologists study.
1:54:35 But nevertheless, the data that archaeologists have generated over the last century or so has been incredibly valuable to me in the work that I do.
1:54:46 But when I look at the Great Sphinx and the studies of archaeology saying that this is the work of the pharaoh Khafre, despite the absence of any single contemporary inscription that ascribes it to Khafre.
1:54:54 And in fact, the presence of other inscriptions that say that it was already there in the time of Khufu, I am not looking at what Egyptologists study.
1:54:59 They just dismiss all of that and lock into the Khafre connection.
1:55:03 At Gobekli Tepe, I’m not really looking at what archaeologists look at.
1:55:09 I’m looking at the alignments of the megaliths and how they seem to track precession of the star Sirius over a period of time.
1:55:12 Archaeologists aren’t interested in any of that.
1:55:15 So I value and respect archaeology.
1:55:19 I think it’s an incredible tool for investigating our past.
1:55:26 But I wish archaeologists would bring a slightly gentler frame of mind to it and a slightly opener perspective.
1:55:33 And also that archaeologists would be willing to trust the general public to make up their own minds.
1:55:42 As though certain archaeologists are afraid of the public being presented with an alternative point of view, which they regard as quote unquote dangerous,
1:55:49 because they somehow underestimate the intelligence of the general public and think the general public are just going to accept that much.
1:56:00 Actually, by condemning those alternative point of view, archaeologists make it much more likely that the general public will accept those alternative point of view because there is a great distrust of experts in our society today
1:56:03 and behaving in a snobbish, arrogant way.
1:56:08 We archaeologists are the only people who are really qualified to speak about the past.
1:56:11 And anybody else who speaks about the past is dangerous.
1:56:15 That actually is not helpful to archaeology in the long term.
1:56:18 There could be a much more positive and a much more cooperative relationship.
1:56:23 And I can see that relationship with a gentleman like Ed Barnhart.
1:56:32 It was very much the case with archaeologist Manti Parsonin from the University of Helsinki and with geographer Alcea Ranzi,
1:56:40 a Brazilian geographer, very, very senior figure who I worked with in the Amazon for Season 2 of Ancient Apocalypse,
1:56:46 looking at these astonishing earthworks that have emerged from the Amazon jungle and which more and more are now being found with LiDAR.
1:56:50 Indeed, we found some of them ourselves with LiDAR while we were there.
1:56:54 Yeah, that was an incredible part of the show that I got a chance to preview.
1:56:57 It’s like there’s all this earthworks.
1:57:08 Yeah, the traces of things built on the ground that probably you can only really appreciate when you look from up above.
1:57:09 That’s right.
1:57:19 So the idea that they built stuff that you can only appreciate when viewed from up above means they had a very kind of deep relationship with the sky.
1:57:24 With the sky and a very good knowledge of geometry as well, because these are geometrical structures.
1:57:30 And some of them even seem to incorporate geometrical games, almost like squaring the circle.
1:57:38 It’s not quite that, but you have a lovely square earthwork with a lovely circle earthwork right in the middle of it.
1:57:40 Whatever else there were, they were geometers.
1:57:47 They were not just builders of fantastically huge earthworks that nobody expected in the Amazon.
1:57:55 Not just builders of cities that we now know existed in the Amazon, but that they were astronomers and mathematicians as well.
1:57:58 Everything we’re talking about is so full of mysteries.
1:58:00 It’s just fascinating, especially the farther back we go.
1:58:03 That’s what I love about the past is the mystery that’s there.
1:58:12 And that’s another thing that I regret about some archaeologists is that their mission seems to be to drain all mystery out of the past.
1:58:24 To suck it dry like some kind of vampire, sucking the blood out of the past and to reduce it to a series of numbers that appear to be scientific.
1:58:26 I think that’s most unfortunate.
1:58:28 The past is deeply mysterious.
1:58:31 The whole story of life on earth is deeply mysterious.
1:58:35 I mean, we were talking about the timeline of human beings.
1:58:41 But if you go back to the formation of the earth itself, if I’ve got the figures right,
1:58:45 it’s about four and a half billion years ago that the earth supposedly formed.
1:58:53 It was then incredibly hot and inhospitable to life for the next several hundred million years.
1:59:04 But it was actually Francis Crick who pointed out something odd that within a hundred million years of the earth being cool enough to support life.
1:59:08 There’s bacterial life all over the planet.
1:59:13 And Crick wrote a book called Life Itself that was published in 1981.
1:59:19 And he suggested that life had been brought here by a process of panspermia.
1:59:26 Now that’s an idea that’s around in circulation that comets may carry bacteria which can seed life on planets.
1:59:30 But Crick actually in life itself was talking about directed panspermia.
1:59:33 He envisaged, this is Crick, not me.
1:59:42 He envisaged an alien civilization far away across the galaxy which faced extinction.
1:59:46 Perhaps a supernova was going to go off in the neighborhood.
1:59:48 They were highly advanced.
1:59:53 Their first thought might have been, let’s get ourselves off the planet and go and populate some other planet.
1:59:56 But the distances of interstellar space were so great.
2:00:09 So their second thought was, let’s preserve our DNA, let’s put bacteria, genetically engineered bacteria into cryogenic chambers and fire them off into the universe in all directions.
2:00:19 And bottom line of Crick’s theory in life itself is one of those cryogenic containers containing bacterial life from another solar system crashed into the early earth.
2:00:22 And that’s why life began so suddenly here on earth.
2:00:36 If we as a human civilization continue, I think that is a one way to create backups of us elsewhere in the universe given the space is to do a life gun and shoot it everywhere.
2:00:38 And there’s just plants.
2:00:51 And you kind of hope that whatever is the magic that makes up human consciousness and if that magic is already there in the initial DNA of the bacteria.
2:00:53 The potential for that magic is there.
2:00:54 The potential is there.
2:01:00 And evolutionary forces will work upon it in different ways in different environments.
2:01:03 But the potential is there. Yes, it’s something that we would do.
2:01:12 If we were facing a complete extinction of life on planet earth, a major global effort would be made to preserve it somehow.
2:01:20 And that might well include firing off cryogenic chambers into the universe and hoping that some of them would land somewhere hospitable.
2:01:24 And as you were mentioning, there’s just so many interesting mysteries along the way here.
2:01:31 For example, I mean, it’s like three billion years, it was single cell organisms.
2:01:41 So it seems like life was pretty good, single cell organisms that there’s no need for multicellularity for animals, for any of this kind of stuff.
2:01:43 So why is that?
2:01:47 It seems like you could adapt much better if you’re a more complicated organism.
2:01:49 It took a really long time to take that leap.
2:01:57 Is it because it’s really hard to do? And what was the forcing function to do that kind of leap?
2:02:01 And the same for us to be selfish and self-obsessed.
2:02:08 For us humans, what was the magic leap to Homo sapiens from the other hominids?
2:02:13 And why did Homo sapiens win out against the Andrithals and the other competitors?
2:02:16 Why are they not around anymore?
2:02:19 So those are all fascinating mysteries.
2:02:28 And it feels like the more we propose sort of radical ideas about our past and take it serious and explore,
2:02:37 the more we’ll be able to sort of figure out that puzzle that leads all the way back to Homo sapiens and maybe all the way back to the origin of life on Earth.
2:02:49 I think that Homo sapiens is the tail end of a very long, deep series of mysteries that goes back right to the beginning of life on this planet and probably long before actually.
2:02:55 Because this planet is part of the universe and God knows what else is out there in the universe.
2:02:59 Why do you think Homo sapiens evolved?
2:03:08 What was the magic thing? There’s a bunch of theories about fire leading to meat, to cooking, which can fuel the brain.
2:03:11 That’s one. The other is like social interaction.
2:03:16 We’re able to use our imagination to construct ideas and share those ideas and tell great stories.
2:03:19 And that is somehow an evolutionary advantage.
2:03:22 Do you have any favorite conceptions?
2:03:24 Well, it’s interesting.
2:03:32 There’s no doubt that anatomically modern humans and Neanderthals coexisted in Europe for at least 10,000 years, probably more than that.
2:03:41 And yet one of the popular views is that anatomically modern humans wiped out the Neanderthals, that we killed them off.
2:03:44 But at the same time, we were into breeding with the Neanderthals.
2:03:52 In a sense, the Neanderthals are not gone. They are still within us today. We are part of Neanderthal.
2:03:55 There’s another theory that I’ve read about.
2:04:05 There is some evidence that Neanderthals were cannibals, that there was ritual cannibalism took place amongst Neanderthals, and particularly the eating of human brains.
2:04:11 And this can cause kuru, which can kill off whole populations.
2:04:14 That’s another suggestion of why the Neanderthals died out.
2:04:17 There’s lots of possibilities that have been put forward.
2:04:24 Maybe we just outcompeted them. Maybe anatomically modern humans had some brain connections that they didn’t have.
2:04:32 Even though the Neanderthal brain was bigger than the brain of anatomically modern human beings, as the old saying goes, size isn’t everything.
2:04:36 Maybe we just had a more compact, more efficient brain.
2:04:45 The fact of the matter is that Neanderthals and Denisovans did not survive the rise of homo sapiens.
2:04:50 For our discussion though, what is interesting is all the hominids seem to be explorers.
2:04:53 They spread. I didn’t know this fact.
2:04:59 The fact that homo erectus was all over the planet more than a million years ago is testament to that.
2:05:04 And I do think that exploration urge is fundamental to humanity.
2:05:17 And I would like to say that’s what I think I’m doing. I’m exercising my urge to explore the past in my own way, making my own path and defining my own route.
2:05:22 That’s the leap from non-human to human.
2:05:34 One of the things you’ve discussed is your idea of what was the leap to human civilization. What is the driver? What is the inspiration for humans to form civilizations?
2:05:36 And for you, that’s shamanism.
2:05:37 Definitely.
2:05:38 Can you explain what that means?
2:05:46 I think that shamanism is the origin of everything, of value in humanity.
2:06:01 I think it was the earliest form of science. When I spend time with shamans in the Amazon, I observe people who are constantly experimenting with plants in a very scientific way.
2:06:12 They’re always trying a pinch of this and a pinch of that in different forms. For example, of the ayahuasca brew to see if it enhances it or makes it makes it different in any way.
2:06:20 The invention of curari is a remarkable scientific feat, which is entirely down to shamans in the Amazon.
2:06:27 They are the scientists of the hunter forager state of society.
2:06:33 And they were the ancient leaders of human civilization.
2:06:37 So I think all civilization arises out of shamanism.
2:06:46 And shamanism is a naturally scientific endeavor where experimentation is undertaken and exploration and investigation of the environment around us.
2:07:04 And what I’m suggesting is that one group, perhaps more than one group, went a bit further than other groups did and used that study of the skies and developed navigational techniques and were able to sail and explore the earth.
2:07:15 But that ultimately what lies behind it is the same curiosity and investigative skill that shamans are still using in the Amazon to this day.
2:07:21 And I do see them as scientists in a very proper use of the word.
2:07:25 But do you think something like ayahuasca was a part of that process?
2:07:34 Yes. Ayahuasca is the result of shamanistic investigation of what’s available in the Amazon.
2:07:39 Of course, ayahuasca is all the fad in Western industrialized societies today.
2:07:44 And some people see it as a miracle cure for all kind of ailments and problems.
2:07:48 And perhaps it is, perhaps it can be in certain ways.
2:07:51 Ayahuasca itself is not an Amazonian word.
2:07:57 It comes from the Quechua language and it means the vine of souls or the vine of the dead.
2:08:03 But the ayahuasca vine is only one of two principal ingredients in the ayahuasca brew.
2:08:08 And the other ingredient are leaves that contain dimethyl tryptamine.
2:08:10 And there are two sources of that.
2:08:13 One is a bush called cicotria viridis.
2:08:16 That’s its botanical name. They call it chacruna in the Amazon.
2:08:24 And its leaves are rich in dimethyl tryptamine DMT, which is arguably the most powerful psychedelic known to science.
2:08:36 And the other source comes from another vine, deplopteris cabrerana, which the leaves of that vine also contain DMT.
2:08:41 So the ayahuasca vine on its own is not going to give you a visionary journey.
2:08:50 And the leaves that contain DMT on their own, whether they come from deplopteris or whether they come from chacruna, are not going to give you a visionary journey.
2:09:00 And the reason they’re not going to give you the visionary journey is because of the enzyme monoaminoxidase in the gut that shuts down DMT when absorbed orally.
2:09:08 Basically, DMT is not accessible orally unless you combine it with a monoaminoxidase inhibitor.
2:09:15 And that’s what I mean when I’m talking about science in the Amazon, because there’s so many tens of thousands, hundreds of thousands of different species of plants and trees in the Amazon.
2:09:23 And they’ve gone around and they found just two or three of them that put together can produce these extraordinary visionary experiences.
2:09:25 Just imagine the number of plants they had to have eaten.
2:09:26 Yeah.
2:09:31 It consumed and smoked all kinds of combinations to arrive at that.
2:09:32 Exactly, exactly.
2:09:35 To realize that this is something very special.
2:09:40 And then to use the principles there to find another form of it.
2:09:46 So ayahuasca is the form that is made with the ayahuasca vine and the leaves of the chacruna plant.
2:10:03 But yahe is made from the ayahuasca vine and the leaves of another vine, Diploplaris cabrarana, which contain not only NNDMT, which is the DMT that everybody’s pretty much familiar with these days, but also five MEO DMT.
2:10:17 And the yahe experience, which I have also had in my view is more intense and more powerful, almost to the point of being overwhelming than the ayahuasca experience.
2:10:29 But the result of this sophisticated chemistry that we find taking place here is a brew which is hideous to drink.
2:10:33 The taste, I find it quite repulsive.
2:10:38 I almost wretched just smelling it in the cup.
2:10:43 But then unleashes these extraordinary experiences.
2:10:46 And it isn’t just pretty visuals.
2:10:51 It’s the sense of encounters with sentient others.
2:10:59 That there are sentient beings that somehow we’re surrounded by a realm of sentience that is not normally accessible to us.
2:11:07 And that what the ayahuasca brew and certain other psychedelics, like some psilocybin mushrooms and high enough dose can do it as well.
2:11:08 LSD can do it.
2:11:18 But ayahuasca is the master in this of lowering the veil to what appears to be a seamlessly convincing other realm, other world.
2:11:24 And of course, the hard line rational scientists will say that’s just all fantasies of your brain.
2:11:31 But I don’t think we fully understand or even close to understanding exactly what consciousness is.
2:11:41 And I remain open to two possibilities that consciousness is generated by the brain is made by the brain in the way that a factory makes cars.
2:11:50 But I also am open to the possibility that the brain is a receiver of consciousness, just as a television set is the receiver of television signals.
2:12:02 And that if that is the case, then we locked into the physical realm, we need our everyday alert problem solving state of consciousness.
2:12:11 And that’s the state of consciousness that Western civilization values and highly encourages.
2:12:21 But these other states of consciousness that allow us to access alternative realities are possibly more important.
2:12:34 It may be apocryphal, but it was reported after Francis Crick’s role and his Nobel Prize for the discovery of the double helix that he finally got it under the influence of LSD.
2:12:39 There’s the classic example of Kerry Mullis and the Polymeres chain reaction.
2:12:42 He said he got that under the influence of LSD.
2:12:53 So the notion that the alert problem solving state of consciousness is the only valuable state of consciousness is disproved by valuable experiences that people have had in a visionary state.
2:13:05 But the question that remains unresolved is those entities that we encounter and not everybody encounters them and you’re certainly not going to encounter them on every ayahuasca trip.
2:13:09 There are ayahuasca journeys where nothing seems to happen.
2:13:13 I suspect something does happen, but it happens at a subconscious level.
2:13:26 I know that shamans in the Amazon regard those trips where actually you don’t see visions as amongst the most valuable and they say you are learning stuff that you’re not remembering, but you’re learning it anyway.
2:13:30 These sentient others that are encountered, what are they?
2:13:33 Are they just figments of our brain on drugs?
2:13:42 Or are we actually gaining access to a parallel reality which is inhabited by consciousness, which is in a non-physical form?
2:13:45 And I’m equally open to that idea.
2:13:49 I think that may be what is going on here with ayahuasca.
2:14:01 But the other thing is that there is a presence within the ayahuasca brew and she is present both in ayahuasca and in yahe.
2:14:08 That’s one of the reasons why the shamans say that actually the master of the process is the ayahuasca vine, not the leaves.
2:14:13 It’s as though the vine has harnessed the leaves to gain access to human consciousness.
2:14:24 And there, if you have sufficient exposure to ayahuasca or yahe, you drink it enough times, I’ve had maybe 75 or 80 journeys with ayahuasca.
2:14:38 You definitely start to feel an intelligent presence with a definite personality which I interpret as feminine and which most people in the West interpreted as feminine and they call her mother ayahuasca.
2:14:47 There are some tribes in the Amazon who interpret the spirit of ayahuasca as male, but in all cases that spirit is seen as a teacher.
2:14:49 That’s fundamentally what ayahuasca is.
2:14:52 It’s a teacher and it teaches moral lessons.
2:15:07 And that’s fascinating that a mixture of two plants should cause us to reflect on our own behavior and how it may have hurt and damaged and affected others and fill us with a powerful wish not to repeat that negative behavior again in the future.
2:15:20 The more baggage you carry in your life, the harder the beating that ayahuasca is going to give you until it forces you to confront and take responsibility for your own behavior.
2:15:26 And that is an extraordinary thing to come from a plant brew in that way.
2:15:38 And I think in yes, I think ayahuasca is the most powerful of all the plant medicines for accessing these mysterious realms, but there’s no doubt you can access them.
2:15:42 They’re all tryptomines, they’re all related to one another in one way.
2:15:48 You can access them through LSD and you certainly can access them through silicide mushrooms as well in large enough dose.
2:15:56 Both possibilities, as you described are interesting and to me, they’re kind of akin to each other.
2:16:17 I wonder what the limit of the brain’s capacity is to create imaginary worlds and treat them seriously and make them real and in those worlds explore and have real sort of moral deep brainstorming sessions with those entities.
2:16:25 So it’s almost like the power of the human mind to imagine taken to its limit.
2:16:26 It is.
2:16:33 And the curious thing is that the same iconography, people paint their visions after ayahuasca sessions.
2:16:44 People were painting in Europe in the cave of Lascaux, for example, and of course they had access to silicide mushrooms in prehistoric Europe.
2:16:49 There’s a remarkable commonality in the imagery that is painted.
2:16:54 I like to give credit to where credit is due and there are two names that need to be mentioned here.
2:17:06 One is the late great Terence McKenna and his book Food of the Gods, where he proposed the idea very strongly that it was our ancestral encounters with psychedelics that made us fully human.
2:17:10 That’s what switched on the modern human mind.
2:17:18 And very much the same idea began to be explored a bit earlier by Professor David Lewis Williams at the University of Woodwatersrand in South Africa.
2:17:39 Fabulous book called The Mind in the Cave, where he is again arguing that these astonishing similarities in cave art and rock art all around the world can only be properly explained by people in deeply altered states of consciousness attempting to remember when they return to a normal everyday state of consciousness.
2:17:47 Attempting to remember their visions and document them on permanent media like the wall of a cave.
2:17:54 So typically you get a lot of geometric patterns, but you also got entities and those entities often are therianthropes.
2:18:06 Part animal, part human in form might have the head of a wolf and the body of a human being, might have the head of a bird and the body of a human being and so on and so forth.
2:18:09 And that they communicate with us in the visionary state.
2:18:17 Interestingly, although this sounds like woo and it is an area that most scientists would steer clear of at risk of their careers.
2:18:30 There is very serious work now being done at Imperial College in London and at the University of California at San Diego where volunteers are being given extended DMT.
2:18:43 There is a new technology, DMTX, where the DMT is fed directly into the bloodstream by drip and it is possible to keep the individual in the peak DMT state.
2:18:54 Which normally when you smoke or vape DMT you are looking if you are lucky at 10 minutes or if you are unlucky if it is a bad journey because those 10 minutes can seem like forever.
2:19:04 But with DMTX, with the drip feeding of DMT into the bloodstream, these volunteers actually could be kept in the peak state for hours.
2:19:11 And unlike LSD, where you rapidly build up tolerance, nobody ever builds up tolerance to DMT, it always hits you with the same power.
2:19:17 Even if you took it yesterday and the day before and you are taking it tomorrow as well, it is still going to have that same power.
2:19:24 There is no tolerance there. So that is how they can use that lack of tolerance to keep volunteers in this state.
2:19:30 And then when they debrief those volunteers, they are also putting them in MRI scanners and looking at what is happening in the brain.
2:19:35 But when they debrief them, they are all talking about encounters with sentient others.
2:19:41 There is even a group now called sentient others where people are exchanging, volunteers are now exchanging their experiences.
2:19:47 They weren’t allowed to do so at the beginning of the experiment, but now that most of them have left it, they are exchanging their experiences.
2:19:52 And it is all about encounters with sentient others who wish to teach their moral lessons.
2:20:00 Now, to me, that is wild. What is going on here? How do we account for this?
2:20:07 Yeah, I get the notion of hallucinations and brightly colored visuals, but the moral lessons that come with it, those are very odd.
2:20:18 Yeah, and would you say that the reason that could give birth to a civilization is it because such visions can help create myths,
2:20:26 and especially like religious myths, that would be a cohesive thing for a large group of people to get around.
2:20:29 Yeah, and can help us to be better members of our own community.
2:20:30 Right, with moral lessons.
2:20:36 Yeah, more contributing members of our community, more caring, more nurturing members of our community.
2:20:38 That’s got to be good for any community.
2:20:50 I’ve said this a dozen times, but I’ll say it again, if I had the power to do so, I would make it a law, an absolute law,
2:20:58 that anybody running for a powerful political position, particularly if that position is president or head of state in any kind of way,
2:21:10 that that person has to undergo the ayahuasca ordeal first, they have to have 10 or 12 sessions of ayahuasca as a condition for applying for the job.
2:21:17 I suspect that most who had had those experiences wouldn’t want to apply for the job anymore.
2:21:19 They would want to live a different kind of life.
2:21:30 And those who did want to carry on being a leader of a nation would be very different people from the people who are leading the nations of the earth into chaos and destruction today.
2:21:32 Yeah, they would be doing it for the right reasons.
2:21:44 I mentioned to you, I recently interviewed Donald Trump and actually brought up this same idea that it would be a much better world if most of Congress and most politicians would take some form of psychedelic at the very least.
2:21:46 I have no doubt that it would be a better world.
2:21:54 I mean, this raises an interesting point, which is the role of government in controlling our consciousness.
2:22:05 And in my opinion, the so-called war on drugs is one of the fundamental abuses of human rights that have been undertaken in the past 60 years.
2:22:19 It should be a Republican issue. If I understand the Republican Party correctly, the Republican Party believes in individual freedom for adults as much as possible and particularly the freedom to make choices over there on bodies.
2:22:26 But in the case of even cannabis, I know there’s one of the great things that’s happening in America.
2:22:43 It’s happening state by state where cannabis is being legalized and that draconian hand of government is being taken off the back of people who are consuming a medicine that is far less harmful than alcohol, which is glorified in our society.
2:22:55 We cannot say that we are free if we allow a government to dictate to us what experiences we may or may not have in our inner consciousness while doing no harm to others.
2:23:01 And the point there is we already have a whole raft of laws that deal with us when we do harm to others.
2:23:08 Do we really need laws that tell us what we may and may or may not experience in the inner sanctum of our own consciousness?
2:23:28 I think it’s a fundamental violation of adult sovereignty and we would have much less drug problems if these drugs were all legalized and made available to people without shaming them, without punishing them in any way, but just part of normal social life.
2:23:34 And you could be sure that you were getting good product rather than really shitty product, which has been cut with all sorts of other things.
2:23:49 Ultimately, the way forward is for adults to take responsibility for their own behavior and for society to allow that to happen and not to have big government taking responsibility for decisions that should be in the hands of individuals.
2:23:57 For me also, it’s exciting some of these substances like psilocybin are being integrated into scientific studies of large scales. It’s really interesting.
2:24:18 We’ve seen a revolution in the way science looks at psychedelics in the last 20, 25 years. They were in that highly demonized category, but again, it’s one of those paradigms which gets overwhelmed by new evidence and it began to be realized that psilocybin and other psychedelics are very helpful in a range of conditions.
2:24:33 From which people suffer post-traumatic stress disorder, the fear of death when you’re suffering from terminal cancer can be overwhelming and it’s been found that psilocybin can remove that.
2:24:46 Deep depressions can be evaporated with one single massive psilocybin journey. They just go away. There’s really good science on this and they are being integrated into conventional medicine more and more.
2:24:53 We’ll see it happening. I’m not sure if it’ll happen as much as fast as I would like to see it happen in my lifetime, but it is going to happen.
2:25:09 I actually just recently found out that he had a TED talk, War on Consciousness, that was taken down and that was just part of just the general resistance because it wasn’t really radical.
2:25:23 I was talking about ayahuasca and I was talking about the view that I hold very strongly that as long as we do no harm to others, sovereign adults should be allowed to make decisions about their own bodies and not face a jail sentence or shaming as a result.
2:25:44 It was a TEDx talk, not a TED talk, organized by a local TED group. They call them TEDx talks. I gave this talk about the War on Consciousness and it was immediately pulled down from TED’s main channel with all kinds of bizarre reasons being given.
2:25:56 Unfortunately it was too late because a number of people had already downloaded the talk and then uploaded it onto other YouTube channels and actually their banning of it made it go viral in a way that would not have happened otherwise.
2:26:07 But again, it’s a sign that points of view that are not acceptable to those in positions of power are simply dismissed and shut down or at least attempts are made to do so.
2:26:17 In general, just along that line of thinking, I’m pretty sure that what we understand about consciousness today will seem silly to humans from 100 years from now.
2:26:32 You bet it will, especially if we harness psychedelics to investigate consciousness. That is what is happening at Imperial College right now is the investigation of the experience.
2:26:48 There are other trials that are looking for the therapeutic potential of D&T, but in this case, they’re looking entirely at the experiences that people have and why they’re so similar from people from different age groups and different genders and different parts of the world are all having the same experiences.
2:27:02 For me, from an engineer perspective, it’s interesting if it’s possible to engineer consciousness in artificial beings. It’s another way to approach the question of how special is human consciousness.
2:27:18 From where does it arise? Is it something that permeates all of life? And then in that case, what is the thing that makes life special? Like what is life? What is these living organisms that we have here that evolve to create humans?
2:27:26 And what is truly special about humans? And it’s both scary and exciting to consider the possibility that we can create something like this.
2:27:36 But why not? We’re a vehicle for consciousness in my view. I think consciousness is present in all life on Earth. I don’t think it’s limited to human beings.
2:27:45 We have the equipment to manifest and express that consciousness in the way that a dog, for example, doesn’t have or a snail doesn’t have or a pigeon doesn’t have.
2:27:57 But when I look at two pigeons sitting on my garden fence and rubbing up close to each other and enjoying each other’s company and taking off together and hanging out together, I think they’re conscious beings.
2:28:16 And I think consciousness is everywhere. I think it’s the basis of everything and I suspect that fundamentally consciousness is non-physical and that it can manifest in physical forms where it can then have experiences that would not be available in the non-physical state. That’s a guess.
2:28:22 That’d be fascinating because then you can construct all kinds of physical forms to manifest the consciousness.
2:28:29 Obviously if consciousness enters, if they become consciousness, isn’t there some suggestion that artificial intelligence is already becoming conscious?
2:28:40 That makes humans really uncomfortable because we are at the top of the food chain. We consider ourselves truly special and to consider that there’s other things that could be special is scary.
2:28:51 Well, look how other people make us uncomfortable too. I mean, look at the state of the world today. All the conflicts that are raging, that’s because we’re afraid.
2:29:03 When I say we, I’m speaking nation by nation, we are afraid of other people. We fear that they’re going to hurt us or damage us in some way and so we seek to stop that. It’s the root of many, many conflicts, this fear.
2:29:14 And so fear of AI may not be such a good idea after all. It might be very interesting to go down that route and see where it comes. Certainly in terms of exploring consciousness, it is very interesting.
2:29:19 Yeah, fear is a useful thing, but it can also be destructive.
2:29:22 Well, it can be destructive and it can shut you down completely.
2:29:30 If you look into the future, maybe the next 100 years, what do you hope are the interesting discoveries in archaeology that we’ll find?
2:29:43 Well, I’d really like to know how the Great Pyramid was built. And we now have with new tech, with scanning technology, it’s now become apparent that there are many major voids within the Great Pyramid.
2:29:51 Right above the Grand Gallery, there’s what looks like a second Grand Gallery that has been identified with remote scanning.
2:30:03 And new chambers, one of them has even been opened up already, are being found as a result of this. So it may be that the Great Pyramid will ultimately give up its secrets.
2:30:11 I often think that the Great Pyramid is partly designed to do that. It’s designed to invite its own initiates.
2:30:17 Some people aren’t interested in the Great Pyramid at all, but some people are fascinated by it and they’re drawn towards it.
2:30:23 And when they’re drawn towards it, it immediately starts raising questions in their minds and they seek answers to their questions.
2:30:30 So it’s like saying, here I stand, investigate me, find out about me, figure out what I am.
2:30:35 Why have I got these two shafts cut into the side of the so-called Queen’s Chamber?
2:30:41 Why do they slope up through the body of the Great Pyramid? Why do they not exit on the outside of the Great Pyramid?
2:30:49 Why when we send a robot up those shafts, do we find them after about 160 feet blocked by a door with metal handles?
2:30:55 Why when we drill through that door to see what’s beyond it three or four feet away, we see another door?
2:31:01 It’s like very frustrating, but it’s saying to us, keep on exploring.
2:31:05 If you’re persistent enough, we’ll eventually give you the answer.
2:31:14 So I’m hoping that that answer will come as to how this most mysterious of monuments was actually built and the inspiration that lay behind it.
2:31:20 Certainly, I’m sure it was never a tomb or a tomb only.
2:31:23 The later pyramids might have been.
2:31:30 Actually, no pharaonic burial has been discovered in any pyramid, but nevertheless, it’s pretty clear that the later pyramids,
2:31:37 the pyramid texts written on the walls, like the Pyramid of Unas, Fifth Dynasty Pyramid at Sakara, were tombs.
2:31:44 But the Great Pyramid, to go to that length to create a tomb, to make it a scale model of the Earth,
2:31:50 to orient it perfectly to True North, to make it six million tons, this is not a tomb.
2:31:55 This is something else. This is a curiosity device. This is something that is asking us to understand it.
2:32:03 And I hope we will understand it. And I hope Egyptologists will be willing to set aside that prejudice that they’re only looking at a tomb
2:32:10 and consider other possibilities. And as New Tech is revealing these previously unknown inner spaces within the Great Pyramid,
2:32:12 I think that’s going to become more and more likely.
2:32:15 So not just the how it was built, but the why.
2:32:16 But the why.
2:32:21 And to you, it seems obvious that there would be a cosmic motivation.
2:32:29 Yeah, very, very much so. As above, so below. Which is an idea in the Hermetica.
2:32:35 The God Hermes for the Greeks was the Greek version of Thoth, the wisdom God of ancient Egypt.
2:32:38 And that’s where that saying comes from. It comes from the Hermetica.
2:32:43 But it’s expressing an ancient Egyptian idea to mirror the perfection of the heavens on Earth.
2:32:47 So you think there’s something interesting to be discovered about the how it was built?
2:32:51 You mean beyond the sort of the ideas of using ramps and what?
2:32:55 Yeah, ramps won’t do it. Ramps won’t do it, nor will wet sand.
2:33:02 It’s true that the ancient Egyptians did haul big objects on sleds on wet sand.
2:33:11 There are even reliefs that show the process where an individual is standing on the front of the sledge pouring water down to lubricate the sand underneath.
2:33:21 And that’s a perfectly respectable way to move a 200 ton block of stone across sand, flat sand, if you have enough people to pull it.
2:33:33 But that is not going to help you get dozens of 70 ton granite blocks, 300 feet in the air, to form the roof of the king’s chamber and the floor of the chamber above it.
2:33:37 And the roof of that chamber and the floor of the chamber above that and so on and so forth.
2:33:40 Wet sand never got those objects up there.
2:33:42 Somehow they were lifted up there.
2:33:48 Now, yeah, ramps are proposed as the solution, but where are the remains of those ramps?
2:33:56 If you’re going to carry blocks weighing up to two or three tons right to the top of the Great Pyramid to complete your work,
2:34:02 you’re going to need a ramp that’s going to extend out into the desert for more than a mile at a 10 degree slope.
2:34:09 And it’s calculated that a 10 degree slope is about the maximum slope that human labor can haul objects up a ramp.
2:34:16 And that ramp can’t just be compacted sand since heavy objects are being hauled up.
2:34:20 It’s going to have to be made of very solid material, almost as solid as the pyramid itself.
2:34:21 Where is it?
2:34:27 We don’t see any trace of those so-called ramps that are supposed to have been involved in the construction of the pyramid.
2:34:28 I think we don’t know.
2:34:29 I think we have no idea it’s built.
2:34:31 That’s why there’s so many different theories.
2:34:33 We haven’t got the answer yet.
2:34:36 But the how of it is one of the big mysteries from our past.
2:34:45 I love the Great Pyramid as a kind of puzzle that was created by the ancient peoples to be solved by later peoples.
2:34:56 I don’t know if you’re aware of the 10,000 year clock that was built by Jeff Bezos and Danny Hillis in Sierra Diablo Mountains in Texas.
2:35:01 So they’re building a clock that takes once a year for 10,000 years.
2:35:08 And it’s supposed to run if there’s a nuclear apocalypse, it just runs.
2:35:17 And it’s an example of modern humans thinking like, okay, if 10,000 years from now and beyond,
2:35:26 if something goes wrong or the future humans that are way different come back and they analyze what happened here,
2:35:30 how can we create monuments that they can then analyze?
2:35:37 And in that way, be curious about, in their curiosity, discover some deep truths about this current time.
2:35:41 It’s an interesting kind of notion of what can we build now?
2:35:42 That would last.
2:35:46 And the answer is that the majority of what we build now wouldn’t last.
2:35:49 It would be gone within a few thousand years.
2:35:55 But what would last is massive megalithic structures like the Great Pyramid.
2:36:02 That would last and it could be used to send a message to the future.
2:36:05 I think Gobekli Tepe serves a similar function.
2:36:06 I mean, there it was.
2:36:10 It was buried 10,400 years ago.
2:36:14 And then for the next 10,000 years, nobody touched it.
2:36:15 Nobody knew it was there.
2:36:24 It took the genius of Klaus Schmidt, the original excavator, to realize what he’d found and what it was.
2:36:29 But the great thing about the sealing of Gobekli Tepe, the deliberate burial of Gobekli Tepe,
2:36:34 is it means that no later culture trod over it and imposed their organic materials on it
2:36:39 and messed up the dating sequences and so on and so forth or vandalized it or used it as a quarry.
2:36:41 It’s all there intact.
2:36:47 So you mentioned that the pyramids and some of the other amazing things that humans have built
2:36:52 was the results of us humans struggling with our mortality.
2:36:55 That’s the ultimate goal.
2:36:59 That seems to me what’s at the heart of many pyramids around the world
2:37:03 is that they’re connected in one way or another to the notion of death
2:37:07 and to the notion of the exploration of the afterlife.
2:37:11 And this is, of course, the fundamental mystery that all human beings face.
2:37:14 We may wish to ignore it.
2:37:19 We may wish to pretend that it’s not going to happen, but we are, of course, all mortal.
2:37:24 Every one of us, all eight billion or however many of us that are on the planet right now,
2:37:27 we’re all going to face death sooner or later.
2:37:30 And the question is, what happens?
2:37:36 And there are a few cultures that really intensely, deeply studied that mystery.
2:37:38 We are not one of them.
2:37:43 The general view of science, I think, is that we’re accidents of evolution.
2:37:45 When we die, the light blinks out.
2:37:46 There’s no more of us.
2:37:47 There’s no such thing as a soul.
2:37:49 But that’s not a proven point.
2:37:51 There’s no experiment that proves that’s the case.
2:37:56 We know we die, but we don’t know whether there’s such a thing as a soul or not.
2:37:58 Yeah, it’s the great mystery.
2:38:00 It’s the great mystery that we all share.
2:38:05 And those cultures that have investigated it and ancient Egypt is the best example.
2:38:10 Have investigated it thoroughly and map out the journey that we make after death.
2:38:15 But that notion of a journey after death and of hazards and challenges along the way,
2:38:19 and ultimately of a judgment, that notion is found right around the world.
2:38:25 And it even manifests into the three monotheistic face that are still present in the world today.
2:38:27 Well, you’re one such human.
2:38:31 And you said you contemplate your own death.
2:38:32 Yeah.
2:38:33 Are you afraid of it?
2:38:34 No.
2:38:36 I’m not afraid of death at all.
2:38:38 I’m curious about death.
2:38:40 I think it could be very interesting.
2:38:44 I think it’s the beginning of the next great adventure.
2:38:46 So I don’t fear it.
2:38:53 And I would like to live as long as my body is healthy enough to make living worthwhile.
2:38:54 But I don’t fear death.
2:38:57 What I do fear is pain.
2:39:03 I do fear the humiliation that old age and the collapse of the faculties can bring.
2:39:08 I do fear the cancers that can strike us down and riddle us with pain and agony.
2:39:11 That I fear very, very much indeed.
2:39:14 But death is going to come to all of us.
2:39:15 I accept it.
2:39:16 It’s going to come to me.
2:39:19 And I’m not going to say I’m looking forward to it.
2:39:25 But when it happens, I’m going to approach it, I hope, with a sense of curiosity and a sense of adventure,
2:39:28 that there’s something beyond this life.
2:39:29 It isn’t heaven.
2:39:30 It isn’t hell.
2:39:32 But there’s something.
2:39:33 The soul goes on.
2:39:37 I think reincarnation is a very plausible idea.
2:39:39 Again, modern science would reject that.
2:39:44 But there’s the excellent work of Ian Stevenson, children who remember past lives,
2:39:49 who found that children up to the age of seven often have memories of past lives.
2:39:55 And in cultures where memories of past lives are discouraged, they tend not to express that much.
2:40:00 But in cultures where memories of past lives are encouraged, like India, they do express it.
2:40:04 And he found several subjects, children under the age of seven in India,
2:40:07 who were able to remember specific details of a past life.
2:40:13 And he was able to go to the place where that past life unfolded and validate those details.
2:40:17 So if consciousness is the basis of everything, if it’s the essence of everything,
2:40:22 and consciousness benefits in some way from being incarnated in physical form,
2:40:24 then reincarnation makes a lot of sense.
2:40:34 All the investment that the universe has put into creating this home for life may have a much bigger purpose than just accident.
2:40:36 What a beautiful mystery this whole thing is.
2:40:37 Yeah, we are immersed in mystery.
2:40:39 We live in the midst of mystery.
2:40:40 We’re surrounded by mystery.
2:40:43 And if we pretend otherwise, we’re deluding ourselves.
2:40:47 And, Graham, thank you so much for inspiring the world to explore that mystery.
2:40:48 Thank you for talking today.
2:40:49 Thank you, Lex.
2:40:51 It’s been a pleasure.
2:40:54 Thanks for listening to this conversation with Graham Hancock.
2:40:58 To support this podcast, please check out our sponsors in the description.
2:41:02 And now, let me leave you with some words from Charles Darwin.
2:41:08 It is not the strongest of the species that survives, nor the most intelligent.
2:41:13 It is the one that is the most adaptable to change.
2:41:17 Thank you for listening, and hope to see you next time.
2:41:21 [MUSIC PLAYING]
2:41:24 [MUSIC PLAYING]
2:41:27 [MUSIC PLAYING]
2:41:30 [MUSIC PLAYING]
2:41:33 (gentle music)

0:00:06 The following is a conversation with Jordan Peterson, his second time on this The Lex
0:00:07 Friedman podcast.
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0:00:46 The thing that makes me feel like home.
0:00:49 The thing I miss when I’m traveling.
0:00:51 That’s a good definition of home.
0:00:55 The place you miss the most when you’re away from it.
0:01:00 I think I’ve read a quote from like Nietzsche or Schopenhauer.
0:01:05 One of those like hardcore, maybe a bit cynical philosophers.
0:01:06 Who was it?
0:01:12 Anyway, the quote was something like home is the place you have tried
0:01:14 to escape the most, but failed.
0:01:17 Oh, boy.
0:01:21 It’s kind of like from the whole school of thought that life is suffering.
0:01:28 And so happiness is the moments where you escape the suffering briefly.
0:01:30 That kind of that kind of perspective on life.
0:01:33 So probably Schopenhauer, probably one of those guys.
0:01:39 And you know, in that same vein, I could say that, you know, taking a nap
0:01:48 on an acely bed, cold bed surface, warm blanket is a kind of escape from the suffering.
0:01:52 It is, no matter what, an escape from the world.
0:01:58 It’s the kind of escape that once you return, you feel so refreshed with new eyes.
0:02:00 Everything is brighter.
0:02:01 Everything’s more hopeful.
0:02:06 Everything radiates the possibility of something good happening.
0:02:09 So I’m a big fan of naps.
0:02:16 Go to sleep.com/lex and use code Lex to get $350 off the pod for ultra.
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0:02:44 the mainstream media or what do we call it, the heterodox media?
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0:03:02 I do think for me, at least personally, consuming news is so much more about protecting
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0:03:14 As a first step, just to protect my mind from all the people who are yelling
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0:03:25 So just protecting my head from that and then calmly just understanding what happened
0:03:29 and what are the different perspectives and what do I think about it?
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0:04:13 Both, I truly believe it is both.
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0:04:20 to fly before it hits the ground.
0:04:22 What a life this is.
0:04:25 My mind has been on a roller coaster lately.
0:04:27 It’s been a rough one.
0:04:29 It’s been a rough one.
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0:04:55 Talking is one of the ways to shine a light on the Jungian shadow.
0:04:56 Try to understand.
0:04:59 Try to understand what’s going on in there.
0:05:03 What are the roots of your particular malady?
0:05:08 Particular kind of psychological distress.
0:05:11 What a fascinating little puzzle this whole thing is.
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0:05:26 This episode is also brought to you by Element, my daily zero sugar and delicious electrolyte
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0:05:28 Let me take a sip.
0:05:31 Delicious.
0:05:35 Watermelon salt always has been, always will be, but there’s a bunch of other flavors.
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0:05:41 it’s actually amazing.
0:05:44 But for me personally, it’s almost a little too amazing.
0:05:50 It almost feels like dessert versus like a refreshing, energizing kind of flavor,
0:05:52 which is watermelon salt.
0:05:56 But to each their own, I’m actually learning that people have fundamentally different
0:06:01 tastes in foods, in ice cream, whatever.
0:06:03 And I think that’s beautiful.
0:06:06 For most of my life, I get stuck on certain foods I like.
0:06:09 And I just keep eating that thing, some creature of habit in a sense.
0:06:11 Because I find a thing that makes me happy.
0:06:13 And I just keep doing the thing that makes me happy.
0:06:15 It’s kind of logical.
0:06:16 I don’t get bored.
0:06:19 I guess I don’t get bored easily because everything is so full of life.
0:06:21 Everything is so full of awesomeness.
0:06:24 And I just don’t need to go to and using this full of awesomeness.
0:06:30 But I also realized that there is a joy inherent to the exploration in itself.
0:06:34 So I guess that’s an argument for me to try different flavors of element.
0:06:37 I have, I gave him a chance.
0:06:39 I still like watermelon salt the most.
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0:06:50 And now, dear friends, here’s Jordan Peterson.
0:07:07 [Music]
0:07:11 You have given a set of lectures on Nietzsche as part of the new Peterson Academy.
0:07:14 And the lectures were powerful.
0:07:18 There’s some element of the contradictions, the tensions, the drama,
0:07:22 the way you like lock in an idea, but then are struggling with that idea.
0:07:25 All of that, that feels like it’s a Nietzschean.
0:07:28 Yeah, well, he has a big influence on me stylistically.
0:07:31 And like in terms of the way I approached writing,
0:07:39 and also many of the people that were other influences of mine were very influenced by him.
0:07:43 So I was blown away when I first came across his writings.
0:07:53 They’re so intellectually dense that I don’t know if there’s anything that approximates that.
0:07:58 Dostoevsky, maybe, although he’s much more wordy, Nietzsche is very succinct,
0:08:00 partly because he was so ill, because he would think all day.
0:08:02 He couldn’t spend a lot of time writing.
0:08:06 And he condenses writings into very short, while this aphoristic style he had.
0:08:09 And it’s really something to strive for.
0:08:15 And then he’s also an exciting writer like Dostoevsky and dynamic.
0:08:18 And romantic in that emotional way.
0:08:20 And so it’s really something.
0:08:21 And I really enjoyed doing that.
0:08:23 I did that lecture that you described.
0:08:27 That lecture series is on the first half of “Beyond Good and Evil,” which is a stunning book.
0:08:34 And that was really fun to take pieces of it and then to describe what they mean
0:08:37 and how they’ve echoed across the decades since he wrote them.
0:08:39 And yeah, it’s been great.
0:08:46 Taking each sentence seriously and deconstructing it and really struggling with it.
0:08:51 I think underpinning that approach to writing requires deep respect for the person.
0:08:55 I think if we approach writing with that kind of respect,
0:09:00 you can take, or well, you can take a lot of writers and really dig in on singular sentences.
0:09:03 Yeah, well, those are the great writers because the greatest writers,
0:09:07 virtually everything they wrote is worth attending to.
0:09:11 No, and I think Nietzsche is in some ways the ultimate exemplar of that because
0:09:17 often when I read a book, I’ll mark one way or another.
0:09:21 I often fold the corner of the page over to indicate something that I’ve found that’s
0:09:22 worth remembering.
0:09:27 I couldn’t do that with a book like “Beyond Good and Evil” because every page ends up marked.
0:09:33 And that’s in marked contrast, so to speak, to many of the books I read now,
0:09:40 where it’s quite frequently now that I’ll read a book and there won’t be an idea
0:09:41 that I haven’t come across before.
0:09:46 And with a thinker like Nietzsche, that’s just not the case at the sentence level.
0:09:51 And I don’t think there’s anyone that I know of who did that to a greater extent than he did.
0:09:55 So there’s other people who whose thought is of equivalent value.
0:10:00 I’ve returned recently, and I’m going to do a course on to the work of this Romanian
0:10:05 historian of religions, Mircea Eliata, who’s not nearly as well known as he should be
0:10:11 and whose work, by the way, is a real antidote to the post-modern nihilistic Marxist
0:10:16 stream of literary interpretation that the universities as a whole have adopted.
0:10:18 And Eliata is like that, too.
0:10:21 I used this book called “The Sacred and the Profane”
0:10:26 quite extensively in a book that I’m releasing in mid-November.
0:10:30 “We who wrestle with God,” and it’s of the same sort.
0:10:33 It’s endlessly analyzable.
0:10:39 Eliata walked through the whole history of religious ideas, and he had the intellect
0:10:40 that enabled him to do that.
0:10:45 And everything he wrote is dreamlike in its density.
0:10:53 So every sentence or paragraph is evocative in an image-rich manner.
0:10:58 And that also, what would you say, deepens and broadens the scope.
0:11:02 And that’s part of often what distinguishes writing that has a literary end
0:11:04 from writing that’s more merely technical.
0:11:10 Like, the literary writings have this imagistic and dreamlike reference space around them.
0:11:17 And it takes a long time to turn a complex image into something semantic.
0:11:25 And so if your writing evokes deep imagery, it has a depth that can’t be captured merely in words.
0:11:29 And the great romantic poetic philosophers, Nietzsche has a very good example.
0:11:30 Dostoevsky is a good example.
0:11:32 So is Mircea Eliata.
0:11:33 They have that quality.
0:11:35 And it’s a good way of thinking about it.
0:11:40 You know, it’s kind of interesting from the perspective of technical analysis of intelligence.
0:11:46 There’s a good book called “The User Illusion,” which is the best book on consciousness that I ever read.
0:11:51 It explains the manner in which our communication is understandable in this manner.
0:11:54 So imagine that when you’re communicating something,
0:11:59 you’re trying to change the way that your target audience perceives and acts in the world.
0:12:01 So that’s an embodied issue.
0:12:08 But you’re using words, which obviously aren’t equivalent to the actions themselves.
0:12:14 You can imagine that the words are surrounded by a cloud of images that they evoke.
0:12:17 And that the images can be translated into actions.
0:12:18 Yeah.
0:12:26 And the greatest writing uses words in a manner that evokes images that profoundly affects perception and action.
0:12:27 And that’s the…
0:12:31 So I would take the manner in which I act and behave.
0:12:33 I would translate that into a set of images.
0:12:35 My dreams do that for me, for example.
0:12:37 Then I compress them into words.
0:12:43 I toss you the words, you decompose them, decompress them into the images, and then into the actions.
0:12:46 And that’s what happens in a meaningful conversation.
0:12:50 That’s a very good way of understanding how we communicate linguistically.
0:12:56 So if the words spring to the visual, full visual complexity,
0:12:59 and then that can then transform itself into action, that’s…
0:13:01 And change in perception, because…
0:13:01 Change in perception.
0:13:02 Well, those are both relevant.
0:13:09 And it’s an important thing to understand because the classic empiricists make the presumption.
0:13:15 And it’s an erroneous presumption that perception is a value-free enterprise.
0:13:19 And they assume that partly because they think of perception as something passive.
0:13:21 You know, you just turn your head and you look at the world and there it is.
0:13:24 It’s like perception is not passive.
0:13:26 There is no perception without action.
0:13:27 Ever.
0:13:28 Ever.
0:13:31 And that’s a weird thing to understand because even when you’re looking at something,
0:13:33 like your eyes are moving back and forth.
0:13:37 If they ever stop moving for a tenth of a second, you stop being able to see.
0:13:40 So your eyes are jiggling back and forth just to keep them active.
0:13:43 And then there’s involuntary movements of your eyes.
0:13:44 And then there’s voluntary movements of your eyes.
0:13:48 Like what you’re doing with your eyes is very much like what a blind person would do
0:13:52 if they were feeling out the contours of an object.
0:13:53 You’re sampling.
0:13:58 And you’re only sampling a small element of the space that’s in front of you.
0:14:03 And the element that you choose to sample is dependent on your aims and your goals.
0:14:04 So it’s value-saturated.
0:14:07 And so all your perceptions are action predicated.
0:14:12 And partly what you’re doing when you’re communicating is therefore not only changing
0:14:18 people’s actions, let’s say, but you’re also changing the strategy that they use to perceive.
0:14:22 And so you change the way the world reveals itself for them.
0:14:26 See, this is why it’s such a profound experience to read a particularly deep thinker.
0:14:31 Because you could also think of your perceptions as the axioms of your thought.
0:14:32 That’s a good way of thinking about it.
0:14:35 A perception is like a, what would you say?
0:14:40 It’s a thought that’s so set in concrete that you now see it rather than conceptualize it.
0:14:44 A really profound thinker changes the way you perceive the world.
0:14:48 That’s way deeper than just how you think about it or how you feel about it.
0:14:53 What about not just profound thinkers, but thinkers that deliver a powerful idea?
0:14:59 For example, utopian ideas of Marx or utopian ideas.
0:15:02 You could say dystopian ideas of Hitler.
0:15:08 Those ideas are powerful and they can saturate all your perception with values.
0:15:15 And they focus you in a way where there’s only a certain set of actions.
0:15:18 Yeah, right. Even a certain set of emotions as well.
0:15:20 And it’s intense and it’s direct.
0:15:26 And they’re so powerful that they completely alter the perception and the words bring to life.
0:15:28 Yeah, it’s like a form of possession.
0:15:32 So there’s two things you need to understand to make that clear.
0:15:40 The first issue is that as we suggested or implied that perception is action predicated,
0:15:43 but action is goal predicated, right? You act towards goal.
0:15:49 And these propagandistic thinkers that you described,
0:15:54 they attempt to unify all possible goals into a coherent singularity.
0:15:58 And there’s advantages of that. There’s the advantage of simplicity, for example,
0:16:03 which is a major advantage. And there’s also the advantage of motivation, right?
0:16:09 So if you provide people with a simple manner of integrating all their actions,
0:16:12 you decrease their anxiety and you increase their motivation.
0:16:16 That can be a good thing if the unifying idea that you put forward is valid.
0:16:22 But it’s the worst of all possible ideas if you put forward an invalid unifying idea.
0:16:26 And then you might say, well, how do you distinguish between a valid unifying idea
0:16:29 and an invalid unifying idea? Now, Nietzsche was very interested in that,
0:16:35 and I don’t think he got that exactly right. But the postmodernists, for example,
0:16:38 especially the ones, and this is most of them, with the neo-Marxists bent,
0:16:42 their presumption is that the fundamental unifying idea is power,
0:16:45 that everything’s about compulsion and force, essentially.
0:16:50 And that that’s the only true unifying ethos of mankind, which is,
0:16:56 I don’t know if there’s a worse idea than that. I mean, there are ideas that are
0:17:00 potentially as dangerous. The nihilistic idea is pretty dangerous,
0:17:04 although it’s more of a disintegrating notion than a unifying idea.
0:17:09 The hedonistic idea that you live for pleasure, for example, that’s also very dangerous.
0:17:13 But if you wanted to go for sheer pathology, the notion that,
0:17:17 and this is Foucault in a nutshell, and Marx, for that matter, that power rules everything,
0:17:24 not only is that a terrible unifying idea, but it fully justifies your own use of power.
0:17:30 I don’t mean the power Nietzsche talks about. His will to power was more his insistence that
0:17:39 a human being is an expression of will rather than a mechanism of self-protection and security.
0:17:43 Like, he thought of the life force in human beings as something that strived
0:17:47 not to protect itself, but to exhaust itself in being and becoming.
0:17:52 It’s like an upward-oriented motivational drive even towards meaning.
0:17:57 Now, he called it the will to power, and that had some unfortunate consequences,
0:18:01 at least that’s how it’s translated. But he didn’t mean the power motivation
0:18:05 that people like Foucault or Marx became so hung up on.
0:18:08 So, it’s not power like you’re trying to destroy the other. It’s power,
0:18:13 full flourishing of a human being, the creative force of a human being.
0:18:18 Yeah. Well, you could imagine that, and you should, you could imagine that you could
0:18:24 segregate competence and ability. Imagine that you and I were going to work on a project.
0:18:31 We could organize our project in relationship to the ambition that we wanted to attain,
0:18:35 and we can organize an agreement so that you were committed to the project voluntarily,
0:18:38 and so that I was committed to the project voluntarily.
0:18:43 So, that means that we would actually be united in our perceptions and our actions
0:18:47 by the motivation of something approximating voluntary play.
0:18:50 Now, you could also imagine another situation where I said,
0:18:55 “Here’s our goal, and you better help me or I’m going to kill your family.”
0:18:58 Well, the probability is that you would be
0:19:06 quite motivated to undertake my bidding. And so then you might say,
0:19:08 “Well, that’s how the world works. It’s power and compulsion.”
0:19:15 But the truth of the matter is that you can force people to see things your way, let’s say,
0:19:19 but it’s nowhere near as good a strategy, even practically,
0:19:23 than the strategy that would be associated with something like voluntary.
0:19:30 Voluntary joint agreement of pattern of movement strategy towards the goal.
0:19:32 See, this is such an important thing to understand because it
0:19:39 helps you start to understand the distinction between a unifying force that’s
0:19:43 based on power and compulsion and one that is much more in keeping,
0:19:47 I would say, with the ethos that governs western societies, free western societies.
0:19:52 There’s really a qualitative difference, and it’s not some morally relativistic illusion.
0:19:58 So, if we just look at the nuance of Nietzsche’s thought,
0:20:04 the idea he first introduced and thus spoke Zarathustra of the Ubermench,
0:20:09 that’s another one that’s very easy to misinterpret because it sounds awfully a lot
0:20:15 like it’s about power. For example, in the 20th century, it was misrepresented and co-opted
0:20:23 by Hitler to advocate for the extermination of the inferior non-Aryan races.
0:20:28 And the dominion of the superior Aryans. Well, that was partly because Nietzsche’s
0:20:34 work also was misrepresented by his sister after his death, but I also think that there’s
0:20:40 a fundamental flaw in that Nietzschean conceptualization. So, Nietzsche, of course, famously announced the
0:20:47 death of God, but he did that in a manner that was accompanied by dire warnings, like Nietzsche said,
0:20:51 because people tend to think of that as a triumphalist statement, but Nietzsche actually said that
0:20:59 he really said something like the unifying ethos under which we’ve organized ourselves
0:21:06 psychologically and socially has now been fatally undermined by, well, by the rationalist proclivity,
0:21:12 by the empiricist proclivity. There’s a variety of reasons. Mostly, it was conflict between the
0:21:17 Enlightenment view, let’s say, and the classic religious view, and that there will be dire
0:21:23 consequences for that. And Nietzsche knew, like Dostoevsky knew, that, see, there’s a proclivity
0:21:29 for the human psyche and for human societies to move towards something approximating a unity,
0:21:35 because the cost of disunity is high, fractionation of your goals, so that means you’re less
0:21:38 motivated to move forward than you might be, because there’s many things competing for your
0:21:44 attention, and also anxiety, because anxiety actually signals something like goal conflict.
0:21:50 So there’s an inescapable proclivity of value systems to unite. Now, if you kill the thing
0:21:56 that’s uniting them, that’s the death of God, they either fractionate and you get confusion,
0:22:03 anxiety, and hopelessness, or you get social disunity, or and you get social disunity, or
0:22:12 something else arises out of the abyss to constitute that unifying force. And Nietzsche
0:22:17 said specifically that he believed that one of those manifestations would be that of
0:22:24 communism, and that that would kill, he said this in Will to Power, that that would kill tens of
0:22:31 millions of people in the upcoming 20th century. He could see that coming 50 years earlier,
0:22:35 and Dostoevsky did the same thing in his book, The Demons. So this is the thing that the A
0:22:41 religious have to contend with. It’s a real conundrum, because I mean, you could dispute the idea that
0:22:47 our value systems tend towards a unity, and society does as well, because otherwise we’re
0:22:53 disunified. But the cost of that disunity, as I said, is goal confusion, anxiety, and hopelessness.
0:22:58 So it’s like a real cost. So you could dispense with the notion of unity altogether, and the
0:23:02 postmodernists did that to some degree. But they pulled off a sleight of hand too, where they replaced
0:23:09 it by power. Now Nietzsche did, he’s responsible for that to some degree, because Nietzsche said,
0:23:15 with his conception of the overman, let’s say, is that human beings would have to create their own
0:23:22 values. Because the value structure that had descended from on high was now shunted aside.
0:23:28 But there’s a major problem with that, many major problems. The psychoanalysts were the first people
0:23:33 who really figured this out after Nietzsche. Because imagine that we don’t have a
0:23:39 relationship with the transcendental anymore that orients us. Okay, now we have to turn to
0:23:47 ourselves. Okay, now if we were a unity, a clear unity within ourselves, let’s say, then we could
0:23:54 turn to ourselves for that discovery. But if we’re a fractionated plurality internally, then when we
0:23:58 turn to ourselves, we turn to a fractionated plurality. Well, that was Freud’s observation.
0:24:02 It’s like, how can you make your own values when you’re not the master in your own house?
0:24:10 Like you’re a war of competing motivations, or maybe you’re someone who’s dominated by the will to
0:24:15 force and compulsion. And so why do you think that you can rely on yourself as the source of values?
0:24:20 And why do you think you’re wise enough to consult with yourself to find out what those values are
0:24:26 or what they should be, say, in the course of a single life? I mean, you know, it’s difficult to
0:24:31 organize your own personal relationship, like one relationship in the course of your life, let alone
0:24:37 to try to imagine that out of whole cloth you could construct an ethos that would be psychologically
0:24:43 and socially stabilizing and last over the long run. It’s like, and of course, Marx, people like that,
0:24:50 the people who reduce human motivation to a single axis, they had the intellectual hubris to
0:24:54 imagine that they could do that. Postmodernists are a good example of that as well.
0:25:04 Okay, but if we lay on the table, religion, communism, Nazism, they are all unifying ethos.
0:25:11 They’re unifying ideas, but they’re also horribly dividing ideas. They both unify and divide. Religion
0:25:22 has also divided people, because in the nuances of how the different peoples wrestle with God,
0:25:26 they have come to different conclusions, and then they use those conclusions that perhaps the people
0:25:31 in power use those conclusions to then start wars, to start hatred, to divide.
0:25:38 Yeah, well, it’s one of the key sub themes in the Gospels is the sub theme of the Pharisees.
0:25:45 And so the fundamental enemies of Christ in the Gospels are the Pharisees and the scribes
0:25:49 and the lawyers. So what does that mean? The Pharisees are religious hypocrites.
0:25:56 The scribes are academics who worship their own intellect. And the lawyers are the legal minds
0:26:04 who use the law as a weapon. And so they’re the enemy of the Redeemer. That’s a subplot in the
0:26:12 Gospels stories. And that actually all means something. The Phariseic problem is that the best
0:26:18 of all possible ideas can be used by the worst actors in the worst possible way. And maybe this
0:26:24 is an existential conundrum is that the most evil people use the best possible ideas to the worst
0:26:30 possible ends. And then you have the conundrum of how do you separate out, let’s say, the genuine
0:26:36 religious people from those who use the religious enterprise only for their own machinations.
0:26:40 We’re seeing this happen online. Like one of the things that you’re seeing happening online,
0:26:47 I’m sure you’ve noticed this, especially on the right wing psychopathic troll side of the
0:26:53 distribution, is the weaponization of a certain form of Christian ideation. And that’s often
0:26:59 marked at least online by the presence of, what would you say, cliches like Christ is king,
0:27:03 which has a certain religious meaning, but a completely different meaning in this sphere of
0:27:09 emerging right wing pathology, right wing, the political dimension isn’t the right dimension
0:27:14 of analysis. But it’s definitely the case that the best possible ideas can be used for the worst
0:27:20 possible purposes. And that also brings up another specter, which is like, well, is there any reliable
0:27:27 and valid way of distinguishing truly beneficial unifying ideas from those that are pathological?
0:27:32 And so that’s another thing that I tried to detail out in these lectures, but also in this
0:27:37 new book is like, how do you tell the good actors from the bad actors at the most fundamental
0:27:42 level of analysis? And good ideas from the bad ideas and your electron truth, they need to also
0:27:51 struggle with. So how do you know, how do you know that communism is a bad idea versus it’s a good
0:27:57 idea implemented by bad actors? Right, right. That’s a more subtle variant of the religious
0:28:00 problem. And that’s what the, that’s what the communists say all the time, the modern day
0:28:06 communists like real communism has never been tried. And you could say, I suppose with some
0:28:13 justification, you could say that real Christianity has never been tried because we always fall short
0:28:20 of the ideal mark. And so, I mean, my rejoinder to the communists is something like every single
0:28:26 time it’s been implemented, wherever it’s been implemented, regardless of the culture and the
0:28:31 background of the people who’ve implemented it, it’s had exactly the same catastrophic consequences.
0:28:37 It’s like, I don’t know how many examples you need of that, but I believe we’ve generated
0:28:44 sufficient examples so that that case is basically resolved. Now, the general rejoinder to that is
0:28:50 it’s really something like, well, if I was in charge of the communist enterprise, the utopia
0:28:56 would have come about, right? But that’s also a form of dangerous pretense. Part of the way, see,
0:29:02 that problem is actually resolved to some degree in the notion of, in the developing notion of
0:29:08 sacrifice that emerges in the Western canon over thousands and thousands of years. So one of the
0:29:13 suggestions, for example, and this is something exemplified in the passion story, is that you
0:29:19 can tell the valid holder of an idea because that holder will take the responsibility for the
0:29:27 consequences of his idea onto himself. And that’s why, for example, you see one way of conceptualizing
0:29:34 Christ in the gospel story is as the ultimate sacrifice to God. So you might ask, well, what’s
0:29:40 the ultimate sacrifice? And there are variants of an answer to that. One form of ultimate sacrifice
0:29:44 is the sacrifice of a child, the offering of a child, and the other is the offering of the self.
0:29:50 And the story of Christ brings both of those together because he’s the Son of God that’s
0:29:57 offered to God. And so it’s a archetypal resolution of that tension between ultimate sacrifice.
0:30:05 Ultimate because once you’re a parent, most parents would rather sacrifice themselves
0:30:09 than their children, right? So you have something that becomes of even more value than yourself.
0:30:16 But the sacrifice of self is also a very high order level of sacrifice. Christ is an archetype
0:30:22 of the pattern of being that’s predicated on the decision to take to offer everything up to the
0:30:28 highest value, right, that pattern of self-sacrifice. And I think part of the reason that’s valid is
0:30:35 because the person who undertakes to do that pays the price themselves. It’s not externalized.
0:30:40 They’re not trying to change anyone else, except maybe by example. It’s your problem.
0:30:45 And like Solzhenitsyn pointed that out too, when he was struggling with the idea of good
0:30:50 versus evil. And you see this in more sophisticated literature, you know, in
0:30:57 really unsophisticated literature or drama, there’s a good guy and the bad guy. And the good guy’s
0:31:07 all good and the bad guy’s all bad. And in more sophisticated literature, the good and bad are
0:31:14 abstracted. You can think of them as spirits. And then those spirits possess all the characters in
0:31:19 the complex drama to a greater or lesser degree. And that battle is fought out both socially and
0:31:27 internally. In the high order religious conceptualizations in the West, if they culminate, let’s
0:31:32 say in the Christian story, the notion is that battle between good and evil is fundamentally
0:31:41 played out as an internal drama. Yeah. So for a religious ethos, the battle between good and evil
0:31:46 is fought within each individual human heart. Right. It’s your moral duty to constrain evil
0:31:51 within yourself. And well, there’s more to it than that, because there’s also the insistence that
0:32:00 if you do that, that makes you the most effective possible warrior, let’s say, against evil itself
0:32:06 in the social world. That you start with the battle that occurs within you in the soul, let’s say.
0:32:11 The soul becomes the battleground between the forces of good and evil. The idea that there’s
0:32:17 an idea there too, which is if that battle is undertaken successfully, then it doesn’t have
0:32:24 to be played out in the social world as actual conflict. You can rectify the conflict internally
0:32:29 without it having to be played out as fate, as Jung put it. So what would you say to Nietzsche,
0:32:36 who called Christianity the slave morality? His critique of religion in that way was slave
0:32:43 morality versus master morality. And then you put an ubermansche into that. See, I would say that the
0:32:50 woke phenomenon is the manifestation of the slave morality that Nietzsche criticized and that
0:32:58 there are elements of Christianity that can be gerrymandered to support that
0:33:08 motive perception and conception. But I think he was wrong in his essential criticism of
0:33:14 Christianity in that regard. Now, it’s complicated with Nietzsche because Nietzsche never criticizes
0:33:21 the Gospel stories directly. What he basically criticizes is something like the pathologies
0:33:27 of institutionalized religion. And I would say most particularly of the, what would you say,
0:33:36 of the sort of casually too nice Protestant form. That’s a thumbnail sketch and perhaps
0:33:43 somewhat unfair. But given the alignment, let’s say, of the more mainstream Protestant movements
0:33:49 with the woke mob, I don’t think it’s an absurd criticism. It’s something like the degeneration
0:33:56 of Christianity into the notion that good and harmless are the same thing or good and empathic
0:34:03 are the same thing, which is simply not true and far too simplified. And so, and I also think
0:34:08 Nietzsche was extremely wrong in his presumption that human beings should take it to themselves to
0:34:12 construct their own values. I think he made a colossal error in that presumption.
0:34:16 And that is the idea of the Ubermench, that the great individual, the best of us,
0:34:22 yeah, should create our own values. Well, and I think the reason that he was wrong about that is
0:34:28 that so when God gives instructions to Adam and Eve in the Garden of Eden, he basically tells them
0:34:33 that they can do anything they want in the walled garden. So that’s the kind of balance between
0:34:37 order and nature that makes up the human environment. Human beings have the freedom,
0:34:45 vouchsafe to them by God to do anything they want in the garden, except to mess with the most
0:34:50 fundamental rule. So God says to people, you’re not to eat of the fruit of the tree of the knowledge
0:34:55 of good and evil, which fundamentally means there is an implicit moral order and you’re
0:35:01 to abide by it. Your freedom stops at the foundation. And you can think about that.
0:35:05 I’d be interested even in your ideas about this as an engineer, let’s say, is that
0:35:15 there is an ethos that’s implicit in being itself. And your ethos has to be a reflection of that.
0:35:20 And that isn’t under your control. You can’t gerrymander the foundation because
0:35:26 your foundational beliefs have to put you in harmony, like musical harmony, with the actual
0:35:33 structure of reality as such. So I can give you an example of that. So our goal in so far as we’re
0:35:39 conducting ourselves properly is to have the kind of interesting conversation that allows both of
0:35:46 us to express ourselves in a manner that enables us to learn and grow, such that we can share that
0:35:51 with everyone who’s listening. And if our aim is true and upward, then that’s what we’re doing.
0:35:57 Well, that means that we’re going to have to match ourselves to a pattern of interaction.
0:36:02 And that’s marked for us emotionally, like you and I both know this. If we’re doing this right,
0:36:06 we’re going to be interested in the conversation. We’re not going to be looking at our watch.
0:36:10 We’re not going to be thinking about what we’re aiming at. We’re just going to communicate. Now,
0:36:14 the religious interpretation of that would be that we were doing something like
0:36:21 making the redemptive logos manifest between us in dialogue. And that’s something that can be shared.
0:36:26 To do that, we have to align with that pattern. I can’t decide that there’s some arbitrary way
0:36:31 that I’m going to play you. I mean, I could do that if I was a psychopathic manipulator. But
0:36:39 to do that optimally, I’m not going to impose a certain mode of a certain a priori aim, let’s
0:36:50 say, on our communication and manipulate you into that. So the constraints on my ethos reflect the
0:36:57 actual structure of the world. And this is the communist presumptions. We’re going to burn
0:37:02 everything down and we’re going to start from scratch. We’ve got these axiomatic presumptions,
0:37:06 and we’re going to put them into place. And we’re going to socialize people so they now
0:37:12 think and live like communists from day one. And human beings are infinitely malleable. And we
0:37:16 can use a rational set of presuppositions to decide what sort of beings they should be. The
0:37:22 transhumanists are doing this too. It’s like, no, there’s a pattern of being that you have to
0:37:26 fall into alignment with. And I think it’s the pattern of being, by the way, that
0:37:32 if you fall into alignment with, it gives you hope, it protects you from anxiety,
0:37:37 and it gives you a sense of harmony with your surroundings and with other people. And none
0:37:42 of that’s arbitrary. But don’t you think we both arrived to this conversation with rigid axioms
0:37:48 that we have, maybe we’re blind to them, but in the same way that the Marxists came with very
0:37:53 rigid axioms about the way the world is and the way it should be, aren’t we calling to that?
0:37:58 Well, we definitely come to the conversation with a hierarchy of foundation laxioms,
0:38:03 right? And I would say the more sophisticated you are as a thinker, the deeper the level at
0:38:08 which you’re willing to play. So imagine first that you have presumptions of different depth,
0:38:15 there’s more predicated on the more fundamental axioms, and then that there’s a space of play
0:38:21 around those. And that space of play is going to depend on the sophistication of the player,
0:38:28 obviously. But those who are capable of engaging in deeper conversations talk about more fundamental
0:38:34 things with more play. Now, we have to come to the conversation with a certain degree of structure,
0:38:39 because we wouldn’t be able to understand each other or communicate if a lot of things weren’t
0:38:46 already assumed or taken for granted. How rigid is the hierarchy of axioms that religion provides?
0:38:51 This is what I’m trying to understand. The rigidity of that- It’s as rigid as play.
0:38:55 Well, play is not rigid at all. No, no, no, no, no, no, no, no, it’s got a rigidity.
0:39:00 There’s some constraints. It took me about 40 years to figure out the answer to that question. So it
0:39:07 wasn’t, I’m serious about that. So it wasn’t, it wasn’t a random answer. So play is very rigid in
0:39:12 some ways. So like, if you and I go out to play basketball or chess, like, there are rules and
0:39:16 you can’t break the rules because then you’re no longer in the game. But then there’s a dynamism
0:39:21 within those rules that’s, well, with chess, it’s virtually infinite. I mean, I think what is it?
0:39:25 There’s more patterns of potential games on a chessboard than there are subatomic
0:39:31 particles in the observable universe. Like, it’s an insane space. So it’s not like there’s not
0:39:37 freedom within it. But by the, it’s, it’s a weird paradox in a way, isn’t it? Because music is like
0:39:43 this too, is that there are definitely rules. And so, and there are things, you can’t throw a basketball
0:39:49 into a chessboard and still be playing chess. But weirdly enough, if you adhere to the rules,
0:39:55 the realm of freedom increases rather than decreasing. And I think you can make the same
0:39:59 case for a playful conversation. It’s like, we’re playing by certain rules and a lot of them are
0:40:05 implicit. But that doesn’t mean that it might mean the reverse of constraint, you know, because
0:40:11 in the seminar, for example, that I was referring to, the Exodus seminar, and then the Gospel
0:40:16 seminar, everybody in the seminar, there’s about eight of us, played fair. Nobody used power.
0:40:21 Nobody tried to prove they were right. They put forward their points. But they were like, here’s
0:40:28 a way of looking at that. Assess it. And they were also doing it genuinely. It’s like, this is what
0:40:33 I’ve concluded about, say, this story. And I’m going to make a case for it. But I’d like to hear
0:40:38 what you have to say, because maybe you can change it, you can extend it, you can find a flaw in it.
0:40:43 And that’s, well, that’s a conversation that has flow and that’s engaging and that other people
0:40:48 will listen to as well. And that’s also, see, I think that one of the things that we can conclude
0:40:54 now, and we can do this even from a neuroscientific basis, is that that sense of engaged meaning
0:40:59 is a marker, not only for the emergence of harmony between you and your environment,
0:41:03 but for the emergence of that harmony in a way that is developmentally
0:41:11 rich, that moves you upward towards, what would you say? Well, I think towards a more effective
0:41:15 and tropic state. That’s actually the technical answer to that. But it makes you more than you
0:41:20 are. And there’s a directionality in that. Well, I would like to sort of, the reason I like talking
0:41:27 about communism, because it has clearly been shown as a set of ideas to be destructive
0:41:35 to humanity. But I would like to understand from an engineering perspective, the characteristics
0:41:42 of communism versus religion, where you can identify religious thought is going to lead
0:41:49 to a better human being, a better society. And communist Marxist thought is not because there’s
0:41:53 ambiguity, there’s room for play in communism and Marxism, because they kind of had a utopian
0:41:59 sense of where everybody’s headed, don’t know how it’s going to happen. Maybe revolution is required,
0:42:05 but after the revolution is done, we’ll figure it out. And there’s an underlying assumption that
0:42:10 maybe human beings are good, and they’ll figure it out once you remove the oppressor. I mean,
0:42:17 all these ideas kind of, until you put them into practice, they can be quite convincing
0:42:22 if you’re in the 19th century. If I was reading, which is kind of fascinating, the 19th century
0:42:30 produced such powerful ideas, Marx and Nietzsche. Oh, fascism too, for that matter. Fascism. So,
0:42:35 you know, if I was sitting there, like, especially if I’m feeling shitty about myself,
0:42:41 a lot of these ideas are pretty powerful as a way to plug the nihilist hole.
0:42:45 Yeah, right. Absolutely. Well, and some of them may actually have an appropriate scope of
0:42:51 application. It could be that some of the foundational axioms of communism, socialism/
0:42:57 communism are actually functional in a sufficiently small social group. Maybe a tribal group even.
0:43:05 Like, I also have a, I’m not sure this is correct, but I have a suspicion that the pervasive attractiveness
0:43:11 of some of the radical left ideas that we’re talking about are pervasive precisely because
0:43:16 they are functional within, say, families, but also within the small tribal groups that people
0:43:23 might have originally evolved into and that once we become civilized, so we produce societies that
0:43:28 are united even among people who don’t know one another, different principles have to apply as
0:43:35 a consequence of scale. So, that’s partly an engineering response, but I think there’s a
0:43:42 deeper way of going after the communist problem. So, I think part of the problem, fundamental
0:43:48 problem with the communist axioms is the notion that the world of complex social interactions
0:43:54 can be simplified sufficiently so that centralized planning authorities can deal with it. And I think
0:44:00 the best way to think about the free exchange rejoinder to that presumption is, no, the sum total
0:44:06 of human interactions in a large civilization are so immense that you need a distributed network of
0:44:11 cognition in order to compute the proper way forward. And so, what you do is you give each actor
0:44:17 their domain of individual choice so that they can maximize their own movement forward and you
0:44:22 allow the aggregate direction to emerge from that rather than trying to impose it from the top down,
0:44:28 which I think is computationally impossible. So, that might be one engineering reason why
0:44:32 the communist solution doesn’t work. Like I read in Solzhenitsyn, for example, that the
0:44:40 central Soviet authorities often had to make 200 pricing decisions a day. Now, if you’ve ever
0:44:47 started a business or created a product and had to wrestle with the problem of pricing,
0:44:53 you’d become aware of just how intractable that is. Like, how do you calculate worth? Well,
0:44:58 there’s the central existential problem of life. How do you calculate worth? It’s not something
0:45:05 like a central authority can sit down and just manage. And there are a lot of inputs that go
0:45:10 into a pricing decision. And the free market answer to that is something like, well, if you get the
0:45:16 price right, people will buy it and you’ll survive. This is a fascinating way to describe how ideas
0:45:22 fail. So, communism perhaps fails because just like with people who believe the earth is flat,
0:45:28 when you look outside, it looks flat. But you can’t see beyond the horizon, I guess.
0:45:34 In the same way with communism, communism seems like a great idea in my family and my
0:45:40 people I love, but it doesn’t scale. And it doesn’t iterate. And that’s a form of scaling too.
0:45:45 Right. Well, I mean, whatever ways it breaks down, it doesn’t scale. And you’re saying religious
0:45:50 thought is the thing that might scale. I would say religious thought is the record of those
0:45:56 ideas that have in fact scaled. Right. And iterated. Does religious thought iterate?
0:46:02 Does that mean there’s a fundamental conservative aspect to religious thought?
0:46:07 Tradition. Yeah. This is why, like Mircea Eliata, for example, who I referred to earlier,
0:46:13 one of the things Eliata did, and very effectively, and people like Joseph Campbell, who in some ways
0:46:20 were popularizers of Eliata’s ideas and Carl Jung’s, what they really did was devote themselves
0:46:26 to an analysis of those ideas that scaled and iterated across the largest possible spans of time.
0:46:32 And so Eliata and Jung, Eric Neumann, they were looking, and Campbell, they were looking at patterns
0:46:38 of narrative that were common across religious traditions that had spanned millennia and found
0:46:42 many patterns. The hero’s myth, for example, is one of those patterns. And it’s, I think,
0:46:46 the evidence that it has its reflection in human neurophysiology and neuropsychology is
0:46:53 incontrovertible. And so these foundational narratives, they last. They’re common across
0:46:59 multiple religious traditions. They unite. They work psychologically, but they also reflect the
0:47:04 underlying neurophysiological architecture. So I can give you an example of that. So the hero myth
0:47:10 is really a quest myth. And a quest myth is really a story of exploration and expansion of adaptation.
0:47:18 Right? So Bilbo, the Hobbit, he’s kind of an ordinary every man. He lives in a very constrained
0:47:23 and orderly and secure world. And then the quest call comes and he goes out and he expands his
0:47:29 personality and develops his wisdom. And that’s reflected in human neuropsychological architecture
0:47:35 at a very low level, way below cognition. So one of the most fundamental elements of the mammalian
0:47:41 brain, and even in lower animal forms, is the hypothalamus. It’s sort of the root of primary
0:47:48 motivation. So it governs lust, and it regulates your breathing, and it regulates your hunger,
0:47:52 and it regulates your thirst, and it regulates your temperature, like really low level biological
0:47:58 necessities are regulated by the hypothalamus. When you get hungry, it’s the hypothalamus. When
0:48:04 you’re activated in a defensively aggressive manner, that’s the hypothalamus. Half the hypothalamus
0:48:10 is the origin of the dopaminergic tracts, and they subsume exploration. And so you could think of
0:48:18 the human motivational reality as a domain that’s governed by axiomatic motivational states, love,
0:48:24 sex, defensive aggression, hunger, and another domain that’s governed by exploration. And the rule
0:48:33 would be something like, when your basic motivational states are sated, explore. Well, and that’s not
0:48:39 cognitive. Like I said, this is deep, deep brain architecture. It’s extraordinarily ancient. And
0:48:45 the exploration story is something like, go out into the unknown and take the risks because the
0:48:52 information that you discover and the skills you develop will be worthwhile, even in sating the
0:48:58 basic motivational drives. And then you want to learn to do that in a iterative manner. So it
0:49:02 sustains across time, and you want to do it in a way that unites you with other people. And there’s
0:49:08 a pattern to that. And I do think that’s the pattern that we strive to encapsulate in our
0:49:11 deep religious narratives. And I think that in many ways, we’ve done that successfully.
0:49:19 What is the believe in God? How does that fit in? What does it mean to believe in God?
0:49:25 Okay, so in one of the stories that I cover in We Who Wrestle With God, which I only recently
0:49:31 begun to take apart, say, in the last two years, is the story of Abraham. And it’s a very cool story.
0:49:36 And it’s also related, by the way, to your question about what makes communism wrong. And
0:49:43 Dostoevsky knew this, not precisely the Abraham story, but the same reason. In notes from underground,
0:49:49 Dostoevsky made a very telling observation. So he speaks in the voice of a cynical nihilistic and
0:49:55 bitter bureaucrat who’s been a failure, who’s talking cynically about the nature of human beings,
0:50:01 but also very accurately. And one of the things he points out with regards to modern utopianism
0:50:05 is that human beings are very strange creatures, and that if you gave them what the socialist
0:50:12 utopians want to give them, so let’s say all your needs are taken care of, all your material needs
0:50:17 are taken care of, and even indefinitely. Dostoevsky’s claim was, you don’t understand human beings
0:50:22 very well, because if you put them in an environment that was that comfortable, they would purposefully
0:50:28 go insane just to break it into bits, just so something interesting would happen. Right. And
0:50:34 he says it’s the human proclivity to curse and complain. And he says this in quite a cynic
0:50:39 and caustic manner, but he’s pointing to something deep, which is that we’re not built for comfort
0:50:45 and security. We’re not infants. We’re not after satiation. So then you might ask, well, what the
0:50:52 hell are we after then? That’s what the Abraham story addresses. And Abraham is the first true
0:50:57 individual in the biblical narrative. So you can think about his story as the archetypal story
0:51:02 of the developing individual. So you said, well, what’s God? Well, in the Abraham story, God has
0:51:08 characterized a lot of different ways in the classic religious texts. Like the Bible is actually a
0:51:14 compilation of different characterizations of the divine with the insistence that they reflect
0:51:21 an underlying unity. In the story of Abraham, the divine is the call to adventure. So Abraham
0:51:28 has the socialist utopia at hand. He’s from a wealthy family, and he has everything he needs.
0:51:34 And he actually doesn’t do anything until he’s in his 70s. Now, hypothetically, people in those
0:51:38 times lived much longer. But a voice comes to Abraham, and it tells him something very specific.
0:51:46 It says, leave your zone of comfort, leave your parents, leave your tent, leave your community,
0:51:54 leave your tribe, leave your land, go out into the world. And Abraham thinks, well, why have I got
0:51:59 naked slave girls peeling grapes and feeding them to me? It’s like, what do I
0:52:03 need an adventure for? And God tells them, and this is the covenant, by the way,
0:52:08 part of the covenant that the God of the Israelites makes with his people. It’s very,
0:52:13 very specific. And it’s very brilliant. He says, if you follow the voice of adventure,
0:52:20 you’ll become a blessing to yourself. So that’s a good deal because people generally live at odds
0:52:27 with themselves. And he says, God says, that’s not all. You’ll become a blessing to yourself in a way
0:52:34 that furthers your reputation among people and validly, so that you’ll accomplish things that
0:52:39 were real, and people will know it, and you’ll be held high in their esteem, and that will be valid.
0:52:46 So that’s a pretty good deal because social people would like to be regarded as of utility
0:52:51 and worth by others, and so that’s a good deal. And God says, that’s not all.
0:52:57 You’ll establish something of lasting permanent and deep value. That’s why Abraham becomes the
0:53:04 father of nations. And finally, he caps it off, and he says, there’s a better element even to it.
0:53:09 There’s a capstone. You’ll do all three of those things in a way that’s maximally beneficial to
0:53:15 everyone else. And so the divinity in the Abrahamic story is making a claim. He says, first of all,
0:53:21 there’s a drive that you should attend to, so the spirit of adventure that calls you out of your
0:53:27 zone of comfort. Now, if you attend to that, and you make the sacrifices necessary to follow that path,
0:53:33 then the following benefits will accrue to you. Your life will be a blessing. Everyone will hold
0:53:37 you in high esteem. You’ll establish something of permanent value, and you’ll do it in a way that’s
0:53:43 maximally beneficial to everyone else. And so think about what this means biologically or from an
0:53:48 engineering standpoint. It means that the instinct to develop that characterizes outward moving
0:53:55 children, let’s say, or adults is the same instinct that allows for psychological stability, that
0:54:02 allows for movement upward in a social hierarchy, that establishes something iterable, and that does
0:54:08 that in a manner that allows everyone else to partake in the same process. Well, that’s a good
0:54:14 deal. And I can’t see how it cannot be true because the alternative hypothesis would be that the spirit
0:54:20 that moves you beyond yourself to develop, the spirit of a curious child, let’s say, what is that
0:54:26 antithetical to your own esteem? Is that antithetical to other people’s best interest? Is it not the
0:54:32 thing that increases the probability that you’ll do something permanent? That’s a stupid theory.
0:54:39 So God is a call to adventure with some constraints. A call to true adventure.
0:54:44 To true adventure. Yeah, and then that’s a good observation because that begs the question,
0:54:51 what constitutes the most true adventure? Well, that’s not fully fleshed out until,
0:54:55 at least from the Christian perspective, let’s say. That’s not fully fleshed out until the
0:55:01 Gospels, because the passion of Christ is the, you could say, this is the perfectly reasonable
0:55:07 way of looking at it. The passion of Christ is the truest adventure of Abraham. That’s a terrible
0:55:14 thing, because the passion story is a catastrophic tragedy, although it obviously has its redemptive
0:55:21 elements. But one of the things that’s implied there is that there’s no distinction between the
0:55:27 true adventure of life and taking on the pathway of maximal responsibility and burden. And I can’t
0:55:32 see how that cannot be true, because the counter hypothesis is, well, Lex, the best thing for
0:55:40 you to do in your life is to shrink from all challenge and hide, to remain infantile, to remain
0:55:45 secure, not to ever push yourself beyond your limits, not to take any risks. Well, no one thinks
0:55:53 that’s true. So basically the maximally worthwhile adventure could possibly be highly correlated
0:56:00 with the hardest possible available adventure? The hardest possible available adventure voluntarily
0:56:06 undertaken. Does it have to be voluntary? Absolutely. How do you define voluntarily? Well,
0:56:14 here’s an example of that. That’s a good question, too. When Christ is the night before the crucifixion,
0:56:21 which in principle he knows is coming, he asks God to relieve him of his burden. And understandably
0:56:25 so. I mean, that’s the scene famously in which he’s sweating, literally sweating blood,
0:56:32 because he knows what’s coming. And the Romans designed crucifixion to be the most agonizing
0:56:38 and humiliating possible, agonizing, humiliating and disgusting possible death. Right? So there
0:56:42 was every reason to be apprehensive about that. And you might say, well, could you undertake that
0:56:48 voluntarily as an adventure? And the answer to that is something like, well, what’s your relationship
0:56:53 with death? Like, that’s a problem you have to solve. And you could fight it and you could be
0:56:58 bitter about it. And there’s reasons for that, especially if it’s painful and degrading. But
0:57:06 the alternative is something like, well, that’s what’s fleshed out in religious imagery always.
0:57:13 It’s very difficult to cast into words. It’s like, no, you, you welcome, you welcome the struggle.
0:57:19 That’s why I called the book, We Who Wrestle With God. You welcome the struggle. And Lex, I don’t
0:57:26 see how you can come to terms with life without construing it something like, construing it as
0:57:31 something like, bring it on, welcome the struggle. And I can’t see that there’s a limit to that.
0:57:35 It’s like, well, I welcome the struggle until it gets difficult. Well,
0:57:42 so there’s not a bell curve, like the struggle of moderation, basically have to welcome
0:57:48 whatever as hard as it gets. And the crucifixion in that way is a symbol of that. Well, and it,
0:57:54 well, it’s, it’s worse than that in some ways, because the crucifixion exemplifies
0:57:59 the worst possible death. But that isn’t the only element of the struggle, because
0:58:06 mythologically, classically, after Christ’s death, he harrows hell. And what that means,
0:58:11 as far as I can tell psychologically, is that you’re not only required, let’s say,
0:58:16 to take on the full existential burden of life and to welcome it, regardless of what it is,
0:58:22 and to maintain your upward aim, despite all temptations to the contrary. But you also have
0:58:28 to confront the root of malevolence itself. So it’s not merely tragedy. And I think the
0:58:32 malevolence is actually worse. And the reason I think that is because I know the literature on
0:58:39 post-traumatic stress disorder. And most people who encounter, let’s say, a challenge that’s so brutal,
0:58:45 that it fragments them, it isn’t mere suffering that does that to people. It’s an encounter with
0:58:50 malevolence that does that to people. They’re owned sometimes, often, by the way, soldier will go
0:58:56 out into a battlefield and find out that there’s a part of him that really enjoys the mayhem.
0:59:02 And that conceptualization doesn’t fit in well with everything he thinks he knows about himself
0:59:08 and humanity. And after that contact with that dark part of himself, he never recovers.
0:59:14 That happens to people. And it happens to people who encounter bad actors in the world, too. If
0:59:20 you’re a naive person and the right narcissistic psychopath comes your way, you are in, like,
0:59:24 mortal trouble because you might die. But that’s not where the trouble ends.
0:59:31 If there’s a young man in their 20s listening to this, how do they escape the pull of
0:59:37 Dostoevsky’s notes from underground? With the eyes open to the world, how do they select the
0:59:44 adventure? So, there’s other characterizations of the divine, say, in the Old Testament story. So,
0:59:48 one pattern of characterization that I think is really relevant to that question is
0:59:56 the conception of God as calling and conscience. Okay, so what does it mean? It’s a description
1:00:02 of the manner in which your destiny announces itself to you. And I’m using that terminology,
1:00:08 and it’s distinguishable, say, from Nietzsche’s notion that you create your own values. It’s like,
1:00:13 part of the way you can tell that that’s wrong is that you can’t voluntarily
1:00:18 gerrymander your own interests, right? Like, you find some things interesting,
1:00:24 and that seems natural and autonomous, and other things you don’t find interesting and you can’t
1:00:31 really force yourself to be interested in them. Now, so what is the domain of interest that makes
1:00:36 itself manifest to you? Well, it’s like an autonomous spirit. It’s like certain things in
1:00:41 your field of perception are illuminated to you. Think, oh, that’s interesting, that’s compelling,
1:00:48 that’s gripping. Rudolf Otto, who studied the phenomenology of religious experience, described
1:00:55 that as numinous. Thing grips you because you’re compelled by it, and maybe it’s also somewhat
1:01:00 anxiety-provoking. It’s the same reaction that, like, a cat has to a dog when the cat’s hair
1:01:05 stands on end. That’s an awe response. And so there’s going to be things in your phenomenological field
1:01:12 that pull you forward, compel you. That’s like the voice of positive emotion and enthusiasm.
1:01:18 Things draw you into the world. It might be love. It might be aesthetic interest. It might be friendship.
1:01:27 It might be social status. It might be duty and industriousness. Like, there’s various domains
1:01:34 of interest that shine for people. That’s sort of on the positive side. God is calling, right?
1:01:40 That would be akin to the spirit of adventure for Abraham. But there’s also God as conscience,
1:01:49 and this is a useful thing to know, too. Certain things bother you. They take root within you,
1:01:54 and they turn your thoughts towards certain issues. Like, there are things you’re interested in that
1:02:00 you’ve pursued your whole life. There are things I’m interested in that I felt as a moral compulsion.
1:02:04 And so you could think, and I think the way you can think about it technically is that
1:02:12 something pulls you forward so that you move ahead and you develop. And then another voice,
1:02:18 this voice of negative emotion says, while you’re moving forward, stay on this narrow pathway,
1:02:24 right? And it’ll mark deviations, and it marks deviations with shame and guilt and anxiety,
1:02:30 regret. And that actually has a voice. Don’t do that. Well, why not? While you’re wandering off
1:02:36 the straight narrow path. So the divine marks the pathway forward and reveals it, but then puts up
1:02:42 the constraints of conscience. And the divine in the Old Testament is portrayed not least as the
1:02:47 dynamic between calling and conscience. What do you do with the negative emotions?
1:02:51 You didn’t mention envy. There’s some really dark ones that can really pull you into some
1:02:57 bad places. Envy, fear. Yeah, envy is a really bad one. Pride and envy are among the worst.
1:03:03 Those are the sins of Cain, by the way, in the story of Cain and Abel. Because Cain fails
1:03:08 because his sacrifices are insufficient. He doesn’t offer his best. And so he’s rejected,
1:03:14 and that makes him bitter and unhappy. And he goes to complain to God and God says to him some
1:03:19 two things. He said, if God tells him if your sacrifices were appropriate, you’d be accepted.
1:03:26 It’s a brutal thing. It’s a brutal rejoinder. And he also says, you can’t blame your misery
1:03:33 on your failure. You could learn from your failure. When you fail, you invited in the spirit of envy
1:03:38 and resentment, and you allowed it to possess you. And that’s why you’re miserable. And so Cain is
1:03:43 embittered by that response, and that’s when he kills Abel. And so you might say, well, how do you
1:03:49 fortify yourself against that pathway of resentment? And part of classic religious practice
1:03:57 is aimed to do that precisely. What’s the antithesis of envy? Gratitude. That’s something you can
1:04:04 practice. Right? I mean, literally practice. I think envy is one of the biggest enemies for a
1:04:12 young person. Because basically, you’re starting from nowhere. Life is hard. You’ve achieved nothing.
1:04:19 And you’re striving, and you’re failing constantly. And you see other people whom you think aren’t
1:04:23 having the same problem. Yeah, and they succeeded. And they could be your neighbor. They could be
1:04:29 succeeding by a little bit or somebody on the internet succeeding by a lot. And I think that
1:04:34 can really pull a person down. That kind of envy can really destroy a person. Yeah, definitely.
1:04:39 Well, the gratitude element would be something like, well, yeah, you don’t know anything,
1:04:47 and you’re at the bottom, but you’re not 80. You know, one of the best predictors of wealth
1:04:53 in the United States is age. So then you might say, well, who’s got it better?
1:05:00 The old rich guy or the young poor guy? And I would say most old rich guys would trade their
1:05:06 wealth for youth. So it’s not exactly clear at all at any stage, who’s got the upper hand? Who’s
1:05:10 got the advantage? And you know, you could say, well, I’ve got all these burdens in front of me
1:05:17 because I’m young and oh my God, or you could say every dragon has its treasure. And then that’s
1:05:23 actually a pattern of perception. You know, I’m not saying that people don’t have their challenges,
1:05:30 they certainly do. But discriminating between a challenge and an opportunity is very, very
1:05:35 difficult. And learning to see a challenge as an opportunity, that’s the beginning of wisdom.
1:05:42 It’s interesting, I don’t know how it works, maybe you can elucidate, but when you have envy
1:05:49 towards somebody, if you just celebrate them, so gratitude, but actually, as opposed to sort of
1:05:54 ignoring and being grateful for the things you have, like literally celebrate that person,
1:06:00 it transforms, it like, it lights the way. I don’t know why that is exactly.
1:06:05 The only reason you’re envious is because you see someone who has something that you
1:06:12 want. Okay, so let’s think about it. Well, first of all, the fact that they have it means that
1:06:19 in principle, you could get it, at least someone has. So that’s a pretty good deal. And then you
1:06:24 might say, well, the fact that I’m envious of that person means that I actually want something.
1:06:28 And then you might think, well, what am I envious of? I’m envious of their attractiveness to women.
1:06:34 It’s like, okay, well, now you know something about yourself. You know that one true motivation
1:06:39 that’s making itself manifest to you is that you wish that you would be the sort of person who
1:06:44 is attractive to women. Now, of course, that’s an extremely common longing among men, period,
1:06:51 but particularly among young men. It’s like, well, what makes you so sure you couldn’t have that?
1:06:56 Well, how about, here’s an answer. You don’t have enough faith in yourself. And maybe you don’t have
1:07:01 enough faith in, well, I would say, the divine. You don’t believe that the world is characterized
1:07:09 by enough potentiality so that even miserable you has a crack at the brass ring. And like,
1:07:13 I talked about this actually practically in one of my previous books, because I wrote a chapter
1:07:19 called “Compare Yourself to Who You Are and Not to Someone Else at the Present Time.” Well, why?
1:07:26 Well, your best benchmark for tomorrow is you today. And you might not be able to have what
1:07:32 someone else has on the particular axis you’re comparing yourself with them on. But you could
1:07:37 make an incremental improvement over your current state, regardless of the direction that you’re
1:07:45 aiming. And it is the case. And this is a law. The return on incremental improvement is exponential
1:07:51 or geometric and not linear. So even if you start, this is why the hero is always born in a lowly
1:07:57 place, mythologically. Christ, who redeems the world, is born in a manger with the animals to
1:08:04 poverty-stricken parents in the middle of a God-versaikened desert in a nondescript time and place
1:08:13 isolated. Well, why? Well, because everyone young struggles with their insufficiency. But that doesn’t
1:08:19 mean that great things can’t make themselves manifest. And part of the insistence in the
1:08:25 biblical text, for example, is that it’s incumbent on you to have the courage to have faith in yourself
1:08:33 and in the spirit of reality, the essence of reality, regardless of how you construe the
1:08:39 evidence at hand. Right. Look at me. I’m so useless. I don’t know anything. I don’t have anything.
1:08:45 It’s hopeless. I don’t have it within me. The world couldn’t offer me that possibility. Well,
1:08:50 what the hell do you know about that? This is what Job figures out in the midst of his suffering in
1:08:57 the book of Job, because Job is tortured terribly by God who makes a bet with Satan himself to bring
1:09:04 him down. And Job’s decision in the face of his intense suffering is, “I’m not going to lose faith
1:09:09 in my essential goodness, and I’m not going to lose faith in the essential goodness of being itself,
1:09:16 regardless of how terrible the face it’s showing to me at the moment happens to be.” And I think,
1:09:24 okay, what do you make of that claim? Well, let’s look at it practically. You’re being tortured by
1:09:30 the arbitrariness of life. That’s horrible. Now you lose faith in yourself and you become cynical
1:09:38 about being. So are you infinitely worse off instantly? And then you might say, well, yeah,
1:09:44 but it’s really asking a lot of people that they maintain faith even in their darkest hours. It’s
1:09:50 like, yeah, that might be asking everything from people. But then you also might ask, this is a
1:09:58 very strange question, is if you were brought into being by something that was essentially good,
1:10:05 wouldn’t that thing that brought you into being demand that you make the best in yourself manifest?
1:10:13 And wouldn’t it be precisely when you most need that, that you’d be desperate enough to
1:10:19 risk what it would take to let it emerge? So you kind of make it seem that reason could
1:10:27 be the thing that takes you out of a place of darkness. So finding that calling through reason,
1:10:34 I think it’s also possible when reason fails you to just take the leap, navigate not by reason,
1:10:40 but by finding the thing that scares you the risk to take the risk, take the leap,
1:10:46 and then figure it out while you’re in the air. Yeah, well, I think that’s always part of a heroic
1:10:52 adventure. Is that ability to cut the Gordian knot? But you could also ask from an engineering
1:10:57 perspective, okay, what are the axioms that make a decision like that possible? And the answer would
1:11:02 be something like, I’m going to make the presumption that if I move forward in good faith, whatever
1:11:06 happens to me will be the best thing that could possibly happen, no matter what it is.
1:11:12 And I think that’s actually how you make an alliance with truth. And I also think that
1:11:16 truth is an adventure. And the way you make an alliance with truth is by assuming
1:11:25 that whatever happens to you, if you’re living in truth, is the best thing that could happen,
1:11:30 even if you can’t see that at any given moment. Because otherwise, you’d say that
1:11:34 truth would be just the handmaiden of advantage. Well, I’m going to say something
1:11:39 truthful, and I pay a price. Well, that means I shouldn’t have said it. Well, that
1:11:45 possibly, but that’s not the only possible standard of evaluation. Because what you’re
1:11:50 doing is you’re making the outcome your deity, right? Well, I just reverse that and say, no, no,
1:11:58 truth is the deity. The outcome is variable, but that doesn’t eradicate the initial axiom.
1:12:08 Where’s the constant, right? What’s the constant? He may be, when you said Abraham was being fed
1:12:13 by naked ladies. That’s an interpolation, obviously, but we’ve been out of keeping for the times.
1:12:20 But it does make me think, sort of in stark contrast in each his own life, that perhaps
1:12:28 getting laid early on in life is a useful starter. Step one, get laid and then go for adventure.
1:12:33 There’s some basic, like a mis-association of- So, I think it’s perfectly reasonable to bring
1:12:38 the sexual element in because it’s a powerful motivating force and it has to be integrated.
1:12:44 I don’t think it’s adventure. It’s romantic adventure. Right, right. But the lack of basic
1:12:52 interaction, sexual interaction, I feel like is the engine that drives towards that cynicism
1:12:59 of the incel. It is, but there’s very little doubt about that. We know perfectly well anthropologically
1:13:06 that the most unstable social situation you can generate is young men with no access to women.
1:13:13 That’s not good. And they’ll do anything, anything to reverse that situation. So,
1:13:20 that’s very dangerous. But then I would also say there’s every suggestion that the pathway
1:13:26 of adventure itself is the best pathway to romantic attractiveness. And we know this
1:13:31 in some ways in a very blunt manner. The Google boys, the engineers who are too, what would you say,
1:13:37 naively oriented towards empirical truth to note when they’re being politically incorrect,
1:13:41 they wrote a great book called A Billion Wicked Thoughts, which I really like. It’s a very good
1:13:48 book and it’s engineers as psychologists. And so they’ll say all sorts of things that no one
1:13:52 with any sense would ever say that happened to be true. And they studied the pattern of
1:13:58 pornographic fantasy and women like pornographic stories, not images. So women’s use of pornographic
1:14:05 pornography is literary. Who are the main protagonists in female pornographic fantasy?
1:14:13 Pirates, werewolves, vampires, surgeons, billionaires, Tony Stark, you know. And so
1:14:18 the basic pornographic narrative is Beauty and the Beast, those five categories.
1:14:26 Parable aggressive male, tamable by the right relationship, hot erotic attraction. And so
1:14:31 I would say to the young men who, and I have many times to the young men who are locked in isolation,
1:14:38 it’s first of all, join the bloody club because the default value of a 15-year-old male on the
1:14:44 mating market is zero. And there’s reason for that. You know, and zero is a bit of an exaggeration,
1:14:48 but not much. And the reason for that is, well, what the hell do you know? Like, you’re not good
1:14:54 for anything. Yeah, you have potential and maybe plenty. And hopefully that’ll be made manifest,
1:14:59 but you shouldn’t be all upset because you’re the same loser as everyone else your age has always
1:15:04 been since the beginning of time. But then you might ask, well, what should I do about it? The
1:15:09 answer is get yourself together. You know, stand up straight with your shoulders back, take on
1:15:15 some adventure, find your calling, abide by your conscience, put yourself together, and you’ll
1:15:21 become attractive. And we know this is, look, we know this is true. The correlation between male,
1:15:32 sexual opportunity and relative masculine status is about 0.6. That’s higher than the
1:15:38 correlation between intelligence and academic achievement. I don’t think that there’s a
1:15:44 larger correlation between two independent phenomena in the entire social science and health literature
1:15:50 than the correlation between relative male social status and reproductive success. It’s by far the
1:15:54 most fundamental determinant. Well, what’s the cause and effect there? It’s a loop. Men are motivated
1:15:59 to attain social status because it confers upon them reproductive success. And that’s not only
1:16:04 cognitively, but biologically. I’ll give you an example of this. There’s a documentary I watch
1:16:08 from time to time, which I think is the most brilliant documentary I’ve ever seen. It’s called
1:16:15 Crumb. And it’s the story of this underground cartoonist, Robert Crumb, who was in high school,
1:16:23 was in the category of males for whom a date was not only not likely, but in unimaginable.
1:16:32 So he was at the bottom of the bottom rung, and almost all the reactions he got from females
1:16:40 wasn’t just, “No, it was like, are you out of your mind?” With that contempt. And then he became
1:16:45 successful. And so the documentary is super interesting because it tracks the utter pathology
1:16:50 of his sexual fantasies because he was bitter and resentful. And if you want to understand
1:16:57 the psychology of serial sexual killers and the like, and you watch Crumb, you’ll find out a lot
1:17:02 more about that than anybody with any sense would want to know. But then he makes this transition.
1:17:06 And partly because he does take the heroic adventure path. And he actually has a family and
1:17:12 children. And he’s actually a pretty functional person, as opposed to his brothers, one of whom
1:17:18 commits suicide, and one of whom is literally a repeat sexual offender. It’s a brutal documentary.
1:17:25 But what he did in his adolescence, after being rejected, was he found what he was interested
1:17:28 in. He was a very good artist. He was very interested in music. And he started to pursue
1:17:33 those sort of single-mindedly. And he became successful. And as soon as he became successful,
1:17:38 and the documentary tracks this beautifully, he’s immediately attractive to women. And then you
1:17:44 might ask too, even if you’re cynical, it’s like, “Well, why do I have to perform for women?”
1:17:47 And the answer to that is something like, “Why the hell should they have anything to do with
1:17:52 you if you’re useless?” They’re going to have infants. They don’t need another one, right?
1:17:57 Partly the reason that women are hypergamous, unless they want males who are of higher status
1:18:02 than they are, is because they’re trying to redress the reproductive burden. And it’s substantial.
1:18:08 I mean, the female of any species is the sex that devotes more to the reproductive function.
1:18:14 That’s a more fundamental definition than chromosomal differentiation. And that’s taken
1:18:19 to its ultimate extreme with humans. And so of course, women are going to want someone around
1:18:26 that’s useful, because the cost of sex for them is an 18-year-old period of dependency with an
1:18:34 infant. So I think the adventure comes first. Heroic adventure comes first. Well, it’s complex,
1:18:37 because the other problem, let’s say, with the crumb boys is that their mother was extremely
1:18:42 pathological, and they didn’t get a lot of genuine feminine nurturance and affection.
1:18:47 Oh, of course. So the family and society are not going to help you most of the time with a
1:18:53 heroic adventure, right? They’re going to be a barrier. Well, in good families, they’re both,
1:18:59 because they put up constraints on your behavior. But like, I’ve interviewed a lot of successful
1:19:06 people about their calling, let’s say, because I do that with all my podcast guests. How did the
1:19:14 path that you took to success make itself manifest? And the pattern is very typical. Almost all the
1:19:20 people that I’ve interviewed had a mother and a father. Now, it’s not invariant, but I’d say
1:19:26 it’s there in 99% of the time. It’s really high. And both of the parents, or at least one of them,
1:19:33 but often both, were very encouraging of the person’s interests and pathway to development.
1:19:39 That’s fascinating. I’ve heard you analyze it that way before, and I had a reaction to that
1:19:43 idea, because you focus on the positive of the parents. I feel like it was the,
1:19:49 maybe I see biographies differently, but it feels like the struggle within the family
1:19:56 was the catalyst for greatness in a lot of biographies. Maybe I’m misinterpreting it.
1:20:02 No, no, no. I don’t think you, I think that that’s a reflection, maybe, correct me if I’m wrong,
1:20:06 I think that’s a reflection of that dynamic between positive and negative emotion.
1:20:11 Like my son, for example, who’s doing just fine. He’s firing on all cylinders, as far as I’m
1:20:15 concerned. He has a nice family. He gets along with his wife. He’s a really good musician. He’s
1:20:23 got a company. He’s running well. He’s a delight to be around. He was a relatively disagreeable
1:20:33 infant. He was tough-minded. He didn’t take no for an answer. There was some tussle in regulating
1:20:38 his behavior. He spent a lot of time when he was two sitting on the steps trying to get his act
1:20:46 together. That was the constraint. That wasn’t something that was, it’s an opposition to him
1:20:52 away, because it was in opposition to the immediate manifestation of his hedonistic desires,
1:20:58 but it was also an impetus to further development. The rule for me, when he was on the stairs was,
1:21:02 as soon as you’re willing to be a civilized human being, you can get off the stairs.
1:21:07 You might think, well, that’s nothing but arbitrary, super-ego, patriarchal, oppressive
1:21:13 constraint. Or you could say, well, no, what I’m actually doing is facilitating his cortical maturation,
1:21:18 because when a child misbehaves, it’s usually because they’re under the domination of some
1:21:23 primordial, emotional, or motivational impulse. They’re angry, they’re over-enthusiastic,
1:21:31 they’re upset, they’re selfish, like it’s narrow, self-centeredness expressed in an
1:21:39 immature manner. But see, okay, tell me if I’m wrong, but it feels like the engine of greatness,
1:21:45 at least on the male side of things, has often been trying to prove the father wrong,
1:21:51 or trying to gain the acceptance of the father. So that tension, where the parent is not encouraging,
1:21:57 like you mentioned, but is basically saying, no, you won’t be able to do this.
1:22:04 Okay, so my observation as a psychologist has been that it’s very, very difficult for someone
1:22:09 to get their act together, unless they have at least one figure in their life that’s encouraging
1:22:15 and shows them the pathway forward. So you can have a lot of adversity in your life,
1:22:20 and if you have one person around who’s a good model and you’re neurologically intact,
1:22:26 you can latch onto that model. Now, you can also find that model in books, and people do that sometimes,
1:22:31 like I’ve interviewed people who had pretty fragmented childhoods, who turned to books and
1:22:37 found the pattern that guided them in, like, let’s say, the adventures of the heroes of the past,
1:22:42 because that’s a good way of thinking about it. And I read a book called Angela’s Ashes that was
1:22:49 written by an Irish author, Frank McCourt’s fantastic book, beautiful book, and his father was
1:22:56 an alcoholic of gargantuan proportions. He just an Irish drinker who drank every scent that came
1:23:04 into the family, and many of whose children died in poverty. And what Frank did is a testament
1:23:08 to the human spirit, is he sort of divided his father conceptually into two elements. There was
1:23:14 sober mourning father who was encouraging and with whom he had a relationship, and then there was
1:23:22 drunk and useless later afternoon, an evening father, and he rejected the negative, and he
1:23:29 amplified his relationship with the positive. Now, like, he had other things going for him,
1:23:36 but he, you know, he did a very good job of discriminating. And I mean, partly the question
1:23:42 that you’re raising is, to what degree is it useful to have a beneficial adversary?
1:23:49 Yeah, and I mean, struggle-free progress is not possible. And I think there are situations
1:23:55 under which where, you know, you might be motivated to prove someone in your
1:24:02 immediate circle wrong, but then that also implies that at some level, for some reason,
1:24:07 you actually care about their judgment. You know, you just didn’t write them off completely.
1:24:13 Well, I mean, that’s why I say there’s an archetype of a young man trying to gain the approval
1:24:21 of his father. And I think that repeats itself in a bunch of biographies that I’ve read.
1:24:28 I don’t know, there must have been an engine somewhere that they found of approval of encouragement,
1:24:33 maybe in books, maybe in the mother, or maybe the role of the parents has flipped.
1:24:36 Well, my father was hard to please, very.
1:24:38 Did you ever succeed?
1:24:40 Yes, but it wasn’t easy ever.
1:24:44 When was the moment when you succeeded?
1:24:50 Late, pretty late, like 40, maybe later.
1:24:56 Was it a gradual or a definitive moment when a shift happened?
1:25:03 My father always was always willing to approve of the things I did that were good,
1:25:09 although he was not effusive by any stretch of the imagination and the standards were very high.
1:25:16 Now, I was probably fortunate for me, you know, and it does bear on the question you’re asking.
1:25:22 It’s like, if you want someone to motivate you optimally, God, it’s complicated because
1:25:25 there has to be a temperamental dance between the two people.
1:25:32 Like, what you really want is for someone to apply the highest possible standards to you
1:25:40 that you’re capable of reaching, right? And that’s a vicious dance because
1:25:46 you have to have a relationship with your child to do that properly, you know, because you want to,
1:25:49 if you want to be optimally motivating as a father, you keep your children on the edge.
1:25:56 It’s like, you might not reward something in your child that you would think would be good
1:25:58 in someone else because you think they could do better.
1:26:03 And so my father was pretty clear about the idea that he always expected me to do better.
1:26:13 And was that troublesome? It was like, I felt often when I was young that there was no pleasing him,
1:26:18 but I also knew that that wasn’t, I knew that that wasn’t right. See, I actually knew that wasn’t right
1:26:26 because I could remember, especially I think when I was very young, that I did things that
1:26:33 he was pleased about. I knew that was possible. So it wasn’t unpredictable and arbitrary,
1:26:37 it was just difficult. It sounds like he’s hit a pretty good optimal,
1:26:43 but it’s for each individual human that optimal differs. Well, that’s why you have to have a
1:26:50 relationship with your children. You have to know them. And well, with yourself too,
1:26:57 and with your wife, you can’t hit that optimal. That optimal is probably love.
1:27:03 Because love isn’t just acceptance. Love is acceptance and encouragement.
1:27:08 And it’s not just that either. It’s also, no, don’t do that. That’s beneath you.
1:27:14 You’re capable of more. And how harsh should that be? It’s like, that’s a really hard question.
1:27:19 You know, like if you really love someone, you’re not going to put up with their stupidity.
1:27:23 Don’t do that. You know, one of the rules I had with my little kids was,
1:27:28 don’t do anything that makes you look like an idiot in public. Why? Because I don’t want you
1:27:34 disgracing yourself. Why not? Because I like you. I think you’re great. And you’re not going to act
1:27:39 like a bloody fool in public so that people get the wrong idea about you. No! What about inside a
1:27:50 relationship? A successful relationship? How much challenge? How much peace? Is a successful
1:27:57 relationship one that is easy, one that is challenging? I would say to some degree that
1:28:05 depends on your temperament. My wife is quite a provocative person. And there are times when I,
1:28:12 I suppose, do I wish that, there are times when I casually wish that she was easier to get along
1:28:17 with. But as soon as I think about it, I don’t think that. Because I’ve always liked her. We
1:28:24 were friends ever since we were little kids. And she plays rough. And I like that as it turns out.
1:28:29 Now, that doesn’t mean it isn’t a pain from time to time. But, you know, and that is going to be
1:28:36 a temperamental issue to some degree and an issue of negotiation. Like, she plays rough, but
1:28:42 fair. And the fair part has been establishing that. It’s been part of our ongoing negotiation.
1:28:47 And part of it is in the play you get to find out about yourself or what your temperament is.
1:28:51 Because I don’t think that that’s clear until it’s tested.
1:28:56 Oh, definitely not. Definitely not. You find out all sorts of things about yourself in a
1:29:00 relationship. That’s for sure. Well, and partly the reason that there is provocativeness,
1:29:05 especially from women in relationship to men, is they want to test them out. It’s like,
1:29:09 can you hold your temper when someone’s bothering you? Well, why would a woman want to know that?
1:29:17 Well, maybe she doesn’t want you to snap and hurt her kids. And so how’s she going to find that out?
1:29:22 Ask you? Well, you’re going to say, well, I’d never do that. It’s like, never, eh? Let’s find out
1:29:29 if it’s never. So we don’t know how people test each other out in relationships, but,
1:29:35 or why exactly, but it’s intense and necessary. What’s your and what’s in general,
1:29:41 should a man’s relationship with temper be? You should have one. And you should be able to regulate
1:29:47 it. Like, that’s part of that attractiveness of the monstrous that characterizes women’s fantasies.
1:29:52 Right? Because, and Nietzsche pointed this out too. Go back to Nietzsche, you know?
1:29:56 Nietzsche, one of Nietzsche’s claims was that most of what passes for morality is nothing but
1:30:02 cowardice. You know, I’d never cheat on my wife. It’s like, is there anybody asking you too?
1:30:08 That you actually find attractive? Or are there dozens of people asking you too that you find
1:30:13 attractive? It’s like, well, I would never cheat. It’s like, no, you just don’t have the opportunity.
1:30:16 Now, I don’t, I’m not saying that everyone’s in that position, you know, that they would cheat
1:30:22 even if they had the opportunity, because that’s not true. But, and it’s the same with regards to,
1:30:27 oh, I’m a peaceful man. It’s like, no, you’re not. You’re just a weak coward. You wouldn’t dare have it
1:30:33 to have a confrontation, physical or metaphysical. And you’re passing it off as morality because
1:30:38 you don’t want to come to terms with the fact of your own weakness and cowardice. And part of the,
1:30:44 that, what I would say is twisted pseudo-Christian morality that Nietzsche was criticizing was
1:30:50 exactly of that sort. And it tied into resentment and envy. And he tied that in explicitly. He said
1:30:59 that failure in life, masked by the morality that’s nothing but weak cowardice, turns to the
1:31:04 resentment that undermines and destroys everything, and that does that purposefully.
1:31:09 Yeah, I think he was criticizing if under the facade of niceness, there’s an ocean of resentment.
1:31:17 Yeah, that’s for sure. That’s also the danger of being too forthcoming with people. See,
1:31:21 this is another thing, let’s say, about my wife, who’s not particularly agreeable. It’s like,
1:31:26 she’s not particularly agreeable, but she’s not resentful. And that’s because she doesn’t give
1:31:32 things away that she, that she isn’t willing to. And if you’re agreeable and nice and you’re conflict
1:31:37 avoidant, you’ll push yourself too far to please the other person. And then that makes you bitter
1:31:42 and resentful. So that’s not helpful. Do you think you’ll be in trouble for saying this on a podcast
1:31:50 later? No, no, we know each other pretty well. And like I said, it’s a trait that I find admirable.
1:31:58 It’s provocative and challenging. And it seems to work. Well, we’ve been together 50 years, so…
1:32:08 Quick pause, bathroom break. If we can descend from the realm of ideas down to history and reality,
1:32:16 I would say the time between World War I and World War II was one of history’s biggest testing of
1:32:27 ideas and really the most dramatic kinds of ideas that helped us understand the nature of good and
1:32:36 evil. I just want to ask you sort of a question about good and evil. Churchill, in many ways,
1:32:44 was not a good man. Stalin, as you’ve documented extensively, was a horrible man.
1:32:54 But you can make the case that both were necessary for stopping an even worse human being in Hitler.
1:33:06 So to what degree do you need monsters to fight monsters? Do you need bad men to be able to
1:33:14 fight off greater evils? It’s everything in its proper place is the answer to that.
1:33:20 You know, we might think that our life would be easier without fear, let’s say. We might say that
1:33:26 our life would be easier without anger or pain, but the truth of the matter is that those things
1:33:30 are beneficial even though they can cause great suffering, but they have to be in their proper
1:33:36 place. And that capacity that could in one context be a terrible force for evil, can in the proper
1:33:44 context be the most potent force for good. A good man has to be formidable. And partly what that means,
1:33:52 as far as I can tell, is that you have to be able to say no. And no means, like, I thought a lot about
1:33:58 no, working as a clinician, because I did a lot of strategic counseling with my clients in a lot
1:34:03 of extremely difficult situations. And I learned to take apart what no meant. And also when dealing
1:34:10 with my own children, because I used no sparingly, because it’s a powerful weapon, let’s say. But
1:34:15 I meant it. And with my kids, what it meant was, if you continue that pattern of behavior, something
1:34:23 you do not like will happen to you with 100% certainty. And when that’s the case, and you’re
1:34:29 willing to implement it, you don’t have to do it very often. With regards to monstrosity, it’s like
1:34:35 weak men aren’t good. They’re just weak. That’s Nietzsche’s observation. That’s partly again why
1:34:43 he was tempted to place the will to power, let’s say, and to deal with that notion in a manner that
1:34:48 when it was tied with the revaluation of all values was counterproductive.
1:34:54 Counterproductive in the final analysis. It’s not like there wasn’t something to what he was
1:35:02 driving at. Formidable men are admirable. And you know, don’t mess with them. Douglas Murray’s a
1:35:10 good example of that. He’s a rather slight guy, but he’s got a spine of steel. And there’s more
1:35:14 than a bit of what’s a monstrous in him. And Jacques Wilink is like that. And Joe Rogan is
1:35:20 like that. And you’re like that. But there’s a different level. I mean, if you look, to me,
1:35:28 Churchill might represent the thing you’re talking about. But World War II, Hitler would not be
1:35:36 stopped without Stalin. Well, I wonder, yes, yes. And if I may insert into this picture of complexity,
1:35:43 Hitler would have not stopped until he enslaved and exterminated the entirety of the Slavic people,
1:35:50 the Jewish people, the Slavic people, the Gypsies, everybody who’s non-Aryan. But then Stalin,
1:35:56 in the mass rape of German women by the Red Army as they marched towards Berlin,
1:36:01 is a kind of manifestation, the full monstrosity that a person can be.
1:36:08 You can easily be in a situation. You can easily, unfortunately, find yourself in a situation where
1:36:13 all you have in front of you are a variety of bad options. You know, that’s partly why,
1:36:17 if you have any sense, you try to conduct yourself very carefully in life, because
1:36:23 you don’t want to be in a position where you’ve made so many mistakes that all the options left
1:36:30 to you are terrible. And so you said, well, was it necessary to ally with Stalin? It’s like, well,
1:36:36 it’s very difficult to second guess the trajectory of something as complex as World War II. But
1:36:42 we could say casually, at least as Westerners have in general, that that alliance was necessary.
1:36:48 Now, I think the mistake that the West made in the aftermath of World War II was in not
1:36:54 dealing as forthrightly with the catastrophes of communism as an ideology as we did with fascism.
1:37:00 And that’s especially true of the intellectuals and the universities. I mean, it was very common
1:37:04 when I was teaching both at Harvard and at the University of Toronto for the students in my
1:37:10 personality class, where we studied Solzhenitsyn, who’s actually an existential psychologist in
1:37:16 many ways and a deep one. None of them knew anything about the Soviet atrocities. None
1:37:21 of them knew anything about what happened in Ukraine in the death of 6 million productive people.
1:37:28 I had no idea that the communists killed tens of millions of people in the aftermath of the
1:37:32 Russian Revolution. And they know even less about Mao and the Great Leap Forward. Yeah, right, right.
1:37:39 Which some estimates are 100 million people. Now, you know, when your error bars are in the tens of
1:37:44 millions, well, that’s a real indication of a cataclysm. And nobody knows how many people died
1:37:50 from direct oppression or indirect in the Soviet Union. 20 million, it seems like a reasonable
1:37:55 estimate. Solzhenitsyn’s upper bound was higher than that. And how do you measure
1:38:02 the intellectual output that was suppressed and killed off, the number of intellectuals,
1:38:06 artists and writers that were put into the gulags? Well, productive farmers, for that matter,
1:38:13 and anyone who was willing to tell the truth. Right, absolutely. So, yeah, catastrophic. And
1:38:20 so I think the West’s failure wasn’t so much allying with Stalin. I mean, it was Douglas MacArthur
1:38:26 who wanted to continue. He thought we should just take the Soviets out after the Second World War.
1:38:33 And they removed him from any position of authority where such a thing might be made possible.
1:38:39 And people were tired. But was MacArthur wrong? Well, he certainly wasn’t wrong
1:38:45 in his insistence that Stalin was as big a monster as Hitler or bigger.
1:38:54 So, the valorization of the leftist proclivity, the radical leftist proclivity is the sin of the
1:39:01 West, I think, more intensely than allying with Stalin. Tricky nuanced topic. But if we look at
1:39:06 the modern day and the threat of communism, Marxism in the United States, to me,
1:39:14 it’s disrespectful to the atrocities of the 20th century to call somebody like Kamala Harris a
1:39:24 communist. But I see the sort of escalation of the extremness of language being used when you call
1:39:30 somebody like Donald Trump a fascist. That makes total sense to then use similar extreme terminology
1:39:35 for somebody like Kamala Harris. But maybe I could ask your evaluation. If you look at the
1:39:39 political landscape today, somebody like Joe Biden and Kamala Harris.
1:39:44 Okay, well, the first thing I would say is that I think that viewing the political landscape
1:39:50 of today as a political landscape is actually wrong. I think it’s not the right frame of reference.
1:39:58 Because what I see happening are a very small percentage of dark tetrad personality types,
1:40:06 so Machiavellian, manipulative, narcissistic, wanting undeserved attention, psychopathic,
1:40:11 that makes them predatory parasites, and sadistic, because that goes along with the other three.
1:40:20 That’s about in the serious manifestation, that’s probably three to five percent of the population.
1:40:27 And they’re generally kept under pretty decent control by civilized people and stable social
1:40:37 interactions. I think that their machinations are disinhibited by cost-free social media communication.
1:40:44 So they gain disproportionate influence. Now, these people want undeserved recognition and
1:40:49 social status and everything that goes along with it, and they don’t care how they get it.
1:40:54 Because when I say they want that, I mean, that’s all they want.
1:41:01 So in the realm of social media, you mentioned yes, but are you also suggesting that they’re
1:41:05 overrepresented in the realm of politics, politicians and so on?
1:41:11 They’re overrepresented in the realm of fractious political discourse, because they can use ideas.
1:41:16 First of all, they can use, let’s say, the benevolent ideas of the right and the benevolent
1:41:23 ideas of the left, either one, and switch back and forth for that matter, as a camouflage for what
1:41:28 they’re actually up to. So you’ve interviewed a lot of people, and you have a really powerful mind,
1:41:33 you have a good read on people. So how do you know when you’re sitting across from a psychopath?
1:41:38 I wouldn’t say that I do know. In normal social circumstances, we have evolved mechanisms to
1:41:42 keep people like that under control. Let’s say that you and I have a series of interactions,
1:41:47 and you screw me over once. I’m not going to forget that. Now, I might not write you off
1:41:52 because of the one time, but if it happens three times, it’s like, we’re not going to play together
1:42:01 anymore. And in normal times, most of our social networks are connected and interacting. So if
1:42:08 you ripped me off three times and I noted that, I’m going to tell everybody I know, and they’re
1:42:13 going to tell everybody they know, and soon everyone will know, and that’s the end of your
1:42:17 tricks. But that assumes that we know who you are, and we’re in continual communication. Well,
1:42:28 all of that’s gone online. So anonymity does that. And so does the amplification of emotional
1:42:35 intensity by the social media platforms and their algorithms. I think what we’re doing,
1:42:43 this is happening on Twitter continually, is we’re giving the 5% of psychopaths a radically
1:42:47 disproportionate voice. And what they’re doing is there’s a bunch of them on the left, and they’re
1:42:51 all, we’re so compassionate. And there’s a bunch of them on the right. And at the moment, they’re
1:42:56 all, we’re so Christian and free speech oriented. It’s like, no, you’re not. You’re a narcissistic
1:43:03 psychopaths. And that’s your camouflage. And you hide behind your anonymity. And you use fractious
1:43:11 and divisive language to, to attract fools and to elevate your social status and your clout.
1:43:18 And, and not only that, to gain, what would you say, satisfaction for your sadistic impulses?
1:43:28 See, the problem is, it’s hard to tell who is the psychopath and who is a heterodox truth seeker.
1:43:34 Yeah. Well, if you were charitable about Tucker Carlson’s recent interview, you’d say that was
1:43:39 exactly the conundrum he faced. And it is hard. Like, I’ve thought about, for example, interviewing
1:43:46 Andrew Tate. And I thought, I don’t think so. And then I thought, why? I figured it’s not obvious
1:43:53 to me at all that he wouldn’t charm me. So I knew this guy, Robert Hare. Robert Hare was the
1:44:01 world’s foremost authority on psychopathy. He established the field of clinical analysis
1:44:06 of psychopathic behavior. And Hare was a pretty agreeable guy. So, you know, he would give people
1:44:10 the benefit of the doubt. And he interviewed hundreds of serious psychopaths, like imprisoned
1:44:17 violent offenders. And he told me in one of our conversations that every time he sat down with
1:44:23 violent offender psychopath, and he had a measure for psychopathy that was a clinical checklist,
1:44:30 so he could identify the psychopaths from just the, say, run-of-the-mill criminals. Every time
1:44:34 he sat down with them, they pulled the wool over his eyes. And it wasn’t an, he videotaped the
1:44:38 interviews. And it wasn’t until later when he was reviewing the videos that he could see what
1:44:46 they were doing. But in person, their tricks were more sophisticated than his detection ability.
1:44:50 Well, okay, this is fascinating because, again, you’re a great interviewer. I would love it if
1:44:58 you interviewed somebody like Putin. So this idea that you are a fool in the face of psychopathy
1:45:02 just doesn’t jive with me. I’m an agreeable guy. That’s the problem. I’ll give people the benefit
1:45:07 of the doubt. Right, right. But that’s good because the way you reveal psychopathy
1:45:15 is by being agreeable, not weak, but seeking with empathy to understand the other person.
1:45:22 And in the details, in the little nuanced ways that they struggle with questions,
1:45:29 the psychopathy is revealed. So from a, we’re kind of just to separate the two things. So one,
1:45:35 overrepresentation of psychopathy online with anonymity, that’s a serious fascinating problem.
1:45:42 But in the interview one-on-one, I don’t know if the job of a human being in conversation is to
1:45:48 not talk to psychopaths, but to talk, I mean, like, how would you interview Hitler?
1:45:52 Well, I’ve, you know, I’ve had very difficult clinical interviews with people
1:45:56 in my clinical practice. And so what do you, how do you approach that?
1:46:01 Well, I really probably approach that the way I approach most conversations. And it’s something
1:46:08 like, I’m going to assume that you’re playing a straight game, but I’m going to watch. And if you
1:46:14 throw in the odd crooked maneuvering, then I’ll note it. And after you do it three times, I’ll
1:46:20 think, okay, I see. I thought we were playing one game, but we’re actually playing another one.
1:46:26 And if I’m smart enough to pick that up, that usually works out quite successfully for me.
1:46:29 But I’m not always smart enough to pick that up.
1:46:33 But see, here’s the nice thing. There’s a one-on-one conversation that’s not recorded,
1:46:38 is different than one that’s listened by a lot of people, because I would venture to,
1:46:43 I trust the intelligence of the viewer and the listener to detect even better than you.
1:46:45 Yes. And I think that’s true, by the way.
1:46:47 To detect the psychopathy.
1:46:52 I’ve had the odd interview with people that I wasn’t happy with having organized,
1:47:00 because I felt that I had brought their ideas to a wider audience that might have been appropriate.
1:47:05 But my conclusion, and the conclusion of my producers and the people I talked to,
1:47:10 was that we could run the interview, the discussion, and let the audience sort it out.
1:47:15 And I would say they do. So I think as a general rule of thumb, that’s true.
1:47:19 And I also think that the long-form interviews are particularly good at that, because
1:47:27 it’s not that easy to maintain a manipulative stance, especially if you’re empty,
1:47:31 for like two and a half hours. So you get tired, you get irritable,
1:47:36 you show that you lose the track, you’re going to start leaking out your mistakes.
1:47:40 And that actually is the case for all the world leaders, I would say,
1:47:49 one hour is too short. Something happens at like two hour plus mark where you start to leak.
1:47:55 And I’m trusting the intelligence of the listener to sort of, to detect that.
1:47:58 Yeah. And it might be the intelligence of the distributed crowd.
1:48:02 And I mean, that is what I’ve seen with the YouTube interviews, is that it’s hard to fool
1:48:10 people as such over a protracted period of time. And I guess it’s partly because everybody
1:48:17 brings a slightly different set of falsehood detectors to the table. And if you aggregate
1:48:22 that, it’s pretty damn accurate. But of course, it’s complicated because
1:48:28 ideas of Nazi ideology spread in the 20s. There was a real battle between Marxism and
1:48:34 Nazism. Oh yeah. And I believe there are some attempts at censorship of Nazi ideology.
1:48:44 Censorship very often does the opposite. It gives the fringe ideologies power if they’re
1:48:51 being censored. Because that’s an indication that the man in power doesn’t want the truth
1:48:55 to be heard, this kind of idea. And that just puts fuel to the fire.
1:49:02 It also motivates the paranoid types because one of the reasons that paranoia spirals out
1:49:07 of control is because paranoid people almost inevitably end up being persecuted. Because
1:49:14 they’re so touchy and so suspicious that people start to walk on eggshells around them as if
1:49:18 there are things going on behind the scenes. And so then they get more distrustful and more
1:49:23 paranoid. And eventually they start misbehaving so badly that they are actually persecuted
1:49:29 often by legal authorities. And it’s down the rabbit hole they go. And so Musk is betting on
1:49:40 that to some degree. He believes that free expression on Twitter X will sort itself out and be of net
1:49:45 benefit. And I follow a lot of really bad accounts on X. Because I like to keep an eye on
1:49:51 the pathology of the left, let’s say, and the pathology of the right, thinking at least in my
1:49:56 clinical way that I’m watching the psychopaths dance around and try to do what their subversion.
1:50:00 And it’s an ugly place to inhabit, that’s for sure. But it’s also the case that
1:50:07 a very tiny minority of seriously bad actors can have a disproportionate influence. And
1:50:12 one of the things I’ve always hoped for for social media channels is that they separate
1:50:17 the anonymous accounts from the verified accounts. They should just be in different categories.
1:50:22 People who will say what they think and take the hits to their reputation,
1:50:27 anonymous types, if you want to see what the anonymous types say, you can see it. But don’t
1:50:32 be confusing them with actual people, because they’re not the same. We know that people are,
1:50:36 we know that people behave more badly when they’re anonymous. That’s a very well established
1:50:41 psychological finding. Well, and I think the danger to our culture is substantive. I think the
1:50:46 reason that everything, perhaps the reason that everything started to go sideways pretty seriously
1:50:52 around 2015 is because we invented these new modes of communication. We have no idea how to
1:50:59 police them. And so the psychopathic manipulators, they have free reign. About 30% of the internet
1:51:04 is pornography. A huge amount of internet traffic is outright criminal. And there’s a
1:51:09 penumbra around that that’s psychopathic narcissistic, trouble making trolls.
1:51:14 And that might constitute the bulk of the interactions online. And it’s partly because
1:51:18 people can’t be held responsible. So the free riders have free reign.
1:51:25 It’s a fascinating technical challenge of how to make our society resilient to the psychopaths
1:51:30 on the left and the right. It might be the fundamental problem of the age, given the
1:51:37 amplification of communication by our social networks. And so to generalize across psychopaths,
1:51:45 you could also think about bots, which behave similar to psychopaths in their certainty and
1:51:48 not caring. They’re maximizing some function. They’re not caring about anything else. Attention,
1:51:52 yeah. Yeah, yeah. Short-term attention, even worse. Yeah, because you might, you know,
1:51:58 that’s another problem. Like, if the algorithms are maximizing for the grip of short-term attention,
1:52:05 they’re acting like immature agents of attention, right? And so then imagine the worst case scenario
1:52:12 is negative emotion, garners more attention. And short-term gratification, garners more attention.
1:52:18 So then you’re maximizing for the grip of short-term attention by negative emotion. I mean,
1:52:24 that’s not going to be a principle if we were talking earlier about, you know, unsustainable,
1:52:32 unifying axioms. That’s definitely, that’s definitely one of them. Maximize for the
1:52:37 spread of negative attention, negative emotion that garners short-term attention. Jesus. Brutal.
1:52:46 I just, I tend to not think there’s that many psychopaths. So maybe to push back a little
1:52:53 bit, it feels like there’s a small number of psychopaths. Three to five percent is the estimate
1:52:57 worldwide. In terms of humans, sure. But in terms of the pattern of stuff we see online,
1:53:03 my hope is that a lot of people on the extreme left and extreme right, or just the trolls in general,
1:53:10 are just young people kind of going through the similar stuff that we’ve been talking about,
1:53:17 trying on the cynicism and the resentment. There is, there’s a drug aspect to it. There’s a pull
1:53:25 to that, to talk shit about somebody, to take somebody down. I mean, there is some pleasure in
1:53:31 that. There’s a dark pull towards that. And I think that’s the sadistic pull. And I think a lot
1:53:36 of people, I mean, you see, when you say sadistic, it makes it sound like some kind of, it’s a pathology.
1:53:41 It’s pleasure in the suffering of others. Right. But I just think that all of us have
1:53:46 the capacity for that. All humans have the capacity for that.
1:53:48 Some more than others, but everyone to some degree.
1:53:53 And when you’re young, you don’t understand the full implications of that on your own self.
1:53:59 So if you participate in taking other people down, that’s going to have a cost on your own
1:54:03 development as a human being. Like it’s going to take you towards the Dostoevsky’s notes from
1:54:08 underground, in the basement, cynical, all that kind of stuff, which is why a lot of young people
1:54:14 try it out. The reason is you get older and older, you realize that there’s a huge cost to that,
1:54:19 so you don’t do it. But there’s young people that, so like, I would like to sort of believe and hope
1:54:24 that a large number of people who are trolls are just trying out the derision. No doubt.
1:54:30 And then so they can be saved. They can be helped. They can be shown that there’s
1:54:36 more growth, there’s more flourishing to celebrating other people. And actually,
1:54:43 and criticizing ideas, but not in the way of derision, lol, but by formulating your own
1:54:49 self in the world, by formulating your ideas in a strong, powerful way, and also removing
1:54:54 the cloak of anonymity and just standing behind your ideas and carrying the responsibility of
1:54:59 those ideas. Yeah. I think all of that is right. I think the idea that that’s more likely to occur
1:55:04 among young people, that’s clear. People, as they mature, get more agreeable and conscientious.
1:55:08 So we actually know that what you said is true technically. It’s definitely the case that there
1:55:14 is an innate tilt towards pleasure in that sort of behavior, and it is associated to some degree
1:55:20 with dominance driving. And I do think it’s true, as you pointed out, that many of the people who
1:55:28 are toying with that pattern can be socialized out of it. In fact, maybe most people, even the
1:55:35 repeat criminal types, tend to desist in their late 20s. So imagine that, so 1% of the criminals
1:55:41 commit 65% of the crimes. So imagine that that 1% are the people that you’re really concerned with.
1:55:48 They often have stable patterns of offending that emerged in very, very young,
1:55:56 like even in infancy, and continued through adolescence and into adulthood. If you keep
1:56:02 them in prison until they’re in the middle of their late 20s, most of them stop. And that might be
1:56:11 the easiest way to understand that might just be delayed maturation. So are most people salvageable?
1:56:17 Yes, definitely. Is everyone salvageable? Well, at some point it becomes,
1:56:22 first of all, they have to want to be salvaged. That’s a problem. But then it also becomes something
1:56:30 like, well, how much resources are you going to devote to that? Like the farther down the
1:56:35 rabbit hole you’ve gone, the more energy it takes to haul you up. So there comes a point where
1:56:40 the probability that you’ll be able to get enough resources devoted to you to rescue you from the
1:56:46 pit of hell that you’ve dug is zero. And that’s a very sad thing. And it’s very hard to be around
1:56:52 someone who’s in that situation, very, very hard. And it seems that it’s more likely that the leaders
1:56:57 of movements are going to be psychopaths. And the followers of movements are going to be the
1:57:04 people that we’re mentioning that are kind of lost themselves to the ideology of the movement.
1:57:09 Well, we know that what you said is true, even historically, to a large degree, because
1:57:16 Germany was successfully denazified. And it’s not like everybody who participated in every
1:57:22 element of the Nazi movement was brought to justice, not in the least. The same thing happened in
1:57:32 Japan. So to some degree, the same thing happened in South Africa. And it’s the case, for example,
1:57:37 also in the stories that we were referring to earlier, the biblical stories, the patriarchs of
1:57:42 the Bible. Most of them are pretty bad people when they first start out. Like Jacob’s a really
1:57:48 good, because Jacob is the one who becomes Israel. He’s a major player in the biblical narrative.
1:57:56 And he’s a pretty bad actor when he first starts out. He’s a mama’s boy. He’s a liar. He steals
1:58:02 from his own brother. And in a major way, he deceives his father. He’s a coward. And yet he
1:58:11 turns his life around. So be careful, the leaders you idolize and worship. But then it’s not always
1:58:17 clear to know who is the good and who’s the evil. Yeah, that’s hard. You have been through some
1:58:22 dark places in your mind over your life. What have been some of your darker hours and how did
1:58:30 you find the light? Well, I would say I started contending with the problem of evil very young,
1:58:34 13 or 14. And that that’s been the main,
1:58:46 that was my main motivation of study for 30 years, I guess, something like that.
1:58:54 At the end of that 30 years, it became more and more, I became more and more interested in
1:58:58 fleshing out the alternative. Like once I became convinced that evil existed,
1:59:07 and that was very young. I always believed that if you could understand something well enough
1:59:15 that you could formulate a solution to it. But it turns out that seeing evil and understanding
1:59:24 that it exists is less complicated than a technical description of its opposite. Like what is good?
1:59:30 You can say, well, it’s not that, for sure. It’s not Auschwitz. How about we start there?
1:59:36 It’s as far from Auschwitz as you can get. It’s as far from enjoying being an Auschwitz
1:59:41 camp guard as you can get. Okay, well, where are you when you’re as far away from that as
1:59:46 you could possibly get? What does that mean? And it does have something to do with play,
1:59:54 as far as I’m concerned. Like I think the antithesis of tyranny is play. So that took me a long time
2:00:01 to figure out that specifically. And so that was very dark. I spent a lot of time studying
2:00:10 the worst behaviors that I could discover abstractly in books, but also in my clinical
2:00:18 practice and in my observations of people. And so that’s rough. More recently, I was very ill
2:00:25 and in a tremendous amount of pain that lasted pretty much without any break for three years.
2:00:33 And what was particularly useful to me then was the strength of my relationships,
2:00:40 my immediate relationships, my friendships. Also, the relationships that I had established
2:00:48 more broadly with people, because by the time I became ill, I was reasonably well known and
2:00:53 people were very supportive when I was having trouble. And that was very helpful. But
2:00:59 it’s certainly the case that it was the connections I had, particularly with my family,
2:01:04 but also with my friends that were the saving grace. And that’s something to know. I mean,
2:01:10 it’s necessary to bear the burdens of the world on your own shoulders. That’s for sure
2:01:15 the burdens of your own existence and whatever other responsibilities you can mount. But
2:01:22 that by no means means that you can or should do it alone. And so, you know, you might say, well,
2:01:31 welcoming the adversity of life as a redemptive challenge is a task that’s beyond the ability
2:01:37 of the typical person or even maybe of anyone. But then when you think, well, you’re not alone,
2:01:42 maybe you’re not alone socially, you’re not alone familial, maybe you’re not alone
2:01:47 metaphysically as well. You know, there’s an insistence. And I think it’s true. There’s an
2:01:53 insistence, for example, in the old and the New Testament alike, that the more darkness you’re
2:01:59 willing to voluntarily encounter, the more likely it is that the spirit of Abraham and the patriarchs
2:02:05 will walk with you. And I think that’s right. Like, I think it’s sort of technically true in that
2:02:11 the best parts of yourself make themselves manifest. If you want to think about it that way,
2:02:15 the best parts of yourself, whatever that means, make themselves manifest when you’re
2:02:20 contending actively and voluntarily with the most difficult challenges.
2:02:25 Why wouldn’t it be that way? And then you could think, well, that’s yourself. It’s like, well,
2:02:33 are the best unrevealed parts of you yourself? Well, no, they’re a kind of metaphysical reality.
2:02:39 They’re not yet manifest. They only exist in potential. They transcend anything you’re currently
2:02:45 capable of, but they have an existence. You could call that yourself, but like it was Jung’s
2:02:50 contention, for example, with regards to such terminology, that the reason we use the term
2:02:56 “self” instead of “God” is because when God was dispensed with, let’s say, by the processes Nietzsche
2:03:02 described, we just found the same thing deep within the instinctive realm, let’s say. We founded it
2:03:07 at the bottom of the things instead of at the top. It’s like, it doesn’t matter. It doesn’t matter
2:03:15 fundamentally. What matters is whether or not that’s a reality. And I think it’s the fundamental
2:03:23 reality, because I do think that the deeper you delve into things, this is what happens to Moses
2:03:28 when he encounters the burning bush. So Moses is just going about his life. He’s a shepherd.
2:03:34 He’s an adult. He has wives. He has children. He has responsibilities. He’s left his home,
2:03:38 and he’s established himself. And so things are pretty good for Moses.
2:03:43 And then he’s out by Mount Horab in that story, but it’s the central mountain of the world. It’s
2:03:49 the same mountain as Sinai, which is the place where heaven and earth touch. And he sees something
2:03:55 that grabs his attention. Right? That’s the burning bush. And bush is a tree. That’s life. That’s the
2:04:01 tree of life. And the fact that it’s on fire is that’s life exaggerated, because everything that’s
2:04:07 alive is on fire. And so what calls to Moses is like the spirit of being itself, and it
2:04:13 attracts him off the beaten track. And he decides to go investigate. So Moses is everyone who goes
2:04:21 off the beaten track to investigate. And so as he investigates, he delves more and more deeply
2:04:28 until he starts to understand that he’s now walking on sacred ground. So he takes off his shoes,
2:04:33 and that’s a symbolic reference of identity transformation. He’s no longer walking the
2:04:39 same path. He no longer has the same identity. He’s in a state of flux. And that’s when
2:04:45 what happens is that he continues to interact with this calling. And Moses asks,
2:04:51 “What it is that’s being revealed?” And God says, “I’m the spirit of being itself.” That’s basically
2:04:57 the answer. “I am what I am.” It’s a more complex utterance than that. “I am what I will be.”
2:05:02 “I am what was becoming.” It’s all of that at the same time. It’s the spirit of being that’s
2:05:08 speaking to him, the spirit of being and becoming. And it tells Moses that he now, because he’s delved
2:05:13 so deeply into something so compelling, his identity has transformed and he’s become the
2:05:19 leader who can speak truth to power. And so he allies himself with his brother Aaron,
2:05:24 who’s the political arm and who can communicate. And he goes back to Egypt to confront the tyrant.
2:05:32 And that’s an indication of that idea that if you wrestle with life properly, that the spirit of
2:05:40 being and becoming walks with you. And it’s like, how can that not be true? Because the
2:05:48 contrary would be that there would be no growth in challenge. You have to be infinitely nihilistic
2:05:59 to believe that. It’s obvious, but it’s also just fascinating that hardship is the thing that ends
2:06:05 up being the catalyst for delving deeply. It’s hardship voluntarily undertaken. And it’s crucially
2:06:10 true. Look, if you bring someone into therapy, let’s say they’re afraid of elevators, and you
2:06:18 trick them into getting near an elevator, you’ll make them worse. But if you negotiate with them so
2:06:26 that they voluntarily move towards the elevator on their own recognizance, they’ll overcome their
2:06:31 fear and they become generally braver. But it has to be voluntary. See, I got to push back
2:06:37 and explore with you the question of voluntarily. Let’s look at Nietzsche. He suffered through
2:06:41 several health issues throughout his life. Migraines, eyesight issues, digestive problems,
2:06:49 depression with suicidal thoughts. And yet he is one of the greatest minds in the history of humanity.
2:06:57 So were these problems that he was suffering arguably involuntarily a feature or a bug?
2:07:02 That’s a good question. The same thing happens in the story of Job, because Job is a good man.
2:07:09 God himself admits it. And Satan comes along and says to God, I see you’re pretty proud of your
2:07:15 man there, Job. God says, yeah, he’s doing pretty well. And Satan says, I think it’s just because
2:07:19 things are easy for him. Let me have a crack at him and see what happens. And God says,
2:07:24 yeah, I think you’re wrong. Do your worst. Right. And that’s how people feel when those
2:07:30 slings and arrows come at them, let’s say like Nietzsche. Well, Job’s response to that. Now,
2:07:35 the story is set up so that what befalls Job is actually quite arbitrary, right? These catastrophes
2:07:41 that you’re describing. The volunteerism in Job is his refusal to despair even in the face of that
2:07:47 adversity. And that seems like something like an expression of voluntary free will. He refuses to
2:07:53 lose faith. And the way the story ends is that Job gets everything back and more. And, you know,
2:08:00 so that’s a dissent and assent story. And a cynic might say, well, the ends don’t justify the means.
2:08:04 And I would say fair enough, but that’s a pretty shallow interpretation of the story.
2:08:10 What it indicates instead is that if you’re fortunate, because let’s not forget that,
2:08:19 and you optimize your attitude even in the face of adversity, that there it’s not infrequently
2:08:23 the case that your fortunes will reverse. You know, when I found that in many situations,
2:08:37 the journalists whose goal was most malicious in relationship to me, who were most concerned with
2:08:44 improving their own, what would you say, fostering their own notoriety and gaining
2:08:51 social status at my expense, were the ones who did me the greatest favor. Those were the interviews
2:08:57 that went viral. And so that’s interesting, you know, because they were definitely the places
2:09:02 where the most disaster was at hand. And I felt that in the aftermath, every time that happened,
2:09:09 my whole family was destabilized for like two months because things, it wasn’t obvious at all
2:09:16 which way the dice were going to roll. But you leaned into that. So in a sense that there’s this
2:09:22 kind of transformation from the involuntary to the voluntary, basically saying bring it on,
2:09:28 that act of bring it on turns the hardship, involuntary hardship into voluntary hardship.
2:09:35 Well, not necessarily, let’s say, but you could say that’s your best bet. Well, you know,
2:09:43 I’m never going to say that you can transcend all catastrophe with the right attitude because
2:09:52 that’s just too much to say. But I could say that in a dire situation, there’s always an element
2:09:59 of choice. And if you make the right choices, you improve the degree, you improve your chances of
2:10:06 success to the maximal possible degree. It might be too much to say, but nevertheless,
2:10:14 this could be true. Victor Frankel, Marcus Aurelius. Well, that’s what the resurrection story
2:10:21 proclaims is that, you know, even under the darkest imaginable circumstances, the fundamental
2:10:29 finale is the victory of the good. And that seems to me to be true.
2:10:37 Do you ever regrets when you look back at your life in the full analysis of it?
2:10:44 Well, as I said, I was very ill for about three years, and it was seriously brutal. Like every,
2:10:50 this is no lie, every single minute of that three years was worse than any single time I’d ever
2:10:58 experienced in my entire life up to that. So that was rough. Was the roughest, the physical or the
2:11:08 psychological pain? Just little pain. Yep. Yeah, I was walking like 10 to 12 miles a day.
2:11:20 Rain or shine, winter, didn’t matter. Not good. And it was, it was worse than that because
2:11:32 as the day progressed, my pain levels would fall until by 10, 11 at night, when I was starting to
2:11:42 get tired, I was approaching, what would you say? I was approaching something like an ordinary bad day.
2:11:50 But as soon as I went to sleep, then the clock was reset and all the pain came back. And so
2:11:54 it wasn’t just that I was in pain, it was that sleep itself became an enemy.
2:12:01 And that’s really rough, man, because sleep is where you take refuge. You know, you’re worn out,
2:12:08 you’re tired, and you go to sleep and you wake up and it’s generally, it’s something approximating
2:12:14 a new day. This was like scissorfish on steroids. And that was, it was very difficult to maintain
2:12:18 hope in that because I would do what I could, like there were times when it took me like an hour
2:12:26 and a half in the morning to stand up. And so I do all that and more or less put myself back
2:12:30 into something remotely resembling human by the end of the day. And then I knew perfectly well,
2:12:35 exhausted if I fell asleep, that I was going to be right at the bottom of the bloody hill again.
2:12:43 And so after a couple of years of that, it was definitely the fact that I had a family that,
2:12:50 that carried me through that. What did you learn about yourself about yourself and about the human
2:12:56 mind from that, from all of those days? Well, I think I learned more gratitude
2:13:05 for the people I had around me. And I learned how fortunate I was to have that and how crucial
2:13:11 that was. My wife learned something similar. She, she was diagnosed with a form of cancer that,
2:13:18 as far as we know, killed every single person who ever had it except her. It’s quite rare. And
2:13:28 her experience was that what really gave her hope and played at least a role in saving her was
2:13:32 the realization of the depth of love that her son in particular had for her.
2:13:37 And that says nothing about her relationship with Michaela, with her daughter. It just so happened
2:13:46 that it was the revelation of that love that made Tammy understand the value of her life in a way
2:13:54 that she wouldn’t have realized of her own accord. We’re very, very, there’s no difference between
2:13:58 ourselves and the people that we love. And there might be no difference between ourselves and
2:14:04 everyone everywhere, but we can at least realize that to begin with in the form of the people that
2:14:09 we love. And I hope I’m better at that than I was. I think I’m better at it than I was.
2:14:17 I’m a lot more grateful for just ordinary, ordinariness than I was, because when I first
2:14:21 recovered, I remember I was standing, first started to recover. I was standing in this
2:14:27 pharmacy waiting for a prescription in a little town and they weren’t being particularly efficient
2:14:31 about it. And so I was in that standing in the aisle for like 20 minutes. And I thought,
2:14:39 I’m not on fire. I could just stand here for like the rest of my life, just not being in pain and
2:14:45 enjoying that. And you know, that would have been something that before that would have been,
2:14:49 you know, I would have been impatient and rare to go because I didn’t have 20 minutes to stand
2:14:54 in the middle of an aisle. And I thought, well, you know, if you’re just standing there and you’re
2:15:01 not on fire, things are a lot better than they might be. And I certainly, I know that. And I think
2:15:07 I remember it almost all the time. You gain a greater ability to appreciate the mundane moments
2:15:14 of life. Yeah, definitely. The miracle of the mundane. Yeah. I think Nietzsche had that
2:15:25 because he was very ill. And so I suspect he had it. You know, and he was regarded by the
2:15:29 inhabitants of the village that he lived in near the end of his life as something approximating a
2:15:35 saint. He apparently conducted himself very admirably, despite all his suffering.
2:15:40 You know, but that still, there’s this tension as there is in much of Nietzsche’s work
2:15:47 between the miracle of the mundane, appreciating the miracle of the mundane versus
2:15:54 fearing the tyranny of the mediocre. It’s more the mediocre and resentful.
2:16:00 Yes, but that’s you giving him a pass or seeing the good. Fair enough, you know.
2:16:06 There’s a kind of, I mean, the tyranny of the mediocre. I always hated this idea that some
2:16:11 people are better than others. And I understand it, but it’s a dangerous idea.
2:16:17 This is why I like the story of Cain and Abel, I would say, because Cain is mediocre,
2:16:24 but that’s because he refuses to do his best. It’s not something intrinsic to him. And I actually
2:16:28 think that’s the right formulation because, you know, I had people in my clinical practice who were,
2:16:37 they were lost in many dimensions from the perspective of comparison. One woman I remember
2:16:44 in particular who, man, she had a lot to contend with. She was not educated. She was not intelligent.
2:16:50 She had a brutal family, like terrible history of psychiatric hospitalization.
2:16:59 And when I met her at a hospital,
2:17:07 she was an outpatient from the psychiatric ward. And she had been in there with people
2:17:13 that she thought were worse off than her. And they were. And that was a long way down.
2:17:20 That was like Dante’s Inferno level down. It was a long term psychiatric inpatient ward.
2:17:27 Some of the people had been there for 30 years. It made one flu over the cuckoo’s nest look like a
2:17:34 romantic comedy. And she had come back to see if she could take some of those people for a walk
2:17:43 and was trying to find out how to get permission to do it. And so, you know, better than other people.
2:17:48 Some people are more intelligent. Some people are more beautiful. Some people are more athletic.
2:17:56 Maybe it’s possible for everyone at all levels of attainment to strive towards the good.
2:18:03 And maybe those talents that are given to people unfairly don’t privilege them in relationship
2:18:08 to their moral conduct. And I think that’s true. Like, there’s no evidence, for example,
2:18:11 that there’s any correlation whatsoever between intelligence and morality.
2:18:18 You’re not better because you’re smart. And what that also implies is if you’re smart,
2:18:23 you can be a lot better at being worse. I think for myself, I’m just afraid
2:18:32 of dismissing people because of my perception of them. Yeah, well, that’s why we have that
2:18:37 metaphysical presumption that everybody’s made in the image of God, right? Despite that immense
2:18:42 diversity of apparent ability. There’s that underlying metaphysical assumption that, yeah,
2:18:51 we all vary in our perceived and actual utility in relationship to any proximal goal. But
2:18:58 all of that’s independent of the question of axiomatic worth. And that preposterous is that
2:19:05 notion appears to be, it seems to me that societies that accept it as a fundamental axiomatic presumption
2:19:11 are always the societies that you’d want to live in if you had a choice. And that to me is an
2:19:17 existence proof for the utility of the presumption. And also, if you treat people like that in your
2:19:26 life, every encounter you have, you make the assumption that it’s an assumption of what would
2:19:31 you, it’s radical equality of worth, despite individual variance and ability, something
2:19:37 like that. Man, your interactions go way better. I mean, everyone wants to be treated that way.
2:19:46 Look, here’s the developmental sequence for you. Naive and trusting. Hurt and cynical. Okay, well,
2:19:53 is hurt and cynical better than naive and trusting? It’s like, yeah, probably. Is that where it ends?
2:20:01 How about cynical and trusting as step three? Right. And then the trust becomes courage. It’s
2:20:09 like, yeah, I’ll put my hand out for you. But it’s not because I’m a fool. And I think that’s
2:20:13 right, because that’s the reistantiation of that initial trust, right, that makes childhood
2:20:21 magical and paradisal. But it’s the admixture of that with wisdom. It’s like, yeah, you know,
2:20:29 we could walk together uphill. But that doesn’t mean, and I’ll presume that that’s your aim.
2:20:32 But that doesn’t mean that I’m not going to watch.
2:20:41 What’s a better life, cynical and safe or hopeful and vulnerable to be hurt?
2:20:47 Oh, you can’t dispense with vulnerable to be hurt. That’s the other realization. It’s like,
2:20:51 you’re going to stake your life on something. You could stake your life on security, but it’s
2:20:56 not going to help. You don’t have that option. So what do you do when you’re betrayed,
2:21:04 ultimately by some people you come across? Grieve and look elsewhere.
2:21:11 Do what you can to forgive. And not least, so you lighten your own burden.
2:21:19 Maybe do what you can to help the person who betrayed you. And if that all proves impossible,
2:21:26 then wash your hands of it and move on to the next adventure. And do it again.
2:21:34 Yeah. Yeah. Boy, this life, something else. Say, we’ve been talking about some
2:21:39 heavy, difficult topics, and you’ve talked about truth in your Nietzsche lectures and elsewhere.
2:21:45 When you think, when you write, when you speak, how do you find what is true?
2:21:49 You know, Hemingway said, all you have to do is write one true sentence.
2:21:55 How do you do that? Well, I would say first that you practice that. It’s like,
2:22:01 it, that question is something, and Hemingway knew this at least to some degree, and he certainly
2:22:08 wrote about it, is that you have to orient your life upward as completely as you can. Because
2:22:13 otherwise you can’t distinguish between truth and falsehood. It has to be a practice. Now,
2:22:17 and for me, I started to become serious about that practice when I realized that
2:22:25 it was individual, it was the immorality of the individual, the resentful, craven, deceitful,
2:22:31 immorality of the individual that led to the terrible atrocities that humans
2:22:36 engage in that make us doubt even our own worth. I became completely convinced of that,
2:22:42 that the fundamental root cause of evil, let’s say, wasn’t economic or sociological,
2:22:50 that it was spiritual, just psychological, and that if that was the case, you had an existential
2:22:57 responsibility to aim upward and to tell the truth, and that everything depends on that. And
2:23:06 I became convinced of that. And so then, look, you set your path with your orientation. That’s
2:23:11 how your perceptions work. As soon as you have a goal, a pathway opens up to you and you can see it,
2:23:17 and the world divides itself into obstacles and things that move you forward. And so the pathway
2:23:23 that’s in front of you depends on your aim. The things you perceive are concretizations of your aim.
2:23:28 If your aim is untrue, then you won’t be able to tell the difference between truth and falsehood.
2:23:32 And you might say, well, how do you know your aim is true? It’s like, well,
2:23:37 you course correct continually, and you can aim towards the ultimate. Are you ever sure that
2:23:43 your aim is the right direction? You become increasingly accurate in your apprehension.
2:23:48 Is it like part of the process to cross the line to go outside the over-to-window,
2:23:53 to dip a toe outside the over-to-window for a bit? Of course. That’s what you do in part and play.
2:24:00 I was at the comedy mothership, and every single comedian was completely reprehensible.
2:24:06 All they were doing was saying things that you can’t say. Well, but it was in play. What I’m
2:24:11 trying to do in my lectures is I’m on the edge. I have a question I’m trying to address, and I’m
2:24:17 trying to figure it out. I don’t know where the conversation is going. Truly. It’s an exploration,
2:24:22 and I think the reason that the audiences respond is because they can feel that it’s a high wire
2:24:28 act, and I could fail. And my lectures have degrees of success. Sometimes I get real fortunate,
2:24:33 and there’s a perfect narrative arc. I have a question. I’m investigating it. It comes to a
2:24:37 punchline conclusion just at the right time, and it’s like the whole act is complete, and sometimes
2:24:43 it’s more fragmented. But I can tell when the audience is engaged because everyone’s silent,
2:24:48 except maybe when they’re laughing. There’s a kind of sense that you’re arguing with yourself
2:24:51 and you’re lecturing. It’s beautiful. It’s really beautiful and powerful to watch.
2:24:55 Like Nietzsche does the same. There’s contradictions in what you’re saying. There’s a struggle
2:24:59 that you’re saying. But I do think that when you’re doing the same on the internet, you get
2:25:04 punished for the deviations. You get punished for the exploration, especially when that explores
2:25:09 outside the overton window. Look, if you’re going to play hard in a conversation
2:25:16 to explore, you’re going to say things that are edgy, that are going to cause trouble,
2:25:20 and that might be wrong. And that’s another reason why free speech protection is so
2:25:25 important. You actually have to protect the right, let’s say in the optimal circumstance,
2:25:29 you have to protect the right of well-meaning people to be wrong. Now, you probably have to
2:25:33 go beyond that to truly protect it. You have to even protect the right of people who aren’t
2:25:39 meaning well to be wrong. And we also need that because we’re not always well-meaning.
2:25:46 But the alternative to that protection would be the insistence that people only say what was
2:25:53 100% right all the time. I’m also, I guess this is a call to our fellow humans not to reduce a
2:26:00 person through a particular statement, which is what the internet tends to want to do.
2:26:04 Especially if it’s the worst thing they ever said. Because God, well,
2:26:07 anyone judged by that standard is doomed unless they’re silent.
2:26:14 But it also just makes you not want to play. Not want to take sort of radical thought experiments
2:26:17 and carry out to a natural conclusion. Yeah, well, that’s kind of the definition of a totalitarian
2:26:23 state. No one’s playing in a totalitarian state ever. But in this case, it’s an emergent one
2:26:29 with psychopaths roaming the landscape, the barbarians. That might be the general pattern
2:26:35 of totalitarianism. Well, in totalitarianism, there’s usually one psychopath, not multiple.
2:26:38 Yeah, but everyone, well, everyone else is complicit, at least in their silence.
2:26:44 Yeah. Does the study of the pathology of psychopaths online, where on you?
2:26:48 Yes, definitely. Do you ever consider doing less of that?
2:26:54 Yes. Yes, definitely. But you know,
2:27:07 probably I experienced most of that on X. But that’s also where I found most of my guests.
2:27:12 That’s also where I get a sense of the zeitgeist, which is necessary, for example,
2:27:16 if you’re going to be a podcast host, it’s necessary for me to make my lectures on point
2:27:23 and up to date, to get a sampling of the current moment. You have to be of the moment in many
2:27:29 ways to function at a high level. There’s a price to be paid for that, because you’re
2:27:37 exposed to everything in a sense. And you can also oversample the darkness.
2:27:41 Yeah. Yeah, definitely. And it can make you more and more cynical.
2:27:45 Yeah. Well, it’s a danger, right? Yeah. Well, luckily for me, I have
2:27:49 many things that counterbalance that, the familial relationships we talked about,
2:27:55 the friendships, and then also all of the public things I do are positive. The lecture tours,
2:28:03 for example, which I’m on a lot, they’re basically 100% positive. So I’m very well
2:28:08 buttressed against that. That’s great to hear.
2:28:14 Darker element. As a fan in the arena watching the gladiators fight, your mind is too important
2:28:21 to be lost to the cynical, to the battles with the abyss.
2:28:26 Well, you have a moral obligation, too, to maintain a positive orientation.
2:28:34 It’s a moral obligation. The future is, of course, rife with contradictory possibilities. And I
2:28:40 suppose in some ways, the more rapid the rate of transformation, the more possibility for
2:28:46 good and for evil is making itself manifest at any moment. But it looks like the best way to
2:28:50 ensure that the future is everything we wish it would be is to
2:28:52 maintain faith that that is the direction that will prevail.
2:28:58 And I think that’s a form of moral commitment when it’s not just naive optimism.
2:29:06 Well, Jordan, thank you for being courageous and being the light amid the darkness for many,
2:29:09 many people. And thank you for once again talking today.
2:29:13 Thanks very much for the invitation and for the conversations. But it’s always a pleasure to see
2:29:21 you. And you’re doing a pretty decent job yourself about there illuminating dark corners and
2:29:27 bringing people upward. I mean, you’ve got a remarkable thing going with your podcast. And
2:29:29 you’re very good at it. Thank you, Jordan.
2:29:34 Thanks for listening to this conversation with Jordan Peterson. To support this podcast,
2:29:38 please check out our sponsors in the description. And now, let me leave you some words from
2:29:45 Friedrich Nietzsche. I would like to learn more and more to see as beautiful that which is necessary
2:29:52 in things. Then I shall be one of those who make things beautiful. Thank you for listening
2:30:08 and hope to see you next time.
2:30:11 (gentle music)

0:00:05 The following is a conversation with the founding members of the Cursor team, Michael Trule,
0:00:10 Swaly Asif, Arvid Lunmark, and Aman Sanger.
0:00:16 Cursor is a code editor based on VS Code that adds a lot of powerful features for AI-assisted
0:00:18 coding.
0:00:24 It has captivated the attention and excitement of the programming and AI communities.
0:00:30 So I thought this is an excellent opportunity to dive deep into the role of AI in programming.
0:00:36 This is a super technical conversation that is bigger than just about one code editor.
0:00:42 It’s about the future of programming and in general the future of human-AI collaboration
0:00:48 in designing and engineering complicated and powerful systems.
0:00:50 And now a quick few second mention of each sponsor.
0:00:54 Check them out in the description, it’s the best way to support this podcast.
0:00:59 We got Encore for unifying your machine learning stack, Masterclass for learning, Shopify for
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0:01:07 Choose what is it my friends.
0:01:13 Also, if you want to get in touch with me for whatever reason, or take a survey or submit
0:01:17 questions for an AMA, all of that will be great.
0:01:19 Go to lexframing.com/contact.
0:01:24 And now onto the full ad reads, I try to make them interesting, but if you skip them please
0:01:28 still check out our sponsors, I enjoy their stuff, maybe you will too.
0:01:34 This episode is brought to you by Encore, a platform that provides data focused AI tooling
0:01:39 for data annotation, curation management, and for model evaluation.
0:01:45 One of the things I love about these guys is they have a great blog that describes cleanly,
0:01:49 I mean it’s technical, but it’s not too technical, but it’s sufficiently technical to where it’s
0:01:52 actually describing the ideas not BS.
0:01:59 Blog posts on sort of the state of the art, like the OpenAI 01 model that was just released.
0:02:05 So sometimes they integrate it into why this is a part of Encore, why this makes sense,
0:02:06 and sometimes not.
0:02:09 And so I love that, I recommend their blog just in general.
0:02:13 That said, when they are looking at state of the art models, they are always looking for
0:02:15 ways to integrate it into their platform.
0:02:19 Basically, it’s a place to organize your data, and data is everything.
0:02:26 This was true before the popularity and the explosion of attention methods of transformers,
0:02:29 and it is still very much true now.
0:02:34 Sort of the non-synthetic, the human generated data is extremely important.
0:02:37 How you generate that data, how you organize that data, how you leverage it, how you train
0:02:43 on it, how you fine tune on it, the pre-training, the post-training, all of it, the whole thing.
0:02:45 It is extremely, extremely important.
0:02:48 And so Encore takes data very seriously.
0:02:54 Anyway, go try out Encore to create, annotate and manage your AI data at Encore.com/Lex.
0:02:58 That’s Encore.com/Lex.
0:03:04 This episode is also brought to you by MasterClass, where you can watch over 200 classes from the
0:03:07 best people in the world and their respective disciplines.
0:03:09 Carlos Santana on guitar, for example.
0:03:10 I loved that one.
0:03:14 There’s a few guitar ones, Tomorello, too, great, great, great stuff, but…
0:03:18 Carlos Santana, his instrumental Europa.
0:03:24 I haven’t quite tried to play that, but it’s on my to-do list, it’s sort of one of those
0:03:30 things you know for sure this is the thing I will play, because it’s too beautiful, it’s
0:03:32 too soulful.
0:03:36 It feels like once you play, you understand something about the guitar that you didn’t
0:03:37 before.
0:03:38 It’s not blues.
0:03:39 It’s not.
0:03:41 It’s not what it is.
0:03:51 It’s some kind of dream-like teleportation into a psychedelic world where the tone is
0:03:56 warmer than anything else I’ve ever heard, and still the guitar can cry.
0:03:57 I don’t know.
0:03:58 I love it.
0:03:59 He’s a genius.
0:04:08 So it’s such a gift that you can get a genius like that to teach us about his secrets.
0:04:13 Get unlimited access to every masterclass and get an additional 15% off an annual membership
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0:04:27 This episode is also brought to you by Shopify, a platform designed for anyone to sell anywhere
0:04:33 with a great looking online store, or a simple looking online store, like the one I put together
0:04:34 at Lex Freeman.com/store.
0:04:39 I have a few shirts on there in case you’re interested.
0:04:45 And speaking of shirts, I’m reminded of thrift stores, which I very much loved for a long
0:04:46 time.
0:04:54 I still love thrift stores, or a nice place to get stuff like kitchen stuff and clothing.
0:04:58 And the kind of clothing you get at thrift stores is actually pretty interesting because
0:05:00 there’s shirts there.
0:05:03 They’re just unlike anything else you would get anywhere else.
0:05:10 So if you’re sort of selective and creative-minded, there’s a lot of interesting fashion that’s
0:05:11 there.
0:05:16 And in terms of t-shirts, there’s just like hilarious t-shirts, t-shirts that are very
0:05:20 far away from the kind of trajectories you have taken in life, or are not, but you just
0:05:24 haven’t thought about it, like a band that you love, but you’ve never would have thought
0:05:26 to wear their t-shirt.
0:05:32 Anyway, a little bit, I think it’s Shopify as the internet’s thrift store.
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0:05:56 This episode is also brought to you by Netsuite, an all-in-one cloud business management system.
0:06:02 Sometimes I think that Netsuite is supporting this podcast because they’re trolling me.
0:06:06 They’re saying, “Hey, Lex, aren’t you doing a little too much talking?
0:06:08 Maybe you should be building more.”
0:06:10 I agree with you, Netsuite.
0:06:12 I agree with you.
0:06:18 And so every time I do an ad read for Netsuite, it is a chance for me to confront my Jungin
0:06:19 shadow.
0:06:26 Some of the demons emerge from the subconscious and ask questions that I don’t have answers
0:06:31 to, questions about one’s mortality and that life is short, and that one of the most fulfilling
0:06:35 things in life is to have a family and kids and all of these things I would very much
0:06:37 like to have.
0:06:43 And also the reality that I love programming and I love building, I love creating cool
0:06:49 things that people can use and share and that would make their life better, all of that.
0:06:53 Of course, I also love listening to podcasts, and I kind of think of this podcast as me
0:06:59 listening to a podcast where I can also maybe participate by asking questions.
0:07:04 So all of these things that you love, but you ask the hard question of like, “Okay,
0:07:08 while life is slipping away, it’s short, it really, really is short.
0:07:13 What do you want to do with the rest of the minutes and the hours that make up your life?”
0:07:14 Yeah.
0:07:16 So thank you for the existential crisis, Netsuite.
0:07:18 I appreciate it.
0:07:22 If you’re running a business, if you have taken the leap into the unknown and started
0:07:27 a company, then you should be using the right tools to manage that company.
0:07:30 In fact, over 37,000 companies have upgraded to Netsuite.
0:07:38 Take advantage of Netsuite’s flexible financing plan at Netsuite.com/Lex, that’s Netsuite.com/Lex.
0:07:43 This episode is also brought to you by The Delicious, The Delicious AG1.
0:07:47 It’s an all-in-one daily drink to support better health and peak performance.
0:07:53 It’s basically a super awesome multivitamin that makes me feel like I have my life together.
0:07:57 Even when everything else feels like it’s falling apart, at least I have AG1.
0:08:00 At least I have that nutritional foundation to my life.
0:08:06 So the fast thing I’m doing, all the carnivore diets, all the physical endurance events and
0:08:12 the mental madness of staying up all night or just the stress of certain things I’m
0:08:19 going through, all of that, AG1 is there, at least they have the vitamins.
0:08:25 Also, sometimes wonder, they used to be called Athletic Greens and now they’re called AG1.
0:08:27 I always wonder, is AG2 coming?
0:08:28 Like, why is it just one?
0:08:32 It’s an interesting branding decision, like AG1.
0:08:38 Me as an OCD kind of programmer type, it’s like, okay, is this a versioning thing?
0:08:42 Okay, is this like AG0.1 alpha?
0:08:45 When’s the final release?
0:08:52 Anyway, the thing I like to say and to consume is AG1, they’ll give you one month supply
0:08:58 of fish oil when you sign up at drinkag1.com/lex.
0:09:02 This is the Lex Freeman podcast, to support it, please check out our sponsors in the
0:09:03 description.
0:09:08 And now, dear friends, here’s Michael, Swale, Arvid, and Aman.
0:09:31 All right, this is awesome, we have Michael, Aman, Swale, Arvid here from the cursor team.
0:09:36 First up, big ridiculous question, what’s the point of a code editor?
0:09:42 So the code editor is largely the place where you build software, and today, or for a long
0:09:47 time, that’s meant the place where you text edit a formal programming language.
0:09:50 And for people who aren’t programmers, the way to think of a code editor is like a really
0:09:56 souped up word processor for programmers, where the reason it’s souped up is code has
0:09:57 a lot of structure.
0:10:03 And so the quote unquote word processor, the code editor, can actually do a lot for you
0:10:08 that word processors in the writing space haven’t been able to do for people editing text there.
0:10:13 And so that’s everything from giving you visual differentiation of the actual tokens in the
0:10:17 code, so you can scan it quickly, to letting you navigate around the code base, like you’re
0:10:21 navigating around the internet with hyperlinks, you’re going to definitions of things you’re
0:10:28 using to error checking, to catch rudimentary bugs.
0:10:33 And so traditionally, that’s what a code editor has meant.
0:10:38 And I think that what a code editor is, is going to change a lot over the next 10 years.
0:10:42 As what it means to build software, maybe starts to look a bit different.
0:10:45 I think also a code editor should just be fun.
0:10:46 Yes.
0:10:47 That is very important.
0:10:48 That is very important.
0:10:54 And it’s actually sort of an underrated aspect of how we decide what to build, like a lot
0:11:00 of the things that we build, and then we try them out, we do an experiment, and then we
0:11:03 actually throw them out because they’re not fun.
0:11:08 And so a big part of being fun is being fast, a lot of the time.
0:11:09 Fast is fun.
0:11:10 Yeah.
0:11:14 That should be a T-shirt.
0:11:18 But fundamentally, I think one of the things that draws a lot of people to building stuff
0:11:24 on computers is this insane integration speed, where in other disciplines, you might be sort
0:11:29 of gate capped by resources or the ability, even the ability to get a large group together
0:11:34 and coding is this amazing thing where it’s you and the computer and that alone, you can
0:11:36 build really cool stuff really quickly.
0:11:42 So for people to know, Cursor is this super cool new editor that’s a fork of VS Code.
0:11:49 It would be interesting to get your kind of explanation of your own journey of editors.
0:11:54 How did you, I think all of you were big fans of VS Code with Co-Pilot.
0:12:00 How did you arrive to VS Code and how did that lead to your journey with Cursor?
0:12:01 Yeah.
0:12:05 So I think a lot of us, all of us were originally fan users.
0:12:06 Pure fan.
0:12:07 Pure fan.
0:12:08 Yeah.
0:12:11 No neo fan, just pure fan and a terminal.
0:12:20 And at least for myself, it was around the time that Co-Pilot came out, so 2021, that
0:12:21 I really wanted to try it.
0:12:26 So I went into VS Code, the only platform, the only co-editor in which it was available.
0:12:33 And even though I really enjoyed using them, just the experience of Co-Pilot with VS Code
0:12:37 was more than good enough to convince me to switch.
0:12:41 And so that kind of was the default until we started working on Cursor.
0:12:43 And maybe we should explain what Co-Pilot does.
0:12:46 It’s like a really nice autocomplete.
0:12:50 It suggests as you start writing a thing, it suggests one or two or three lines how to
0:12:52 complete the thing.
0:12:57 And there’s a fun experience in that, you know, like when you have a close friendship
0:13:02 and your friend completes your sentences, like when it’s done well, there’s an intimate
0:13:03 feeling.
0:13:08 There’s probably a better word than intimate, but there’s a cool feeling of like, holy shit,
0:13:10 it gets me.
0:13:14 And then there’s an unpleasant feeling when it doesn’t get you.
0:13:19 And so there’s that kind of friction, but I would say for a lot of people, the feeling
0:13:21 that it gets me overpowers that it doesn’t.
0:13:25 And I think actually one of the underrated aspects of get up Co-Pilot is that even when
0:13:29 it’s wrong, it’s like a little bit annoying, but it’s not that bad because you just type
0:13:33 another character and then maybe then it gets you, or you type another character and then
0:13:34 it gets you.
0:13:35 So even when it’s wrong, it’s not that bad.
0:13:38 Yeah, you can sort of iterate and fix it.
0:13:43 I mean, the other underrated part of Co-Pilot for me sort of was just the first real, real
0:13:44 AI product.
0:13:47 It’s like the first language model consumer product.
0:13:52 So Co-Pilot was kind of like the first killer app for LMS.
0:13:53 Yeah.
0:13:55 And like the beta was out in 2021.
0:13:56 Right.
0:13:57 Okay.
0:14:00 So what’s the origin story of cursor?
0:14:05 So around 2020, the scaling loss papers came out from open AI.
0:14:10 And that was a moment where this looked like clear predictable progress for the field where
0:14:13 even if we didn’t have any more ideas, looks like you can make these models a lot better
0:14:16 if you had more compute and more data.
0:14:21 By the way, we’ll probably talk for three to four hours on the topic of scaling loss.
0:14:22 Yes.
0:14:27 But just to summarize, it’s a paper and a set of papers and set of ideas that say bigger
0:14:32 might be better for model size and data size in the realm of machine learning.
0:14:34 It’s bigger and better, but predictively better.
0:14:35 Okay.
0:14:36 That’s another topic of conversation.
0:14:37 Yeah.
0:14:40 So around that time, for some of us, there were like a lot of conceptual conversations
0:14:43 about what’s this going to look like?
0:14:46 What’s the story going to be for all these different knowledge worker fields about how
0:14:51 they’re going to be made better by this technology getting better?
0:14:56 And then I think there were a couple of moments where the theoretical gains predicted in that
0:15:00 paper started to feel really concrete and it started to feel like a moment where you
0:15:06 could actually go and not do a PhD if you wanted to work on, do useful work in AI.
0:15:10 I actually felt like now there was this whole set of systems one could build that were really
0:15:11 useful.
0:15:14 And I think that the first moment we already talked about a little bit, which was playing
0:15:15 with the early bit of copilot.
0:15:18 Like that was awesome and magical.
0:15:22 I think that the next big moment where everything kind of clicked together was actually getting
0:15:23 early access to GPT-4.
0:15:29 So it was sort of end of 2022 was when we were tinkering with that model.
0:15:32 And the step of incapability is felt enormous.
0:15:37 And previous to that, we had been working on a couple of different projects we had been
0:15:41 as a copilot because of scaling odds, because of our prior interests in the technology.
0:15:46 We had been tinkering around with tools for programmers, but things that are very specific.
0:15:52 So we were building tools for financial professionals who have to work within a Jupyter notebook
0:15:55 or playing around with, can you do static analysis with these models?
0:16:01 And then the stuff up in GPT-4 felt like, look, that really made concrete the theoretical
0:16:06 gains that we had predicted before felt like you could build a lot more just immediately
0:16:07 at that point in time.
0:16:13 And also, if we were being consistent, it really felt like this wasn’t just going to
0:16:14 be a point solution thing.
0:16:16 This was going to be all the programming that was going to flow through these models.
0:16:20 And it felt like that demanded a different type of programming environment, a different
0:16:22 type of programming.
0:16:26 And so we set off to build that sort of larger vision around that.
0:16:28 There’s one that I distinctly remember.
0:16:32 So my roommate is an IMO Gold winner.
0:16:35 There’s a competition in the US called the Putnam, which is sort of the IMO for college
0:16:36 people.
0:16:40 And it’s this math competition is exceptionally good.
0:16:50 So Sheng Tong and Amon, I remember, it’s sort of June of 2022, had this bet on whether
0:16:56 the like 2024 June or July, you were going to win a gold medal in the IMO with like
0:16:57 models.
0:16:59 IMO is International Math Olympiad.
0:17:00 Yeah.
0:17:02 IMO is International Math Olympiad.
0:17:05 And so Arvid and I are both also competed in it.
0:17:09 So it was sort of personal.
0:17:13 And I remember thinking, Matt, this is not going to happen.
0:17:21 This was like, even though I sort of believed in progress, I thought, IMO Gold, like Amon
0:17:22 is just delusional.
0:17:23 Yeah.
0:17:27 That was the, and to be honest, I mean, I was, to be clear, very wrong.
0:17:31 But that was maybe the most prescient bet in the group.
0:17:36 So the, the new results from DeepMind, it turned out that you were correct.
0:17:37 That’s what the–
0:17:38 Well, it was technically not.
0:17:41 Technically incorrect, but one point away.
0:17:43 Amon was very enthusiastic about this stuff.
0:17:48 And before Amon had this like scaling loss t-shirt that he would walk around with, he
0:17:51 had the like charts and like the formulas on it.
0:17:54 So you like felt the AGI, or you felt the scaling loss?
0:17:55 Yeah.
0:18:01 I distinctly remember there was this one conversation I had with Michael where before
0:18:05 I hadn’t thought super deeply and critically about scaling laws.
0:18:09 And he kind of posed the question, why isn’t scaling all you need?
0:18:12 Or why isn’t scaling going to result in massive gains in progress?
0:18:15 And I think I went through like the, like the stages of grief.
0:18:22 There is anger, denial, and then finally at the end, just thinking about it, acceptance.
0:18:29 And I think I’ve been quite hopeful and optimistic about progress since.
0:18:33 I think one thing I’ll caveat is, I think it also depends on like which domains you’re
0:18:34 going to see progress.
0:18:40 Like math is a great domain because especially like formal theorem proving because you get
0:18:44 this fantastic signal of actually verifying if the thing was correct.
0:18:47 And so this means something like RL can work really, really well.
0:18:52 And I think like you could have systems that are perhaps very superhuman to math and still
0:18:53 not technically have AGI.
0:18:54 Okay.
0:18:57 So can we take it all the way to cursor?
0:18:58 And what is cursor?
0:19:04 It’s a fork of VS code and VS code is one of the most popular editors for a long time.
0:19:06 Like everybody found love with it.
0:19:07 Everybody left Vim.
0:19:13 I left Emacs for, sorry.
0:19:19 So unified in some fundamental way, the developer community.
0:19:23 And then you look at the space of things, you look at the scaling laws, AI is becoming
0:19:24 amazing.
0:19:30 And you decided, okay, it’s not enough to just write an extension for your VS code because
0:19:32 there’s a lot of limitations to that.
0:19:37 We need, if AI is going to keep getting better, better, better, we need to really like rethink
0:19:40 how the AI is going to be part of the editing process.
0:19:45 And so you decided to fork VS code and start to build a lot of the amazing features we’ll
0:19:48 be able to talk about.
0:19:49 But what was that decision like?
0:19:55 Because there’s a lot of extensions, including co-pilot of VS code that are doing sort of
0:19:56 AI type stuff.
0:19:59 What was the decision like to just fork VS code?
0:20:05 So the decision to do an editor seemed kind of self-evident to us for at least what we
0:20:07 wanted to do and achieve.
0:20:10 Because when we started working on the editor, the idea was, these models are going to get
0:20:12 much better, their capabilities are going to improve, and it’s going to entirely change
0:20:16 what you build software, both in a, you will have big productivity gains, but also radical
0:20:20 and not like the active building software is going to change a lot.
0:20:25 And so you’re very limited in the control you have over a code editor if you’re a plug
0:20:28 into an existing coding environment.
0:20:31 And we didn’t want to get locked in by those limitations.
0:20:34 We wanted to be able to just build the most useful stuff.
0:20:35 Okay.
0:20:41 Well, then the natural question is, VS code is kind of with co-pilot a competitor.
0:20:43 So how do you win?
0:20:46 Is it basically just the speed and the quality of the features?
0:20:52 Yeah, I mean, I think this is a space that is quite interesting, perhaps quite unique
0:20:57 where if you look at previous tech waves, maybe there’s kind of one major thing that
0:21:00 happened and unlock a new wave of companies.
0:21:06 But every single year, every single model capability or jump you get model capabilities,
0:21:13 you now unlock this new wave of features, things that are possible, especially in programming.
0:21:18 And so I think in AI programming, being even just a few months ahead, let alone a year
0:21:22 ahead, makes your product much, much, much more useful.
0:21:28 I think the cursor a year from now will need to make the cursor of today look obsolete.
0:21:33 And I think, you know, Microsoft has done a number of like fantastic things, but I don’t
0:21:37 think they’re in a great place to really keep innovating and pushing on this in the way that
0:21:39 a startup can.
0:21:42 Just rapidly implementing features.
0:21:49 And push, yeah, like and kind of doing the research experimentation necessary to really
0:21:50 push the ceiling.
0:21:53 I don’t know if I think of it in terms of features as I think of it in terms of like
0:21:56 capabilities for programmers.
0:22:01 It’s that like, you know, as, you know, the new one model came out and I’m sure there
0:22:06 are going to be more models of different types, like longer context and maybe faster.
0:22:14 Like, there’s all these crazy ideas that you can try and hopefully 10% of the crazy ideas
0:22:17 will make it into something kind of cool and useful.
0:22:24 And we want people to have that sooner to rephrase it’s like an underrated fact is we’re
0:22:25 making it for ourselves.
0:22:30 When we started cursor, you really felt this frustration that, you know, models, you could
0:22:33 see models getting better.
0:22:35 The cobalt experience had not changed.
0:22:39 It was like, man, these guys like the ceiling is getting higher.
0:22:41 Like, why are they not making new things?
0:22:42 Like they should be making new things.
0:22:45 They should be like, like, where’s all the alpha features?
0:22:47 There were no alpha features.
0:22:51 It was like, I’m sure it was selling well.
0:22:55 I’m sure it was a great business, but it didn’t feel, I’m one of these people that
0:23:00 really want to try and use new things and it was just, there was no new thing for like
0:23:01 a very long while.
0:23:02 Yeah.
0:23:03 It’s interesting.
0:23:08 I don’t know how you put that into words, but when you compare a cursor with cobalt,
0:23:12 cobalt pretty quickly became, started to feel stale for some reason.
0:23:13 Yeah.
0:23:19 I think one thing that I think helps us is that we’re sort of doing it all in one where
0:23:23 we’re developing the UX and the way you interact with the model.
0:23:28 At the same time as we’re developing like how we actually make the model give better
0:23:33 answers, so like how you build up the prompt or like how do you find the context and for
0:23:36 a cursor tab, like how do you train the model.
0:23:41 So I think that helps us to have all of it, like sort of like the same people working
0:23:43 on the entire experience end to end.
0:23:44 Yeah.
0:23:47 It’s like the person making the UI and the person training the model like sit to like
0:23:50 18 feet away.
0:23:52 Often the same person even.
0:23:53 Yeah.
0:23:57 Often even the same person, so you can create things that are sort of not possible if you’re
0:24:00 not talking, you’re not experimenting.
0:24:03 And you’re using like you said cursor to write cursor.
0:24:04 Of course.
0:24:05 Oh, yeah.
0:24:06 Yeah.
0:24:07 Well, let’s talk about some of these features.
0:24:14 Let’s talk about the all knowing, the all powerful praise B to the tab, auto complete
0:24:17 on steroids basically.
0:24:18 So how does tab work?
0:24:19 What is tab?
0:24:24 To highlight and summarize at a high level, I’d say that there are two things that cursor
0:24:25 is pretty good at right now.
0:24:27 There are other things that it does.
0:24:31 But two things that it helps programmers with.
0:24:36 One is this idea of looking over your shoulder and being like a really fast colleague who
0:24:41 can kind of jump ahead of you and type and figure out what you’re going to do next.
0:24:47 And that was the original idea behind, that was kind of the kernel, the idea behind good
0:24:51 auto complete was predicting what you’re going to do next, but you can make that concept
0:24:56 even more ambitious by not just predicting the characters after your cursor, but actually
0:24:58 predicting the next entire change you’re going to make the next diff, the next place
0:25:01 you’re going to jump to.
0:25:07 And the second thing cursor is pretty good at right now too, is helping you sometimes
0:25:13 jump ahead of the AI and tell it what to do and go from instructions to code.
0:25:16 And on both of those, we’ve done a lot of work on making the editing experience for
0:25:21 those things ergonomic and also making those things smart and fast.
0:25:24 One of the things we really wanted was we wanted the model to be able to edit code for
0:25:25 us.
0:25:30 That was kind of a wish and we had multiple attempts at it before we had sort of a good
0:25:34 model that could edit code for you.
0:25:39 Then after we had a good model, I think there had been a lot of effort to make the inference
0:25:45 fast for having a good experience.
0:25:50 And we’ve been starting to incorporate, I mean, Michael sort of mentioned this like
0:25:52 ability to jump to different places.
0:25:57 And that jump to different places, I think, came from a feeling of, you know, once you
0:26:04 accept an edit, it’s like, man, it should be just really obvious where to go next.
0:26:08 It’s like, I’d made this change, the model should just now that like the next place to
0:26:11 go to is like 18 lines down.
0:26:18 If you’re a WIM user, you could press 18jj or whatever, but like, why am I doing this?
0:26:20 Like the model should just know it.
0:26:24 And then so the idea was, you know, you just press tab, it would go 18 lines down and then
0:26:28 make it show you the next edit and you would press tab.
0:26:31 So it was just you, as long as you could keep pressing tab.
0:26:35 And so the internal competition was how many tabs can we make someone press.
0:26:41 Once you have like the idea, more sort of abstractly, the thing to think about is sort
0:26:45 of like, how are the edits sort of zero entropy?
0:26:50 So once you’ve sort of expressed your intent and the edit is, there’s no like new bits
0:26:56 of information to finish your thought, but you still have to type some characters to
0:27:00 like make the computer understand what you’re actually thinking, then maybe the model should
0:27:07 just sort of read your mind and all the zero entropy bits should just be like tabbed away.
0:27:08 Yeah.
0:27:09 That was sort of the abstract.
0:27:12 And this is an interesting thing where if you look at language model loss on different
0:27:19 domains, I believe the bits per byte, which is kind of character normalize loss for code
0:27:23 is lower than language, which means in general, there are a lot of tokens in code that are
0:27:27 super predictable, a lot of characters that are super predictable.
0:27:32 And this is I think even magnified when you’re not just trying to autocomplete code, but
0:27:37 predicting what the user is going to do next in their editing of existing code.
0:27:41 And so, you know, the gold cursor tabs, let’s eliminate all the low entropy actions you
0:27:43 take inside of the editor.
0:27:47 When the intent is effectively determined, let’s just jump you forward in time, skip
0:27:48 you forward.
0:27:52 Well, what’s the intuition and what’s the technical details of how to do next cursor
0:27:54 prediction?
0:27:58 That jump, that’s not, that’s not so intuitive, I think to people.
0:27:59 Yeah.
0:28:03 I think I can speak to a few of the details on how to make these things work.
0:28:09 They’re incredibly low latency, so you need to train small models on this, on this task.
0:28:15 In particular, they’re incredibly pre-filled token hungry, what that means is they have
0:28:19 these really, really long prompts where they see a lot of your code, and they’re not actually
0:28:21 generating that many tokens.
0:28:27 And so the perfect fit for that is using a sparse model, meaning an MOE model.
0:28:31 So that was kind of one, one break, one break that we made that substantially improved performance
0:28:32 at longer context.
0:28:38 The other being a variant of speculative coding that we kind of built out called speculative
0:28:39 edits.
0:28:46 These are two, I think, important pieces of what make it quite high quality and very fast.
0:28:47 Okay.
0:28:51 So MOE, make sure of experts, the input is huge, the output is small.
0:28:52 Yeah.
0:28:53 Okay.
0:28:57 So what else can you say about how to make, is like caching play a role in this particular
0:29:02 model. Caching plays a huge role because you’re dealing with this many input tokens.
0:29:08 If every single keystroke that you’re typing in a given line, you had to rerun the model
0:29:13 on all of those tokens passed in, you’re just going to, one, significantly degrade latency,
0:29:16 two, you’re going to kill your GPUs with load.
0:29:22 So you need to design the actual prompts used for the model such that they’re caching
0:29:28 aware and then you need to reuse the KV cache across requests, just so that you’re spending
0:29:30 less work, less compute.
0:29:37 Again, what are the things that Tab is supposed to be able to do kind of in the near term,
0:29:46 just to sort of linger on that? Generate code, fill empty space, also edit code across multiple
0:29:51 lines and then jump to different locations inside the same file and then launch.
0:29:56 And hopefully jump to different files also. So if you make an edit in one file and maybe
0:30:03 you have to go to another file to finish your thought, it should go to the second file also.
0:30:09 The full generalization is like next action prediction. Sometimes you need to run a command
0:30:14 in the terminal and it should be able to suggest the command based on the code that you wrote
0:30:22 to. Or sometimes you actually need to, like it suggests something, but it’s hard for you
0:30:26 to know if it’s correct because you actually need some more information to learn. You need
0:30:29 to know the type to be able to verify that it’s correct.
0:30:33 So maybe it should actually take you to a place that’s like the definition of something
0:30:38 and then take you back so that you have all the requisite knowledge to be able to accept
0:30:39 the next completion.
0:30:47 Also providing the human the knowledge. Yes. Right. Can you integrate like, I just gone
0:30:53 to know a guy named Prime Jen who I believe has an SS, you can order coffee via SSH?
0:30:56 Oh yeah. We did that.
0:30:57 We did that.
0:31:04 So can that also the model do that? Like feed you and provide you with caffeine. Okay,
0:31:11 so that’s the general framework. Yeah. And the magic moment would be if it is programming
0:31:17 is this weird discipline where sometimes the next five minutes, not always, but sometimes
0:31:19 the next five minutes of what you’re going to do is actually predictable from the stuff
0:31:23 you’ve done recently. And so can you get to a world where that next five minutes either
0:31:27 happens by you disengaging and it taking you through or maybe a little bit more of just
0:31:30 you seeing next step what it’s going to do and you’re like, okay, that’s good. That’s
0:31:35 good. That’s good. And you can just sort of tap, tap, tap through these big changes.
0:31:39 As we’re talking about this, as you mentioned, one of the really cool and noticeable things
0:31:44 about cursor is that there’s this whole diff interface situation going on. So like the
0:31:50 model suggests with the red and the green of like, here’s how we’re going to modify
0:31:54 the code. And in the chat window, you can apply and it shows you the diff and you can
0:31:58 accept the diff. So maybe can you speak to whatever direction of that?
0:32:05 We’ll probably have like four or five different kinds of diffs. So we we have optimized the
0:32:11 diff for for the autocomplete. So that has a different diff interface than then when
0:32:16 you’re reviewing larger blocks of code. And then we’re trying to optimize another diff
0:32:23 thing for when you’re doing multiple different files. And sort of at a high level, the difference
0:32:30 is for when you’re doing autocomplete, it should be really, really fast to read. Actually,
0:32:35 it should be really fast to read in all situations. But in autocomplete, it’s sort of you’re really
0:32:40 like your eyes focused in one area, you can’t be in too many, the humans can’t look in
0:32:41 too many different places.
0:32:43 So you’re talking about on the interface side?
0:32:48 On the interface side. So it currently has this box on the side. So we have the current
0:32:53 box. And if you try to delete code in some place and tries to add other code, it tries
0:32:58 to show you a box on the side, maybe show it if we pull it up and cursor.com. This is
0:32:59 what we’re talking about.
0:33:05 So that that box, it was like three or four different attempts at trying to make this
0:33:12 this thing work, where first the attempt was like these blue crossed out line. So before
0:33:18 it was a box on the side, it used to show you the code to delete by showing you like,
0:33:22 like Google Doc style, you would see like a line through it, then you would see the
0:33:28 new code. And that was super distracting. And then we tried many different, you know,
0:33:33 there was there was sort of deletions, there was trying to read highlight. Then the next
0:33:40 iteration of it, which is sort of funny, would you would hold the on Mac, the option button.
0:33:44 So it would, it would sort of highlight a region of code to show you that there might
0:33:51 be something coming. So maybe in this example, like the input and the value would get would
0:33:57 all get blue. And the blue was to highlight that the AI had a suggestion for you. So instead
0:34:01 of directly showing you the thing, it would show you that the AI, it would just hint that
0:34:05 the AI had a suggestion. And if you really wanted to see it, you would hold the option
0:34:11 button. And then you would see the new suggestion. And if you release the option button, you
0:34:14 would then see your original code.
0:34:17 So that’s, by the way, that’s pretty nice, but you have to know to hold the option button.
0:34:18 Yeah.
0:34:21 So by the way, I’m not a Mac user, but I got it.
0:34:25 It was, it was a button, I guess, you people have.
0:34:29 It’s, you know, it’s again, it’s just, it’s just not intuitive. I think that’s the, that’s
0:34:30 the key thing.
0:34:33 And there’s a chance this, this is also not the final version of it.
0:34:41 I am personally very excited for making a lot of improvements in this area. Like we often
0:34:48 talk about it as the verification problem where these diffs are great for small edits,
0:34:55 for large edits, or like when it’s multiple files or something, it’s actually a little
0:34:59 bit prohibitive to, to review these diffs.
0:35:04 So there are like a couple of different ideas here. Like one idea that we have is, okay,
0:35:09 you know, like parts of the diffs are important. They have a lot of information. And then parts
0:35:15 of the diff are just very low entropy. They’re like example, like the same thing over and
0:35:16 over again.
0:35:20 And so maybe you can highlight the important pieces and then gray out the not so important
0:35:26 pieces. Or maybe you can have a model that looks at the diff and sees, oh, there’s a
0:35:31 likely bug here. I will like mark this with a little red squiggly and say like, you should
0:35:37 probably like review this part of the diff. And ideas in that vein, I think are exciting.
0:35:43 Yeah, that’s a really fascinating space of like UX design engineering. So you’re basically
0:35:49 trying to guide the human programmer through all the things they need to read and nothing
0:35:50 more.
0:35:51 Yeah.
0:35:52 Like optimally.
0:35:59 I want an intelligent model to do it. Like currently diff algorithms are, they’re like,
0:36:05 they’re just like normal algorithms. There is no intelligence. There’s like intelligence
0:36:09 that went into designing the algorithm, but then there is no like you don’t care if it’s
0:36:13 about this thing or this thing, as you want a model to do this.
0:36:20 So I think the general question is like, Matt, these models are going to get much smarter.
0:36:26 As the models get much smarter, the changes they will be able to propose are much bigger.
0:36:29 So as the changes gets bigger and bigger and bigger, the humans have to do more and more
0:36:34 and more verification work. It gets more and more and more hard. Like just you need, you
0:36:41 need to help them out. It’s sort of, I don’t want to spend all my time reviewing code.
0:36:46 Can you say a little more across multiples files, diff?
0:36:51 Yeah. I mean, so GitHub tries to sell this, right? With code review. When you’re doing
0:36:57 code review, you’re reviewing multiple diffs across multiple files. But like Arvid said
0:37:02 earlier, I think you can do much better than code review. You know, code review kind of
0:37:07 sucks. Like you spend a lot of time trying to grok this code that’s often quite unfamiliar
0:37:14 to you. And it often like doesn’t even actually catch that many bugs. And I think you can
0:37:19 significantly improve that review experience using language models, for example, using
0:37:23 the kinds of tricks that Arvid had described of maybe pointing you towards the regions
0:37:31 that actually matter. I think also, if the code is produced by these language models
0:37:38 and it’s not produced by someone else, like the code review experience is designed for
0:37:44 both the reviewer and the person that produced the code. In the case where the person that
0:37:48 produced the code is a language model, you don’t have to care that much about their experience.
0:37:53 And you can design the entire thing around the reviewer, such that the reviewer’s job
0:38:00 is as fun, as easy, as productive as possible. And I think that that feels like the issue
0:38:06 with just kind of naively trying to make these things look like code review. I think you
0:38:09 can be a lot more creative and push the boundary on what’s possible.
0:38:15 Because one idea there is, I think, ordering matters. Generally, when you review a PR,
0:38:20 you have this list of files and you’re reviewing them from top to bottom. But actually, you
0:38:24 actually want to understand this part first, because that came logically first. And then
0:38:28 you want to understand the next part. And you don’t want to have to figure out that
0:38:32 yourself. You want a model to guide you through the thing.
0:38:36 And is the step of creation going to be more and more in natural language? Is the goal
0:38:38 versus with actual…
0:38:43 I think sometimes, I don’t think it’s going to be the case that all of programming will
0:38:48 be natural language. And the reason for that is, you know, if I’m programming with Swalla
0:38:54 and Swalla is at the computer and the keyboard. And sometimes, if I’m like driving, I want
0:39:00 to say to Swalla, hey, like implement this function. And that that works. And then sometimes
0:39:05 it’s just so annoying to explain to Swalla what I want him to do. And so I actually take
0:39:11 over the keyboard and I show him, I write like part of the example. And then it makes sense.
0:39:15 And that’s the easiest way to communicate. And so I think that’s also the case for AI.
0:39:19 Like sometimes the easiest way to communicate with AI will be to show an example and then
0:39:23 it goes and does the thing everywhere else. Or sometimes, if you’re making a website,
0:39:28 for example, the easiest way to show to the AI what you want is not to tell it what to
0:39:34 do, but, you know, drag things around or draw things. And yeah. And like maybe eventually
0:39:38 we will get to like brain machine interfaces or whatever and kind of like understand what
0:39:42 you’re thinking. And so I think natural language will have a place. I think it will not definitely
0:39:46 not be the way most people program most of the time.
0:39:52 I’m really feeling the AGI with this editor. It feels like there’s a lot of machine learning
0:39:57 going on underneath. Tell me about some of the ML stuff that makes it all work.
0:40:03 The cursor really works via this ensemble of custom models that we’ve trained alongside,
0:40:07 you know, the frontier models that are fantastic at the reasoning intense things. And so cursor
0:40:12 tab, for example, is a great example of where you can specialize this model to be even better
0:40:17 than even frontier models if you look at evals on the task we set it at. The other domain
0:40:22 which it’s kind of surprising that it requires custom models, but it’s kind of necessary
0:40:29 and works quite well is in apply. So I think these models are like the frontier models are
0:40:33 quite good at sketching out plans for code and generating like rough sketches of like
0:40:41 the change, but actually creating diffs is quite hard for frontier models for your training
0:40:49 models. Like you try to do this with sonnet with 01 any frontier model and it really messes
0:40:56 up stupid things like counting line numbers, especially in super, super large files. And
0:41:00 so what we’ve done to alleviate this is we let the model kind of sketch out this rough
0:41:06 code block that indicates what the change will be. And we train a model to then apply
0:41:13 that change to the file. And we should say that apply is the model looks at your code.
0:41:19 It gives you a really damn good suggestion of what new things to do. And the seemingly
0:41:26 for humans trivial step of combining the two you’re saying is not so trivial contrary to
0:41:32 popular perception. It is not a deterministic algorithm. Yeah. I think like you see shallow
0:41:38 copies of apply elsewhere. And it just breaks like most of the time because you think you
0:41:43 can kind of try to do some deterministic matching and then it fails, you know, at least 40%
0:41:51 of the time. And that just results in a terrible product experience. I think in general this
0:41:56 regime of you are going to get smarter and smarter models. And like so one other thing
0:42:03 that apply, it lets you do is it lets you use fewer tokens with the most intelligent models.
0:42:10 This is both expensive in terms of latency for generating all these tokens and cost.
0:42:16 So you can give this very, very rough sketch and then have your small models go and implement
0:42:20 it because it’s a much easier task to implement this very, very sketched out code. And I think
0:42:25 that this regime will continue where you can use smarter and smarter models to do the planning
0:42:30 and then maybe the implementation details can be handled by the less intelligent ones.
0:42:35 Perhaps you’ll have, you know, maybe 01, maybe it’ll be even more capable models given an
0:42:43 even higher level plan that is kind of recursively applied by Sonic and then the applied model.
0:42:47 Maybe we should talk about how to make it fast. I feel like fast is always an interesting
0:42:48 detail.
0:42:51 Yeah. How do you make it fast?
0:42:57 Yeah. So one big component of making it fast is speculative edits. So speculative edits
0:43:02 are a variant of speculative decoding and maybe be helpful to briefly describe speculative
0:43:08 decoding. With speculative decoding, what you do is you can kind of take advantage of the
0:43:14 fact that, you know, most of the time, and I’ll add the caveat that it would be when
0:43:22 you’re memory bound in language model generation. If you process multiple tokens at once, it
0:43:26 is faster than generating one token at a time. So this is like the same reason why if you
0:43:32 look at tokens per second with prompt tokens versus generated tokens, it’s much, much faster
0:43:39 for prompt tokens. So what we do is instead of using what speculative decoding normally
0:43:44 does, which is using a really small model to predict these draft tokens that your larger
0:43:50 model will then go in and verify. With code edits, we have a very strong prior of what
0:43:56 the existing code will look like, and that prior is literally the same exact code. So
0:44:01 what you can do is you can just feed chunks of the original code back into the model,
0:44:05 and then the model will just pretty much agree most of the time that, okay, I’m just going
0:44:10 to spit this code back out. And so you can process all of those lines in parallel, and
0:44:12 you just do this with sufficiently many chunks, and then eventually you’ll reach a point of
0:44:18 disagreement where the model will now predict text that is different from the ground truth
0:44:23 original code. It’ll generate those tokens, and then we kind of will decide after enough
0:44:29 tokens match the original code to restart speculating in chunks of code. What this actually ends
0:44:36 up looking like is just a much faster version of normal editing code. So it looks like a
0:44:41 much faster version of the model rewriting all the code. So we can use the same exact
0:44:48 interface that we use for diffs, but it will just stream down a lot faster.
0:44:53 And then the advantage is that while it’s streaming, you can just also start reviewing
0:45:01 the code before it’s done. So there’s no big loading screen. So maybe that is part of the
0:45:02 advantage.
0:45:05 So the human can start reading before the thing is done.
0:45:11 I think the interesting riff here is something like speculation is a fairly common idea nowadays.
0:45:16 It’s not only in language models. There’s obviously speculation in CPUs, and there’s
0:45:20 speculation for databases and speculation all over the place.
0:45:28 Let me ask this sort of the ridiculous question of which LLM is better at coding. GPT, Claude,
0:45:32 who wins in the context of programming? And I’m sure the answer is much more nuanced because
0:45:37 it sounds like every single part of this involves a different model.
0:45:46 Yeah, I think there’s no model that Pareto dominates others, meaning it is better in
0:45:55 all categories that we think matter. The categories being speed, ability to edit code, ability
0:45:59 to process lots of code, long context, you know, a couple of other things and kind of
0:46:01 coding capabilities.
0:46:06 The one that I’d say right now is just kind of net best is Sonic. I think this is a consensus
0:46:07 opinion.
0:46:12 Our one’s really interesting and it’s really good at reasoning. So if you give it really
0:46:18 hard programming interview style problems or lead code problems, it can do quite, quite
0:46:25 well on them. But it doesn’t feel like it kind of understands your rough intent as well as
0:46:28 Sonic does.
0:46:34 If you look at a lot of the other frontier models, one qualm I have is it feels like they’re
0:46:39 not necessarily over, I’m not saying they train on benchmarks, but they perform really
0:46:44 well on benchmarks relative to kind of everything that’s kind of in the middle. So if you try
0:46:47 it on all these benchmarks and things that are in the distribution of the benchmarks
0:46:51 they’re evaluated on, you know, they’ll do really well, but when you push them a little
0:46:56 bit outside of that, so I think the one that kind of does best at kind of maintaining that
0:47:00 same capability, like you kind of have the same capability in the benchmark as when you
0:47:03 try to instruct it to do anything with coding.
0:47:09 But another ridiculous question is the difference between the normal programming experience versus
0:47:14 what benchmarks represent? Like where do benchmarks fall short, do you think, when we’re evaluating
0:47:15 these models?
0:47:20 By the way, that’s like a really, really hard. It’s like critically important detail, like
0:47:28 how different benchmarks are versus like real coding. Where real coding, it’s not interview
0:47:35 style coding, it’s you’re doing these, you know, humans are saying like half broken English
0:47:41 sometimes and sometimes you’re saying like, oh, do what I did before. Sometimes you’re
0:47:48 saying, go add this thing and then do this other thing for me and then make this UI element
0:47:54 and then, you know, it’s just like a lot of things are sort of context dependent. You
0:47:58 really want to like understand the human and then do what the human wants as opposed to
0:48:04 sort of this, maybe the way to put it is sort of abstractly is the interview problems are
0:48:15 very well specified. They lean a lot on specification while the human stuff is less specified.
0:48:21 Yeah. I think that this benchmark question is both complicated by what, so I just mentioned,
0:48:27 and then also, what Amon was getting into is that even if you like, you know, there’s
0:48:31 this problem of like the skew between what can you actually model on a benchmark versus
0:48:34 real programming and that can be sometimes hard to encapsulate because it’s like real
0:48:39 programming is like very messy and sometimes things aren’t super well specified, what’s
0:48:44 correct or what isn’t. But then it’s also doubly hard because of this public benchmark
0:48:48 problem and that’s both because public benchmarks are sometimes kind of hill climbed on. Then
0:48:53 it’s like really, really hard to also get the data from the public benchmarks out of
0:48:59 the models. And so, for instance, like one of the most popular like agent benchmarks
0:49:05 sweet bench is really, really contaminated and the training data of these foundation
0:49:09 models. And so if you ask these foundation models to do a sweet bench problem, you actually
0:49:12 don’t give them the context of a code base. They can like hallucinate the right file pass.
0:49:18 They can hallucinate the right function names. And so it’s also just the public aspect of
0:49:19 these things is tricky.
0:49:25 Yeah, like in that case, it could be trained on the literal issues or pull requests themselves.
0:49:30 And maybe the labs will start to do a better job or they’ve already done a good job at
0:49:34 decontaminating those things, but they’re not going to emit the actual training data
0:49:38 of the repository itself. Like these are all like some of the most popular Python repositories
0:49:44 like Sympi is one example. I don’t think they’re going to handicap their models on Sympi and
0:49:50 all these popular Python repositories in order to get true evaluation scores on these benchmarks.
0:49:56 I think that given the dearths and benchmarks, there have been like a few interesting crutches
0:50:01 that places that build systems with these models or build these models actually use to
0:50:05 get a sense of are they going in the right direction or not. And in a lot of places,
0:50:09 people will actually just have humans play with the things and give qualitative feedback
0:50:14 on these like one or two of the foundation model companies, they have people who that’s
0:50:19 a big part of their role. And internally, we also qualitatively assess these models and
0:50:22 actually lean on that a lot in addition to like private evals that we have.
0:50:23 It’s like the vibe.
0:50:31 Yeah, the vibe benchmark, human benchmark. You pull in the humans to do a vibe check.
0:50:32 Yeah.
0:50:37 I mean, that’s kind of what I do like just like reading online forums and Reddit and
0:50:45 X just like, well, I don’t know how to properly load in people’s opinions because they’ll
0:50:51 say things like, I feel like Claude or GPT’s gotten dumber or something. They’ll say I
0:50:58 feel like and then I sometimes feel like that too. But I wonder if it’s the model’s problem
0:50:59 or mine.
0:51:06 Yeah, with Claude, there’s an interesting take I heard where I think AWS has different
0:51:14 chips. And I suspect they have slightly different numerics than Nvidia GPUs. And someone speculated
0:51:20 that Claude’s degraded performance had to do with maybe using the quantized version that
0:51:27 existed on AWS bedrock versus whatever was running on Anthropics GPUs.
0:51:31 I interviewed a bunch of people that have conspiracy theories. I’m glad we spoke to
0:51:32 this conspiracy theory.
0:51:40 Well, it’s not like conspiracy theory as much. Humans are humans and there’s these details
0:51:48 and you’re doing like these queasy monoflops and chips are messy and man, you can just
0:51:55 have bugs. Like bugs are, it’s hard to overstate how hard bugs are to avoid.
0:52:00 That’s the role of a good prompt in all of this. You will mention that benchmarks have
0:52:11 really structured, well-formulated prompts. What should a human be doing to maximize success?
0:52:15 And what’s the importance of what the humans? You wrote a blog post on, called it Prompt
0:52:16 Design.
0:52:23 Yeah, I think it depends on which model you’re using and all of them are slightly different
0:52:26 and they respond differently to different prompts.
0:52:35 But I think the original GPT-4 and the original sort of readable models last year, they were
0:52:40 quite sensitive to the prompts and they also had a very small context window.
0:52:46 And so we have all of these pieces of information around the codebase that would maybe be relevant
0:52:49 in the prompt. Like you have the docs, you have the files that you add, you have the
0:52:54 conversation history, and then there’s a problem like how do you decide what you actually
0:52:57 put in the prompt and when you have a limited space.
0:53:01 And even for today’s models, even when you have long context, filling out the entire
0:53:06 context window means that it’s slower. It means that sometimes the model actually gets
0:53:09 confused and some models get more confused than others.
0:53:14 And we have this one system internally that we call pre-empt, which helps us with that
0:53:26 a little bit. And I think it was built for the era before where we had 8,000 token context
0:53:33 windows. And it’s a little bit similar to when you’re making a website, you sort of,
0:53:38 you want it to work on mobile, you want it to work on a desktop screen, and you have
0:53:45 this dynamic information, which you don’t have, for example, if you’re designing a print
0:53:48 magazine, you know exactly where you can put stuff.
0:53:52 But when you have a website or when you have a prompt, you have these inputs, and then
0:53:55 you need to format them to always work. Even if the input is really big, then you might
0:53:58 have to cut something down.
0:54:02 And so the idea was, okay, let’s take some inspiration. What’s the best way to design
0:54:08 websites? Well, the thing that we really like is React and the declarative approach where
0:54:18 you use JSX in JavaScript, and then you declare, this is what I want, and I think this has
0:54:24 higher priority, or this has higher Z index than something else. And then you have this
0:54:30 rendering engine in web design, it’s like Chrome, and in our case, it’s a preempt renderer,
0:54:34 which then fits everything onto the page. And as you declare it, it will decide what
0:54:40 you want, and then it figures out what you want. And so we have found that to be quite
0:54:46 helpful. And I think the role of it has sort of shifted over time, where initially it was
0:54:52 to fit to these small context windows. Now it’s really useful because it helps us with
0:54:58 splitting up the data that goes into the prompt and the actual rendering of it. And so it’s
0:55:02 easier to debug because you can change the rendering of the prompt and then try it on
0:55:06 old prompts, because you have the raw data that went into the prompt. And then you can
0:55:11 see, did my change actually improve it for this entire eval set.
0:55:14 So do you literally prompt with JSX?
0:55:18 Yes, yes. So it kind of looks like React, there are components, we have one component
0:55:25 that’s a file component, and it takes in the cursor, usually there’s one line where the
0:55:29 cursor is in your file, and that’s probably the most important line because that’s one
0:55:32 you’re looking at. And so then you can give priorities, so like that line has the highest
0:55:38 priority, and then you subtract one for every line that is farther away. And then eventually
0:55:42 when it’s rendered, it figures out how many lines can actually fit in its centers around
0:55:43 that thing.
0:55:44 That’s amazing.
0:55:48 Yeah. And you can do like other fancy things where if you have lots of code blocks from
0:55:53 the entire code base, you could use retrieval and things like embedding and re-ranking scores
0:55:57 to add priorities for each of these components.
0:56:02 So should humans, when they ask questions, also use, try to use something like that?
0:56:07 Like would it be beneficial to write JSX in the problem? The whole idea is this should
0:56:09 be loose and messy.
0:56:15 I think our goal is kind of that you should just do whatever is the most natural thing
0:56:21 for you. And then we, our job is to figure out how do we actually like retrieve the relative
0:56:23 event thing so that your thing actually makes sense.
0:56:28 Well, this is sort of the discussion I had with Arvin of Proplexity is like his whole
0:56:33 idea is like you should let the person be as lazy as he wants.
0:56:39 But like, yeah, that’s a beautiful thing. But I feel like you’re allowed to ask more
0:56:41 of programmers, right?
0:56:45 So like if you say just do what you want, I mean, humans are lazy. There’s a kind of
0:56:52 tension between just being lazy versus like provide more as be prompted almost like the
0:56:59 system pressuring you or inspiring you to be articulate not in terms of the grammar
0:57:05 of the sentences, but in terms of the depth of thoughts that you convey inside the problems.
0:57:12 I think even as a system gets closer to some level of perfection, often when you ask the
0:57:17 model for something, you just are not not enough intent is conveyed to know what to
0:57:22 do. And there are like a few ways to resolve that intent. One is the simple thing of having
0:57:28 models just ask you, I’m not sure how to do these parts based on your query. Could you
0:57:29 clarify that?
0:57:38 I think the other could be maybe if you there are five or six possible generations, given
0:57:42 the uncertainty present in your query so far, why don’t we just actually show you all those
0:57:44 and let you pick them?
0:57:53 How hard is it to, for the model to choose to speak, talk back? Sort of versus, it’s
0:58:00 hard to sort of like how to deal with the uncertainty. Do I do I choose to ask for more information
0:58:02 to reduce the ambiguity?
0:58:09 So I mean, one of the things we do is it’s like a recent addition is try to suggest files
0:58:18 that you can add. So while you’re typing, one can guess what the uncertainty is and maybe
0:58:27 suggest that like, you know, maybe maybe you’re writing your API. And we can guess using the
0:58:34 commits that you’ve made previously in the same file that the client and the server is
0:58:41 super useful. And there’s like a hard technical problem of how do you resolve it across all
0:58:46 commits, which files are the most important given your current prompt. And we’re still
0:58:53 sort of initial versions ruled out. And I’m sure we can make it much more accurate. It’s
0:58:54 very experimental.
0:58:58 But then the idea is we show you like, do you just want to add this file, this file, this
0:59:04 file also, to tell, you know, the model to edit those files for you. Because if maybe
0:59:08 you’re making the API, like, you should also edit the client and the server that is using
0:59:13 the API and the other one resolving the API. And so that will be kind of cool as both there’s
0:59:18 the phase where you’re writing the prompt and there’s before you even click enter, maybe
0:59:20 we can help resolve some of the uncertainty.
0:59:25 To what degree do you use agentic approaches? How use for our agents?
0:59:33 We think agents are really, really cool. Like, I think agents is like, it’s like you resemble
0:59:37 sort of like a human, it’s sort of like the things like you can kind of feel that it,
0:59:43 like you’re getting closer to AGI because you see a demo where it acts as a human would
0:59:53 and and it’s really, really cool. I think agents are not yet super useful for many things.
1:00:00 I think we’re getting close to where they will actually be useful. And so I think there
1:00:06 are certain types of tasks where having an agent would be really nice. Like, I would
1:00:10 love to have an agent, for example, if like we have a bug where you sometimes can’t command
1:00:17 C and command V inside our chat input box. And that’s a task that’s super well specified.
1:00:21 I just want to say like in two sentences, this does not work. Please fix it. And then
1:00:27 I would love to have an agent that just goes off, does it? And then a day later, I come
1:00:31 back and I reviewed the thing. You mean it goes finds the right file?
1:00:36 Yeah, it finds the right files, it like tries to reproduce the bug, it like fixes the bug
1:00:39 and then it verifies that it’s correct. And this is could be a process that takes a long
1:00:46 time. And so I think I would love to have that. And then I think a lot of programming,
1:00:52 like there is often this belief that agents will take off over all of programming. I don’t
1:00:57 think we think that that’s the case, because a lot of programming, a lot of the value is
1:01:02 in iterating, or you don’t actually want to specify something upfront, because you don’t
1:01:06 really know what you want until you’ve seen an initial version, and then you want to iterate
1:01:11 on that, and then you provide more information. And so for a lot of programming, I think you
1:01:15 actually want a system that’s instant, that gives you an initial version instantly back,
1:01:18 and then you can iterate super, super quickly.
1:01:24 What about something like that Rethink came out replete agent that does also like setting
1:01:28 up the development environment and installing software packages, configuring everything,
1:01:31 configuring the databases, and actually deploying the app?
1:01:32 Yeah.
1:01:36 Is that also in the set of the things you dream about?
1:01:40 I think so. I think that would be really cool. For certain types of programming, it would
1:01:41 be really cool.
1:01:43 Is that within scope of Cursor?
1:01:49 Yeah. We’re aren’t actively working on it right now, but it’s definitely like we want
1:01:56 to make the programmers life easier and more fun. And some things are just really tedious,
1:02:00 and you need to go through a bunch of steps, and you want to delegate that to an agent.
1:02:04 And then some things, you can actually have an agent in the background while you’re working,
1:02:08 like let’s say you have a PR that’s both back end and front end, and you’re working in the
1:02:12 front end, and then you can have a background agent that doesn’t work and figure out kind
1:02:17 of what you’re doing. And then when you get to the back end part of your PR, then you
1:02:23 have some initial piece of code that you can iterate on. And so that would also be really
1:02:25 cool.
1:02:30 One of the things we already talked about is speed. But I wonder if we can just link
1:02:36 around that some more in the various places that the technical details involved in making
1:02:41 this thing really fast. So every single aspect of Cursor, most aspects of Cursor feel really
1:02:46 fast. Like I mentioned, the apply is probably the slowest thing. And for me, I’m sorry,
1:02:47 the pain.
1:02:53 I know. It’s a pain. It’s a pain that we’re feeling and we’re working on fixing it.
1:02:58 Yeah. I mean, it says something that feels, I don’t know what it is, like one second or
1:03:04 two seconds. That feels slow. That means that actually shows that everything else is just
1:03:09 really, really fast. So is there some technical details about how to make some of these models,
1:03:14 how to make the chat fast, how to make the diffs fast? Is there something that just jumps
1:03:15 to mine?
1:03:18 Yeah. I mean, so we can go over a lot of the strategies that we use. One interesting thing
1:03:28 is cache warming. And so what you can do is if as the user is typing, you can have, you’re
1:03:33 probably going to use some piece of context. And you can know that before the user is done
1:03:39 everything. So as we discussed before, reusing the KV cache results in lower latency, lower
1:03:44 costs, cross requests. So as the user starts typing, you can immediately warm the cache
1:03:50 with like, let’s say the current file contents. And then when they’ve pressed enter, there’s
1:03:55 very few tokens. It actually has to prefill and compute before starting a generation.
1:03:57 This will significantly lower TTFD.
1:03:59 Can you explain how KV cache works?
1:04:09 Yeah. So the way transformers work, one of the mechanisms that allow transformers to
1:04:14 not just independently, like the mechanism that allows transformers to not just independently
1:04:19 look at each token, but see previous tokens are the keys and values to tension. And generally
1:04:25 the way attention works is you have at your current token some query. And then you’ve
1:04:30 all the keys and values of all your previous tokens, which are some kind of representation
1:04:37 that the model stores internally of all the previous tokens in the prompt. And like by
1:04:43 default, when you’re doing a chat, the model has to, for every single token, do this forward
1:04:48 pass through the entire model. That’s a lot of matrix multiplies that happen. And that
1:04:49 is really, really slow.
1:04:54 Instead, if you have already done that, and you stored the keys and values, and you keep
1:04:59 that in the GPU, then when I’m, let’s say I have sorted for the last end tokens, if
1:05:06 I now want to compute the output token for the N plus one token, I don’t need to pass
1:05:11 those first end tokens through the entire model because I already have all those keys
1:05:16 and values. And so you just need to do the forward pass through that last token. And
1:05:21 then when you’re doing attention, you’re reusing those keys and values that have been computed,
1:05:26 which is the only kind of sequential part, or sequentially dependent part of the transformer.
1:05:31 Is there like higher level caching of like caching of the prompts or that kind of stuff
1:05:32 that could help?
1:05:40 I see. Yeah, there’s other types of caching you can kind of do. One interesting thing
1:05:48 that you can do for Cursor Tab is you can basically predict ahead as if the user would
1:05:54 have accepted the suggestion and then trigger another request. And so then you’ve cached,
1:05:57 you’ve done the speculative, it’s a mix of speculation and caching, right? Because you’re
1:06:03 speculating what would happen if they accepted it. And then you have this value that is cached,
1:06:07 this suggestion. And then when they pressed tab, the next one would be waiting for them
1:06:13 immediately. It’s a kind of clever heuristic slash trick that uses a higher level caching
1:06:20 and can give the, it feels fast, despite there not actually being any changes in the model.
1:06:25 And if you can make the KV cache smaller, one of the advantages you get is like, maybe
1:06:29 you can speculate even more. Maybe you can guess, here’s the 10 things that could be
1:06:36 useful. Like, you predict the next 10, and then it’s possible the user hits the one
1:06:40 of the 10. It’s like much higher chance than the user hits like the exact one that you
1:06:45 show them. Maybe they type in other character and we sort of hit something else in the cache.
1:06:52 So there’s all these tricks where the general phenomena here is, I think it’s also super
1:07:00 useful for RAL is, you know, maybe a single sample from the model isn’t very good. But
1:07:07 if you predict like 10 different things, turns out that one of the 10, that’s right is the
1:07:12 probability is much higher. There’s these Passet K curves. And, you know, part of RAL,
1:07:19 like what RAL does is, you know, you can exploit this Passet K phenomena to make many different
1:07:26 predictions. And one way to think about this, the model sort of knows internally has like,
1:07:29 has some uncertainty over like, which of the K things is correct, or like, which of the
1:07:36 K things does the human want. So when we RAL are, you know, cursor tab model, one of the
1:07:44 things we’re doing is we’re predicting which like, which of the 100 different suggestions
1:07:48 the model produces is more amenable for humans. Like, which of them do humans more like than
1:07:53 other things? Maybe, maybe like, there’s something where the model can predict very far ahead
1:07:59 versus like a little bit and maybe somewhere in the middle and, and, you know, just, and
1:08:03 then you can give a reward to the things that humans would like more and sort of punish
1:08:06 the things that it won’t like and sort of then train the model to output the suggestions
1:08:09 that humans would like more. You have these like RAL loops that are very useful that exploit
1:08:14 these Passet K curves. Oman maybe can, can you go into even more detail?
1:08:21 Yeah, it’s a little, it is a little different than speed. But I mean, like, technically
1:08:24 you tie it back in because you can get away at the smaller model if you RAL your smaller
1:08:30 model and it gets the same performance as the bigger one. That’s like, and, and while
1:08:35 I was mentioning stuff about KB, about reducing the size of your KB cache, there are other
1:08:41 techniques there as well that are really helpful for speed. So kind of back in the day, like
1:08:47 all the way two years ago, people mainly use multi-hat attention. And I think there’s been
1:08:55 a migration towards more efficient attention schemes like group query or multi-query attention.
1:09:01 And this is really helpful for then with larger batch sizes, being able to generate the tokens
1:09:08 much faster. The interesting thing here is this now has no effect on that time to first
1:09:15 token pre-fill speed. The thing this matters for is now generating tokens. And, and why
1:09:21 is that? Because when you’re generating tokens, instead of being bottlenecked by doing the
1:09:26 super paralyzable matrix multiplies across all your tokens, you’re bottlenecked by how
1:09:32 quickly it’s for long context with large batch sizes by how quickly you can read those cache
1:09:38 keys and values. And so then how that’s memory bandwidth and how can we make this faster?
1:09:42 We can try to compress the size of these keys and values. So multi-query attention is the
1:09:47 most aggressive of these. Where normally with multi-hat attention, you have some number
1:09:57 of attention heads and some number of kind of query, query heads. Multi-query just preserves
1:10:03 the query heads, gets rid of all the key value heads. So there’s only one kind of key value
1:10:11 head and there’s all the remaining query heads. With group query, you instead, you know, preserve
1:10:19 all the query heads. And then your keys and values are kind of, there are fewer heads
1:10:23 for the keys and values, but you’re not reducing it to just one. But anyways, like the whole
1:10:26 point here is you’re just reducing the size of your KV cache.
1:10:28 And then there is the MLA.
1:10:35 Yeah, multi-latent. That’s a little more complicated. And the way that this works is it kind of
1:10:41 turns the entirety of your keys and values across all your heads into this kind of one
1:10:46 latent vector that is then kind of expanded inference time.
1:10:52 But MLA is from this company called DeepSeq. It’s quite an interesting algorithm. Maybe
1:11:00 the key idea is sort of in both MQA and in other places, what you’re doing is sort of
1:11:10 reducing the number of KV heads. And the advantage you get from that is there’s less of them,
1:11:16 but maybe the theory is that you actually want a lot of different, like you want each
1:11:23 of the keys and values to actually be different. So one way to reduce the size is you keep
1:11:28 one big shared vector for all the keys and values. And then you have smaller vectors
1:11:34 for every single token so that when you, you can store the only the smaller thing. There’s
1:11:38 some sort of like lower rank reduction. And the lower rank reduction, and at the end of
1:11:43 the time, when you eventually want to compute the final thing, remember that like your memory
1:11:47 bound, which means that like, you still have some compute left that you can use for these
1:11:55 things. And so if you can expand the latent vector back out, and somehow like, this is
1:12:01 far more efficient because you’re reducing like, for example, maybe like reducing like
1:12:04 32 or something, like the size of the vector that you’re keeping.
1:12:10 Yeah, there’s perhaps some richness and having a separate set of keys and values and query
1:12:16 that kind of pairwise match up versus compressing that all into one. And that interaction at
1:12:17 least.
1:12:25 Okay. And all of that is dealing with being memory bound. Yeah. And what I mean, ultimately,
1:12:29 how does that map to the user experience, trying to get the Yeah, the two things that
1:12:35 map to is you can now make your cash a lot larger, because you’ve less space allocated
1:12:38 for the KV cash, you need to cash a lot more aggressively and a lot more things. So you
1:12:44 get more cash hits, which are helpful for reducing the time to first token for the reasons
1:12:49 that were kind of described earlier. And then the second being when you start doing inference
1:12:53 with more and more requests and larger and larger batch sizes, you don’t see much of
1:12:58 a slowdown in as it’s generated the tokens, the speed of that,
1:13:02 it also allows you to make your prompt bigger for certain. Yeah. Yeah. So like the basic
1:13:08 the size of your KV cash is both the size of all your prompts multiplied by the number
1:13:11 of prompts being processed in parallel. So you could increase either those dimensions,
1:13:17 right, the batch size, or the size of your prompts without degrading the latency of generating
1:13:18 tokens.
1:13:23 Arvid, you wrote a blog post shadow the workspace iterating on code in the background. So what’s
1:13:29 going on? So to be clear, we want there to be a lot of stuff happening in the background
1:13:35 and we’re experimenting with a lot of things. Right now, we don’t have much of that happening,
1:13:40 other than like the cash warming or like, you know, figuring out the right context that
1:13:45 goes into your command key prompts, for example. But the idea is, if you can actually spend
1:13:53 computation in the background, then you can help the user maybe like, at a slightly longer
1:13:57 time horizon than just predicting the next few lines that you’re going to make. But actually,
1:14:02 like in the next 10 minutes, what are you going to make? And by doing it in the background,
1:14:08 you can spend more computation doing that. And so the idea of the shadow workspace that
1:14:15 we implemented, and we use it internally for like experiments, is that to actually get
1:14:20 advantage of doing stuff in the background, you want some kind of feedback signal to give
1:14:24 back to the model. Because otherwise, like you can get higher performance by just letting
1:14:29 the model think for longer. And so like, oh, one is a good example of that. But another
1:14:35 way you can improve performance is by letting the model iterate and get feedback. And so
1:14:41 one very important piece of feedback when you’re a programmer is the language server,
1:14:47 which is this thing it exists for most different languages, and there’s like a separate language
1:14:52 server per language. And it can tell you, you know, you’re using the wrong type here,
1:14:56 and then gives you an error, or it can allow you to go to definition and sort of understands
1:15:01 the structure of your code. So language servers are extensions developed by like there’s a
1:15:05 typescript language to be developed by the typescript people, a Rust language to be developed
1:15:09 by the Rust people, and then they all interface over the language server protocol to VS code.
1:15:13 So that VS code doesn’t need to have all of the different languages built into VS code,
1:15:17 but rather you can use the existing compiler infrastructure.
1:15:19 For linting purposes?
1:15:24 It’s for linting. It’s for going to definition. And if we’re like seeing the right types that
1:15:25 you’re using.
1:15:27 So it’s doing like type checking also?
1:15:32 Yes, type checking and going to references. And that’s like when you’re working in a big
1:15:38 project, you kind of need that. If you don’t have that, it’s like really hard to code in
1:15:39 a big project.
1:15:45 Can you say again how that’s being used inside cursor, the language server protocol communication
1:15:46 thing?
1:15:50 So it’s being used in cursor to show to the programmer just like in VS code. But then
1:15:57 the idea is you want to show that same information to the models, the I/O models. And you want
1:16:01 to do that in a way that doesn’t affect the user because you want to do it in background.
1:16:08 And so the idea behind the Chattel workspace was, okay, like one way we can do this is
1:16:14 we spawn a separate window of cursor that’s hidden. And so you can set this flag in an
1:16:15 electron that’s hidden.
1:16:19 There is a window, but you don’t actually see it. And inside of this window, the AI agents
1:16:24 can modify code however they want, as long as they don’t save it because it’s still the
1:16:29 same folder, and then can get feedback from the linters and go to definition and iterate
1:16:30 on their code.
1:16:36 So like literally run everything in the background? Like as if, right, maybe even run the code?
1:16:41 So that’s the eventual version. And that’s what you want. And a lot of the blog post
1:16:47 is actually about how do you make that happen? Because it’s a little bit tricky. You want
1:16:52 it to be on the user’s machine so that it exactly mirrors the user’s environment. And
1:16:57 then on Linux, you can do this cool thing where you can actually mirror the file system
1:17:04 and have the AI make changes to the files. And it thinks that it’s operating on the file
1:17:12 level. But actually, that’s stored in memory. And you can create this kernel extension to
1:17:21 make it work. Whereas on Mac and Windows, it’s a little bit more difficult. But it’s
1:17:23 a fun, technical problem in this way.
1:17:29 One maybe hacky but interesting idea that I like is holding a lock on saving. And so
1:17:33 basically you can then have the language model kind of hold the lock on saving to disk. And
1:17:38 then instead of you operating in the ground truth version of the files that are saved
1:17:41 to disk, you actually are operating what was the shadow workspace before and these unsaved
1:17:45 things that only exist in memory that you still get linter errors for. And you can code in.
1:17:49 And then when you try to maybe run code, it’s just like there’s a small warning that there’s
1:17:53 a lock. And then you kind of will take back the lock from the language server if you’re
1:17:56 trying to do things concurrently or from the shadow workspace if you’re trying to do things
1:17:57 concurrently.
1:18:02 That’s such an exciting future, by the way. It’s a bit of a tangent. But like to allow
1:18:09 a model to change files. It’s scary for people. But like it’s really cool to be able to just
1:18:16 like let the agent do a set of tasks and you come back the next day and kind of observe.
1:18:18 Like it’s a colleague or something like that.
1:18:23 And I think there may be different versions of like runability where for the simple things
1:18:27 where you’re doing things in the span of a few minutes on behalf of the user as they’re
1:18:32 coming, it makes sense to make something work locally in their machine. I think for the
1:18:36 more aggressive things, we’re making larger changes that take longer periods of time.
1:18:41 You’ll probably want to do this in some sandbox remote environment. And that’s another incredibly
1:18:47 tricky problem of how do you exactly reproduce or mostly reproduce to the point of it being
1:18:53 effectively equivalent for running code, the user’s environment, what does remote sandbox?
1:18:59 I’m curious what kind of agents you want for coding. Do you want them to find bugs? Do
1:19:03 you want them to like implement new features? Like what agents do you want?
1:19:08 So by the way, when I think about agents, I don’t think just about coding. I think so
1:19:13 for the practice, this particular podcast, there’s video editing and a lot of if you
1:19:18 look in Adobe, a lot of there’s code behind. It’s very poorly documented code, but you
1:19:25 can interact with Premiere, for example, using code and basically all the uploading everything
1:19:30 I do on YouTube, everything as you could probably imagine, I do all that through code and including
1:19:37 translation and overdubbing all this. So I envision all those kinds of tasks so automating
1:19:42 many of the tasks that don’t have to do directly with the editing. So that, okay, that’s what
1:19:49 I was thinking about. But in terms of coding, I would be fundamentally thinking about bug
1:19:56 finding, like many levels of kind of bug finding and also bug finding, like logical bugs, not
1:20:03 logical, like spiritual bugs or something. One’s like, sort of big directions of implementation,
1:20:04 that kind of stuff.
1:20:12 Yeah. I mean, it’s really interesting that these models are so bad at bug finding when
1:20:17 just naively prompted to find a bug. They’re incredibly poorly calibrated.
1:20:21 Even the smartest models, even 01.
1:20:25 How do you explain that? Is there a good intuition?
1:20:31 I think these models are really strong reflection of the pre-training distribution. And I do
1:20:36 think they generalize as the loss gets lower and lower. But I don’t think the loss and
1:20:42 the scale is quite, the loss is low enough such that they’re like really fully generalizing
1:20:47 in code. Like the things that we use these things for, the frontier models that they’re
1:20:53 quite good at are really code generation and question answering. And these things exist
1:20:57 in massive quantities in pre-training with all of the code on GitHub and the scale of
1:21:04 many, many trillions of tokens and questions and answers on things like Stack Overflow and
1:21:05 maybe GitHub issues.
1:21:12 And so when you try to push under these things that really don’t exist very much online,
1:21:16 like for example, the cursor tap objective of predicting the next edit given the edits
1:21:22 done so far, the brittleness kind of shows. And then bug detection is another great example
1:21:26 where there aren’t really that many examples of like actually detecting real bugs and then
1:21:33 proposing fixes and the models just kind of like really struggle at it. But I think it’s
1:21:36 a question of transferring the model like in the same way that you get this fantastic
1:21:43 transfer from pre-trained models just on code in general to the cursor tab objective, you’ll
1:21:48 see a very, very similar thing with generalized models that are really good at code to bug
1:21:51 detection. It just takes like a little bit of kind of nudging in that direction.
1:21:55 Like to be clear, I think they sort of understand code really well. Like while they’re being
1:22:02 pre-trained, like the representation that’s being built up like almost certainly like
1:22:07 somewhere in the stream, there’s the model knows that maybe there’s something sketchy
1:22:13 going on, right? It sort of has some sketchiness, but actually eliciting the sketchiness too.
1:22:20 Like actually like part of it is that humans are really calibrated on which bugs are really
1:22:25 important. It’s not just actually saying like there’s something sketchy. It’s like it’s
1:22:29 just a sketchy trivial. It’s the sketchy like you’re going to take the server down.
1:22:30 Yeah.
1:22:35 Like part of it is maybe the cultural knowledge of like why is the staff engineer, staff engineer,
1:22:40 the staff engineer is good because they know that three years ago, like someone wrote a
1:22:46 really sketchy piece of code that took the server down. And as opposed to like, as opposed
1:22:52 to maybe there’s like, you know, you just, this thing is like an experiment. So like
1:22:56 a few bugs are fine. Like you’re just trying to experiment and get the feel of the thing.
1:23:00 And so if the model gets really annoying when you’re writing an experiment, that’s really
1:23:04 bad. But if you’re writing something for super production, you’re like writing a database,
1:23:08 right? You’re writing code in Postgres or Linux or whatever, like your Linus Torvalds.
1:23:13 It’s sort of unacceptable to have even in that case. And just having the calibration
1:23:18 of like, how paranoid is the user? Like,
1:23:22 But even then, like if you’re putting in a maximum paranoia, it’s still just like doesn’t
1:23:23 quite get it.
1:23:24 Yeah. Yeah.
1:23:30 I mean, but this is hard for humans too, to understand what, which line of code is important,
1:23:35 which is not. Like you, I think one of your principles on a website says, if a code can
1:23:41 do a lot of damage, one should add a comment that say this, this, this line of code is,
1:23:42 is dangerous.
1:23:51 And all caps, 10 times, 10 times, no, you say like, for every single line of code inside
1:23:56 the function, you have to, and that’s quite profound. That says something about human
1:24:03 beings because the engineers move on, even the same person might just forget how it can
1:24:06 sink the Titanic, a single function. Like you don’t, you might not intuit that quite
1:24:09 clearly by looking at the single piece of code.
1:24:16 Yeah. And I think that, that one is also partially also for today’s AI models where if you actually
1:24:22 write dangerous, dangerous, dangerous in every single line, like the models will pay more
1:24:26 attention to that and will be more likely to find bucks in that region.
1:24:32 That’s actually just straight up a really good practice of a labeling code of how much
1:24:34 damage this can do.
1:24:39 Yeah. I mean, it’s controversial. Some people think it’s ugly, swallow it.
1:24:42 Well, I actually think it’s like, in fact, I actually think this is one of the things
1:24:48 that I learned from Arvid is, you know, like I sort of aesthetically, I don’t like it,
1:24:53 but I think there’s certainly something where like, it’s useful for the models and humans
1:24:59 just forget a lot. And it’s really easy to make a small mistake and cause like, bring
1:25:05 down, you know, like just bring down the server and like, like, of course we like test a lot
1:25:08 of whatever, but there’s always these things that you have to be very careful.
1:25:12 Yeah. Like with just normal dock strings, I think people will often just skim it when
1:25:18 making a change and think, oh, I know how to do this. And you kind of really need to
1:25:22 point it out to them so that that doesn’t slip through.
1:25:26 Yeah. You have to be reminded that you can do a lot of damage. That’s like, we don’t
1:25:31 really think about that. Like, you think about, okay, how do I figure out how this works so
1:25:35 I can improve it. You don’t think about the other direction that it could do this.
1:25:41 Until we have formal verification for everything, then you can do whatever you want and you
1:25:45 know for certain that you have not introduced a bug if the proof pass.
1:25:48 But concretely, what do you think that future would look like?
1:25:56 I think people will just not write tests anymore and the model will suggest, like you write
1:26:03 a function. The model will suggest a spec and you review the spec. And in the meantime,
1:26:08 smart reasoning model computes a proof that the implementation follows the spec. And I
1:26:10 think that happens for most functions.
1:26:13 Don’t you think this gets at a little bit, some of the stuff you were talking about earlier
1:26:18 with the difficulty of specifying intent for what you want with software? Where sometimes
1:26:22 it might be because the intent is really hard to specify, it’s also then going to be really
1:26:24 hard to prove that it’s actually matching whatever your intent is.
1:26:27 What do you think that spec is hard to generate?
1:26:35 Yeah, or just like for a given spec, maybe you can, I think there is a question of like
1:26:39 can you actually do the formal verification? Like is that possible? I think that there’s
1:26:41 like more to dig into there.
1:26:42 But then also-
1:26:43 Even if you have the spec?
1:26:44 If you have the spec-
1:26:45 How do you map the spec?
1:26:47 Even if you have the spec, is the spec written in natural language?
1:26:48 Yeah, how do you map the spec?
1:26:51 No, the spec would be formal.
1:26:56 I think that you care about things that are not going to be easily well specified in the
1:26:57 spec language.
1:26:58 I see, I see.
1:26:59 Yeah.
1:27:00 Yeah.
1:27:01 Maybe an argument against formal verification is all you need.
1:27:02 Yeah.
1:27:04 But there’s this massive docking-
1:27:07 Replacing something like unit tests, sure.
1:27:08 Yeah.
1:27:09 Yeah.
1:27:14 I think you can probably also evolve the spec languages to capture some of the things
1:27:18 that they don’t really capture right now.
1:27:21 But I don’t know, I think it’s very exciting.
1:27:25 And you’re speaking not just about like single functions, you’re speaking about entire code
1:27:26 bases.
1:27:30 I think entire code bases is harder, but that is what I would love to have.
1:27:32 And I think it should be possible.
1:27:38 Because you can even, there’s like a lot of work recently where you can prove formally
1:27:40 verified down to the hardware.
1:27:44 So like through the, you formally verify the C code and then you formally verify through
1:27:49 the GCC compiler and then through the very log down to the hardware.
1:27:53 And that’s like incredibly big system, but it actually works.
1:27:57 And I think big code bases are sort of similar in that they’re like multi-layered system.
1:28:02 And if you can decompose it and formally verify each part, then I think it should be possible.
1:28:05 I think the specification problem is a real problem, but.
1:28:07 How do you handle side effects?
1:28:12 Or how do you handle, I guess, external dependencies like calling the Stripe API?
1:28:14 Maybe Stripe would write a spec for their API.
1:28:18 But like, you can’t do this for everything, like, can you do this for everything you use?
1:28:22 Like, how do you, how do you do it for, if there’s a language, like maybe, maybe like
1:28:26 people will use language models as primitives in the programs they write and there’s like
1:28:27 a dependence on it.
1:28:29 And like, how, how do you now include that?
1:28:32 I think you might be able to prove, prove that still.
1:28:33 Prove what about language models?
1:28:40 I think if it feels possible that you could actually prove that a language model is aligned,
1:28:46 for example, or like, you can prove that it actually gives the, the right answer.
1:28:47 That’s the dream.
1:28:48 Yeah, that is.
1:28:54 I mean, that’s, if it’s possible, that’s your, I have a dream speech that will certainly
1:29:00 help with, you know, making sure your code doesn’t have bugs and making sure AI doesn’t
1:29:01 destroy all of human civilization.
1:29:08 So the, the full spectrum of AI safety to just bug finding, so you said the models struggle
1:29:09 with bug finding.
1:29:10 What’s the hope?
1:29:15 You know, my hope initially is, and I can let Michael, Michael chime in too, but it’s
1:29:21 like this, it should, you know, first help with the stupid bugs, like it should very
1:29:23 quickly catch the stupid bugs.
1:29:27 Like off by one error is like, sometimes you write something in a comment and do the other
1:29:28 way.
1:29:29 It’s like very common.
1:29:30 Like I do this.
1:29:33 I write like less than in a comment and like, I maybe write the greater than sorry or something
1:29:34 like that.
1:29:39 And the model is like, you look sketchy, like, you sure you want to do that?
1:29:41 But eventually it should be able to catch harder bugs too.
1:29:42 Yeah.
1:29:48 And I think that it’s also important to note that this is having good bug finding models
1:29:53 feels necessary to get to the highest reaches of having AI do more and more programming for
1:29:57 you where you’re going to, you know, if the AI is building more and more of the system
1:30:00 for you, you need to not just generate, but also verify.
1:30:04 And without that, some of the problems that we’ve talked about before with programming
1:30:08 with these models will just become untenable.
1:30:13 So it’s not just for humans, like you write a bug, I write a bug, find the bug for me,
1:30:18 but it’s also being able to verify the AI’s code and check it is really important.
1:30:19 Yeah.
1:30:20 And then how do you actually do this?
1:30:23 Like we have had a lot of contentious dinner discussions of how do you actually train a
1:30:24 bug model?
1:30:30 But one very popular idea is, you know, it’s kind of potentially easy to introduce a bug
1:30:31 than actually finding the bug.
1:30:36 And so you can train a model to introduce bugs in existing code.
1:30:43 And then you can train a reverse bug model, then that can find bugs using this synthetic
1:30:44 data.
1:30:46 So that’s like one example.
1:30:49 But yeah, there are lots of ideas for how to achieve this.
1:30:53 You can also do a bunch of work, not even at the model level of taking the biggest models
1:30:58 and then maybe giving them access to a lot of information that’s not just the code.
1:31:02 But it gets kind of a hard problem to like stare at a file and be like, where’s the bug?
1:31:04 And you know, that’s hard for humans often, right?
1:31:07 And so often you have to run the code and being able to see things like traces and step
1:31:09 through a debugger.
1:31:12 There’s another whole another direction where it like kind of tends toward that.
1:31:15 And it could also be that there are kind of two different product form factors here.
1:31:17 It could be that you have a really specialty model that’s quite fast.
1:31:20 That’s kind of running in the background and trying to spot bugs.
1:31:24 And it might be that sometimes sort of sort of Arvid’s earlier example about, you know,
1:31:27 some nefarious input box bug, it might be that sometimes you want to like, there’s, you
1:31:30 know, there’s a bug, you’re not just like checking hypothesis free, you’re like, this
1:31:31 is a problem.
1:31:35 I really want to solve it and you zap that with tons and tons and tons of compute and
1:31:39 you’re willing to put in like $50 to solve that bug or something even more.
1:31:41 Have you thought about integrating money into this whole thing?
1:31:46 Like I would pay probably a large amount of money for if you found a bug or even generated
1:31:50 code that I really appreciated, like I had a moment a few days ago when I started using
1:32:01 Cursor or generated a perfect, like perfect three functions for interacting with the
1:32:08 YouTube API to update captions and for localization like different in different languages.
1:32:11 The API documentation is not very good.
1:32:15 And the code across like if I Google that for a while, I couldn’t find exactly, there’s
1:32:19 a lot of confusing information and Cursor generated perfectly.
1:32:23 And I was like, I just said back, I read the code, I was like, this is correct, I tested
1:32:24 it is correct.
1:32:30 I was like, I want to tip on a button that goes, yeah, there’s $5.
1:32:34 One that’s really good just to support the company and support what the interface is.
1:32:41 And the others that probably send a strong signal like, good job, right, there’s as much
1:32:43 stronger signal than just accepting the code, right?
1:32:46 You just actually send like a strong good job.
1:32:53 But and for bug finding, obviously, like there’s a lot of people, you know, that would pay
1:32:58 a huge amount of money for a bug, like a bug bounty thing, right?
1:33:00 Is that you guys think about that?
1:33:03 Yeah, it’s a controversial idea inside the company.
1:33:10 I think it sort of depends on how much you believe in humanity almost, you know, like,
1:33:15 I think it would be really cool if like, you spend nothing to try to find a bug.
1:33:18 And if it doesn’t find a bug, you spend $0.
1:33:22 And then if it does find a bug, and you click accept, then it also shows like, in parentheses,
1:33:26 like $1 as you spend $1 to accept the bug.
1:33:30 And then of course there’s a worry like, okay, we spent a lot of computation, like maybe
1:33:32 people will just copy paste.
1:33:34 I think that’s a worry.
1:33:39 And then there is also the worry that like introducing money into the product makes it
1:33:43 like kind of, you know, like it doesn’t feel as fun anymore, like you have to like think
1:33:47 about money and all you want to think about is like the code.
1:33:52 And so maybe it actually makes more sense to separate it out and like you pay some fee
1:33:55 like every month, and then you get all of these things for free.
1:33:59 But there could be a tipping component, which is not like it costs us.
1:34:00 It still has that like dollar symbol.
1:34:06 I think it’s fine, but I also see the point where like, maybe you don’t want to introduce
1:34:07 it.
1:34:09 Yeah, I was gonna say the moment that feels like people do this is when they share it,
1:34:13 when they have this fantastic example, they just kind of share it with their friend.
1:34:16 There is also a potential world where there’s a technical solution to this like honor system
1:34:21 problem too, where if we can get to a place where we understand the output of the system
1:34:24 more, I mean, to the stuff we were talking about with like, you know, error checking
1:34:26 with the LSP and then also running the code.
1:34:30 But if you could get to a place where you could actually somehow verify, oh, I have fixed
1:34:35 the bug, maybe then the bounty system doesn’t need to rely on the honor system too.
1:34:40 How much interaction is there between the terminal and the code, like how much information
1:34:45 is gained from few, if you run the code in the terminal, like can you use, can you do
1:34:51 like a loop where it runs, runs the code and suggests how to change the code if the code
1:34:57 and runtime gives an error is right now, they’re separate worlds completely.
1:35:01 Like I know you can like do control K inside the terminal to help you write the code.
1:35:08 You can use terminal contacts as well inside of Jackman K kind of everything.
1:35:12 We don’t have the looping part yet, though we suspect something like this could make
1:35:13 a lot of sense.
1:35:16 There’s a question of whether it happens in the foreground too, or if it happens in
1:35:19 the background like what we’ve been discussing.
1:35:20 Sure.
1:35:21 The background is pretty cool.
1:35:24 Like we do running the code in different ways, plus there’s a database side to this,
1:35:29 which how do you protect it from not modifying the database, but okay.
1:35:32 I mean, there’s certainly cool solutions there.
1:35:41 There’s this new API that is being developed for it’s not an AWS, but you know, it certainly
1:35:42 is I think it’s in planet scale.
1:35:47 I don’t know planet scale was the first one to added it’s disability sort of add branches
1:35:53 to a database, which is like if you’re working on a feature and you want to test against
1:35:56 the prod database, but you don’t actually want to test against the prod database, you
1:35:59 could sort of add a branch to the database and the way they do that is to add a branch
1:36:04 to the right head log and there’s obviously a lot of technical complexity in doing it
1:36:05 correctly.
1:36:09 I guess database companies need new things to do.
1:36:14 They have good databases now.
1:36:19 And I think like Turbo Buffer, which is one of the databases we use as is going to add
1:36:29 hope maybe branching to the right head log and so maybe the AI agents will use branching
1:36:32 to like test against some branch.
1:36:36 And it’s sort of going to be a requirement for the database to like support branching
1:36:37 or something.
1:36:39 It’d be really interesting if you could branch a file system, right?
1:36:40 Yeah.
1:36:43 If you like everything needs branching, it’s like that.
1:36:44 Yeah.
1:36:45 Yeah.
1:36:48 It’s like that’s the problem with the multiverse, right?
1:36:50 If you branch and everything, that’s like a lot.
1:36:53 I mean, there’s obviously these like super clever algorithms to make sure that you don’t
1:36:58 actually use a lot of space or CPU or whatever.
1:36:59 Okay.
1:37:00 This is a good place to ask about infrastructure.
1:37:03 So you guys mostly use AWS?
1:37:04 What are some interesting details?
1:37:05 What are some interesting challenges?
1:37:08 Why did you choose AWS?
1:37:10 Why is AWS still winning?
1:37:11 Hashtag.
1:37:14 AWS is just really, really good.
1:37:15 It’s really good.
1:37:23 Like whenever you use an AWS product, you just know that it’s going to work.
1:37:28 Like it might be absolute hell to go through the steps to set it up.
1:37:30 Why is the interface so horrible?
1:37:32 Because it’s just so good.
1:37:33 It doesn’t need to.
1:37:34 It’s the nature of winning.
1:37:37 I think it’s exactly, it’s just nature of the winning.
1:37:38 Yeah.
1:37:39 Yeah.
1:37:41 But AWS, you can always trust like it will always work.
1:37:45 And if there is a problem, it’s probably your problem.
1:37:46 Yeah.
1:37:47 Okay.
1:37:52 Is there some interesting like challenges to, you guys have a pretty new startup to get
1:37:55 scaling to like, to so many people on.
1:37:56 Yeah.
1:38:02 I think that there, it has been an interesting journey, adding, you know, each extra zero
1:38:06 to the request per second, but you run into all of these with like, you know, the general
1:38:09 components you’re using for, for caching and databases, run into issues as you make things
1:38:10 bigger and bigger.
1:38:13 And now we’re at the scale where we get like, you know, into overflows on our tables and
1:38:15 things like that.
1:38:19 And then also there have been some custom systems that we’ve built, like for instance
1:38:25 our retrieval system for computing a semantic index of your code base and answering questions
1:38:29 about a code base that have continually, I feel like been one of the trickier things
1:38:30 to scale.
1:38:34 I have a few friends who are super, super senior engineers and one of their sort of
1:38:40 lines is like, it’s very hard to predict where systems will break when you scale them.
1:38:45 You can sort of try to predict in advance, like there’s always something weird that’s
1:38:50 going to happen when you add this extra zero and you thought you thought through everything,
1:38:52 but you didn’t actually think through everything.
1:39:01 But I think for that particular system, we’ve, so what the, for concrete details, the thing
1:39:08 we do is obviously we upload when like, we chunk up all of your code and then we send
1:39:12 up sort of the code for, for embedding and we embed the code.
1:39:18 And then we store the embeddings in a database, but we don’t actually store any of the code.
1:39:22 And then there’s reasons around making sure that we don’t introduce client bugs because
1:39:25 we’re very, very paranoid about client bugs.
1:39:34 We store much of the details on the server, like everything is sort of encrypted.
1:39:39 So one of the technical challenges is always making sure that the local index, the local
1:39:44 code base state is the same as the state that is on the server.
1:39:49 And the way, sort of technically we ended up doing that is, so for every single file,
1:39:52 you can sort of keep this hash.
1:39:56 And then for every folder, you can sort of keep a hash, which is the hash of all of
1:39:57 its children.
1:40:00 And you can sort of recursively do that until the top.
1:40:04 And why, why do something, something complicated?
1:40:07 One thing you could do is you could keep a hash for every file.
1:40:11 Then every minute you could try to download the hashes that are on the server, figure
1:40:13 out what are the files that don’t exist on the server.
1:40:17 Maybe you just created a new file, maybe you just deleted a file, maybe you checked out
1:40:23 a new branch and try to reconcile the state between the client and the server.
1:40:29 But that introduces like absolutely ginormous network overhead, both, both on the client
1:40:30 side.
1:40:35 I mean, nobody really wants us to hammer their Wi-Fi all the time if you’re using cursor.
1:40:39 But also like, I mean, it would introduce like ginormous overhead on the database.
1:40:47 I mean, it would sort of be reading this tens of terabyte database, sort of approaching
1:40:54 like 20 terabytes or something database like every second, that’s just, just kind of crazy.
1:40:56 You definitely don’t want to do that.
1:41:01 And what you do, you sort of, you just try to reconcile the single hash, which is at
1:41:02 the root of the project.
1:41:06 And then if, if something mismatches, then you go, you find where all the things disagree.
1:41:09 Maybe you look at the children and see if the hashes match and if the hashes don’t match,
1:41:13 go look at their children and so on, but you only do that in this scenario where things
1:41:14 don’t match.
1:41:16 And for most people, most of the time the hashes match.
1:41:20 So it’s a kind of like hierarchical reconciliation of hashes.
1:41:21 Yeah, something like that.
1:41:22 Yeah.
1:41:23 It’s called a Merkel tree.
1:41:24 Yeah.
1:41:25 Yeah.
1:41:28 This is cool to see that you kind of have to think through all these problems.
1:41:32 And I mean, the point of, like the reason it’s gotten hard is just because like the number
1:41:38 of people using it and, you know, some of your customers have really, really large code
1:41:43 bases to the point where, you know, we, we originally reordered our code base, which
1:41:48 is, which is big, but I mean, it’s just not the size of some company that’s been there
1:41:52 for 20 years and sort of has a ginormous number of files and you sort of want to scale that
1:41:53 across programmers.
1:41:58 There’s all these details where like building the simple thing is easy, but scaling it to
1:42:02 a lot of people, like a lot of companies is obviously a difficult problem, which is sort
1:42:06 of, you know, independent of actually, so that there’s part of this scaling our current
1:42:11 solution is also, you know, coming up with new ideas that obviously we’re working on,
1:42:14 but then, but then scaling all of that in the last few weeks, months.
1:42:15 Yeah.
1:42:18 And there are a lot of clever things, like additional things that, that go into this
1:42:24 indexing system. For example, the bottleneck in terms of costs is not storing things in
1:42:27 the vector database or the database. It’s actually embedding the code. And you don’t
1:42:32 want to re-embed the code base for every single person in a company that is using the same
1:42:37 exact code, except for maybe there’s a branch with a few different files or they’ve made
1:42:41 a few local changes. And so, because again, embeddings are the bottleneck you can do is
1:42:45 one clever trick and not have to worry about like the complexity of like dealing with branches
1:42:53 and, and the other databases where you just have some cash on the actual vectors computed
1:43:00 from the hash of a given chunk. And so this means that when the end person at a company
1:43:04 goes and embeds their code base, it’s, it’s really, really fast. And you do all this without
1:43:08 actually storing any code on our servers at all. No code data is stored. We just store
1:43:12 the vectors in the vector database and the vector cash.
1:43:18 What’s the biggest gains at this time you get from indexing the code base? I could just
1:43:23 out of curiosity, like what, what benefit do users have? It seems like longer term,
1:43:27 there’ll be more and more benefit. But in the short term, just asking questions of the
1:43:32 code base. What, what’s the use, what’s the usefulness of that?
1:43:39 I think the most obvious one is just you want to find out where something is happening in
1:43:44 your large code base. And you sort of have a fuzzy memory of, okay, I want to find the
1:43:49 place where we do X. But you don’t exactly know what to search for in a normal text search.
1:43:54 As you ask a chat, you hit command enter to ask with, with the code base chat. And then
1:43:58 very often it finds the right place that you were thinking of.
1:44:02 I think like you, like you mentioned, in the future, I think there’s only going to get
1:44:08 more and more powerful where we’re working a lot on improving the quality of our retrieval.
1:44:11 And I think the ceiling for that is really, really much higher than people give credit
1:44:12 for.
1:44:16 One question that’s good to ask here, have you considered and why haven’t you much
1:44:21 done sort of local stuff to where you can do the, it seems like everything we just discussed
1:44:25 is exceptionally difficult to do. To go, to go to the cloud, you have to think about
1:44:32 all these things with the caching and the, you know, large code base with a large number
1:44:35 of programmers are using the same code base. You have to figure out the puzzle of that.
1:44:41 A lot of it, you know, most software just does stuff, this heavy computational stuff
1:44:45 locally. Have you considered doing sort of embeddings locally?
1:44:49 Yeah, we thought about it. And I think it would be cool to do it locally. I think it’s
1:44:55 just really hard. And, and one thing to keep in mind is that, you know, some of our users
1:45:00 use the latest MacBook Pro. And, but most of our users, like more than 80% of our users
1:45:07 are in Windows machines, which, and many of them are not very powerful. And so local models
1:45:14 really only works on the, on the latest computers. And it’s also a big overhead to, to, to build
1:45:19 that in. And so even if we would like to do that, it’s currently not something that we
1:45:23 are able to focus on. And I think there are, there are some people that, that, that do that.
1:45:29 And I think that’s great. But especially as models get bigger and bigger and you want
1:45:34 to do fancier things with like bigger models, it becomes even harder to do it locally.
1:45:39 Yeah. And it’s not a problem with like weaker computers. It’s just that, for example, if
1:45:45 you’re some big company, you have big company code base, it’s just really hard to process
1:45:49 big company code base, even on the beefiest MacBook Pros. So even if it’s not even a
1:45:55 matter of like, if you’re, if you’re just like a student or something, I think if you’re
1:46:00 like the best programmer at a big company, you’re still going to have a horrible experience
1:46:05 if you do everything locally. You could, you could do edge and sort of scrape by, but like,
1:46:07 again, it wouldn’t be fun anymore.
1:46:10 Yeah. Like at approximate nearest neighbors and this massive code base is going to just
1:46:17 eat up your memory and your CPU. And that’s, and that’s just that. Like, let’s talk about
1:46:22 like also the modeling side where as I’ve already said, there are these massive headwinds
1:46:29 against local models where one thing seems to move towards MOEs, which like one benefit
1:46:35 is maybe there are more memory bandwidth bound, which plays in favor of local versus using
1:46:43 GPUs or using NVIDIA GPUs. But the downside is these models are just bigger in total.
1:46:47 And you know, they’re going to need to fit often not even on a single node of multiple
1:46:53 nodes. There’s no way that’s going to fit inside of even really good MacBooks. And I
1:46:59 think especially for coding, it’s not a question as much of like, does it clear some bar of
1:47:04 like the models good enough to do these things? And then like we’re satisfied, which may be
1:47:08 the case for other other problems and maybe where local models shine, but people are always
1:47:13 going to want the best, the most intelligent, the most capable things. And that’s going
1:47:17 to be really, really hard to run for almost all people locally.
1:47:22 Don’t you want the most capable model? Like you want, you want Sonya too?
1:47:23 And also with O1.
1:47:29 I like how you’re pitching me. Would you be satisfied with an inferior model? Listen,
1:47:34 I’m, yes, I’m one of those, but there’s some people that like to do stuff locally, especially
1:47:40 like really, there’s a whole obviously open source movement that kind of resists and it’s
1:47:46 good that they exist actually because you want to resist the power centers that are growing
1:47:47 are.
1:47:52 There’s actually an alternative to local models that I am particularly fond of. I think it’s
1:47:58 still very much in the research stage, but you could imagine to do homomorphic encryption
1:48:03 for language model inference. So you encrypt your input on your local machine, then you
1:48:10 send that up and then the server can use loss of computation. They can run models that you
1:48:14 cannot run locally on this encrypted data, but they cannot see what the data is. And
1:48:18 then they send back the answer and you decrypt the answer and only you can see the answer.
1:48:25 So I think that’s still very much researched and all of it is about trying to make the
1:48:30 overhead lower because right now the overhead is really big. But if you can make that happen,
1:48:36 I think that would be really, really cool and I think it would be really, really impactful
1:48:39 because I think one thing that’s actually kind of worrisome is that as these models
1:48:44 get better and better, they’re going to become more and more economically useful and so more
1:48:52 and more of the world’s information and data will flow through one or two centralized actors.
1:48:58 And then there are worries about there can be traditional hacker attempts, but it also
1:49:04 creates this kind of scary part where if all of the world’s information is flowing through
1:49:12 one node in plain text, you can have surveillance in very bad ways. And sometimes that will happen
1:49:18 for, you know, initially will be like good reasons like people will want to try to protect
1:49:23 against like bad actors using AI models in bad ways. And then you will add in some surveillance
1:49:27 code and then someone else will come in and, you know, you’re in a slippery slope and then
1:49:36 you start doing bad things with a lot of the world’s data. And so I’m very hopeful that
1:49:38 we can solve homomorphic encryption for language modeling.
1:49:42 Yeah, doing privacy preserving machine learning, but I would say like that’s the challenge
1:49:48 we have with all software these days. It’s like there’s so many features that can be
1:49:53 provided from the cloud and all this increasingly relying it and make our life awesome, but
1:49:56 there’s downsides and that’s why you rely on really good security to protect from basic
1:50:03 attacks. But there’s also only a small set of companies that are controlling that data,
1:50:07 you know, and they obviously have leverage and they could be infiltrated in all kinds
1:50:09 of ways. That’s the world we live in.
1:50:14 Yeah, I mean, the thing I’m just actually quite worried about is sort of the world where
1:50:21 means the entropic has this responsible scaling policy and so we’re on like the low ASLs,
1:50:26 which is the entropic security level or whatever, of the models, but as we get to like code
1:50:36 and code ASL 3, ASL 4, whatever models, which are sort of very powerful. But for mostly
1:50:41 reasonable security reasons, you would want to monitor all the prompts. But I think that’s
1:50:46 sort of reasonable and understandable where everyone is coming from, but Matt, it’d be
1:50:52 really horrible if all the world’s information is sort of monitored that heavily. It’s way
1:50:59 too centralized. It’s like this really fine line you’re walking where on the one side,
1:51:05 you don’t want the models to go rogue. On the other side, it’s humans. I don’t know
1:51:11 if I trust all the world’s information to pass through three model providers.
1:51:19 What do you think is different than cloud providers? Because I think a lot of this data would never
1:51:27 have gone to the cloud providers in the first place where this is often like you want to
1:51:31 give more data to the EIA models. You want to give personal data that you would never
1:51:38 have put online in the first place to these companies or to these models. And it also
1:51:47 centralizes control, where right now, for cloud, you can often use your own encryption
1:51:56 keys and it just can’t really do much. But here is just centralized actors that see the
1:51:59 exact plaintext of everything.
1:52:03 On the topic of context, that’s actually been a friction for me. When I’m writing code in
1:52:09 Python, there’s a bunch of stuff imported. You could probably intuit the kind of stuff
1:52:17 I would like to include in the context. How hard is it to auto figure out the context?
1:52:24 It’s tricky. I think we can do a lot better at computing the context automatically in
1:52:28 the future. One thing that’s important to notice, there are trade-offs with including
1:52:34 automatic context. The more context you include for these models, first of all, the slower
1:52:39 they are. And the more expensive those requests are, which means you can then do less model
1:52:44 calls and do less fancy stuff in the background. Also, for a lot of these models, they get
1:52:49 confused if you have a lot of information in the prompt. The bar for accuracy and for
1:52:57 relevance of the context you include should be quite high. But already we do some automatic
1:53:00 context in some places within the product. It’s definitely something we want to get a
1:53:03 lot better at.
1:53:11 I think that there are a lot of cool ideas to try there. Both on the learning better retrieval
1:53:16 systems, like better embedding models, better re-rankers, I think that there are also cool
1:53:21 academic ideas. Stuff we’ve tried out internally, but also the field is grappling with writ
1:53:26 large, about can you get language models to a place where you can actually just have the
1:53:31 model itself understand a new corpus of information. And the most popular, talked about version
1:53:34 of this is, can you make the context windows infinite? Then if you make the context windows
1:53:38 infinite, can you make the model actually pay attention to the infinite context? And
1:53:41 then after you can make it pay attention to the infinite context, to make it somewhat
1:53:45 feasible to actually do it, can you then do caching for that infinite context? You don’t
1:53:47 have to re-compute that all the time.
1:53:51 But there are other cool ideas that are being tried that are a little bit more analogous
1:53:56 to fine-tuning of actually learning this information and the weights of the model. And it might
1:54:01 be that you actually get sort of a qualitatively different type of understanding if you do it
1:54:04 more at the weight level than if you do it at the in-contact learning level. I think
1:54:08 the journey, the journey is still a little bit out on how this is all going to work in
1:54:13 the end. But in the interim, us as a company, we are really excited about better retrieval
1:54:16 systems and picking the parts of the code base that are most relevant to what you’re
1:54:18 doing. We could do that a lot better.
1:54:23 Like one interesting proof of concept for the learning this knowledge directly in the
1:54:31 weights is with VS Code. So we’re in a VS Code fork and VS Code, the code is all public.
1:54:36 So these models in pre-training have seen all the code. They’ve probably also seen questions
1:54:41 and answers about it. And then they’ve been fine-tuned in RLHF to be able to answer questions
1:54:45 about code in general. So when you ask it a question about VS Code, you know, sometimes
1:54:51 it’ll hallucinate, but sometimes it actually does a pretty good job at answering the question.
1:54:56 And I think like this is just by, it happens to be okay. But what if you could actually
1:55:02 like specifically train or post train a model such that it really was built to understand
1:55:08 this code base? It’s an open research question, one that we’re quite interested in. And then
1:55:12 there’s also uncertainty of like, do you want the model to be the thing that end to end is
1:55:16 doing everything, i.e., it’s doing the retrieval and its internals, and then kind of answering
1:55:22 the question, creating the code? Or do you want to separate the retrieval from the frontier
1:55:26 model where maybe, you know, you’ll get some really capable models that are much better
1:55:32 than like the best open source ones in a handful of months. And then you’ll want to separately
1:55:36 train a really good open source model to be the retriever, to be the thing that feeds
1:55:42 in the context to these larger models. Can you speak a little more to the post training
1:55:48 a model to understand the code base? What do you mean by that? Is this a synthetic data
1:55:54 direction? Yeah, I mean, there are many possible ways you could try doing it. There’s certainly
1:55:58 no shortage of ideas. It’s just a question of going in and like trying all of them and
1:56:04 being empirical about which one works best. You know, one very naive thing is to try to
1:56:11 replicate what’s done with the S code and these frontier models. So let’s like continue
1:56:14 pre-training, some kind of continued pre-training that includes general code data, but also
1:56:20 throws in a lot of the data of some particular repository that you care about. And then in
1:56:25 post training, meaning in let’s just start with instruction fine tuning, you have like
1:56:29 a normal instruction fine tuning data set about code, but you throw in a lot of questions
1:56:36 about code in that repository. So you could either get ground truth ones, which might
1:56:39 be difficult, or you could do what you kind of hinted at or suggested using synthetic
1:56:49 data, i.e. kind of having the model ask questions about various pieces of the code. So you kind
1:56:54 of take the pieces of the code, then prompt the model or have a model propose a question
1:56:59 for that piece of code, and then add those as instruction fine tuning data points. And
1:57:04 then in theory, this might unlock the model’s ability to answer questions about that code
1:57:05 base.
1:57:11 Let me ask you about OpenAI 01. What do you think is the role of that kind of test time
1:57:13 compute system in programming?
1:57:18 I think test time compute is really, really interesting. So there’s been the pre-training
1:57:24 regime, which will kind of, as you scale up the amount of data and the size of your model,
1:57:29 get you better and better performance, both on loss and then on downstream benchmarks,
1:57:35 and just general performance when we use it for coding or other tests. We’re starting
1:57:41 to hit a bit of a data wall, meaning it’s going to be hard to continue scaling up this
1:57:47 regime. And so scaling up test time compute is an interesting way of now increasing the
1:57:54 number of inference time flops that we use, but still getting like, as you increase the
1:57:59 number of flops you use inference time, getting corresponding improvements in the performance
1:58:02 of these models. Traditionally, we just had to literally train a bigger model that always
1:58:06 uses, that always used that many more flops. But now we could perhaps use the same size
1:58:12 model and run it for longer to be able to get an answer at the quality of a much larger
1:58:17 model. And so the really interesting thing I like about this is there are some problems
1:58:22 that perhaps require 100 trillion parameter model intelligence trained on 100 trillion
1:58:30 tokens. But that’s like maybe 1%, maybe like 0.1% of all queries. So are you going to spend
1:58:36 all of this effort, all this compute training a model that costs that much and then run
1:58:43 it so infrequently? It feels completely wasteful when instead you get the model that can, you
1:58:48 train the model that’s capable of doing the 99.9% of queries, then you have a way of inference
1:58:54 time running it longer for those few people that really, really want max intelligence.
1:59:00 How do you figure out which problem requires what level of intelligence? Is that possible
1:59:06 to dynamically figure out when to use GPT-4, when to use a small model and when you need
1:59:09 the 0.1?
1:59:14 I mean, yeah, that’s an open research problem, certainly. I don’t think anyone’s actually
1:59:20 cracked this model routing problem quite well. We’d like to, we have initial implementations
1:59:26 of this for things, for something like cursor tab. But at the level of like going between
1:59:33 4.0 sonnet to 0.1, it’s a bit trickier. There’s also questions like what level of intelligence
1:59:41 do you need to determine if the thing is too hard for the four level model. Maybe you need
1:59:46 the 0.1 level model. It’s really unclear.
1:59:51 You mentioned there’s a pre-training process, then there’s post-training, and then there’s
1:59:57 like test time compute that fair does sort of separate. Where’s the biggest gains?
2:00:02 Well, it’s weird because like test time compute, there’s like a whole training strategy needed
2:00:06 to get test time to compute to work. And the really, the other really weird thing about
2:00:13 this is no one, like outside of the big labs and maybe even just open AI, no one really
2:00:18 knows how it works. Like there’ve been some really interesting papers that show hints of
2:00:25 what they might be doing. And so perhaps they’re doing something with tree search using process
2:00:31 reward models. But yeah, I think the issue is we don’t quite know exactly what it looks
2:00:35 like. So it would be hard to kind of comment on like where it fits in. I would put it in
2:00:39 post-training, but maybe like the compute spent for this kind of forgetting test time
2:00:45 compute to work for a model is going to dwarf pre-training eventually.
2:00:50 So we don’t even know if 01 is using just like chain of thought, RL, we don’t know how they’re
2:00:53 using any of these. We don’t know anything.
2:00:57 It’s fun to speculate.
2:01:01 If you were to build a competing model, what would you do?
2:01:06 Yeah, so one thing to do would be, I think you probably need to train a process reward
2:01:11 model, which is so maybe we can get into reward models and outcome reward models versus process
2:01:16 reward models. Outcome reward models are the kind of traditional reward models that people
2:01:21 are trained for these four language models, language modeling. And it’s just looking at
2:01:24 the final thing. So if you’re doing some math problem, let’s look at that final thing you’ve
2:01:30 done, everything, and let’s assign a great how likely we think like what’s the reward
2:01:35 model for this outcome. Process reward models instead try to grade the chain of thought.
2:01:42 And so OpenAI had some preliminary paper on this I think last summer where they use human
2:01:47 labellers to get this pretty large several hundred thousand dataset of grading chains
2:01:48 of thought.
2:01:54 Ultimately, it feels like I haven’t seen anything interesting in the ways that people use process
2:02:02 reward models outside of just using it as a means of affecting how we choose between
2:02:06 a bunch of samples. So like what people do in all these papers is they sample a bunch
2:02:12 of outputs from the language model and then use the process reward models to grade all
2:02:16 those generations alongside maybe some other heuristics and then use that to choose the
2:02:18 best answer.
2:02:23 The really interesting thing that people think might work and people want to work is tree
2:02:28 search with these process reward models because if you really can grade every single step
2:02:34 of the chain of thought, then you can kind of branch out and explore multiple paths of
2:02:38 this chain of thought and then use these process reward models to evaluate how good is this
2:02:40 branch that you’re taking?
2:02:45 Yeah when the quality of the branch is somehow strongly correlated with the quality of the
2:02:49 outcome at the very end. It’s like you have a good model of knowing which branch to take.
2:02:52 So not just in the short term and like in the long term.
2:02:55 And like the interesting work that I think has been done is figuring out how to properly
2:03:01 train the process or the interesting work that has been open sourced in people I think
2:03:07 talk about is how to train the process reward models maybe in a more automated way. I could
2:03:12 be wrong here, could not be mentioning some papers. I haven’t seen anything super that
2:03:17 seems to work really well for using the process reward models creatively to do tree search
2:03:18 and code.
2:03:23 This is kind of an AI safety maybe a bit of a philosophy question. So open AI says that
2:03:27 they’re hiding the chain of thought from the user. And they’ve said that that was a difficult
2:03:33 decision to make. They instead of showing the chain of thought, they’re asking the model
2:03:37 to summarize the chain of thought. They’re also in the background saying they’re going
2:03:42 to monitor the chain of thought to make sure the model is not trying to manipulate the user,
2:03:46 which is a fascinating possibility. But anyway, what do you think about hiding the chain of
2:03:47 thought?
2:03:51 One consideration for open AI, and this is completely speculative, could be that they
2:03:56 want to make it hard for people to distill these capabilities out of their model. It
2:04:00 might actually be easier if you had access to that hidden chain of thought to replicate
2:04:05 the technology. Because that’s pretty important data like seeing the steps that the model took
2:04:06 to get to the final result.
2:04:08 So you could probably train on that also.
2:04:12 And there was sort of a mirror situation with this, with some of the large language model
2:04:20 providers, and also this is speculation. But some of these APIs used to offer easy access
2:04:25 to log probabilities for all the tokens that they’re generating. And also log probabilities
2:04:30 for the prompt tokens. And then some of these APIs took those away. And again, complete speculation.
2:04:35 But one of the thoughts is that the reason those were taken away is if you have access
2:04:39 to log probabilities, similar to this hidden chain of thought, that can give you even more
2:04:44 information to try and distill these capabilities out of the APIs, out of these biggest models,
2:04:46 and to models you control.
2:04:54 As an asterisk on also the previous discussion about us integrating O1, I think that we’re
2:04:55 still learning how to use this model.
2:05:01 So we made O1 available in Cursor because when we got the model, we were really interested
2:05:05 in trying it out. I think a lot of programmers are going to be interested in trying it out.
2:05:13 But O1 is not part of the default Cursor experience in any way up. And we still haven’t found
2:05:21 a way to yet integrate it into the editor in a way that we reach for every hour, maybe
2:05:22 even every day.
2:05:30 And so I think the jury is still out on how to use the model. And we haven’t seen examples
2:05:35 yet of people releasing things where it seems really clear like, oh, that’s like now the
2:05:36 use case.
2:05:40 The obvious one to turn to is maybe this can make it easier for you to have these background
2:05:46 things running, to have these models in loops, to have these models be agentic. But we’re
2:05:48 still discovering.
2:05:54 To be clear, we have ideas. We just need to try and get something incredibly useful before
2:05:56 we put it out there.
2:06:04 But it has these significant limitations. Even barring capabilities, it does not stream.
2:06:08 And that means it’s really, really painful to use for things where you want to supervise
2:06:13 the output. And instead you’re just waiting for the wall text to show up.
2:06:17 Also, it does feel like the early innings of test time computing search where it’s just
2:06:25 like a very, very much of v0. And there’s so many things that don’t feel quite right.
2:06:32 And I suspect in parallel to people increasing the amount of pre-training data and the size
2:06:36 of the models and pre-training and finding tricks there, you’ll now have this other thread
2:06:40 of getting searched to work better and better.
2:06:50 So let me ask you about strawberry tomorrow eyes. So it looks like GitHub co-pilot might
2:06:56 be integrating 01 in some kind of way. And I think some of the comments are saying, does
2:07:01 this mean cursor is done? I think I saw one comment saying that.
2:07:03 I thought, time to shut down cursor.
2:07:05 Time to shut down cursor. Thank you.
2:07:07 So is it time to shut down cursor?
2:07:14 I think this space is a little bit different from past software spaces over the 2010s where
2:07:18 I think that the ceiling here is really, really, really incredibly high. And so I think that
2:07:22 the best product in three to four years will just be soon much more useful than the best
2:07:30 product today. And you can wax poetic about Mote’s this and brand that. And this is our
2:07:34 advantage. But I think in the end, just if you don’t have, like if you stop innovating
2:07:40 on the product, you will lose. And that’s also great for startups. That’s great for
2:07:44 people trying to enter this market because it means you have an opportunity to win against
2:07:50 people who have, you know, lots of users already by just building something better.
2:07:55 And so I think, yeah, over the next few years, it’s just about building the best product,
2:08:01 building the best system, and that both comes down to the modeling engine side of things.
2:08:03 And it also comes down to the editing experience.
2:08:08 Yeah. I think most of the additional value from cursor versus everything else out there
2:08:15 is not just integrating the new model fast, like a one, it comes from all of the kind
2:08:19 of depth that goes into these custom models that you don’t realize are working for you
2:08:25 in kind of every facet of the product, as well as like the really thoughtful UX with
2:08:27 every single feature.
2:08:32 All right. From that profound answer, let’s descend back down to the technical. You mentioned
2:08:34 you have a taxonomy of synthetic data.
2:08:35 Oh, yeah.
2:08:36 Can you please explain?
2:08:43 Yeah. I think there are three main kinds of synthetic data. The first is so what is synthetic
2:08:49 data first? So there’s normal data, like non-synthetic data, which is just data that’s naturally
2:08:55 created, i.e. usually it’ll be from humans having done things. So from some human process,
2:09:01 you get this data. Synthetic data, the first one would be distillation. So having a language
2:09:08 model kind of output tokens or probability distributions over tokens. And then you can
2:09:14 train some less capable model on this. This approach is not going to get you a net like
2:09:19 more capable model than the original one that has produced the tokens. But it’s really useful
2:09:24 for if there’s some capability you want to elicit from some really expensive high latency
2:09:31 model, you can then distill that down into some smaller task specific model. The second
2:09:40 kind is when one direction of the problem is easier than the reverse. And so a great
2:09:46 example of this is bug detection, like we mentioned earlier, where it’s a lot easier
2:09:51 to introduce reasonable looking bugs than it is to actually detect them. And this is
2:09:58 probably the case for humans too. And so what you can do is you can get a model that’s not
2:10:02 training that much data that’s not that smart to introduce a bunch of bugs and code. And
2:10:06 then you can use that to then train, use a synthetic data to train a model that can be
2:10:12 really good at detecting bugs. The last category I think is I guess the main one that feels
2:10:19 like the big labs are doing for synthetic data, which is producing text with language
2:10:26 models that can then be verified easily. So like, you know, an extreme example of this
2:10:31 is if you have a verification system that can detect if language is Shakespeare level
2:10:36 and then you have a bunch of monkeys typing in typewriters, like you can eventually get
2:10:39 enough training data to train a Shakespeare level language model. And I mean, this is
2:10:45 the case, like very much the case for math, where verification is actually really, really
2:10:52 easy for formal, formal languages. And then what you can do is you can have an OK model
2:10:58 generate a ton of rollouts, and then choose the ones that you know have actually proved
2:11:01 the ground truth here at theorems and train that further. There’s similar things you can
2:11:07 do for code with Lee code like problems, or where if you have some set of tests that you
2:11:12 know correspond to if something passes these tests, it is actually solved the problem.
2:11:14 You could do the same thing where we verify that it’s past the test and then train the
2:11:19 model in the output set of past the tests. I think it’s gonna be a little tricky getting
2:11:26 this to work in all domains or just in general, like having the perfect verifier feels really,
2:11:31 really hard to do with just like open-ended miscellaneous tasks. You get the model or
2:11:35 more like long horizon tasks, even in coding.
2:11:40 That’s because you’re not as optimistic as Arvid. But yeah. So yeah, so that third category
2:11:43 requires having a verifier.
2:11:46 Yeah. Verification is, it feels like it’s best when you know for a fact that it’s correct.
2:11:52 And like, then it wouldn’t be like using a language model to verify. It would be using
2:11:54 tests or formal systems.
2:11:59 Or running the thing too, doing like the human form of verification where you just do manual
2:12:01 quality control.
2:12:02 Yeah.
2:12:04 But like the language model version of that where it’s like running the thing and it actually
2:12:05 understands the output.
2:12:06 Yeah.
2:12:07 No, that’s true.
2:12:08 For somewhere between.
2:12:14 Yeah. I think that’s the category that is most likely to result in like massive gains.
2:12:22 What about RL with feedback side, RLHF versus RLAIF? What’s the role of that in getting
2:12:25 better performance on the models?
2:12:36 Yeah. So RLHF is when the reward model you use is trained from some labels you’ve collected
2:12:38 from humans giving feedback.
2:12:45 I think this works if you have the ability to get a ton of human feedback for this kind
2:12:47 of task that you care about.
2:12:54 RLAIF is interesting because you’re kind of depending on, like this is actually kind
2:13:01 of going to, it’s depending on the constraint that verification is actually a decent bit
2:13:05 easier than generation because it feels like, okay, like what are you doing? You’re using
2:13:08 this language model to look at the language model outputs and then improve the language
2:13:09 model.
2:13:15 But no, it actually may work if the language model has a much easier time verifying some
2:13:20 solution than it does generating it. Then you actually could perhaps get this kind of recursive,
2:13:23 but I don’t think it’s going to look exactly like that.
2:13:30 The other thing you could do is that we kind of do is like a little bit of a mix of RLAIF
2:13:34 and RLHF where usually the model is actually quite correct. And this is in the case of
2:13:40 the cursor tab at picking between like two possible generations of what is the better
2:13:45 one. And then it just needs like a hand, a little bit of human nudging with only like
2:13:54 on the order 50, 100 examples to like kind of align that prior the model has with exactly
2:13:55 with what you want.
2:13:59 It looks different than I think normal RLHF. We’re usually training these reward models
2:14:02 and tons of examples.
2:14:09 What’s your intuition when you compare generation and verification or generation and ranking?
2:14:12 Is ranking way easier than generation?
2:14:20 My intuition would just say, yeah, it should be like this is kind of going back to like
2:14:25 if you if you believe P does not equal NP, then there’s this massive class of problems
2:14:30 that are much, much easier to verify given a proof than actually proving it.
2:14:35 I wonder if the same thing will prove P not equal to NP or P equal to NP.
2:14:37 That would be, that would be really cool.
2:14:45 That’d be of whatever feels metal by AI who gets the credit, another open philosophical
2:14:46 question.
2:14:56 I’m actually surprisingly curious what like a good bet for one AI will get the feels
2:14:57 metal will be.
2:14:59 Isn’t this a Mons specialty?
2:15:01 I don’t know what a Mons bed here is.
2:15:04 Oh, sorry, Nobel Prize or feels metal first?
2:15:05 Feels metal.
2:15:06 Feels metal level.
2:15:07 Feels metal comes first.
2:15:08 Feels metal comes first.
2:15:09 Well, you would say that, of course.
2:15:14 But it’s also this like isolated system in verify and sure.
2:15:16 Like I don’t even know if I don’t need to do this.
2:15:17 I feel like I have much more to do there.
2:15:22 I felt like the path to get to IMO was a little bit more clear because it already could get
2:15:26 a few IMO problems and there are a bunch of like, there’s a bunch of low hanging fruit
2:15:30 given the literature at the time of like what what tactics people could take.
2:15:36 I think I’m one much less first in the space with deer improving now and to, yeah, less
2:15:41 intuition about how close we are to solving these really, really hard open problems.
2:15:43 So you think it’ll be feels metal first?
2:15:46 It won’t be like in physics or in.
2:15:47 Oh, 100%.
2:15:51 I think I think that’s probably likely like it’s probably much more likely that it’ll
2:15:52 get them.
2:15:53 Yeah.
2:15:54 Yeah.
2:15:57 Well, I think it’s both to like, I don’t know, like BSD, which is a bird’s wing turn
2:16:02 die or conjecture or like Riemann iPods or any one of these like hard, hard math problems
2:16:07 or just like actually really hard, it’s sort of unclear what the past you to get even a
2:16:09 solution looks like.
2:16:13 Like we don’t even know what a path looks like, let alone, and you don’t buy the idea
2:16:17 that this is like an isolated system and you can actually, you have a good reward system
2:16:22 and it feels like it’s easier to train for that.
2:16:25 I think we might get feels metal before AGI.
2:16:30 I mean, yeah, I’d be very happy, very happy.
2:16:37 But I don’t know if I think 20, 20 H, 20, 30 feels metal feels metal.
2:16:38 All right.
2:16:44 It’s feels like forever from now, given how fast things have been going.
2:16:47 Speaking of how fast things have been going, let’s talk about scaling laws.
2:16:55 So for people who don’t know, maybe it’s good to talk about this whole idea of scaling
2:16:56 laws.
2:16:57 What are they?
2:17:00 Where do you think stand and where do you think things are going?
2:17:04 I think it was interesting, the original scaling laws paper by OpenAI was slightly wrong because
2:17:11 I think of some issues they did with learning rate schedules and then Chinchilla showed
2:17:13 a more correct version.
2:17:16 And then from then, people have again kind of deviated from doing the compute optimal
2:17:22 thing because people start now optimizing more so for making the thing work really well
2:17:26 given an inference budget.
2:17:31 And I think there are a lot more dimensions to these curves than what we originally used
2:17:37 of just compute, number of parameters and data.
2:17:38 Like inference compute is the obvious one.
2:17:41 I think context length is another obvious one.
2:17:47 So let’s say you care about the two things of inference compute and then context window.
2:17:52 Maybe the thing you want to train is some kind of SSM because they’re much, much cheaper
2:17:55 and faster at super, super long context.
2:17:59 And even if maybe it is 10x worse scaling properties during training, maybe if you spend
2:18:04 10x more compute to train the thing to get the same level of capabilities, it’s worth
2:18:09 it because you care most about that inference budget for really long context windows.
2:18:13 So it’ll be interesting to see how people kind of play with all these dimensions.
2:18:17 So yeah, I mean you speak to the multiple dimensions, obviously the original conception
2:18:22 was just looking at the variables of the size of the model as measured by parameters and
2:18:25 the size of the data as measured by the number of tokens and looking at the ratio of the
2:18:26 two.
2:18:32 And it’s kind of a compelling notion that there is a number or at least a minimum and
2:18:36 it seems like one was emerging.
2:18:41 Do you still believe that there is a kind of bigger is better?
2:18:49 I mean, I think bigger is certainly better for just raw performance and raw intelligence.
2:18:55 I think the path that people might take is I’m particularly bullish on distillation.
2:19:01 How many knobs can you turn to if we spend like a ton, ton of money on training, get
2:19:07 the most capable cheap model, really, really caring as much as you can because the naive
2:19:10 version of caring as much as you can about inference time compute is what people have
2:19:16 already done with the Lama models or just overtraining the shit out of 7B models on
2:19:19 way, way, way more tokens than is essential optimal.
2:19:24 But if you really care about it, maybe the thing to do is what Gemma did, which is let’s
2:19:31 not just train on tokens, let’s literally train on minimizing the KL divergence with
2:19:37 the distribution of Gemma 27B, so knowledge distillation there.
2:19:42 And you’re spending the compute of literally training this 27B model, the billion parameter
2:19:46 model on all these tokens just to get out this smaller model.
2:19:50 And the distillation gives you just a faster model, smaller means faster.
2:19:56 Yeah, distillation in theory is I think getting out more signal from the data that you’re
2:19:57 training on.
2:20:01 And it’s like another, it’s perhaps another way of getting over, not like completely over,
2:20:05 but like partially helping with the data wall, where like you only have so much data to train
2:20:09 on, let’s like train this really, really big model on all these tokens and we’ll distill
2:20:10 it into a smaller one.
2:20:16 And maybe we can get more signal per token for this, for this much smaller model than
2:20:18 we would have originally if we trained it.
2:20:22 So if I gave you $10 trillion, how would you spend it?
2:20:25 I mean, you can’t buy an island or whatever.
2:20:34 How would you allocate it in terms of improving the big model versus maybe paying for HF and
2:20:35 the RLHF?
2:20:36 Yeah.
2:20:42 Yeah, I think there’s a lot of these secrets and details about training these large models
2:20:46 that I just don’t know and only privy to the large labs.
2:20:50 And the issue is I would waste a lot of that money if I even attempted this because I wouldn’t
2:20:53 know those things.
2:20:59 Suspending a lot of disbelief and assuming like you had the know-how and operate or if
2:21:03 you’re saying like you have to operate with like the limited information you have now.
2:21:04 No, no, no.
2:21:09 Actually, I would say you swoop in and you get all the information, all the little heuristics,
2:21:17 all the little parameters, all the parameters that define how the thing is trained.
2:21:22 If we look in how to invest money for the next five years in terms of maximizing what
2:21:23 you call raw intelligence.
2:21:25 I mean, isn’t the answer like really simple?
2:21:28 You just try to get as much compute as possible.
2:21:33 Like at the end of the day, all you need to buy is the GPUs and then the researchers
2:21:39 can find all the like they can sort of you can tune whether you want to train a big model
2:21:41 or a small model.
2:21:45 Well this gets into the question of like, are you really limited by compute and money
2:21:50 or are you limited by these other things and I’m more privy to Arvid’s beliefs that
2:21:54 we’re sort of idea limited but there’s always like.
2:21:59 But if you have a lot of compute, you can run a lot of experiments.
2:22:04 So you would run a lot of experiments versus like use that compute to train a gigantic
2:22:05 model.
2:22:10 I would, but I do believe that we are limited in terms of ideas that we have.
2:22:15 I think, yeah, because even with all this compute and like, you know, all the data you
2:22:21 could collect in the world, I think you really are ultimately limited by not even ideas but
2:22:27 just like really good engineering like even with all the capital in the world, would you
2:22:32 really be able to assemble like there aren’t that many people in the world who really like
2:22:34 make the difference here.
2:22:40 And there’s so much work that goes into research that is just like pure, really, really hard
2:22:45 engineering work as like a very kind of hand wavy example.
2:22:48 If you look at the original transformer paper, you know how much work was kind of joining
2:22:54 together a lot of these really interesting concepts embedded in the literature versus
2:22:58 then going in and writing all the codes like maybe the CUDA kernels, maybe whatever else.
2:23:04 I don’t know if it ran in GPUs or TPUs originally, such that it actually saturated the GPU performance,
2:23:05 right?
2:23:07 Getting known she’s here to go in and do all this code, right?
2:23:11 And know it’s like probably one of the best engineers in the world or maybe going a step
2:23:15 further like the next generation of models, having these things, like getting model parallelism
2:23:20 to work and scaling it on like, you know, thousands of, or maybe tens of thousands of like V100s,
2:23:23 which I think GBD3 may have been.
2:23:27 There’s just so much engineering effort that has to go into all of these things to make
2:23:28 it work.
2:23:35 If you really brought that cost down to like, you know, maybe not zero, but just made it
2:23:40 10x easier, made it super easy for someone with really fantastic ideas to immediately
2:23:47 get to the version of like the new architecture they dreamed up that is like getting 50, 40%
2:23:53 utilization on the GPUs, I think that would just speed up research by a ton.
2:23:58 I mean, I think if you see a clear path to improvement, you should always sort of take
2:23:59 the low hanging fruit first, right?
2:24:04 I think probably open the eye and all the other labs did the right thing to pick off
2:24:10 the low hanging fruit, where the low hanging fruit is like sort of, you could scale up
2:24:19 to a GPT4.25 scale and you just keep scaling and like things keep getting better and as
2:24:25 long as like, there’s no point of experimenting with new ideas when like everything is working
2:24:30 and you should sort of bang on it and try to get as much as much juice out of the possible
2:24:33 and then maybe when you really need new ideas for.
2:24:38 I think if you’re spending 10 trillion dollars, probably want to spend some, you know, then
2:24:41 actually like reevaluate your ideas, like probably your idea limited at that point.
2:24:47 I think all of us believe new ideas are probably needed to get, you know, all the way there
2:24:56 to AGI and all of us also probably believe there exist ways of testing out those ideas
2:25:02 at smaller scales and being fairly confident that they’ll play out.
2:25:07 It’s just quite difficult for the labs in their current position to dedicate their
2:25:14 very limited research and engineering talent to exploring all these other ideas when there’s
2:25:21 like this core thing that will probably like improve performance for some like decent amount
2:25:22 of time.
2:25:25 Yeah, but also these big labs like winning.
2:25:31 So they’re just going wild, okay.
2:25:34 So how big question looking on to the future.
2:25:38 You’re now at the center of the programming world.
2:25:43 How do you think programming, the nature programming changes in the next few months,
2:25:47 in the next year, in the next two years, in the next five years, 10 years?
2:25:52 I think we’re really excited about a future where the programmers in the driver’s seat
2:25:54 for a long time.
2:26:00 And you’ve heard us talk about this a little bit, but one that emphasizes speed and agency
2:26:05 for the programmer and control, the ability to modify anything you want to modify, the
2:26:08 ability to iterate really fast on what you’re building.
2:26:16 And this is a little different, I think, than where some people are jumping to in the space
2:26:24 where I think one idea that’s captivated people is can you talk to your computer?
2:26:27 Can you have it built software for you as if you’re talking to like an engineering department
2:26:32 or an engineer over Slack and can it just be this sort of isolated text box?
2:26:38 And part of the reason we’re not excited about that is, you know, some of the stuff
2:26:39 we talked about with latency.
2:26:44 But then a big reason we’re not excited about that is because that comes with giving up
2:26:45 a lot of control.
2:26:49 It’s much harder to be really specific when you’re talking in the text box.
2:26:53 And if you’re necessarily just going to communicate with a thing like you would be communicating
2:26:58 with an engineering department, you’re actually advocating tons of really important decisions
2:26:59 to the spot.
2:27:04 And this kind of gets at fundamentally what engineering is.
2:27:08 I think that some people who are a little bit more removed from engineering might think
2:27:12 of it as, you know, the spec is completely written out and then the engineers just come
2:27:14 and they just implement.
2:27:19 And it’s just about making the thing happen in code and making the thing exist.
2:27:24 But I think a lot of the best engineering, the engineering we enjoy involves tons of
2:27:30 tiny micro decisions about what exactly you’re building and about really hard tradeoffs between,
2:27:35 you know, speed and cost and just all the other things involved in a system.
2:27:41 And we want as long as humans are actually the ones making, you know, designing the software
2:27:44 and the ones specifying what they want to be built.
2:27:47 And it’s not just like company run by all AIs.
2:27:52 We think you’ll really want the humor, the human in a driver seat dictating these decisions.
2:27:56 And so there’s the jury still out on kind of what that looks like.
2:28:01 I think that, you know, one weird idea for what that could look like is it could look
2:28:06 like you kind of, you can control the level of abstraction you view a code base at.
2:28:12 And you can point at specific parts of a code base that may like maybe you digest a code
2:28:15 base by looking at it in the form of pseudocode.
2:28:20 And you can actually edit that pseudocode too and then have changes get made down at
2:28:23 the sort of formal programming level.
2:28:29 And you keep the like, you know, you can gesture at any piece of logic in your software component
2:28:30 of programming.
2:28:33 You keep the inflow text editing component of programming.
2:28:36 You keep the control of, you can even go down into the code.
2:28:40 You can go at higher levels of abstraction while also giving you these big productivity
2:28:41 gains.
2:28:44 It’d be nice if you can go up and down the abstraction stack.
2:28:45 Yeah.
2:28:46 And there are a lot of details to figure out there.
2:28:48 That’s sort of like a fuzzy idea.
2:28:49 Time will tell if it actually works.
2:28:53 But these principles of control and speed in the human in the driver seat we think are
2:28:55 really important.
2:28:58 We think for some things, like Arvid mentioned before, for some styles of programming you
2:29:02 can kind of hand it off chatbot style, you know, if you have a bug that’s really well
2:29:03 specified.
2:29:08 But that’s not most of programming and that’s also not most of the programming.
2:29:10 We think a lot of people value.
2:29:12 What about like the fundamental skill of programming?
2:29:20 There’s a lot of people like young people right now kind of scared, like thinking because
2:29:23 they like love programming, but they’re scared about like, will I be able to have a future
2:29:26 if I pursue this career path?
2:29:30 Do you think the very skill of programming will change fundamentally?
2:29:34 I actually think this is a really, really exciting time to be building software.
2:29:42 We remember what programming was like in, you know, 2013, 2012, whatever it was.
2:29:50 And there was just so much more cruft and boilerplate and, you know, looking up something
2:29:51 really gnarly.
2:29:53 And that stuff still exists.
2:29:55 It’s definitely not at zero.
2:29:59 But programming today is way more fun than back then.
2:30:04 It’s like we’re really getting down to the delight concentration and all the things
2:30:07 that really draw people to programming, like for instance, this element of being able to
2:30:12 build things really fast and speed and also individual control, like all those are just
2:30:15 being turned up a ton.
2:30:18 And so I think it’s just going to be, I think it’s going to be a really, really fun time
2:30:20 for people who build software.
2:30:22 I think that the skills will probably change too.
2:30:26 I think that people’s taste and creative ideas will be magnified and it will be less
2:30:32 about maybe less a little bit about boilerplate text editing, maybe even a little bit less
2:30:36 about carefulness, which I think is really important today.
2:30:39 If you’re a programmer, I think it’ll be a lot more fun.
2:30:41 What do you guys think?
2:30:42 I agree.
2:30:49 I’m very excited to be able to change, like just, one thing that happened recently was
2:30:52 like we wanted to do a relatively big migration to our code base.
2:30:58 We were using async local storage in Node.js, which is known to be not very performant and
2:31:00 we wanted to migrate to a context object.
2:31:04 And this is a big migration that affects the entire code base.
2:31:09 And Swall and I spent, I don’t know, five days working through this, even with today’s
2:31:10 AI tools.
2:31:17 And I am really excited for a future where I can just show a couple of examples and then
2:31:20 the AI applies that to all of the locations.
2:31:24 And then it highlights, oh, this is a new example, like what should I do and then I
2:31:25 show exactly what to do there.
2:31:29 And then that can be done in like 10 minutes.
2:31:33 And then you can iterate much, much faster than you can, then you don’t have to think
2:31:38 as much upfront and stand at the blackboard and like think exactly like how are we going
2:31:42 to do this because the cost is so high, but you can just try something first.
2:31:44 And you realize, oh, this is not actually exactly what I want.
2:31:47 And then you can change it instantly again after.
2:31:53 And so, yeah, I think being a programmer in the future is going to be a lot of fun.
2:31:54 Yeah.
2:31:57 I really like that point about, it feels like a lot of the time with programming.
2:32:00 There are two ways you can go about it.
2:32:06 One is like, you think really hard, carefully upfront about the best possible way to do it.
2:32:10 And then you spend your limited time of engineering to actually implement it.
2:32:14 But I must refer just getting in the code and like, you know, taking a crack at it, seeing
2:32:19 it, how it kind of lays out, and then iterating really quickly on that.
2:32:21 That feels more fun.
2:32:25 Yeah, like just being to generate the boilerplate is great.
2:32:31 So you just focus on the difficult design, nuanced difficult design decisions, migration.
2:32:34 I feel like this is, this is a cool one.
2:32:38 Like, it seems like large language models able to basically translate from one program
2:32:43 language to another or like translate, like migrate in the general sense of what my grade
2:32:44 is.
2:32:46 But that’s in the current moment.
2:32:51 So, I mean, the fear has to do with like, okay, as these models get better and better,
2:32:53 then you’re doing less and less creative decisions.
2:32:58 And is it going to kind of move to a place where it’s, you’re operating in the design
2:33:03 space of natural language, where natural language is the main programming language.
2:33:07 And I guess I get asked that by way of advice, like, if somebody’s interested in programming
2:33:10 now, what do you think they should learn?
2:33:19 Like do they, you guys started in some Java and I forget the, oh, some PHP.
2:33:20 Objective C.
2:33:21 Objective C.
2:33:22 There you go.
2:33:23 Yeah, absolutely.
2:33:27 I mean, in the end, we all know JavaScript is going to win.
2:33:28 And not TypeScript.
2:33:33 It’s just, it’s going to be like vanilla JavaScript, it’s just going to eat the world and maybe
2:33:34 a little bit of PHP.
2:33:40 And I mean, it also brings up the question of like, I think Don Knuth has a, this idea
2:33:45 that some percent of the population is geeks and like, there’s a particular kind of psychology
2:33:52 in mind required for programming and it feels like more and more that expands the kind of
2:33:58 person that should be able to can do great programming might expand.
2:34:04 I think different people do programming for different reasons, but I think the true, maybe
2:34:12 like the best programmers are the ones that really love, just like absolutely love programming,
2:34:21 for example, they’re folks in our team who literally when they’re, they get back from
2:34:26 work, they go and then they boot up cursor and then they start coding on their side projects
2:34:31 for the entire night and they stay up till 3 AM doing that.
2:34:37 And when they’re sad, they said, I just really need to code.
2:34:42 And I think like, you know, there’s that level of programmer where like this obsession
2:34:49 and love of programming, I think makes really the best programmers.
2:34:55 And I think these types of people will really get into the details of how things work.
2:35:01 I guess the question I’m asking that exact program, let’s think about that person.
2:35:06 When the super tab, the super awesome praise be the tab is succeeds.
2:35:11 You keep pressing tab, that person in the team loves to curse the tab more than anybody
2:35:12 else.
2:35:13 Yeah.
2:35:17 And it’s also not just like, like pressing tab is like the just pressing tab.
2:35:21 That’s like the easy way to say it in the catch phrase, you know, but what you’re actually
2:35:27 doing when you’re pressing tab is that you’re injecting intent all the time while you’re
2:35:29 doing it.
2:35:30 Sometimes you’re rejecting it.
2:35:32 Sometimes you’re typing a few more characters.
2:35:38 And that’s the way that you’re sort of shaping the things that’s being created.
2:35:43 And I think programming will change a lot to just what is it that you want to make.
2:35:45 It’s sort of higher bandwidth.
2:35:50 The communication to the computer just becomes higher and higher bandwidth as opposed to like
2:35:53 just typing as much lower bandwidth than communicating intent.
2:36:00 I mean, this goes to your manifesto titled engineering genius.
2:36:04 We are an applied research lab building extraordinary productive human AI systems.
2:36:10 So speaking to this like hybrid element to start, we’re building the engineer of the
2:36:17 future, a human AI programmer that’s an order of magnitude more effective than any one engineer.
2:36:21 This hybrid engineer will have effortless control over their code base and no low entropy
2:36:23 keystrokes.
2:36:28 They will iterate at the speed of their judgment, even in the most complex systems.
2:36:34 Using a combination of AI and human ingenuity, they will outsmart and out engineer the best
2:36:36 pure AI systems.
2:36:38 We are a group of researchers and engineers.
2:36:42 We build software and models to invent at the edge of what’s useful and what’s possible.
2:36:47 Our work has already improved the lives of hundreds of thousands of programmers.
2:36:51 And on the way to that, we’ll at least make programming more fun.
2:36:53 So thank you for talking today.
2:36:54 Thank you.
2:36:55 Thanks for having us.
2:36:56 Thank you.
2:36:57 Thank you.
2:37:01 Thanks for listening to this conversation with Michael, Swalle, Arvid, and Aman.
2:37:04 To support this podcast, please check out our sponsors in the description.
2:37:11 And now, let me leave you with a random, funny, and perhaps profound programming code I saw
2:37:13 on Reddit.
2:37:18 Nothing is as permanent as a temporary solution that works.
2:37:21 Thank you for listening and hope to see you next time.
2:37:22 Bye.
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