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0:00:21 Hi everyone, welcome to the A6NZ podcast.
0:00:22 I’m Sonal.
0:00:26 I’m here today with a very special guest visiting Silicon Valley, the former prime minister
0:00:32 of the United Kingdom, Mr. Tony Blair, who now runs an institute for global change working
0:00:35 with governments, policymakers and others all around the world.
0:00:39 Also joining us, we have Andreessen Horowitz, managing partner Scott Cooper, who has a new
0:00:44 book just out called Secrets of Sandhill Road, Venture Capital and How to Get It.
0:00:49 And given that startups are drivers of economic growth and innovation, Cooper also often weighs
0:00:54 in on various policy issues, especially those that affect the flow of capital, people and
0:00:55 ideas around the world.
0:00:58 And that’s the focus and theme of this episode.
0:01:02 It’s more of a hallway-style conversation where we invite our audience to sort of eavesdrop
0:01:04 on internal meetings and convos.
0:01:09 We discuss the intersection of governments and technology and where policy comes in,
0:01:13 focusing mainly on the mindsets that are required for all of this.
0:01:17 But then we do also suggest a few specific things we can do when it comes to supporting
0:01:21 tech change for the many, not just for the few.
0:01:22 Welcome Tony.
0:01:23 Thank you.
0:01:24 Did you ask me to call you that?
0:01:25 Thank you.
0:01:26 Can everyone know?
0:01:27 He said it was okay.
0:01:28 And Cooper, welcome.
0:01:29 Thank you.
0:01:30 So let’s just get started.
0:01:34 I think the context is that there’s so much discussion right now about tech in the context
0:01:35 of inequality.
0:01:40 One of the points of view that I have, particularly coming from a background where my family came
0:01:44 from India, et cetera, is that it’s also very democratizing.
0:01:49 And a lot of people can do new things in better ways because of technology.
0:01:53 But I think the big question, the question I think we care about today is how do we bring
0:01:58 more people into the system and make sure that tech benefits everyone?
0:02:03 The first thing I would say from the political perspective is that technology is essentially
0:02:04 an empowering and enabling thing.
0:02:08 So I regard it as benign, but it’s got vast consequence.
0:02:10 So the question is how do you deal with the consequence?
0:02:14 How do you access the opportunities and mitigate its risks and disbenefits?
0:02:17 So that is, I think, the right framework to look at it.
0:02:24 But because it’s accelerating in its pace of change and because the change is so deep,
0:02:28 and I look upon this technological revolution as like the 21st century equivalent of the
0:02:32 19th century industrial revolution, it’s going to transform everything.
0:02:37 So I think the fundamental challenge is that the policy makers and the change makers are
0:02:39 not in the right dialogue with each other.
0:02:44 And this is where misfortune will lie if you end up with bad regulation or bad policy and
0:02:49 where the tech people kind of go into their little huddle, because I say this with great
0:02:55 respect, but you come to this Silicon Valley and it’s like walking into another planet,
0:02:56 frankly.
0:02:57 Yes, that’s actually really interesting.
0:03:00 I’m personally offended by that comment.
0:03:03 Now I think the difficulty is that, yes, you’re right, it’s very empowering.
0:03:07 On the other hand, it’s actually quite frightening to people because you kind of all understand
0:03:11 it and the rest of the world doesn’t quite understand it.
0:03:16 And as far as they do understand it, they find it somewhat dystopian and it’s look.
0:03:19 I was actually sitting with some people from my old constituency in the north of England
0:03:24 a few months back and I said to them, I wonder what’s going to happen when we have driverless
0:03:28 cars and their attitude was, it’s never going to happen.
0:03:34 And the role of a politician is to be able to, in a sense, articulate to the people those
0:03:37 changes and then fit them into a policy framework that makes sense.
0:03:41 And that’s the worry because if the politicians don’t understand it, they’ll fear it.
0:03:43 If they fear it, they’ll try and stop it.
0:03:46 You articulated the vision that we’ve always had, which is we’ve always invested around
0:03:48 this theme called software is eating the world.
0:03:53 It’s exactly what you describe, which is technology no longer kind of sits in its own box.
0:03:57 It really is the case that technology will permeate almost every industry over time.
0:04:01 I think that’s where the big change is happening now is it used to be that technology was a
0:04:02 piece of the puzzle now.
0:04:04 Every company is a technology company.
0:04:05 Yeah, exactly.
0:04:07 So that is the kind of board on which people are playing.
0:04:11 So the issue, I think, is this, is how do you get the structured dialogue between the
0:04:13 change makers and the policy makers?
0:04:16 What would you say the number one thing if you could give advice to entrepreneurs in
0:04:21 the Valley that they should do differently to engage this kind of a framework that you’re
0:04:22 describing?
0:04:26 My advice would be stop looking at your own narrow interest in what you’re doing and
0:04:31 understand you’ve got a collective interest in making the world of policy and politics
0:04:33 understand the technology.
0:04:34 What’s going to happen?
0:04:41 How you get A, the right system of regulation and B, how you allow government to enable
0:04:43 these transformative changes.
0:04:44 Yes.
0:04:45 Well, I actually have a question for both of you.
0:04:48 Today is the 30th anniversary of the web, the World Wide Web.
0:04:52 And I just had Google Doodle this morning and a note from Tim Berners-Lee, his original
0:04:55 memo, information management, colon, a proposal.
0:04:59 The question I have is that a lot of people would argue that the best technologies can
0:05:04 develop when you don’t try to, A, priority predict the consequences because, A, you cannot
0:05:09 their complex adaptive systems and, B, there was an environment of quote permissionless
0:05:13 innovation that allowed the web to thrive because the original makers may have foreseen
0:05:17 some apps, but the whole point is that the innovation is what allowed it to thrive.
0:05:20 So I’d love to hear from both of you on how to balance that perspective.
0:05:21 So I agree with that.
0:05:25 I think though what’s different is we used to be able to compartmentalize technology.
0:05:29 It was a piece of software that you used at work to help you be more productive.
0:05:33 But if technology really is going to be part and parcel of everything, then I think it
0:05:37 changes the nature of how we think about that responsibility because it is regulated industries
0:05:42 in many cases that have been largely immune over time from technology in a way that appears
0:05:43 to be different today.
0:05:45 So I would say that then there are two questions that derive from that.
0:05:48 One is how do you make regulation intelligent?
0:05:53 How do you make it abide by the public interest or enhance the public interest, but at the
0:06:00 same time not dampen that creative and in a way entrepreneurial drive behind the development
0:06:01 of new ideas?
0:06:06 And then secondly, what are the ways that government should be working with those that
0:06:08 are going to be impacted by technology?
0:06:11 If you’re in the car industry, it’s going to be a huge change, right?
0:06:15 I mean, if you get these driverless cars, it’s going to change obviously jobs.
0:06:17 It’s going to change insurance.
0:06:22 It’s going to change the method of production, what you produce, probably change the concept
0:06:23 of car ownership in some way.
0:06:24 Absolutely.
0:06:25 It might even reshape entire cities.
0:06:26 Everything will be impacted by it.
0:06:27 Exactly.
0:06:32 I am fascinated by the potential of technology to allow African nations and governments to
0:06:36 circumvent some of the legacy problems we have within our systems.
0:06:41 And that goes for everything from basic healthcare and education through to how you help agricultural
0:06:45 small holders develop a better yield, cooperate better together, and link up better with the
0:06:46 market.
0:06:49 And in fact, one thing that’s happening in Africa today is there are applications of
0:06:52 tech that are growing up in interesting ways.
0:06:55 So my point is, you’ve got all these different facets.
0:07:00 And yet at the moment, the curious thing is, if you were to go to virtually any European
0:07:06 country or if you were to come here and say, okay, name the top four issues, where would
0:07:09 technology be in that list?
0:07:10 Would it be at the top of the list is what you’re saying?
0:07:12 No, I think it wouldn’t be in the list.
0:07:16 Cooper, you spent a lot of time actually in your role as a partner in front of Congress
0:07:20 and various entities giving testimonies about policy and curious for your take on this.
0:07:21 Yeah.
0:07:25 There is a concern I often hear from entrepreneurs, which is, how do we know if we go there?
0:07:29 How does that not just bring us into the fold of regulation and therefore have negative
0:07:32 consequences versus, you know, we talk about things out here.
0:07:35 Sometimes you do things you ask for permission later is a better strategy.
0:07:36 Right.
0:07:37 Ask for forgiveness.
0:07:38 Ask for permission.
0:07:39 Yeah.
0:07:40 I complete to get that.
0:07:44 That’s why I think it’s got to be a big, it’s got to be done in a big way from the collectivity
0:07:47 rather than individual people going because of course you’re absolutely right what will
0:07:50 happen is that the entrepreneurs think, okay, if I go and say, I’ve got the following five
0:07:53 problems that I can see in this technology I’m developing, they’re going to regulate
0:07:54 it away.
0:07:55 Yeah.
0:08:01 I think the hard question will be you’re getting people and companies that will enormous power.
0:08:06 I mean, not just the big tech players, but the others as well.
0:08:11 So I think one of the things that in a sense, it’s my question to you is, how do you manage
0:08:18 to get into that dialogue with policymakers where, you know, these very powerful people
0:08:23 recognize that in the end, you know, however powerful they are, they are not more important
0:08:25 than the public interest.
0:08:28 Part of we believe our role is to help provide, you know, visibility.
0:08:31 I wouldn’t, I don’t want to say education because I think politicians are very well
0:08:35 educated and certainly well meaning, but to connect the divide between, in our case, DC
0:08:36 and Silicon Valley.
0:08:40 And so we will often reach out to regulators, legislators and help them understand this is
0:08:43 what’s happening from an innovation perspective and therefore these are things that you might
0:08:45 want to anticipate that you need to think about.
0:08:49 So autonomous driving is a great example, right, which you mentioned is in order to
0:08:53 make that work in the United States, we probably need forward-looking governments to say there
0:08:58 are test zones or areas where we might have almost regulatory free zones for testing purposes,
0:09:01 right, that have proper supervision, but to enable something that otherwise might not
0:09:03 exist ahead of its time.
0:09:07 Obviously, you’ve got specific micro issues, I mean, they can be big in their impact like
0:09:11 driverless cars, but there is a specific thing, they’ve got specific issues attached to them.
0:09:18 But where does the tech sector go if it wants to engage on, you know, the bigger macro question
0:09:24 of how do you redesign government, by the way, as well as individual sectors, because
0:09:27 government itself is going to have to change.
0:09:28 That organization doesn’t exist today, at least.
0:09:30 I’m not aware of where you would do that.
0:09:34 And I think the other problem with it is we have to think beyond geographic and national
0:09:39 borders on this stuff, because technology and capital are free-flowing in our society.
0:09:44 You almost need a United Nations or some kind of, you know, type of organization to convene
0:09:45 to have those discussions.
0:09:46 Yeah.
0:09:47 I would say there’s a couple of things, too.
0:09:48 There’s a couple of factors.
0:09:54 One, there are obviously lobbying entities like the NVCA, there’s the Internet Association,
0:09:56 which a lot of major companies are a part of.
0:10:02 Then there is a group of players, like there’s a group of think tanks and a middle layer,
0:10:06 and then the government agencies themselves have been soliciting testimony.
0:10:10 Cooper has actually done testimony on all kinds of topics, from Cypheus to crypto to
0:10:12 various different topics.
0:10:18 But what’s really interesting to me, especially, is there’s organizations like 18F and USDS
0:10:23 in the US government, at least, where you have technologists doing literally rotating
0:10:24 apprenticeships.
0:10:28 It’s like the rotating missions, essentially, where they go for three years and they’re
0:10:30 contributing to actually reinventing government systems.
0:10:32 Now, this is a very important addition about it.
0:10:33 Yes.
0:10:34 I think it is, too.
0:10:36 And what’s really amazing is that it’s got tangible impacts.
0:10:41 So a specific example is we have a huge Veterans Administration that doesn’t get great healthcare.
0:10:46 So they redesigned the VA site in order to make sure that people who have accessibility
0:10:49 issues can use the site in a friendly way.
0:10:51 There’s many more applications of the types of things they’re doing.
0:10:53 We’ve actually had them on this podcast.
0:10:54 But I think those are some avenues.
0:10:56 But to Cooper’s point, there is no single entity.
0:11:01 I will say that at Wired, I edited a big set of op-eds around the ITU, which is sort of
0:11:03 like a UN for the internet.
0:11:06 And it was during the WC-12 hearings, which you might recall.
0:11:11 I think Hama Dune Touré was the head of the commission, and I edited him as well.
0:11:16 And what’s fascinating to me is that there’s a lagging versus a leading approach to it,
0:11:20 because you’re sort of taking the data that’s passed, not really looking forward.
0:11:23 And that was what I saw as a big drawback when I was working with the WC-12 op-ed.
0:11:28 So I’m curious for your take on how do we shift it, so you are listening to those being
0:11:34 affected by technology, but with the point of view that spins it forward for future generations.
0:11:38 Because if we had listened to all the farmers in the first wave of the Industrial Revolution,
0:11:43 we may not have many of the things today, but their grandkids are benefiting from those
0:11:44 things.
0:11:45 Yeah, no, absolutely.
0:11:48 So look, I think there are two caps that I see, and I just look at this from the side
0:11:54 of the, as a we’re ordinary politician, because I think there are initiatives that are happening
0:11:58 inside government where people or departments will get it, and therefore they’ll embrace
0:12:02 it and bring in smart people to help them and so on.
0:12:04 But I think there are two sort of lacunae.
0:12:10 One is your average politician does not understand a lot about this, and that is not sort of
0:12:12 a disrespect to your average politician.
0:12:18 It’s that it’s new, it’s complicated, it takes you time to get your head around it.
0:12:22 My eldest son is in technology, and I am always trying to get him to explain blockchain to
0:12:23 me.
0:12:24 We’re big on crypto.
0:12:25 I know.
0:12:31 I remember you sent me the other day saying, “This is the idiot’s guide to crypto currency,
0:12:33 and I still couldn’t understand it.”
0:12:35 I’m going to send you our crypto cannon.
0:12:36 We took a stab at Compound Water Resources.
0:12:37 Right.
0:12:38 But you probably shouldn’t test me on it.
0:12:39 But that is one lacunae.
0:12:44 Those people have to understand this is like the 19th century Industrial Revolution.
0:12:47 So you’ve got to get your ordinary politicians to understand it.
0:12:51 And then there’s another lacunae which is, I think, in getting the dialogue at the top
0:12:56 level between particularly the Americans and the Europeans, because I also think it would
0:13:00 be immensely helpful if we had a more transatlantic approach.
0:13:01 I think there’s a third piece.
0:13:03 There’s an incentives problem.
0:13:07 I would imagine if you did a survey of most politicians, they would say, “My fundamental
0:13:11 role is how do I improve long-term economic growth and job sustainability for my constituents?”
0:13:15 I mean, if people kind of cut through a lot of the politics, that’s really why they think
0:13:16 they’re there.
0:13:19 Look, they want to make a better life and make a better opportunity for their constituents.
0:13:23 The problem we have, though, is because their short-term incentive program is to get reelected,
0:13:25 which I understand is a good thing from a political perspective.
0:13:29 It’s very hard for them to take that long-term view because the shorter-term opportunity
0:13:34 is to say, “Look, I really need to do no harm to my constituents and by allowing technology,
0:13:39 which might be in the short-term, displacing and unsettling to job growth and other stuff,
0:13:41 particularly for different segments of the population.”
0:13:44 It’s very hard, I would imagine, as a politician to square those two things, which is how do
0:13:49 I help my constituents understand, you know, to Sonal’s point that, yes, over a period
0:13:54 of time, it was a good idea to have industrialized farming as opposed to pure manual agrarian
0:13:55 farming.
0:13:58 But that’s an incredibly unsettling thing, particularly in the U.S. here, if every two
0:14:01 years you have to get reelected or you go find a new job.
0:14:07 By the way, this happened in the 19th century and you had whole new politics created around
0:14:08 it.
0:14:10 And I think there are two things that are important here.
0:14:16 First of all, I think the technology will, in some way, provide solutions to what is
0:14:23 a constant dilemma for an ordinary politician, which is we need to do more for our people,
0:14:27 but we can’t just keep spending more and taxing more.
0:14:31 If the technology can help unlock part of that, that is something they’re prepared to
0:14:32 go for.
0:14:37 And secondly, with most politicians, if they’re able to see this within a longer-term perspective,
0:14:42 what you say to them, “Look, we’ll help you and guide you through this process of change,
0:14:44 but in the end, it’s a beneficial change.”
0:14:47 And what I found when I was in government is some of the most difficult reforms we put
0:14:54 through, for example, around education reform, healthcare reform, we were able, in some ways,
0:15:01 with at least some people, to say this short-term difficulty is going to be worth it.
0:15:03 How did you pull that off, though?
0:15:05 Was it the education, the explanation?
0:15:07 Was it consensus building?
0:15:12 I mean, let me take a very specific example, which, of course, is under attack now, but
0:15:18 we introduced tuition fees in the UK, but my point was very, very simple, that universities
0:15:22 are going to be major engines of economic growth in the future, in particular because
0:15:26 of the link between university and technology and the development of technology.
0:15:31 And therefore, we cannot afford for UK universities not to be able to get the best talent, and
0:15:34 they’re going to have to therefore have an extended funding base.
0:15:35 They can’t get it all from government.
0:15:39 And my point is, if you get it all from government in the end, some governments will start to
0:15:43 slice it away, and you’re always hand-to-mouth as universities.
0:15:49 And I reckon when we did that, it was very difficult, in fact, it was extremely difficult,
0:15:55 but in the end, you were able to say to people, “Look, if we want to save our position as
0:16:00 a country that, along with the US, probably has most high-quality universities in the
0:16:02 top 50 in the world, then we’ve got to be prepared to do that.”
0:16:07 Now, some people, by the way, rejected it, and today it’s a big political issue again,
0:16:12 but you can get to at least some form of alignment between long-term and short-term.
0:16:15 It’s a fundamental rethinking of what the role of government is, quite frankly, right?
0:16:19 Which is, again, if you take the premise that the overall objective for government is to
0:16:23 create economic conditions that hopefully generate long-term economic growth and sustainability
0:16:25 for individuals and companies, then you’re right.
0:16:29 Maybe the ancillary role of government is, how do we deal with short-term issues that
0:16:32 have market dislocations for people?
0:16:35 Maybe that’s a more proper way to describe what the role of government is, in many cases.
0:16:38 I think the other thing would be, I think there’s another question for politics which
0:16:43 would be very challenging, because what would be weird is if the whole of the world is undergoing
0:16:46 this revolution and politics is just kind of staying fixed.
0:16:49 The type of people who go into politics, what happens often is people leave university,
0:16:53 they’re going to become a researcher for an MP, and then they become an MP, and then they
0:16:55 become a minister, but they have no experience of the outside world, right?
0:16:59 So that’s one, and it becomes a constraint over time, and then the types of people who
0:17:00 work in government.
0:17:04 So you were saying something about the people who’ve been brought into, say, the Veterans
0:17:06 Administration here.
0:17:12 So how do you actually open up public service and then get a greater interoperability between
0:17:15 public service and the private sector?
0:17:20 Because all of the sort of pressure certainly coming from the media has been not to allow
0:17:25 that to happen, and not to allow politicians to have anything other than they’re usually
0:17:26 just focused on…
0:17:29 Okay, let’s say we all agree, which I think we do, that there needs to be a connection
0:17:32 between all the entities working together, no question.
0:17:36 More engagement, more explanation, more understanding, thinking of consequence.
0:17:37 I think those are all table stakes.
0:17:41 The question now is, how do you then think about unintended consequences?
0:17:46 Because the story, to me, is not that bad things have bad consequences.
0:17:51 It’s that often the worst consequences come from very well-intended things.
0:17:53 And quite frankly, the perfect example that comes to mind is GDPR.
0:17:57 Yeah, to make it concrete, there’s been a, over the last several months, and I’m sure
0:18:00 probably more so in Europe as well, there’s been a number of articles talking about when
0:18:05 you look at kind of the broad impact of GDPR, essentially, it’s endured largely to the benefit
0:18:08 of the very large incumbents, which was probably not what’s intended to do.
0:18:09 Because they’ve got the resource to better handle.
0:18:10 That’s exactly right.
0:18:14 And the analogy we have here in the States was the Dodd-Frank legislation that came out
0:18:18 of the global financial crisis, where financial institutions had to comply with a whole new
0:18:20 set of regulations.
0:18:24 What it really did here in the U.S. was, it really entrenched those incumbents very well,
0:18:27 and it made it very hard for startup financial institutions to grow.
0:18:31 It was very hard for a new institution to get a banking license for many years, in part
0:18:33 because of the regulatory cost of doing so.
0:18:35 And so, how do you balance that?
0:18:38 And maybe the answer is, look, it’s an education problem, but well-meeting and politicians
0:18:43 certainly expect and intend that regulation is the appropriate way to deal with these things.
0:18:47 It does, in some cases, interfere with the overall goal of entrepreneurship in a startup
0:18:48 formation.
0:18:53 That’s why I think that the attitude of the technology sector to engagement with government
0:18:54 is so important.
0:18:58 Because if you’re engaging with government saying, look, we understand there’s a massive
0:19:01 set of issues here, and we’re really going to sit down and work with you as to how we
0:19:07 get the right answer, then government’s in a position where they regard you as a partner.
0:19:11 But I think for this moment in time, a bit like, actually, the aftermath of the financial
0:19:17 crisis, government kind of regards, you’re looking after yourselves, but we got to look
0:19:19 after the public.
0:19:21 And that’s where it leads to poor regulation.
0:19:28 I mean, poor regulation is nearly always the consequence of a failure on the regulating
0:19:31 side to really understand what’s going on.
0:19:35 And on the founder’s side, what I’m hearing is to really communicate the benefits of the
0:19:36 technology upfront.
0:19:40 And to be so defensive that you’re just thinking all the time, how can we ward these people
0:19:41 off?
0:19:46 But here’s the thing, you can sometimes, if you have wealth, which a lot of these big
0:19:51 tech players do, and power, and you also have access, and you can go and see whoever they
0:19:57 want to see, it can sometimes mask your essential underlying vulnerability.
0:19:58 Interesting.
0:19:59 Right.
0:20:05 And your vulnerability is the comes a point when suddenly the mood flips, and it doesn’t
0:20:10 matter how much wealth and power and access you have, you’re the target.
0:20:12 So that point, everything changes.
0:20:16 So if you want to avoid that, I think it’s got to be a dialogue that’s structured and
0:20:22 it requires not just things happening between the tech sector and government, but for people
0:20:28 like my own institute, to use our sort of convening power, the political side, to say
0:20:30 to the politicians, look, let’s get our heads around this.
0:20:32 Here’s my essential challenge.
0:20:37 How do you take this technological revolution and as a politician, weave it into a narrative
0:20:39 of optimism about the future?
0:20:40 Yes.
0:20:41 I want that too.
0:20:42 Right.
0:20:43 Yeah.
0:20:44 So what’s driving the populism is pessimism.
0:20:46 If people are pessimistic, then they look for someone to blame.
0:20:52 If people are optimistic, they look for something to aspire to, and that’s the essential difference.
0:20:54 It’s really interesting also, Sonal and I have been having this conversation, and we’ve
0:20:58 been having this conversation in the U.S. about ESG, right, which obviously, you know, certainly…
0:20:59 Environmental social governance.
0:21:00 Right.
0:21:02 Which Europe is way ahead of the U.S. there, and Larry Fink, who’s the head of BlackRock
0:21:05 here, has written this letter, you know, appealing to CEOs, and it really goes to the same issue
0:21:09 you’re talking about, which is fundamentally, what is the role of the corporation and how
0:21:12 do corporations think about obviously enhancing value for their shareholders, but also to your
0:21:17 point recognizing that they impact constituents in many other ways, and I think that’s kind
0:21:21 of the dialogue we ought to be having with politicians, which is, look, we can create
0:21:25 a world where it’s compatible to have, you know, maximizing shareholder opportunity,
0:21:28 but also recognizing and being a part of the broader community discussion about the impact
0:21:29 on society.
0:21:35 The other thing is to recognize that when we create these things, we have some obligation
0:21:36 to share.
0:21:40 It comes out of fundamental macroeconomics, right, which is we can improve growth for
0:21:44 a country by either population growth and/or productivity growth, right?
0:21:47 Those are the two levers in theory that we can impact, and if we can frame the discussion
0:21:50 around technology, that’s a lot of where the U.S. has done well, right?
0:21:54 We’ve generally, obviously times are changing, but we’ve generally been very open to immigration
0:21:58 and thought about population growth as a way to help improve the lot for people generally,
0:22:01 and we’ve also been very open to productivity growth, right, in the form of technology and
0:22:05 automation, and if we can frame it that way, but also to your point, recognize that there
0:22:09 are going to be disintermediations along the way, and part of our responsibility is to help
0:22:14 from a training and education perspective, and even potentially the role of government
0:22:19 in subsidizing the transition from less automated to more automated society.
0:22:21 What happens to education in all of this?
0:22:23 I don’t think we have a singular point of view on it.
0:22:27 We have talked about education a lot on this podcast and shared a diversity of views, but
0:22:32 I think a couple of the high level things are that universities are huge drivers, of course,
0:22:36 as you mentioned, of innovation, and in every study of regional innovation, every innovation
0:22:43 cluster is successful because of the collaboration between universities, government, local, entrepreneurial
0:22:44 communities.
0:22:47 The other key point, however, is it’s a combination of top-down and bottom-up.
0:22:52 People who have tried top-down, industrial, planned, smart cities or things like Silicon
0:22:53 Valley never work.
0:22:56 The only bottom-up ones alone don’t necessarily work.
0:22:57 You need a combination of the two.
0:23:00 That’s the number one finding, but the second thing, and this is a big topic we talk about
0:23:05 on this podcast, is the importance of apprenticeship and a new type of education that really thinks
0:23:07 about skills-based education.
0:23:12 We have this elitist attitude that education has to be a certain way when, in fact, in
0:23:16 this day and age, especially with increased automation and the need for jobs, we might
0:23:19 want to be really thinking about very specific skills-based education.
0:23:24 It’s actually fascinating because, in fact, my eldest son’s got a company that’s on apprenticeships
0:23:28 and technology, so that’s exactly what he does.
0:23:32 I think it’s really, really interesting because of the idea that you don’t necessarily have
0:23:33 to go to university.
0:23:38 Well, there are alternative universities coming about too, like we’re investors in
0:23:39 Udacity.
0:23:40 There’s just Lambda School.
0:23:44 There’s all these interesting types of containers where people can get what they call nano-degrees
0:23:46 or microskills or specific skills.
0:23:50 There’s so much that’s actually in play, because the point I want to raise here, this is kind
0:23:55 of an underlying theme to me, is that technology, as you pointed out, you can take an optimistic
0:23:56 view.
0:23:59 It also gives you the means to address many of the problems that we are complaining about
0:24:04 because when I think of some of the trade-offs between waiting for a government to update
0:24:12 policy, what I love is that a mass of users on a platform can essentially vote with saying
0:24:18 leave that platform, and immediately that platform is going to act the next day in a
0:24:20 way that a lawmaker cannot overnight.
0:24:22 Yes, from a political perspective.
0:24:24 You want this thing at least to have some sort of rational-
0:24:25 Of course.
0:24:26 It shouldn’t be mobbed.
0:24:31 But I think the other thing is, if you take, so a lot of what drove, for example, Brexit
0:24:35 in the UK is, apart from the immigration issue, was this idea of communities, people left
0:24:36 behind.
0:24:41 So what is it that technology would do to go into those communities and help people gain
0:24:45 better education, get connectivity to the world, because in the end, this is what it’s
0:24:46 all about.
0:24:47 If you’re not connected, you’re not really–
0:24:48 If you’re left behind.
0:24:49 Right.
0:24:55 So I think one big question is, how does the technology sector help us as policymakers
0:25:00 reach those people for whom the conversation we’ve just been having may be sort of scratching
0:25:02 their heads and thinking about what these guys are on.
0:25:03 That’s a fantastic question.
0:25:05 And actually, it’s interesting because we’re investors in NationBuilder, which is one of
0:25:10 the companies that mobilize a lot of the communities that actually organize for pro and for con
0:25:11 around these things.
0:25:14 So a quick thing, I do want to make sure we actually give answers because we’re asking
0:25:15 a lot of questions.
0:25:19 So can you both give a little bit more on what concretely we need to do?
0:25:23 So from the point of view of my institute, what we’re doing is we’re creating a tech
0:25:25 and public policy center.
0:25:29 And the idea is to bring a certain number of people who really understand the tech side
0:25:32 and a certain number of people who come from the public policy side, put them together
0:25:33 in a structured way.
0:25:35 I will kind of curate that.
0:25:43 And out of it should come what I call serviceable policy and a narrative about the future,
0:25:46 which makes sense of this technological revolution.
0:25:50 And then to link up with politicians, not just in the UK and Europe, but actually over
0:25:55 here and create a sense that this technological revolution should be at the center of the
0:25:56 political debate.
0:25:57 How do we handle it?
0:26:00 How do we, as I say, mitigate its risks and access its opportunities?
0:26:03 So that’s one very specific thing.
0:26:07 And then I think the other thing, frankly, is just to be out there, myself and a number
0:26:13 of other people, at least of access to the airwaves, to say, guys, we’ve got to switch
0:26:14 the conversation.
0:26:18 You’ve got to put this technology question at the heart of the political debate.
0:26:22 Now the solution, some people may go to the left, some people may go to the right.
0:26:24 Some people will never be in between.
0:26:25 But make it the conversation.
0:26:29 Put it at the top four of those priorities for every country, every organization.
0:26:30 I think that’s right.
0:26:32 Fundamentally, we’re talking around the issues.
0:26:36 It’s either immigration or its income inequality or other things that drive the debate.
0:26:40 But the fundamental question is exactly that, which is how do we move forward with broader
0:26:41 economic growth initiatives?
0:26:46 So sitting here in Silicon Valley, any individual company is probably better off, quite frankly,
0:26:49 taking the break glass first and then ask for forgiveness later.
0:26:53 And so it’s, I think, the idea of having kind of solving that collective action problem
0:26:56 through a convening organization makes a lot of sense.
0:26:59 But you come to the issues of very traditional income inequality.
0:27:03 Now there is a perfectly good question as to whether you raise the minimum wage, and
0:27:04 if so, by how much?
0:27:07 And my government was coming to introduce the minimum wage in the UK, so I’m very familiar
0:27:10 with all those arguments.
0:27:17 But in the end, there is a whole other dimension to that individual, which is about the world
0:27:20 that’s changing and their place in it, and whether they’re going to have the skills and
0:27:21 the aptitude to be able to.
0:27:25 So you’re just saying completely at every level reframe that technology is at the center
0:27:26 of that.
0:27:27 Right.
0:27:31 So it’s not that you displace traditional questions of taxation and inequality, but the
0:27:38 truth of the matter is it’s going to be probably in the long term more significant for that
0:27:43 individual and for the society if this technological revolution is handled properly.
0:27:47 So if you had a debate in the UK at the moment about our healthcare system, our national
0:27:53 health service, it would be, should we spend 10 billion pounds a year more on it or 5 billion?
0:27:59 But how do you change the whole of the way we implement care for people because of technology
0:28:00 is going to have a much bigger impact?
0:28:01 I agree.
0:28:05 I guess the only thing I would add to this, because I think about this a lot, interestingly,
0:28:10 is that we treat technology like this word, this homogenous, nebulous entity.
0:28:16 And the reality is that every single instance so depends on the specific technology.
0:28:19 So my call to action, I guess, would be to think about it very specifically.
0:28:25 The way we think about AI, that’s such a broad phrase and it’s a very scary phrase that
0:28:30 suggests everything from generalized intelligence to very specific automation that gets your
0:28:33 bank account updated automatically.
0:28:34 So I think there’s two things to this.
0:28:38 One that we need to be incredibly specific about what technology we’re talking about,
0:28:39 in what context.
0:28:43 And then B, we also dial in the right degree to what we’re talking about at what point
0:28:44 because I don’t really make a difference.
0:28:45 I completely agree with that.
0:28:50 I mean, I think the only thing I would say is right now we’re actually far away from
0:28:53 even getting the specifics.
0:28:54 Yeah, you’ve got a good point.
0:28:55 That’s fair.
0:28:59 You know, I was just saying to people in politics when we were campaigning and they’d say, you
0:29:03 know, I’d say, right, because we were, we campaigned in the slogan 99% right, new labor,
0:29:04 new Britain.
0:29:05 Right.
0:29:06 And they say, no, but it’s much more complicated now.
0:29:09 I say, okay, guys, it is, but actually it’s complicated, but sometimes you need to go
0:29:10 straight.
0:29:11 I hear you.
0:29:15 You’re saying that you’re saying that when you go back to your old constituency in constituency
0:29:19 in Northern England, they don’t care about the specifics, they just need to have their
0:29:20 fears established.
0:29:22 The first thing that you need to persuade them of, you’ve got to say to them, guys, technology
0:29:24 is going to change the world and we’ve got to prepare for it.
0:29:25 They’re not there yet.
0:29:29 When you get them there, then obviously in all sorts of different ways, but this is where
0:29:35 I think that the gulf that there is between the technology sector and the policies and
0:29:36 therefore the people is so big.
0:29:40 How do we deal with the fundamental challenges that we have as we talked about earlier from
0:29:44 an incentive perspective and short tenures on, you know, office and people’s ability
0:29:45 to be in office?
0:29:48 Is that a conversation you think that the country and the nations are prepared to have?
0:29:50 I think so, but it’s a very good question.
0:29:55 I would also say that in all of the change that’s going to happen, I mean, this is a
0:29:58 whole topic for another podcast, probably with the different people.
0:30:08 But how you exchange information and the validation of that information is an essential part of
0:30:12 having a democratic debate and that is a big problem in today’s world.
0:30:19 So I think it is possible to have that conversation with people, but all political conversations
0:30:25 today are extremely difficult because they happen in such a fevered environment with
0:30:31 so much polarization and the interaction between conventional and social media makes a rational
0:30:33 debate occasionally extremely difficult.
0:30:35 With that qualification I answer this.
0:30:39 I think the better way to approach that problem is to say, how do we make the U.S. and/or Europe
0:30:45 or other places attractive to entrepreneurship and encourage people to think about the regulatory
0:30:49 framework and the economic framework as, you know, want to be participants in these markets
0:30:53 as opposed to the anti, you know, kind of policies we have, which is let’s make it harder for
0:30:56 free flow of capital and try to stave off those opportunities.
0:31:00 It used to be that 90% of venture capital and entrepreneurship happen in the U.S. literally
0:31:05 almost as early as 20 years ago, and if you look at those numbers today, it’s about 50%.
0:31:09 And so the amount of kind of capital that’s kind of been distributed globally and therefore
0:31:12 the amount of opportunity set distributed globally is interesting.
0:31:15 We have to think about this beyond kind of regional borders.
0:31:18 We will have talent in people that are free flowing across geographies.
0:31:21 And so we have to think about this from a broader, you know, global initiative.
0:31:25 Well, you guys, I just want to say thank you for joining the A6NZ podcast.
0:31:26 Thank you.

0:00:03 Hi, and welcome to the A16Z podcast. I’m Hannah.
0:00:08 This episode is all about how artificial intelligence is coming to the doctor’s office, how it will
0:00:13 impact the nature of doctor-patient interactions, diagnosis, prevention, prediction, and everything
0:00:14 in between.
0:00:20 The conversation between A16Z’s general partner Vijay Pandey and Dr. Eric Topol, cardiologist
0:00:25 and chair of innovative medicine at Scripps Research, is based on Topol’s book, “Deep
0:00:29 Medicine,” and touches on everything from how AI’s deep phenotyping can shift our
0:00:34 thinking from population health to understanding the medical health essence of you, how the
0:00:38 industry might respond, the challenges in integrating and introducing the technology
0:00:43 into today’s system, what the doctor’s visit of the future might really look like, and
0:00:47 ultimately how AI can make healthcare more human.
0:00:50 Before we talk about technology and we talk about all the things that are changing the
0:00:54 world or making huge impacts, it’s interesting to just think like, “What should a doctor
0:00:55 be doing?”
0:00:56 And how do you see that?
0:01:02 That’s really what I was pondering and I really did this deep look into AI.
0:01:07 I actually didn’t expect it to be this back to the future story, but in many ways, I think
0:01:13 it turns out that as we go forward, particularly in a longer-term view, the ability to outsource
0:01:19 so many things with help from AI and machines, I think is going to get us back.
0:01:20 It could.
0:01:21 It could.
0:01:24 That’s a big if, to where we were back in the ’70s and before.
0:01:28 What was better then was that doctors were spending much more time with us, right?
0:01:29 Exactly.
0:01:34 That gift of time, the human side, which is the center of medicine that’s been lost.
0:01:40 The big business of healthcare and all of its components like electronic records and
0:01:48 relative value units and all this stuff basically has sucked out any sense of intimacy and time.
0:01:51 And it’s also accompanied by lots of errors.
0:01:58 But of course, it’s not a gimme because administrators want more efficiency, more product to be.
0:02:02 I think you put this on Twitter where it’s like some kid drew like a drawing of going
0:02:07 to the doctor and the picture was the doctor with their back turned working on a computer.
0:02:12 And that is what happens too much, but yet we’re talking about technology coming in.
0:02:15 So how does this all work out that more technology means less computer?
0:02:23 I think that is a kind of fundamental of the problem of doctors not even making eye contact
0:02:30 and as a child to draw that picture, how unnerving that was her trip to the pediatrician natural
0:02:35 language processing can actually liberate from keyboards.
0:02:41 And so it’s already being done in some clinics and even in the UK in emergency rooms.
0:02:49 And so if we keep that up and build on that, we can eliminate that whole distraction doctors
0:02:52 and nurses and clinicians being data clerks and this is ridiculous.
0:02:58 So the fact that voice recognition is just moving so fast in terms of accuracy and speed
0:02:59 is really encouraging.
0:03:03 Alex is a very basic version of voice recognition, but you’re talking about something much more
0:03:04 sophisticated.
0:03:07 That’s something where they’re actually doing NLP, they’re doing transcriptions of doctors
0:03:09 and have to take notes.
0:03:13 If you were more sophisticated, you could put an ontology onto this such that you’re
0:03:17 not just getting like a transcript of what’s going on, but that you have very machine learning
0:03:19 friendly organized data.
0:03:20 Exactly.
0:03:26 So the notes that are synthesized from the conversation are far better than the notes
0:03:32 that you would get an Epic or Cerner where 80% are cut and pasted and they’re error laden.
0:03:39 So I mean, just as we Google AI published in JAMA of their experience, I think it’s really
0:03:45 going much faster because the accuracy of the transcription and the synthesized note is
0:03:50 far better than what we have today and it exceeds professional medical transcriptionists in
0:03:51 terms of accuracy.
0:03:52 Yeah.
0:03:55 And so I’m imagining like what the doctor visits like then.
0:03:59 So we’ve got maybe NLP so the doctor doesn’t have to be transcribing and not interacting
0:04:00 with Epic.
0:04:02 Who knows what’s in the back end, but doesn’t even matter anymore.
0:04:03 Right.
0:04:10 So we’ve got billing and all that headache for like a PCP, all that’s a huge headache.
0:04:13 That’s all part of that conversation because you say, “Well, Mr. Jones, we’re going to
0:04:18 have you have lab tests for such and such and then we’re going to get this scan and
0:04:20 it’s all done through the conversation.”
0:04:22 We could bring in other technologies, right?
0:04:28 We’ve thought about just how imaging or other type of diagnosis comes in and we’ve seen
0:04:33 all these cool things about how machine learning can improve this, but then also it’s fine
0:04:38 because at the same point, and you bring this up in the book that over-diagnosing also can
0:04:39 be very difficult.
0:04:40 Yeah.
0:04:44 Like it was very stunning where you talked about how the incidence of thyroid cancer
0:04:47 is going up, but the mortality’s been flat.
0:04:48 Exactly.
0:04:53 And so I guess the challenge will be is how can we bring in these technologies in so doctors
0:04:56 can do the things they should be doing and not the things they shouldn’t be doing.
0:04:57 Right.
0:05:03 Well, I think there is getting arms around a person’s data, this whole deep phenotyping.
0:05:10 So no human could actually integrate all this data, not only the whole electronic record,
0:05:18 which all too often is incomplete, but also pulling together sensor data, genomic data,
0:05:24 gut microbiome, all the things that you’d want to be able to come up with not only better
0:05:29 diagnoses, but also the better strategy for prevention or treatment.
0:05:34 So I think what’s going to make life easier for both doctors and for the patients is having
0:05:39 that data fully processed and distilled.
0:05:44 In the book, I tell the story about my knee replacement and how it was a total fiasco.
0:05:49 Part of that was because my orthopedist who did the surgery wasn’t in touch with my congenital
0:05:50 condition.
0:05:57 And so that hopefully is going to be something we can transcend in the future.
0:06:02 And this is one thing that computers do, can do very well as logistics and coordination.
0:06:05 There’s tons of cases where you might like thyroid cancer, we’re just talking about maybe
0:06:09 you have to bring in an endocrinologist in addition to an oncologist.
0:06:13 And it’s shocking that often there’s no discussion there, there’s no communication.
0:06:19 But yet the challenge in my mind is how does computer magically know things that we can’t
0:06:20 do right now?
0:06:27 Well, that’s I think where the complementarity, the synergy between machines and people is
0:06:35 so ideal because we just have early satiety with data, whereas deep learning has insatiable
0:06:36 appetite.
0:06:38 And so that contrasts.
0:06:45 But we have, as doctors and humans, we have just great contextual abilities, the judgment,
0:06:53 the wisdom experience, and just the features that can basically build on that machine
0:06:58 processing because we don’t ever want to trust an algorithm for a serious matter.
0:07:04 But if it tees it up and we have oversight and we fit it into that person’s story, that
0:07:06 I think is the best of both worlds.
0:07:08 Well, here I’m really curious because what is the ground truth?
0:07:13 Because generally we don’t trust an individual person as like, as knowing everything either,
0:07:14 right?
0:07:19 But the ground truth would be like a second opinion or a third opinion or a fourth opinion
0:07:22 or even like, you know, a board to look at something.
0:07:24 And that would be what I think most people would view as the ground truth.
0:07:29 The ground truth, when it’s applied to training an algorithm, of course, is knowing that it
0:07:32 is the real deal, that it is really true.
0:07:40 And I think a great example of that is in radiology because radiologists have a false
0:07:44 negative rate of 32 percent, false negative.
0:07:49 And that’s the basis for most of the litigation in radiology, which is over the course of
0:07:54 a career, a third of radiologists get sued mostly because they miss something.
0:08:00 So what you have are algorithms with ground truths that are trained on, you know, hundreds
0:08:05 of thousands of scans so that whether it’s a chest x-ray, a CT scan or MRI, whatever
0:08:08 it is, that it’s not going to miss stuff.
0:08:12 But then, of course, you’ve got that over read by the trained radiologists.
0:08:20 So they’re, you know, I think that’s an example of how we can use AI to really rev up accuracy.
0:08:23 Somebody is going to need to interpret the algorithms or sort of be on top and that in
0:08:29 a sense, the doctor is freed from the stuff the doctor shouldn’t be doing, like the BS
0:08:32 accounting or the typing or all these things.
0:08:34 And that’s not the best use of doctors time.
0:08:39 And it’s funny because it seems like the best use of doctors time is in understanding what
0:08:43 these tests would mean, whether it be an AI test or a cholesterol or whatever, and in
0:08:45 how to communicate with the patient.
0:08:48 And so that seems to be a theme running through the book.
0:08:51 So for the first half, in terms of the understanding, what do you think that’s going to look like?
0:08:55 I mean, one of the things I’ve been constantly wondering about is whether there’ll be a new
0:08:56 medical specialty.
0:09:00 Like, you know, because you don’t have radiology without like CT or X-rays, right?
0:09:03 So presumably you don’t have radiology a hundred years ago.
0:09:05 I do have AIology or something like that.
0:09:12 Saurabh Jha, who is a radiologist at Penn, he and I Penn, a JAMA editorial about the
0:09:16 information specialist radiologists and pathologists.
0:09:21 Their foundation is reviewing patterns and information.
0:09:26 But what’s interesting is this an opportunity for them to connect with patients because
0:09:27 they don’t.
0:09:32 You know, right now radiologists never sees a patient radiologists live in the basement.
0:09:35 The pathologists look at the slides that that group of pathologists.
0:09:40 But they actually want to interact with patients and they have this unique insight because
0:09:43 they’re like the honest brokers, they’re not, they don’t want to do a surgery.
0:09:46 They want to give you their expertise.
0:09:49 And so I think that’s what we’re going to see a pretty substantial change.
0:09:54 And as you’re touched on this new specialty, it’ll look different than the way it is today.
0:09:58 In that case, it almost seems like everybody is better off.
0:10:01 The doctor is better off because of pathologists and not just looking at slides, but actually
0:10:02 is dealing with patients.
0:10:08 And presumably the patients better off because you have these false negatives get found.
0:10:12 The pathologists, you know, they have remarkable discordance when they look at slides and to
0:10:18 be able to have that basically looked at as if hundreds of thousands of them were reviewed,
0:10:24 getting back to your second, third and nth opinion to get that as input for them to help
0:10:26 and consult with a patient.
0:10:28 I think it’s really a bonus.
0:10:31 You’re talking about radiology here in pathology, but I mean, we could sort of think about this
0:10:33 as an issue for diagnosis in general.
0:10:36 And you know, there’s one thing you point in the book, I think really beautifully, you
0:10:40 know, you said once trained doctors are pretty much wedged into their level of diagnostic
0:10:42 performance throughout their career.
0:10:46 That’s kind of an amazing thing is that doctors, I guess, go through CPE and so on.
0:10:49 But like, you only go through med school once and that’s an intense process.
0:10:52 You learn a lot, but you can’t go through med school all the time.
0:10:53 No, it’s so true.
0:10:58 And that gets me to, you know, Danny Kahneman’s book about thinking fast and slow and the
0:11:05 system one that is the reflexive thinking that happens automatically versus what we
0:11:11 want are reflective thinking, which is system two, which takes time.
0:11:17 And it turns out that if a doctor doesn’t think of the diagnosis of a patient in the
0:11:21 first five minutes is over 70% error rate.
0:11:25 And that’s how actually that’s how much time is the average with patients.
0:11:31 So we have the problem as you’ve alluded to of kind of a plateauing early on in a career,
0:11:35 but we also suffer because of lack of time with the system one thinking.
0:11:40 The thought is that the machine learning is reflecting what system two would look like
0:11:44 because it’s trained from doctors sort of doing system two.
0:11:45 Exactly.
0:11:53 That brings in the integration of what would be the ground troops of thousands for that
0:11:55 particular dataset.
0:11:57 So I think it has a potential.
0:12:03 And of course, a lot of this stuff needs validation, but there’s a lot of promissory studies to
0:12:05 date that suggests that’s going to be very possible.
0:12:11 When I think about what ML or artificial intelligence AI could do, there’s like two axes.
0:12:17 There’s one like just scale, like the fact that you can scale up servers on Amazon trivially
0:12:19 much more than you could scale up human beings.
0:12:22 We could scale up a thousand servers right now or 10,000 servers right now.
0:12:25 I don’t think we could call 10,000 doctors and get them here.
0:12:29 The other thing you could do is that you could sort of talk about other axes is like sort
0:12:31 of intelligence or capability.
0:12:36 And you’re talking about both in a sense that you can scale up not just the fact that you
0:12:42 could have like a resident or med school students sort of doing things and having a lot of them,
0:12:48 but you in addition to that, you have in a sense a doctor through AI that’s in diagnostics
0:12:50 that’s better than any single doctor.
0:12:51 Right.
0:12:55 I think that’s really what is going to be one of the big early changes in this new AI
0:13:00 medical era is that diagnosis is going to get so much better.
0:13:05 Right now we have over 12 million serious errors a year in the United States.
0:13:09 And they’re not just costly, but they hurt people.
0:13:14 So this is a real opportunity to upgrade that and that’s a much more of a significant problem
0:13:16 than most people realize.
0:13:20 And so so far we’ve been talking about things that feel like the sci-fi fantasy version of
0:13:21 stuff, right?
0:13:26 I mean like because we’ve got like this doctor of sorts through diagnosis that can do what
0:13:30 no single doctor could do, presumably at scale it’s doing this at lower cost.
0:13:35 It’s allowing human beings to do the things they should be doing.
0:13:37 Is there any dystopian turn here or how does this?
0:13:39 It knows no shortage of those.
0:13:42 And so how could this go wrong and what can we do to prevent it?
0:13:47 Well, I mean I think one of the things we’ve harped on is that you’ve got to have human
0:13:48 oversight.
0:13:54 We can’t trust an algorithm absolutely because for any serious matter, because if we do that
0:13:59 and it has a glitch or it’s been hacked or it has some kind of adversarial input, it
0:14:01 could hurt people at scale.
0:14:03 So that’s one of the things that we got to keep an eye on.
0:14:11 For example, if an algorithm gets approved by the FDA, oftentimes it’s these days it’s
0:14:14 an in silico retrospective sort of thing.
0:14:20 And if we just trust that without seeing how it performs in a particular venue, a particular
0:14:24 cohort of people, these are things that we just shouldn’t accept blindly.
0:14:30 So there’s lots of deep liabilities and it runs from of course privacy, security, the
0:14:31 ethics.
0:14:37 There are many aspects that are not ideal about this, but when you think about the need
0:14:41 and how much it could provide to help medicine, I think those are the trade-offs that we have
0:14:43 to really consider.
0:14:47 What we should be doing now and what people could be doing now to sort of anticipate this?
0:14:48 Or do you think it’s like too early?
0:14:51 I mean, because people have these algorithms now.
0:14:54 What do we see in one year versus five years versus 10 years?
0:14:57 Well, it is rolling out in other parts of the world.
0:15:03 I just finished this review with the NHS and that was fascinating because they are really
0:15:04 going after this.
0:15:10 They are the leading in the world force in genomics and now they want to be in AI.
0:15:15 So they already have emergency rooms that are using that are liberated from keyboards
0:15:18 and they are going after this.
0:15:23 And this course in the middle of Brexit, so that’s kind of amazing.
0:15:27 But China is really implementing this that you could say, well, maybe too fast because
0:15:32 out of desperation or need, but one of the advantages that we don’t recognize with China,
0:15:37 not just that they have scale in terms of people, but they have all the data for each
0:15:38 person.
0:15:40 And we have no data for each person.
0:15:46 Basically, our data is just spread around all these different doctors and health systems.
0:15:47 Nobody has all their data.
0:15:52 And that is a big problem because without the inputs that are complete.
0:15:53 For like you personally.
0:15:54 Yeah.
0:15:55 Yeah.
0:15:56 Then what are you going to get out of that?
0:16:00 So this is a, this, we are at a handicapped position in this country.
0:16:05 And the other thing of course is we have no strategy as a nation, whereas China, UK and
0:16:10 many other countries, they are developing or have developed planning and strategy and
0:16:13 put in resources here as a nation.
0:16:14 We have zero resources.
0:16:19 In fact, we have proposed cuts to the same, you know, granting agencies that would potentially
0:16:20 help.
0:16:23 And so what, what should one do at that scale?
0:16:27 Like, you know, there’s various things people propose, you know, is this something to have
0:16:31 a new national institute of health, you know, in this area?
0:16:35 Is there, I mean, I think when I think about the government playing a role, I think I want
0:16:40 them to try to help them build the marketplace and set the rules, but we have to be careful
0:16:42 that we don’t put too much regulation as well.
0:16:46 I mean, what are, what do you, I mean, when you say we don’t, we don’t have a strategy,
0:16:47 what’s missing?
0:16:48 What should we be doing?
0:16:52 We don’t have no national planning or strategies.
0:17:00 How is AI not only for healthcare, but in general, how is it going to be cultivated and made
0:17:02 transformative?
0:17:07 The experience I had in the UK was really interesting because there they not only have the will,
0:17:12 but they have a whole wing of the NHS for education and training.
0:17:13 You just think about it.
0:17:20 They talked about professions within medicine that are going to have a more of their daily
0:17:21 function.
0:17:25 So, we’re not well prepared, you know, who should take leave them?
0:17:29 One of the problems we have that you’re touching on is our professional organizations haven’t
0:17:31 really been so forward thinking.
0:17:36 They mainly are centered on maintaining reimbursement for their constituents.
0:17:43 The, you know, entities like NIH and NSF and others could certainly be part of the solution.
0:17:47 What you want to do here, I think, is to really accelerate this.
0:17:52 We’re in the middle of an economic crisis in healthcare, which is in the US, the worst
0:17:53 outlier.
0:18:00 I mean, we, we spending over $11,000 per person and we have the worst outcomes, life expectancy
0:18:06 going down three years in a row, childhood mortality, infant mortality, maternal mortality.
0:18:09 The worst people don’t realize that.
0:18:14 Then you have the UK and so many other countries that are at the $4,000 per year level and
0:18:16 they have outcomes that are far superior.
0:18:21 So, if we use this, we could actually reduce inequities.
0:18:27 We could make for a far better business model, paradoxically, but we’re not grabbing the
0:18:28 opportunity.
0:18:31 Maybe there’s another solution we could think about, which you also point to in the book,
0:18:36 which is what if, what can we do to drive through consumer action?
0:18:39 For instance, a lot of our healthcare is sick here, right?
0:18:40 What happens when we get sick?
0:18:41 That’s almost all of it.
0:18:44 What about, what can we do to stay healthy?
0:18:47 First thing I think of is diet and lifestyle, right?
0:18:50 That could go a long way in so many diseases, so many things that we deal with.
0:18:52 So, actually, you touch on diet.
0:18:56 Before we even talking about like diagnosing whether you have cancer, should we be diagnosing
0:18:57 what you should be having for lunch?
0:19:01 I couldn’t agree more that that should be a direction.
0:19:09 We have had this so naive notion that everyone should have the same diet, and we never got
0:19:16 that right as a country, but now we know without any question that people have an individualized
0:19:19 and highly heterogeneous response.
0:19:21 That’s not just through glucose spikes.
0:19:26 If you and I ate the exact same food, the exact same amount, the exact same time, our
0:19:30 glucose response would be very different, but also triglyceride response would be different,
0:19:32 and they don’t track together.
0:19:37 So, what we’re learning is if you get all this multimodal data, not just your gut microbiome
0:19:43 and sensor data and your sleep and your activity, your stress level, and what exactly you eat
0:19:47 and drink, we can figure out what would be promoting your health.
0:19:50 We’re not there yet, but we’re seeing some pretty rapid progress.
0:19:55 What’s intriguing to me is that in cases, there are cases now, especially, let’s say,
0:20:00 just glucose, where you can take technology to develop for type one or type two diabetics.
0:20:05 Now, I’m not diabetic, but I actually had the sort of, I was about to say joy, but at
0:20:09 least the intellectual intrigue of having a CGM on me for two weeks.
0:20:10 Yeah.
0:20:11 So, I got to play all these experiments.
0:20:12 Right.
0:20:13 Right?
0:20:14 So, I tried white rice as brown rice.
0:20:15 Yeah.
0:20:16 How’s ice cream?
0:20:20 Wine versus scotch, all the important questions one has to figure out.
0:20:21 Exactly.
0:20:26 And actually, it was actually a surprise to me that how, for instance, I did not spike
0:20:27 on ice cream.
0:20:28 Spiked on brown rice.
0:20:29 Yeah.
0:20:32 I don’t think I prepared to go on the ice cream diet just yet.
0:20:33 Yeah.
0:20:34 And I don’t think you would prescribe that either, right?
0:20:37 But I think the idea is that it’s just different for everybody, right?
0:20:39 So, maybe you spike on ice cream, I don’t.
0:20:45 And that, you know, what’s I think been kind of so annoying about nutrition is that we
0:20:48 hear all these conflicting things, but perhaps part of the reason why we’re hearing these
0:20:50 conflicting things is that it is so individual.
0:20:51 Exactly.
0:20:56 And that it’s so complicated and such a fundamental data science problem that it probably takes
0:20:57 something like machine learning to figure it out.
0:20:59 Well, I think that’s central.
0:21:02 If we didn’t have machine learning, we wouldn’t have known this.
0:21:06 And only, you know, thanks to the group and the Wiseman Institute in Israel, they cracked
0:21:07 the case on this.
0:21:09 So, Aaron Segal’s work?
0:21:10 Yeah.
0:21:11 Aaron Segal.
0:21:15 And now, it’s been replicated by many others and it’s being extended.
0:21:17 What would be promoting your health?
0:21:22 And right now, it’s, you know, these proxy metrics like your glucose or your lipids in
0:21:30 the blood, but eventually we’ll see how outcomes and prevention can be fostered by your diet.
0:21:31 It’s really kind of mind blowing.
0:21:34 How difficult data science problem it seems nutrition is.
0:21:35 Yeah.
0:21:39 The problem VJ is a number of levels and the sea of data.
0:21:44 I mean, we’re talking about terabytes of data to crack the case for each individual.
0:21:48 So it’s not even just your gut microbiome of the species of bacteria and their density,
0:21:54 but now we know it’s the sequence of those bacteria that are part of the story.
0:21:59 Then you have, of course, these continuous glucose every five minutes for a couple of
0:22:00 weeks.
0:22:01 That’s a lot of data.
0:22:09 Besides that, you’ve got, you know, all your physical activity, your sensors for stress,
0:22:15 you know, your sleep data, and then even your genomics.
0:22:20 So when you add all this together, this is a real challenge.
0:22:23 No human being could assimilate all this data.
0:22:28 But what’s interesting is not only at the individual level, but then with thousands of people.
0:22:33 So take everything we just talked about, multiply by thousand or hundreds of thousands.
0:22:35 That’s how we learn here.
0:22:41 And so what I think is the biggest thing about the AI underappreciation is the things that
0:22:43 we’re going to learn that we didn’t know.
0:22:49 Like, for example, another great example is when you give a picture of a retina to international
0:22:55 retina expert, and you say, is this from a man or a woman, the chance of them getting
0:22:56 right is 50/50.
0:23:02 But you can train an algorithm to be over 97, 98% accurate.
0:23:06 And there’s so many examples like that, like you wouldn’t miss polyps in a colonoscopy,
0:23:08 which is a big issue.
0:23:13 Or you would be able to see your potassium through your smartwatch level in your blood
0:23:14 without any blood.
0:23:20 And then the imagination just runs wild, as far as what you could do when you train things.
0:23:27 And so training your diet with this torrent of data, not just from you, but from a population
0:23:29 is, I think, a realistic direction.
0:23:33 And what I think is interesting about this is that it’s something where, A, we don’t
0:23:39 need the AMA or NIA or anything else to get involved in terms of diet.
0:23:43 And B, actually, people want to take care of these problems, because I think most people
0:23:44 are motivated.
0:23:45 We just don’t know what to do.
0:23:46 Right.
0:23:52 And so many aspects of it, like now chronobiology is really this hot topic.
0:23:54 That’s about your circadian rhythm.
0:23:57 And should you eat only for eight hours during the day?
0:23:58 Well, certain people, yes.
0:24:03 But the whole idea that there’s this thing for everyone, we got to get over that.
0:24:08 That’s what deep phenotyping is all about, to learn about the medical health essence
0:24:09 of you.
0:24:13 And we haven’t had the tools until now to do that.
0:24:14 OK.
0:24:17 So there’s a ton of data, but a lot of it seems kind of subjective.
0:24:18 Right?
0:24:20 I mean, did I sleep well or not?
0:24:24 How do you sort of overcome the fact that not everything is quantitative, like my cholesterol
0:24:25 level?
0:24:30 Well, it turns out that was kind of old medicine where we just talked about your symptoms.
0:24:34 But new medicine is with all sorts of objective metrics.
0:24:39 So a great example of this is state of mind or mood.
0:24:43 And that’s going to be transformative for mental health, because now everything from
0:24:52 how you type on your smartphone to the voice, which is so rich in terms of tone in a nation,
0:24:57 to your breathing pattern, to your facial recognition of yourself.
0:25:02 I mean, there’s all these ways to say, you know, you’re VGA, you’re really depressed.
0:25:03 Yeah.
0:25:04 You know that you’re depressed.
0:25:12 So the point being is that you have objective metrics of one’s mental health as a cardiologist
0:25:13 for all these years.
0:25:18 I’d have these patients they come and tell me, I feel my heart’s fluttering.
0:25:20 And I would put in the note, the heart’s fluttering.
0:25:22 That was so unhelpful.
0:25:26 Now I can say, well, you know, you should be able to record this on your phone or if
0:25:33 you have a smartwatch and when your heart flutters, just send me the PDF of that.
0:25:38 And we have the diagnosis that is real world, no longer subjective.
0:25:39 The whole different look really.
0:25:45 And by the way, did the patient who has the fluttering records their, their cardiogram,
0:25:47 they don’t have to wait for me.
0:25:53 They already have an automated read from AI that’s more accurate than a doctor.
0:25:55 There is something very anecdotal about the doctor visit.
0:25:57 He’s not right there in the moment.
0:25:58 Yeah.
0:25:59 Right.
0:26:00 It’s a one off.
0:26:01 Yeah.
0:26:02 It’s a one off.
0:26:05 And so it’s a fine thing because people wonder about, let’s say the knock on a wearable will
0:26:08 be that it’s not like a eight point EKG or something like that.
0:26:10 But on the other hand, it’s there with you all the time.
0:26:11 Yeah.
0:26:12 No, exactly.
0:26:16 And then there’s this contrived aspect of going to see the doctor where you, a lot of
0:26:19 people find that very stressful.
0:26:23 And when we talk about white coat hypertension, we don’t even know what normal blood pressure
0:26:28 is because we need to check that out in thousands, hundreds of thousands of people in their real
0:26:30 world to find out what’s normal.
0:26:36 We’ve already had this chaos of the American Heart Association saying that they changed
0:26:42 the blood pressure guidelines on the basis of no data, speaking of, of lack of some objective
0:26:43 metric.
0:26:44 Yeah.
0:26:47 Well, so one other area that I thought was really intriguing and just to me, this was
0:26:52 almost paradoxical, the concept of AI being useful for empathy.
0:26:54 Because I would have thought like, if we’re thinking about the things that a computer
0:26:58 is good at, like multiplying numbers, that’s going to be something like they’re going to
0:26:59 beat humans at any day.
0:27:04 I would have thought that empathy would be the one, like the last bastion of what we’re
0:27:06 good at and what the computer is good at.
0:27:08 But how does AI get to empathy?
0:27:12 Because as we started the conversation with us about how as a key part of what a doctor
0:27:15 does, we’re like, what can AI do there?
0:27:19 Well, we are missing that in a big way today.
0:27:21 And how do we get it back?
0:27:27 Well, I think how we get it back is we take this deep phenotyping, we do deep learning
0:27:35 about the person, and that’s all outsourced with oversight for a doctor or clinician.
0:27:42 Now, when you have this remarkable improvement in productivity in workflow and efficiency
0:27:46 and accuracy, all of a sudden, you have the gift of time.
0:27:54 If we just lay down, as doctors have over decades for administrators to go ahead and
0:28:03 just drive revenue and basically have no consideration for patients or doctors, we’re not going to
0:28:05 see any growth of empathy.
0:28:09 We’re not going to see the restoration of care in healthcare.
0:28:15 But if we stand up and if we say that time, all that benefit of the AI part, the machine
0:28:19 support, and by the way, that’s also at the patient level.
0:28:23 So the patients now, with their algorithmic support, they’re decompressing the doctor
0:28:24 load too.
0:28:25 Yeah, they’re doing some of it.
0:28:29 A lot of simple things, ear infections, skin rashes and all that sort of stuff that’s not
0:28:35 life threatening or serious, but that’s bypassing a doctor potentially almost completely.
0:28:43 So between this flywheel of algorithmic performance enhancement, if we stand up for patients, then
0:28:45 we have all this time to give back.
0:28:51 Once we have time to give back, then we tap into why did humans go into the medical profession
0:28:52 in the first place.
0:28:57 And the reason was because they want to care for their fellow human being, but they lost
0:28:58 their way.
0:29:03 And now we have the peak burnout and depression and suicide in the history of the medical
0:29:04 profession.
0:29:08 And by the way, not just in the US, in many parts of the world, and how are we going to
0:29:10 get that back?
0:29:15 Because it turns out, if you have a burnout doctor, you have a doubling of errors and it’s
0:29:20 a vicious cycle, you have errors, and they get more burnout, more depressed.
0:29:21 So we have to break that up.
0:29:29 And I think if we can get people, so there’s time together, and that real reason why the
0:29:34 mission of healthcare is brought back, we can do this.
0:29:38 It’s going to take a lot of activism, it’s not going to be easy, and it’s going to take
0:29:39 a while.
0:29:42 But if we don’t start planning for this now, it’s not going to happen.
0:29:45 How do you think that changes for how do you become a doctor?
0:29:50 I mean, getting into med school and all the training is really difficult.
0:29:53 What does the future of medical education look like?
0:29:59 Right now, pre-med degree is a lot of biology and chemistry, not too much effort in psychology
0:30:06 or empathy or in statistics or in machine learning.
0:30:07 What does that look like in the future?
0:30:10 I think we’re missing the mark there.
0:30:18 We continue to cultivate Brainiacs, who have the highest MCAT scores and grade point averages,
0:30:22 and oftentimes relatively low on emotional intelligence.
0:30:24 So we have tilted things.
0:30:25 We want to go the other way.
0:30:30 We want to emphasize who are the people who have the highest interpersonal skills, communicative
0:30:35 abilities, and who really are the natural empathetic people.
0:30:40 Because a lot of that Brainiac work is going to be machine generated.
0:30:45 And so it’s something that we all start to lean in that direction.
0:30:46 Yeah.
0:30:49 Now, and it’s intriguing because I think there’s a chicken and egg problem here because I think
0:30:51 first this has to be put in.
0:30:55 Often in these eddies, big changes, there will be resistance.
0:30:56 Who’s going to be fighting this?
0:31:00 The resistance we have to anticipate is going to be profound.
0:31:08 One of the problems is that the medical profession, it may not be ossified, but it’s very difficult
0:31:09 to change.
0:31:16 The only changes that have occurred rapidly, like the adoption of robotics and surgery,
0:31:19 were because it enhanced revenue.
0:31:21 These are none of these things are going to enhance revenue.
0:31:24 They’re actually going to potentially be a hit.
0:31:27 We have all these interests that this is going to challenge.
0:31:33 But for example, we could get remote monitoring of everyone in their home instead of being
0:31:38 in a hospital room, unless they were needing an intensive care unit.
0:31:42 Now, do you think hospitals are going to allow that to happen?
0:31:47 Because they could be gutted, and then they won’t know what to do with all their facilities.
0:31:50 So the American Hospital Association is not going to like this.
0:31:51 So I’ll be delusional here.
0:31:53 They’re not going to like it revenue-wise.
0:31:58 Would they say that would put patients in danger, because obviously at home you don’t
0:31:59 have what a hospital has?
0:32:00 Well, you know what the interesting thing is?
0:32:03 I don’t know if you can get in more danger than going into our hospitals.
0:32:05 One in four people are harmed.
0:32:07 Yeah, they mean sepsis and infection.
0:32:10 The main thing, nose and commu infections from the hospital.
0:32:15 But other medication errors and other things, the comfort of your own home.
0:32:19 You can actually sleep, you’d be with your loved ones, the convenient.
0:32:22 But most importantly, just think of the difference in expense.
0:32:29 You could buy years of broadband data plan for one night in the hospital, which is $5,000
0:32:30 on average.
0:32:31 It’s amazing.
0:32:37 We have the tools to do that now, but you’re not seeing it being seriously undertaken because
0:32:38 of the conflicts.
0:32:43 So if you think about how all this has to actually happen, we talked about what’s possible.
0:32:48 But if you get to nuts and bolts, it’s interesting to think who’s going to do it.
0:32:53 Because if you take just a pure data scientist who doesn’t understand the medicine.
0:32:58 I don’t know if that would be enough, but also I don’t know if you could take a doctor
0:33:01 that doesn’t understand data science.
0:33:06 And so is it going to be teams of commingial groups that get this together?
0:33:09 Because there will be iterations between the data science and the biology and the clinical
0:33:14 aspects that have to come one after the other to be able to make these advances.
0:33:19 We need machines and people to get the best of both worlds.
0:33:28 So in the book, that example of how we cracked the potassium case between Mayo Clinic cardiologists
0:33:30 and a live core data scientist.
0:33:36 And what was amazing about that experience to review with them was that the cardiologists
0:33:40 thought you should only look at one part of the cardiogram, which historically known as
0:33:45 so-called QT interval, because it was known to have something to do with potassium.
0:33:51 But when that flunked and the algorithm was a farce, the data scientist said, well, why
0:33:52 are you so biased?
0:33:54 Why don’t we just look at the entire cardiogram?
0:33:58 And by the way, Mayo, you only gave us a few million cardiograms.
0:34:01 And why don’t you give us all the cardiograms?
0:34:02 So then they nailed it.
0:34:08 So the whole idea is that the biases that we have that are profound.
0:34:15 But when you start de-biasing both the data scientist and the doctors, the medical people,
0:34:18 then you start to get a really great result.
0:34:23 One of the scariest stories I saw was that this algorithm was getting cancer, no cancer
0:34:29 right with crazy high accuracy, like AUC of like 1.0, like never making a mistake.
0:34:33 And it turned out that there was some subtle difference between like a high Tesla magnet
0:34:39 and a low Tesla magnet, and that the patients who were very sick to start off with were
0:34:42 always getting one type of scan, and that a human being couldn’t tell the difference,
0:34:47 but that the machine was picking up some signal, not of whether it was cancer, no cancer,
0:34:52 but whether they were getting like the fancy measurement or the less complicated one.
0:34:56 Or another great example is like there’s a classic example where they’re I think predicting
0:35:01 tumors and they had rulers for the size of the tumor on all the tumor ones.
0:35:03 And so really ML was a great ruler detector.
0:35:04 Yeah.
0:35:10 The whole idea that as a pathologist we can’t see in a slide the driver mutation, but you
0:35:13 could actually train the algorithms.
0:35:17 So when the pathologist is looking at it, it’s already giving you what is the most likely
0:35:19 driver mutation.
0:35:20 It’s incredible.
0:35:27 And that does get me to touch on the deep science side of this, which we aren’t recognizing
0:35:31 is way ahead of the medical side, the ability to upend the microscope.
0:35:35 You don’t have to use fluorescence or H and E. You just train.
0:35:37 So you forget staining.
0:35:43 The idea that you used to be hard to find rare cells, just train the algorithms to find
0:35:44 the rare cells.
0:35:50 I mean, we’re seeing some things in science, no less in drug discovery, in processing cancer
0:35:57 and sequencing data and certainly in neuroscience, it’s a real quiet revolution that’s much
0:36:01 further ahead than on the medical side because there’s no regulatory hurdles.
0:36:05 And you make a good point because I think it’s tempting to just try to do what the human
0:36:08 can do better or what the human can do better now, try to do as well.
0:36:12 But now you’re talking about doing things that no human being could do.
0:36:13 Yeah.
0:36:17 Imaging plus genomics where the genomics read out, let’s say, or whatever the blood assay
0:36:18 is, the gold standard.
0:36:21 I don’t want to predict what the pathologist would say.
0:36:25 I want to predict the biopsy, I want to predict the blood or whatever the true gold standard
0:36:26 is behind it.
0:36:27 Right.
0:36:30 And if you’re training on the best labels, you can do things that no human being could
0:36:31 do.
0:36:36 Well, you know, this may be the most important point is that we have to start having imagination
0:36:44 because we don’t even have any idea of the limitless things that we could teach machines.
0:36:46 Because I’m getting stunned almost on a weekly basis.
0:36:49 I said, I never would have thought of that.
0:36:52 And so just fast forward, here we are in 2019.
0:36:53 What’s it going to be like?
0:36:56 You know, a few years of all the things, like when the Mayo Clinic told me they could look
0:37:01 at a 12-lead cardiogram for millions and be able to say this person’s going to get
0:37:06 atrial fibrillation in their life with X percent probability, I said, really?
0:37:07 And they’ve done it.
0:37:10 And so I never would have expected that.
0:37:11 Yeah.
0:37:16 That’s a really fun point because, and you could either think of two ways that the human
0:37:21 beings aren’t being imagined enough or what does imagination mean for an algorithm?
0:37:22 Right.
0:37:28 Well, if we get into heavy and unsupervised learning, we’re a bit limited by the annotation
0:37:31 and the ground truth going back to that.
0:37:35 You can only imagine things when you have those for supervised learning.
0:37:40 But you know, as we go forward, we’ll have more of those data sets to work with and we’ll
0:37:47 be better at going forward without, well with federated data sets and unsupervised learning.
0:37:50 So the opportunities going forward are pretty enthralling.
0:37:51 Yeah.
0:37:54 Well, the unsupervised learning is interesting because you can finally just, you know, and
0:37:57 for those who aren’t familiar with the term, it’s kind of like trying to find the clusters
0:38:01 to sort of not have the labels, but to see the lay of the land.
0:38:04 And that’s interesting because no human being can sort of, especially in high-dimensional
0:38:07 space, like visualize that and see that.
0:38:08 And so that’s one thing.
0:38:13 The second thing is that if you just throw all of the data in and maybe have the algorithm
0:38:18 make sure that it’s not overfitting, that it’s not trying to find an overly complicated story
0:38:23 almost like, you know, these conspiracy theories are like human beings overfitting for the
0:38:26 moon landing being a hoax or something like that when there’s a simpler explanation for
0:38:27 things.
0:38:30 If you keep it to a simple explanation, the computer can try everything.
0:38:31 Yeah.
0:38:32 Yeah.
0:38:33 So like you talked about, it could look at the whole cardiogram.
0:38:39 We could look at things that we don’t look at because either we’re expert enough to
0:38:42 know that couldn’t possibly write even if it is.
0:38:43 Or we just don’t have the time.
0:38:48 It reminds me sometimes these algorithms almost like children in that kids just don’t know
0:38:49 until they’ll try things.
0:38:50 Yeah.
0:38:53 And that’s where imagination and creativity often comes from.
0:38:55 I couldn’t agree with you more.
0:38:58 So we’ve been spending a lot of time talking about diagnosis, but prediction is another
0:39:00 thing that is really important.
0:39:03 I’d call that a real soft spot in AI.
0:39:09 And I told the story of my father-in-law who kind of was my adopted father just in the
0:39:13 book about how he was on death’s door.
0:39:19 He was about to come to our house to die and he was resurrected.
0:39:23 But any algorithm would have said he was a goner.
0:39:29 And so the idea that at the individual level, you could predict accurately whether it’s
0:39:33 end of life or when you’re going to die or in the hospital.
0:39:37 This is how long you’re going to stay or you’re going to be readmitted, all these things.
0:39:38 We’re not so good at that.
0:39:45 We can have a general sense from a population level, but so far prediction hasn’t really
0:39:51 panned out nearly as well as classification, diagnosis, triage, that kind of stuff.
0:39:57 And I still think that that’s one of the shakier parts because then you’re going to tell a
0:40:01 person about a prediction, we’re not very good at that.
0:40:06 When we talk to people with cancer and we tell them their prognosis, it’s all over
0:40:08 the place in reality.
0:40:13 And so the question is, are algorithms really going to do better or are they just going
0:40:17 to give us a little more precision, maybe not much?
0:40:21 Is there enough information to ever predict anything like that?
0:40:25 Well, that’s a part of the problem too is that the studies that have been done to date,
0:40:30 things like predicting Alzheimer’s, predicting all sorts of outcomes you can imagine, they’re
0:40:36 not with complete data, they’re just taking what you can get, like what’s in one electronic
0:40:41 health record, one system, rather than everything about that person.
0:40:43 So maybe it will get better when we fill in the holes.
0:40:47 I always think about what would be the interesting challenges to work on.
0:40:48 That’s like one of the most interesting ones.
0:40:54 I think it is because there you could improve the efficiency if you knew who are the people
0:40:58 at the highest risk and who you want to change the natural history or what their algorithm
0:41:01 is predicting if it’s something that’s an adverse outcome.
0:41:06 So eventually we’ll probably get there, but it isn’t nearly as refined as the other areas.
0:41:11 But if you combine all these things together, this thing where you’re monitoring your body
0:41:16 every five minutes and your diet and your exercise and your drugs and you have all this
0:41:19 longitudinal data, that’s something that no one’s ever had before.
0:41:27 Yeah, well, you’re bringing up a big hole in the story, which is multimodal data processing.
0:41:29 We are not doing it yet.
0:41:35 Like a perfect example is like in diabetes, people have a glucose sensor and the only
0:41:40 algorithm they have tells them if the glucose is going up or down, that’s pretty dumb.
0:41:45 Why isn’t it factoring in everything they eat and drink and their sleep and activity
0:41:47 and the whole works.
0:41:51 Some day we’ll have multimodal algorithms, but we’re not there yet.
0:41:55 Well, so let’s go back to where we start, you know, a visit to the doctor in the future.
0:42:01 And like the good news is that the doctor doesn’t have to do any of the typing or recording
0:42:06 AIs sort of figuring out the diagnosis and that the doctor has all the time now to actually
0:42:09 be empathetic and communicate, which is great.
0:42:11 But is that now all that’s left?
0:42:15 No, no, not at all, because human touch.
0:42:18 So when you go to see a doctor, you want to be touched.
0:42:21 That’s the exam part of this.
0:42:28 People, when they get examined for their heart and you don’t even take off their shirt, they
0:42:30 know, they know there’s a shortcut going on.
0:42:31 Yeah, that’s interesting.
0:42:41 They have a thorough exam because they know that that’s part of the real experience.
0:42:44 And so what we’re talking about is the exam may change.
0:42:46 Like, you know, for example, I don’t use a stethoscope.
0:42:50 I use a smartphone ultrasound and do an echocardiogram.
0:42:54 And I show it to the patient together as we’re doing it in real time, which the person would
0:42:55 never see.
0:42:59 And by the way, they wouldn’t know what love dub looks like, but you sure can see or sounds
0:43:02 like, but you sure can show them.
0:43:09 So the tools of the physical exam may change, but the actual hands-on aspects of it and
0:43:15 the interaction with the person, the patient, and that that’s the intimacy.
0:43:16 And we’ve lost that too.
0:43:23 You know, the physical exams have really gotten very much a detraction from what they used
0:43:24 to be.
0:43:25 I mean, we need to get back to that.
0:43:28 That’s what people want when they go see a doctor.
0:43:33 And people have deprecated exams because essentially they said they weren’t of value, but it sounds
0:43:36 like what was being done was not the part that needed to be done.
0:43:41 Well, when you’re dealing with analog tools and, you know, they can be so superseded by
0:43:42 the things we have today.
0:43:46 And when you’re sharing them with the patient, so here’s what you have.
0:43:51 And then you send them the video files or the metrics that they can look at, you know,
0:43:54 when they get home and get more familiar with their body.
0:44:00 It’s not only the physical exam that happens instantaneously in the encounter, but the
0:44:05 ability to have that archived data that people get more, they learn about themselves.
0:44:08 That’s all part of that awareness that’s important.
0:44:11 And you know, you talked about back to the future, there might be another sci-fi analogy.
0:44:15 I think there’s some Star Trek episodes like this where actually the group that has the
0:44:18 highest technology is the one where the technology is invisible.
0:44:19 Yeah.
0:44:23 And it sounds like that’s where the, all of this is going to be in the background.
0:44:24 That’s right.
0:44:28 You really are interacting with a person and this person now has just these powers that
0:44:29 they couldn’t have before.
0:44:30 Yeah.
0:44:31 I’m with you all the way.
0:44:32 Well, thank you so much.
0:44:33 This has been fantastic.
0:44:34 I really enjoyed it.

0:00:03 The content here is for informational purposes only,
0:00:05 should not be taken as legal business tax
0:00:07 or investment advice or be used to evaluate
0:00:10 any investment or security and is not directed
0:00:14 at any investors or potential investors in any A16Z fund.
0:00:18 For more details, please see a16z.com/disclosures.
0:00:21 – Hey, I’m Andrew Chen from Injuries and Horowitz
0:00:24 and today we have Justin Kahn, who is one
0:00:26 of our repeat entrepreneurs that we are very excited
0:00:28 to be working with on Atrium.
0:00:31 And so we’re gonna talk a bunch about
0:00:33 what is it like to be a repeat entrepreneur?
0:00:34 I think we were just going through the list.
0:00:37 There’s like five different companies on there
0:00:39 and you’ve learned a ton from every single one.
0:00:42 And so we’re gonna do a series of sort of compare
0:00:45 and contrast across quite a number of topics.
0:00:47 But as a very first step,
0:00:50 I think it’d be awesome to have Justin talk about
0:00:53 some of the companies that you’ve been involved in.
0:00:56 And I know the company you were running when we first met
0:00:57 where you were running around with a camera on your head
0:01:00 is not actually even your first one.
0:01:01 There’s one before that called Kiko.
0:01:03 So why don’t you talk about Kiko first?
0:01:04 – Sure, yeah.
0:01:06 So I’ve been an internet entrepreneur here
0:01:09 for the last 14 years since 2005.
0:01:11 Our very first company was called Kiko.
0:01:13 It was kind of like Google Calendar,
0:01:16 but it came out one month before Google Calendar came out
0:01:20 and wasn’t really that good when I’m honest about it.
0:01:23 And so that company didn’t work out super good.
0:01:26 We ended up fire selling it on eBay
0:01:28 after several failed acquisition attempts
0:01:30 with the Sullivan Valley players.
0:01:34 And then after we did that, we started another company.
0:01:39 This one was even less well thought out than Kiko was actually,
0:01:42 which was the idea was we would create our own
0:01:45 live video reality TV show on the internet.
0:01:48 I do think that tapped into several things
0:01:49 that were in the zeitgeist
0:01:50 that actually have become popular now.
0:01:54 But unfortunately, we as talent in our own show
0:01:56 were not very entertaining and not very popular.
0:01:58 And so we launched this live streaming show.
0:02:00 We called it Justin TV.
0:02:03 ‘Cause I was the only one in our four founders
0:02:05 who was stupid enough to put the camera on his head
0:02:09 and be like, I’ll make myself the subject of this show.
0:02:12 And you literally wore, I remember you wore a backpack.
0:02:15 – Yeah, so this was 2007.
0:02:19 So it was pre-iPhone, pre-good cellular internet.
0:02:21 So we had this computer in a backpack
0:02:25 with like multiple cell phone modem connections.
0:02:27 And we hooked it up to a camera.
0:02:30 So there was a camera, this computer virtualized the video
0:02:33 as a webcam, basically sent it over to the server.
0:02:36 And then we had this very hacky way of streaming it out
0:02:39 to the millions of people watching,
0:02:40 actually not millions, hundreds,
0:02:43 but the people who were watching at home,
0:02:46 they were following along, it was actually pretty fun.
0:02:48 They could text, we put our number up there,
0:02:51 they would text me, usually fairly offensive things actually.
0:02:55 But then eventually we launched this show,
0:02:57 people started coming because they were like,
0:02:59 what is this guy doing, this is crazy.
0:03:02 They were like, you’re very boring, I hate your show,
0:03:04 but I want to create my own live video stream.
0:03:05 So how are you doing it?
0:03:07 And then the light bulb kind of went off
0:03:10 and we said, aha, let’s create a live video platform.
0:03:12 And that became Justin TV, the platform.
0:03:15 And then after that, we ran that for a couple of years,
0:03:17 I’ve condensed it because it’s a very long story,
0:03:19 but we raised a bunch of money,
0:03:21 ran it for a couple of years,
0:03:24 hit the nuclear winter of video startups
0:03:26 where all the other video startups in Silicon Valley died.
0:03:30 We were pretty scrappy and survived on like a ramen budget.
0:03:33 Eventually decided we needed to pivot to some new ideas.
0:03:36 And from there we incubate a few ideas internally.
0:03:38 One of them was a video app called social cam,
0:03:40 which we spun off and eventually sold to Autodesk.
0:03:45 And another one was a site that we,
0:03:48 my co-founder really thought of,
0:03:50 which was the idea was focus on,
0:03:53 let’s focus on the video game related video on our site.
0:03:57 And that became Twitch, which kind of grew and grew and grew.
0:03:59 We eventually pivoted the entire company to Twitch.
0:04:02 My co-founder Emmett was the CEO of the company
0:04:06 and eventually sold it to Amazon for $970 million in 2014.
0:04:08 About five years ago.
0:04:10 Along the way started some other companies
0:04:11 to start this company called exec,
0:04:14 which is in the errand running/home cleaning space.
0:04:16 It’s kind of like a handy year home joy.
0:04:19 We actually ended up selling it to handy,
0:04:22 which through an act of God sold to Angie’s List this year.
0:04:26 And then more recently,
0:04:28 I’d been a partner Y Combinator for a couple of years
0:04:30 and then incubated a few companies.
0:04:34 One of those was this video Q&A app called Whale.
0:04:37 And then just fast forwarding all the way
0:04:38 up till the present day,
0:04:42 decided I want to really put all my eggs back in one basket,
0:04:45 precariously thrown around basket.
0:04:47 And so I decided I’d start a new company.
0:04:48 That idea was Atrium,
0:04:51 which is a technology enabled law firm for startups,
0:04:53 really trying to solve all the problems that I had
0:04:56 as an entrepreneur when dealing with legal.
0:04:58 To make legal faster, more price predictable,
0:05:00 more transparent for me, the business owner,
0:05:02 you know, the business manager.
0:05:05 And that’s what we set out to do about two years ago.
0:05:06 It’s going pretty well.
0:05:08 We’re serving a bunch of startups here in Silicon Valley
0:05:09 with all of their needs.
0:05:12 And it’s been great, but I’m sure we’ll get to that.
0:05:13 – Awesome, yeah.
0:05:18 Well, I think one place where I’m going to start on this
0:05:23 is a lot of the advantages of being a repeat entrepreneur
0:05:25 are that, you know, you can raise more money
0:05:29 and there’s more, you know, maybe easier to recruit talent.
0:05:30 And there’s, you know, there’s all these advantages
0:05:32 that kind of, you know, come along with that.
0:05:35 One of the disadvantages I find ends up being that,
0:05:38 you know, there’s so many like distractions, right?
0:05:39 Like you could, there’s a million different things
0:05:40 you could do.
0:05:42 There’s a lot of things pulling at your attention.
0:05:45 I’m really fascinated by, you know, your movement
0:05:48 from going from YC and an incubator
0:05:51 where you can maybe kind of dip into a lot of little things
0:05:53 versus kind of putting all your eggs in one basket
0:05:54 and trying to like start a company.
0:05:56 Like, you know, to talk to us about that,
0:05:57 that kind of decision.
0:05:59 – Yeah, that’s a great question.
0:06:01 So once you become, you know, successful in some way
0:06:03 in Silicon Valley, whether that’s, you know,
0:06:04 you’ve been the executive at a company
0:06:06 that’s, you know, rocket ship unicorn
0:06:08 or you’ve started a company, you know,
0:06:09 the world opens up, right?
0:06:11 People want you to be a VC.
0:06:15 They want you to, you know, work on projects with them.
0:06:18 You can start any company that you want,
0:06:19 which is great, right?
0:06:20 But there’s just paradox of choice
0:06:22 and focus can be a huge problem.
0:06:25 I know for a lot of friends of mine, it has been as well.
0:06:27 As an investor, as a partner at YCom,
0:06:28 there were some parts I really liked.
0:06:31 You know, I loved helping early stage founders out
0:06:34 and like really working with them on these problems that,
0:06:36 like I felt like for five to 10% of them,
0:06:37 it was like life changing, right?
0:06:39 Like me helping them out in a way,
0:06:40 I came up with some great idea
0:06:42 or helped them at a critical moment.
0:06:43 That was life changing.
0:06:44 But then there was a large majority of them
0:06:46 that probably could have been listening
0:06:48 to a YouTube video of me or this podcast
0:06:50 and like you get the same information, right?
0:06:54 And so I didn’t feel like the feedback cycle
0:06:56 was fast enough also as an investor.
0:06:57 And I didn’t really feel ultimately,
0:06:58 like after the first couple of years
0:07:00 that I was continuing to learn and grow.
0:07:02 And you know, I’m still in my 30s.
0:07:03 I was like, I need to do something
0:07:05 where I’m going to be forced to grow.
0:07:09 And really the number one vehicle for personal growth
0:07:11 that I’ve ever experienced in my life has been startups.
0:07:13 You know, so I went back to when I knew
0:07:17 and I really decided that in order to grow as a founder,
0:07:19 you know, we had a pretty big outcome with Twitch
0:07:21 and I’d seen a lot of different things
0:07:24 in order to really grow to the next level.
0:07:26 I would have to do something that potentially
0:07:27 could be even bigger.
0:07:29 And so I really felt like it deserved,
0:07:30 that deserved my full attention.
0:07:33 And so that’s kind of how I decided.
0:07:35 I don’t necessarily think it’s the right answer
0:07:37 for everyone because what I didn’t mention
0:07:39 was that I had selective memory at the time
0:07:42 and I forgot just how painful starting a startup could be.
0:07:45 And so for the first couple months of atrium,
0:07:46 I was like, oh man, this is a dream.
0:07:47 I’ve like finally leveled up.
0:07:48 I learned all these skills.
0:07:51 I’ve like, I made it as a founder.
0:07:53 And then reality boom set in.
0:07:56 And of course there were nothing ever goes according to plan.
0:07:58 There were pains, there were struggles.
0:08:01 And that was, you know, that’s part of the journey.
0:08:03 But then the good part is, of course,
0:08:06 when you experience pain, that is a catalyst for learning.
0:08:08 And so I really got what I wanted in the end,
0:08:10 which was forced to learn.
0:08:11 – Right, that’s great.
0:08:13 And I know one of the big differences
0:08:16 among some of the companies that you started
0:08:17 in the past versus atrium,
0:08:20 and something that you’ve talked a lot about is,
0:08:21 you had this sort of succession
0:08:24 of very like consumer oriented startup,
0:08:26 sort of like watching other people play video games.
0:08:28 Like that’s as consumers, basically.
0:08:33 And so very interestingly, atrium is a B2B thing.
0:08:36 And why did you choose B2B?
0:08:37 Was it just for novelty?
0:08:39 Was it just to push yourself?
0:08:43 Or do you think that there’s something different in mind
0:08:47 that maybe takes advantage of some of your new found skills?
0:08:48 – Yeah, well, Twitch, I guess, is really the,
0:08:50 it’s like the ultimate consumption thing,
0:08:51 ’cause it’s you’re consuming someone,
0:08:53 consuming video games.
0:08:54 – Right, right.
0:09:00 – For me, I felt like maybe this was an analysis
0:09:01 I’ve done like retroactively,
0:09:03 but I think I’ve had this discussion
0:09:05 with a couple people with multiple time founders.
0:09:07 And when you’re an early stage founder,
0:09:09 when we started Kiko and then Twitch,
0:09:10 when we started Twitch, we were like,
0:09:12 or just in TV, it was like we were 23 years old.
0:09:14 And we had no skills.
0:09:17 I never had a real full time job in my life.
0:09:18 And even though we were programmers,
0:09:20 we were like new college grad programmers.
0:09:22 We were not good.
0:09:24 We were horrible managers.
0:09:26 We basically had nothing going for us.
0:09:28 When you have nothing going for you,
0:09:31 except for your like willing to put in hard amount,
0:09:35 like long hours on a lot of blood, sweat, and tears,
0:09:38 then you should focus on things that are like,
0:09:40 where there’s a lot of market risk,
0:09:43 because ideas where there’s market risk,
0:09:45 you can potentially win those, right?
0:09:50 Now, as someone who has abilities and skills,
0:09:53 where I’ve learned skills over the last 14 years,
0:09:57 you wanna focus much more on like execution risk things.
0:09:59 And so I felt like B2B startups
0:10:01 are more about execution risk.
0:10:03 I felt like this legal market,
0:10:05 particularly was already a big market,
0:10:06 and you just have to figure out
0:10:08 how to do it 10 times better, right?
0:10:09 And I felt like I had a roadmap
0:10:11 for how to do that in my head.
0:10:14 And so that’s why I felt that this was a better use of time.
0:10:17 ‘Cause some of the consumer startups that I had incubated
0:10:18 and played around with post Twitch,
0:10:20 actually it was really hard to find product market fit, right?
0:10:22 Like, I don’t have any advantage
0:10:25 in finding product market fit with a consumer app
0:10:27 more than the 22 year old Justin.
0:10:28 – You might have a disadvantage.
0:10:29 – Yeah, I probably have a disadvantage
0:10:31 ’cause I’m like already more set in my ways.
0:10:34 I’m like less in tune with the culture.
0:10:36 I’m like, I don’t know what the kids are doing.
0:10:40 So, with the Justin of today, invent the Twitch of today.
0:10:41 Like, I don’t think so, right?
0:10:43 Like I’m an old guy now, man.
0:10:44 (laughing)
0:10:45 It’s game over for me.
0:10:48 – I wanna unpack this market risk, execution risk.
0:10:49 ‘Cause that’s obviously,
0:10:51 it’s such an important distinction,
0:10:55 but very colloquial, kind of in our understanding of it.
0:10:57 What do you mean by market risk
0:10:59 and sort of maybe new entrepreneurs
0:11:00 can have an advantage in market
0:11:02 and tackling something with new market?
0:11:04 How do you know if an idea has a lot of market risk?
0:11:05 – Sure, that’s great.
0:11:08 So, Twitch and H&M are the perfect examples, right, almost.
0:11:10 So, Twitch, it’s like when we started,
0:11:12 when we pivoted Justin DV to Twitch,
0:11:13 or even Justin DV is a good example,
0:11:15 but Twitch is the best one probably.
0:11:17 When we pivoted Justin DV to Twitch,
0:11:20 nobody believed that there would, this was a market, right?
0:11:22 No one believed, no investors,
0:11:25 very few of our even internal employees believed,
0:11:27 and even the founders were skeptical.
0:11:29 Emmett deserves the credit here, ’cause he had belief,
0:11:31 but a lot of the other co-founders were skeptical.
0:11:34 I’m like, does this exist as a business?
0:11:37 And so, the good part is that the competition there
0:11:39 was very low, right?
0:11:41 There weren’t experienced entrepreneurs being like,
0:11:43 this is gonna be a huge business, we should compete.
0:11:47 So, really it was, the entire thing was market risk
0:11:49 and figuring out, do we have product market fit,
0:11:50 how do we build product market fit?
0:11:51 – ‘Cause in that case,
0:11:53 you’re trying to be the first in the category.
0:11:54 There’s no substitutes really.
0:11:57 You’re watching someone else play street fighter
0:11:59 at the arcade, that’s a substitute.
0:12:00 – Exactly, you have a lottery ticket, right?
0:12:01 – You have a lottery, yeah.
0:12:04 – It’s a lottery ticket, and if you pivot a bunch of times
0:12:05 and listen to your customers,
0:12:06 you might be buying more and more lottery tickets.
0:12:08 But your lottery tickets are just as valuable
0:12:11 as the experienced entrepreneur’s lottery tickets
0:12:12 that he’s buying.
0:12:14 So, he’s a fool to play that game,
0:12:16 and I don’t think you see as many experienced entrepreneurs
0:12:18 playing that same game.
0:12:20 Whereas, with execution risk businesses,
0:12:23 my lottery ticket now is way bigger than the guy
0:12:26 who’s like the 22-year-old Justin, right?
0:12:30 So, for a B2B startup, I know, oh, I can attract talent.
0:12:32 I can hire a sales team.
0:12:34 I can raise capital.
0:12:36 And so, it’s a lot more, for something
0:12:39 where it’s very established that that’s gonna be a business,
0:12:41 or some business is gonna be in there,
0:12:45 it’s like he’s stupid to play against me, you know?
0:12:47 – Right, that makes sense.
0:12:48 Well, you know, and I always find it funny
0:12:51 that in the consumer startup world,
0:12:54 that if you look at the last kind of decade of hits,
0:12:56 if you were to tell people, oh, yeah,
0:12:58 the biggest hits are gonna be this app
0:13:00 that lets you get in strangers’ cars,
0:13:02 this app that lets you stay at someone
0:13:04 you don’t know’s like house,
0:13:07 an app where you swipe left and right
0:13:09 in order to meet people on the internet,
0:13:12 and an app that lets you watch other people play video games.
0:13:16 You would be like, that’s the craziest list of
0:13:18 billion-dollar companies I’ve ever heard.
0:13:21 And yet, that that is actually how consumer
0:13:23 the internet actually, you know,
0:13:25 the ecosystem unfolds, it’s insane.
0:13:27 – It takes a lot of people who have nothing to lose
0:13:30 to discover those ideas, right?
0:13:33 – Right, yeah, and so one of the things,
0:13:35 one of the clear advantages in all of this
0:13:38 is that, you know, let’s talk about fundraising,
0:13:42 and the decision on whether or not to raise
0:13:44 a bunch of money out of the gate,
0:13:48 versus doing the kind of, you know,
0:13:51 ramen profitable, you know, cockroach thing, right?
0:13:54 That’s sort of like, you know, one common contrast,
0:13:55 and then obviously you had a very unique
0:13:57 fundraising strategy as well.
0:13:59 So maybe talk about that decision,
0:14:01 and then kind of how you ended up pursuing it.
0:14:03 – Look, I’m not convinced that raising a ton of money
0:14:05 out of the gate is the right strategy.
0:14:08 You know, the Silicon Valley is littered
0:14:10 with dead bodies of these companies,
0:14:13 you know, the Juceros, and the Kools,
0:14:15 and like all these companies that have raised
0:14:17 a ton of money, and then like they,
0:14:20 when you have a ton of money, you spend a ton of money, right?
0:14:23 Now there’s other companies like the Jet.coms
0:14:24 that, you know, they made it work.
0:14:28 So, you know, I’m not convinced it’s the worst strategy ever,
0:14:29 but I’m not convinced it’s the best.
0:14:32 But for me personally, you know,
0:14:34 where it’s an execution risk business,
0:14:37 I’m too rich to like, fuck around with the like,
0:14:39 you know, okay, I’m just gonna do a seed round,
0:14:42 and like, and just like, take a long time, right?
0:14:45 Like for me, speed to market and execution
0:14:47 was really important, and I felt also like
0:14:48 this market really supported and required it,
0:14:51 because, you know, it is the legal space,
0:14:54 there is, you know, so a lot of value
0:14:57 in making sure that the clients and the talent,
0:15:00 the attorneys talent, and other legal providers,
0:15:03 like, think this company’s gonna be around, right?
0:15:05 So that’s very important.
0:15:06 And so, you know, this is a strategy,
0:15:08 I don’t necessarily recommend to anyone
0:15:11 who can raise a ton of money that this is the right strategy,
0:15:13 it’s just the strategy that we picked.
0:15:16 In terms of the tactics of like how we did our round,
0:15:18 especially our Series A, you know, my idea was,
0:15:22 because VCs are kind of naturally adjacent to legal,
0:15:25 right, like, for example, when you fund a company,
0:15:27 they need someone to help them on the legal side
0:15:30 with all of the, you know, paperwork
0:15:32 and the execution of that funding round.
0:15:35 We felt like getting a lot of VCs on our side
0:15:37 would be a good tactic, and so we ended up going out
0:15:40 and raising money for our seed round of, you know,
0:15:44 over 90 different investors from all over Silicon Valley
0:15:46 because I felt like it would be really good
0:15:48 to have those investors on our side
0:15:50 and recommending Atrium as a channel partner,
0:15:54 and so that was our tactic there.
0:15:56 – Right, and I think it’s something where,
0:16:00 you know, to your point, if something
0:16:03 that you’re working on is primarily execution,
0:16:05 then that means that, you know, you can,
0:16:08 there are times and places where you can use money
0:16:10 to solve it versus, it seems like, you know,
0:16:12 part of the market risk thing is it just, you know,
0:16:14 to your point, it sort of lets you buy more lottery tickets,
0:16:17 but it may not accelerate the process
0:16:19 of actually doing it, right, and so I think that,
0:16:21 that sort of feels like one main difference,
0:16:23 and then the other one is, you know,
0:16:25 that just to build on what you’re saying is that
0:16:28 if you are in an industry where trustworthiness
0:16:31 is really important, then being well-capitalized is key,
0:16:33 you know, the same way you wouldn’t, you know,
0:16:36 if you’re gonna, you know, for example,
0:16:39 you know, build a FinTech startup where, you know,
0:16:41 you’re gonna ask consumers to trust their money
0:16:44 with you, you know, like you wanna be legit,
0:16:46 you wanna be well-capitalized, you wanna have, like,
0:16:48 you know, super strong executives
0:16:50 and board members and investors, and like,
0:16:52 and that’s a strategy, kind of, that’s a little bit,
0:16:54 kind of, like, self-perpetuating as well.
0:16:55 – Yeah, that’s a great example.
0:16:57 If you’re gonna build, like, a new bank, right,
0:16:59 like an online, or like a mobile-first bank,
0:17:01 which is, I think, some people are doing,
0:17:05 would you wanna raise, you know, $1 million,
0:17:07 or $1 million seed round, or a $5 million seed round,
0:17:11 or a $50 million series A out the gate?
0:17:12 Obviously, if it’s available to you,
0:17:15 you want more money, ’cause you know people need banks, right?
0:17:17 It’s just a matter of can you do it better, right?
0:17:19 That’s a perfect example, and there’s, you know,
0:17:20 there’s many others here in Silicon Valley.
0:17:22 I think we’ve actually shifted more
0:17:25 as the cycle has changed over the last 10 years,
0:17:26 the tech cycle.
0:17:29 We’ve shifted more to these execution-risk startups,
0:17:31 and so, you know, you’ve consequently seen,
0:17:33 I mean, it’s a chicken and egg thing, really,
0:17:35 what came first, but like, you’ve seen these more and more,
0:17:37 like, bigger rounds, I’d say,
0:17:39 that are supporting these companies.
0:17:41 – Well, you know, one of my,
0:17:44 one of the partners here, Chris Dixon,
0:17:46 has talked about the idea that, you know,
0:17:50 if you have a set of problems that has not been able
0:17:52 to get solved and improved in, you know,
0:17:55 the 20 years of the modern internet,
0:17:59 then maybe all the techniques that we pioneered
0:18:01 in the last, you know, decade plus,
0:18:04 like being asset-light and just throwing software,
0:18:06 and just shipping really quickly, and being really lean,
0:18:09 like, maybe those techniques don’t work for a reason
0:18:12 in healthcare and fintech and legal services
0:18:14 in real estate, and like, you know,
0:18:15 some set of those things, right?
0:18:17 And so then, very quickly, you have to think,
0:18:19 okay, well, you know, if those techniques don’t work,
0:18:20 otherwise, it would have,
0:18:21 someone would have tried it already,
0:18:23 it would have, it would have sort of, you know,
0:18:25 a little bit like efficient market hypothesis.
0:18:26 – Yeah, that’s the thing.
0:18:27 – It would have kind of like happened already,
0:18:31 like, maybe you need a foundationally different approach.
0:18:34 And so, I think that is actually one of the reasons why
0:18:35 there’s more of these like, quote unquote,
0:18:37 full-stack startups that are going after
0:18:39 these really, really difficult areas.
0:18:40 ‘Cause like, you know, otherwise,
0:18:41 you wouldn’t be able to do it.
0:18:44 – Yeah, well, running experiment right now.
0:18:46 – Yes, yeah, right, no, I think that’s right,
0:18:48 I think that’s right.
0:18:50 So, you know, one of the fun topics,
0:18:52 one thing that I have a tremendous amount of respect
0:18:55 for you on is you’re very, you know,
0:18:58 you’re always on the leading edge,
0:19:00 thinking about, you know, self-improvement,
0:19:03 how to sort of, you know, your own, you know,
0:19:07 personal performance at work, you know, at home, et cetera.
0:19:10 And obviously, one of the big things about, you know,
0:19:12 running a company is that it is enormously stressful.
0:19:13 Right? – Yeah.
0:19:16 – And so, talk to us about like, you know,
0:19:18 when you were a first-time entrepreneur,
0:19:21 kind of Kiko, Justin TV, you know,
0:19:22 how did you think about, you know,
0:19:26 managing the stress of, you know, running a company?
0:19:27 And what was your approach there?
0:19:29 And then let’s talk about kind of like,
0:19:30 the new and improved Justin now, you know,
0:19:32 kind of 10 years later.
0:19:33 – Yeah, so in the early days, you know,
0:19:37 10 years ago, I was not doing anything
0:19:39 in terms of like improving myself.
0:19:44 In fact, I think I used to think about people’s attributes,
0:19:47 maybe not your skills so much in terms of like, you know,
0:19:48 it’s your skills at programming or stuff like that,
0:19:50 but more of like your attributes.
0:19:52 Like, I don’t know if you ever played Dungeons and Dragons,
0:19:55 but when you create a character in Dungeons and Dragons,
0:19:57 you roll this like 20-sided die and, you know,
0:20:00 your strength, it’s like 14, your intelligence,
0:20:02 you roll it and it’s six or whatever,
0:20:04 and that’s what you have, you can’t change it.
0:20:06 And so I felt like people’s attributes
0:20:07 were kind of like that.
0:20:09 And so I never worked on that very much,
0:20:11 self-improvement stuff outside of, you know,
0:20:12 like yeah, I became a better programmer
0:20:14 ’cause we were programmed, you know,
0:20:17 but I didn’t work on things to like make myself,
0:20:20 I don’t know, smarter, right, or harder working,
0:20:22 or like awake more hours of the day, right,
0:20:25 like alert more hours a day or anything like that.
0:20:26 So it was very happened, you know,
0:20:28 everything was kind of accidental.
0:20:30 Like we would, you know, I was not dealing with stress
0:20:31 well at all.
0:20:35 If there was a problem in the company,
0:20:37 I would be very emotionally avoided to it,
0:20:41 or I would like drown my sorrows in like alcohol, right,
0:20:43 which is not a very good coping mechanism at all.
0:20:46 And so, and then like, you know,
0:20:47 in terms of even just down to like
0:20:49 what we were like eating at lunch,
0:20:51 I was, you know, we were talking about this this earlier,
0:20:53 but I would, we would have like pizza every day
0:20:56 at lunch at Justin TV, and then I’d like fall asleep.
0:20:57 Like I’d go into like a carb comb,
0:21:00 like a carb comb in the afternoon, like every day.
0:21:04 And so now more recently, like with atrium,
0:21:06 it’s been a tremendous vehicle
0:21:08 for my own personal discovery,
0:21:10 because I experienced the stress again.
0:21:12 You know, I was like, oh my God, it’s stressful.
0:21:16 Again, this is crazy, why is it so stressful?
0:21:18 And so I started looking for ways to deal with that,
0:21:21 and I really, I mean,
0:21:24 if I had a number of things that started working for me
0:21:26 after I started really exploring it,
0:21:27 a number of things that started working for me
0:21:31 starting last year, you know, everything from,
0:21:33 friend of mine recommended a daily gratitude journal.
0:21:35 So I’ve just been doing that every day,
0:21:37 writing this gratitude journal, five minute journal.
0:21:39 You write down the three things every morning
0:21:40 that you’re grateful for.
0:21:42 And that seems like a very simple thing
0:21:44 and kind of hokey, actually, when I first heard about it.
0:21:48 But what I realized was it helps recontextualize
0:21:49 all the ups and downs that you experience
0:21:51 as an entrepreneur, especially, I mean,
0:21:53 the downs really, like throughout the day,
0:21:54 they’re not as big of a deal,
0:21:55 because in the morning, you’re writing down,
0:21:58 like, wow, I have this opportunity.
0:22:00 You know, I remember the day we came in and pitched,
0:22:02 you guys here, I wrote my gratitude journal,
0:22:05 I get to pitch, you know, Andrewson Horowitz.
0:22:06 That’s amazing, right?
0:22:09 Even whatever happens, that’s an incredible opportunity.
0:22:13 It puts me in the top 0.01% of people in the world
0:22:15 without opportunity, you know?
0:22:18 So that was like pretty amazing.
0:22:20 And then, you know, every day there’s something,
0:22:21 like if you really think about it,
0:22:24 there’s so many amazing things that happen to you
0:22:27 as a human being here in Silicon Valley.
0:22:29 Even just like actually one thing I think in the morning,
0:22:31 oftentimes it’s like the supply chain,
0:22:33 the global supply chain to deliver coffee
0:22:35 from like around the world,
0:22:38 so that I can grind up fresh coffee
0:22:41 and like a pour over in my Chem-X in the morning.
0:22:41 – Right.
0:22:42 – That’s amazing.
0:22:43 – It is amazing. – Like if you think about it.
0:22:44 – It’s totally amazing. – Yeah.
0:22:45 – Right.
0:22:47 – So the gratitude journal really working for me.
0:22:47 And then another thing–
0:22:49 – You’re still eating pizza for lunch every day?
0:22:52 – Yeah, so diet, another thing, I stopped eating pizza.
0:22:54 – So, entirely?
0:22:56 – No, well I started eating a ketogenic diet,
0:22:58 which is a high-fat diet,
0:23:01 but really the reason for me is like,
0:23:05 I just don’t get as tired in the day anymore.
0:23:06 And so that’s been really helpful.
0:23:07 And last year I was experimenting,
0:23:11 I did like some one meal a day, days,
0:23:13 you know, like four days a week.
0:23:15 Some of these weird like Jack Dorsey diets,
0:23:17 kind of Jack Dorsey light or whatever,
0:23:19 but it’s been good for me.
0:23:21 You know, you had to do what your body feels like,
0:23:21 it feels good.
0:23:24 So, you know, that’s some diet and exercise,
0:23:25 pretty religious about exercising,
0:23:28 you know, try to do something every day during the work days.
0:23:29 – Yeah.
0:23:31 – And then the last thing that was really big
0:23:33 is meditation for me.
0:23:35 Start off just with headspace, you know,
0:23:38 I’m not the type of person that people, you know,
0:23:40 assume would be a heavy meditator
0:23:42 or very introspective or anything like that.
0:23:46 But for me, just like starting off with headspace last year,
0:23:48 and then now I’ve been doing transcendental meditation,
0:23:51 which is kind of what Ray Dahlio talks about
0:23:53 and the principles, that’s worked really well for me
0:23:58 to just be more present during the day in my life.
0:24:02 It’s pretty amazing, amazingly profound effect.
0:24:03 – I feel like your Twitter stream
0:24:05 is part of your gratitude journal.
0:24:06 – Yeah.
0:24:07 – ‘Cause I read your Twitter stream and I’m like,
0:24:09 oh, this is like very philosophical.
0:24:11 You know, it’s not like just pulling out like,
0:24:15 oh, here’s a blurb from the latest S1 or something.
0:24:17 Like you’re like, you know, you’re like sharing ways
0:24:20 that the startup community can, you know,
0:24:21 to think about themselves.
0:24:23 – Yeah, the way I think about it is like,
0:24:25 if you want something to be part of your identity,
0:24:26 talk about it.
0:24:28 If you want to learn something, teach it.
0:24:29 You know, and I really believe that.
0:24:31 So for me putting out, you know,
0:24:33 what I’ve been doing on the mindfulness side
0:24:37 and for myself to be more emotionally kind of stable
0:24:40 throughout the days and weeks and months,
0:24:42 as a startup founder, that’s been really valuable to me.
0:24:44 So if I can spread that to other people,
0:24:46 it’s gonna help reinforce it as an identity for me,
0:24:48 but it’s also gonna hopefully help those people as well.
0:24:49 – Right.
0:24:51 You know, one thing I’ve been working on a lot as well,
0:24:54 the last thing I’ll say is just working on realizing
0:24:56 that your attachment out in the very early days
0:24:59 of Justin TV and Twit, Kiko and all these companies,
0:25:03 I had a huge ego attachment to the outcome of the company.
0:25:04 – Yeah.
0:25:06 – Right, my identity and the companies
0:25:07 were like very intertwined.
0:25:10 And more recently, I realized that was the same
0:25:11 at Atrium actually.
0:25:13 Like again, I was like creating that same pattern,
0:25:15 but it was like, it’s a very unhealthy pattern.
0:25:18 So what I realized was I needed to start telling,
0:25:21 reminding myself that no matter what happens
0:25:24 with this company, I’m not gonna be any happier
0:25:25 or any less happy in the long run.
0:25:27 There might be a short burst of unhappiness
0:25:30 if it fails or happiness if it succeeds amazingly,
0:25:33 but you’re not gonna get any happier.
0:25:35 Really, if you’re relying on outside things,
0:25:38 external factors to drive your inner happiness,
0:25:40 you will always be disappointed in the long run.
0:25:43 And the funny thing is I’ve actually run the experiment,
0:25:45 not an A/B test, but a linear experiment on that,
0:25:48 because we’ve had more and more success over time.
0:25:51 Like we started off really paying ourselves,
0:25:54 I think with Kiko, $7,800 a month,
0:25:57 but we only paid ourselves every other month.
0:26:00 And so then we raised funding for Justin D.V.
0:26:02 and we were able to make a little salary,
0:26:05 then we became profitable, we made a lot more,
0:26:07 and then we sold one company, then we sold a lot of companies.
0:26:09 And so we kind of ramped over time.
0:26:14 And after the basics of Maslow’s hierarchy of needs,
0:26:17 we’re taking care of, I felt like I could go out to eat.
0:26:18 I don’t know if that’s on one of those Maslow’s
0:26:20 hierarchy of needs, but after that basic thing,
0:26:22 I could afford to go out to eat.
0:26:24 None of it never mattered.
0:26:26 It never made me sustainably more happy.
0:26:28 And so just reminding yourself of that
0:26:30 and trying to remove those attachments in your mind,
0:26:31 it’s easier said than done,
0:26:34 but having that as like an active practice
0:26:36 has been really important to me.
0:26:38 – Let’s talk about mentorship as part of that, right?
0:26:41 So when you’re building Kiko, Justin D.V.
0:26:45 and you’re a first time entrepreneur,
0:26:49 there’s a lot of experienced people
0:26:52 who are kind of like, go ahead of you in the thing.
0:26:55 And so that’s fantastic ’cause you can learn a ton.
0:26:57 Would love to hear kind of who you thought of
0:26:59 as your kind of lifelong mentors,
0:27:01 people that have helped you for a long time.
0:27:04 And then the other problem that’s super interesting is,
0:27:06 then you start atrium and you’ve had
0:27:08 some major successes behind you.
0:27:11 And then in some ways, the number of people
0:27:13 you can learn from is a much smaller pool
0:27:16 and sort of like, how do you kind of curate your mentorship?
0:27:17 Maybe how has it changed over time?
0:27:20 That’s a broad topic, but would love to hear your opinion.
0:27:21 – Sure, absolutely.
0:27:23 So like the best part about Silicon Valley, in my opinion,
0:27:26 is that there are people here who have done it before
0:27:27 who are willing to help you.
0:27:30 That we would never have made it here even day one
0:27:35 without people who helped us when it was like economically
0:27:37 bad, waste of time.
0:27:40 We weren’t like, didn’t look like a hot prospect
0:27:41 company or anything, right?
0:27:44 So those people will, Paul Graham, great example,
0:27:45 founder of Y Combinator, Paul Buhay,
0:27:47 who is like a partner in Y Combinator,
0:27:50 but they invented Gmail inside of Google.
0:27:52 These are people who invested in us very early on,
0:27:54 mentored us very early on and helped us out.
0:27:57 And that was pretty amazing.
0:28:01 And really that ethos perpetuates itself,
0:28:03 because then as a partner in Y Combinator,
0:28:05 even today as an entrepreneur,
0:28:08 where there’s like lots of conflicting,
0:28:09 competing interests for my time,
0:28:12 I always make sure to spend a little bit of time
0:28:14 mentoring other startups,
0:28:16 because it’s kind of like a pay it forward thing.
0:28:19 And you do the thing that people did for you
0:28:21 when you were younger.
0:28:25 – One of the really obvious sources of mentorship
0:28:26 is actually peer mentorship, right?
0:28:28 And some of the folks that kind of came up with you
0:28:32 at the same time and ended up running
0:28:34 really interesting companies on their own.
0:28:37 And I know they all live in DeBose with you
0:28:39 in San Francisco.
0:28:40 Talk to us about some of your friends
0:28:42 that you consider your mentors.
0:28:44 – Yeah, so it’s great to have friends
0:28:46 who are kind of doing the same path in a lot of ways,
0:28:48 but are one or two steps ahead of you.
0:28:49 So it’s still a case.
0:28:52 Obviously Emmett, my co-founder who’s still running Twitch,
0:28:53 it’s over a thousand people.
0:28:56 I think it’s like 1500 people or something like that.
0:28:59 My brother, who’s COO of Cruise,
0:29:01 co-founder of Cruise, the self-driving car company.
0:29:02 And they’re like over a thousand people.
0:29:05 And Steve, the founder of Reddit,
0:29:08 they’re like 400 something people or whatever.
0:29:11 So, you know, seeing what their problems are,
0:29:13 you know, obviously the problems are always the same,
0:29:14 actually the problems are always like,
0:29:16 I don’t have the right alignment among my team
0:29:19 and I don’t have the right executive team.
0:29:21 It’s usually some variation of those things.
0:29:24 But, you know, hearing it from the horse’s mouth
0:29:28 is super helpful in terms of making decisions for me.
0:29:31 And then, you know, even outside of DeBose,
0:29:32 sometimes I venture into Soma
0:29:33 and having some of those, you know,
0:29:35 kind of early YC founders who have really made it,
0:29:38 you know, like the Drew from Dropbox, for example,
0:29:41 and just knowing like how do they think about
0:29:44 every, all those things, executive hiring, et cetera,
0:29:45 it’s been really helpful, helpful to me.
0:29:48 So, luckily here at Silicon Valley,
0:29:49 I have a lot of great resources.
0:29:51 And the last thing I’ll shout out is,
0:29:55 I have this new executive coach, Matt Mocharri,
0:29:57 who’s like amazing.
0:29:58 This guy is the guru.
0:30:01 He’s, you know, mentors a lot of different fast-growing
0:30:02 startups around here.
0:30:04 I just talked to him for like a 360 thing.
0:30:05 – Yes. – Yeah, yes.
0:30:07 – This guy. – Yeah, that was great.
0:30:09 – Incredible, highly recommend, can’t speak,
0:30:11 he’s like changing my life.
0:30:13 So, I feel like he’s an angel sent down from Heaven
0:30:16 to teach me, finally, after 14 years,
0:30:19 how to like manage a system.
0:30:21 And I’ve learned a lot from him, so.
0:30:21 – That’s great.
0:30:23 – Those are kind of three sets, you know,
0:30:26 executive coach, the peer mentors,
0:30:28 and then kind of those early stage mentors
0:30:29 that I had back in the day.
0:30:30 – That’s great.
0:30:33 And I know one of the topics that you must end up
0:30:36 talking about often is that when you’re building something
0:30:39 that has a little bit more just execution risk,
0:30:42 you know, you’ve raised some real money
0:30:44 to kind of get started.
0:30:46 A lot of it ends up being sort of like organizational,
0:30:47 complexity, company culture.
0:30:51 I know this is the big, big, big, big area focus for you.
0:30:53 And that’s something that is very different
0:30:54 when you’re trying to build something for the long run
0:30:56 versus when you’re kind of just trying to find
0:30:59 product market fit, and it’s kind of like 10 people,
0:31:00 and you’re just like, is this even going to make it?
0:31:02 Like, let’s not even work on this.
0:31:03 – Yeah.
0:31:05 – So talk to us about kind of how your approach has changed
0:31:07 on building the company and your leadership style.
0:31:09 – That is a great question because it’s something
0:31:10 I think a lot about.
0:31:15 So I had never thought before a couple of months ago,
0:31:18 and this may sound stupid in a way,
0:31:20 but I’d never thought, what is the kind of company
0:31:22 that I want to show up to work at?
0:31:25 So 14 years later, finally thinking about it.
0:31:28 But the real answer is like, when you’re a 22 year old,
0:31:30 just starting your company, or you’re in Silicon Valley
0:31:33 and you’re thinking funding rounds and exits,
0:31:35 you’re always thinking, what’s the next milestone?
0:31:37 Like, how do I just claw my way desperately?
0:31:39 However, whatever it takes, how do I get to that
0:31:40 next milestone?
0:31:41 It’s do or die.
0:31:43 And for some, you know, oftentimes it is do or die.
0:31:46 You don’t have the luxury, oftentimes, of thinking
0:31:48 about what kind of, or you feel like you don’t have
0:31:50 the luxury of thinking about what kind of company
0:31:51 you want to build culturally.
0:31:54 And so, I started at Atrium actually very much
0:31:56 in the same way, but like, what are the metrics milestones
0:31:57 we want to hit?
0:31:58 What’s the next metrics milestone?
0:32:00 What do we need to get to for a Series B
0:32:02 or a next round of funding?
0:32:05 And so, the problem with that was that a year in,
0:32:07 I realized, oh, shoot, I need to like,
0:32:10 there are like a lot of things that I’ve neglected
0:32:13 that are actually affecting our ability to execute.
0:32:15 And the number one thing there was,
0:32:18 what’s the culture going to be?
0:32:21 People didn’t know, like, what’s the alignment aspect
0:32:22 of like, what are we building?
0:32:24 What kind of company are we?
0:32:25 Who are we?
0:32:26 What are we building?
0:32:28 And then, what’s the culture?
0:32:29 How are we being intentional about it?
0:32:32 So, we did a lot to work on that,
0:32:35 ran through a collaborative values process over a year ago
0:32:36 where we brought the whole company together,
0:32:38 figure out what we care about.
0:32:40 And then more recently, I’ve been thinking about,
0:32:43 after a lot of this self-work in terms of making myself
0:32:46 feel kind of consistently good every day
0:32:49 and move my attachments to the outcome,
0:32:53 I’ve realized there’s a set of principles
0:32:55 that I want to implement at the company.
0:32:57 And that I think that execution will flow
0:32:59 from those things, right?
0:33:01 If we build a company that has a high empathy
0:33:04 for each other, where we have care for each other,
0:33:06 where people are very collaborative,
0:33:10 where people feel like the locus of control
0:33:12 for what’s happening is inside of them,
0:33:13 instead of outside of them.
0:33:16 Things are happening through them, not to them.
0:33:19 I think that all the execution will actually flow from that.
0:33:21 One of the things I never understood before,
0:33:23 which I feel like I really understand now,
0:33:26 is that saying that culture eats strategy, right?
0:33:28 I felt like I had very good strategy with Atrium,
0:33:31 but I forgot about culture in that first part of the company,
0:33:34 and now I realized how important it is.
0:33:37 So one of the things I’ll say that we’re doing
0:33:39 is recently I read this book called
0:33:41 “The 15 Commitments of Conscious Leadership,”
0:33:42 which is an amazing book,
0:33:46 but it’s really about building a certain type of company,
0:33:49 what the authors call a conscious company.
0:33:54 But I would centered around that locus of control question.
0:33:56 Do you have radical responsibility
0:33:59 for what’s going on in your life, in your company,
0:34:00 regardless of who you are?
0:34:03 And read that book, and I realized this is the type
0:34:05 of company that I want to work at.
0:34:08 It’s a company populated by team members
0:34:11 who really believe in these principles.
0:34:13 And so we’re kind of going through a process
0:34:15 of trying to implement that at our company.
0:34:18 And really culture is one of the highest.
0:34:20 It went from something that I didn’t prioritize
0:34:22 to my top priority.
0:34:24 – Yeah, well, and I think it’s really interesting
0:34:29 because you’d started Kiko out of school, right?
0:34:33 And so unlike some folks who maybe,
0:34:35 they go and they work at Google or Facebook or something,
0:34:36 and they maybe have a template
0:34:39 for the company culture they want to create,
0:34:41 this is something that you kind of had to learn
0:34:44 and adjust over many, many kind of company iterations
0:34:46 of various companies that you built.
0:34:48 – That’s right, we had never worked at a place
0:34:51 with good culture or a culture, right?
0:34:53 Because we had always worked at our own company,
0:34:55 so we were just making it up as we went along.
0:34:57 When you’re not intentional about your culture
0:34:59 or type of company you want to be,
0:35:02 then the culture ends up being the accidental collection
0:35:05 of good and bad choices and personality quirks
0:35:08 and good and bad behaviors that your founding
0:35:12 and executive teams propagate, right?
0:35:13 And it’s accidental, right?
0:35:17 And often times there’s things that are not good behaviors,
0:35:19 they get propagated culturally.
0:35:22 And often times people justify it because they’re like,
0:35:26 well, they conflate the correlation with causation, right?
0:35:29 They’re like, because we, you know,
0:35:34 or have behaviors where maybe we’re like a low empathy
0:35:35 company, let’s say, but they don’t call it that.
0:35:36 They’re just like, we make decisions
0:35:38 based on like meritocracy, right?
0:35:40 And they’re like, but the best idea is gonna win,
0:35:43 but then maybe that’s like because that’s a behavior
0:35:44 that they’ve propagated,
0:35:46 but that might not be the real reason
0:35:49 why they’ve actually been winning, right?
0:35:51 I think a lot of companies in Silicon Valley
0:35:55 kind of succeed despite their management actually,
0:35:56 not because of it.
0:35:59 And what I mean by that is like the idea was so good
0:36:02 that a bunch of 25-year-olds could run the company, right?
0:36:04 That core product market was so good,
0:36:05 it was just a rocket ship
0:36:07 and then people were just trying to hang on.
0:36:09 Now, eventually I think they do figure it out,
0:36:10 but often times in those early days,
0:36:12 and I think it’s actually quite, you know,
0:36:14 not intentional and often times not that good.
0:36:15 – Yeah.
0:36:18 You know, in our conversation today,
0:36:20 we’ve talked about all the things that you’ve changed.
0:36:21 – Yeah. – Right?
0:36:24 You’ve changed, you know, from consumer to B to B,
0:36:27 you’ve changed, you know, how fast you fundraise,
0:36:30 you know, there’s been a lot of different changes.
0:36:33 You know, is there anything that you feel like you,
0:36:35 like there’s a core that you’re like,
0:36:37 okay, there’s this thread that I’m trying to do the same
0:36:39 between all the companies,
0:36:41 or is it just really like iterating very quickly
0:36:44 and, you know, you’re doing a lot that’s different?
0:36:46 – Well, I think that core ethos of, yeah,
0:36:50 iterating quickly, you know, that’s like a YC ethos,
0:36:52 that’s something that we really did,
0:36:54 carry on at the very beginning.
0:36:59 So speed was, you know, something that’s pretty important.
0:37:04 I think that really being helpful in the community,
0:37:06 not just your startup, I mean, including your startup,
0:37:08 but also the community of startups,
0:37:09 that’s something that we learn,
0:37:11 the behavior we learn from, you know, the early days of YC,
0:37:14 and even like our community of friends who were founders,
0:37:17 who all became successful, you know, helped each other out,
0:37:19 and then now today, you know, it’s an ethos
0:37:21 that would take the atrium to really build a company
0:37:24 that’s, you know, kind of for startups, you know,
0:37:26 by startups that helps out these, you know,
0:37:28 fast-growing startups.
0:37:30 So that ethos is probably something
0:37:33 that’s pretty similar, you know, kind of similar
0:37:35 to like what you guys have at Andreessen, right?
0:37:38 Which is like, if we can, you know, be the most helpful
0:37:40 in terms of providing these networks of services,
0:37:43 that is something that is going to, you know,
0:37:45 kind of pay dividends for us as a brand.
0:37:47 You know, that’s something I believed personally,
0:37:50 and then also at each room throughout my entire career.
0:37:51 – Right, right.
0:37:54 Yeah, I mean, as you know,
0:37:56 one of the things that’s great about the Bay Area
0:37:59 is that it ends up being this very long-running,
0:38:02 relationship-driven place where, you know,
0:38:05 you meet people like, I mean, we met like 10 plus years ago,
0:38:07 right, and that’s the kind of interesting thing
0:38:11 where there’s many, many cases where you can work together.
0:38:14 And so, you know, focusing on value creation,
0:38:16 as opposed to like, how am I going to try to position myself
0:38:18 to like capture the most value?
0:38:21 Like, I think that, you know, like that certainly runs,
0:38:22 like I think it’s one of the very special things
0:38:24 about the Bay Area. – Absolutely.
0:38:26 If I think about where we’re at, you know,
0:38:29 where all the people who I, you know, saw in the early days,
0:38:32 like 13 years ago, all these different founders,
0:38:33 you know, these two-person startups
0:38:35 that won’t even anything, where they’re at,
0:38:37 their company might not have succeeded,
0:38:40 but they have like created some value here in Silicon Valley
0:38:42 by being in that ecosystem, being helpful,
0:38:44 and then, you know, maybe becoming an executive
0:38:45 at someone else’s company,
0:38:47 or becoming an investor, early-stage investor
0:38:48 at another company that really worked.
0:38:51 And so, there really is like a feeling
0:38:55 of if you kind of get out what you put into this community,
0:38:57 and that’s one of the things I really love about it.
0:39:02 – How do you think about the idea that, you know,
0:39:05 when you’re first getting started,
0:39:07 you’re a first-time entrepreneur,
0:39:09 there’s kind of like low expectations, right?
0:39:11 ‘Cause you’re like, maybe this’ll work,
0:39:11 maybe this won’t work,
0:39:15 people’s expectations of you are like kind of low too,
0:39:16 ’cause they’re kind of like, I don’t know,
0:39:18 who knows, you know, Justin’s often in San Francisco
0:39:20 doing this thing, he’s running around with a camera
0:39:24 on his head, and it’s just kind of a fun thing.
0:39:27 And then, now, several companies later,
0:39:30 because, you know, also you’ve raised money,
0:39:32 and because you’ve done a lot, et cetera,
0:39:34 you know, the expectations must be higher.
0:39:36 Like, how do you think about, you know,
0:39:38 how do you think about those expectations
0:39:39 managing your own, you know,
0:39:41 your own expectations around that?
0:39:46 – Well, I always think that every entrepreneur’s expectations
0:39:49 for themselves are very exceedingly high, right?
0:39:52 If you’re the type of person who was a PM or engineer
0:39:53 at, you know, one of these fan companies,
0:39:55 and then you’re like, I’m gonna start a startup
0:39:57 ’cause I see other people doing it,
0:39:59 then you don’t think, you don’t go into it thinking,
0:40:01 well, I’m just gonna create like a whatever,
0:40:04 something that’s like a nice small business, right?
0:40:06 You go in being like, I’m gonna raise series A
0:40:08 from Andreessen, and we’re gonna be, you know,
0:40:10 this product that just goes,
0:40:12 the next Snapchat or whatever.
0:40:16 And so I think that, like, it’s always a battle
0:40:20 against your own, like, the kind of devil
0:40:22 on your shoulders telling you, you’re not good enough,
0:40:24 you know, you’re not doing well enough,
0:40:25 you could be doing better.
0:40:27 And I think the way that you win that battle
0:40:31 is by really internalizing and realizing
0:40:36 that whatever happens, you’re gonna be fine.
0:40:38 And you’re probably gonna be the same,
0:40:40 you’re not gonna be happier or less happy, actually.
0:40:43 And now most people do not actually successfully
0:40:46 internalize that well enough, in my opinion.
0:40:48 But it is true, I firmly believe it’s true,
0:40:51 and it’s something that the sooner you start practicing
0:40:54 that in your head, really, you know,
0:40:58 feeling that and experiencing that in yourself internally,
0:41:00 then the happier you’ll be.
0:41:02 And that’s not to say that a lot of, you know,
0:41:04 people I come into contact with, like, friends even,
0:41:06 or people who work for me are like,
0:41:11 well, my drive, like, that need to win at all costs
0:41:12 is my edge.
0:41:14 But I really don’t believe, I mean,
0:41:15 maybe that’s true for other people,
0:41:17 but for me, I never found that to be the case.
0:41:21 It’s like, no, it was just like the kind of unhappiness
0:41:23 that was created around it that would make it
0:41:25 actually less sustainable for me to continue on,
0:41:27 because I was always, you know, you can’t,
0:41:28 human beings don’t wanna live in a high stress,
0:41:30 high anxiety, stay for too long.
0:41:31 That’s how you can burn out, right?
0:41:35 So startups are not, I mean, contrary to popular belief,
0:41:37 it’s not a sprint, it’s a marathon, right?
0:41:39 There’s these overnight successes,
0:41:42 Twitch came out of nowhere, eight years from
0:41:44 incorporating that company to selling it,
0:41:45 almost exactly eight years, right?
0:41:47 So that is a long time.
0:41:49 And in order to last a long time,
0:41:52 you need to figure out a way that you are okay
0:41:54 with what’s going on, and what’s going on
0:41:57 is always gonna involve bad things.
0:41:59 Like things, you know, there’s gonna be good
0:42:00 and there’s gonna be bad.
0:42:01 So if you don’t figure out a way
0:42:04 that you psychologically are okay with that,
0:42:06 you’re gonna give up, and if you give up,
0:42:09 you’re never gonna see it to the ultimate potential
0:42:12 that whatever your startup is can be.
0:42:12 – Great.
0:42:18 I’m gonna throw in kind of two last questions in here.
0:42:23 One is, you know, what are you reading these days?
0:42:25 Do you have any sort of recommendations, podcasts
0:42:27 that you like, kind of media consumption
0:42:30 is kind of a way to learn yourself?
0:42:31 – Yeah.
0:42:33 – And anything sort of especially super impactful
0:42:37 over the last couple years that you wanna reference?
0:42:38 – Yeah, that’s great.
0:42:40 So reading a lot, actually,
0:42:43 ’cause I deleted all the entertainment apps off my phone,
0:42:45 including the browser, and I’d locked it
0:42:47 so that I don’t can install new apps
0:42:48 ’cause I was a total phone addict.
0:42:49 That includes Twitch.
0:42:50 – Wait, how do you lock?
0:42:51 How do you lock your–
0:42:51 – So you can lock installing new apps,
0:42:52 you can delete the app store
0:42:54 and put a passcode lock on it.
0:42:55 – Oh.
0:42:56 – And I gave my wife the passcode,
0:42:58 or she put in a passcode, I don’t know what it is.
0:42:59 – Right.
0:43:00 – So I can’t–
0:43:01 – This is like a parental lock.
0:43:02 – It’s a parental lock, exactly.
0:43:04 So I don’t have control over my own phone anymore.
0:43:07 But the consequence of that is I read a lot more books,
0:43:09 which is good.
0:43:12 A couple of ones that have been particularly impactful to me.
0:43:15 Number one is this book called The Untethered Soul.
0:43:17 So this book changed my life.
0:43:20 It’s really about the idea is that you are not
0:43:22 the thing that you think you are.
0:43:25 Most people go through life thinking they are the experiences
0:43:27 that they’ve experienced, the thoughts that they have,
0:43:29 or the emotions that they have.
0:43:33 But really, you’re just the observer of these things
0:43:34 that are happening.
0:43:36 And by creating, it’s almost like you’re watching a movie,
0:43:39 right, like a movie that has all the five senses,
0:43:40 plus emotions, plus thoughts.
0:43:45 So like a seven dimensional movie of the just end life, right?
0:43:49 And I think that was a very important message to me
0:43:52 to realize, like an internalize that like actually
0:43:55 these attachment, these things that I think will happen
0:43:57 that will like drive happiness, like experiences or events
0:43:59 or whatever will never actually drive
0:44:01 true internal happiness.
0:44:04 So amazing book, The Untethered Soul, highly recommend it.
0:44:07 Another book that I think made me a much better leader,
0:44:10 I was this book I read called Leadership and Self Deception.
0:44:12 Amazing book.
0:44:14 The book, the premise of the book is really there’s two ways
0:44:17 to treat other people inside the box and outside the box,
0:44:21 but really I mean like without empathy or with empathy,
0:44:25 like treating them like an object or a person, right?
0:44:28 And the idea, the fundamental idea behind the book
0:44:31 is if you treat people like an object,
0:44:33 number one, they don’t like it, right?
0:44:36 But the second thing is, if you treat people like objects
0:44:38 who are just there to fulfill something for you, right?
0:44:40 Like at work, it would be like to, you know,
0:44:43 hit some number or metric or whatever, do some job.
0:44:46 The problem is that you will lie to yourself about
0:44:50 when there’s negative situations about what your role is,
0:44:51 you’ll self deceive, you’ll say,
0:44:53 oh, this person is 100% at fault
0:44:55 and I’m 0% at fault.
0:44:58 And I found myself actually doing that a lot of times.
0:45:02 I realized, you know, I felt like I was,
0:45:07 you know, had a high degree of empathy for other people,
0:45:09 but I realized that was for people who,
0:45:10 I felt like we’re performing really good
0:45:13 where my observation was like high performance.
0:45:16 But I wasn’t, where my observation,
0:45:19 where my feeling was that there wasn’t high performance,
0:45:23 I felt like I would slip into this like low empathy mode.
0:45:25 And the problem is that when you’re in that mode,
0:45:27 you don’t admit, what are the things that I have done?
0:45:29 What, you know, Justin, what are the things I have done
0:45:30 to contribute to that situation?
0:45:34 So examples could be put someone in the wrong job,
0:45:37 didn’t give them clear enough criteria for success or failure,
0:45:39 didn’t support them with the right resources, right?
0:45:42 There’s many reasons why I could have contributed
0:45:44 to some situation failing.
0:45:47 And I found that like I would lie to myself in those situations.
0:45:51 So, you know, that was a really important book for me.
0:45:53 And those are probably two ones I really recommend.
0:45:55 – That’s great.
0:45:57 Are you doing a lot of podcasts right now?
0:45:59 – Listening to them? – Yeah.
0:46:01 – Been listening to– – That kind of entertainment.
0:46:03 – Yeah, I listened to some podcasts.
0:46:06 I’ve been listening to some of the Joe Rogan experience.
0:46:09 – Nice. – And that’s probably the,
0:46:10 this is probably it.
0:46:12 I like, I just listened to one with Alex Honol,
0:46:15 where he’s talking about climbing, you know,
0:46:16 so it’s pretty interesting.
0:46:20 – I’m gonna ask you the time travel question.
0:46:24 So if you were today, an enlightened, repeat entrepreneur,
0:46:27 to go back to yourself when, you know,
0:46:30 you’re 22, 23, just getting started doing this thing.
0:46:34 You know, what advice would you give yourself?
0:46:37 – When I’m just getting started, you know,
0:46:40 probably join Facebook.
0:46:45 No, but the, maybe the real answer,
0:46:47 I’ve actually, you know, I don’t really regret
0:46:49 any of the, like, economic choices or anything.
0:46:52 I think I’ve had this tremendous opportunity
0:46:55 to, like, build and discover these new things
0:46:59 and build companies and I wouldn’t trade it for anything.
0:47:01 I think the thing I could have done better
0:47:03 or, like, learned, you know, back then is,
0:47:07 you know, self-improvement is a thing.
0:47:08 You should probably, like, work on that.
0:47:09 Maybe that’s number one.
0:47:10 The second thing would be–
0:47:11 – Stop eating pizza at work.
0:47:12 – Yeah, stop eating pizza at work.
0:47:14 Number three would be,
0:47:18 you should, you know, things take time.
0:47:20 Like, don’t be in such a, you know,
0:47:22 it’s not, like, a one-year, one-and-done,
0:47:24 like, no, you’re a billionaire, you know?
0:47:26 Like, if you look at the, let’s say,
0:47:29 any sort of, like, the Amazon share price
0:47:30 or, like, market cap over time, right?
0:47:32 It looks, like, even through, like,
0:47:36 the last couple years looks like an exponential curve, right?
0:47:39 And so, you know, if Bezos had been, like, your,
0:47:41 you know, like, 15,
0:47:44 which is a long time to be starting a company,
0:47:46 like, oh, man, I made it, I’m done,
0:47:47 like, I’m gonna retire, like,
0:47:50 well, you know, the company would look a lot different,
0:47:52 right, so, you know, things take time
0:47:54 and I have to constantly remind myself that.
0:47:57 I think humans, you know, here in Silicon Valley,
0:47:59 especially, but then human beings in general,
0:48:01 are wired to, like, always want the new,
0:48:02 you know, look for the new thing.
0:48:03 What’s new?
0:48:05 What are you, like, what’s your new thing?
0:48:07 Something that everybody’s asking about in Silicon Valley?
0:48:10 They’re always, you know, that’s a question here.
0:48:12 But the best entrepreneurs here,
0:48:13 the ones who have created lasting companies
0:48:16 and lasting value, they stick with their thing
0:48:19 for decades, you know?
0:48:21 And that’s what impresses me most now
0:48:25 and I wish that I had kind of realized that, you know,
0:48:25 before.
0:48:28 – Awesome.
0:48:29 – Better late than never.
0:48:30 (laughing)
0:48:31 – Awesome.
0:48:33 Justin, thank you for coming by.
0:48:33 – Yeah.
0:48:35 – Thanks for having me. – Really good discussion.
0:48:45 [BLANK_AUDIO]

0:00:02 Hi, and welcome to the A16Z podcast.
0:00:03 I’m Hannah.
0:00:05 Deep learning has come to the life sciences.
0:00:09 Lately it seems every week a published study comes out with code on top.
0:00:14 In this episode, A16Z general partner on the bio fund Vijay Pandey and Bart Remsundar talk
0:00:17 about how AI and ML is unlocking the field in a new way.
0:00:22 In a conversation around their recently published book, Deep Learning for the Life Sciences,
0:00:25 written along with co-authors Peter Eastman and Patrick Walters.
0:00:30 The book aims to give developers and scientists a toolkit on how to use deep learning for
0:00:35 genomics, chemistry, biophysics, microscopy, medical analysis, and other areas.
0:00:36 So why now?
0:00:40 What is it about ML’s development that is allowing it to finally make an impact in this
0:00:41 field?
0:00:43 And what is the practical toolkit?
0:00:44 The right problems to attack?
0:00:46 The right questions to ask?
0:00:51 Above and beyond that, as this deep learning toolkit becomes more and more accessible, biology
0:00:54 is becoming democratized through ML.
0:00:57 So how is the hacker ethos coming to the world of biology?
0:01:01 And what might open source biology truly look like?
0:01:05 So Bart, we spent a lot of time thinking about deep learning and life sciences.
0:01:10 It’s a great time, I think, for people to become practitioners in this space, especially
0:01:15 for people maybe that’s never done machine learning before from the life sciences side,
0:01:18 or maybe people from the machine learning side to get into life sciences.
0:01:22 But maybe the place to kick it off is what’s special about now?
0:01:23 Why should people be thinking about this?
0:01:28 The challenge of programming biology has been that we don’t know biology, and we make up
0:01:33 theoretical models, and the computers are wrong, and biologists and chemists understandably
0:01:36 get grumpy and say, “Why are you wasting my time?”
0:01:40 But with machine learning, the advantage is that we can actually learn from the raw data.
0:01:43 And all of a sudden, we have this powerful new tool there.
0:01:45 It can find things that we didn’t know before.
0:01:50 And this is why it now is the time to get into it, really to enable that next wave of breakthroughs
0:01:51 in the core science.
0:01:57 The part that still blows me away is just how fast this field is moving, and it feels
0:02:03 like it’s a combination of having the open source code on places like GitHub and Archive,
0:02:07 and there’s a paper or a week that’s impactful when it used to be maybe a paper or a quarter
0:02:09 or a paper a year.
0:02:13 And the fact that code is coming with the paper, it’s just layering on top.
0:02:17 That seems to me to be the critical thing that’s different now.
0:02:21 I think when you can clone a repo off GitHub, you also don’t have new insights just because
0:02:23 I’m using a new language.
0:02:27 And now that thousands of people are getting into it, I think all of a sudden you’ll find
0:02:32 lots of semi-self-taught biologists who are really starting to find new, interesting things.
0:02:33 And that is why it’s exciting.
0:02:38 It’s like the hacker ethos, but kind of coming into the bio world, which has typically been
0:02:40 much more buttoned down now.
0:02:44 I think anyone who can clone a repo can start really making a difference.
0:02:47 I think that’s going to be where the real long-term impact arises from these types
0:02:48 of efforts.
0:02:53 You don’t need a journal subscription to get archive or to get the code, which is actually
0:02:54 that alone is kind of amazing.
0:02:59 It wasn’t that long ago where a lot of academics offer was sold, and it was maybe sold for
0:03:01 $500, which is very material.
0:03:02 That’s one piece.
0:03:08 You connect that to the concept of now AI or ML can unlock things in biology.
0:03:12 Then biology is becoming democratized as kind of your point.
0:03:17 And so let’s talk about that because we’re still learning biology collectively.
0:03:20 What is it about deep learning in biology now?
0:03:22 Because biology’s old, machine learning is old.
0:03:23 What’s new now?
0:03:26 Deep learning has this question all over the place.
0:03:27 Why does it work now?
0:03:30 The first neural nets kind of popped out in the 1950s.
0:03:32 And I think it’s really a combination of things.
0:03:38 I think that part of it is the hardware, really, the hardware, the software, the growth of kind
0:03:42 of rapid linear algebra stacks that have made it accessible.
0:03:47 I think also an underappreciated part of it is the growth of the cloud and the internet
0:03:48 really.
0:03:51 Neural nets are about as janky now as it used to be in the ’80s.
0:03:55 The difference is that I can now pull up a blog post where someone says, “Oh, these things
0:03:56 are janky.
0:03:57 Here’s the 17 things I did.
0:03:59 I can copy, paste that into my code.”
0:04:01 And all of a sudden, I’m a neural net expert.
0:04:02 It’s all quite that easy.
0:04:07 It turns it to a tradecraft almost that you can learn by just working through it.
0:04:09 That’s why the deep learning tool again has been accessible.
0:04:14 Then you get to biology, and the question is why biology, why now?
0:04:17 And I think you’re actually the question’s a little deeper.
0:04:21 I think that it’s really about, I think, representation learning.
0:04:27 So we have now reached this point where I think we can learn representations of molecules
0:04:28 that are useful.
0:04:33 This has been something that in the science of chemistry, we’ve been doing a long time.
0:04:38 There’s been all sorts of hand-encoded representations of parts of molecular behavior that we think
0:04:39 are important.
0:04:44 But I think now using the new technology from image processing, from word processing, we
0:04:47 can begin to learn molecular representations.
0:04:50 To be fair, I actually don’t think we’ve really broken through there.
0:04:55 If you look at what’s happening in images or text, there are five years ahead of us.
0:05:00 Well, let me break in here because just for the listeners to give a sense for why representation
0:05:05 is important, and one of my pet examples is that if I gave anybody, say, two five-digit
0:05:07 numbers to add, it’d be trivial.
0:05:12 If I gave you those same five-digit numbers in Roman numerals and you wanted to add them,
0:05:14 the representation there would make this insane.
0:05:15 And what would you do?
0:05:21 Well, you would convert into appropriate representation where the operations are trivial or obvious.
0:05:26 And then the operation is done, and maybe it re-encodes, auto-encodes back to the other
0:05:27 representation.
0:05:28 So this is the problem.
0:05:32 It’s like when you have a picture, representations are obvious because it’s pixels, and computers
0:05:34 love pixels.
0:05:39 And maybe even for DNA, DNA is like a one-dimensional image, and so you have bases that are kind
0:05:40 of like pixels.
0:05:44 We used to joke early days that we would just take a photograph with a small molecule and
0:05:46 then use all the other stuff, but that’s kind of insane too.
0:05:51 And so with the right representation, things become transparent and obvious with the wrong
0:05:53 representation becomes hard.
0:05:54 This is really at the heart of machine learning.
0:05:59 It’s that there’s something about the world that I want to compute on, but computers only
0:06:06 accept very limited forms of input, zero ones, tack strings, like simple structures.
0:06:11 Whereas if you take a molecule, a molecule is like a frighteningly complex entity.
0:06:16 So one thing that we often don’t realize is that until 100 years ago, we barely had any
0:06:17 idea what a molecule was.
0:06:23 It’s this alarmingly strange concept that although we see little diagrams in 10th grade
0:06:26 chemistry or whatever, that isn’t what a molecule is.
0:06:31 It’s a much weirder, weirder quantum object, dynamic, kind of shifting, flowing.
0:06:33 We barely understand it even now.
0:06:37 So then you just really start asking the question of what is water, for example?
0:06:40 Is it the three characters, H2O?
0:06:43 Is it two hydrogens and oxygen?
0:06:45 Is it some quantum construct?
0:06:47 Is it this dynamic vibrating thing?
0:06:49 Is it this bulk mass?
0:06:52 There’s so many layers to kind of the science of it.
0:06:55 So what you really want to do is you’ve got to pick one, and this is where it gets really
0:06:56 hard, right?
0:07:01 Like, if I’m thirsty, what I care about in water is a glass of water.
0:07:06 If I’m trying to answer deep questions about the structure of Neptune, I might want a slightly
0:07:08 different representation of water.
0:07:14 The power of the new deep learning techniques is we don’t necessarily have to pick a representation.
0:07:17 We don’t have to say water is X or water is Y.
0:07:22 Instead, you say, let’s do some math, and let’s take that math and let the machine really
0:07:27 learn the form of water that it needs to answer the question at hand.
0:07:32 So one form of mathematical construct is thinking of a molecule as a graph.
0:07:37 And if you do this, you can begin to do these graph-deep learning algorithms that can really
0:07:41 extract meaningful structure from the molecule itself.
0:07:46 We’ve learned, finally, that here’s a general enough mathematical form we can use to extract
0:07:52 meaningful insights about molecules or these critical biological chemical entities that
0:07:56 we can then use to answer real questions in the real world.
0:08:00 What I think is interesting here in particular is that so much has been developed on images,
0:08:03 and there’s a lot of biology that’s images, and so we could just spend the whole time
0:08:08 talking about images, and it could be microscopy or radiology and tons of good stuff there.
0:08:12 But there’s a lot of biology that’s more than images, and molecules is a good example.
0:08:16 For a long time, it seemed like deep learning was being so successful in images that that’s
0:08:17 all it really did.
0:08:23 And if you could take your square peg and put in whatever holes you got, it would work.
0:08:26 What you’re talking about for graphs is kind of an interesting evolution of this, because
0:08:30 a graph and an image are different types of representations.
0:08:35 But at a technical level, convolutional networks for images or graph convolutions for graphs
0:08:39 are kind of a sort of borrowing a concept at a higher level.
0:08:44 The biology version of machine learning is starting to sort of grow up and starting to
0:08:49 not just be a direct copy of what was done with images and in other areas, but now starting
0:08:50 to be its own thing.
0:08:55 A five-year-old can really point out the critical points in an image, but you almost
0:08:58 need a PhD to understand the critical points of a protein.
0:09:04 So you have this like dual kind of weights, a burden of understanding, so it’s taken
0:09:09 a while for the biological machine learning approach to really mature because we’ve had
0:09:13 to spend so much time even figuring out the basics.
0:09:18 But now we’re finally at this point where it feels like we are diverging a little bit
0:09:23 from the core trunk of what people have done for images or text.
0:09:26 In another five years, I’m going to be blown away by what this thing does.
0:09:28 It’s going to understand more deeply.
0:09:35 So we kind of have this sort of connection between democratization of ML, ML into biology,
0:09:38 democratization into biology, but I don’t think we’re there yet.
0:09:42 I think for ML, I think there really is a sense of democratization.
0:09:49 You could code on your phone and do some useful things or certainly on a laptop, a cheap laptop.
0:09:51 But for biology, what is missing?
0:09:53 One is data, and there’s a fair bit of data.
0:09:58 In the book, we talk about the PDB, we talk about other data sets, and there are publicly
0:10:02 available data sets, but somehow that doesn’t get you into the big leagues.
0:10:06 So like if in this vision of democratizing biology, what’s left to be done?
0:10:12 In some ways, the democratization of ML is a teensy bit of an illusion even.
0:10:17 It’s because that the core constructs were mathematically invented, that there is this
0:10:24 convolutional neural net or its cousins, the LSTM or the other forms of core mathematical
0:10:28 breakthroughs that have been designed, that you can take these building blocks and just
0:10:30 apply them straight out.
0:10:35 In biology, as you pointed out earlier, I think we don’t have those core building blocks
0:10:36 just yet.
0:10:41 We don’t know what the LEGO pieces are that would enable a newcomer to really start to
0:10:44 do breakthrough work.
0:10:45 We’re closer than we were.
0:10:49 I think we’ve had the beginnings of a toolbox, but we’re not there yet.
0:10:53 Let’s think about what happened on the ML side as inspiration for the Bio side.
0:10:54 How much is it driven through academia?
0:10:56 How much driven through companies?
0:10:59 Because what I’m getting at is that there’s a lot of IO in academia.
0:11:02 I don’t know if we’re seeing that being made open sourced in companies.
0:11:07 We’re getting to this really weird set of influences where in order for companies to
0:11:09 gain influence, they need to open source.
0:11:14 This is why 10 years ago, I can’t imagine that Google would have open sourced TensorFlow.
0:11:20 It would have been core proprietary technology, but now they know that if they don’t do that,
0:11:24 developers will shift to some other platform by some other company.
0:11:25 Exactly.
0:11:30 It’s weird that the competitive market forces are driving democratization.
0:11:35 Most of high torch basically are Facebook-based and TensorFlow is from Google.
0:11:37 Let’s say Google kept TensorFlow proprietary.
0:11:39 What would be so bad for them if they did that?
0:11:41 What if everybody outside used high torch?
0:11:45 I think there’s a really neat analogy to the financial sector.
0:11:50 A lot of financial banks have masses of functional programs that they keep under the hood, under
0:11:51 the covers.
0:11:55 If you look at Jane Street, or I believe Standard and Chartered, or a few other of these other
0:12:00 big institutions, lots and lots of functional code hiding behind those walls.
0:12:04 But that really hasn’t really infiltrated further out.
0:12:09 This actually, I think, in the long run weakens them because it’s harder to train, it’s harder
0:12:12 to find new talent, it’s more specialized.
0:12:17 A lot of the code base at Google is proprietary, like the original MapReduce was never put
0:12:18 out there.
0:12:22 This I think has actually caused them a little bit of a problem in that new developers coming
0:12:27 in have to spend months and months and months getting up to speed with the Google stack,
0:12:32 whereas if you look at TensorFlow, it doesn’t take any time at all, someone could walk in
0:12:34 and basically be able to write TensorFlow.
0:12:36 They’ve been using it for months to years.
0:12:37 Exactly.
0:12:42 And I think at the scale that Big Tech is at, this is just like, it’s a powerful market
0:12:43 advantage.
0:12:45 They’re almost outsourcing their education process.
0:12:48 And I guess if they don’t put it out, someone else will, and then they’ll learn on their
0:12:49 platform.
0:12:52 Yes, but then maybe what is the missing part in biology?
0:12:57 We’ve got pharma, a huge force there, but they have very specific goals.
0:13:02 A lot of agricultural companies, but it’s much more disparate.
0:13:08 Yeah, it’s dramatically hard to actually take an existing organization and turn it into
0:13:10 an AI machine learning organization.
0:13:17 So one thing I’ve honestly been surprised by is that when I’ve seen companies or organizations
0:13:22 I know try to incorporate AI into their drug discovery process, it ends up taking them
0:13:27 years and years and years, because they’re fighting all these upstream battles, weeks
0:13:32 to get their old computing system to upgrade to the right version of their numerical library
0:13:35 so they could even install TensorFlow.
0:13:41 And then they had all these things about who can actually say, upgrade the core software,
0:13:44 who is it this department?
0:13:47 How much do they need to talk to the biologists, to the chemists?
0:13:52 And the fact is that pharma and existing big codes are not built this way.
0:13:57 That’s not their core expertise, whereas if you look at Facebook or Google, they’ve been
0:14:02 machine learning for almost two decades now, from the first AdWords model.
0:14:07 So in some sense, they had to change very little about their culture, like, yeah, there’s
0:14:11 a slight difference instead of this function, use that function, but whatever.
0:14:15 But the core culture was there, and I think the culture, the people, changing that is
0:14:20 going to be dramatically hard, which is why I think it will really take, I think, ten
0:14:24 years and a generation of students who have been trained in the new way to come in and
0:14:25 shift it.
0:14:26 Yeah.
0:14:27 Well, Google was a startup too, right?
0:14:31 I think, you know, the thesis was that, and is that, that startups will be able to build
0:14:32 a new culture.
0:14:37 And I think the key thing that I think we’re seeing sort of boots on the ground is that
0:14:41 culture has to be not, here’s your data scientists are machine learning people in one room and
0:14:45 you’re biologists in another room, that they’d have to be the same.
0:14:50 What’s intriguing to me is just the size of the bio market.
0:14:55 Biology is healthcare, it’s agriculture, it’s food, it could be the future of manufacturing.
0:14:59 There’s so many different places that biology plays a role to date and will play a role,
0:15:02 but it just means that I think, I think to the point we’re talking about these companies
0:15:06 just are being built right now.
0:15:12 There’s I think this whole host of challenges here because biology is hard and building kind
0:15:17 of like that selective understanding of like, you know, of the 10 best practices that existed.
0:15:19 Five are actually still best practices.
0:15:23 The other five we need to toss out a window and stick in a deep learning model.
0:15:27 That kind of very painstaking process of experimentation and understanding.
0:15:31 That I think is like where the really hard innovation is happening.
0:15:32 And that’s going to take time.
0:15:36 You’re never going to be able to replace like a world-class biologist with any machine learning
0:15:37 program.
0:15:43 A world-class biologist is typically fricking brilliant and they often bring a set of understanding
0:15:46 that no programmer or no computer scientist can.
0:15:51 Now, the flip side holds true and I think that merger, as you said, that’s where like
0:15:53 there’s power for magic dynamism.
0:15:57 One really interesting factoid I heard from an entrepreneur in the space is that, you
0:16:03 know, the best biologists that they could hire had a market rate that was lower than
0:16:10 a introductory, intermediate, you know, front-end dev and, you know, of course, front-end is
0:16:11 very hard engineering.
0:16:15 I don’t want to put that down, but there’s so many fewer of these biologists, so there’s
0:16:21 almost this market imbalance of how is it possible that, you know, you can take really
0:16:27 a world-class biologist of whom there’s maybe a couple of hundred in the world and not have
0:16:29 them be valued properly by the market.
0:16:32 So do you even out those pay scales in one company?
0:16:37 Do you like have two awkward pay ladders that coexist and create tension in your company?
0:16:41 These are the types of like really hard operational questions that almost have nothing to do with
0:16:43 the science, but at the heart of it they do.
0:16:45 Maybe it’s interesting to talk about like how we can help people get there.
0:16:49 Yeah, so what’s like the training they should be doing, maybe we could even go like super
0:16:50 nuts and bolts.
0:16:52 So I got my laptop, what do I do?
0:16:58 So I mean, like, I guess there’s a couple key packages we install, like TensorFlow, maybe
0:17:00 DeepGam, something like that.
0:17:04 Python is often already installed, let’s say on a Mac, is that it?
0:17:06 And then we start going through papers and books and code.
0:17:11 I think the first place really is to, you need to form an understanding of like what
0:17:14 are the problems even that you can think about.
0:17:19 I think if you’re not trained as a biologist, and even if you are, you might not see that
0:17:25 intersection of these are the problems where biological machine learning can or cannot work.
0:17:29 And that I think is really what the book tries to teach you, as in like, what’s the frame
0:17:30 of thinking?
0:17:34 What’s the lens at which you look at this world and say that, oh, that is data coming
0:17:36 out of a microscope.
0:17:40 I should spend 30 minutes, spin up a connet, and iterate on that.
0:17:46 This is a really gnarly thing about how I prepare my like, you know, C.Elegant samples.
0:17:49 I don’t think the deep learning is going to help me here.
0:17:52 And I think it’s that blend of knowledge that the book tries to give you.
0:17:53 It’s like a guidebook.
0:17:57 When you see a new problem, you ask, is this a machine learning problem?
0:17:59 If so, let me use these muscles.
0:18:03 If it’s not a machine learning problem, well, I know that I need to talk to someone who
0:18:05 does know these things.
0:18:06 And that’s what we try to give.
0:18:08 Andring has a great rule of thumb.
0:18:12 If, you know, a human can do it in a second, deep learning can probably figure it out.
0:18:19 So start with something like say microscopy, you have an image coming in and an expert
0:18:22 can probably eyeball and say, interesting, not interesting.
0:18:24 So there’s this binary choice.
0:18:30 And there’s some arcane black box that was trained within the expert’s head and experience.
0:18:34 That’s actually the sort of thing machine learning is like made to solve.
0:18:38 So really ask yourself, like, when you see something like that, is there some type of
0:18:44 perceptual input coming in, image, sound, text, and increasingly molecules, a weird
0:18:49 new form of perception, almost magnetic or quantum, but you have perceptual input coming
0:18:50 in.
0:18:56 And is there a simple right, wrong, left, right, intensity type answer that you want
0:18:57 from it?
0:19:00 If you do, that’s really a machine learning problem at its heart.
0:19:01 Well, so that’s one type of machine learning.
0:19:06 And I think the benefit there of that, what human can do in a second, deep learning can
0:19:12 do, especially since, in principle, on the cloud, you could spin up 100,000, 10,000 servers.
0:19:15 Suddenly you’ve got 10,000 people working to solve the problem.
0:19:17 And then they go back to something else.
0:19:19 That’s just something you can’t do with people.
0:19:24 Or you’ve got 10,000 people working 24/7, as necessary, can’t do that with people.
0:19:28 But there’s another type of machine learning, which is to do things people can’t.
0:19:32 Or maybe more specifically, do things individual people can’t, but maybe crowds could.
0:19:37 So like we see this in radiology, right, where the machine learning can have accuracies
0:19:41 greater than any individual, akin to what, let’s say, the consensus would be, which would
0:19:43 be the gold standard.
0:19:47 That’s maybe the real exciting part, sort of the so-called superhuman intelligence.
0:19:49 Where are the boundaries of possibilities there?
0:19:54 One of the biggest problems really with deep learning is that you have some like strange
0:19:56 and crazy prediction.
0:20:02 Now I think that there’s a fallacy that people fall into of trusting the machine too easily.
0:20:07 Because 90% of the time that’s going to be garbage.
0:20:12 And I think that really kind of the challenge of picking out these bits of superhuman insight
0:20:16 is to know how to shave off the trash predictions.
0:20:17 Yeah.
0:20:19 Is 90% an exaggeration or is it really 90%?
0:20:23 I like nice round numbers, so that might have just been something I picked out.
0:20:26 But there’s like this great example, I think, in medicine.
0:20:32 So there’s scans coming in and the deep learning algorithm is doing like amazing at predicting
0:20:33 it.
0:20:38 And then like they dug into it and it turned out that the scans came from three centers.
0:20:43 One of them had like some type of center label that was like the trauma center or something.
0:20:44 There’s the other non-trauma center.
0:20:49 The deep learning algorithm had like a kindergartner told to do this, learn to identify the trauma
0:20:52 flag and flag those and uptake those.
0:20:57 So if you did this like naive statistics of blending them all out, you’d look amazing.
0:20:58 But really it’s looking for a sticker.
0:20:59 Yeah.
0:21:00 I mean, there’s tons of examples like that.
0:21:04 One with the pathologist with the ruler in there and it’s becoming a ruler detector and
0:21:05 so on.
0:21:09 Like, you know, this AUC like a sense of accuracy of close to 1.0.
0:21:14 We all got to be very suspicious of that because just running a second experiment wouldn’t
0:21:16 predict the first experiment with that type of accuracy.
0:21:18 Anything that’s too good to be true probably is.
0:21:19 Yeah.
0:21:23 I think then you get into the really subtle challenges, which is that, you know, the algorithm
0:21:28 tells me this molecule should be non-toxic to a human and should have effect on this,
0:21:29 you know, indication.
0:21:31 Do I trust it?
0:21:34 Is it possible that there’s a false pattern learned there?
0:21:37 Humans make these types of mistakes all the time, right?
0:21:42 Like if you have any type of like actual biotech, you know that there’s gonna be molecules
0:21:44 made or DCs that are disproven.
0:21:49 So you’re getting into the hard core of learning, which is that, is this real?
0:21:51 The reality is we don’t have answers to these.
0:21:55 They were really kind of trending into the edge of machine learning today, which is that,
0:21:58 is this a causal mechanism?
0:21:59 Does A cause B?
0:22:01 Is it a spurious correlation?
0:22:04 And now we’re getting to that place where humans aren’t necessarily better.
0:22:09 We talk about some techniques for interpreting, for looking at kind of what informed the decision
0:22:11 of the deep learning algorithm.
0:22:16 And we do provide a few kind of tips and tricks to start thinking about it, but the reality
0:22:19 is that’s kind of the hard part of machine learning.
0:22:20 It’s the edge.
0:22:24 The interpreting chapter is one of my favorite ones because it’s often sort of become so-called
0:22:29 common wisdom that machine learning is a black box, but in fact, it doesn’t have to be and
0:22:32 there’s lots of things to do and we are quite prescriptive there.
0:22:36 So the interpretability I think also is frankly what’s going to make human beings more at
0:22:38 peace with this.
0:22:40 And this isn’t anything unique to machine learning.
0:22:46 If you had some guru who’s just spouting off stuff and said, “Buy this stock X and short
0:22:51 the stock Y and put all your life savings into it,” you probably would be thinking, “Okay,
0:22:54 well, maybe, but why?”
0:22:57 So I think this is just human nature and there’s no reason why our interaction with machines
0:22:59 would be any different.
0:23:04 What I think is interesting is human beings are notoriously bad at causality.
0:23:07 We kind of attribute things to be causal when they’re not causal at all.
0:23:12 We do that in our lives from why did that person give me that cup of coffee to why did
0:23:13 that drug fail?
0:23:15 All these different reasons.
0:23:17 There’s two big misconceptions about machine learning.
0:23:18 One is lack of interpretability.
0:23:22 The second one is correlation doesn’t mean causation, which is true, but somehow people
0:23:26 take that to mean it’s impossible to compute causality.
0:23:30 And that’s the part that I think people have to really be educated on because there are
0:23:33 now numerous theories of causality.
0:23:36 And you could use probabilistic generative models, PGMs.
0:23:38 There’s lots of ways to go after causality.
0:23:40 The whole trick though is you need time series data.
0:23:43 What’s beautiful about biology or at least in healthcare is that we’ve got time series
0:23:45 data in many cases.
0:23:51 So now perhaps finally there’s the ability to really understand causality in a way that
0:23:55 human beings couldn’t because we’re so bad at it and machines are good at it and we’ve
0:23:56 got the data.
0:24:00 Can you think of a place where in your experience the algorithms have succeeded in teasing out
0:24:03 a causal structure that people missed?
0:24:10 Yeah, so I think in healthcare we always think about what is leading to various changes like
0:24:15 this drug having adverse effects, this diet having positive or negative effects.
0:24:20 All of these things are being understood in the category of real world evidence, which
0:24:23 is a big deal in pharma these days.
0:24:28 And if you think about it like a clinical trial is really a poor man surrogate for not
0:24:32 understanding causality because if we don’t understand causality you’ve got to do this
0:24:36 thing where it’s double blind, we start from scratch and I’m following it in time and we
0:24:37 see it.
0:24:42 If you understood causality you might be able to just get a lot of results from just mining
0:24:43 the data itself.
0:24:47 As a great example you can’t do clinical trials for all pairs of drugs.
0:24:51 I mean just doing for a single drug is ridiculously expensive and important, but all pairs of
0:24:54 drugs would never happen, but people take pairs of drugs all the time.
0:24:59 And so their adverse effects from real world data is probably the only way to do it and
0:25:03 we can actually get causality and there’s tons of interesting journal medicine papers
0:25:07 sort of saying, “Aha, we found this from doing data analyses.”
0:25:09 I think that’s just starting out.
0:25:14 Honestly, I think that bio-AI drug discovery needs to take a page from the self-driving
0:25:19 car companies, in the neighboring self-driving car world, simulators are all the rage.
0:25:25 And really because it’s that same notion of causality almost, like there’s a structure
0:25:30 to the world like pedestrians walk out, chickens, alligators, whatever, crazy thing.
0:25:34 I saw this for the picture, it happens.
0:25:39 So I think there they’ve built this amazing infrastructure of being able to run these
0:25:44 repeated experiments, almost a randomized clinical trials, but informed by real data.
0:25:49 We don’t yet have that infrastructure in bio-world and I know there’s a couple of exciting
0:25:53 startups are starting to kind of move towards that direction, but I think it’s when we
0:25:58 can really probe the causality at scale and then in addition to just probing it, when
0:26:04 the simulator is wrong, use the new data point that came in and have the simulator learn
0:26:05 to fix itself.
0:26:09 That’s when you get to this really amazing feedback loop that could really revolutionize
0:26:10 biology.
0:26:15 Yeah, so we talked about some basic nuts and bolts about how to get started and the framing
0:26:17 of questions, which is a key part.
0:26:22 So let’s say people, they’re set up, they’ve got their question, where do they go from
0:26:23 there?
0:26:27 I mean, in a sense, we’re talking about something closer to open source biology and to the extent
0:26:33 that biology is programmable and synthetic biology is, I think, very much, it’s been around
0:26:35 for a while, but I think it’s really starting to crest.
0:26:39 How do these pieces come together such that we could finally get to this sort of open source
0:26:41 biology democratization of biology?
0:26:45 A big part of this is really the growth of community.
0:26:49 There are people behind all these GitHub pages that you see.
0:26:54 There’s real decentralized, powerful organizations that, if you look at the Linux Foundation,
0:26:59 if you look at, say, the Bitcoin Core Foundation, there are networks of open source contributors
0:27:02 really that form this brain trust.
0:27:03 It’s very diffuse.
0:27:08 It’s not centralized in the Stanford, Harvard, Med Department, or whatever.
0:27:11 And I think what we’re going to see is the advent of similar decentralized brain trusts
0:27:13 in the bio world.
0:27:17 It’s in a network of experts who are kind of spread across the world and who kind of
0:27:20 contribute through these code patches.
0:27:22 And that, I think, is not at all new to the software world.
0:27:24 We’ve seen that for decades.
0:27:25 It’s totally new to biology.
0:27:26 It’s alien.
0:27:32 Like, you would be surprised how much skepticism there can be at the idea that a non-Harvard
0:27:35 trained, say, biologist can come up with a deep insight.
0:27:37 We know that to be a fact, right?
0:27:43 There is multiple PhDs worth of work in just like the Linux kernel that that community
0:27:46 really doesn’t care to get that stamp of approval.
0:27:50 So I think we’re going to see the similar parallel kind of knowledge base that grows
0:27:51 organically.
0:27:55 But it takes time because you’re talking about the building of another kind of almost educational
0:27:57 structure, which is this new and exciting direction.
0:28:00 Here’s the challenge I worry about the most, which is that, like, so you’re building a
0:28:05 Linux kernel, you can test whether it works or doesn’t work relatively easily.
0:28:09 Even as it is, there’s this huge reproducibility crisis in biology.
0:28:14 So how does one sort of become immune from that, or at least not tainted by that?
0:28:15 How do you know what to trust?
0:28:18 And this is a really, really interesting question.
0:28:23 And this is kind of shading a little bit almost into the crypto world, right?
0:28:27 Like you could potentially think about this experiment where you have like a molecule.
0:28:31 You don’t know what’s going to happen to it, but maybe you create a prediction market
0:28:34 that talks about the future and the small kill.
0:28:38 And you could then begin to create these historical records of predictions.
0:28:42 And we all know there are kind of like expert drug pickers at Big Pharma who can like eyeball
0:28:45 and say that is going through, that is failing.
0:28:48 And five years later, you’re like, well, shit, okay, yes, I was right.
0:28:52 There is the beginnings of infrastructure for these feedback mechanisms, but it’s a really
0:28:53 hard problem.
0:28:54 Yeah.
0:28:55 I’m trying to think though what that would be like.
0:28:59 The huge thing is like, you could imagine if it was a simple question, like, is this
0:29:01 drug soluble?
0:29:03 Someone might run a cheap software calculation.
0:29:07 Someone might do the experiment and there’s different levels of cost of having different
0:29:09 levels of certainty.
0:29:14 You’re essentially describing a decentralized research facility.
0:29:16 Maybe the question is who would use it?
0:29:21 This is, I think, the really hard part because I think that biopharma tends to be a little
0:29:24 more risk averse for good reasons than many other industries.
0:29:29 But I actually think that in the long run, this could be really interesting because if
0:29:34 you have multiple assets in a company, you could kind of like, disbundle the assets and
0:29:39 then you could start to get this much more granular understanding of like, what assets
0:29:41 actually do well, what assets don’t.
0:29:47 And if you make it okay for people to like, place a bet on these assets, all of a sudden
0:29:53 it’s de-risk because if you’re a big farmer and you’re like, I don’t really believe that
0:30:00 Alzheimer’s molecule does what is claimed, but I’m going to say like 15% odds it goes
0:30:01 through.
0:30:04 I’ll just invest 15% of what I would have in another world.
0:30:07 The trick is, and especially what we’re talking about now is the world of financial instruments
0:30:11 as well, is the trick is you have to be able to know how to risk an asset.
0:30:15 And so it could be in the end, one of the first interesting applications of deep learning,
0:30:20 machine learning is to use all the available data to give the maximum likelihood estimator
0:30:22 of what we think this asset is going to be.
0:30:25 It prices the asset and then people can go from there.
0:30:29 It’s kind of a fun world where we’re sort of thinking about how the financial world,
0:30:33 machine learning world and biology come together to kind of decentralize it and democratize
0:30:34 it.
0:30:40 I think there’s opportunities to kind of like, allow for more risks, the long tail to be played
0:30:41 out.
0:30:45 You don’t have as many interesting hypotheses that grow dead in the water because it wasn’t
0:30:48 de-risk enough for a big bet.
0:30:53 So, you know, what I think the big takeaway for me here is that there is that possible
0:30:56 world, but I forget if this is the way you learned how to program.
0:31:03 The way many of us did is I learned when I was like 11 on like actually a TI99A and
0:31:07 I was just playing around with it and I learned so much because it was, I could just get my
0:31:09 hands right in it.
0:31:13 And I think kind of, my hope for the book is that it’s kind of the equivalent in biology
0:31:16 that people can get their hands in it and I don’t know where they’re going to go with
0:31:17 it.
0:31:19 I don’t know if they go where we’re describing.
0:31:22 That’s one of the possible, any futures, but I think that’s what we’re hopefully being
0:31:23 able to give people.
0:31:25 We are opening out the sandbox.
0:31:30 Here’s what we’ve learned in kind of these very exclusive academic institutions.
0:31:36 Let’s throw the gate open, say here’s as much as we know as we can try to distill it down
0:31:38 and do what you will with it.
0:31:42 Like open source means no permission, so go to town and hopefully do something good for
0:31:44 the world is kind of the dream.
0:31:45 That sounds fantastic.
0:31:46 Well, thank you so much for joining us.
0:31:47 Thank you for having me.

0:00:04 I’m Chris Lyons, and I lead the Cultural Leadership Fund here at Andreessen Horowitz,
0:00:08 a strategic investment vehicle that connects the world’s greatest cultural leaders to
0:00:10 the best new technology companies.
0:00:15 This segment of the A16Z podcast was based on an event hosted by the CLF in which we
0:00:20 featured a special early screening of Van Jones’s new series, The Redemption Project, followed
0:00:25 by a fireside chat between Van Jones and Chaka Senghor.
0:00:29 The Redemption Project is an eight-part series that looks at victims’ families in a life-altering
0:00:34 crime as they come together to actually meet their offender in hopes of finding personal
0:00:35 healing or peace.
0:00:40 It’s a rare glimpse into the U.S. prison system and also the incredible human potential
0:00:43 for redemption through restorative justice.
0:00:47 In this episode, Jones brought together a police officer who was shot and the man who
0:00:51 committed the crime decades earlier when he was only 17 years old.
0:00:54 In addition to the conversation between Van and Chaka, you’ll also hear two spoken
0:00:56 word performances.
0:01:00 Both artists are formally incarcerated inmates who have contributed to The Beat Within, an
0:01:05 organization and publication that serves over 5,000 youth annually through workshops
0:01:11 operated across California County juvenile halls and encourages literacy, self-expression,
0:01:15 healthy and supportive relationships with adults from their community.
0:01:19 First off, we’ll open up with Kevin Gentry performing his piece, My Heart.
0:01:24 And please note, there is some profanity and mature material in this episode.
0:01:32 For all intents and purposes, this piece, I loosely call it a piece, it’s more a letter
0:01:38 and the recipients of which are going to become readily apparent as I read this.
0:01:43 Excuse me, I’m sorry, may I please have just a few minutes of your time to say how much
0:01:47 I’m sorry for destroying your life.
0:01:51 Strong words that fall so short I can only imagine.
0:01:56 How can I, especially I, even begin to measure the impact of what I’ve done.
0:02:02 The loss, the pain, the emptiness, the sorrow, the guilt, what ifs, if onlys.
0:02:03 Is that a good start?
0:02:05 Maybe, I don’t know.
0:02:11 For so long I have dreamt of just how, what to say, the right words, but everything just
0:02:13 feels so flat.
0:02:18 So now here I am, resigned to having faith in the process, releasing my heart to you
0:02:23 through the words, praying that they will do, sparing even the slightest amount of any
0:02:25 additional hurt.
0:02:31 In no way did you deserve these years of torment, the anguish, the pain, the emptiness, perhaps
0:02:37 even bearing the burden of having to be strong for others when support was the furthest thing
0:02:39 from your mind.
0:02:43 You didn’t deserve such a fate, I’m sorry.
0:02:48 Sorry that on that faithful day I largely treated others like I felt.
0:02:55 Empty and devoid of any value, I saw your loved one as an object, though human, an obstacle
0:02:57 to my hopes and dreams.
0:03:03 Hopes and dreams are belonging and feeling relevant in the eyes of others, relevant so
0:03:10 unattainable it seemed for so long, so empty such a void I felt barren to the core.
0:03:14 My attempts to self heal I thought while I was perfecting.
0:03:21 If I get more I’ll be more, value was in the end more, irrelevance was in the knot.
0:03:29 In genius I believed back then, feel bad, fill with stuff, feel good, but not for long.
0:03:32 Try again, something’s wrong.
0:03:40 The pattern I repeated a revolving door in my life, try to feel, feeling full, just temporary,
0:03:43 once again feeling empty setting in.
0:03:50 The writing expectation that a life, his life, our lives should be unrestrained and unimpeded
0:03:55 by the untrue self defeating and outwardly destructive thoughts and behavior of someone
0:03:57 just as me.
0:04:04 To stand in the way with an idea, a belief in some time, to cowardly step with hollow purpose
0:04:07 to fill a void that was never real.
0:04:13 Your loved ones so deserving of everything good, unaffected by me, unfortunately there
0:04:15 wasn’t me.
0:04:20 But thank God there is also you through which his life still lives.
0:04:26 Through the memories and lessons in love, the affection and joy and promise and hope
0:04:31 and countless other memories I’m sure, though I cut them way too short.
0:04:37 Now illuminated to the precious sanctity of life, the gift of the beauty and purpose that
0:04:44 lies within us all, staying ever mindful that I will never grasp the gravity of the destruction
0:04:46 I caused you that day.
0:04:55 I stay primed and fueled to walk boldly, purposefully, into any and every venue to answer my call.
0:05:00 To carry his memory in my heart to others with a message of life, of promise even on
0:05:03 the lowest rung to all.
0:05:09 Hope is eternal, believe it, a bright future can spring from even the darkest past.
0:05:16 The words that I now utter, I do so to breathe life into those who may feel that they have
0:05:24 guest but last.
0:05:26 And now we’ll hear from Van Jones and Shaka Sengor.
0:05:31 Shaka was most recently the executive director of the Anti-Rocidicism Coalition, a New York
0:05:36 Times bestselling author for his memoir Writing My Wrongs, Life, Death and Redemption in an
0:05:41 American prison and star of the highly anticipated One Man Show.
0:05:46 Van Jones is an American news commentator, author, co-founder of several non-profit organizations
0:05:52 including Reform and Yes We Code, host of The Van Jones Show and co-host of CNN’s political
0:05:54 debate show Crossfire.
0:05:58 Their conversation is all about the redemption project, the American prison system and how
0:06:03 we can normalize rehabilitation and restorative justice in our culture.
0:06:09 The journey toward redemption is one I understand on a very personal level.
0:06:14 And you and I, we’ve been friends for a while and we’ve had a chance to talk about, you
0:06:18 know, what does redemption look like for people?
0:06:24 What is something that you would say really stood out to you as a lesson that we can all
0:06:30 take away to create space for redemption to happen?
0:06:35 Doing this whole series has changed me in ways I haven’t really caught up to yet.
0:06:42 You know, now when I’m on TV and we’re supposed to be tearing each other up over some tweet
0:06:49 or some other nonsense that’s going on, which is terrible stuff, but I have a hard time
0:06:58 getting as petty and shitty as you have to be to do good television.
0:07:04 And then jeopardizing my career, I have to figure out some way to get petty again.
0:07:08 I have some answers for you.
0:07:13 That one was the hardest one for me to do because my dad used to be a cop.
0:07:20 And my uncle Milton just retired from Memphis City Police Force a couple of years ago.
0:07:25 And so that one was hard for me as much as I do criminal justice stuff and as much as
0:07:29 I’ve like, you know, been against police brutality.
0:07:33 That’s always your fear when you have a family member who’s a cop.
0:07:41 And you can see me struggling in this episode to be my usual sort of like open self.
0:07:42 Like I was really tight.
0:07:47 You know, I was really trying, but I wasn’t succeeding in this episode.
0:07:52 And I told Jason, I said, I don’t think this is going to go well.
0:07:58 Tom has admitted that he’s got racial bias, which was a big deal.
0:08:01 You know, this is not going to go well.
0:08:04 This is going to be a shit show.
0:08:08 And I guess one has to go terribly, like that was basically my view.
0:08:13 And so I didn’t have any hope in that one.
0:08:17 I was just waiting for him to come out and, you know, say some stuff that wasn’t going
0:08:18 to work.
0:08:26 And as soon as the door opened, just something changed, both of them became something different
0:08:30 than they had been up until the moment they saw each other.
0:08:36 Something fell away and, you know, between men, there’s almost always some shielding
0:08:43 in a patriarchal society, like you’ll tell a woman you just met more than you’ll tell
0:08:46 your homeboy, you know, for 20 years about how you actually feel.
0:08:49 You know, it’s just the trap.
0:08:54 And between white and black people, there’s always a lot of gulf.
0:09:03 And between cops and black people, it’s like planetary levels of gulf and all just disappeared.
0:09:08 And you saw these two guys who had literally tried to kill each other, laugh at each other,
0:09:14 saw each other, have this conversation that I bet they couldn’t have with any other human
0:09:16 being.
0:09:18 And I haven’t processed it.
0:09:22 And there’s a lot of stuff in this series I haven’t processed.
0:09:23 Yeah, I can imagine.
0:09:26 I struggle with this episode.
0:09:31 You know, I’ve watched a few episodes, I’ve actually struggled with all of them.
0:09:38 And you know, for those who may not know my story, I was convicted of second degree homicide.
0:09:45 And while I was in prison, I got into an altercation and I punched the officer in the neck and
0:09:47 almost killed him.
0:09:54 The family of the man whose life I’m responsible for taking, one of them reached out to me
0:09:58 and extended a letter of forgiveness during my incarceration.
0:10:04 The officer that I got into the conflict with in prison advocated for me to die in solitary
0:10:05 confinement.
0:10:12 And so as I’ve done this work over the years, that’s one of the areas of my life I haven’t
0:10:14 been able to reconcile.
0:10:21 So watching Jason come out and seeing that through the lens of his 17-year-old self,
0:10:26 and knowing where he was back then, and knowing that I was him back then.
0:10:33 And I’m thinking about this larger conversation that this is presenting to the world about
0:10:35 how do we see what’s possible.
0:10:41 You know, I’ve been out of prison almost nine years now, I’ve been highly successful
0:10:46 and been able to do a lot of work in this space and prevent acts of violence in communities
0:10:48 throughout the country.
0:10:56 But the reality is, for many men like Jason, like myself, society just says, “Watch our
0:10:57 hands of them.
0:10:58 They’re broken.
0:10:59 They’re beyond repair.
0:11:00 Throw them away.
0:11:02 Let them die in prison.”
0:11:08 And one of the things that really struck me was that restorative justice gives space for
0:11:13 people who have been hurt by the Jason’s of the world to have their say.
0:11:17 And we saw what happens when you create space for that.
0:11:22 You know, Tom’s a remarkable man, Christie is an extraordinary woman.
0:11:27 And the courage that the exhibit was honest, you know, she went from, you know, “I want
0:11:35 them to die in prison because we can’t kill them because of a particular crime to forgiveness.”
0:11:41 And so as we think about this show, how do we amplify that part of the message?
0:11:46 How do we get people to understand that people do change in a very real way?
0:11:54 Well, look, I mean, part of what’s crazy about this show is that it exists at all.
0:12:00 You know, CNN has put this at nine o’clock on Sundays, which is prime time.
0:12:03 And that’s Anthony Bourdain’s slot.
0:12:06 Against getting my thrones now.
0:12:13 So they either really like it or they really don’t.
0:12:23 Our idea was we wanted to do media that would be healing, that would be positive, that would
0:12:29 be transformative and, you know, living in Hollywood and all that, you know, you get
0:12:35 a lot of side-eye looks at you when you talk that way, as you know, until you actually
0:12:40 can produce something that makes the point, you’re just one of those people talking in
0:12:45 the cafe that everybody rolls their eyes at, which is half the population of LA.
0:12:51 Luckily, Jana’s best friend from college, Antonia, married to a guy named Jason Cohen.
0:12:58 Jason Cohen is the guy that did “Facing Fear,” that Oscar-nominated film about a former U.S.
0:13:02 neo-Nazi who reconciled with his victim of violence.
0:13:08 So Jason, having done that film, said, “Hey, let’s do this.
0:13:10 Let’s do this kind of a series.”
0:13:15 So we just went totally renegade, you know, seeing in I’m not allowed to do anything without
0:13:19 their permission on camera, but we just went totally renegade, shot something.
0:13:21 It wasn’t a good idea.
0:13:23 Let me stop you there, right?
0:13:30 So you basically what you’re saying is that you are willing to compromise your career.
0:13:34 You’re standing something you’ve worked long and hard for.
0:13:38 Most people would, you know, who talk a lot, especially people on social media, they would
0:13:43 love to be on CNN sharing their opinions and views and thoughts.
0:13:51 And you were willing to sacrifice or compromise that because you felt so strongly about the
0:13:53 importance of this mission.
0:13:59 Yeah, but yeah, because who gives a shit if we’re going to just be up here, I mean, you’re
0:14:00 the same way.
0:14:03 I mean, you could, people in this room are the same way.
0:14:04 Look.
0:14:06 I might not quit my job.
0:14:08 And you’re about to quit.
0:14:11 I got a seven-year-old.
0:14:14 But honestly, like that’s how we got the messy truth on the air.
0:14:18 I think it’s a very important point is that we have to take chances.
0:14:23 I mean, for me, I felt like this is the moment.
0:14:28 I feel like criminal justice reform is finally becoming a mainstream conversation.
0:14:33 The problem that we have right now is that there’s a level that people won’t go to.
0:14:36 So we can have the conversation about innocence, right?
0:14:40 And that’s an important conversation because that begins to chip away at people’s confidence
0:14:43 in the system that innocent people are being put away in prison.
0:14:48 So that used to be risky to say that our system, our American system, is putting people to
0:14:49 death who are innocent.
0:14:50 That was radical.
0:14:52 But we’ve been able to establish that.
0:14:55 Then we went to the nonviolent drug offenders.
0:14:57 They’re guilty, but they’re guilty of stuff that you did in college.
0:14:59 So why are they in prison?
0:15:00 Or maybe you did this weekend.
0:15:05 So don’t raise your hand.
0:15:08 And so now that’s been established.
0:15:13 But then the way that the danger is that then, well, okay, but if you’re not innocent and
0:15:17 if you’re not nonviolent, well, then we really don’t have to care about you at all.
0:15:22 And we have all these funerals in the community and we have all this harm and we can’t talk
0:15:23 about it.
0:15:30 And I said, this true crime genre has to be hacked and used for something positive because
0:15:34 true crime on the left wing, it’s about exoneration.
0:15:35 Like who done it?
0:15:38 Well, we’ve got to exonerate the person because they’re actually innocent.
0:15:41 Or on the right wing, it’s catch a killer.
0:15:45 But true crime as a who done it genre doesn’t get to the truth because a lot of times we
0:15:47 know who did it.
0:15:48 We already know who did it.
0:15:53 It’s about the truth long after the crime, which is that growth is possible for people
0:15:55 who have done harm.
0:16:00 And healing is sometimes impossible for people who’ve been harmed because of separation.
0:16:04 Because we don’t let people actually eventually come back together.
0:16:06 And so I say it was worth the risk.
0:16:07 And so we did it.
0:16:08 It was a little bit nuts.
0:16:10 We showed it to CNN.
0:16:15 Look, that day when we did the first one, I literally, I cried so hard when it was over
0:16:17 that my nose started bleeding.
0:16:21 Like, because my blood pressure was so high, it was just such an intense thing to see a
0:16:24 man who would kill someone’s mother sit down with the daughter 20 years later and try to
0:16:26 explain.
0:16:29 And we showed that to CNN.
0:16:35 And at that point, you know, we had no other people to go talk to.
0:16:38 It wasn’t like there’s thousands of people for us to go talk to, but CNN said if you
0:16:40 can find more, shoot it.
0:16:41 So we shot it.
0:16:42 Why am I saying all this?
0:16:47 I’m saying this to say that from my point of view, we’re at a point where those of us
0:16:53 who have privilege earned or otherwise, those of us who have positions of power, those of
0:16:59 us who have positions where, you know, people have to listen to what we say.
0:17:00 We have to push.
0:17:03 The Phadra Ellis Lampkins is here.
0:17:08 And she’s an African-American entrepreneur in the tech space, female.
0:17:13 You know, they say, like, that’s like a plaid unicorn or something, like, you know, it’s
0:17:16 not even supposed to exist in fantasy land.
0:17:22 And yet she’s building a company called Promise, pushing technology to solve some of these problems
0:17:25 in the community and winning, right?
0:17:27 You know, she doesn’t have to do that.
0:17:31 She could have taken an easy job and not try or put together a company to, like, you know,
0:17:36 make, you know, I don’t know, pictures or something, I don’t know.
0:17:40 But she’s doing the hard thing the hard way for the right reasons.
0:17:43 So all I’m saying is this.
0:17:47 The culture is not a show about criminal justice, first of all.
0:17:51 We have to market it that way and promote it that way, but it’s not about that.
0:17:52 It’s about humanity.
0:17:58 All of us have done something that we profoundly regret and don’t have any way to apologize
0:17:59 for.
0:18:03 All of us have had something done to us that’s hard to get past, and the stakes are higher
0:18:06 in our show, but this is humanity.
0:18:08 This is the human condition.
0:18:13 And yet in our culture, empathy is no longer trendy.
0:18:15 Compassion is no longer trendy.
0:18:19 It’s about the cancel culture, the call-out culture, and it’s poison.
0:18:21 This is the human condition.
0:18:26 We have to be able to listen to each other, to forgive each other, to hold each other,
0:18:27 to help each other.
0:18:28 That’s not fashionable.
0:18:31 And so we want to put some medicine back in the culture.
0:18:35 This show is our attempt to put some medicine back in the culture, and a little bit of medicine
0:18:37 can go a long way.
0:18:42 And so, you know, that’s what we’re trying to do.
0:18:43 [APPLAUSE]
0:18:47 I really want to push the envelope a little bit.
0:18:48 Eight episodes.
0:18:49 Yes, sir.
0:18:52 One of the episodes of “The Aviation Restorative Justice” happening, right?
0:18:55 In small pockets throughout the country, some prisons are a lot more progressive with
0:18:58 creating space for that.
0:19:02 But the reality is, it doesn’t happen for everybody.
0:19:07 So a lot of men, I work with men and women every day to come home from prison.
0:19:14 As executive director, anti-recidivism coalition, our staff has comprised 54% of system-impacted
0:19:16 men to come out of prison.
0:19:19 A lot of them have armed robberies.
0:19:20 Homicides.
0:19:21 Attempted murder.
0:19:26 I have scores of friends who are coming home after the “Warm Drugs” campaign.
0:19:33 Thousands of men and women come home every day who have served 15, 20, 30 years in prison.
0:19:38 They haven’t gone through a restorative justice process, because for years, our prison system
0:19:41 was designed for nothing more than punishment.
0:19:44 And that’s somebody who was deeply immersed in that environment.
0:19:46 And I know the type of works it takes to get there, right?
0:19:50 I know what it takes to transform a life.
0:19:54 I can honestly say I was super blessed and fortunate, because I was actually literate
0:19:55 when I’m with the prison.
0:19:57 And so I was able to read books that inspired me.
0:20:03 I was able to read Malcolm and read Mandela and read books about personal transformation
0:20:05 in these things, right?
0:20:06 And then I put the work in.
0:20:09 That’s not the norm in prison.
0:20:12 This is not the norm in prisons throughout the country.
0:20:21 And so one of the things that I’m always thoughtful about is how do we normalize restorative justice?
0:20:24 How do we normalize redemption?
0:20:26 You watch somebody in their worst moment.
0:20:30 It’s one of the things that I love that Sheriff Tom spoke about is that he met him in his
0:20:31 worst moment.
0:20:33 He met Jason in his worst moment, right?
0:20:36 But he was also in his worst moment.
0:20:40 And now we have many men and women coming home, and I deal with them all the time, and they’re
0:20:44 broken and they haven’t been able to make peace.
0:20:48 We think about the victim and them working through their trauma.
0:20:52 But there’s also work that those of us who have perpetrated a violent crime have to do
0:20:55 on our own.
0:21:00 And when I say on our own, oftentimes on our own, because in most cases we’re scary.
0:21:02 People are afraid.
0:21:04 You’ve killed another human being.
0:21:09 I don’t know if I can trust when you’re upset or when you’re angry or when things aren’t
0:21:13 going your way that you won’t react in that manner again.
0:21:20 So how do we create a space where there’s more honesty about what’s really not working?
0:21:23 We know about the policies and things like that, right?
0:21:29 But once the policies work, there’s real human beings coming home with deep, deep trauma.
0:21:35 My first 10 years in prison, I was in solitary my second year, and I ended up in solitary
0:21:38 my seventh year that extended to my 11th year.
0:21:43 So I did a total of seven years in hell, and I’m fortunate to have that breakthrough.
0:21:50 But what about the men and women who don’t have space to reconcile their past?
0:21:52 And what is our responsibility?
0:21:57 Ultimately, I guess the question is, what is our societal responsibility when it comes
0:22:02 to welcoming those men and women home in a healthy way?
0:22:06 I think this is the key question for American society.
0:22:10 I don’t have an answer, but it’s the key question.
0:22:11 We have…
0:22:17 People has become almost numb to throw out the numbers, but we have the biggest incarceration
0:22:23 industry in the world here in the United States, trafficking in human flesh, trafficking in
0:22:25 human bodies.
0:22:28 On the stock exchange, you have private prison companies that get more money than more people
0:22:32 who are locked up, and there’s no business model in de-incarceration.
0:22:35 The business model is in incarceration.
0:22:40 But what I do know is this, this is a political problem, kind of.
0:22:42 It’s a policy problem, kind of.
0:22:45 It’s an economic problem, kind of.
0:22:48 It’s a spiritual problem, for sure.
0:22:49 Absolutely.
0:22:55 It’s a spiritual problem, and separation is the enemy.
0:22:56 That’s the problem.
0:23:02 And unfortunately, you have now both political parties preaching separation and superiority.
0:23:08 Those red state people, those big it’s those idiots, those Trump voters, they’re terrible.
0:23:13 It’s almost like we in the blue, we’re good, they’re bad.
0:23:15 And it’s almost like a colonial thing.
0:23:22 Like the people in the red state, these unwashed heathens that need to be conquered and converted
0:23:31 to the NPR religion, and force fed some kale until they can rise up to our level of
0:23:32 civilization.
0:23:34 I mean, this is how people talk.
0:23:38 Separation and superiority, and then, of course, you know how the other side does.
0:23:40 And so, for me, it’s a spiritual problem.
0:23:42 Separation is the enemy.
0:23:49 And so, I have discovered all these diamonds behind those prison walls.
0:23:50 Absolutely.
0:23:52 No pressure, no diamonds.
0:23:53 There are diamonds behind those walls.
0:24:00 There are people behind those walls that are much wiser, much braver, much stronger, much
0:24:06 more creative than 99.99% of people who are on the outside.
0:24:12 When I worked in the Obama White House on a Friday, I was at San Quentin doing my work.
0:24:17 And then on Monday, I was in the Obama White House reporting for work.
0:24:20 So I went from the jailhouse to the White House in 72 hours.
0:24:26 And even under the Obama administration, the smartest people in the Obama administration
0:24:29 were no smarter than the smartest people at San Quentin.
0:24:35 But the wisest people at San Quentin were wiser than anybody in Washington, D.C.
0:24:39 All I know is that I have to tell the truth as I see it.
0:24:40 Absolutely.
0:24:45 And part of it is, you know, telling people, “Look, I went to Yale Law School.
0:24:50 I saw more kids doing drugs at Yale than I ever saw doing drugs and housing projects.”
0:24:51 Period.
0:24:54 And none of those kids even saw a police office.
0:24:58 They got in trouble when they went to rehab or France.
0:25:03 They sure didn’t go to prison.
0:25:08 And yet four or five blocks away, those kids, you know, doing fewer drugs because they had
0:25:12 less money and selling fewer drugs because they were dealing with a different clientele,
0:25:15 they almost all at least got arrested if they didn’t go to prison.
0:25:19 And yet now we sit here and say, “Well, I can’t, my God, I can’t hire you.
0:25:20 You’re a drug felon.”
0:25:24 You know what I mean?
0:25:30 So the hypocrisy of a society where almost everybody’s addicted to something and nobody
0:25:35 can survive, think about this, these phones we carry around, if I told you right now that
0:25:41 for the past three months we have been audio taping and video taping, everything you’ve
0:25:49 been doing, and we’re now about to show it on the screen, you would run out of here because
0:25:52 none of us are as good all the time as we’re supposed to be.
0:25:53 Absolutely.
0:25:57 And nobody wants to be defined by their worst moment or their worst mistake, as you said
0:25:58 many times.
0:26:03 And so for me, I don’t know, but I do know that everybody in here has a lot of power
0:26:04 in the matter.
0:26:09 And everybody in here has a lot of ability to turn it.
0:26:12 And I think it’s trying to happen.
0:26:17 I think the fact that this many people are here, the fact that CNN put this up, I think
0:26:19 it’s trying to happen.
0:26:25 You know, your voice, Topeka Sam’s voice, Lewis Reed’s voice, the voice of people who
0:26:30 are directly impacted, people who are coming out of prison, you’re right, everybody doesn’t
0:26:33 come out of prison as whole as you.
0:26:36 Everybody doesn’t come out of prison and have Oprah as their best friend.
0:26:40 In fact, most people who haven’t gone to prison don’t have those things.
0:26:44 So art as a tool to shift culture.
0:26:52 How important is art and technology towards shifting this larger idea culturally?
0:26:58 You know, the opposite of humanization is criminalization.
0:27:01 If you can criminalize a whole population of people, all everybody in the neighborhood
0:27:05 is bad, all the people from that racial group are bad.
0:27:10 If you can criminalize a whole population, then you dehumanize them and then anything
0:27:14 can be done and people won’t respond to it if it’s my child.
0:27:20 Everybody says, oh my God, my child’s on drugs, give him 17 years in prison.
0:27:21 Nobody says that.
0:27:22 People say my child needs help.
0:27:28 And so what I would say is that the opposite of criminalization, though, is humanization.
0:27:32 And so art and technology, which helps us to humanize and spread these stories is really
0:27:33 critical.
0:27:34 Thank you.
0:27:40 Hey, Shalucha, we love y’all.
0:27:41 All right.
0:27:46 A round of applause for Shalucha and Van.
0:27:51 Now we’ll enjoy a performance by Missy Hart, who will share an amazingly powerful piece
0:27:55 called “Bloom,” a trilogy, and the titles of the three different poems are “Just Us,
0:28:02 The Dream,” and “What’s Your Seed?”
0:28:06 Before I share these pieces, I want to share a big part of myself.
0:28:09 And I feel it’s really important to really paint a picture of the power of healing and
0:28:11 redemption, and creative art therapies.
0:28:15 I’m from Norfolk, Redwood City, California, it’s not too far from here.
0:28:18 My beautiful struggle began when my father committed suicide before I was two.
0:28:21 So I was raised by my strong single mother, who had to work multiple jobs.
0:28:26 She came up out of the gang culture as well, and not just working jobs, but taking care
0:28:27 of my grandmother, who was mentally ill.
0:28:29 But most of the time it was me and my brother taking care of her.
0:28:31 So I had to grow up really fast.
0:28:37 And during that time, growing up in the streets and trying to find my identity, we all go
0:28:41 through those times trying to find our identity, and being biracial, and a lesbian growing up
0:28:45 in the late ’90s, early 2000s, I tried to find my place, you know, and I found my place
0:28:46 in the streets.
0:28:50 And I started gang banging when I was 10, and being a girl smaller than everyone else,
0:28:51 I had to go hard.
0:28:55 In the streets, you were all in, or you’re not, you’re not going to survive.
0:28:59 So I was fully committed, went all in, caught my first case when I was 11, when they just
0:29:03 passed Prop 21, and then I went to the system.
0:29:06 When I was 13, I started writing for the B. And the B really gave me a voice, gave me
0:29:09 a way to express my truths in my way.
0:29:13 Because going through the system, you’re constantly trying to go through all these therapies
0:29:17 and stuff, but you don’t even have language growing up and not being shown what you’re
0:29:21 feeling, or you just learn to speak with the language of violence and aggression.
0:29:24 And that’s what I learned to speak.
0:29:28 So over the next few years, I was in and out of the system, I became a ward of the court,
0:29:32 so I was in group homes, being locked up, and then being on the run, and then just in
0:29:33 this constant cycle.
0:29:38 And it wasn’t until I got released two months before my 18th birthday, and my mom’s boy
0:29:41 didn’t want me at the house, so I was homeless, serving crack on Army Block.
0:29:46 I don’t know if y’all from the city, but I on the blade on 2/6, and then I started changing
0:29:47 my life.
0:29:50 And then when I caught a tent of murder charges day before my 18th birthday, I fought the
0:29:52 whole case in solitary.
0:29:57 But by the grace of God, I was taken and arrested when I did, because where I lived that back
0:30:00 home, my boy ended up stabbing his due to death not even an hour later.
0:30:03 So if I didn’t get arrested when I did, I would be in there for murder.
0:30:05 He’s doing 25 with L right now.
0:30:09 And I just really, you know, just started to see that my chances were running out.
0:30:12 And I got out, and you don’t change overnight, it’s a process, you know, and putting that
0:30:13 work in.
0:30:16 But it’s so important.
0:30:19 And I got out, you know, in and out of county, but then, you know, I started to change my
0:30:23 life and really see that education was the way to liberate myself.
0:30:26 So I went back to adult school, got my high school diploma.
0:30:30 And then I went to community college, when Jason was saying, like, you know, just having
0:30:32 someone believe in you, that is so powerful.
0:30:35 It may seem so little, but just even in times when you don’t believe in yourself and you’re
0:30:39 just raised to be taught in a system like broken down, you’re identity to nothing, you
0:30:42 know, and the beat really gave us our voice, and the beat really planted that seed for me
0:30:46 because now I’m doing, like, all these amazing things that I can’t even imagine back then.
0:30:50 So I went to community college, ended up winning a full ride to UC Santa Cruz where I attend
0:30:52 now I’m studying psychology and the history of consciousness.
0:30:53 Thank you.
0:30:54 Right on.
0:31:03 And I also just want a national scholarship to go study abroad this fall where I’m going
0:31:07 to study psychology, neuroscience, come back, go to DC, do an internship, come back, and
0:31:11 then I’m planning to get my PhD in positive psychology and my end goal.
0:31:12 Thank you.
0:31:16 Thank you, thank you.
0:31:19 My end goal is to open a group home with an art therapy program because our creative
0:31:23 art therapy is like, it’s so powerful, I can’t stress it enough, like, there’s no words
0:31:28 that I can even put it, express to explain how powerful it is.
0:31:31 So yeah, without further ado, I’m just going to spray my pieces then.
0:31:35 So thank you, and I just want to say thank you because this is a privilege of me being
0:31:39 on the stage because a lot of my loved ones and people I don’t even know, you know, we
0:31:43 lost to the streets and the system, they don’t get the same opportunity, and I’m just thankful
0:31:46 and I don’t just do this for myself, but I do this for my people and everyone’s still
0:31:50 behind those walls and who are lost, you know, and I’m just, I got this motto, it’s called
0:31:51 be the change, lead the way.
0:31:57 Alright, so this is a bloom, so this first one’s called Just Us, a little spin of justice.
0:32:00 Is it just us who see no justice and no peace struggling to achieve the American dream?
0:32:05 A dream just to have an opportunity to succeed, but somehow it’s so far to reach.
0:32:08 Car in the system that’s designed to keep us at war, at war with each other, a storm
0:32:10 is brewing right outside of your door.
0:32:14 Is it our choice to endure, or is there a power much greater than the plans of the hate-filled
0:32:17 hearts, wedging a war on the people and the power within, a power capable in manifesting
0:32:22 a revolutionary change, a change that ripples through generations in time, seeded in this
0:32:24 message trying to reach your mind through a rhyme, because you see the powers in the
0:32:28 people and the passion that’s in our hearts, but change only start when you shine the light
0:32:32 on the dark, beginning within ourselves and branching out to the people, educating each
0:32:36 other to fight for our rights to be equal, no one who walks upon this earth is illegal,
0:32:39 all this misguided hate and bigotry is spiritually lethal, empowerment for each other starts
0:32:44 with the peaceful, not the deceitful, don’t let the con steer you wrong, the power you
0:32:48 hold within remains strong, you just gotta believe in the power of your seed to plant
0:32:52 amongst the weeds of the world’s evil deeds, to grow strong like a tree to feed the minds
0:32:56 of the future, but first you must take your time to find your design that creates change
0:33:00 in people’s lives one day at a time, then you will see it begins within thee, so may
0:33:04 the life you lead be the life of the sea, be the change lead the way and ask yourself,
0:33:06 what can I do today?
0:33:17 Thank you, thank you, so this next one is called the dream, many underestimate the power
0:33:21 of the mind, but what really lies inside the complexity of the emotional pathways that
0:33:27 lead us to act in a certain way, what drives us to manifest positive change, is it love,
0:33:31 is it pain, maybe it’s the dream that we all dare to scheme, this dream to be free and
0:33:35 all live in peace, but it seems just to be that, but a dream, a dream that seems impossible
0:33:37 to conceive or is it?
0:33:41 We create our limitations gate, it’s the power of your mind that can grow with time or deplete
0:33:46 with lies depending on what vibe you choose to feed inside, it begins with the light that
0:33:49 burns deep and bright, the young activists that just wants to raise their fists to fight
0:33:53 for the people in and out of sight because you see it’s not just about you or me, but
0:33:57 we, together we can be this dream that we dream, but in order to achieve this dream we
0:34:01 all need to see that I am you and you are me, that beating in your chest is your first
0:34:04 clue, purpose, you feel that?
0:34:07 That’s what we need to remember when faced with the choice to endeavor, it’s the power
0:34:10 of your mind that leads you to believe that you can achieve all that you seek, it just
0:34:14 leaves one question, what’s your seed?
0:34:22 Thank you, and you know to like kind of answer the question, like if you don’t got no purpose
0:34:24 when you get out, you’re going to end up right back in because you got nothing that’s going
0:34:27 to bring you up out of that, so I just want to say it because that’s whatever, everyone
0:34:30 has a seed, everyone has a seed to plant, you got to believe in that seed and believe in
0:34:31 yourself.
0:34:36 So this last one is called what’s your seed, I actually wrote this one when I started changing
0:34:40 my life when I went to community college, I wrote it before the other two, so it means
0:34:42 a lot to me, so.
0:34:45 Like a scientist gone mad, creativity flows out of me, like knowledge to history, like
0:34:49 wise words to a revolutionary, like the power in the people, but nobody is listening, while
0:34:53 the falling clock of life just keeps on ticking, life hitting you with trials and tribulations
0:34:57 and man you still don’t get it, you don’t need to wake the fuck up and get on with it,
0:35:00 if not when judgment day comes don’t look at me to save you because I wasn’t the one,
0:35:03 while you’re gone and with your funds, steady stacking your funds, you feel the biggest
0:35:06 test kid, you had to prove you was worthy of the sun, instead of bringing peace to the
0:35:10 world, they brought hay and guns, put them in the kids’ heads, said have some fun, then
0:35:13 prove to the world cops are just out of killing the dumb, while we’re all blinded by the government’s
0:35:17 thumb, and you all fail to see we’re all kids of the sun, I’m on this world of righteousness,
0:35:21 steady fighting the wicked, seems like growth, love and spirituality is extremely restricted,
0:35:25 kids caught in the cycle, like to the shelter world it’s explicit, on your worst enemies
0:35:29 you wouldn’t wish it, I know because I lived it, but this life is not a burden, nah, because
0:35:33 I’m that seed planted in the garden of grief, they try to drown me statistically, put me
0:35:37 through some shit you wouldn’t even believe, but instead of dying, I rose from the depths
0:35:40 of despair, to breathe truths for share, soon I found myself the heir to the knowledge’s
0:35:44 lair, now it’s up to me to train my mind, to learn how to share, many stop and stare,
0:35:48 but not many opt to care, they’d rather shop and hate than bring these kids up and congratulate,
0:35:52 designing our futures fate, and it doesn’t look pretty, so before the last grain of sand
0:35:57 falls in God’s land, what will you build to grow on with sand, thank you.
0:36:01 Thanks again for listening to this episode of the A16Z Podcast, and if you want to learn
0:36:05 more about the Culture Leadership Fund, please visit A16Z.com.