AI transcript
0:00:06 I’m Guy Kawasaki and this is Remarkable People. This is a special issue of the Remarkable People
0:00:14 Podcast because we are using an episode from the Possible Podcast. The Possible Podcast explores
0:00:21 the future of possibilities, which makes perfect sense. This podcast is co-hosted by Aria Finger,
0:00:28 Reed Hoffman’s Chief of Staff and Reed Hoffman himself. They in turn have a very special guest
0:00:36 and that guest is none other than the Bill Gates. So you’re going to hear from Aria, Reed and Bill
0:00:44 today and never in a million years that I think Bill Gates, Mr. Windows would ever be on my podcast,
0:00:50 which just goes to show you that if you wait by the side of a river, you never know what’s going
0:01:06 to pass by. Mahalo and Aloha. AI for material science, biology, it is a gigantic accelerator.
0:01:14 So take whatever green product you think is going to be the hardest to get the zero green premium,
0:01:22 rethink how hard that’s going to be because the AI tools are so phenomenal at accelerating all of
0:01:38 these paths of innovation. Hi, I’m Reed Hoffman and I’m Aria Finger. We want to know what happens
0:01:43 if in the future everything breaks humanity’s way. What we can possibly get right if we leverage
0:01:50 technology like AI and our collective effort effectively. We’re speaking with technologists,
0:01:57 ambitious builders and deep thinkers across many fields, AI, geopolitics, media, healthcare,
0:02:03 education and more. These conversations showcase another kind of guest, whether it’s inflections
0:02:10 pie, open AI’s GPT-4 or other AI tools. Each episode, we use AI to enhance and advance our
0:02:16 discussion. In each episode, we seek out the brightest version of the future and learn what
0:02:28 it’ll take to get there. This is possible. How do you settle on a topic of discussion for someone
0:02:34 with as much going on as Bill Gates? He’s a technologist, business leader and philanthropist
0:02:39 who works to solve some of the world’s biggest problems using technology to address poverty,
0:02:43 climate change, global disease and educational disparities worldwide.
0:02:50 For just a snapshot of Bill’s contributions to a better future for humanity, he co-founded
0:02:55 Breakthrough Energy Ventures, which has $3.5 billion to invest in reducing greenhouse gas
0:03:01 emissions. The Gates Foundation has distributed more than $7.8 billion to improve medical research,
0:03:09 access to immunizations and more. Bill led the effort to reduce global child mortality by 50%
0:03:16 and continues to work to eradicate and lessen the impact of diseases worldwide. The Foundation’s
0:03:22 commitment to improving education in the US and abroad has been equally strong. As just one example,
0:03:28 in 2022, the Foundation pledged $1 billion to improve math education in the US in the wake
0:03:35 of the COVID pandemic. Bill is quite literally one of the most impactful people in the world,
0:03:39 with a unique capability to steer technology that can change society for good.
0:03:45 So, that’s what we’re talking about today. Climate change, medicine, education and the
0:03:51 latest in AI. Plus, the way all of these issues will play off each other to impact our society.
0:03:57 As someone with a broad view of cutting-edge tech in these big areas, what opportunities
0:04:03 is Bill Gates seeing and what’s the trajectory of nascent applications for AI in medicine,
0:04:09 education and energy? Here’s our conversation with Bill Gates.
0:04:17 So, Bill, we’ve known each other for some time now. We probably don’t want to quite date it,
0:04:24 but it’s been a while. And one of the things that I love about doing podcasts with my friends is
0:04:28 that I actually, in research, learned some things that I didn’t actually know before. And apparently,
0:04:35 there’s this kind of trifecta of three criteria. It’s, “Will it have a big impact? Will you learn
0:04:41 something? Will it be fun?” What are some of the projects that immediately come to mind that are
0:04:47 the kind of the most interesting that meet that trifecta for you? What in that goes, “Ah, that’s
0:04:56 something that met all three and is fun and exciting for you?” Well, until age 20, I got to read about
0:05:02 lots and lots of things. So, I range quite broadly, including auditing a lot of courses
0:05:09 at Harvard that I wasn’t even signed up for. Weirdly, then when I got into software,
0:05:19 I had to suppress my sort of normal desire to be polymathic and be monomaniacal. And so, from age,
0:05:28 you know, 20 to 35, I didn’t stay up to date on geology. By the time I was 30, I started
0:05:35 cheating and reading some other things, in particular, when I turned over the CEO role. So,
0:05:41 it’s nice now that partly because the foundation touches on a lot of things, I do
0:05:47 get to range pretty widely. And there’s a few topics like climate, you know, then you have to
0:05:55 learn about weather and materials and energy. And so, it’s a great excuse for learning things.
0:06:04 Nowadays, global health gets a lot of my attention. And so, you know, I do put a lot of energy into
0:06:10 that because it really fits all of those criteria. And it’s such an underinvested field, you know,
0:06:17 that you can invent tools that save millions, you can save lives for less than $1,000 per life.
0:06:24 But, you know, now you’ve got climate, you’ve got AI, no shortage of interesting topics
0:06:31 in the world today for somebody like me. And, you know, my ability to know people who can help
0:06:36 educate me in the online tools, the combination means you don’t have to worry about getting
0:06:41 confused because someone, I know someone who will straighten me out.
0:06:46 So, one of your projects that actually hit all three for me is your recent Netflix series. I
0:06:52 loved it. What’s next? The Future with Bill Gates. And I will humbly say, I think it has a lot of
0:06:58 DNA in common with this podcast. It’s about the future. It’s about what could possibly go right.
0:07:03 So, can you just tell us a little bit about how is that experience making that show? And is there
0:07:07 any memorable moment, maybe an outtake that didn’t make it into the final cut?
0:07:13 Well, five years ago, I did a documentary with Davis Guggenheim inside Bill’s brain,
0:07:20 and he picked things I was working on that could fail, nuclear fusion, polio, and
0:07:28 magic toilets that don’t need sewer systems. And that was an interesting paradigm, you know,
0:07:34 why was I putting money into those when essentially no one else was? This one is quite
0:07:40 different because it takes topics that, like misinformation, I don’t know the answer. I mean,
0:07:46 literally, that’s one of the few problems I say, okay, young people, we screwed this one up.
0:07:52 You better create around it. You know, is AI going to help? Is AI going to hurt?
0:07:59 It was fascinating talking through with my kids, did they want to be on the series, you know,
0:08:06 and to them, like, ah, that doesn’t seem like a priority. And then D.B. is like, yeah, you know,
0:08:13 dad, you’re so out of it on this digital stuff, you still try to send me email, let me straighten
0:08:19 you out on these things, which, you know, really became the case. You know, I met people that I
0:08:26 hadn’t talked to much before. I’d never met Lady Gaga before. And, you know, so that was kind of
0:08:34 a privilege and, you know, a very interesting person. You know, on the global health, we had so
0:08:40 much footage, you know, that’s the one that I worry will get the viewership that the others will get,
0:08:46 because, you know, we really do know what to do in that space. It’s kind of amazing. And
0:08:51 because it’s far away, people, I think, would learn a lot, because, you know, they’re not
0:08:58 confronted with 500,000 malaria deaths a year. And the fact we see a path to drive that to zero.
0:09:03 I mean, I think the tagline, come for Lady Gaga, stay for global health. Like, you’ve got it, you’ve
0:09:13 got it. Absolutely. Netflix might use that on its rotation. What are you currently most excited
0:09:21 about in terms of the technologies that will make a massive difference of changing what’s
0:09:27 possible at scale? Yeah, the current situation is that all the things I’m working on that relate
0:09:34 to innovation, whether it’s climate or these health issues, a lot malnutrition, infectious disease,
0:09:42 or, you know, digital tools for, say, teaching or health, the pace of innovation is faster than I
0:09:47 would have expected. And I have a pretty high expectation. I go to product meetings and say,
0:09:53 how come we can’t do this twice as fast? Just do that many times a day. And yet innovation is even
0:10:02 exceeding my best hope. So it’s super promising in all of those areas. You know, take malnutrition,
0:10:07 almost half the kids in Africa, their brain and bodies don’t develop. And we haven’t understood
0:10:12 chemically, they’re getting enough calories. You know, what are the micronutrients or the mix
0:10:20 in their diet that causes them to be, on average, five inches shorter than they would be and,
0:10:27 you know, 20 IQ points less capable than they should be, which for them and their countries is
0:10:32 pretty profound. And now, with the latest tools of science, you know, looking at these bacteria in
0:10:39 the gut, the microbiome, and, okay, how do we influence that? We clearly have a path to solve
0:10:49 malnutrition. And, you know, people should go, wow, that is a very, very big deal in terms of
0:10:56 the uplift that comes out of that. You know, on the energy side, things like either getting
0:11:05 fission or fusion to provide both very cheap and constant reliable electricity, you know,
0:11:12 that’s a longer time frame in the fusion case. But, you know, there’s a ton of companies I’m
0:11:18 invested in, in five. So deep understandings of complex science things, including
0:11:24 all these diseases, this next 20 years is going to be mind blowing.
0:11:30 And actually, knowing a little bit about the malnutrition, go the next level depth on the
0:11:34 bullion. So make it a little more tangible so people could say, oh, my God, we’re there now.
0:11:44 Yeah. Well, with malnutrition, if you don’t get the right vitamins during pregnancy or in your
0:11:49 first several years, you can never catch up. You know, so it’s a sad fact you don’t get to go back
0:11:57 and say, okay, eat your Wheaties, and you get the IQ points and the physical capabilities.
0:12:04 And it’s weird that you’re just missing small amounts of things like vitamin A and vitamin D.
0:12:11 And so how do you solve that? Well, you could fortify a food like U.S. breakfast cereals are
0:12:19 fortified, but you have to find some food that even the poorest households who are the most
0:12:26 likely to be malnourished because they’re not getting eggs and milk and meat. And it turns out
0:12:32 these bullion cubes are preferably bought by low income households because it gives them something
0:12:39 tasty. And it’s very cheap relative to how tasty it is. So now we’re going to put a lot of vitamins,
0:12:45 particularly vitamin A, into that bullion cube. It raises the price about 3 percent to do that.
0:12:51 Something I love about what you do is you identify the problem, you bring data to it,
0:12:55 you test it, figure out, especially because you’re working with populations who are low
0:12:59 income in Africa, the poorest of the poor, even if you’re raising the price 3 percent,
0:13:04 you have to build that into everything that’s happening. And so now we want to get into all
0:13:09 of the different areas that you’re focused on. And so let’s start with climate. I was lucky enough
0:13:13 to have dinner with you and Reid and a few others a few weeks ago. And one of the interesting things
0:13:18 you were talking about is you were saying how one of the interventions that we need to do
0:13:23 is around cows. I feel like everyone sort of knows that maybe cows contribute to climate,
0:13:28 but you were giving me very specific interventions that can move the needle. Could you talk more
0:13:36 about those? Yeah, so there’s basically two ways to help with cows. Cows are about 5 percent of global
0:13:44 emissions, which is pretty unbelievable. And if your goal is to get to zero, you don’t get to
0:13:52 skip the cows or the steel or the cement or any of those big areas. So there’s a whole class of
0:14:00 solutions of making meat without cows. Today, it doesn’t taste as good and it costs too much.
0:14:05 It’s going through a little bit of a lull, but those companies impossible beyond Memphis and
0:14:14 others are pursuing that. In terms of the cows, we actually have, we pursued many solutions. So
0:14:22 one is to vaccinate the cows in a way that they’re gut bacteria that emit the methane, which is also
0:14:29 called natto gas or CH4, which is the second most important greenhouse gas. You can vaccinate them
0:14:35 and that species of bacteria isn’t there. Their stomachs are very special because they can eat
0:14:43 grass. It’s a three-stage fermentation process, basically. There’s another way you can change
0:14:51 what they eat and you could either put that in their water or their feed. There is a drug to change
0:14:57 the microbiome, not a vaccine, but a drug. That looks very promising. And then there’s a solution
0:15:05 where you stick a sort of a metal thing into the skin of the cow and it actually burns the methane.
0:15:14 And all of these look to be quite cheap and implementable even in Africa. And so this is one
0:15:21 where I wasn’t hopeful when I got started a decade ago. And now it’s just a question of which
0:15:27 solution for which country ends up being the best. Yeah, it’s similar amazing to what you did with
0:15:32 the toilet and all the rest. It doesn’t look like it’s possible. Now it is and it makes a huge
0:15:37 difference. One of the other funny things when we were talking about the discovery of the work
0:15:42 around cows over the dinner tables, once you focus on it, it’s not just the systemic climate change,
0:15:48 but it’s also the questions about, for example, what this can do for quality of life in Africa.
0:15:53 So say a little bit more about milk production and cow breeds and other things. It’s like,
0:16:00 I never thought Bill as the cow expert, but here we are. Well, I didn’t grow up knowing much about
0:16:08 cows. So protein is a very important part of a good diet and foods with proteins tend to be
0:16:16 very expensive. So if you can make chickens and cows live longer and be more productive,
0:16:25 then that’s super beneficial. So the West has taken these cows, these Holsteins, and driven them up
0:16:33 so they make 30 liters of milk a day, whereas the normal cows make less than three liters a day. So
0:16:39 you have this factor of 10 productivity through the genetics of that cow. Now you can’t just fly
0:16:46 a Holstein down to Africa because the heat and the diseases, it’s not adapted, but if you do the
0:16:52 crossbreeding properly, only giving up a little bit of the productivity, say down to 20 liters,
0:16:58 still a factor of six better, you can improve those cows. You also take the idea of grazing,
0:17:04 where the cows are going out into areas that are now being fenced off, and you can change it so the
0:17:09 cows largely stationary, and the food, which they call fodder, is brought to the cow. And so you
0:17:16 avoid these incredible conflicts between the grazers and the other farmers. And so it’s one of the
0:17:24 most exciting foundation things. And once we do the R&D and get those good cows in, it’s private
0:17:30 market sustainable. We’re further along with chickens, so I was in Ethiopia a few weeks ago,
0:17:37 seen that we’ve cut the price of chickens in half. Women who do this, they make extra money,
0:17:44 they give some of the extra eggs to their kids as well as selling them. So it’s taking, but it’s
0:17:51 leveraged off of the West that spent the last 100 years doing selective breeding of both chickens
0:17:56 and cows. So obviously, when we’re thinking about chickens and cows, it’s climate change,
0:18:03 it’s malnutrition, it’s potentially war and conflict. If we zoom out on climate change,
0:18:09 what is the sort of statistic that for you sort of represents the gravity of the situation,
0:18:13 or what are you working towards to decrease when you think about climate in general?
0:18:21 Well, the size of the emissions, which is over 50 billion tons CO2 equivalent per year,
0:18:29 and the pie chart of, okay, one of the big five areas, like transport is one of the big five,
0:18:37 but then cars, planes, boats, trains, underneath that electricity, which is coal,
0:18:47 napto gas, industry, buildings, agriculture. I want everybody to have that pie chart in their head,
0:18:56 and the theory of change, which is that we need to make all those things without them costing more.
0:19:03 I always say there’s two numbers, 50 billion and zero. Zero is what we want emissions to be,
0:19:12 and zero is what we want, the extra cost of green cement, green beef, green rice, green car.
0:19:20 We want it even at the low end where you park on the street, because even if the world should pay
0:19:28 a lot for green things, they won’t, because it’s a global problem. The real problem is for future
0:19:33 generations. The negative impacts have been somewhat overstated for the current generation,
0:19:39 actually, but because it accumulates and gets worse over time, if it causes us to help those
0:19:47 future generations, it’s not a terrible thing. The damage is mostly in poor countries.
0:19:53 It’s almost like people didn’t realize weather’s always been a problem, and they’re like, oh,
0:19:58 every bad weather thing, that’s climate. No, there was bad weather before, and it’s
0:20:03 slightly getting worse, but it’s near the equator where your absolute temperatures
0:20:09 defeat outdoor work and the current crops that we have. That’s where you’re
0:20:13 getting into conditions that humans have never thrived in.
0:20:19 Well, on the electricity side, and we’ll get to nuclear in a minute, because you and I share a
0:20:24 passion in that being a great source, and there’s all kinds of things. One, the need for electricity.
0:20:30 Second, you have good, scalable, clean, cost-effective power. You can solve all kinds of ills.
0:20:37 What are the other energy sources of renewables and green energy that are capturing your attention
0:20:47 outside of nuclear? Well, of course, we want to keep driving solar to be cheaper. That’s gone
0:20:53 way better than was expected, and there’s some new things using perovskites that’ll
0:20:59 drive the efficiency up of that. We want to drive wind costs down, including offshore wind,
0:21:05 which is still quite a bit of a premium. We want to improve energy storage, but it’s not
0:21:13 realistic to think we’ll completely solve that problem, which is why you need nuclear in the mix
0:21:22 as well. Geothermal actually looks like it might play a role. The western half of the United States
0:21:29 actually has pretty good hot rocks, and then there’s geothermal companies that want to dig
0:21:38 really deep holes. That’s more early stage, but Fervo and one other company are showing that they
0:21:44 can actually get reasonably good pricing, and now they’re scaling up. Google just did a
0:21:52 purchase agreement at a premium to help them scale up, which is all the tech companies are
0:21:59 very oriented towards not raising their emissions, which will take advantage of that to get
0:22:05 these products onto a learning curve. Eventually, we want to have a zero-green premium, but somebody
0:22:13 has to help get us, which solar was very subsidized, and then under certain definitions has now gotten
0:22:21 to a zero-green premium. There’s a few things like title that probably is pretty limited,
0:22:28 solar panels in space, maybe some people have even talked about, okay, put the whole data center up
0:22:34 there. It’s just bits, actually moving bits from space to ground is easier than moving energy
0:22:40 from space to ground. In particular, because launch costs are down, you can at least dream
0:22:50 of those things. That’s kind of a far out thing, but should be in the portfolio of innovation.
0:22:55 One of the things that I think that it’s useful to highlight for most people is like, for example,
0:22:59 a current discussion of AI is always going to be all its electricity, but the investment that all
0:23:05 the hyperscaler companies are putting into the kind of how we make clean data centers, how we have
0:23:10 clean power and all the rest is like that subsidy for the R&D. These advanced purchase agreements
0:23:16 are exactly the kind of thing where you have different ways of conceptualizing public-private
0:23:24 good. I say that rich countries, rich companies, and rich individuals should bootstrap the market
0:23:31 for these green products. We should buy clean aviation fuels. Some nations may mandate that
0:23:37 for private aviation, which that would definitely be a good thing because it would get the volume
0:23:43 to go up so that eventually we can get something with either a zero or very low green premium
0:23:50 into commercial aviation, which is another 6% of the emissions we have to get all the way to zero.
0:23:57 It’s important to remember that all that data is under demand. It’s big numbers,
0:24:03 and it’s coming quickly over the next six or seven years, but it’s not as much as electric cars or
0:24:10 electric heat pumps. Our climate solution, because you can’t avoid using the energy somehow,
0:24:19 the only non-hydrocarbon way we really know how to make energy in a portable form is electricity.
0:24:23 So we get rid of coal and natural gas, but you have to make a lot more electricity
0:24:31 to replace the either heating or industrial capacity that those direct hydrocarbons provided.
0:24:37 Can you tell us a little more about solar? You were telling me actually about the amazing
0:24:42 increase in ability of solar panels and what percentage of the sun they capture for energy,
0:24:47 which was fascinating, but you also said that you don’t think batteries aren’t going to be here
0:24:52 in time. Or is it we just need more time to get to batteries where they are? The cost is too high,
0:24:58 and I feel like not having the batteries we need is what is inhibiting solar from being the energy
0:25:04 source that can save us all. Most places we should be adding solar as fast as we can,
0:25:10 and we’re actually limited by the grid capacity. So I love solar. The efficiency
0:25:16 started out at like 10%. It’s in the 20s now. It could get as high as 40 with
0:25:22 new approaches over the particularly prospect over the next decade or so,
0:25:28 but it’s not just a 24-hour storage problem. Lithium ion batteries are now sodium,
0:25:36 and others will solve your 24-hour problem. But you have periods of time, like the Midwest gets
0:25:42 a cold front where you get 10 or 12 days. All the batteries ever made in history for every car,
0:25:49 every computer, wouldn’t store a day of electricity. And if you’re only using a battery once a year,
0:25:54 that is, you charge it and it’s sitting there for this unusual thing, that’s super expensive
0:26:02 electricity. Instead of getting the capital value out 365 times a year, you get it out once. So
0:26:09 various seasonal and bad weather things where you don’t want to shut off power, particularly if it’s
0:26:16 being used to heat homes, it’s way more complicated than people think. Electricity doesn’t move long
0:26:22 distances today. It’s mostly coal or natural gas plants that are fairly near to the usage.
0:26:28 And yes, there’s innovation and transmission, actually fairly exciting stuff, but we’re going
0:26:34 to have to have a mix. In particular, if you look at a country like Japan, where there’s
0:26:43 essentially almost no solar potential there, and even the wind has periods where you have too much
0:26:52 or too little, the US happens to be very blessed. We have incredible wind and solar resources. And
0:26:57 these open source models that are now, we’re modeling now, okay, what does that energy system
0:27:04 look like, are part of seeing, okay, when can we get there? And there’s a lot of these goals that
0:27:11 are not well thought through. It’s going to be harder than we wished it would be.
0:27:16 Well, one of the things that you already mentioned, gestured at before, is that part of the reason why
0:27:23 we need nuclear fission and fusion is because of the fact that there are all these awesome renewables,
0:27:31 solar, wind, hydro, et cetera. Well, hydro can do a little bit more 24, but yes,
0:27:36 but they have limitations on when they generate and when they don’t. And so, and then battery
0:27:42 storage is a challenge. And so, you and I have invested in some fusion things together. Say a
0:27:50 little bit about what’s the hope and the possibilities for fusion? Fusion is where you take the big
0:27:56 atoms like uranium. And as they split, you get energy. And fusion is where you take the small
0:28:03 atoms, primarily hydrogen, as you put them together, you release energy. The middle of the periodic
0:28:13 table is the most stable. And so, you’re getting relativistic energy through that mass decrease.
0:28:22 Fusion is very difficult. It involves temperatures that are like the center of the sun in millions of
0:28:30 degrees. And so, that’s plasma physics, which we know a lot more about. We’re using AI tools
0:28:38 to study those things. And now there’s a variety of techniques. Tokamak, which Commonwealth Fusion
0:28:44 system is using being the one with the most credible schedule for, say, 10 years from now.
0:28:52 Most of the others are probably more like 15 years away. But at some point, fusion energy
0:29:00 will be extremely cheap. And it’s not, it doesn’t have the same waste problems that fission does.
0:29:06 You know, I think those are solvable problems. And I’m investing in that because that’s more
0:29:11 like a six-year time frame if everything went perfectly. So, we, society as a whole,
0:29:18 even though a lot more money is coming into it, we’re still under investing in fission and fusion,
0:29:25 given that the value of cheap electricity specifically is so fundamental to society.
0:29:31 I mean, if somebody says to you, “Oh, we have a water shortage,” no, there’s a lot of water.
0:29:37 It just takes energy to move that water and desalinate it. So, if energy is cheap, you have
0:29:45 infinite, wonderful water everywhere in the world. But the energy is too expensive right now
0:29:51 to do that at scale, even recycling things. You know, why can’t we, you know,
0:29:56 atoms don’t leave the planet, tiny bit of hydrogen. But it’s all there. And the reason we don’t is
0:30:03 that the energetic costs of restoring things to their original state is too high.
0:30:10 Well, one of the, for our listeners, a terapower super awesome way of kind of actually using
0:30:15 nuclear fission waste as fuel. And so, getting a compounding effect is one of the things that
0:30:19 you guys have been working on for a number of years, maybe even 10. Yeah, 2006 is when
0:30:25 the fission company terapower got started. Exactly. Now, one of the things that always
0:30:29 bemuses me about some of the current public dialogue around AI is like, “Oh, is this going
0:30:33 to accelerate climate change because of the electricity cost?” And I think what, you know,
0:30:39 most of these people are not realizing how we can also use AI to help us with climate.
0:30:45 So, because if we can get a lot more intelligence applied to various problems,
0:30:48 that can help us with climate. Say a little bit about how you’re thinking in that arena.
0:30:57 You know, the extra electricity load is, you know, it’s there, but it’s like a 10 percent
0:31:04 add-on. And it’ll challenge the way that we do green accounting a little bit. And I wish fission
0:31:11 and fusion were sooner because this sort of gold rush for AI back-end capacity is kind of the next
0:31:20 eight years. And even fission will only be able to make a modest contribution in the 2030 time frame
0:31:30 to that electricity supply. So, the value of AI in solving the scientific problems of,
0:31:36 okay, how do you grow, how do you make plants more productive? Okay, you model photosynthesis and you
0:31:44 model how you change the plant genetics in order to double the productivity. That’s a very profound
0:31:51 advance, improving photositic efficiency. In fact, you know, because it’s kind of a far out thing,
0:32:00 the foundation is the primary thunder of that as we show that it can work. Okay, other people will
0:32:10 come into that. But anyway, AI for material science, biology, it is a gigantic accelerator. So, take
0:32:15 whatever green product you think is going to be the hardest to get the zero green premium,
0:32:24 rethink how hard that’s going to be because the AI tools are so phenomenal at accelerating all of
0:32:28 these paths of innovation. Yeah, and actually, one of the things I’ve been thinking about is that
0:32:35 while it’s a big electricity cost for training these scale learning machines, once you have that
0:32:39 intelligence, like that’s how we’ve made everything. It’s through intelligence. Once you have that
0:32:45 intelligence to amplify across the board, like applying that to climate change has got to be like
0:32:51 there’s got to be some multiplier effect of we get this much actually savings and carbon and
0:32:54 other kinds of things through the application of this electricity. I don’t know what the multiplier
0:33:02 will end up being, but I’m certain it’s there. No, it’s absolutely there. There are some goals like
0:33:11 not going above 1.5 degrees that even with AI being a net positive contributor, because of the
0:33:20 difficulty of scaling in all the areas in all the countries, some of those goals we will miss.
0:33:29 But we will avoid the level of heating that would be disastrous, and we will need to do some adaptation
0:33:36 particularly in poor countries. So I want to switch gears. Another area where I think you’re
0:33:44 probably best known is global health. And I think it’s an area where AI can do so much. And
0:33:48 my husband is a public health data scientist, so he’s particularly excited about this area of the
0:33:54 interview. You have focused on the eradication of disease. And I think, but fact check me,
0:34:00 in 1980, the WHO declared that smallpox was eradicated, and that’s the first and only disease
0:34:06 that we have eradicated. You’ve said, let’s tackle polio, let’s tackle malaria. How do you pick
0:34:11 what is the next disease you’re going to tackle? What an amazing ambition. And then how do you go
0:34:21 after it? Yeah, so most diseases, we’re going for a burden reduction. Only very few diseases,
0:34:28 should you try to go for eradication, because it’s very, very hard to get to zero. And right now,
0:34:36 with polio, we’re in Afghanistan, we’re in Gaza, we’re in Somalia, we’re in DRC, and we’re having
0:34:44 to execute high coverage vaccination campaigns against misinformation and violence in the toughest
0:34:51 places in the world. So it’s very, very hard. Polio’s close, there’s one called Guinea Worm,
0:34:58 which is combined to Africa, where President Carter just got to 100. He’s been a champion of that.
0:35:06 So we’re hoping he’ll be alive not only to vote in the election, but also to come to the Guinea
0:35:11 Worm celebration party. It’s going to take a couple years, so he’s going to have to hang on
0:35:18 a little longer. So the magic thing that happened at the turn of the century was people got serious
0:35:24 about global health, about really measuring, okay, kids die of diarrhea, but what caused that
0:35:30 diarrhea? They died of pneumonia, malaria, okay, it’s more clear what that is. But let’s, even though
0:35:36 there’s no market, the people who died of malaria happen in kids’ ear, it’s not like you can make
0:35:42 a business case of, hey, go to Silicon Valley and do a malaria startup and look at that spreadsheet.
0:35:46 The line that says life saved will look good, but the line that says
0:35:54 profit will have a lot of red numbers because they can’t afford these tools. So medical science
0:36:00 is very distorted towards rich world conditions and even amongst rich world conditions towards
0:36:07 cancer and a few other things. So the incentive system is potentially could be improved. But
0:36:12 the Gates Foundation, that’s our place we come to fill in, is that the things that aren’t market
0:36:18 driven, like getting diarrheal vaccines cheap enough for all the kids of the world, not just
0:36:24 the rich kids who don’t die of diarrhea, but that also used to be a half million, now down
0:36:31 to 100,000. So as we went from 10 million under five deaths per year at the turn of the century
0:36:37 to five million, diarrhea was one of our best. Pneumonia, we got a vaccine out for that, which
0:36:43 was a very expensive vaccine that we worked with all the vaccine companies, Western and
0:36:52 Asian, to get those prices down. And so we’re basically driven by the inequity where we say,
0:37:00 why do mothers die on childbirth 20 times as much in Africa? Why do kids die 50 times as much
0:37:07 in their first five years in import countries, particularly Africa, but also Southeast Asia?
0:37:12 And so we’re taking all of those and saying, okay, let’s find the best scientists. Let’s
0:37:19 understand the field conditions. Is this stuff deliverable? Will it be accepted? We have crazy
0:37:26 ways of killing mosquitoes that that loan doesn’t get rid of malaria. But if you treat a lot of
0:37:34 humans and the reinfection rate has been massively reduced, then you can get to the point the US got
0:37:41 to because we had malaria where you’ve cleared during the low season, that’s the winter here and
0:37:46 the dry season there. You’ve actually cleared the pyrocyte. So there’s no humans. So there’s no
0:37:55 reinfection taking place. And in the next, hopefully, well less than 20 years, we have five
0:38:01 years now we need to do the tools. So our goal is to finish polio during this five years and then
0:38:09 with new tools get the credibility to get the world to fund a malaria eradication starting in 2030.
0:38:22 Totally awesome. One of the other projects we work on together is AI and drug discovery.
0:38:25 And so this is one of the ones I think we may actually even begin to see
0:38:34 some of the earliest kind of global benefit from. So we’re in what areas of AI drug discovery are
0:38:38 you kind of most focused on and think can make a kind of a global health difference?
0:38:46 Yeah. So understanding protein and molecular shape space is a perfect AI problem because
0:38:55 we have databases, the protein database that we have 150,000 molecules, we know the shapes. And so
0:39:01 we’ve trained AI on those, their ability to predict the shapes and therefore the drugable
0:39:11 sites in these proteins. That is accelerating medical discovery. There was actually a company
0:39:17 called Schrodinger that was doing it pre-AI. But now there might be 20 times as many people
0:39:28 and progressing much faster because AI is very, very well suited to this. And eventually AI won’t
0:39:35 just model the low level what the shape is, but it’ll model the cell and the organ and the organism.
0:39:43 And so even complex disease dynamics, it’s beyond human understanding to map out all
0:39:49 the different things that go on. The AI models as you gather the data, which will be the limiting
0:39:57 factor will help you understand over nutrition, malnutrition way better than we do today. So
0:40:06 most things put aside neurological in the next 10 to 20 years, I would say the likelihood of dramatic
0:40:14 medical advances, even in the neurological one thing, Alzheimer’s, I’d say those would be solved.
0:40:18 Then I love talking to these companies about, okay, which part of the problem they’re solving.
0:40:23 I mean, one of the things that strikes me is I feel like it’s super fashionable today to be,
0:40:28 oh, the world is terrible. The world’s a dumpster fire. It’s getting worse. The past 30 years have
0:40:35 been horrible. The next 20 are horrible. And yet talking to you, I’m like, no, stop. If we actually
0:40:40 look at the data, things have been getting better, especially in global health over the last 30 years.
0:40:47 And often because of AI, the future’s bright. There’s so much that can happen. And you just
0:40:53 sort of touched on not just sort of the technological progress, but also some of the sort of bureaucratic,
0:41:01 some of the other things that perhaps AI or else can unlock. Are there things that AI can unlock
0:41:05 that are just boring, administrative or helping healthcare workers that aren’t sort of about
0:41:09 cutting edge technology, but are other ways that AI can help in the public health field?
0:41:13 You know, in the same way that the micro computer revolution
0:41:20 allowed me at a young age to think, okay, computing will be free. Therefore, what would
0:41:27 an individual do with free computing? And, you know, Paul Allen and I kind of saw that and said,
0:41:33 okay, software is the only thing that will hold that back. Whereas older people kind of thought,
0:41:38 ooh, computers are expensive. And so the idea of, oh, it’ll be doing spreadsheets or word processor,
0:41:45 they’d be like, are you kidding me? That’s just too expensive. Here, it’s even more mind blowing
0:41:52 that you could say white collar worker capability. And eventually, although robotics is still
0:42:00 very specialized, eventually horizontal blue collar productivity will be very inexpensive.
0:42:07 So, you know, I take an MRI diagnosis that a friend has, and, you know, chat GBT does such a
0:42:15 good job of explaining it, you know, and showing, you know, where it got that material. The creativity,
0:42:24 the fluency is kind of mind blowing. And so all of us should have this crutch of,
0:42:29 yes, if you just want to know what fertilizer is, Wikipedia was good. But if you want to know what
0:42:36 a trip with a 16 year old for four days with a budget of $4,000 to Italy in August looks like,
0:42:45 nobody wrote that thing. But the AI, if it’s connected up properly, it is mind blowingly
0:42:55 good at those things. So we’re already all, you know, in our life of writing poems or speeches or
0:43:00 understanding or having complex material summarized, you know, we’re already getting a
0:43:10 huge benefit. And, you know, that, you know, a lot of white collar work, you know, should already be
0:43:15 either more productive or, you know, drive towards, towards higher quality.
0:43:20 So actually, one of the things this conversation is reminding me of that I haven’t actually ever asked
0:43:27 you is, so, you know, there was this decade or so of a lot of focus on personalized medicine.
0:43:32 Where are we at currently on that? Is that improving? Is that, is the, is the promise
0:43:40 of that turning out at all? I always am put off by people’s fascinations with n equals one medicines.
0:43:48 You know, a half million kids die of malaria. You know, millions of people are diagnosed every
0:43:56 year with Alzheimer’s. So I’m just the impersonalized medicine guy. I’m like, you know, the world does
0:44:03 not have the resources to do n equals one solutions. If some super rich person, you know,
0:44:10 funds that, maybe it helps the scientific discovery path, but, you know, I couldn’t bring myself
0:44:16 to be involved with that because it’s so unjust to take finite resources.
0:44:23 Eventually, yes, understanding everyone’s genetics and saying, okay, your drug dose is
0:44:30 different because of this. And so, you know, I’m kind of taking a provocative position
0:44:36 on this. The science that goes under that name is very good science. The people who work on that
0:44:43 are very well intended. It’s just, you know, we have rare diseases. We’ve created such an incentive
0:44:52 for them versus widespread conditions. We’re not really allocating that effectively, particularly,
0:44:56 I mean, the insane stuff is the diseases of the poor countries.
0:45:02 So can you actually say more about, you’ve traveled recently, Africa, Asia, all over about the
0:45:07 conditions with healthcare workers on the ground. What have you seen there and how can we help improve
0:45:14 them? Well, people should understand that most people in Sub-Saharan Africa never meet a doctor,
0:45:21 not when they’re born, not when they’re sick, not when they die. And so this is not a doctor’s
0:45:28 thing. So your image of healthcare, healthcare for most people in those countries is primary
0:45:36 healthcare. A modestly trained person who can give you semantibiotics, bed net, vaccines, very,
0:45:44 very importantly, to give a pregnant woman their prenatal checks, you know, now we’ll get an ultrasound
0:45:49 evaluation and we’ll see, okay, which 10% of pregnancies might be complicated. And then the
0:45:56 woman does need to get to somewhere where there’s trained personnel who could do a C-section.
0:46:02 So we can see with this AI trained ultrasound, is it going to be a complicated pregnancy and the
0:46:09 predictions are stunningly accurate and go to all that trouble, which you couldn’t afford to do for
0:46:15 all pregnancies. And, you know, so the greatest shortage of doctors in the world is there. And
0:46:22 so the idea that in native language through your smartphone, which, okay, not everybody has, but
0:46:30 times on our side, even in the poor countries on this, you will get health advice. And a lot of
0:46:37 the diagnostic tools will be available at a point of care where individuals, you know, can take a
0:46:43 lot of people experience this with lateral flow COVID tests, you know, now we’re trying to convert
0:46:52 those to be point of care, but molecular tests and still super cheap, but also more sensitive, more
0:47:01 accurate. And so healthcare is, you know, it’s in a, we only have $100 per person per year
0:47:11 versus in the US, where we spend $15,000 per person per year. So it’s, you know, it’s 150 times
0:47:20 different. And, you know, in fact, there is triage involved in, okay, which things should you treat.
0:47:29 Now, even things like blood pressure, cholesterol, obesity, my hope is that not only are the cost
0:47:35 of those treatments going down, we’re also going to put them into forms where it’s like yearly dosing.
0:47:42 And so the cost in the delivery system of getting GLP one to all Africans,
0:47:47 you know, 10, 15 years from now, you know, we’ll be able to do that.
0:47:54 So in addition to the elevation of humanity through all forms of kind of global medicine
0:48:01 and what is actually in fact healthcare, education is another area that you and the
0:48:07 Foundation work on intensely. And, you know, obviously, people have encountered AI a lot with
0:48:11 this, you know, it’s kind of like, you know, what does it mean? What are some of the,
0:48:20 the kind of maybe more surprising or other kinds of use of AI and education, whether it’s,
0:48:26 you know, kind of a globally available tutors or other kinds of, what are some of the AI for
0:48:29 education for the world that’s kind of captured some of your attention?
0:48:35 Well, I think the first thing to admit is that tech lovers like myself
0:48:42 have talked about the benefit of technology being used in education for our whole career.
0:48:49 And the actual benefit for the average student has been very, very modest. If you’re a motivated
0:48:57 student who can get on Khan Academy two hours a night or watch YouTube videos about photosynthesis,
0:49:05 wow, you know, people like ourselves, we are able to learn in a way that’s unprecedented.
0:49:10 You know, there’s a company called the Great Courses where I, when I’m on the treadmill, I love
0:49:17 watching those things, just so much great stuff out there. But the current math achievement
0:49:23 of a high school graduate in the U.S. is not better than 100 years ago. It’s not like medicine
0:49:29 where there’s new tools and new understanding. If I said, oh, in 1900, the best math teacher
0:49:37 was then, you couldn’t contradict me because it may be true. And so we’ve done a lot,
0:49:44 and, you know, I’m a believer. But AI, because of the fluency and personalization,
0:49:53 I think we can have very high aspirations of how we mix social experience in the classroom,
0:50:00 experiences with the teacher, and working on your own, that correcting your pronunciation as you
0:50:06 read things, immediately telling you, no, you didn’t get this math problem right, you know,
0:50:10 not turning in homework. So two days later, after the poor teacher spent all this time,
0:50:16 you’re like, well, was that a manipulation error? Or was that a conceptual understanding error? The
0:50:24 AI on Conomego is on the cutting edge of this, but there’s others like CK12Mini. It’ll say, yes,
0:50:32 those two minuses, you didn’t cancel those properly as opposed to, no, you set the problem
0:50:38 up wrong because, you know, these two trains, you didn’t get the, when they pass each other,
0:50:47 algebraic equation properly. And, you know, so the idea that it will learn how a tutor
0:50:53 keeps students motivated, you know, using the domain like sports or health or construction that
0:51:01 the student can relate to, the promise of having fantastic personal tutors in the inner city,
0:51:08 U.S. and in poor countries is super exciting. And, you know, talk about an area that’s
0:51:16 underfunded, you know, global education is, and the magic formula for countries uplifting themselves
0:51:22 is to have good health and good education. And then their economy grows, their tax collection
0:51:27 grows, and they’re very self-sufficient. And that’s, you know, why we want to help
0:51:31 countries get out of the poverty trap, not because it’s an endless guilt thing, but
0:51:38 if we help them get there, not only morally, but economically, stability, you know,
0:51:45 many good things flow from those countries being well off. Global education is a very
0:51:53 underdeveloped field, but particularly now with AI, you know, I’m encouraging philanthropists
0:51:57 to get in and showing that there are things like structured pedagogy
0:52:05 where the teacher’s given a very clear way of teaching that we are seeing some very good results.
0:52:10 No, I really appreciate. I think some people are like, oh, the boy who cried wolf. It’s like,
0:52:15 you told me 20 years ago that, you know, MOOCs were going to do it or that whatever. And so,
0:52:21 you know, people are skeptical. Are we, you know, are we finally there? Can you tell us more about
0:52:25 the First Avenue Elementary School in Newark? And obviously you’re super concerned about equity.
0:52:30 Is this something that is replicable that could scale? Well, so I love Khan Academy,
0:52:36 but it was mostly used by motivated students. And so for the last, I think it’s like eight years,
0:52:41 they’ve been saying, okay, how do we get into the classroom? How do we work with the teachers,
0:52:46 explain the stuff? You know, yes, the computers, the internet stuff is getting more pervasive.
0:52:52 The pandemic actually helped a bit with that. So Saul and I were amongst the first two people
0:53:01 who open AI was nice enough to let us mess around with early chat GPT-4. And a lot of the cool things,
0:53:08 like having it write songs and poems, actually Saul taught me that stuff. I was like, I wouldn’t
0:53:15 have known to ask. It can write like Shakespeare. Wow. And so he, you know, has put a lot of
0:53:21 Khan resources in. He gets support from the Gates Foundation and many others, has created this Khan
0:53:28 Amigo. And he, last school year, he had it in a small number of schools, but including this New
0:53:35 Jersey, Newark, New Jersey school. And so I went there to meet the teachers, to meet the students,
0:53:43 and see, you know, so you meet a kid who clearly is ahead of his class. And sort of the factory-based
0:53:49 model of, you know, 30 kids in a classroom, you definitely have a problem where you need to do
0:53:58 remediation and catch kids up. But you’re also pained by that kid who’s ahead, you know, and maybe
0:54:05 checking out or being disruptive. And yet, you know, you think, wow, we want to drive that. So the
0:54:12 personal tutor aspect allows that student to sometimes be off on his own, sometimes helping
0:54:18 the other students. And this, you know, Khan dashboard along with Khan Amigo, which is seen,
0:54:22 you know, so when you walk in and your teacher in the morning, instead of people handing in a
0:54:26 homework new up deal, that you just go to your dashboard and say, okay, who connected in last
0:54:34 night? How many hints did they need? How far did they get in the progression? And, you know,
0:54:40 you’re giving feedback, you know, you can have the parents connected up to that. Even the thing
0:54:46 where a paper gets turned in, you don’t turn in the paper, you turn in the AI session. So you can
0:54:54 just say to the AI, okay, how much did the student do? What’s your suggestion on how we get them to
0:55:03 either help with the first draft or help with the grammar or the logic? So it’s great to see it being
0:55:10 there and seeing it in person reminded me of embedding it in. And always with teachers, whenever
0:55:16 you have some new thing, there’s maybe 10% of the teachers that latch onto it and you get these great
0:55:22 results. And then when you tell the other 90%, you must use this, those results almost always just
0:55:30 disappear. And so, okay, how do we make this one one that that scales, you know, so that humility
0:55:38 of how far we haven’t come is, even with AI, we will have to do that. But what I saw made me even
0:55:44 more optimistic. So one of the things that people sometimes miss about the approach that you and
0:55:50 the Foundation bring to these problems is, you know, not just quantification of like, okay,
0:55:57 cost per life saved, etc., but also the systems thinking. And so what are some of the kind of
0:56:04 non-tech related levers for change and education? Like, what are some policy things that either we
0:56:09 as Americans or, you know, the world should be thinking about to improve education?
0:56:16 And there’s a lot of very good data about not having cell phones in the school. And, you know,
0:56:22 some great work going on there. There’s very good data about boys should probably start later than
0:56:29 they do school, school day should start later. There’s all the learning out of the charter school
0:56:36 movement, which it’s hard stop because it shows that long school day, long school year are incredibly
0:56:44 beneficial. Engaging the parents in a, you know, here’s where kids having challenge and communicating
0:56:49 with them, although these digital tools are going to make that far easier for the parents
0:56:55 who want to engage in that. You know, we’re seeing now in communities where there are charters,
0:56:59 the, even though most of the kids are in the public schools, those public schools
0:57:04 essentially compete. They either adopt those practices or find their own ways.
0:57:12 So we’re, you know, there are places like New Orleans or DC or Austin where school performance
0:57:19 is up. And so we ought to make sure that even as we try to put AI into this, some of those
0:57:25 learnings are incorporated. I mean, amen. As a mother of three boys, I’m like, how are they
0:57:31 going to compete with the girls? What is happening? So to switch gears from education, I think I’m
0:57:36 going to steal one of your own questions, which is if you had the opportunity to meet with someone
0:57:45 from the year 2100, what would your questions be for them? Wow. How did you deal with the AI
0:57:51 challenge opportunity? You know, did, you know, you said earlier that I have this
0:57:57 view that life is improving, which is objectively true. There’s always footnotes like nuclear
0:58:03 weapons, bioterrorism, and now AI needs to be added to that list. But, you know, the past 50 years,
0:58:11 life in general, if you’re a woman, if you’re gay, I mean, they, it’s kind of sad in a way that
0:58:20 because we’re so problem oriented, we’re not very reflective about, hey, 5 million kids a year aren’t
0:58:24 dying. You know, like you meet with climate people and they say, oh no, climate’s going to ruin the
0:58:29 world. Do you think you’re going to go back to 10 million a year dying? No way. You’re not going to
0:58:35 go back. We, it is a super big headwind because of the impacts through agricultural. So, you know,
0:58:42 I bring my, hey, the world is pretty good, but that 2100 person, I hope to hear how they avoided,
0:58:50 I’ll call it the four footnotes, AI, nuclear weapons, bioterror weapons, and polarization,
0:58:57 you know, people being able to get along and cooperate, including in how governance adapts,
0:59:04 you know, which AI will force governance to come up with different way of taxing people
0:59:12 and regulating things. And it’s a little scary that it’s happening at a time when the, the broad
0:59:20 trust in government is, is at a very low point, both relatively and absolutely. What are some of
0:59:27 the things that you think people should anticipate coming with AI in the next three to five years?
0:59:32 Well, it’s so mind blowing, you know, sometimes hard to get your head around it. No one expected
0:59:38 the white collar thing to come before the blue collar thing. You know, so in like life 3.0,
0:59:44 they have these hills where the computers are doing the easy things and, you know, warehouse work,
0:59:49 which we can’t yet do, was down there in the lowlands and helping diagnose, you know, was
0:59:57 way up the hill or writing legal briefs or code. And so it’s, we’re surprised at that order, but,
1:00:04 you know, the so-called blue collar, horizontal robot that can be told, you know, go to this
1:00:09 construction site and help go to this restaurant, go to this hotel and clean the rooms, you know,
1:00:16 even if the price is such that in the home, it only drops by for an hour, doesn’t live there at,
1:00:25 at first. You know, those things are, I believe, within, easily within the next decade.
1:00:31 Well, and actually going back to your original vision of a PC on every desk with software that
1:00:37 would help people do their work and lives and so forth, what do you think is going to come now?
1:00:41 I think a lot of people haven’t realized that part of what’s happening with current AI is
1:00:46 essentially the largest programming language will be natural language, e.g. English,
1:00:51 and everyone will have a coding assistant, not just the PC, but a coding assistant.
1:00:54 What do you think going, going back to those kind of earliest, what do you,
1:00:56 how do you think that will transform the world?
1:01:03 Well, the ability to navigate data, you know, which a long time ago, you had to have some
1:01:08 IT guy write a thing and, you know, okay, what’s the header and footer and, you know, report,
1:01:13 there’s a thing called RPG, report generation language, you know, and COBOL, you have this
1:01:22 section in the picture. Anyway, you know, a lot of that stuff, it’s so obsolete, it, it, it just
1:01:29 makes you laugh. The idea you can sit down and engage in a dialogue about data in a very rich way
1:01:37 means that, you know, our ability to run businesses better, you know, understand
1:01:44 bottlenecks, adapt to changing things will be so incredible and it won’t require custom software.
1:01:49 And in fact, the whole complexion of the software market, how many applications will there be,
1:01:54 you know, at first, what we have is everybody adding AI to every application and saying,
1:01:59 okay, pay me extra because now I got a little AI. But in fact, the number of applications you need,
1:02:06 you know, think of a college which has a scheduling app, a finance app, support the,
1:02:13 the student app, you know, they, that should all be one thing that every encounter with the student
1:02:22 to the college is all maintained in a rich way. So the software, software applications
1:02:26 will be very different. And I’m, you know, trying to figure out, okay, how quickly
1:02:33 does that happen? It’s incredibly beneficial that these, this software is more adapting to you,
1:02:38 including creating user interfaces dynamically than you going, okay, I use this software package
1:02:43 for this. And then I go to this website and do this, you’re a low level clerk, you know, even
1:02:50 looking at your email, the email is so stupid that you have to figure out it only can time sort
1:02:54 the order. It doesn’t know what’s important. And then you have your messages, and you have to go
1:02:58 back and forth. And you’re the one who puts it little folders and things. I mean, I thought the
1:03:05 semantic level of interaction with the computer would be higher by now, even without AI. But now
1:03:12 with AI, the idea that it, that very high level tasks that I’m doing, you know, I’m working on my
1:03:18 budget, I’m considering buying a new home that it will be working with you, not at the spreadsheet
1:03:25 cell level. But at the, oh, you know, let’s break this task down in a high level form. Here’s what
1:03:32 I can automatically go in and do for you. It’s, you know, super revolutionary. You know, we’ll all
1:03:39 have an agent that is a utilitarian help you get things done. You know, it reads everything you
1:03:47 read, but the things you meant to read, it reads. And then, you know, you, your agent can figure
1:03:53 out, okay, which parts of that are important enough to take your time to understand.
1:04:03 We are now switching to rapid fire. I’ll let Reed ask the first question.
1:04:09 Is there a movie, song, or book that fills you with optimism for the future?
1:04:15 Well, the Better Angels of our Nature, Stephen Pinker sort of documents how
1:04:24 violent death, you know, lifespan, education have gone. And, you know, there’s some lessons of
1:04:30 why we’ve done well. Doesn’t guarantee, and he doesn’t say that’s, that’s the future. But,
1:04:36 you know, if you have one book, which should get you back into the mindset of, okay, how far
1:04:42 have we come? What should we feel good about? I’d recommend that one. Fabulous. And what is a question
1:04:50 that you wish people asked you more often? How does malnutrition work? You know, a lot of things I
1:04:57 think about are boring and we should solve without most people having to ever figure out toilets and
1:05:06 nuclear reactors and, you know, understanding disease. I’m surprised people aren’t more curious,
1:05:12 you know. When I first said, what do kids die of? I had a hard time finding out. And I would have
1:05:19 thought, well, isn’t, shouldn’t we all be asking that kind of thing? It’s more important than GDP.
1:05:25 Exactly. I love it. So where do you see progress or momentum outside of your industry? Of course,
1:05:31 that’s very broad that inspires you. Well, when India is an example of a country where
1:05:39 oh, there’s plenty of things that are difficult there, the health, nutrition, education is improving
1:05:47 and they’re stable enough and generating their own government revenue enough that it’s very likely
1:05:54 that 20 years from now people will be dramatically better off. And it’s kind of a laboratory to try
1:05:59 things that then, when you prove them out in India, you can take to other places. And so
1:06:07 our biggest non-U.S. office for the foundation is in India. And then most number of pilot rollout
1:06:13 things we’re doing anywhere in the world are with partners in India. You know, if you go there and
1:06:19 you’ve never been, you might think, whoa, this is a chaotic place. And, you know, you’re not used to
1:06:25 so many levels of income all being on the street at the same time, but you will get a sense of the
1:06:32 vibrancy. All right. Last question. Can you leave us with a final thought about what is possible to
1:06:37 achieve if everything breaks our way in the next 15 years and what’s the first step to get there?
1:06:45 The potential positive path is so good that it will force us to rethink how should we use our time?
1:06:53 You know, you can almost call it a new religion or a new philosophy of, okay, how do we stay connected
1:07:00 with each other, not addicted to these things that will make video games look like nothing in terms of
1:07:08 the attractiveness of spending time on them. So it’s fascinating that we will, the issues of,
1:07:16 you know, disease and enough food, of climate, if things go well, those will largely become
1:07:24 solved problems. And, you know, so the next generation does get to say, okay, given that
1:07:30 some things that were massively in shortage are now not, how do we take advantage of that? You
1:07:39 know, do we ban AI being used in certain endeavors so that humans get some, you know, like, you
1:07:45 don’t want robots playing baseball, probably, because they’ll be too good. So we’ll keep them off.
1:07:53 The field, okay, how broadly would you go with that? We are so used to this shortage world that,
1:08:01 you know, I hope I get to see how we start to rethink these deep meaning questions.
1:08:06 Bill, a tour de force. Thank you. Yeah, great talking to you.
1:08:19 Possible is produced by Wonder Media Network. It’s hosted by R.F. Finger and me, Reid Hoffman.
1:08:24 Our showrunner is Sean Young. Possible is produced by Katie Sanders, Edie Allard,
1:08:31 Sarah Schleid, Adrienne Bain, and Paloma Moreno Jimenez. Jenny Kaplan is our executive producer
1:08:39 and editor. Special thanks to Soria Yalamanchili, Saida Sapieva, Ian Alice, Greg Beato, Parth Patil
1:08:46 and Ben Rales. And a big thanks to Aubrey Bogdanovich, Ian Saunders, Christy Anthony, Alex Reed,
1:08:53 Jen Krajasek, David Sanger, Larry Cohen, Alicia Sammond, Sean Simons, Denali Wiraman,
1:09:03 Andrea Dremer, John Ryder, the whole team at Gates Ventures and Little Monster Media Company.
0:00:14 Podcast because we are using an episode from the Possible Podcast. The Possible Podcast explores
0:00:21 the future of possibilities, which makes perfect sense. This podcast is co-hosted by Aria Finger,
0:00:28 Reed Hoffman’s Chief of Staff and Reed Hoffman himself. They in turn have a very special guest
0:00:36 and that guest is none other than the Bill Gates. So you’re going to hear from Aria, Reed and Bill
0:00:44 today and never in a million years that I think Bill Gates, Mr. Windows would ever be on my podcast,
0:00:50 which just goes to show you that if you wait by the side of a river, you never know what’s going
0:01:06 to pass by. Mahalo and Aloha. AI for material science, biology, it is a gigantic accelerator.
0:01:14 So take whatever green product you think is going to be the hardest to get the zero green premium,
0:01:22 rethink how hard that’s going to be because the AI tools are so phenomenal at accelerating all of
0:01:38 these paths of innovation. Hi, I’m Reed Hoffman and I’m Aria Finger. We want to know what happens
0:01:43 if in the future everything breaks humanity’s way. What we can possibly get right if we leverage
0:01:50 technology like AI and our collective effort effectively. We’re speaking with technologists,
0:01:57 ambitious builders and deep thinkers across many fields, AI, geopolitics, media, healthcare,
0:02:03 education and more. These conversations showcase another kind of guest, whether it’s inflections
0:02:10 pie, open AI’s GPT-4 or other AI tools. Each episode, we use AI to enhance and advance our
0:02:16 discussion. In each episode, we seek out the brightest version of the future and learn what
0:02:28 it’ll take to get there. This is possible. How do you settle on a topic of discussion for someone
0:02:34 with as much going on as Bill Gates? He’s a technologist, business leader and philanthropist
0:02:39 who works to solve some of the world’s biggest problems using technology to address poverty,
0:02:43 climate change, global disease and educational disparities worldwide.
0:02:50 For just a snapshot of Bill’s contributions to a better future for humanity, he co-founded
0:02:55 Breakthrough Energy Ventures, which has $3.5 billion to invest in reducing greenhouse gas
0:03:01 emissions. The Gates Foundation has distributed more than $7.8 billion to improve medical research,
0:03:09 access to immunizations and more. Bill led the effort to reduce global child mortality by 50%
0:03:16 and continues to work to eradicate and lessen the impact of diseases worldwide. The Foundation’s
0:03:22 commitment to improving education in the US and abroad has been equally strong. As just one example,
0:03:28 in 2022, the Foundation pledged $1 billion to improve math education in the US in the wake
0:03:35 of the COVID pandemic. Bill is quite literally one of the most impactful people in the world,
0:03:39 with a unique capability to steer technology that can change society for good.
0:03:45 So, that’s what we’re talking about today. Climate change, medicine, education and the
0:03:51 latest in AI. Plus, the way all of these issues will play off each other to impact our society.
0:03:57 As someone with a broad view of cutting-edge tech in these big areas, what opportunities
0:04:03 is Bill Gates seeing and what’s the trajectory of nascent applications for AI in medicine,
0:04:09 education and energy? Here’s our conversation with Bill Gates.
0:04:17 So, Bill, we’ve known each other for some time now. We probably don’t want to quite date it,
0:04:24 but it’s been a while. And one of the things that I love about doing podcasts with my friends is
0:04:28 that I actually, in research, learned some things that I didn’t actually know before. And apparently,
0:04:35 there’s this kind of trifecta of three criteria. It’s, “Will it have a big impact? Will you learn
0:04:41 something? Will it be fun?” What are some of the projects that immediately come to mind that are
0:04:47 the kind of the most interesting that meet that trifecta for you? What in that goes, “Ah, that’s
0:04:56 something that met all three and is fun and exciting for you?” Well, until age 20, I got to read about
0:05:02 lots and lots of things. So, I range quite broadly, including auditing a lot of courses
0:05:09 at Harvard that I wasn’t even signed up for. Weirdly, then when I got into software,
0:05:19 I had to suppress my sort of normal desire to be polymathic and be monomaniacal. And so, from age,
0:05:28 you know, 20 to 35, I didn’t stay up to date on geology. By the time I was 30, I started
0:05:35 cheating and reading some other things, in particular, when I turned over the CEO role. So,
0:05:41 it’s nice now that partly because the foundation touches on a lot of things, I do
0:05:47 get to range pretty widely. And there’s a few topics like climate, you know, then you have to
0:05:55 learn about weather and materials and energy. And so, it’s a great excuse for learning things.
0:06:04 Nowadays, global health gets a lot of my attention. And so, you know, I do put a lot of energy into
0:06:10 that because it really fits all of those criteria. And it’s such an underinvested field, you know,
0:06:17 that you can invent tools that save millions, you can save lives for less than $1,000 per life.
0:06:24 But, you know, now you’ve got climate, you’ve got AI, no shortage of interesting topics
0:06:31 in the world today for somebody like me. And, you know, my ability to know people who can help
0:06:36 educate me in the online tools, the combination means you don’t have to worry about getting
0:06:41 confused because someone, I know someone who will straighten me out.
0:06:46 So, one of your projects that actually hit all three for me is your recent Netflix series. I
0:06:52 loved it. What’s next? The Future with Bill Gates. And I will humbly say, I think it has a lot of
0:06:58 DNA in common with this podcast. It’s about the future. It’s about what could possibly go right.
0:07:03 So, can you just tell us a little bit about how is that experience making that show? And is there
0:07:07 any memorable moment, maybe an outtake that didn’t make it into the final cut?
0:07:13 Well, five years ago, I did a documentary with Davis Guggenheim inside Bill’s brain,
0:07:20 and he picked things I was working on that could fail, nuclear fusion, polio, and
0:07:28 magic toilets that don’t need sewer systems. And that was an interesting paradigm, you know,
0:07:34 why was I putting money into those when essentially no one else was? This one is quite
0:07:40 different because it takes topics that, like misinformation, I don’t know the answer. I mean,
0:07:46 literally, that’s one of the few problems I say, okay, young people, we screwed this one up.
0:07:52 You better create around it. You know, is AI going to help? Is AI going to hurt?
0:07:59 It was fascinating talking through with my kids, did they want to be on the series, you know,
0:08:06 and to them, like, ah, that doesn’t seem like a priority. And then D.B. is like, yeah, you know,
0:08:13 dad, you’re so out of it on this digital stuff, you still try to send me email, let me straighten
0:08:19 you out on these things, which, you know, really became the case. You know, I met people that I
0:08:26 hadn’t talked to much before. I’d never met Lady Gaga before. And, you know, so that was kind of
0:08:34 a privilege and, you know, a very interesting person. You know, on the global health, we had so
0:08:40 much footage, you know, that’s the one that I worry will get the viewership that the others will get,
0:08:46 because, you know, we really do know what to do in that space. It’s kind of amazing. And
0:08:51 because it’s far away, people, I think, would learn a lot, because, you know, they’re not
0:08:58 confronted with 500,000 malaria deaths a year. And the fact we see a path to drive that to zero.
0:09:03 I mean, I think the tagline, come for Lady Gaga, stay for global health. Like, you’ve got it, you’ve
0:09:13 got it. Absolutely. Netflix might use that on its rotation. What are you currently most excited
0:09:21 about in terms of the technologies that will make a massive difference of changing what’s
0:09:27 possible at scale? Yeah, the current situation is that all the things I’m working on that relate
0:09:34 to innovation, whether it’s climate or these health issues, a lot malnutrition, infectious disease,
0:09:42 or, you know, digital tools for, say, teaching or health, the pace of innovation is faster than I
0:09:47 would have expected. And I have a pretty high expectation. I go to product meetings and say,
0:09:53 how come we can’t do this twice as fast? Just do that many times a day. And yet innovation is even
0:10:02 exceeding my best hope. So it’s super promising in all of those areas. You know, take malnutrition,
0:10:07 almost half the kids in Africa, their brain and bodies don’t develop. And we haven’t understood
0:10:12 chemically, they’re getting enough calories. You know, what are the micronutrients or the mix
0:10:20 in their diet that causes them to be, on average, five inches shorter than they would be and,
0:10:27 you know, 20 IQ points less capable than they should be, which for them and their countries is
0:10:32 pretty profound. And now, with the latest tools of science, you know, looking at these bacteria in
0:10:39 the gut, the microbiome, and, okay, how do we influence that? We clearly have a path to solve
0:10:49 malnutrition. And, you know, people should go, wow, that is a very, very big deal in terms of
0:10:56 the uplift that comes out of that. You know, on the energy side, things like either getting
0:11:05 fission or fusion to provide both very cheap and constant reliable electricity, you know,
0:11:12 that’s a longer time frame in the fusion case. But, you know, there’s a ton of companies I’m
0:11:18 invested in, in five. So deep understandings of complex science things, including
0:11:24 all these diseases, this next 20 years is going to be mind blowing.
0:11:30 And actually, knowing a little bit about the malnutrition, go the next level depth on the
0:11:34 bullion. So make it a little more tangible so people could say, oh, my God, we’re there now.
0:11:44 Yeah. Well, with malnutrition, if you don’t get the right vitamins during pregnancy or in your
0:11:49 first several years, you can never catch up. You know, so it’s a sad fact you don’t get to go back
0:11:57 and say, okay, eat your Wheaties, and you get the IQ points and the physical capabilities.
0:12:04 And it’s weird that you’re just missing small amounts of things like vitamin A and vitamin D.
0:12:11 And so how do you solve that? Well, you could fortify a food like U.S. breakfast cereals are
0:12:19 fortified, but you have to find some food that even the poorest households who are the most
0:12:26 likely to be malnourished because they’re not getting eggs and milk and meat. And it turns out
0:12:32 these bullion cubes are preferably bought by low income households because it gives them something
0:12:39 tasty. And it’s very cheap relative to how tasty it is. So now we’re going to put a lot of vitamins,
0:12:45 particularly vitamin A, into that bullion cube. It raises the price about 3 percent to do that.
0:12:51 Something I love about what you do is you identify the problem, you bring data to it,
0:12:55 you test it, figure out, especially because you’re working with populations who are low
0:12:59 income in Africa, the poorest of the poor, even if you’re raising the price 3 percent,
0:13:04 you have to build that into everything that’s happening. And so now we want to get into all
0:13:09 of the different areas that you’re focused on. And so let’s start with climate. I was lucky enough
0:13:13 to have dinner with you and Reid and a few others a few weeks ago. And one of the interesting things
0:13:18 you were talking about is you were saying how one of the interventions that we need to do
0:13:23 is around cows. I feel like everyone sort of knows that maybe cows contribute to climate,
0:13:28 but you were giving me very specific interventions that can move the needle. Could you talk more
0:13:36 about those? Yeah, so there’s basically two ways to help with cows. Cows are about 5 percent of global
0:13:44 emissions, which is pretty unbelievable. And if your goal is to get to zero, you don’t get to
0:13:52 skip the cows or the steel or the cement or any of those big areas. So there’s a whole class of
0:14:00 solutions of making meat without cows. Today, it doesn’t taste as good and it costs too much.
0:14:05 It’s going through a little bit of a lull, but those companies impossible beyond Memphis and
0:14:14 others are pursuing that. In terms of the cows, we actually have, we pursued many solutions. So
0:14:22 one is to vaccinate the cows in a way that they’re gut bacteria that emit the methane, which is also
0:14:29 called natto gas or CH4, which is the second most important greenhouse gas. You can vaccinate them
0:14:35 and that species of bacteria isn’t there. Their stomachs are very special because they can eat
0:14:43 grass. It’s a three-stage fermentation process, basically. There’s another way you can change
0:14:51 what they eat and you could either put that in their water or their feed. There is a drug to change
0:14:57 the microbiome, not a vaccine, but a drug. That looks very promising. And then there’s a solution
0:15:05 where you stick a sort of a metal thing into the skin of the cow and it actually burns the methane.
0:15:14 And all of these look to be quite cheap and implementable even in Africa. And so this is one
0:15:21 where I wasn’t hopeful when I got started a decade ago. And now it’s just a question of which
0:15:27 solution for which country ends up being the best. Yeah, it’s similar amazing to what you did with
0:15:32 the toilet and all the rest. It doesn’t look like it’s possible. Now it is and it makes a huge
0:15:37 difference. One of the other funny things when we were talking about the discovery of the work
0:15:42 around cows over the dinner tables, once you focus on it, it’s not just the systemic climate change,
0:15:48 but it’s also the questions about, for example, what this can do for quality of life in Africa.
0:15:53 So say a little bit more about milk production and cow breeds and other things. It’s like,
0:16:00 I never thought Bill as the cow expert, but here we are. Well, I didn’t grow up knowing much about
0:16:08 cows. So protein is a very important part of a good diet and foods with proteins tend to be
0:16:16 very expensive. So if you can make chickens and cows live longer and be more productive,
0:16:25 then that’s super beneficial. So the West has taken these cows, these Holsteins, and driven them up
0:16:33 so they make 30 liters of milk a day, whereas the normal cows make less than three liters a day. So
0:16:39 you have this factor of 10 productivity through the genetics of that cow. Now you can’t just fly
0:16:46 a Holstein down to Africa because the heat and the diseases, it’s not adapted, but if you do the
0:16:52 crossbreeding properly, only giving up a little bit of the productivity, say down to 20 liters,
0:16:58 still a factor of six better, you can improve those cows. You also take the idea of grazing,
0:17:04 where the cows are going out into areas that are now being fenced off, and you can change it so the
0:17:09 cows largely stationary, and the food, which they call fodder, is brought to the cow. And so you
0:17:16 avoid these incredible conflicts between the grazers and the other farmers. And so it’s one of the
0:17:24 most exciting foundation things. And once we do the R&D and get those good cows in, it’s private
0:17:30 market sustainable. We’re further along with chickens, so I was in Ethiopia a few weeks ago,
0:17:37 seen that we’ve cut the price of chickens in half. Women who do this, they make extra money,
0:17:44 they give some of the extra eggs to their kids as well as selling them. So it’s taking, but it’s
0:17:51 leveraged off of the West that spent the last 100 years doing selective breeding of both chickens
0:17:56 and cows. So obviously, when we’re thinking about chickens and cows, it’s climate change,
0:18:03 it’s malnutrition, it’s potentially war and conflict. If we zoom out on climate change,
0:18:09 what is the sort of statistic that for you sort of represents the gravity of the situation,
0:18:13 or what are you working towards to decrease when you think about climate in general?
0:18:21 Well, the size of the emissions, which is over 50 billion tons CO2 equivalent per year,
0:18:29 and the pie chart of, okay, one of the big five areas, like transport is one of the big five,
0:18:37 but then cars, planes, boats, trains, underneath that electricity, which is coal,
0:18:47 napto gas, industry, buildings, agriculture. I want everybody to have that pie chart in their head,
0:18:56 and the theory of change, which is that we need to make all those things without them costing more.
0:19:03 I always say there’s two numbers, 50 billion and zero. Zero is what we want emissions to be,
0:19:12 and zero is what we want, the extra cost of green cement, green beef, green rice, green car.
0:19:20 We want it even at the low end where you park on the street, because even if the world should pay
0:19:28 a lot for green things, they won’t, because it’s a global problem. The real problem is for future
0:19:33 generations. The negative impacts have been somewhat overstated for the current generation,
0:19:39 actually, but because it accumulates and gets worse over time, if it causes us to help those
0:19:47 future generations, it’s not a terrible thing. The damage is mostly in poor countries.
0:19:53 It’s almost like people didn’t realize weather’s always been a problem, and they’re like, oh,
0:19:58 every bad weather thing, that’s climate. No, there was bad weather before, and it’s
0:20:03 slightly getting worse, but it’s near the equator where your absolute temperatures
0:20:09 defeat outdoor work and the current crops that we have. That’s where you’re
0:20:13 getting into conditions that humans have never thrived in.
0:20:19 Well, on the electricity side, and we’ll get to nuclear in a minute, because you and I share a
0:20:24 passion in that being a great source, and there’s all kinds of things. One, the need for electricity.
0:20:30 Second, you have good, scalable, clean, cost-effective power. You can solve all kinds of ills.
0:20:37 What are the other energy sources of renewables and green energy that are capturing your attention
0:20:47 outside of nuclear? Well, of course, we want to keep driving solar to be cheaper. That’s gone
0:20:53 way better than was expected, and there’s some new things using perovskites that’ll
0:20:59 drive the efficiency up of that. We want to drive wind costs down, including offshore wind,
0:21:05 which is still quite a bit of a premium. We want to improve energy storage, but it’s not
0:21:13 realistic to think we’ll completely solve that problem, which is why you need nuclear in the mix
0:21:22 as well. Geothermal actually looks like it might play a role. The western half of the United States
0:21:29 actually has pretty good hot rocks, and then there’s geothermal companies that want to dig
0:21:38 really deep holes. That’s more early stage, but Fervo and one other company are showing that they
0:21:44 can actually get reasonably good pricing, and now they’re scaling up. Google just did a
0:21:52 purchase agreement at a premium to help them scale up, which is all the tech companies are
0:21:59 very oriented towards not raising their emissions, which will take advantage of that to get
0:22:05 these products onto a learning curve. Eventually, we want to have a zero-green premium, but somebody
0:22:13 has to help get us, which solar was very subsidized, and then under certain definitions has now gotten
0:22:21 to a zero-green premium. There’s a few things like title that probably is pretty limited,
0:22:28 solar panels in space, maybe some people have even talked about, okay, put the whole data center up
0:22:34 there. It’s just bits, actually moving bits from space to ground is easier than moving energy
0:22:40 from space to ground. In particular, because launch costs are down, you can at least dream
0:22:50 of those things. That’s kind of a far out thing, but should be in the portfolio of innovation.
0:22:55 One of the things that I think that it’s useful to highlight for most people is like, for example,
0:22:59 a current discussion of AI is always going to be all its electricity, but the investment that all
0:23:05 the hyperscaler companies are putting into the kind of how we make clean data centers, how we have
0:23:10 clean power and all the rest is like that subsidy for the R&D. These advanced purchase agreements
0:23:16 are exactly the kind of thing where you have different ways of conceptualizing public-private
0:23:24 good. I say that rich countries, rich companies, and rich individuals should bootstrap the market
0:23:31 for these green products. We should buy clean aviation fuels. Some nations may mandate that
0:23:37 for private aviation, which that would definitely be a good thing because it would get the volume
0:23:43 to go up so that eventually we can get something with either a zero or very low green premium
0:23:50 into commercial aviation, which is another 6% of the emissions we have to get all the way to zero.
0:23:57 It’s important to remember that all that data is under demand. It’s big numbers,
0:24:03 and it’s coming quickly over the next six or seven years, but it’s not as much as electric cars or
0:24:10 electric heat pumps. Our climate solution, because you can’t avoid using the energy somehow,
0:24:19 the only non-hydrocarbon way we really know how to make energy in a portable form is electricity.
0:24:23 So we get rid of coal and natural gas, but you have to make a lot more electricity
0:24:31 to replace the either heating or industrial capacity that those direct hydrocarbons provided.
0:24:37 Can you tell us a little more about solar? You were telling me actually about the amazing
0:24:42 increase in ability of solar panels and what percentage of the sun they capture for energy,
0:24:47 which was fascinating, but you also said that you don’t think batteries aren’t going to be here
0:24:52 in time. Or is it we just need more time to get to batteries where they are? The cost is too high,
0:24:58 and I feel like not having the batteries we need is what is inhibiting solar from being the energy
0:25:04 source that can save us all. Most places we should be adding solar as fast as we can,
0:25:10 and we’re actually limited by the grid capacity. So I love solar. The efficiency
0:25:16 started out at like 10%. It’s in the 20s now. It could get as high as 40 with
0:25:22 new approaches over the particularly prospect over the next decade or so,
0:25:28 but it’s not just a 24-hour storage problem. Lithium ion batteries are now sodium,
0:25:36 and others will solve your 24-hour problem. But you have periods of time, like the Midwest gets
0:25:42 a cold front where you get 10 or 12 days. All the batteries ever made in history for every car,
0:25:49 every computer, wouldn’t store a day of electricity. And if you’re only using a battery once a year,
0:25:54 that is, you charge it and it’s sitting there for this unusual thing, that’s super expensive
0:26:02 electricity. Instead of getting the capital value out 365 times a year, you get it out once. So
0:26:09 various seasonal and bad weather things where you don’t want to shut off power, particularly if it’s
0:26:16 being used to heat homes, it’s way more complicated than people think. Electricity doesn’t move long
0:26:22 distances today. It’s mostly coal or natural gas plants that are fairly near to the usage.
0:26:28 And yes, there’s innovation and transmission, actually fairly exciting stuff, but we’re going
0:26:34 to have to have a mix. In particular, if you look at a country like Japan, where there’s
0:26:43 essentially almost no solar potential there, and even the wind has periods where you have too much
0:26:52 or too little, the US happens to be very blessed. We have incredible wind and solar resources. And
0:26:57 these open source models that are now, we’re modeling now, okay, what does that energy system
0:27:04 look like, are part of seeing, okay, when can we get there? And there’s a lot of these goals that
0:27:11 are not well thought through. It’s going to be harder than we wished it would be.
0:27:16 Well, one of the things that you already mentioned, gestured at before, is that part of the reason why
0:27:23 we need nuclear fission and fusion is because of the fact that there are all these awesome renewables,
0:27:31 solar, wind, hydro, et cetera. Well, hydro can do a little bit more 24, but yes,
0:27:36 but they have limitations on when they generate and when they don’t. And so, and then battery
0:27:42 storage is a challenge. And so, you and I have invested in some fusion things together. Say a
0:27:50 little bit about what’s the hope and the possibilities for fusion? Fusion is where you take the big
0:27:56 atoms like uranium. And as they split, you get energy. And fusion is where you take the small
0:28:03 atoms, primarily hydrogen, as you put them together, you release energy. The middle of the periodic
0:28:13 table is the most stable. And so, you’re getting relativistic energy through that mass decrease.
0:28:22 Fusion is very difficult. It involves temperatures that are like the center of the sun in millions of
0:28:30 degrees. And so, that’s plasma physics, which we know a lot more about. We’re using AI tools
0:28:38 to study those things. And now there’s a variety of techniques. Tokamak, which Commonwealth Fusion
0:28:44 system is using being the one with the most credible schedule for, say, 10 years from now.
0:28:52 Most of the others are probably more like 15 years away. But at some point, fusion energy
0:29:00 will be extremely cheap. And it’s not, it doesn’t have the same waste problems that fission does.
0:29:06 You know, I think those are solvable problems. And I’m investing in that because that’s more
0:29:11 like a six-year time frame if everything went perfectly. So, we, society as a whole,
0:29:18 even though a lot more money is coming into it, we’re still under investing in fission and fusion,
0:29:25 given that the value of cheap electricity specifically is so fundamental to society.
0:29:31 I mean, if somebody says to you, “Oh, we have a water shortage,” no, there’s a lot of water.
0:29:37 It just takes energy to move that water and desalinate it. So, if energy is cheap, you have
0:29:45 infinite, wonderful water everywhere in the world. But the energy is too expensive right now
0:29:51 to do that at scale, even recycling things. You know, why can’t we, you know,
0:29:56 atoms don’t leave the planet, tiny bit of hydrogen. But it’s all there. And the reason we don’t is
0:30:03 that the energetic costs of restoring things to their original state is too high.
0:30:10 Well, one of the, for our listeners, a terapower super awesome way of kind of actually using
0:30:15 nuclear fission waste as fuel. And so, getting a compounding effect is one of the things that
0:30:19 you guys have been working on for a number of years, maybe even 10. Yeah, 2006 is when
0:30:25 the fission company terapower got started. Exactly. Now, one of the things that always
0:30:29 bemuses me about some of the current public dialogue around AI is like, “Oh, is this going
0:30:33 to accelerate climate change because of the electricity cost?” And I think what, you know,
0:30:39 most of these people are not realizing how we can also use AI to help us with climate.
0:30:45 So, because if we can get a lot more intelligence applied to various problems,
0:30:48 that can help us with climate. Say a little bit about how you’re thinking in that arena.
0:30:57 You know, the extra electricity load is, you know, it’s there, but it’s like a 10 percent
0:31:04 add-on. And it’ll challenge the way that we do green accounting a little bit. And I wish fission
0:31:11 and fusion were sooner because this sort of gold rush for AI back-end capacity is kind of the next
0:31:20 eight years. And even fission will only be able to make a modest contribution in the 2030 time frame
0:31:30 to that electricity supply. So, the value of AI in solving the scientific problems of,
0:31:36 okay, how do you grow, how do you make plants more productive? Okay, you model photosynthesis and you
0:31:44 model how you change the plant genetics in order to double the productivity. That’s a very profound
0:31:51 advance, improving photositic efficiency. In fact, you know, because it’s kind of a far out thing,
0:32:00 the foundation is the primary thunder of that as we show that it can work. Okay, other people will
0:32:10 come into that. But anyway, AI for material science, biology, it is a gigantic accelerator. So, take
0:32:15 whatever green product you think is going to be the hardest to get the zero green premium,
0:32:24 rethink how hard that’s going to be because the AI tools are so phenomenal at accelerating all of
0:32:28 these paths of innovation. Yeah, and actually, one of the things I’ve been thinking about is that
0:32:35 while it’s a big electricity cost for training these scale learning machines, once you have that
0:32:39 intelligence, like that’s how we’ve made everything. It’s through intelligence. Once you have that
0:32:45 intelligence to amplify across the board, like applying that to climate change has got to be like
0:32:51 there’s got to be some multiplier effect of we get this much actually savings and carbon and
0:32:54 other kinds of things through the application of this electricity. I don’t know what the multiplier
0:33:02 will end up being, but I’m certain it’s there. No, it’s absolutely there. There are some goals like
0:33:11 not going above 1.5 degrees that even with AI being a net positive contributor, because of the
0:33:20 difficulty of scaling in all the areas in all the countries, some of those goals we will miss.
0:33:29 But we will avoid the level of heating that would be disastrous, and we will need to do some adaptation
0:33:36 particularly in poor countries. So I want to switch gears. Another area where I think you’re
0:33:44 probably best known is global health. And I think it’s an area where AI can do so much. And
0:33:48 my husband is a public health data scientist, so he’s particularly excited about this area of the
0:33:54 interview. You have focused on the eradication of disease. And I think, but fact check me,
0:34:00 in 1980, the WHO declared that smallpox was eradicated, and that’s the first and only disease
0:34:06 that we have eradicated. You’ve said, let’s tackle polio, let’s tackle malaria. How do you pick
0:34:11 what is the next disease you’re going to tackle? What an amazing ambition. And then how do you go
0:34:21 after it? Yeah, so most diseases, we’re going for a burden reduction. Only very few diseases,
0:34:28 should you try to go for eradication, because it’s very, very hard to get to zero. And right now,
0:34:36 with polio, we’re in Afghanistan, we’re in Gaza, we’re in Somalia, we’re in DRC, and we’re having
0:34:44 to execute high coverage vaccination campaigns against misinformation and violence in the toughest
0:34:51 places in the world. So it’s very, very hard. Polio’s close, there’s one called Guinea Worm,
0:34:58 which is combined to Africa, where President Carter just got to 100. He’s been a champion of that.
0:35:06 So we’re hoping he’ll be alive not only to vote in the election, but also to come to the Guinea
0:35:11 Worm celebration party. It’s going to take a couple years, so he’s going to have to hang on
0:35:18 a little longer. So the magic thing that happened at the turn of the century was people got serious
0:35:24 about global health, about really measuring, okay, kids die of diarrhea, but what caused that
0:35:30 diarrhea? They died of pneumonia, malaria, okay, it’s more clear what that is. But let’s, even though
0:35:36 there’s no market, the people who died of malaria happen in kids’ ear, it’s not like you can make
0:35:42 a business case of, hey, go to Silicon Valley and do a malaria startup and look at that spreadsheet.
0:35:46 The line that says life saved will look good, but the line that says
0:35:54 profit will have a lot of red numbers because they can’t afford these tools. So medical science
0:36:00 is very distorted towards rich world conditions and even amongst rich world conditions towards
0:36:07 cancer and a few other things. So the incentive system is potentially could be improved. But
0:36:12 the Gates Foundation, that’s our place we come to fill in, is that the things that aren’t market
0:36:18 driven, like getting diarrheal vaccines cheap enough for all the kids of the world, not just
0:36:24 the rich kids who don’t die of diarrhea, but that also used to be a half million, now down
0:36:31 to 100,000. So as we went from 10 million under five deaths per year at the turn of the century
0:36:37 to five million, diarrhea was one of our best. Pneumonia, we got a vaccine out for that, which
0:36:43 was a very expensive vaccine that we worked with all the vaccine companies, Western and
0:36:52 Asian, to get those prices down. And so we’re basically driven by the inequity where we say,
0:37:00 why do mothers die on childbirth 20 times as much in Africa? Why do kids die 50 times as much
0:37:07 in their first five years in import countries, particularly Africa, but also Southeast Asia?
0:37:12 And so we’re taking all of those and saying, okay, let’s find the best scientists. Let’s
0:37:19 understand the field conditions. Is this stuff deliverable? Will it be accepted? We have crazy
0:37:26 ways of killing mosquitoes that that loan doesn’t get rid of malaria. But if you treat a lot of
0:37:34 humans and the reinfection rate has been massively reduced, then you can get to the point the US got
0:37:41 to because we had malaria where you’ve cleared during the low season, that’s the winter here and
0:37:46 the dry season there. You’ve actually cleared the pyrocyte. So there’s no humans. So there’s no
0:37:55 reinfection taking place. And in the next, hopefully, well less than 20 years, we have five
0:38:01 years now we need to do the tools. So our goal is to finish polio during this five years and then
0:38:09 with new tools get the credibility to get the world to fund a malaria eradication starting in 2030.
0:38:22 Totally awesome. One of the other projects we work on together is AI and drug discovery.
0:38:25 And so this is one of the ones I think we may actually even begin to see
0:38:34 some of the earliest kind of global benefit from. So we’re in what areas of AI drug discovery are
0:38:38 you kind of most focused on and think can make a kind of a global health difference?
0:38:46 Yeah. So understanding protein and molecular shape space is a perfect AI problem because
0:38:55 we have databases, the protein database that we have 150,000 molecules, we know the shapes. And so
0:39:01 we’ve trained AI on those, their ability to predict the shapes and therefore the drugable
0:39:11 sites in these proteins. That is accelerating medical discovery. There was actually a company
0:39:17 called Schrodinger that was doing it pre-AI. But now there might be 20 times as many people
0:39:28 and progressing much faster because AI is very, very well suited to this. And eventually AI won’t
0:39:35 just model the low level what the shape is, but it’ll model the cell and the organ and the organism.
0:39:43 And so even complex disease dynamics, it’s beyond human understanding to map out all
0:39:49 the different things that go on. The AI models as you gather the data, which will be the limiting
0:39:57 factor will help you understand over nutrition, malnutrition way better than we do today. So
0:40:06 most things put aside neurological in the next 10 to 20 years, I would say the likelihood of dramatic
0:40:14 medical advances, even in the neurological one thing, Alzheimer’s, I’d say those would be solved.
0:40:18 Then I love talking to these companies about, okay, which part of the problem they’re solving.
0:40:23 I mean, one of the things that strikes me is I feel like it’s super fashionable today to be,
0:40:28 oh, the world is terrible. The world’s a dumpster fire. It’s getting worse. The past 30 years have
0:40:35 been horrible. The next 20 are horrible. And yet talking to you, I’m like, no, stop. If we actually
0:40:40 look at the data, things have been getting better, especially in global health over the last 30 years.
0:40:47 And often because of AI, the future’s bright. There’s so much that can happen. And you just
0:40:53 sort of touched on not just sort of the technological progress, but also some of the sort of bureaucratic,
0:41:01 some of the other things that perhaps AI or else can unlock. Are there things that AI can unlock
0:41:05 that are just boring, administrative or helping healthcare workers that aren’t sort of about
0:41:09 cutting edge technology, but are other ways that AI can help in the public health field?
0:41:13 You know, in the same way that the micro computer revolution
0:41:20 allowed me at a young age to think, okay, computing will be free. Therefore, what would
0:41:27 an individual do with free computing? And, you know, Paul Allen and I kind of saw that and said,
0:41:33 okay, software is the only thing that will hold that back. Whereas older people kind of thought,
0:41:38 ooh, computers are expensive. And so the idea of, oh, it’ll be doing spreadsheets or word processor,
0:41:45 they’d be like, are you kidding me? That’s just too expensive. Here, it’s even more mind blowing
0:41:52 that you could say white collar worker capability. And eventually, although robotics is still
0:42:00 very specialized, eventually horizontal blue collar productivity will be very inexpensive.
0:42:07 So, you know, I take an MRI diagnosis that a friend has, and, you know, chat GBT does such a
0:42:15 good job of explaining it, you know, and showing, you know, where it got that material. The creativity,
0:42:24 the fluency is kind of mind blowing. And so all of us should have this crutch of,
0:42:29 yes, if you just want to know what fertilizer is, Wikipedia was good. But if you want to know what
0:42:36 a trip with a 16 year old for four days with a budget of $4,000 to Italy in August looks like,
0:42:45 nobody wrote that thing. But the AI, if it’s connected up properly, it is mind blowingly
0:42:55 good at those things. So we’re already all, you know, in our life of writing poems or speeches or
0:43:00 understanding or having complex material summarized, you know, we’re already getting a
0:43:10 huge benefit. And, you know, that, you know, a lot of white collar work, you know, should already be
0:43:15 either more productive or, you know, drive towards, towards higher quality.
0:43:20 So actually, one of the things this conversation is reminding me of that I haven’t actually ever asked
0:43:27 you is, so, you know, there was this decade or so of a lot of focus on personalized medicine.
0:43:32 Where are we at currently on that? Is that improving? Is that, is the, is the promise
0:43:40 of that turning out at all? I always am put off by people’s fascinations with n equals one medicines.
0:43:48 You know, a half million kids die of malaria. You know, millions of people are diagnosed every
0:43:56 year with Alzheimer’s. So I’m just the impersonalized medicine guy. I’m like, you know, the world does
0:44:03 not have the resources to do n equals one solutions. If some super rich person, you know,
0:44:10 funds that, maybe it helps the scientific discovery path, but, you know, I couldn’t bring myself
0:44:16 to be involved with that because it’s so unjust to take finite resources.
0:44:23 Eventually, yes, understanding everyone’s genetics and saying, okay, your drug dose is
0:44:30 different because of this. And so, you know, I’m kind of taking a provocative position
0:44:36 on this. The science that goes under that name is very good science. The people who work on that
0:44:43 are very well intended. It’s just, you know, we have rare diseases. We’ve created such an incentive
0:44:52 for them versus widespread conditions. We’re not really allocating that effectively, particularly,
0:44:56 I mean, the insane stuff is the diseases of the poor countries.
0:45:02 So can you actually say more about, you’ve traveled recently, Africa, Asia, all over about the
0:45:07 conditions with healthcare workers on the ground. What have you seen there and how can we help improve
0:45:14 them? Well, people should understand that most people in Sub-Saharan Africa never meet a doctor,
0:45:21 not when they’re born, not when they’re sick, not when they die. And so this is not a doctor’s
0:45:28 thing. So your image of healthcare, healthcare for most people in those countries is primary
0:45:36 healthcare. A modestly trained person who can give you semantibiotics, bed net, vaccines, very,
0:45:44 very importantly, to give a pregnant woman their prenatal checks, you know, now we’ll get an ultrasound
0:45:49 evaluation and we’ll see, okay, which 10% of pregnancies might be complicated. And then the
0:45:56 woman does need to get to somewhere where there’s trained personnel who could do a C-section.
0:46:02 So we can see with this AI trained ultrasound, is it going to be a complicated pregnancy and the
0:46:09 predictions are stunningly accurate and go to all that trouble, which you couldn’t afford to do for
0:46:15 all pregnancies. And, you know, so the greatest shortage of doctors in the world is there. And
0:46:22 so the idea that in native language through your smartphone, which, okay, not everybody has, but
0:46:30 times on our side, even in the poor countries on this, you will get health advice. And a lot of
0:46:37 the diagnostic tools will be available at a point of care where individuals, you know, can take a
0:46:43 lot of people experience this with lateral flow COVID tests, you know, now we’re trying to convert
0:46:52 those to be point of care, but molecular tests and still super cheap, but also more sensitive, more
0:47:01 accurate. And so healthcare is, you know, it’s in a, we only have $100 per person per year
0:47:11 versus in the US, where we spend $15,000 per person per year. So it’s, you know, it’s 150 times
0:47:20 different. And, you know, in fact, there is triage involved in, okay, which things should you treat.
0:47:29 Now, even things like blood pressure, cholesterol, obesity, my hope is that not only are the cost
0:47:35 of those treatments going down, we’re also going to put them into forms where it’s like yearly dosing.
0:47:42 And so the cost in the delivery system of getting GLP one to all Africans,
0:47:47 you know, 10, 15 years from now, you know, we’ll be able to do that.
0:47:54 So in addition to the elevation of humanity through all forms of kind of global medicine
0:48:01 and what is actually in fact healthcare, education is another area that you and the
0:48:07 Foundation work on intensely. And, you know, obviously, people have encountered AI a lot with
0:48:11 this, you know, it’s kind of like, you know, what does it mean? What are some of the,
0:48:20 the kind of maybe more surprising or other kinds of use of AI and education, whether it’s,
0:48:26 you know, kind of a globally available tutors or other kinds of, what are some of the AI for
0:48:29 education for the world that’s kind of captured some of your attention?
0:48:35 Well, I think the first thing to admit is that tech lovers like myself
0:48:42 have talked about the benefit of technology being used in education for our whole career.
0:48:49 And the actual benefit for the average student has been very, very modest. If you’re a motivated
0:48:57 student who can get on Khan Academy two hours a night or watch YouTube videos about photosynthesis,
0:49:05 wow, you know, people like ourselves, we are able to learn in a way that’s unprecedented.
0:49:10 You know, there’s a company called the Great Courses where I, when I’m on the treadmill, I love
0:49:17 watching those things, just so much great stuff out there. But the current math achievement
0:49:23 of a high school graduate in the U.S. is not better than 100 years ago. It’s not like medicine
0:49:29 where there’s new tools and new understanding. If I said, oh, in 1900, the best math teacher
0:49:37 was then, you couldn’t contradict me because it may be true. And so we’ve done a lot,
0:49:44 and, you know, I’m a believer. But AI, because of the fluency and personalization,
0:49:53 I think we can have very high aspirations of how we mix social experience in the classroom,
0:50:00 experiences with the teacher, and working on your own, that correcting your pronunciation as you
0:50:06 read things, immediately telling you, no, you didn’t get this math problem right, you know,
0:50:10 not turning in homework. So two days later, after the poor teacher spent all this time,
0:50:16 you’re like, well, was that a manipulation error? Or was that a conceptual understanding error? The
0:50:24 AI on Conomego is on the cutting edge of this, but there’s others like CK12Mini. It’ll say, yes,
0:50:32 those two minuses, you didn’t cancel those properly as opposed to, no, you set the problem
0:50:38 up wrong because, you know, these two trains, you didn’t get the, when they pass each other,
0:50:47 algebraic equation properly. And, you know, so the idea that it will learn how a tutor
0:50:53 keeps students motivated, you know, using the domain like sports or health or construction that
0:51:01 the student can relate to, the promise of having fantastic personal tutors in the inner city,
0:51:08 U.S. and in poor countries is super exciting. And, you know, talk about an area that’s
0:51:16 underfunded, you know, global education is, and the magic formula for countries uplifting themselves
0:51:22 is to have good health and good education. And then their economy grows, their tax collection
0:51:27 grows, and they’re very self-sufficient. And that’s, you know, why we want to help
0:51:31 countries get out of the poverty trap, not because it’s an endless guilt thing, but
0:51:38 if we help them get there, not only morally, but economically, stability, you know,
0:51:45 many good things flow from those countries being well off. Global education is a very
0:51:53 underdeveloped field, but particularly now with AI, you know, I’m encouraging philanthropists
0:51:57 to get in and showing that there are things like structured pedagogy
0:52:05 where the teacher’s given a very clear way of teaching that we are seeing some very good results.
0:52:10 No, I really appreciate. I think some people are like, oh, the boy who cried wolf. It’s like,
0:52:15 you told me 20 years ago that, you know, MOOCs were going to do it or that whatever. And so,
0:52:21 you know, people are skeptical. Are we, you know, are we finally there? Can you tell us more about
0:52:25 the First Avenue Elementary School in Newark? And obviously you’re super concerned about equity.
0:52:30 Is this something that is replicable that could scale? Well, so I love Khan Academy,
0:52:36 but it was mostly used by motivated students. And so for the last, I think it’s like eight years,
0:52:41 they’ve been saying, okay, how do we get into the classroom? How do we work with the teachers,
0:52:46 explain the stuff? You know, yes, the computers, the internet stuff is getting more pervasive.
0:52:52 The pandemic actually helped a bit with that. So Saul and I were amongst the first two people
0:53:01 who open AI was nice enough to let us mess around with early chat GPT-4. And a lot of the cool things,
0:53:08 like having it write songs and poems, actually Saul taught me that stuff. I was like, I wouldn’t
0:53:15 have known to ask. It can write like Shakespeare. Wow. And so he, you know, has put a lot of
0:53:21 Khan resources in. He gets support from the Gates Foundation and many others, has created this Khan
0:53:28 Amigo. And he, last school year, he had it in a small number of schools, but including this New
0:53:35 Jersey, Newark, New Jersey school. And so I went there to meet the teachers, to meet the students,
0:53:43 and see, you know, so you meet a kid who clearly is ahead of his class. And sort of the factory-based
0:53:49 model of, you know, 30 kids in a classroom, you definitely have a problem where you need to do
0:53:58 remediation and catch kids up. But you’re also pained by that kid who’s ahead, you know, and maybe
0:54:05 checking out or being disruptive. And yet, you know, you think, wow, we want to drive that. So the
0:54:12 personal tutor aspect allows that student to sometimes be off on his own, sometimes helping
0:54:18 the other students. And this, you know, Khan dashboard along with Khan Amigo, which is seen,
0:54:22 you know, so when you walk in and your teacher in the morning, instead of people handing in a
0:54:26 homework new up deal, that you just go to your dashboard and say, okay, who connected in last
0:54:34 night? How many hints did they need? How far did they get in the progression? And, you know,
0:54:40 you’re giving feedback, you know, you can have the parents connected up to that. Even the thing
0:54:46 where a paper gets turned in, you don’t turn in the paper, you turn in the AI session. So you can
0:54:54 just say to the AI, okay, how much did the student do? What’s your suggestion on how we get them to
0:55:03 either help with the first draft or help with the grammar or the logic? So it’s great to see it being
0:55:10 there and seeing it in person reminded me of embedding it in. And always with teachers, whenever
0:55:16 you have some new thing, there’s maybe 10% of the teachers that latch onto it and you get these great
0:55:22 results. And then when you tell the other 90%, you must use this, those results almost always just
0:55:30 disappear. And so, okay, how do we make this one one that that scales, you know, so that humility
0:55:38 of how far we haven’t come is, even with AI, we will have to do that. But what I saw made me even
0:55:44 more optimistic. So one of the things that people sometimes miss about the approach that you and
0:55:50 the Foundation bring to these problems is, you know, not just quantification of like, okay,
0:55:57 cost per life saved, etc., but also the systems thinking. And so what are some of the kind of
0:56:04 non-tech related levers for change and education? Like, what are some policy things that either we
0:56:09 as Americans or, you know, the world should be thinking about to improve education?
0:56:16 And there’s a lot of very good data about not having cell phones in the school. And, you know,
0:56:22 some great work going on there. There’s very good data about boys should probably start later than
0:56:29 they do school, school day should start later. There’s all the learning out of the charter school
0:56:36 movement, which it’s hard stop because it shows that long school day, long school year are incredibly
0:56:44 beneficial. Engaging the parents in a, you know, here’s where kids having challenge and communicating
0:56:49 with them, although these digital tools are going to make that far easier for the parents
0:56:55 who want to engage in that. You know, we’re seeing now in communities where there are charters,
0:56:59 the, even though most of the kids are in the public schools, those public schools
0:57:04 essentially compete. They either adopt those practices or find their own ways.
0:57:12 So we’re, you know, there are places like New Orleans or DC or Austin where school performance
0:57:19 is up. And so we ought to make sure that even as we try to put AI into this, some of those
0:57:25 learnings are incorporated. I mean, amen. As a mother of three boys, I’m like, how are they
0:57:31 going to compete with the girls? What is happening? So to switch gears from education, I think I’m
0:57:36 going to steal one of your own questions, which is if you had the opportunity to meet with someone
0:57:45 from the year 2100, what would your questions be for them? Wow. How did you deal with the AI
0:57:51 challenge opportunity? You know, did, you know, you said earlier that I have this
0:57:57 view that life is improving, which is objectively true. There’s always footnotes like nuclear
0:58:03 weapons, bioterrorism, and now AI needs to be added to that list. But, you know, the past 50 years,
0:58:11 life in general, if you’re a woman, if you’re gay, I mean, they, it’s kind of sad in a way that
0:58:20 because we’re so problem oriented, we’re not very reflective about, hey, 5 million kids a year aren’t
0:58:24 dying. You know, like you meet with climate people and they say, oh no, climate’s going to ruin the
0:58:29 world. Do you think you’re going to go back to 10 million a year dying? No way. You’re not going to
0:58:35 go back. We, it is a super big headwind because of the impacts through agricultural. So, you know,
0:58:42 I bring my, hey, the world is pretty good, but that 2100 person, I hope to hear how they avoided,
0:58:50 I’ll call it the four footnotes, AI, nuclear weapons, bioterror weapons, and polarization,
0:58:57 you know, people being able to get along and cooperate, including in how governance adapts,
0:59:04 you know, which AI will force governance to come up with different way of taxing people
0:59:12 and regulating things. And it’s a little scary that it’s happening at a time when the, the broad
0:59:20 trust in government is, is at a very low point, both relatively and absolutely. What are some of
0:59:27 the things that you think people should anticipate coming with AI in the next three to five years?
0:59:32 Well, it’s so mind blowing, you know, sometimes hard to get your head around it. No one expected
0:59:38 the white collar thing to come before the blue collar thing. You know, so in like life 3.0,
0:59:44 they have these hills where the computers are doing the easy things and, you know, warehouse work,
0:59:49 which we can’t yet do, was down there in the lowlands and helping diagnose, you know, was
0:59:57 way up the hill or writing legal briefs or code. And so it’s, we’re surprised at that order, but,
1:00:04 you know, the so-called blue collar, horizontal robot that can be told, you know, go to this
1:00:09 construction site and help go to this restaurant, go to this hotel and clean the rooms, you know,
1:00:16 even if the price is such that in the home, it only drops by for an hour, doesn’t live there at,
1:00:25 at first. You know, those things are, I believe, within, easily within the next decade.
1:00:31 Well, and actually going back to your original vision of a PC on every desk with software that
1:00:37 would help people do their work and lives and so forth, what do you think is going to come now?
1:00:41 I think a lot of people haven’t realized that part of what’s happening with current AI is
1:00:46 essentially the largest programming language will be natural language, e.g. English,
1:00:51 and everyone will have a coding assistant, not just the PC, but a coding assistant.
1:00:54 What do you think going, going back to those kind of earliest, what do you,
1:00:56 how do you think that will transform the world?
1:01:03 Well, the ability to navigate data, you know, which a long time ago, you had to have some
1:01:08 IT guy write a thing and, you know, okay, what’s the header and footer and, you know, report,
1:01:13 there’s a thing called RPG, report generation language, you know, and COBOL, you have this
1:01:22 section in the picture. Anyway, you know, a lot of that stuff, it’s so obsolete, it, it, it just
1:01:29 makes you laugh. The idea you can sit down and engage in a dialogue about data in a very rich way
1:01:37 means that, you know, our ability to run businesses better, you know, understand
1:01:44 bottlenecks, adapt to changing things will be so incredible and it won’t require custom software.
1:01:49 And in fact, the whole complexion of the software market, how many applications will there be,
1:01:54 you know, at first, what we have is everybody adding AI to every application and saying,
1:01:59 okay, pay me extra because now I got a little AI. But in fact, the number of applications you need,
1:02:06 you know, think of a college which has a scheduling app, a finance app, support the,
1:02:13 the student app, you know, they, that should all be one thing that every encounter with the student
1:02:22 to the college is all maintained in a rich way. So the software, software applications
1:02:26 will be very different. And I’m, you know, trying to figure out, okay, how quickly
1:02:33 does that happen? It’s incredibly beneficial that these, this software is more adapting to you,
1:02:38 including creating user interfaces dynamically than you going, okay, I use this software package
1:02:43 for this. And then I go to this website and do this, you’re a low level clerk, you know, even
1:02:50 looking at your email, the email is so stupid that you have to figure out it only can time sort
1:02:54 the order. It doesn’t know what’s important. And then you have your messages, and you have to go
1:02:58 back and forth. And you’re the one who puts it little folders and things. I mean, I thought the
1:03:05 semantic level of interaction with the computer would be higher by now, even without AI. But now
1:03:12 with AI, the idea that it, that very high level tasks that I’m doing, you know, I’m working on my
1:03:18 budget, I’m considering buying a new home that it will be working with you, not at the spreadsheet
1:03:25 cell level. But at the, oh, you know, let’s break this task down in a high level form. Here’s what
1:03:32 I can automatically go in and do for you. It’s, you know, super revolutionary. You know, we’ll all
1:03:39 have an agent that is a utilitarian help you get things done. You know, it reads everything you
1:03:47 read, but the things you meant to read, it reads. And then, you know, you, your agent can figure
1:03:53 out, okay, which parts of that are important enough to take your time to understand.
1:04:03 We are now switching to rapid fire. I’ll let Reed ask the first question.
1:04:09 Is there a movie, song, or book that fills you with optimism for the future?
1:04:15 Well, the Better Angels of our Nature, Stephen Pinker sort of documents how
1:04:24 violent death, you know, lifespan, education have gone. And, you know, there’s some lessons of
1:04:30 why we’ve done well. Doesn’t guarantee, and he doesn’t say that’s, that’s the future. But,
1:04:36 you know, if you have one book, which should get you back into the mindset of, okay, how far
1:04:42 have we come? What should we feel good about? I’d recommend that one. Fabulous. And what is a question
1:04:50 that you wish people asked you more often? How does malnutrition work? You know, a lot of things I
1:04:57 think about are boring and we should solve without most people having to ever figure out toilets and
1:05:06 nuclear reactors and, you know, understanding disease. I’m surprised people aren’t more curious,
1:05:12 you know. When I first said, what do kids die of? I had a hard time finding out. And I would have
1:05:19 thought, well, isn’t, shouldn’t we all be asking that kind of thing? It’s more important than GDP.
1:05:25 Exactly. I love it. So where do you see progress or momentum outside of your industry? Of course,
1:05:31 that’s very broad that inspires you. Well, when India is an example of a country where
1:05:39 oh, there’s plenty of things that are difficult there, the health, nutrition, education is improving
1:05:47 and they’re stable enough and generating their own government revenue enough that it’s very likely
1:05:54 that 20 years from now people will be dramatically better off. And it’s kind of a laboratory to try
1:05:59 things that then, when you prove them out in India, you can take to other places. And so
1:06:07 our biggest non-U.S. office for the foundation is in India. And then most number of pilot rollout
1:06:13 things we’re doing anywhere in the world are with partners in India. You know, if you go there and
1:06:19 you’ve never been, you might think, whoa, this is a chaotic place. And, you know, you’re not used to
1:06:25 so many levels of income all being on the street at the same time, but you will get a sense of the
1:06:32 vibrancy. All right. Last question. Can you leave us with a final thought about what is possible to
1:06:37 achieve if everything breaks our way in the next 15 years and what’s the first step to get there?
1:06:45 The potential positive path is so good that it will force us to rethink how should we use our time?
1:06:53 You know, you can almost call it a new religion or a new philosophy of, okay, how do we stay connected
1:07:00 with each other, not addicted to these things that will make video games look like nothing in terms of
1:07:08 the attractiveness of spending time on them. So it’s fascinating that we will, the issues of,
1:07:16 you know, disease and enough food, of climate, if things go well, those will largely become
1:07:24 solved problems. And, you know, so the next generation does get to say, okay, given that
1:07:30 some things that were massively in shortage are now not, how do we take advantage of that? You
1:07:39 know, do we ban AI being used in certain endeavors so that humans get some, you know, like, you
1:07:45 don’t want robots playing baseball, probably, because they’ll be too good. So we’ll keep them off.
1:07:53 The field, okay, how broadly would you go with that? We are so used to this shortage world that,
1:08:01 you know, I hope I get to see how we start to rethink these deep meaning questions.
1:08:06 Bill, a tour de force. Thank you. Yeah, great talking to you.
1:08:19 Possible is produced by Wonder Media Network. It’s hosted by R.F. Finger and me, Reid Hoffman.
1:08:24 Our showrunner is Sean Young. Possible is produced by Katie Sanders, Edie Allard,
1:08:31 Sarah Schleid, Adrienne Bain, and Paloma Moreno Jimenez. Jenny Kaplan is our executive producer
1:08:39 and editor. Special thanks to Soria Yalamanchili, Saida Sapieva, Ian Alice, Greg Beato, Parth Patil
1:08:46 and Ben Rales. And a big thanks to Aubrey Bogdanovich, Ian Saunders, Christy Anthony, Alex Reed,
1:08:53 Jen Krajasek, David Sanger, Larry Cohen, Alicia Sammond, Sean Simons, Denali Wiraman,
1:09:03 Andrea Dremer, John Ryder, the whole team at Gates Ventures and Little Monster Media Company.
Tune in this week for a feed swap! How many lives will be saved with the help of AI over the next decade?
Reid Hoffman and Aria Finger sat down with Bill Gates to discuss his main areas of focus: climate change, energy, global health, and education—and how AI will help transform each of them. Taking a bird’s-eye view of society’s challenges, it’s easy to give in to pessimism. But as one of the most influential people in the world, Bill Gates has a unique perspective on how far humanity has come and what our potential—and timelines—for meaningful change really look like. He gets granular on everything from cows (5% of global emissions) to disease reduction and eradication (Guinea worm disease). At each turn, he has data at his fingertips to ground his beliefs. So, what current set of innovations is Bill most excited about? And what is realistically on the horizon for AI, climate change, energy, global health, and education?
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