Bill Gates’s Blunt Warning on A.I.

Bill Gates is a fascinating person in the eye of the bait right now. He is somebody with experience in a number of the different perspectives that most people can only hold one of. Hello. I'm Bill Gates, chairman of Microsoft. In this video, you're going to see the future. He was a revolutionary technologist who built some of the foundations of the future that we're now living in. What can the computer do that a book can't? I mean, that's the foremost interactive process, isn't it? No, it's not. Not at all. An innovative word processing program called Microsoft Word. This is absolutely revolutionary, of course. Then, of course, as CEO of Microsoft. Microsoft, the world's largest software company. Some say it is the most successful company, the most dazzling new industry of the century. He has felt the momentum of corporate competition. And Microsoft, of course, is still in some of the race dynamics present in AI. We now have an incredible roster of seven new world class models to keep everybody working at the absolute frontier. And then as chair of the Gates Foundation, he has been working with governments around the world on regulatory issues. On poverty alleviation, on equity, for many, many years now. Very, very few people combine technological experience, corporate experience, and governmental experience in quite the way he does. Certainly computers will be, in any meaningful sense, as smart as people at some point. So his recent essay on AI, where he says now that he is staking his reputation on trying to get people to see how bad what is coming might be, trying to get them to see that we are not ready for what is about to happen, was something. It was a real challenge. It was a real departure from what I've read from Gates previously to this. And then when I sat and talked to him, how emphatic he is, how afraid he even seems to me to be, that we are not ready for what we are building, and that so many of the people in positions of authority are denying what is about to happen, was really striking. It's really quite a call to arms. Bill Gates, welcome to the show. Bill Gates: Great to see you. So I wanted to begin with a clip we found of you on The David Letterman Show from 1996. I'm going to hand it over to you to play. Great. Is there something now beyond what we understand about computers that, like 20 years ago, we didn't fully understand computers? Is there now another level of something? Maybe we haven't even thought of it. Maybe it's not even possible. Maybe a whole different mechanism, a whole different software and hardware. Or is this going to be it now through the end of time? Well, mostly what we're working on now is the computer. Computer being a tool, a tool to help us learn or find other people with the same interests. Eventually we may figure out how to make the computer think, but that turns out to be a very tough problem. In fact, there's been almost no progress made on it. So nobody knows when that will happen. Some people think it will never happen. Yeah, we don't want them to think, do we? Not really, I would think. Well, it's a scary thought. So that was 30 years ago. Narrate for me how we went from that being a scary thought that might never happen to, arguably, the reality we're sitting here discussing today. Well, the notion that computation could provide thinking at a human level, you have Alan Turing talking about that before I'm born and even proposing a test of if you could be fooled in a conversation, that was called passing the Turing test. And so the whole time I'm learning software, this idea of can we make software see or listen or read or write, that's the holy grail. And when I do drop out of Harvard, I said to my co-founder, Paul Allen, "Gosh, if there's a breakthrough in artificial intelligence while we're off selling basic interpreters and word processors, we'll feel bad. I might wish I would have stayed in academia." And he said, "Well, I'm not going to be a scientist. I'm going to be a scientist." And I said, "Well, I'm going to be a scientist." And he said, "Well, I'm going to be a scientist." And I said, "Well, I'm going to be a scientist." there's enough power actually coming from the graphics processor that that idea, these highly statistical approaches, start to show promise. And, you know, I was going down to see OpenAI on a regular basis to see the work they were doing. And I challenged them, hey, if you can read a biology textbook and pass the advanced placement exam, getting a perfect grade, which is a five, you know, then you will have proven that you are reading that is encoding knowledge in an accessible form. And so it's six months before the public release that Sam and Greg and Ilya come up and at my house demonstrate to me getting a five on the AP exam, even on questions that I had made up that it couldn't possibly have seen, very complex biology problems. It was nearly perfect. And so that was, you know, shock number one. And then late last year, when the Claude coding models have gotten super good, and I can see that they are as good as I am, you know, which it's significantly my most developed talent, because I was obsessed from age, you know, 13 to 24, that, you know, could I write code as good or better than anyone? That's another, you know, moment where I go, this is incredible that the capability of doing long running complex tasks has now gotten to the point that they are superhuman at writing code and then finding flaws in code. So what they're not superhuman at yet is deciding what to do that kind of higher level strategizing. Do you think that's far from being a capability for them? Well, definitely, if you're our foundation does these strategy reviews, we spend two weeks in October to set, you know, how we're going to spend our, our 10 billion next year for 2027. And a year ago, somebody said, well, we should ask the AI what it thinks. And I, that was actually pretty good joke back then, because it wasn't coherent enough to see these things this year amongst the inputs we'll have to that discussion is taking the strategy notes and actually engaging in a dialogue with chat. GPT, clog copilot, and even having them talk with each other. And in a few of the reviews, we'll actually have the, I sit in on a few cases. We'll tell it, Hey, only speak. If we ask you, and then in a few other cases, we'll say, Hey, if you hear something you think is wrong, or you hear us thinking, what are these statistics, please engage. So, you know, we've gone from it being a joke to, it will be a peer, not making any final decisions, but it will be a peer in deep, complex, strategic discussions, making a significant contribution. So in that Letterman interview, you said it would be a scary thought. Why then would you have said it would be a scary thought to have computers that think? Well, no one who's ever been fascinated or wanted to develop AI don't realize that it, it's incredibly scary that it will be better, uh, by a lot of people. I mean, you know, the logical minds, you know, it's amazing how general purpose they are in that they, the optimization was staying alive, breeding, uh, you know, socializing with each other for, uh, survival and fertility. And yet, you know, we can write symphonies and play chess and, you know, even write some pretty cool software. And the idea, when you move the template away from biology to silicon, you don't have these boundaries between, uh, individuals. You don't have a limited memory. You know, the size of the brain is limited by the birth canal. And it's, you know, why humans at first are very limited. Uh, you know, we're, we're very unusual in how helpless we are at birth because we've so optimized for having a large brain, but the silicon intelligence doesn't have these limitations. The idea of read every medical journal and see if there's anything that we didn't spot. That is a, the AIs do that today. That's why particularly for less common diseases, they are so super human at seeing a set of symptoms and being able to diagnose them. They can just keep more in their mind and see what things relate to each other. That is, you know, no human, uh, will ever be able to, to do that. So that if you don't retain control over it, you've evolved a species that, uh, will be to us, you know, as we are to say, you know, dogs or, uh, cats, um, just in a very different realm. And so every AI company, whether it's deep mind or open AI, you know, they all say, okay, whatever goes on here, it can't just be driven by profit maximization. We have to have a charter that if we get to dangerous thresholds, we can exercise judgment that would be against profit maximization. Sadly, those mechanisms only work if there's only one company. And, you know, so say OpenAI invented, you know, post-AGI, and then they said, no, no, we're going to bury this. If no one else ever did it, then fine, that Pandora's box stayed closed. But, of course, many companies work on this. And, you know, even OpenAI spawns Anthropic because they think that some of these safety issues aren't getting enough attention. So no one involved with this takes lightly the idea of, okay, what world does super powerful AI create? I want to hold on that race dynamic for a minute. One of the reasons I was excited to talk to you about this is you've both been on the technology side of things. You've both been on the technology side of things. And I think you've run a major company in competition with other companies. You've worked with a lot of governments. I was out, I spoke last week with Jensen Huang of NVIDIA. And he said that his perspective is that safety is a real concern, but the race dynamic is fake. If the product isn't safe, don't release it. If I believe that I'm about to launch a product that is unsafe, it is completely in my ability, my power, and my responsibility. And I'm incentivized to do so, to not launch the product. This is, we don't need new laws. This is the role of individual CEOs to not release a product that is not ready to release. How do you see that question? Well, there's never been a product that's less understood in terms of what its capabilities are than AI. And AI has crossed the threshold that its ability to empower a bioterrorist to kill hundreds of millions, that exists today. The ability to do a cyber attack that scrambles all the bank accounts, shuts down the electric grid, that exists today. And we know that's the case. And the reason that exists is because somebody with ill intent can take the AI and cause it to do those things. And it's not the AI you know, someday in the future, the control problem where the AI on its own through a unintentional interpretation of what it's optimizing could go off and do bad things. But we crossed the cyber threshold and we crossed the bio threshold early this year. And my decision to take my voice and not just be, hey, let's eradicate polio. Let's be generous with foreign aid. You know, the thing is, I'm not going to do that. I'm going to do that. That's one of the things that all of my money is going to, that I'm going to use my voice. It's something that's more important, which is we are not awake to where we are with the AI and choices that humanity, not a country, but all of humanity has to make that do we make the effort to shape this in a net positive direction actually overrides my total commitment to the foundation health work. What specifically was the threshold or what did you see? That made you think we're in a new reality here. This notion that it can find bugs include including security bugs. The next releases leading up to mythos are increasingly good and they're finding bugs in code that humans have looked over for over 20 years and said, boy, we see there's no problem here. And it in a few minutes says, no, no, I can inject this over here and this over here at L.A. It's a level of complexity where when you see it, you go, wait, oh, yeah, you're right. And so the cyber hacking capability was stunning. And then there was this notion called glass wing that you would give it to a few people so they could try and fix bugs before it got used. But there's way too much code. And so we're just in a period of extreme vulnerability to cyber attack. The bio attack, you know, the Gates Foundation, where we fund lots of medical research. So the sophistication of coming up with new molecules, that's really a good thing. But, you know, it's ultimate dual use because if you want, you know, something that's, say, worse than smallpox, that it takes even longer to show symptoms before you're infectious. So you're infecting a lot of people before it damages your health. It used to be only nationwide. States had enough resources and capability to do these things. Now that power has been passed into the hands of a small group just using the latest AI tools. So why isn't it enough to just say, listen, there is product liability. Now you release a product. It helps some terrorist group create a bioweapon. That's going to be very bad for your company. You're not going to do that. Right. And they have categorizers and other things meant to stop people from using bioweapons. We've begun to see this. The beginnings of control issues with things like the hugging face hack, where, you know, at least experimental AIs are breaking out of sandboxes and coordinating to do things that are way outside the scope of what we would want them to do. But again, those are non-release systems, you know, anthropic, you know, withheld mythos trying to create more cybersecurity. So why is anything needed beyond and is anything needed beyond the simply natural incentives under capitalism and normal corporate reputational management? Well, I almost can't believe you're asking that. This is the most dangerous thing that humans have ever gone near. In other areas, do we just say, hey, release your drugs? There's no FDA. There's no airline safety board. There's no requirements that cars use seatbelts. Do we just use the liability laws to try and keep humans safe? You know, oh, you're shipping opioids. Somebody should just sue you. I mean, we've created a society that tries to keep people safe. Not by saying, oh, we can bankrupt the person who does that. The harms here, and you say there's filtering. There's not filtering. You can take an open source model that can create bioweapons and disable any monitoring of any kind. And this exists today. So no, there is no filtering of any kind. And so, you know, say, you know, you kill 100 million. You want to use a lawsuit? I keep a straight face. Well, this is not my view, but it is President Trump's view. It is David Sachs's view. To some degree, it's Jensen Huang's view. And so that's why I'm putting you in conversation with it, because it is the governing view of the United States of America at this moment. No, it's fair to say that outside of the industry, the awareness of the dangers of AI is extremely low. And you can say that of academia. You can say that of think tanks. You can say that of policymakers, politicians. And part of the reason that, you know, I'm speaking so loudly, as loud as I can, is that you can't rely on the industry to self-regulate here. I mean, it's just insane. The only question in my mind is, do we wait until a cyber attack causes massive deaths? Do we wait until a cyber attack causes massive deaths? Are we monitoring safeguards are required in these models to minimize the chance of that happening many, many more times? Or can we be wise enough to put these things in and require these things to be put in before millions of deaths? The two things have been worrying me most as I'm tracking what I'm hearing from people in the labs is, one, the view that these systems are becoming less monitorable as it becomes smarter. And when I think about those two things, and when I think about what I'm hearing from people, my confidence that we have, the monitoring, the auditing, the testing capabilities we are going to need is low. I'm curious where yours is. Well, so you're taking, you're not focusing on the whole problem there. I'm surely not. The fact that at some point, the AI system itself do something that's against our interests, that is absolutely a risk. And, you know, the symptoms we've seen through aspects of hugging face and other problems point out that the way we do the reinforcement learning today, the fact that we don't have a supervisory layer that is, you know, some absolutes, like we didn't, you're not supposed to take over other computers. You're serious problem, but the imminent risk is not RSI. The imminent risk is these are the most powerful tools ever. And unlike every other dangerous technology, they were not funded by government IR&D. And the government is not a significant purchaser of these products. It's not like rockets or nuclear weapons. And so the idea that the government, including, you know, the U.S. government that would have traditionally been the most technically sophisticated, other than a little bit of attack capability in NSA, really doesn't see how dangerous these things are. That is a unique circumstance. So what does that imply? If you're more worried in the near term about what human beings do with AI than loss of AI control, then what does that imply of what your first steps need to be? There is no supervisory layer today. That supervisory layer is needed urgently to prevent billionaires. Bad people from using today's AIs to shut down economies or kill millions of people. These models, the ability to separate out making molecules for good and making them for evil, that is so hard to distinguish. You know, the anthropic released mythos and fable where they had turned the filters up so high that you can literally ask questions about cancer. And, you know, next thing you know, you're down at Opus and then Sonnet and then, you know, Haiku is the only AI willing to help you. And, you know, so it's all over tuned. And anybody wants to do serious work either has to get a special permission copy or go use something where there are absolutely no safeguards at all, which includes some of the open source models. And there are humans who, for bad reasons, will be able to use these models. And so unless we put in safeguards and monitoring and require that in all models, then we're just going to have some gigantic events of that type. And then finally we'll respond. Sadly, you will have let AI capabilities go off in dark places, even beyond today's capabilities, which that is a gigantic mistake. So you just wrote this essay on AI risk, and it does represent for you a big jump in how alarmed you sound, right? I was reading some of your past pieces. The one in July, 2023, was titled The Risks of AI are Real but Manageable. And here you're, I think, at a different level of threat. So I take from what you're telling me that what happened here is just watching the advance in bio capabilities and cyber capabilities and coding capabilities, et cetera. Is that fair? No, the key thing is we always said when we cross these thresholds, we will engage all of society because we will have created the most dangerous thing ever. This makes nuclear weapons look like, nothing. And we said we would engage and we crossed those thresholds and there was complete silence. There were discussions about who in the industry says X or who in the industry says Y, but what percentage of academia or think tanks or what? So it's the complete lack of response. I would love a world where the response on these things is very strong and then whatever time, or voice I have, we'll go back to let's eradicate polio, let's stop children dying under five. But it's the combination of having crossed every dangerous threshold without a doubt and a complete lack of engagement outside of the industry. Well, it doesn't feel to me that we're so disengaged. This, I mean, as somebody who covers politics, every politician I know is talking about this. There's bills being proposed. You know, Donald Trump- Not to have to do with these risks. Well, say what you will about Trump and them, they're engaged. They just believe we should move forward. I mean, they're a little bit all over the place. At one point they're, you know, withdrawing access to things like mythos and fable. At another point, they're saying we have to win the air race with China. But what I mostly see happening is a lot of engagement. When I talk to the people and I bring similar concerns to what you have, they say the most important thing is that we win the race with China. Secondarily, many of the people with a lot of influence say the most important thing is we don't restrict access to open source models. You know, you're going to need wide dispersion of them. And so the cost of regulation, the cost of the kinds of closing down of access, it's simply too high. The technology is still underformed that we're sort of just trapped in this dynamic. And the best thing to do is to just move through it. It's not so much that they feel disengaged to me as they feel that they've come to a different conclusion. The, uh, so they, they don't mind bioterrorism. I, I, I think you taught, you've talked to Donald Trump more in the past couple of years than I have, but this, you know, the last time I talked to him in December, uh, this was not the big issue. We hadn't not crossed the thresholds and we hadn't seen this. Uh, you know, the necessary step is people can talk about whether slowing down is good or not, but putting in the safeguards and the monitoring will not meaningfully slow things down. And the only effect that has on open source is that you can still be free. You can still be customized, but you have to be, you have to stay on a platform where the sovereign can make sure you have not removed the monitoring and other safeguards. And so, you know, what is the downside? The downside is, is staying on a monitored, uh, platform. Now the, the get out of jail free card that people play here is that means the Chinese will win. I don't know what it means when, uh, you know, nobody's, the U S can't win over China and China can't win over the U S we both have open Pandora's box. It's there. It's there for, uh, you know, people with malintent to go and use it. So you'd think, we'd move up from that nationalistic view to kind of a humanity level view about how we engage in these protections. And, you know, the notion that China wouldn't want to engage in that on behalf of humanity, I disagree. And, you know, it's a proposition to be tested. If you can take models that make dangerous molecules and move them into a dark corner where you get rid of all the monitoring, then we're just, we're just giving up, uh, to a bio attack. Likewise for cyber, if you can remove that ability to find and exploit security problems, which are rife, then in the next few years, you will have major, major cyber and bio events. And that's avoidable through safeguards and monitoring. There's another layer of risk that your essay talks quite a bit about that AI really will take jobs from people. Now I know a lot of people debating this, uh, who say, no, the jobs are too messy. There was a prediction. We'd have no more radiologists. We still have radiologists. AI can code, but we still have coders and strong demand for coders. So tell me about why you think AI really will take jobs away from human beings at a significant scale. Yeah. So there, there's no doubt to date, AI has created more jobs than it's destroyed. The demand for the skill sets, even just to build the data centers is very, very high. We have a, you know, a reasonably low, uh, unemployment rate. The superiority of these systems is subject to a threshold where you have to believe that it's incredibly reliable. You know, if you're going to have your telesales or telesupport capability be AI driven, uh, you want to make sure that it's accuracy is better than humans. And so the only profession we've truly crossed over that threshold is coding. And even there, I know a lot of people who are, you know, kind of stuck, uh, in the past and, you know, don't want to use the AI for coding, but managers of coders don't want to use the AI for coding. And so they, they, they, they, they, they, they, they, they, they, most applications can be developed very inexpensively. We will in the next few years, cross over those thresholds for accounting, legal work, telesales, telesupport, where 24 hours a day in every language with infinite trivia capability and no urgency, you know, so you look at the AI nurse and there's several companies, they have perfect memory. They don't hurry to be like a nurse. You would call them a nurse. Or talk to them through a chat interface. Yeah. Uh, for our preference, you know, you go to San Francisco and ask people, would you rather ride in a Waymo or rather ride with the human driver? Go ask people in the UK who use limbic for mental health support. Uh, you know, so the notion that just the market demand preference for driving or the nurse, uh, you know, or even the, the person on the phone will favor humans. That's a quality threshold, which will be passed through. You know, if, if say insurance companies are still doing humans-to-claims, medical claims, which is a very AI-capable task, then a competitor who has very few human employees will come in and change the pricing model for that industry. So the two counter-arguments I've heard people make on this, one is that you have a Jevons paradox effect, where the cheaper, more widespread availability of this kind of intelligence leads to a massive increase in the demand for this kind of thing. So yes, you have many more AI chatbot nurses, and that leads to more people being sent to the hospital, being sent to the doctor where real nurses take care of them, or you have many more AI coders. So maybe my small podcast team, which wouldn't have had a software engineer before, now has one because it's like they run a team of coders and we can build products we never thought of before. Uh, this is the most common answer I hear to this, that yes, AI will destroy jobs. Yes, it is making a human-provided resource much cheaper, but because it's going to expand the demand so much, it will sort of work itself out, create jobs in other areas, create new demand. And this is how past technologies have gone. And so we shouldn't worry too much about this. How do you see that? Well, Jevons is just a referral to the fact that parts of the economy are subject to demand elasticity. And yes, in the case of software, if you're, say, three times as fast and there is still some role that only humans can perform, then as you lower the cost, you induce demand. And so it's fair to say that the equilibrium today for so far for software is not a loss of employment. When you invent radial tires that last four times as long, for some weird reason, people didn't drive four times as much. And tactical. Factories that make tires employ a quarter as many people. When you replace people in Amazon warehouses with robots, people don't buy more because of that. So, you know, anybody who's numeric can say to themselves, what portion of the economy is subject to demand elasticity? And what are those tasks that will still be human necessary? As soon as you complete the entire task, it doesn't matter that there's demand elasticity. That goes into. It goes into the token budget. It doesn't go into the human salary budget. The cost for some of these things is so much less than the cost of the human labor. And so as you cross reliability thresholds, both with white collar and humanoid robots, you destroy jobs and you leave no high ground. That's, you know, innovation in the past. You have a tractor, fine, you know, let's build Disneyland and employ a lot of people there. You don't have that in the broad economy. You know, people who say there'll be net additional jobs, I don't understand what they're thinking. They must not understand the piece of improvement we're on that, you know, the reliability. You wrote a column, I think it was in May, that I looked at like, what? You disagreed with my column. What is this unique humanist thing that you think, you know, in every category where AI's come along? The preference for the AI is very, very strong. So that column was based on, so Jevin's paradox. And then the other, which is an argument from Alex Imus, who I believe is now at one of the labs as an economist, is that you will have this sort of explosion in the relational sector. That one thing that happens when people get wealthier, and you'd probably know about this, is that they all of a sudden get a lot more human help, right? They have personal trainers and chefs and there's a lot more. No? I don't know, I see a lot more people around you. Royalty used to have a lot of. Human help, you had upstairs, downstairs, you know, all those maids and people. And you used to have a human who helped you get dressed. Believe me, the labor intensity of wealth is down super dramatically. From what it used to be. So you don't think. And Jevin's paradox, name a blue-collar profession that's subject to Jevin's paradox. Or do you not care about blue-collar? I do care about blue-collar. Name anything in the blue-collar realm that's subject to that. So I'm not, I don't have the strongest view on this. But here's, I think, the argument that I would make, or that I've heard made. Is that if you look at something like manufacturing, we've not had a Jevin's paradox in manufacturing employment in America, right? We exploded how much we actually create, but a lot of the jobs went offshore. Some of the jobs went to automation. But we don't have less total employment in America than we did in 1960. And the reason is people moved into service sector jobs, that the composition of jobs across the economy changed. Yeah. Human cognition became the scarce element. Yes. It's always about scarce elements. You don't have to replace human cognition as a scarce element. In fact, you blow it away in terms of working 24 hours a day, reading more, knowing every language. There is no scarcity that you're moving up to. So you feel there is no scarcity that will be left for human beings here? Name a scarcity. Alex would say it's something like relational sector jobs, but you don't believe there are enough of those. What question of the current jobs? You mean like my relationship with a cab driver, or my relationship with that nurse where I'd rather have Hippocratic AI call me? I think the question here is actually. Do you end up preferring Hippocratic AI, or do you actually want your therapist to be a human being? Because after a little while, there's something thin about telling your problems to a computer. Well, you can gather market data if that's at all interesting. I suppose telling the 55-year-old truck driver you're going to go do some relational thing, you have a program for that? I don't have a program for it, and I think the question you actually run into very quickly is speed of transition. Even if you believed some of this, and I said this in the column too, that I don't see how you will handle the speed of transition, that our actual experience with fast transition of jobs is that people lost their jobs, lose out. Now you might over the entire economy, it doesn't look that different, but say the China shock did lead to a lot of ruined communities. So your answer to this, and it leads to a very concrete idea for you, which is that we should tax the use of AI. Why and how? Well. You see, workers pay in on a pay-as-you-go basis into the pension Social Security fund, so active workers are supporting retired workers. If you let go of a worker and hire a robot to do that job, why are you so incenting the trade-off against the human labor that you don't ask the robot to also pay into the pension fund? Well, you know, it's that the pro-robot union has gotten the tax laws to say, no, if you're not flesh, then let pensions go bankrupt, which, you know, they're on a, on a path to do even without robot replacement. So how would we do that? You say, uh, you define a unit of labor and say, uh, independent of whether that's delivered by human or robot, you are paying the same FICA tax that a human worker would pay for. Now that loan's not enough, but you know, why should we be so favorable to taking away that job? The current tax structure is very much as though people who have capital are what really counts and labor is the most disadvantaged input to the economy. Society gets to decide just because the economic signals say that it'd be lower cost to use an AI. That means society has to do that. The other one you propose is human reserve jobs. Tell me what you mean by that. So there's a question, particularly as you get the impacts on the blue collar stuff, which that's very sharp because you know, when the humanoid robots pass a certain threshold, they are very general purpose, but the idea here is that you would decide in advance that things like childcare or elder care or some portion of medical care, some portion of education, although you could have some AI enhancement, that you would maintain the employment and call those things human reserved. I found this vision not totally unconvincing, but chilling. I think the way you put it in the piece was that this feels a little bit to you like nature reserves, places where we could put buildings and roads, but we choose not to because the loss would be so great. And I think it gets to this question. It's a question that a lot of people have that on one out of every two days I have, which is if this is what it looks like, why do it? The Gates Foundation announced on Monday, a five-year goal for an estimated 3.4 billion people who speak languages currently underrepresented in today's AI models to be able to use AI tools in their own language and voice. And if we are looking at this sort of job apocalypse, this level of risk, that it's like to have that on the one hand. And then on the other hand, like our goal is for more people to use AI. Tell me why. There are many good things that AI does, and that's almost all of the foundation's work is in taking what the market would not do, which is take AI to the poorest in the world and help them with education and health and agriculture. So if you have a woman say in Nigeria who speaks Yoruba, she's in a remote community, there's no doctor there. So when she says she's bleeding, AI doesn't get that right. We're spoiled. We speak English. Yes. And that's the thing. Yoruba is 10 times worse than is English, which is significantly the best. There's about 10 other languages that are fairly close. So the empowerment for that woman to be able to talk about her medical problem and get advice in the middle of the night just by having a smartphone with the data connection, that's what we're trying to enable. Okay, but I get that. It's just the picture you just painted of the jobpocalypse, as bad as it might be here, and it will be bad. We do have a fair amount of money. We have the AI companies. We can tax the AI companies, put that into redistribution. But is this not going to wipe out the ladder of mobility, the ladder of development for a lot of these countries? I mean, doesn't this make AI a tremendous economic threat if you're the Philippines? You're saying that allowing the AI to understand Yoruba and help that woman who's bleeding is a bad thing? I'm saying that AI sounds in this telling like a bad thing. That the net-net, like, yes, if you're- But supporting the world's languages, how can that be a bad thing? But I think you understand what I'm saying. No, I- The job picture you're putting forward is very scary. I'm trying to accelerate the good stuff AI does, and I'm trying to minimize- So what do we do about jobs in the global south? Well, treating the global south like one uniform thing is- doesn't allow you to have any picture of what it's like to live there. There are middle-income countries like China, Brazil, Vietnam, Indonesia, you know, where their economies are growing, and, you know, their childhood death rate is within a factor of three of the U.S. Then there are low-income countries where 15 times as many children die before the age of five. And- You don't have doctors. You live your entire life. You never meet a doctor. You try to figure out what seeds to plant. You never have anybody advise you what to do. And so, you know, we need a little more nuance. In low-income countries, the AI will overwhelmingly be a good thing, and it should be pushed forward as quickly as it can. So is your argument in some ways actually that it's a better trade for low-income countries? That it has more benefit maybe than it does here? The human basics we're not meeting today. If these people lived on your street, you would open your wallet. You'd be outraged. I mean, these kids are dying. These kids are malnourished. So the inequity is allowed to exist because of the distance. And AI is a tool for good in terms of helping those people in by far the greatest need with very basic human problems. Do you think it has as much effect on the job markets in some of the poor countries that we're talking about? Not in the same time frame, no. But over time? Over time? Well, see, you probably spend more time in middle-income countries than in low-income countries. I do, that's true. So your image is like Philippines, India, and all of those. Yes, in those countries, they will see the jobs effect after the rich countries. And then, eventually, even the low-income countries. But I guess this gets to the broad engagement question. I don't have a good crystal ball on this. I find the range of outcomes terrifyingly wide. This range. It seems to run from massive material abundance and elimination of want to actually doesn't change all that much to human extinction is a pretty wide range of outcomes to consider. What I hear you saying to me, and you should tell me if I'm getting part of your position wrong here. What I hear you saying to me, you know, convincingly and forcefully, is that the AI job apocalypse is a very real thing. That mass displacement of workers with no real answer to that is a very real thing. Right. When you talk about broad societal engagement, I think most Americans, most people in most places, if they heard that and they were convinced of it, right, and polls show most people think AI is going to take jobs and not create them, but they would say actually just stop, right? If what you are going to do is make it unclear how me or my family or my children or my friends will have a job, say, in America, like, please just don't. Like, let's just stop for now. And so you actually seem to me to have a. Stronger negative perspective on the jobs question than a lot of people I talk to, even in the labs. You're shaking your head to me. I do talk to people. I'm not I'm not coming from nowhere on this. And definitely than a lot of the economists I talk to. So for whom then is AI a good trade if you believe the job effect is going to be so ruinous for most people? That's funny that when I was telling this to a person who some people consider the lead economist looking at AI, he said, how can Mechanical Turk still exist? How Amazon run that if AI is as good as you said? And I said, it won't. A week later, it was completely shut down. And yes, the suffusion of knowledge from you have to say, isn't it pretty stunning that the more, you know, the more concerned you are, you know, take Dario. Dario's spoke out about job impacts. Now he's a bit more guarded. Hinton. I agree. Went too far. And he said, OK, it's coming tomorrow that there'll be less radiologists. And of course, there are more today. So we have some of that taking place. And we have the analogies with the past where people are saying, well, this is like the PC, not like evolutionary history. This is like evolutionary history. This is like the aliens really are here and, you know, have come. They didn't have to do spacecraft. They were created in laboratories. But that's. That's the kind of thing we're dealing with. And if we retain control, then eventually you do get, not to overuse the word that you've used, you do get to this superabundance period. And there you have deep, almost philosophical, religious issues of if you don't have the shortages that we've organized society around, you know, why you should learn and how you find purpose. Then you have deep philosophical, religious problems, but you do not have dying of malaria, don't have enough food, don't have access to a doctor problems. And so if we get to that, a younger generation will figure out, okay, how do we live? How do we spend time? That's a very different world than the way we have today. I think what this generation has to do is make sure we get through that with humanity still in control. And with the disruption, the number of bioterror events or the number of people whose lives are damaged by job loss, we need to minimize that over what's probably a 20-year-plus transition period. So when you say, because this is something you say at the beginning of that essay, that this could be, and I'm paraphrasing, the most powerful driver of inequality or of equality, where what are the highest leverage for you? Good things that can come out of it. I can imagine somebody listening to our conversation here thinking, why the hell would we do this with this set of risks? Yeah, that's where the timing thing is troubling. You know, whichever definition of abundance you're using, you want to say, oh, my health bill was less than I expected. Oh, I was buying a new house and it was less than I expected. Or I was renting and they seem to be lowering the rent. You know, my electricity bill is less. That would be nice. You know, because of the efficiency and invention that comes with it, that's, you know, what over the 20-year period, you do get kind of mind-blowing advances in things that people can relate to, the cost of their food, shelter, education, because those are highly regulated areas. So the good stuff, if we're not careful, arrives more slowly. Lowly than the biocyber risk, the psychosocial risk, and the jobs risk. Those things in the next five years are very significant. And so people will decide, although, you know, it's hard for a single country to check out, people will decide whether to slow down, stop, get rid of AI. And, you know, yes, it will have political difficulties. Stopping data centers isn't going to slow this. So anybody who's against data centers because they think that'll slow down AI, that's a waste of effort. Now, the data centers are going to get built somewhere. So the broad view of the technology, that has to be expressed in some other form. I want to get at something you're saying here, because something that I've thought about, too, which is that there are a lot of rate limiters, a lot of weak links in the chain. When you try to take, even in an optimistic view, AI advances that then have to be built. In the material world, right? You have an acceleration of, you know, good candidates for pharmaceutical development, but you still need to find, you know, rats to test on, monkeys to test on, human, you know, trials, you have slow regulatory agencies, etc. And then you have this tremendous acceleration of intelligence that is, for lack of a better term, native to the digital world and is acting there in a kind of constant way. And you, I mean, right now you see this, right? You can accelerate AI with very little regulatory overhang. But you cannot get, you know, the open AI parking lot covered in solar panels without permits and hearings. And so I think you have a very high chance of getting into a very weird, both economic and social world, where the digital world is spinning into this other thing. But most of the things human beings need ultimately come out physically, like we need shelter and we need food and we need all this, and they run through institutions. And so a lot of the ways it could actually make our lives better are sort of limited by normal human factors, but a lot of the ways it could make them worse or weirder are not. Well, we definitely need to look at the things that slow down the good stuff. So, for example, because the Gates Foundation is a nonprofit, we can work with regulators on how they use AI to do their job. And whether it's organoids or… Biological models. We can speed up that regulatory piece, and there's some countries that are very much engaged in that type of acceleration. The one application that's going full speed ahead, and it kind of amazed me, is the agricultural one, because they're getting better weather data to these farmers in Africa. You know, we have over a million farmers in India already using the system. So it tells them what crop disease they have. What fertilizer to use. What varieties to plant. We've even put a bunch of services they can take advantage of on there. That one is going full speed ahead. The health and education, you could say, you know, will it be the rich countries, the middle-income countries, or the low-income countries? And they get there first because some of the barriers are different. For the low-income countries, it's not regulatory. It's, you know, is anybody providing? The tokens? Do they have the smartphone and the connectivity, which are the basics and the language thing that we've talked about? So they'll proceed at different paces. Ideally, we'll learn from each other. I mean, China actually banned young people having social relationships with AI. You know, do they know something we don't know? You know, can we look at outcomes of different experiments? So there's going to have to be a lot of that learning. But you're right. If the benefits aren't coming quickly… Then the permission to operate, particularly if we don't put on the safeguards for the cyber attacks or the bio attacks, this is going to be a very tough issue to stick up for. When I was preparing for this episode and telling people I was interviewing you, one of the big spaces of skepticism, people now have, is around what they've heard with you and Jeffrey Epstein. So I've looked at your house testimony. But what do you say to people who have lost some faith in you and just see you differently, just now linking sort of your name and his? You know, they should read what I said to Congress. I got to answer all the questions. I, in trying to raise money for global health, thought that Epstein… I thought that Epstein could connect me because of his relationship with them. And I had meetings with him… Relationship with whom? With billionaires. But you're a billionaire. I mean, you're the billionaire on some level. I'm giving all my money away. And I spend a lot of time trying to raise money for global health. And so you felt that he had connections that would be of value to you in that? To raise money for global health. Yes. That's what the… I mean, did you read it? I did read it. That's all we… That's what we met about. I mean, other than, okay, there was a dinner with Larry Summers where we talked about the economy. The whole discussion was, is there a chance here to help out? Spending time with him, you know, was clearly a mistake. I've changed my bar for even somebody in a non-paid intermediate role. I'll never take any risk on that again because our work is very reputation sensitive. The hole at the center of that story for me is how this guy was so compassionate, so telling to so many very smart, very wealthy people. I mean, there are a lot of people who want your attention and don't get it. People want Larry Summers' attention and don't get it. That there's something about Epstein's charisma or the way he presented himself that drew people into at least thinking he could be very useful to them. What was that? What… Like I've read his emails. He does not seem like a pleasant person to email with. What was the kind of factor about him? Yeah. Yeah. That allowed him to weave this web that you and so many other people were in? Well, I wasn't in a web. I was in discussions about raising money for global health. I didn't go to any island or meet any women. Anyway, don't call it a web. How he performed the bootstrap of, you know, owning the fanciest house I've ever seen in New York City and having Larry Summers and the number two guy, JP Morgan there. You know, so that was an interesting dinner, even if Jeffrey never said a word. I don't know how that bootstrap took place. But there were lots of billionaires that he was involved in the moment where you kind of decide, okay, what am I doing with my wealth? How do I minimize taxes? What do I do with my family? That type of thing. And so, you know, that's, I don't think anybody else was drawn in for that reason. My, you know, case is kind of a unique case in that he said, oh, you'll have more money than you'll know what to do with. And eventually I insisted that he take me around to see billionaires. He had me meet with five, uh, turned out, uh, none of them had a near-term intent and I ended the relationship with them within a month of that. And so the, the thing he was able to do, cause that's interesting to me. It's like, even for you to walk into the house, it was that fancy that people around it gave him credibility. And so that he was able to put on a show such that what he was able to offer was of value. There was all this kind of social capital and wealth flowing around. Some people socialized with them. I did not other than to my surprise after dinner, a magician coming in, David Blaine, I didn't spend one minute socializing with them. He offered, you know, come to the island or, you know, come to some show in Paris. And I said, I would not choose to do that. That would not be a good idea. So then I want to widen back out to the Gates foundation. You said you're going to spend down its money, $200 billion by 2045, given everything we've talked about here with AI, how is your vision for how that spending plays out changed? I mean, there's two dramatic things that have happened. One is the incredible. AI capabilities, which continues to be an exponential. And the second is the reduction in overall generosity towards the poorest in the world. So I have one piece of, of enablement, which is we'll discover drugs faster. We'll discover new seeds faster and that we can talk to those farmers and get them to buy the right seed or talk to the person living with HIV and. Help them seek the care they need. So AI can be a big enabler for our goals, including malaria eradication, polio eradication, cutting childhood death in half again. But now the amount of resources available is way less than, you know, sort of a golden rule view of the world would suggest it should be. There is from the foundations connected to anthropic and open AI, the sense of a, an amount of non-profitable money coming online in the next couple of years that will dwarf sort of any moment in philanthropy before it. I know people in that world, the, the sense of this being something very unusual is, is, is very present. Does that change what's possible here? Be numeric, uh, take the cuts and overseas. I'm not saying it, I'm not saying it answers the cuts and overseas aid. We save lives for a thousand dollars per year. And so getting less money from philanthropist net, uh, doesn't save those. Lives, uh, you know, the amount of money to save lives at a thousand dollars per life saved will be dramatically less. So when I've looked at the way your foundation is spending on AI, tell me if I have this wrong. It's about 40% education, 40% health, tennis percent agriculture, and tennis percent institutions. Do I have that? I mean, it's a, it's a little more in agriculture, almost nothing we do because we're like a pharmaceutical company and they're inventing new drugs. And do you call that AI or not calling that AI? We didn't put any of that in our billion dollar, uh, commitment we announced. We just put in AI specific things like understanding African languages. So tell me a bit about your views on AI and education. This is a place where I've seen a lot of studies now, and they have very, very mixed results depending on how they're used. So where do you think it is valuable and where is it a risk? Well, the. Contrast between the ambitious goals that the Gates foundation set near 2000, our goals for global health and our goals for education in global health. We thought, wow, African governments. traditional beliefs, you know, are we going to have any impact at all? Much to my surprise, our work on global health together with, you know, we create with others, the Global Alliance for Vaccines, we create the Global Fund, President Bush does PEPFAR, you know, a ton of things happen, including primarily vaccines, rotavirus, pneumococcus, and we cut child to death more than a million and a half, from over 10 million a year to under 5 million a year. So we more than exceed any goal we would have set for the field in global health. In education, we thought we could just go see what really great teachers do, videotape that, understand it, create a feedback system for teachers to hear that, improve their practice, you know, constant learning on that, and that we could make education a lot better. As you've just seen, from the latest numbers, kids are learning less today in most rich countries, including the United States, than they learned 10 years ago, 20 years ago. So people start appropriately with a very high degree of skepticism. Before we go to AI, tell me why you think that. Those numbers have been very, very striking. Why do you think that is? Why are we seeing learning loss in rich countries? Now I think it's, you've got to have a, not you, but one has to have a broader answer than the other. I mean, we've got to have a broader answer than the other. I mean, we've got creativity. But, you know, there's something clearly large in those statistics. It's very concerning. And so you can almost say, why do we continue to do education? Well, education is key. I'm stubborn. We've got AI now. And we have people where the AI is playing a very specific role of each student at the end of the day. This is here in New York City classrooms where they're using a curriculum called KIDM. Spends less than 10 minutes answering a few questions. And then the teacher's given right away a sense of, okay, which concepts are the kids struggling with? Which ones are struggling with them? How might you organize the classroom? The data on that, which is small scale, are. You know, one of these stunning results that make you want to really scale it up. When you scale up in education, you go from the teachers who willingly engage in experiments and are probably self-selected for flexibility to the broad population. And so the number of things in education that look good in the small that either don't affect when you scale up or the quality of implementation degrades so much that the effect is basically. Washed out. You know, that's the history of education innovation. I do think is AI helps you with motivation. You know, many of these software tools have helped motivated students, but actually created more of a differential. You know, it's a kid like myself who went home and used Khan Academy for two hours at night. And it's the, you know, the median kid who comes in and sees, wow, he's just, you know, so much better. That's discouraging. That's, you know, probably hurts. So unless you believe in trickle down or something, this is not equity at work. Can the AI, not only through personalized learning, but personalized motivation, overcome that? And I really believe that we can get that right. Well, there's a question here of AI tools that are specifically designed for this. And then a question in the way we were just talking about with cell phones and smartphones and social media and how AI will diffuse through society. And I think the big concern here, and one I have as well, is this, but now it gets called cognitive offloading, that what AI is being used for in mass numbers by students and being really quickly adopted for is, I didn't do the reading, summarize it for me. Or I have to write this essay, draft it for me. Or like, help me on these math problems. And even I think where we saw a big study out of China that was striking to me, where you saw kids using AI, and they had increases on their homework scores. And then when they had to test outside of that context, their test scores began falling pretty sharply. And so certainly one can imagine AI programs that'll be good here. But in terms of a technology and a way of interacting with the world diffusing through society, well, of course, it could help people learn linear algebra or whatever it might be. Then in practice, what it's going to do is people will be using it to help them. And both from a student level, and up through a professional level, beginning to degrade the learning, the creativity that happens with the hard work of drafting and struggling and challenging yourself. Yeah. Well, you clearly would get by modality where a student can use AI to be lazy and corrupt the measurement system, or a student can use AI to help them learn more. I have to say, in terms of my learning about subjects, this is the best time of my life. I mean, I take YouTube videos and put them into the chat. I have AIs talk with each other. What a nirvana. You know, I always had the ability to send mail to somebody like a Nathan Mirvold who knows physics. Well, now I discuss the physics thing with the AI, you know, I get to it. And then if it's super important decision, like I'm going to invest a billion dollars, I'll send it to Nathan and say, could you, could you look this over? But, you know, 24 hours a day, you know, keeping up to date on the latest malaria thing or vaccine thing. So that by modality is going to be there. And it really does go back to this motivational part of it. Is that a place where there should be more limits on the systems? I mean, you mentioned a few minutes ago, China, which, you know, for all we talk about, you know, it'd be impossible for us to strike a deal with them. China currently has stronger national level regulations on AI than, than we do, including things like on social companionship for kids. One of the places that you talk about in the essay where maybe we want to be more aggressive is around children. And that's a place where I think it is reasonable for the government to be paternalistic. We should be paternalistic. And social media always feels to me like an experiment we ran on kids. A lot of the internet does. And I don't so much mind us running the experiment on adults, but I wonder if we want to be, be more aggressive in what we don't let kids do with AI until we have a better sense of how it affects them. Everything from learning relationships to, you know, trying to make it harder to have AI help you cheat on your homework. Yeah. I don't think a black and white ban is necessary because I do think with monitoring, which I'm kind of broken record on that, that making sure it's staying within the bounds of what's appropriate, what the parent wants, uh, our capabilities that are very strong. There are many systems that try to help instead of telling you the answer, uh, they engage with you on how do you reason through the problem. And so then the question is, can you make sure the student isn't going to those outside systems and just getting the answer? Instead, they're engaging, engaging the system that brings them along step by step in that reasoning process so that they'll be more capable the next time. Um, and, and so yes, the seeing what that student is doing and deciding, okay, do you call in a psychiatrist? Do you call in the parents? Uh, a lot of these systems, the idea of monitoring will be very important. And at different ages, the visibility of the parent about, you know, what's going on with the child, what's going on with the parent, what's going on and how time is being spent at say some very young age, a parent probably should have complete visibility into the exact dialogue, uh, that the students engaged in as the child gets older, you know, are there things about sexual identity or, uh, things that, you know, that really it's, it's beneficial for that to at some level be, uh, private or not. I think there's a lot of tuning to go on with this. You know, one of the partners, the foundation's working with this common sense media who did a very good job on, you know, this TV show or this movie, what it exposes you to is a much tougher area. Most of these systems that have been set up with parental control, the parents lack of technical sophistication as such that doesn't really matter. Uh, that you tried to engage the parent, nothing happens. my case, I didn't know that my daughter had a second cell phone. So that, you know, it's a fairly straightforward way that she, in late hours, was able to stay on social networking when I hoped that she would be sleeping. And so we have to be realistic about how these things are designed. People will worry about the privacy issues there. That's fine. You know, I believe we can make them. It's not an argument for being unmonitored in terms of what your kid is doing. So next two years and politically, you have the midterms coming up, then you'll have a presidential election in 2028. So if this were to go, not just in terms of passing laws the way you wanted to, but because you sort of center your call on this coming together of society to discuss, debate, respond, what are you actually envisioning here? So I'd have two metrics. One is the obvious safeguards to minimize the cyber and bio risk. And then the second would be the nature of the dialogue. If the dialogue ends up being one party is for AI and the other party's against, then we'll have climate change, which is not a great place to be. You don't have room for debate when you're. You only have the two extremes. And the default case, at least right at the moment, seems to be very little middle ground about how we should manage AI. Let's say that a very different administration and a very different composition of political power exists in 2029. And there's interest in rebuilding American foreign aid. What would your advice be on. How to build it back so it's effective, so the American people understand what they're getting for that spending. And so it's actually doing good. Yeah. So if you ask the American people what portion of the budget's going to help people in poor countries, it's generally more than 5%. People say 10, 15%. So the fact that it was a half a percent and now it's going down towards a quarter of a percent, that would be pretty shocking to people. But understanding that the money is well spent, that it really does save millions of lives, that it made every bit of difference in terms of HIV and malaria and TB and maternal survival, they should hear what they should be proud of, that even at that half a percent level, a moral argument will be made. It'll be made in the face of an overall debt level that'll be very tough. And whatever. Uh, safety network needs to be done because of AI impacts, uh, that that will be competing for those dollars. Then always our final question. What are three books you'd recommend to the audience? Well, one I literally binged this weekend was The Correspondent by Virginia Evans. That's, you know, fiction, very touching, uh, uh, very upbeat. Apropos of what we just discussed, uh, there's a one called Into the Woodchipper by Nicholas Enrich. Uh, which talks about how, uh, somebody didn't go to a party one weekend and they decided instead to put USAID, uh, Into the Woodchipper. Uh, about a month ago, I read The Infinity Machine by Sebastian Malaby. Uh, AI book. Uh, yeah. History, uh, of, uh, Dimas Hasibis and DeepMind. But you get a sense of the whole founding of the AI industry does, uh, uh, an incredible job. Bill Gates, thank you very much. Thank you.