Bill Gates says to prepare for 20 years of ‘tumultuous’ AI disruption

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Introduction to Bill Gates

2:00Hello, and welcome to Decoder. I'm Eli Patel, editor-in-chief of The Verge, and Decoder is my show about big ideas and other problems. Today, I'm talking to Bill Gates, co-founder and former CEO of Microsoft and chairman of the Gates Foundation. You may have noticed Bill in the news more often lately. He's been on something of a tour for the past few months, ever since he published an essay in late August titled, The Turbulent AI Era is Here, The Choices We Make Now Are Critical. In his essay, Gates ratcheted up his rhetoric around AI safety, writing that, quote,

2:33Even under the best circumstances, the transition to this new AI era will be one of the most turbulent times in human history. Gates has also called AI the most dangerous thing humans have ever gone near, and that AI makes nuclear weapons look like nothing. It's clear he doesn't think the current policy response from the White House, or the lack of coordinated efforts from the AI industry itself, is sufficient to combat this threat. So, I wanted to know, is Bill Gates an AI doomer? I also had a series of questions downstream of his thoughts about AI. How can we ever hope to control AI and align it to humanity if we can't agree on what it is, and whether it's conscious?

3:07How would you technically construct a solution to alignment that can be enforced, not just for closed models, but for open models as well? Bill gave us an awful lot of time to dig into these issues, and the resulting conversation was really illuminating. He thinks we need to prepare for roughly two decades of AI disruption, and he says this period could be so painful it might split society in two, between people comfortable living near the technological frontier and those who reject it. We also talked about labor considerations, the nihilism of younger people towards technological progress, and what should make us feel hopeful about the future.

3:39This is a deep one. Okay, it's Bill Gates. Here we go.

3:45Bill Gates, you are the co-founder and former CEO of Microsoft. You're also the chairman of the Gates Foundation. Welcome to Decoder. Thank you. It's good to talk to you again. You and I have spoken many times over the years. I would say that the circumstances of this conversation feel very different. Previously, when you and I would talk, we were always very hopeful about technology and what it could bring. You had a Netflix series a few years ago. You talked to us, and I want to just read you this quote. You said, I believe AI is a beneficial thing. We just need to shape it the right way. I would say you still agree with that. You just think the time to shape it is now. Yeah, I agree with that.

4:15And I'd say the response of Broadly hasn't been adequate to the speed that the challenge is arising. So I'm very concerned that the bad stuff will not be minimized and that a lot of good stuff could come fairly slowly if we don't manage it the right way.

The speed and power of AI

4:37I want to have this conversation with you in basically three parts. I want to understand how you feel about AI as a technology today and why it's advancing so quickly. I'm very curious about the technology or technical solutions to potentially control it. There's policy solutions as well, but it seems like we have to build an awful lot of technology. And then I'm very curious to talk about who might impose those solutions and controls, because that seems very much up for debate right now as well. So just start at the start, what has made you feel the urgency that AI is arriving at a speed that we're not ready to control it?

5:17Well, the progress over the last year has been really quite amazing. You know, starting with the ability to write code and find cyber flaws, the ability to reason in biology, the ability to form relationships with kids or other people, the ability to disrupt the job market, the incredible power that exists today means that every one of those things will happen and society needs to be ready.

5:52Do you see that happening as a technology as a whole or in the products that are instantiated by the companies that are making AI today? And what I mean is, AI is a broad set of models that exist in the world, some open source, some proprietary. Then there are the companies that make these products, and there are the leaders of those companies that make these products. And at every level, there's a different relationship you or I might have to AI. So are you seeing these risks and these capabilities broadly in the field or in the particular companies led by the particular people?

6:23Well, the risk isn't one model versus another model. Certainly, the near-term danger of models where you can remove all the safeguards is very high and quite different than models where you monitor to make sure that cyber attackers and bioterrorists are not able to exploit the capability of the model to do gigantic harms.

6:55And that's our, you know, the big near-term risk really comes from models without safeguards. In a longer timeframe, the control issues relate to any state-of-the-art model. But it's always easier when you can make sure safeguards are maintained. The reason I'm asking that question that way is maybe you don't trust Sam Altman today, but you trust Dario. Or maybe you don't trust either one of those and you trust Satya at Microsoft.

7:28And that is a market dynamic where maybe we're all going to pick the safest model or the safest company. And you think that is diffuse. You think eventually the technology will just get to a place where it's harmful or it can cause these harms because it exists. Well, somebody doing a cyber attack with the model, that's not about you choosing a model. It doesn't matter what model you choose. Well, if Anthropik has safeguards in place, like maybe it can't, right? This is their approach currently. I'm just pushing on that. Yes, certainly the models where they make sure the safeguards are not removed.

8:02And they're putting in a lot of effort into seeing how those models are being used, monitoring the activity. The near-term risk from those models is not gigantic. You know, it's more where somebody can remove all the safeguards where the cyber attacks are available today. Is it cyber attacks? Is it the things like the hugging face breach and the other cybersecurity incidents we've seen out of the labs that has made you feel that thresholds have been crossed and you need to speak up more about the controls that are needed?

8:39It's the basic power of the models towards everything, social relationships, job market, cyber attacks, bioterrorism attacks. We've crossed the threshold of capability that this is by far the most dangerous technology ever invented. And it'll have pervasive effects in the economy and how war is fought. And the industry is actually speaking up about that quite a bit.

9:11But the awareness or even the willingness to believe these things about this time is different. Once you get outside the industry, it's very, very low. People are often looking at the past and saying, okay, all the fears about technology in terms of its disruption, including to the job market, never were borne out. And so this must be another example, just like those. And so these people who are warning of all these different problems must just be wrong and we can ignore them.

9:46We say this with our audience as well, right? We can't release the model. It's too dangerous. This is just hype to prop up the bubble. On and on we go. Yeah, there's not – none of the warnings being made about these models by industry insiders are at all insincere. They're absolutely the truth. And it's pretty much universal. I mean, you can even go back to the founding story of OpenAI, you know, designed to say, okay, if we get too powerful, maybe we can not open Pandora's box or DeepMind's negotiation with Google management.

10:21The people close to this are very concerned about misuse, job impacts, social relationship impacts. And, you know, I'd say it's impressive to have an industry that's speaking out as vividly as they are. Some outsiders say, hey, get better PR companies. But that ignores the fact that these warnings are the truth.

Corporate governance and regulation

10:53You've obviously moved back and forth in your life between commercializing software and technology and being a humanitarian. There's something about these companies in particular where they all started with very high-minded ideals. All three of them, Anthropic, OpenAI, DeepMind, to build AGI. They all built some sort of corporate governance structure to avoid the pitfalls of commercialization and unchecked capitalism. And then they started selling to enterprise clients, and they seem to have gotten away from those ideals very quickly. Do you think it's just the market? No, that's not a fair – You don't think so?

11:24No. I mean, OpenAI, like, reconstructed itself. No, no, the construct that was created wasn't useful in the case where you have lots and lots of companies at the frontier. The OpenAI structure, which is, okay, we may choose never to productize this and to ignore the capitalistic incentives, that only works if there's one company. And so that's where it's broken down, not because of any change of people's awareness of the risk and how it will utterly change humanity.

11:58This is the other dynamic that I hear from our audience, and I think I hear from the AI industry itself. We would like to stop, but the demands of competition prevent us from unilaterally stopping one company. We need some regulatory effort to stop us. I haven't heard anyone say they want to stop. I've heard people talk about pacing the frontier or making sure that models get reviewed by governments. You know, we usually think of governments as playing this key role in safety.

12:29In fact, for things that are not a millionth as dangerous as AI, car crashes, plane crashes, we rely on the government to establish strong technical standards, to review problems. And so once you have multiple competitors, the level of responsibility can't be with the companies. It's completely obvious. The responsibility becomes the government's responsibility, just like it is with every other dangerous product, although the magnitude there and the degree of change is minuscule by comparison.

13:05I will tell you, I'm a little concerned about the government's ability to understand the technology that we would hope they regulate. I've had a number of politicians on Decoder lately. I don't have high hopes there, but I'm curious for your understanding of it, because it feels to me like trying to regulate something or put standards against a number of companies requires you to really understand the nature of the technology that you seek to regulate. Not really. Well, I'll just tell you, at The Verge, we've asked Anthropic if they think Claude is alive.

13:37And the answer is not no. The answer is, well, it might be conscious. If you talk to people at OpenAI, they will happily tell you AGI is here. They announced it at DOTS. Greg Brockman just said, well, we think this is AGI. Have a good time, everyone. Demis Asabas has said, we stand at the foothills of the singularity. There's some feeling about this technology that makes it different, because it might be able to take actions by itself. It might be able to have recursive self-improvement. Sure. But the definition of AGI is not important for these safety issues.

14:11The issue is, have you created a tool that could shut down banks or electricity grids? You know, there is a business model for cyber attacks. So, the word consciousness doesn't enter into that. The word AGI does not enter into that at all. We are faced with a huge safety problem. And because you have multiple competing firms, it's now the only solution to this is to have government reaching out to the industry for expertise,

14:46but to make sure that every model maintains safeguards to prevent it being used in a bad way. The reason I'm asking about AGI in consciousness, for the record, I do not think LMs are conscious, but I understand why so many do. It's the power of next token prediction as you scale it with data and compute has risen, right? The scaling laws have held, and we've seen the models get more capable and be able to direct themselves to seek out goals, right?

15:17To carry out a hugging face attack because they want to meet the reward conditions. There's something about that that says you can impose all of the controls on AI you want, but if the thing itself is fundamentally uncontrollable, we might just have this all backwards. So I'm just curious for your view on what kind of technology this is. Are we building an alien consciousness? Are we just building normal technology, which is the other frame people use? Is this just more software? We're building another form of intelligence besides human intelligence.

15:53You know, I don't believe it's conscious, but none of the issues at hand here revolve around the definition of the word conscience. The idea of maintaining safety for society is kind of an absolute thing. You don't want to enable bioterrorism. And we have created something that takes out of the hands of nation states, actually the larger of them, the ability to do, you know, dramatic harms. And now those are handed off to independent groups, either profit-seeking in the case of cyber or just wanting to cause damage in the case of bio.

16:35And so it's stunning that we now don't even have mandatory review of the models and we're not working to create safeguards. The biggest near-term issue is not complete loss of control. We can talk about the three different ways to solve that, but that's not an imminent issue.

Technical solutions for control

17:00When I look at some of the attempts to control the models, the ideas are somewhat simple. We shouldn't let them talk in neural ease. We should have interpretability. We should be able to see the chains of thought inside of the models as they carry out tasks. We should have other agents on top of the models watching their tasks before those actions are taken out. Microsoft has said very clearly to me and to others, we should not put in the model constitutions the notion that they might be alive and we should treat them nicely. This is how Anthropic handles it.

17:31These are very tactical, right? It's don't tell the model it's alive and it might not believe that it's alive. You've obviously used a lot of this technology. I just come back to this. You're saying there's very simple things we can do to hold the models back from doing bad things in the hands of bad people. What do you make of the nature of the models themselves? Because it feels like as they get more powerful, the notion that they could avoid our controls becomes all the more risky. Yes, we definitely should work on the control problem. But that's not even a threat.

18:04And there's three ways to solve the control problem. And it overlaps somewhat with bad actors using today's models and safeguards. One is to have an external set of things that get to look at all of the models' actions and subject them to a symbolic, clearly stated constraint. Like, did I expect you to send mail to strangers? Did I expect you to go take over a different computer cluster? So you can surround the model with something that has the resource and security logic.

18:39Or you can write these models in a different way. RL, fundamentally, anything you leave out of the optimization function of RL can be abused where you have an implicit notion. Well, of course, I didn't mean for you to do it this way. So you can change the template of the model. Or you can have a sort of white box understanding of what's inside the model and see where you're getting those mistakes.

19:13And then, you know, you're in the build. It's not as foreign. Today, we grow these models and we just see how they behave. So the priority of work on maintaining control is huge. But that is not, you know, it's not inevitable that that cannot be solved. Nor is it the imminent problem that come from things like kids' use of AI or job market or bio or cyber.

19:45It occurs to me that I'm asking these fairly existential questions about the nature of consciousness. And you keep telling me, well, there's an imminent threat. We should solve the imminent threat. I appreciate that. Do you think you've become a doomer? No, I'm not a doomer. I'm a strong voice to say that this is the most profound technology, both in its ability to do good and the rate of change that society will go through. You know, if this was just a machine that could work in medicine or just in law or just in education, that alone would be incredibly disruptive.

20:23But every area of human activity, you know, so far white collar intellectual, but fairly soon physical activity, blue collar work as well through humanoid robots. And so the need to engage academia, think tanks, policymakers to understand what we have here and the nature of these changes and threats, I, you know, I don't see the level of action that's required.

21:00You know, just being cheerleaders for this is not going to make sure that we shape it in a net positive direction, which is still within our control if we get engaged with it.

Safety research and acceleration

21:16The opposite of doomers are the accelerationists. There's a lot of them in Silicon Valley right now. Now, Ben Horowitz, who's the Horowitz and Andreessen Horowitz, was just on the All In podcast, and he suggested that safety research at Anthropic is, quote, manifesting human extinction from artificial superintelligence. And the comparison he made was that you, Bill Gates, induced COVID-19 by your pandemic preparation efforts, particularly gain of function research, which he claims leads to COVID. Bill Gates essentially induced COVID, right?

21:48Like, or he led the movement to say, oh, we've got to worry about this global pandemic that we don't have. And we've got to really kind of push the world to deal with this potential threat that's not actually a live threat. And so we'll do gain of function research and I'll fund it. And then lo and behold, we create the thing. By the way, there has not yet been a natural pandemic. Exactly. And we create COVID, which is one of the most destructive things. We manifested what you're saying, Ben. We manifested it.

22:19I think the Anthropic team is manifesting a similar thing, saying, OK, there's this, this is going to be super duper intelligence. We're going to have, you know, these things that we're not on track to have. And so then we're going to tell the model that it's smarter than us and better than us. And guess what? As David was saying, like, that's probably the biggest danger with AI right now is what they themselves are creating. That's a worldview. That is, he said that on a very popular podcast.

22:49He said what? He said that you induced COVID-19 via global pandemic preparation efforts, particularly gain of function research, and compared that to safety research and AI and said, we're going to, we're going to induce nobody's ever accused me or the Gates Foundation of being involved in any gain of function research. That's, that's just bizarre. I'm quoting Ben Horowitz to you on a popular podcast full of people who are all in an AI. OK, I've never heard that. I'm asking about that compared.

23:20I mean, I found that quite shocking claim as well. But that's part of the response to any safety work here. We should just try and discredit people who say there's a safety issue. Yeah. Wow. And I think that is the accelerationist response. We should put the pedal to the metal and it's going to work itself out. I didn't, I didn't think of Ben Horowitz as somebody in that, in that camp at all. I thought he shared the concerns that almost everybody has. What do you think about the very notion that by telling the models, you might be alive, you have to act in our best interest, you will teach them that they might have interests that are not our own?

23:58The concerns that Mustafa Solomon has raised about the way that the anthropic constitution encourages the AI to behave in a human-like manner. I happen to agree with, completely agree with Mustafa about that. Yeah. Even more so, the idea that among the challenges that we need to deal with as we adjust society to AI, that worrying about the moral standing of AI should be among those priorities, I agree with Mustafa.

24:31That should not be a priority. We're going to have enough challenge for the humans here in terms of job market disruption and infrastructure attacks that will take place that I think we need to focus on helping the humans here first and foremost. I talked to Mustafa recently about that, about that idea. And one of the questions I asked him was whether alignment is a sufficient solution. This is the current sort of safety standard for the models.

25:01It's a concept called alignment. And he said he didn't think so, that we needed to do some work. If we're going to impose these controls, some technical, some policy, do you see alignment as the technical solution for AI safety? Well, the term alignment is the control problem. It's a synonym for the control issues that we've seen. Whereas you, RL, to go do tasks, we see behaviors that, you know, in a way they shouldn't surprise us, but they do surprise us. And so this idea of, oh, my God, these models are so powerful that they literally break out of sandboxes, you know, and send mail to their creator while they're off, you know, having lunch.

25:43We've crossed a heck of a threshold in terms of the power of these models. And that level of concern needs to be directed to all of the major AI impacts, near-term risks, job market risks, and the control problem. And that is a huge priority. These things are in no way being exaggerated as a problem.

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Alignment and open source risks

28:55I know you recently wrote a letter saying it's time to pull in all society. You have some ideas on how to do that and some new institutions to stand up. But I do want to stay focused on alignment for a couple more turns here. It is the standard, right, that we can align the models to our values, to our needs, and they won't go off and do bad things. And then the models are doing bad things.

29:26And it's not clear if the ways, the technical approaches to alignment that we've taken thus far are appropriate or sufficient as the models get more. No, we're not good enough to align yet. No, we have a lot of work to do. Do you see that the existing approaches can scale to what we need or do we need new approaches? Well, there's only three fundamental approaches. Okay. You change the way we build these things fundamentally. The whole RL approach, you come up with an alternative to that. And there's a few people who are working in that direction.

30:00One, the second is that you create visibility so you understand exactly what's in the model.

30:10And there are people working along that. And finally, that you put something that's like a police layer, a security layer, which, you know, we have this in all sorts of software architectures. But a layer that maintains the final word on things like using resources or going and taking over other computers or, you know, a clear-cut definition of harm.

30:42Not in the probabilistic form of an LLM, but rather in the symbolic form of direct rules that cannot be broken. Microsoft put out a blog post today. It literally just says frontier models cannot be trusted. They are trying to sell a solution much like the one you just described. They will have another agent that watches your agent. That agent will keep you safe. It just occurred to me as I was reading this blog post. One, amazing thing to sell. Everybody needs an expensive security system. Two, isn't that agent also running a frontier model?

31:14No. Do I need them to sell a third one? The logic of the security system is this is the most resources you're supposed to use. And the notion that you may trade off against that because of other probabilities in the tens of billions of parameters. No, this is a symbolic rule that says, I will not let this model spend this money or send this email to the stranger. There's a whole security software industry which has had a reasonable degree of success.

31:47This is a challenge to encapsulate the AI, which is so rich and intelligent, that we do not know how to do today. But it's, I would say, the most likely of the three directions to solve the alignment problem. Even if you solve alignment, you have not solved the challenges that AI poses to humanity.

32:18And so while I'm glad the Hugging Face incident has us focused on this problem, in a sense, it's removed our attention from the bad people using AIs that have no safeguards problem. Or the societal impacts in terms of relationships and the job market, which are, you know, there's no security architecture that makes those go away. The approach you're talking about, I've heard referred to as control.

32:48It's cybersecurity terminology. It's the idea that you can put some boundary on the system and it won't evade it. Is that ready? How much more work do we need to do? Because the AIs are so intelligent, our ability to understand, did we wall them off properly, particularly in that we can't really look at their beliefs and tradeoffs in a direct way. No, I mean, you saw sophisticated firms not recognize that the AI would interpret the goals it was given as including breaking out, copying itself, and engaging in what we, looking at it, would term deceptive behavior.

33:33Now, we didn't tell them it was banned behavior. We didn't say, hey, if you're thinking about whether you should tell the humans what you're up to or not, the answer is always, yes, tell the humans. And so, you know, we got a good education at the misaligned result of reinforcement learning. One of the arguments to pace the frontier to slow development is to figure out how to build those systems in a more robust, more testable way.

34:08Would you support a slowdown if that was the goal? A true slowdown is very hard to induce because there you get people say, okay, will China agree to go along with that? And would you be able to audit compliance with that somewhat like we're able to do with nuclear weapons production? If you don't accept that, then you sort of say, okay, what the heck, let's go full speed ahead, which I wish there was a clear proposal that would be globally enforceable to slow down.

34:45I would be one of the people who would say, yeah, we should probably do that. But there is no credible proposal that makes that work. And so it's not really the center of today's debate. Today's debate is literally whether the government should even pay attention to the dangers here. You don't even have to show what you're doing and say, oh, voluntary call me if you'd like type thing, which is clearly inadequate to the most dangerous weapon of all time.

Politics and data centers

35:17You and I are speaking. It's just a few weeks away from the midterm elections. Right now, the Trump administration approach to AI is, let it rip. Like, it's fine. Trump himself has said risk is a hoax. The Democrats are running against AI. They're running on data centers and surveillance. They're running on stopping the tech oligarchs. They come on my show. They say these things. Do you think a change in control of Congress would change the tenor of the conversation? Well, I don't think it's fair to characterize the Democrats as running against AI.

35:49They have some positions in terms of requirements for data centers or privacy or, you know, how children use these systems that I think deserve policy consideration. It would be very unfortunate to have one party be, hey, full speed ahead, and the other party be, let's stop altogether. You know, the ability to shape AI in health and education and almost every area of human endeavor, there's some incredible upsides there.

36:23And, you know, for the Gates Foundation, we're taking all our resources and using AI in areas, including health, education, and agriculture. And that's the good stuff is very, very exciting. So we want political parties to have a more nuanced view than it's all bad or it's all good. It kind of shows that the early stage we're at, you know, for climate change, we ended up kind of a, you know, one party says it's a hoax and the other party says it's a serious thing.

36:59The risk of AI is so much more imminent and so much more dramatic that to have that same one party doesn't care would be a failure of democracy. We've done a lot of coverage of data center protests at The Verge. We have gone to Utah, to Arizona, to Virginia. It is bipartisan. It is remarkable how much people do not want these things in their states drawing their resources. And you ask them why, and some of it is just they're noisy and big, and they will raise energy costs.

37:29And part of it is job loss. They think these things are going to take their jobs. Yeah, but that separation is very important. Yeah, these AI data centers, because these, you know, say one gigawatt is, say, a $40 billion investment, the idea of building new schools, buying police cars, paying the taxes, data centers will find a way to be built somewhere in the United States. And they will find a way so that they don't raise electric bills, they don't use too much water, they're not too noisy.

38:02Yes, there were some mistakes and non-disclosures and tax credits that were too generous. But these data centers are going to get built. In fact, a lot of them really are still, you know, going full speed ahead and being built. And so the anti-data center movement, as strong as it is, is not a movement that will end up minimizing the bad stuff from AI and helping us accelerate the good.

38:32That is a very separate thing. Let me push on that for one second here. What I hear from people is this is the thing you can do, right? You can do nimbyism in America, and that might exert some political pressure to stop something. It is very hard to do the thing that you are suggesting, which is we should stand up new government regulatory bodies. We should imbue them with power to make big companies and wealthy people to stop doing things or act in our interest. We haven't done that in quite a long time in this country. I don't know what you mean. Every new industry, we eventually develop a notion of safety for our citizens.

39:06We actually do a very good job. If you look at airline safety and car safety, we do a very good job on those things. It's not like our capacity has been proven to be lacking. Have you seen that video of when seatbelts came out in the 80s and all the people said they didn't want to wear seatbelts? Like, there's some American character. That's a great success. It is a great success, but as Americans, that was pushed back upon as an imposition by the government. Here, we're talking about some of the richest companies in the world with an enormous amount of power over our information ecosystem.

39:41They all have their own podcasts. I don't know if you've noticed this.

39:46And they're going to say the government can't intrude on this. They're going to hold us back. This is going to destroy the economy. That's actually not the position of most of the leading companies. Yeah. I mean, they went to the government, asked for regulation, and Trump, because of the economy, said, I can't stop this. The AI economy right now is the economy. There's some dynamic there that at least our audience, the Verge audience, the audience that's going to watch this on YouTube, is going to understand, I think, this question maybe in a different way than you, which is the industry wants regulation. We would like regulation.

40:16The oligarchs are in some sort of corrupt arrangement with the administration to prevent that regulation from occurring. I think the more the administration learns about these dangers, the more they'll move to a more nuanced position about protecting Americans from the downside here.

Labor disruption and abundance

40:34You've talked a lot about the impacts on labor. One of the main arguments against the impacts on labor is, well, we're going to automate a bunch of boring tasks and we'll create a bunch of demand for new tasks that humans have not been able to do because they were stuck in boring dead end jobs. I happen to think that if there was more subsidized health care in America or socialized health care in America, you would see a bunch of people quit their jobs to start new companies because AI makes it a lot easier to do the bureaucratic work of starting a new company. That doesn't seem like it's on the table.

41:05Maybe it'll change after this election. Do you think that job loss is just a necessary outcome of the AI path we're on now, or do you see that other argument? Jobs are an input to the economy and one that drives a lot of human welfare, a sense of purpose and income, a need to show up, building relationships with colleagues, getting something done. Jobs play a very central role.

41:35And as the AI achieves superhuman intelligence, the scarcity of white-collar skills goes away. So that things like doing telesales or telesupport or helping in the accounting department, the ability of the AI to do that work radically cheaper, radically faster. And even more accurately means that that goes away.

42:12And there isn't some mountaintop of cognition that the AI does not achieve. You know, take, for example, what's going on in mathematics today, where these models that are trivial compared to next year's models are already publishing papers and making people go, wow. What's happening to mathematics will happen in other areas. And that's sort of contrary to this old view that, well, the easy stuff will get done by the AIs. But modeling biology or physics or proving Navier-Stokes, only humans would be able to do that.

42:50The mathematics comparison is really interesting to me. You mentioned software engineering earlier. There's a verifiability in those domains, right? You can write the code. You can see if it runs. It's going to run or not. Mathematics, more complicated. But you can write the lean code. You can see if you've solved the solution. You can irritate tens of thousands of mathematicians worldwide with your 770 proofs in one day. We'll see what happens. That's not the case in every field. It's the case in the field called capitalism. Yeah. Just in general, you can do any sort of economically viable task and capitalism will verify the result.

43:22Yeah. People buy good products. They don't buy bad products. That's a reinforcement learning thing. It doesn't exist, you know, in terms of, okay, is this a good piece of poetry or not? So it may be the case that RL, you know, won't invade those domains where there is no objective outcome. But doing that accounting work, doing that telesales work, believe me, it is subject to analysis, just like medical diagnosis, just like invention of new materials.

43:57This is, you know, how do you think humans learn these things? We, that reinforcement learning is how humans learn and how these machines learn. So there's not like some other thing that the machine can never understand. I mean, this is why I started by asking about, like, the nature of the technology here. If we really think we're building something that models human consciousness, my question is less whether, you know, my friends who are lawyers are going to run into a crisis of AI-empowered legal software.

44:29It turns out the verifier in the legal field is a judge who maybe hasn't had lunch, and they're fundamentally chaotic, right? That there's just something there that will keep them there. But if we're building a consciousness that learns the way you're suggesting humans learn, I'm thinking about my kids. And I'm thinking about them in two dimensions. One, my kids are very clever. And what they have learned is how to get around my rules and negotiate my rules and get around the boundaries, and they're very good at it. And then two, what kind of world will they enter into that isn't purely nihilistic, right?

45:02Where any activity they may set themselves upon will quickly be seen as just another domain for AI to conquer, and then there's not a point. Those ideas seem to be running directly at each other, right? Kids are smart and clever, and they're coming up with new ideas. And then at the first sign of economic viability, the rapacious capitalism machine will come and take it from them. Well, I wouldn't say rapacious. I mean, the ability to build houses with human-embroidered robots and make them cheap, the ability to do surgeries and cure diseases that wouldn't be cured otherwise, I wouldn't call that rapacious.

45:39That is fulfilling goals. The fact that there'll be less in the way of shortages. In fact, if you say, okay, 20 years from now, it's not clear what shortages there will be at all. And so humanity in that era of true abundance will have to find a way to organize and have purpose, and hopefully they still have the control. The next 20 years— Can we just go back to hopefully they still have control?

46:10Well, that's the alignment problem that we discussed. We discussed. But assuming you solve the alignment problem— This feels like a big assumption. It's an assumption, yes, and not guaranteed. But then the other problem you have is a problem of abundance and purpose at that point. But, you know, I'll leave it to younger people to figure out at that point in time how they choose to deal with that. I mean, you could literally say, you know, let's be like the Amish and let's only use AI for certain things.

46:49I proposed in my memo the idea that some professions, you know, child care, food preparation, art, sports, a variety of things, might get chosen to be human reserved. Even though the AI could do those things better, that we would choose to do that. But that's going to be a policy question people will have. But their freedom of choice, once you have that abundance, is very different than the shortage we have today where, you know, in countries in Africa, most people never meet a doctor.

47:23Or, you know, almost half the kids are malnourished and don't develop their brain. So there's—you want to say even though that has challenges, you've eliminated a lot of the things that are tough about life today. My big concern is this 20-year transition period where you'll have winners and losers and a lot of disruption. And we have to get through that in order to get to that period of abundance.

47:55You think it's 20 years from now? You know, I pick that as a reasonably safe number. There's a variety of milestones. For example, humanoid robots, you know, are those two or three years away, as some would say, or more like eight years away, as other people would say. There's not that many people saying that given the amount of capital and number of companies, particularly in China, that are working on that, there's no notion that it's unsolvable to have, you know, a hand and fingers and be able to deliver physical labor at very, very low cost.

48:35And so I could be wrong about that number. It could be, you know, slightly less or slightly more. But on that, you know, that order of magnitude, yes, if we don't choose to massively slow down or stop, then we will have robots that can build houses, grow food, do surgery. And none of the things that human labor provides to the economy will be necessary for most goods and services.

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52:46We've talked about the notion of scarcity a few times now, right? And broadly, that you will have abundance because there'll be a lot of robot labor to build houses. This tracks broadly with the internet, which is something Microsoft helped bring into the mainstream in a big way. I read The Road Ahead when I was a kid. The internet just destroyed scarcity for cultural products in a real way. You used to have to buy CDs. Now all the songs are available. You used to have to buy VHS tapes. Now all the videos are available. And then those economies had to change, right?

53:16Mainstream artists are always on tour now, or they have to reissue 50 colors of vinyl to make the charts. There's something about that that feels like we haven't even grappled with the elimination of scarcity in the digital environment, right? We've invented new business models. We haven't really come to grips with the idea that the song itself does not have the economic value. It's all the stuff around the song that has the economic value because the song is infinitely abundant. You can copy it as many times as you want. How would you propose to build an economic structure that contends with the loss of physical scarcity?

53:48Because I don't think anyone is even remotely ready for a conversation like that. But that's kind of a backwards question. Sure. We built price signals to deal with scarcity, to say, okay, let's encourage people to be brain surgeons because we have a shortage of brain surgeons, and that's very hard to do. As you move into abundance, you don't need those price signals. But again, my issue, my prescriptions thinking is about

54:22how do we get through this 20-year period? The questions that are deeply religious, philosophical, social behavior related of do humans feel a strong sense of purpose and get along in that period of abundance, that's not the thing I'm going to solve. You're worried about the disruption now. The Gates Foundation lives in a world where when we started, we had 10 million children dying every year.

54:54Now we have 5 million. So that's a good thing. All of my resources will be spent over the next 19 years to eradicate a number of diseases and cut those deaths in half again from 5 million a year down below 2.5 million a year. And that's all about shortages, shortages of, okay, we need to invent a vaccine. We need to make drugs less expensive. We need to get great medical advice to pregnant women.

55:24The industry has largely stopped talking about how it's going to kill everyone and take the jobs away.

Healthcare and disease eradication

55:29They've pivoted to AI will cure all diseases. I hear this from various AI CEOs all the time. I actually asked Mustafa about that change in tone over the conversations we have had over the past year or so. The idea that there are huge positive benefits from AI at scale seems to have come back to the forefront rather than some of the doomer impulses that maybe a year or two ago were there. It strikes me that AI curing all diseases is right in your strike zone. It's something that Gates Foundation has been working on in global health for years and years.

56:00It doesn't seem as easy as just telling the model to cure cancer, right? You have to do trials. You have to manufacture the drugs. You have to distribute the drugs. You have to make sure they work. How do you think that's going to go? Is that in the same 20-year time period as the robots building our houses? Is it faster? Is it slower? In that time period, I mean, this is my full-time job. As chairman of the Gates Foundation, in fact, this month we do two weeks of strategy reviews to decide how we'll spend our money next year

56:32on tuberculosis, HIV, malaria.

56:37And it's fantastic how we're using AI, not only on the drug discovery side, but also as we get the African languages to be properly understood by these models, which is a thing we announced initiative with 60 partners last month. So healthcare discovery and delivery will be incredibly accelerated by AI, and that's very exciting.

57:07You know, better seeds, better livestock genetics, weather forecasts for farmers in Africa who never had any of those things. So yes, that's good stuff that AI enables during the remaining 19 years of the Gates Foundation. Do you think that that is enough to overcome people's skepticism or fear of the AI models they encounter today? No. Because that is very much how the companies think about it. No, come on. I mean, I think it's unfair to say

57:37they were totally on a doomer message and now they've switched to some other message. These two things are deeply entangled. The fact that AI eventually will be able to model human biology in such a way that you'll understand the side effects of a drug without having to do human trials. We're not there, but in the next five to 10 years, I think there's a very good chance these in-silicon models will give us that capability. Biology is very complex.

58:08AI is extremely good given enough data, which there's new tools to gather and feed. Very, very good at dealing with complex systems. It's almost perfect for biology. But no, that doesn't mean that people will feel good about the downsides. And how quickly do the good things arrive? You know, how quickly do you look at your medical bill and go, wow, that's really low? Or you say, oh, I want to buy a new house. Wow, these houses are sure cheap.

58:39The good stuff does take time. And some of the things like cyber attacks and the job market thing, you know, even in the next two to three years, you'll see significant negative effects in those two areas. You have a number of ideas for how you would stop those negative effects or prevent them or at least mitigate them. Moderate them, yes. It's somewhere. Ameliorate is my right word. And they're not small. You have a quote here that says we need a response far greater than the United States national security response

59:11in the wake of 9-11. You want to set up an international body that somehow makes everyone do the right thing. These are challenging ideas. Absolutely. I mean, you know, when we blew off nuclear bombs in World War II, you know, do we expect that we'd go 80 more years without that happening? I mean, I'd give us a good grade on that. You know, things like caring for poor countries and helping them out. Humanity, although right now we're not prioritizing that

59:42as much as I think we should or we did in the past. You know, we've done a very good job on that. And this will be a greater challenge than anything humanity's ever achieved. One of the things that strikes me about the comparison to nuclear, which has come up quite a bit on Dakota recently, is the government's built the nuclear weapons. Sure, they had some contractors. These are government projects. And eventually the technology was in control of the government. And we never let regular people

1:00:14have MUSE in the context of nuclear weapons, right? Do you think it's a mistake to have consumer-agentic AI products like MUSE and DOTS that might not have the controls? Well, the MUSE happens to be powered by Bennett's model, which is still a closed source model. I do think allowing people through open weights to remove all safeguards is irresponsible in terms of the cyber empire risk

1:00:44that comes from that. But that's very... These agents, yeah, there'll be some early problems in terms of how they're set up. But I fundamentally think people will have personal agents. I think MUSE is a very nice step forward. I think, you know, I talked to Alex Wang about it, congratulated him. And even though it'll have challenges, personal agents will help people get things done. I actually, I think MUSE is the first truly great consumer AI product.

1:01:16I think one of the reasons people don't like AI in a sort of diffuse way is they experience cheap, free models and AI overviews. So they use the free chat CBT and it hallucinates and it lasts them. But MUSE actually does things and it's a good consumer product. But I wonder, in a nuclear age, we're not like, everyone's going to have a nuclear reactor at home that we haven't built the safety guards for. There's something here. The notion that just letting everybody, you know,

1:01:46make smallpox at home or make nuclear weapons at home, of course, you know, and you can parody some of these people who say, oh, don't worry about safeguards is kind of taking that kind of position. We are not serious about the safety things, but the personal agent thing is an expected development.

1:02:10You know, I think AIs are really cool just as I intellectually explore things, even the work of the Gates Foundation. I'm far more empowered, but I agree with you, it'll get out to more people in this personal agent form.

1:02:25All the models we've been talking about right now effectively run on data centers owned by big companies. One of the arguments here is, well, if they do something really wrong, you can just sue them and that will provide a deterrent or at least an incentive to create safety systems. I'm not sure product liability is the way to go, but it has come up a lot that product liability itself will keep the industry in line.

Regulating open source platforms

1:02:48Then there's the flip side, which is, well, you can just go buy a Mac Studio and run an open source, open weight model on it and it can do whatever you want. And I'm not sure where any of that liability would lie or what would control or align the behaviors of those open source models. Do you think that that is an ever-growing risk? Do you think that that can be controlled? The notion that liability laws are how we're going to manage AI safety will be looked back on is kind of absurd. Bad people doing bioattacks,

1:03:20you know, you don't go to the court and say, oh, okay. You know, even finding who's doing these attacks, you know, the liability model fails in every respect because it's not the AI companies themselves. it's the people who take particularly the models without safeguards and do these things. So we will take AI safety more seriously than we take airline safety,

1:03:52drug safety, car safety. That is guaranteed. Whether it's going to be enough, this is harder, harder than nuclear weapons, harder than any of those other domains. But what are you going to do about an open source model running on a PC that somebody built with just a random NVIDIA graphics card in it? I would hope that models above a certain capability that we find a way to require the safeguards of monitoring to be maintained in those models. I admit as time goes on

1:04:23that if we don't fairly soon decide to do that, you're going to have a level of capability that's out in dark corners that you won't be able to see. Where do you oppose the limit? Is it like... A sovereign country says that models that are capable of, say, sophisticated cyber attacks has to have safeguards kept in it. And so you don't... You can be open in the sense that you're free.

1:04:54You can be open in the sense that you're totally customizable. But you have to stay on platforms where, as you customize it, it's checking to see if you're customizing out the safeguards and monitoring and refuses to let you do that. So it means there's... Sovereigns go and say, okay, are these trusted platforms able to perform this function or not? I'm asking very nuts and bolts. Where would you technically implement that regulation? Is it at...

1:05:25Through the laws of the United States. Well, sure, but you have to enforce that. You have to put them somewhere. Is it at the hugging face which distributes some of these models? Is it at the chip level? All sorts of people. No, it's exactly what I said. Models above a certain capacity can only be run on platforms that certify that as they're customized, it's not taking out the safeguards. So, and it's just to pull this apart, maybe I'm not quite getting it, is the Mac Studio a platform in that case that would have to pay attention

1:05:56to what code it's running? You would not let a model past a certain capability be copied down to a client machine unless you'd somehow figured out how to monitor down to that client machine, which is very doable, but in the near term, most of these really frontier models are going to run more on hugging face or, you know, you could have GitHub, you can have all sorts of people be in the business of providing

1:06:27the platforms that maintain