Bill Gates’s Blunt Warning on A.I.

The Ezra Klein Show

Bill Gates discusses the profound risks and opportunities presented by advancing artificial intelligence, specifically focusing on the dange

Key takeaways

• Bill Gates believes AI has reached a point where it can identify security bugs and create biological threats that were previously only possible for nation-states.
• The transition of human cognition from a scarce element to a silicon-based commodity could lead to the automation of coding, accounting, and legal work.

Main topics

• Existential AI risks
• Economic automation and job loss

Notable quotes

The idea when you move the template away from biology to silicon, you don't have these boundaries between individuals.

Conclusion

The episode serves as a high-stakes warning that proactive governance

Transcript preview

Speaker 1 (0:00) Hey, it's Anna Martin from The New York Times. What if I told you you'll never have to hear this line again? Speaker 4 (0:06) Wherever you get your podcasts. Speaker 1 (0:08) Yeah, no, I actually can't tell you that. But you know what I can tell you? A place I think you might want to go. It's the Shows tab in The New York Times app. The place to listen to and now watch full episodes of New York Times shows. The Shows tab. Find it in The New York Times app. Explore The Shows tab and follow a show today. Speaker 4 (0:51) 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. Speaker 5 (1:01) Hello, I'm Bill Gates, chairman of Microsoft. In this video, you're going to see the future. Speaker 4 (1:08) He was a revolutionary technologist who built some of the foundations of the future that we're now living in. Speaker 3 (1:12) What can the computer do that a book can't? I mean, that's the foremost interactive process, Speaker 5 (1:17) isn't it? No, it's not. Not at Speaker 6 (1:19) all. Speaker 5 (1:19) An Speaker 3 (1:20) innovative word processing program called Microsoft Word. Speaker 6 (1:23) This is absolutely revolutionary, of course. Speaker 4 (1:25) Then, of course, a CEO of Microsoft. Speaker 6 (1:28) 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 Speaker 4 (1:35) the momentum of corporate competition. And Microsoft, of course, is still in some of the race dynamics present in AI. We Speaker 3 (1:42) now have an incredible roster of seven new world-class models to keep everybody working at the absolute frontier. And Speaker 4 (1:48) 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 Speaker 5 (2:05) computers will be, in any meaningful sense, as smart as people at some point. Speaker 4 (2:10) 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 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. Speaker 4 (2:50) Bill Gates, welcome to the show. 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. Speaker 6 (3:01) 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, you know, a whole different mechanism, a whole different software and hardware. Or is this going to be it now through the end of time? Speaker 5 (3:20) Mostly what we're working on now is the 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. Speaker 6 (3:41) Yeah, we don't want them to think, do we? Not really, Speaker 5 (3:43) I would think. It's a scary thought. Speaker 4 (3:45) 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. Speaker 5 (3:55) 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, can we make software see or listen or... read or write. You know, 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, you know, selling basic interpreters and word processors, you know, we'll feel bad. You know, I might wish I would have stayed in academia. You know, so that's... 1975, Progress on AI is mostly going down dead ends like prologue expert systems. And there's a small group, including Jeffrey Hinton and a few others, that are working on neural nets. And eventually there's enough power, actually coming from the graphics processor, that that idea, these highly statistical approaches, start to show promise. Speaker 5 (5:23) 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 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 CLAWD 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 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 in finding flaws Speaker 4 (6:52) 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, Speaker 5 (7:07) our foundation does these strategy reviews, we spend two weeks in October to set, you know, how we're going to spend 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 a 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, cloud, copilot, and even having them talk with each other. And in a few of the reviews, we'll actually have the AI sit in. In 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. 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. Speaker 4 (8:15) 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? Speaker 5 (8:23) Well, no one who's ever been fascinated or wanted to develop AI don't realize that It's incredibly scary that... it will be better. Biological minds, you know, it's amazing how general purpose they are in that the optimization was staying alive, breeding, you know, socializing with each other for 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 individuals. You don't have a limited memory. You know, the size of the brain is limited by the birth canal. It's, you know, why humans at first are very limited. You know, 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, the AIs do that today. That's why, particularly for less common diseases, they are so superhuman 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 no human. will ever be able to do that. So that, if you don't retain control over it, you've evolved a species that will be to us, you know, as we are to say, you know, dogs or cats, just in a very different realm. And so every AI company, whether it's DeepMind or OpenAI, 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 Speaker 4 (11:17) 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, but 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. Speaker 5 (11:43) If I believe that I'm about to launch a product that is unsafe, Speaker 5 (11:50) It is completely in my ability, my power, and my responsibility, and I'm incentivized to do so to not launch the product. This Speaker 4 (12:01) 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. Speaker 4 (12:09) How Speaker 5 (12:09) 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 things that all of my money is going to, that I am going to use my votes. It's something that's more important, which is we are not awake to where we are with the eye 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. Speaker 4 (13:51) What specifically was the threshold or what did you see that made you think we're in a new reality here? This notion that Speaker 5 (14:00) 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 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 Glasswing 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, 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. 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 nation 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 Speaker 4 (15:37) 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 the beginnings of control issues with things like the... hugging face hack where 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, anthropic 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'm almost... Can't believe you're Speaker 5 (16:25) asking that. This is the most dangerous thing that humans have ever gone near. Speaker 3 (16:30) In Speaker 5 (16:31) other Speaker 3 (16:31) areas, Speaker 5 (16:32) 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 almost can't keep Speaker 4 (17:25) a straight face. Speaker 5 (17:26) Well, Speaker 4 (17:26) 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. Speaker 5 (17:38) 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 damage and a bio attack causes massive damage. And then the 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 Speaker 4 (18:41) 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. OpenAI said this about Astra 6, and people inside OpenAI have been raising the alarm that these systems seem to know when they're being tested. There's more situational awareness. And so the testing environment is no longer as indicative necessarily of what they will do. So that's one. And the second is that all the labs are moving as quickly as they can towards recursive self-improvement where you have the AI coder moving not just from speeding up the