Do you think you've become a doomer?
No, I'm not a doomer. I hate being the bearer of, you know, concerning news on this, but it's even worse not to tell people that this is coming and we have choices as humans to make about how we shape it.
He 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 in AI, and said, "We're going to induce."
Nobody's ever accused me or the Gates Foundation of being involved in any gain of function research.
You know what some of the YouTube commenters are going to say? They're going to say, "Why should we trust Bill Gates? He had a relationship with Jeffrey Epstein." How do you respond to that?
Bill Gates, you are the co-founder and former CEO of Microsoft. You're also the chairman of the Gates Foundation. Welcome to Decoder.
It's good to talk to you again. You and I have spoken many times over the years. I would say that the circumstance of this conversation feels 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. And I'd say the response broadly hasn't been adequate to the speed at which 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.
I 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 technological 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 let's 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?
Well, 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.
Do 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 there's AI as a broad set of models that exist in the world. Some open source, some proprietary. Then there are the companies that make these products. Then 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?
Well, 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.
And that's our big near-term risk really comes from models without safeguards. In a longer time frame, the control issues relate to any state-of-the-art model. But it's always easier when you can make sure safeguards are maintained.
There's a 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. And that is a market dynamic where maybe we're all going to pick the safest model or safest company. And you think that is diffuse? You think eventually technology will just get to a place where it's harmful or can cause these harms because it exists?
Well somebody doing a cyber attack with a model—that's not about choosing a model; it doesn't matter what model has safeguards in place like maybe they can't right? This is their approach currently—but yes certainly models where they make sure safeguards are not removed—they put effort into seeing how models are used monitoring activity—the near-term risk from those models is not gigantic; more where somebody can remove all safeguards—cyber attacks available today.
White-collar skills go away. So 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 goes away. And there isn't some mountaintop of cognition that the AI does not achieve. 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.
The 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 is 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. Yeah, people buy good products, they don't buy bad products. That's a reinforcement learning thing. It doesn't exist in terms of, okay, is this a good piece of poetry or not? So it may be the case that RL 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. This is, you know, how do you think humans learn these things? 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 the nature of the technology here. If we really think we're building something that models human consciousness, my question is less whether my friends who are lawyers are going to run into a crisis of AI-empowered legal software. It 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? Where 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 humanoid 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. That 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'? And that's—
Well, that's the alignment problem that 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, let's be like the Amish and let's only use AI for certain things. I proposed in my memo the idea that some professions, 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 in countries in Africa most people never meet a doctor, or almost half the kids are malnourished and don't develop their brain. So you want to say, even though that has challenges, you've eliminated a lot of the things that are tough about life today.
Yeah, my big concern is this 20-year transition period where you 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.
You 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, 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.
And so I could be wrong about that number. It could be slightly less or slightly more. But on 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.
We've talked about the notion of scarcity a few times now, right? 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? Mainstream 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, but 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? Because 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 prescription thinking, is about how 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, you know, that's not the thing I'm going about.
The Gates Foundation lives in a world where, you know, when we started, we had 10 million children dying every year. Now 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.
The industry has largely stopped talking about how it's going to kill everyone and take all the jobs away. They'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. It 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 on tuberculosis, HIV, malaria. And 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 an initiative we announced with 60 partners last month.
So healthcare discovery and delivery will be incredibly accelerated by AI, and that's very exciting. 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 is enough to overcome people's skepticism or fear of the AI models they encounter today? Because that is very much how the companies think about it.
No, come on. I think it's unfair to say they 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 5 to 10 years, I think there's a very good chance these in silico models will give us that capability. Biology is very complex. AI is extremely good, given enough data, which there are 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 cheap.' The good stuff does take time, and some of the things like cyber attacks and the job market thing, even in the next 2 to 3 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. In your letter, yes, it's somewhere, 'ameliorate' might be the 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 in 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, when we blew off nuclear bombs in World War II, did we expect that we'd go 80 more years without that happening? I mean, I'd give us a good grade on that. Things like caring for poor countries and helping them out, humanity, although right now we're not prioritizing that as much as I think we should or we did in the past, 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 Decoder recently, is the governments 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 have nukes in the context of nuclear weapons, right? Do you think it's a mistake to have consumer agentic AI products like Muse and Dots? They might not have the controls.
Well, Muse happens to be powered by Bettis's model, which is still a closed model. I do think allowing people, through open weights, to remove all safeguards, is irresponsible in terms of the cyber and bio risk that comes from that. But 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 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 think Muse is the first truly great consumer AI product. I think one of the reasons people don't like AI a lot, in a sort of diffuse way, is they experience cheap free models and AI overviews, or they use the free ChatGPT and it hallucinates and it lies to them. But Muse actually does things, and it's a good consumer product. But I wonder, in the 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 make smallpox at home, or make nuclear weapons at home, of course, and you can parody some of these people saying, 'Oh, don't worry about safeguards,' is kind of taking that position. We are not serious about the safety things. But the personal agent thing is an expected development. 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.
All 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. Then 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 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 as kind of absurd. Bad people doing bio attacks, you know, you don't go to the court and say, 'Oh, okay.' Even finding who's doing these attacks, 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, drug safety, car safety. That is guaranteed. Whether it's going to be enough, this is harder than nuclear weapons, harder than any of those other domains.
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 and monitoring to be maintained in those models. I admit, as time goes on, that 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 would you set the limit? Is it like—
