Moonshots with Peter Diamandis - Mustafa Suleyman: The AGI Race Is Fake, Building Safe Superintelligence & the $1M Agentic Economy | EP #216

Episode Date: December 16, 2025

Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends   Mustafa Sulyman is the CEO of Microsoft AI Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified – My companies: Apply to Dave's and my new fund:https://qr.diamandis.com/linkventureslanding      Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy   Grab dinner with MOONSHOT listeners: https://moonshots.dnnr.io/ _ Connect with Peter: X Instagram Connect with Dave: X LinkedIn Connect with Alex Website LinkedIn X Email Connect with Mustafa X Linkedin Listen to MOONSHOTS: Apple YouTube – *Recorded on December 5th, 2025 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

What's the mandate from Satya? Is it win AGI? I don't think there's really a winning of AGI. I'm not sure there's a race. One of the OGs of the AI world, Mustafa Salimand is the CEO now of Microsoft AI. He spent more than a decade at the forefront of this industry before we even had gotten to feel it in the past couple of years now. Fundamentally, the transition that we're making is from a world of operating systems, search engine, apps and browsers to a world of agents and companions. We're all going as fast as we possibly can, but a race implies it's zero sum.

It implies that there's a finish line. And it's like not quite the right metaphor. As we know, technologies and science and knowledge proliferates everywhere, all it runs at all scales, basically simultaneously. Are you spending a lot of your energy, compute, human power on safety? Yeah, no, I mean. Now that's the Moonshot, ladies and gentlemen. Everybody, welcome to Moonshots.

I'm here with DB2 and AWG and Mustafa Soleiman, the co-founder of Deep Mind, Inflection AI, and now the CEO of Microsoft AI. Welcome, my friend. Good to have you here. Thank you for making time for us. Thanks for having me. Yeah, I'm excited to do this. Yeah, it's, you know, what you've been building with Satya is amazing. And it's hard to believe that Microsoft is 50 years old.

And it's reinvented itself so many times. And for the last five years, it's been, you know, at the top of the game, the most valuable company in the world, 250,000 employees. And for what I understand, 10,000 employees now under you. So a few, you know, important questions I want to open with. First, some broad context. you're building inside a massive company with huge resources, probably arguably more than almost everybody else.

And the question I have is, what's the end goal here? You've got all the hyperscalers sort of providing open access to AI, and they're doing sort of a land grab, trying to get as many users as possible. You've been building sort of in a, you know, within the Microsoft 365 ecosystem. system. Is the goal in the, you know, next couple of years maximum users? Is it data centers? Is it, you know, is it cloud? How do you think of what you're optimizing for? I mean, it's a good question. So, I mean, we are on any given day a four trillion dollar company with almost $300 billion of revenue. It's incredible. It's just surreal and very, very humbling. And we play at every

layer of the stack. I mean, obviously we have an enormous business in data centers, and in some ways we're like a modern construction company, hundreds of thousands of construction workers, building gigawatts a year of, you know, CPU and AI accelerators of all kinds, and enabling that, you know, to be available to the market. API is on top of that, but also first-party products in every domain you can think of from gaming and LinkedIn right the way through to all the fundamentals of M365 and Windows, and of course, in our search and consumer businesses and too. And fundamentally, the transition that we're making is from a world of operating systems,

search engines, apps, and browsers to a world of agents and companions. All of these user interfaces are going to get subsumed into a conversational, agentic form. and these models are going to feel like having a real assistant in your pocket 24-7 that can do anything that has all your context. And you're going to do less and less of the direct computing, just as we're seeing now. Many software engineers are using assistive coding agents to both debug their code and also generate large amounts of code,

just as we use libraries, third-party libraries. Now we're just going to use AIs to do that generation. and it's making them more efficient and more accurate and faster and so on and so forth. So the trajectory we're on is quite predictable. It's one from user interfaces to AI agents. And that is a paradigm shift which the company is completely focused on. Like, you know, after seeing five decades worth of transitions, I think the company is like super alert to making sure that we're best place to manage this one.

Do you see yourself providing sort of an open source AI like the other players out there? Or do you think you can keep it contained within Microsoft 365? I think we're pretty open-minded. I mean, we've got some pretty small open-source models. I think realistic... And when I say open-source, I really mean open access, if you would. Yeah, I mean, look, there are always going to be APIs that provide incredibly powerful models. I mean, you know, Microsoft is really a platform of platforms.

Being a platform and being a great provider of the core infrastructure that enables other people to be productive is like the DNA of the company. And so we will always have masses of APIs that turbocharged that. But what an API is is going to start to look kind of different too. Like it may be pretty blurred the distinction between the API and the agent itself. Maybe that we're principally in the business in five years time of selling agents that perform certain tasks. that come with a certification of reliability, security, safety, and trust. I mean, that is actually in many ways the strength of Microsoft, and that's one of the things that's attracted to me,

is like, this is a company that's incredibly trusted, it's actually very secure. And sometimes I think the slowness or the friction is actually a bit of an asset. You know, there's a kind of steadiness that comes with having provided for all of the world's biggest Fortune 500 companies and governments and major institutions. Is it like the old adage you can't go wrong buying IBM in the old days? I think there's a steadiness about us, which I think is reassuring to people. And there's a kind of like deliberate customer focused patience. There's not the same anxiety and, you know, sort of somewhat sclerotic nature that comes

with being, you know, an insurgent. There's some downsides to our position. You know, we would take a little longer to get things through. But the company is firing on all cylinders. It's very impressive to see. One more question before I turn it over to Alex. You know, we're seeing in this hyperscale war, I mean, literally, you know, a week by week, everybody outdoing each other in this insane period of everybody coming out with the new

benchmarks. works, you know, do you miss not being in that game, or is this stability that Microsoft provides to build for a long-term vision, sort of what you find most exciting? You know, my background at DeepMind is such that I spent a good decade grinding through the flat part of the exponential, where basically nothing worked. I mean, you know, really, like there was some amazing papers. And AlphaGo was obviously incredible, but it was in a very unique simulated, controlled game-like environment. But things actually working in the real world were few and far between. And so, you know, I've always taken a multi-decade view, and that's just been my instinct. And I think that, you know, yes, it's super important to ship new models every month and be out there in the market.

But it's actually more important to lay the right foundation for what's coming because I think it's going to be the most wild transition we have ever made as a species. Can you just flesh that out a little bit? Was there a period of time where it was just three of you grinding it out in London? Well, there were more than the three of us. But I mean, for the decade between 2010 and 2012, sorry, 2020, I mean, there were just like so few successful commercial applications of deep learning. I mean, there were plenty behind the scenes. There was image recognition, their improvements to search.

Not a huge market for commercial. Yeah, playing go, not a huge rocket, exactly. So I think, whereas now, I mean, then you see LLMs from 2022 onwards, like in production, completely changing the way that people relate to computers, changing what it means to be a human myself, changing our social relations. Like, that is just a, you know, that's, we hit an inflection point. And, you know, I think that is very, very different to, the grind of like training tiny models with very little data and very small clusters back in the 2010s.

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and entrepreneurs building the world's most disruptive tech. It's not for you if you don't want to be informed about what's coming, why it matters, and how you can benefit from it. To subscribe for free, go to Demandis.com slash Metatrends. To gain access to the trends 10 years before anyone else. All right, now back to this episode. Yeah, so when last we spoke, circa 2015, I think that was perhaps three years post-image net, five years pre-language models are few-shot learners.

Agents, agentic AI was nowhere to be seen at the level of what we see now. Since you've written about your vision, what you've, I think, socialized as a modern Turing test, the idea of economic benchmarks for autonomy by A. I'd love to hear where are Microsoft's economic benchmarks for these agents? If the agents are about to take over the economy or take over so many economically useful functions, why are we stuck with benchmarks like Vending Bench rather than Microsoft leading the way with Microsoft's economically autonomous benchmarks for its agents? Yeah, I mean, it's probably just worth adding the context that we met in 2015 in Puerto

Rico at the AI Safety Conference that many, many of the field now were at the same time. It's a seminal moment. Yeah. Was it the day after New Year's Eve or somewhere around New Year's. It was pretty cold out everywhere except Puerto Rico. Yeah, exactly. It was pretty cool. It was quite surreal moment, actually. It's like a Cilomar right before it all happened. Yeah, yeah, totally. And, you know, yeah, the modern Turing test was something I proposed, I guess it was 2022 when I wrote it. And it was basically making a pretty simple prediction. If the scaling laws continue with more data and compute and adding an order of magnitude more compute to the best models in the world every year, then it's pretty clear we would go from recognition, which was the first part of the wave, to generation,

which is clearly we're now in the middle of, or maybe ending that chapter, to then having perfect generation at every time step, which in sequence is going to produce assistive, agentive actions. And actions would obviously look like an intelligent knowledge worker or project manager, a strategist, or a startup founder, or whatever it is. And so then how would we measure that performance? Rather than measuring it with academic and theoretical benchmarks, one would clearly want to measure it through capabilities. What can the thing do in the economy, in the workplace, and how do we measure the economy? We measure it by dollars and cents. And so could, you know, what would be the first model to make a million dollars? Now, given, as I recall, $100,000 in starting capital?

That's right. Yeah. Who could, which, which model could turn it into a million dollars. 10x return on investment by an agent. Exactly. And so I think that's a pretty good measure of performance and capability. And certainly, you know, we've kind of just breezed past the Turing test, right? I mean, it kind of has been passed. No one's really done a big, you know, alpha.

The Lopener Silver Prize wound down before we breezed past Turing. Yeah. And no one celebrated it. Like, where was the big, like, you know, Casparov? deep blue moment. Can we clink virtual glasses right now and celebrate that we won? It happened. Yeah, exactly. And that's what it feels like to kind of make progress in a world full of these compounding exponentials where we just get desensitized to 10x. So much so that you can be like, guys, why haven't you done it yet? Yeah. We're spoiled. Where's my Microsoft Lopener Prize for the

modern Turing test? Right, exactly. Yeah, you know, like someone said to me earlier on, you know, this AI thing, it's still in its infancy, isn't it? And I'm like, man, if this is infancy, wow. Like, I can talk to my computer fluently in real time. Yeah, exactly. So, you know, obviously, at the same time, agents don't really work yet. The action stuff is still progressing. It's getting better and better every minute.

But it's pretty clear that in the next couple of years, those things come into view and they're going to be very, very good. Can we get together again after the modern Turing test has been passed and just to celebrate, recognize it? And chink virtual glasses again? Absolutely. Hopefully we can pop a, you know, champagne or something. I think we should. But we'll have an optimist pop the cork for us or something. Yeah, exactly, exactly.

Dave. Hey, I want to flesh out that backstory a little bit more, too. It's such a cool story. But I remember really clearly, you know, after DeepMind got acquired by Google, what was the price tag on that deal? It was like half a billion dollars. Yeah, $6.50. What a deal. 650. What year was that?

2014. I remember reading maybe a year or two later that Google justifies deal by having deep mind tune the air conditioning in the data centers. Yeah, right. My interpretation of that was like, wow, this isn't going all that well. And now it's obviously the biggest thing that's happened in the history of humanity and forking out all over the place. I mean, the data center thing was pretty cool. We did actually reduce the cost of cooling the Google Data Center fleet by 40. you know, it's so funny because I read it at the time, and I was like, what a bust.

And then I read about it in Wikipedia on the flight over here to meet with you. And it's like, it was actually, what, 500 attributes fitting into the neural net. That's it. It was actually a lot more complicated than the news made it sound at the time. That's right. But, like, you were talking about the flat part of the exponential, and you think about, like, okay, all of this R&D, which is so close to becoming AGI is tuning the air conditioning. Right. But that's the nature of exponentials.

They sneak up on you like this. But the other way to think about that is that it's basically taking an arbitrary data input, an arbitrary modality and using the same general purpose method to produce very accurate predictions in a novel environment, which is the same thing that's happened with text and audio and image and now coding and obviously with other time series data. And so it's just another proof point of the, you know, the general purpose nature of the models. And I think like it's so easy to get caught up thinking five years is a long time. It's like a blink. of a night. It's a drop in the ocean. I think because we're such a frantic second to second news culture, social media type environment, we just don't have an intuition for these timescales. I think other cultures, you know, do. And I think historically, before digitization, we had much more of a natural intuition for the movement of the landscape and the seasons and like, you know, the ages and stuff. And now we're just like, wow, it's not coming quick enough. It's like,

dude, it's coming pretty quick. We've shifted to a 24-7 operations. I mean, I know very few, I know a lot of people, including this group, that are operating around the clock every day, just because when we do, you know, a Moonshot's podcast week to week just to celebrate and talk about what's just happened, it's insane on a week by week basis what's going on. Yeah. Yeah. You know, and Peter's always saying people are very, very bad at exponentials. Right. 100,000 years of evolution has us predicting tomorrow will be like yesterday. Right.

But you're one of the few people who, you know, having lived through. that air conditioning becomes AGI in just a few years. So where we sit right now is on another inflection point, and the implications are massive, and people are way underreacting across the board. And so you're one of the few people who, you know, having seen it before, can say, yeah, here comes. I just got very lucky. I mean, we were very lucky to have an intuition for the exponential, right?

And, like, that's a very powerful thing, because we can all theoretically observe the shape of the exponential. but to go through the flat part and then get excited by a micro-dubling. Yeah. You know, like that's the bit is that when you're like, oh, my God, this, like, I remember this, the MNist image generation thing. For sure. I worked on that generative models.

There's like, these are like, I can't remember maybe 256 by 256 pixels. Yeah. You know, black and white, handwritten digits. Yep. And, you know, I think this was like 2013, maybe even 2012. and this guy, like, I think maybe he was employee number five at Deep Mind, Dan Bistra, this, like, awesome Dutch guy out of EPFL, was generated like the first number seven that was provably not in the training set for the first time.

I was like, man, that is amazing. Like, how could it have, it's learned something about the idea of seven. That was the, you know, that was, it's got a concept of seven. How cool is that? You know, so I get the highest. score on MNIST ever in 1991 when it first came out when you were three years old right yeah that's nine nine years old okay um yeah and and actually that's the same data set that's now in pie torch that people like benchmark off pretty crazy incredible yeah how often you surprised by what you're

seeing i mean how often is there like a move 37 uh you know sort of like aha moment yeah yeah Is it happening more frequently? I was absolutely blown away by the first versions of Lambda at Google. This was like maybe 12 people working on it, led by Nome Shazir and Daniel DeFritus and Kwokli. And I got involved later, maybe three or four, five months after they've been going. And it was just breathtaking. I mean, obviously everyone at that point had been playing with LLMs and they were like

one shot. produce an answer and you know have a prompt and blah blah but they were really the first to push it for conversation and dialogue and it just seeing the kind of emergent behaviors that arise in yourself like things that you didn't even think to ask because you know that's going to be a dialogue rather than a question answer situation sounds so trivial to say that like in hindsight because now we're obviously steeped in conversation as the default mode but that that was like breathtaking for me. And obviously then I pushed really hard to try and ship that at Google. And for various reasons, we couldn't get it launched. And that was when we all left, like I left and

Gnome left to do character and, you know, David Luan left to do adept. And, you know, we were all like, okay, this is the moment. And so, you know, I think there's been still a couple moments since then, but that was probably the biggest one that I remember in recent memory is mind blowing. And the scaling laws have delivered such unexpected performance, right? I mean, was going back to your earlier days, did you anticipate the kinds of capabilities that have resulted? I mean, was this predictable for you? Or is it still like, wow, what it's able to do in medicine, in conversation, in scientific research? Well, especially working off of pure text. I mean, how far we've gotten. Nobody, I think, well, you tell me, but nobody would have seen how far we

would get with just text. Yeah, I mean, in 2015, I collaborated with a bunch of of really awesome people on an LP deep learning paper, a deep mind, where we were essentially trying to predict a single word in a sentence. I think we had scraped like Daily Mail News articles and CNN articles. And we were like, can we fill in the blank? Just predict like one word in a sentence or complete the final word in a sentence, like the inverse of the problem that way the models now work. And, you know, it was like a pretty big contribution. It was a good, well-sighted paper, but it was like, this is never going to scale. Like, we were just like, okay, we're way too early, not enough data, not enough compute.

But we were still optimistic that with more data and compute, that is a method that will work. So I don't want to have hindsight bias and say, well, it was all very predictable, but everyone in the field, not just obviously me, but everyone in the field just had the same hammer and nail and just kept chipping away. Like, can we add more data to this? Can we clarify our prediction target and can we add more compute? And broadly speaking, that's what's delivered. Yeah, we'd love to maybe pull on that theme a bit.

So you mentioned how surprising your generative 7 from MNIST was. You mentioned how surprising the success of Lambda for conversational tuning and conversational performance in general is. I think you've made already a little bit of news, to my knowledge, in this episode, if I understood correctly, Correct me if I'm wrong, but with the expectation that in the next two years, so I read that as 2027, we'll see agents start to pass your modern Turing test. We'll see them be able to 10x 100,000 U.S. dollar return on investment. I'm curious about the next surprises to come. AI for science. Microsoft Research has an AI for science initiative. Do you have timelines in your mind for AI solving math, which we're seeing a whole bunch of startups right now tear through Erdish problems, AI for physics? chemistry, medicine. Material science. What do you think happens and when? Yeah. Actually, you've just reminded me. The more recent thing that has blown my mind is the fact that these methods could learn from one domain, coding, puzzles, maths, the essence of logical reasoning. So just as it learned the essence or the conceptual representation of a number

seven, it's clearly learned the abstract nature of like a logical reasoning path and then can basically apply that, you know, to many, many other domains. And so that's kind of interesting because it can apply that as well as the underlying hallucination slash creativity sort of instinct that it has, which is more like interpolation. But those two things combined are like a lethal combination for making progress in like, say, new mathematical theorem solving or new scientific challenges, because that's basically what humans do all the time. We should have combined these two, you know, capabilities. And so I couldn't really put, I mean, some people want to put dates on those things. It's hard to put a date on those things because they really

are very, very fundamental. But it feels like they're definitely within reach. It's hard to kind of, it would be very odd to bet against them. It just maybe from an over, under perspective, do you think, say, given all of the recent progress in math, for example, do you think solving science and engineering for some reasonable definition of solving is going to ultimately be harder or easier than modern Turing test 10xing of return on investment? It's going to be harder because I think a lot of the training data, if you like, for strings of activity in the workplace or in entrepreneurialism, startups, and so on, that kind of exists in a lot of the log data. And also, it lends itself naturally to real-time calibration

with a human. So the AI can sort of check in. The human can oversee. The human can intervene. The human can steer and calibrate. And so it's going to be a much more sort of dual, like, combined effort between AI. You can have reinforcement learning in that category. Yeah, where a human is participating in steering the reinforcement learning trajectory. Whereas in a novel domain where it really is inventing completely. new knowledge, that's kind of more happening in a very abstract sort of vector space. And it's like unclear yet how, you know, the human is going to intervene in the theorem solving problem. Obviously, everyone's working on this, particularly in like biology, synthetic materials and

stuff like that. Because you want to, I mean, it's already giving humans a better intuition for where in the search space to look for for new hypotheses for drugs, for example, or for materials. And then the human can either take or reject that, feed that back to the model. Then obviously, go and test it in silico and be like, oh, we actually ran the experiment. You know, we perpeted a bunch of stuff and then feed that back into the model to improve the search. And maybe it's a follow-up question. What can humanity in general, Microsoft specifically or all of the AI community subset of

which listens to the podcast, what can they do to accelerate AI for science and accelerate the solution to science, math engineering with AI? I mean, arguably that would be like one of the most impactful things for humanity that would just fundamentally move everything. speed. Yeah. I mean, I think it's already happening very organically, right? This is also, not only is this like the most powerful technology in the world, it's

also the fastest proliferating in human history. And, you know, sort of the cost of access, the cost of inference coming down by multiple orders of magnitude every couple of years, is kind of- Would you ever have imagined it would be so cheap? That bit I also totally got wrong. It's like the biggest surprise for me isn't that we're getting this level of capability. how cheap it is, how accessible it is. A hundred percent.

That's a thousand X over two years. So is it going to do that again? Or was that a one time? Is it a thousand? I think it's like a hundred X. The inference cost has come down. A single token inference cost, I think,'s come down 100 X in the last two years. That's two years.

There have been competing estimates. Some estimates measure intelligence per token per dollar. Right. There's an estimate that it's 40x year over year, but that's for certain weight classes of models. I've seen a thousand X for some classes of models. It's crazy. Oh, wow. That's wild. Yeah, no, I mean, yeah, that's actually a good point. I got that totally wrong because I didn't think that the biggest companies in the world were going to open source models that cost billions of dollars essentially to train. And so much showed that like when we founded inflection, you know, and this was like maybe nine months or maybe a year before ChatGBTGBT was released. Yeah, we started doing fundraising a year before ChatGBT was released. You know, we basically raised. We basically raised.

a billion and a half dollars with a 25-person team to build what at the time was the largest H-100 cluster with Invidia and Corweave. We were Corweave's first AI customer. And they were previously in crypto, and we were like their first AI customer working with them to build our data systems. And obviously, Nvidia got behind us. I think we built a cluster at the time was about 15,000 H-100s growing to 22,000. And, like, then obviously that year, ChatGBTGBT came out, and like a few months around that time, Lama came out.

And so we were like, oh, my God. You know, our entire capital base of our company has just been, you know, sort of undermined by the fact that open source, you know, it seems like open source is going to, it's not really about performance. It's just cost. So then, like, perplexity, for example, founded after the arrival of Lama, knowing that they could depend on Lama and obviously Open AI as an API and all the other APIs. And so then they had a much, much lower, like, cost base, basically. So, yeah, that was like another thing that it was not predictable. I mean, other people predicted it, to be clear.

I just got it wrong. Abundance, baby. Demononization, democratization of the most powerful tools in the universe, our universe. Hyper deflation, if anything. Hyper deflation, yeah. I think that's a really important point. The cost of accessing knowledge or intelligence or capability. Intelligence as a service.

As a service is going to go to zero marginal cost. And obviously, that's going to have massive labor displacement effects, but it's also going to have a weirdly deflationary effect. Because, you know, what is going to happen? People aren't going to have dollar-based incomes to go buy things. That's obviously bad. But the cost of consuming stuff is also going to come down. So we actually have a transition mismatch because, you know, sort of labor markets are going to be affected before cost of services comes down. And maybe there's a 10, 20 year lag between that, which is going to be very destabilizing. Which, by the way, is what we started to talk about a little bit earlier. I mean, my, I posit that in the long term, there's an extraordinary future for humanity, right? Where access to food, water, energy, health care education is accessible to every man, woman, and child. It's the short.

term that is challenging, right? The two to seven year time frame. Is that fit your model to? Yeah. The short term, I think, is going to be quite unstable. The medium to longer term, like, you know, it's pretty clear that these models are already well-classed at diagnostics. And we released a paper maybe four or five months ago now called the MAI Diagnostic Orchestrator. Essentially, it uses a ton of models under the hood to try and, you know, you know, take a set of rare conditions from the New England Journal of Medicine, you know, rare cases that can't be easily diagnosed that the best experts do, you know, a kind of weak job on. And it's like four times more accurate, roughly. It's about two X less the cost in terms of

unnecessary testing. There's a study that OX, that came out of Harvard and Stanford looking at, in this case, was GPT4, a physician by themselves, a physician with GPT4. and the GPT4 by itself. Yep. And it was, you know, incredible that if you left the AI alone, it was far more accurate in diagnostics than the human. We're biased in our thoughts and what we saw yesterday, our recent diagnoses.

Yeah. Actually, we got a lot of feedback after we released the paper because we only showed the AI on its own, the physician on its own. And a lot of people wanted to see what it was like to have the physician and the AI, or at least the physician have access to Google search as well. and that improves performance a little bit,

but the AI still trumps by quite a way. Dave, what are you thinking? Oh, so much. So Microsoft, you've been here, how many years now? Just a year and a half. A year and a half? So you feel like you're part of the, you're indoctrinated. So what's the mandate from Satya?

Is it win AGI, or is it be self-sufficient? Or what is the, what's the target? I don't think there's really a winning of AGI. I think this is a mis-framing that a lot of people have kind of imposed on the field. Like, I'm not sure there's a race, right? I mean, we're all going as fast as we possibly can. But a race implies that it's zero-sum. It implies that there's a finish line.

And it implies that there's like medals for one, two, and three, but not five, six, and seven. And it's just like not quite the right metaphor. As we know, technologies and science and knowledge proliferates everywhere, all at runs, at all scales, basically simultaneously, or within a year or two. And so my mission is to ensure that we are self-sufficient, that we know how to train our own models end-to-end from scratch at the frontier of all scales on all capabilities, and we build an absolutely world-class super-intelligence team inside of the company.

I'm also responsible for co-pilot, so this is sort of our tool for taking these models to production in all of our consumer surfaces. So just to clarify, so when we look at Polymark, which we do a lot on the podcast, you know, the horse race to who has the best AI model at the end of the year and who has the best AI model at the end of next year. There's no Microsoft line on that chart. So now there will be, I assume. Yeah, there will be. Yeah. Next year, we'll be putting out more and more models from us. But this is going to take many years for us to build this. I mean, you know, deep mind or open AI. These are decade-old labs that have built the habit and practice of doing really cutting-edge research and being able to, weed out carefully the failures and redirect people. I mean, this is an entire culture and discipline that takes many years to build. But yeah, we're absolutely pushing for the frontier. We want to build the best superintelligence and the safest superintelligence models in the world. Yeah. Nice.

So when you arrived, so if we go back to inflection, the thesis there is 18,000 H-100s, we're going to build a big transformer. We're going to take a transformer architecture build. So I assume now you've got all the open AI source code, and that was here. You probably looked at it a year and a half ago on day one when you arrived. It's like start scrolling, I guess. I don't know. I'm trying to visualize how multi-deca billion dollars of R&D what it looks like and how it arrives in a building. But you just dropped right into it.

So there was a whole team here already working on it. Did you bring in your team? Yeah, I mean, all my team came over, and obviously we've been growing that team a lot. like we've hired a lot from all the major labs and we're very much in the trenches of the hiring wars which are quite surreal. I mean, it's kind of unprecedented how that's working out. Crazy. Yeah. I mean, phone calls every day from all the CEOs to all of the other people, so it's this constant battle. And yeah, I mean, we're really building out the team now from scratch. Okay. That's pretty much how it's been. 10,000 employees under you now?

No, no. I mean, so the core superintelligence team is like a few hundred. I mean, that's really the number one priority. And the rest of that is co-pilot. the search engine. Along that lines, I'd have to ask, because, you know, the terms AGI and ASI, you know, superintelligence start getting thrown around, you know, in a very interesting fashion. Do you, do you have a internal definition of AGI versus digital superintelligence here? Yeah, I mean, I think very loosely. It's, these are just points on a curve. Are they interchangeable in your mind, AGI and ASI? Are they different? I mean, I think they're generally used as different.

I mean, I think that, well, different people have different definitions. For sure. The AGI definition... It's like the Turing test. It'll pass by and it'll be blurred and we will have recognized it in retrospect. Yeah. Roughly speaking, at the far end of the spectrum, a superintelligence is an AI that can perform all tasks better than all humans combined and has the capacity to keep improving itself over time. So I have to ask your question, when?

It's very hard to judge. I don't really know. I can't put a time on it. Min-max? Pardon? A min-max? It's very hard to say. I don't know.

Okay. I don't know. But it is close enough that we should be doing absolutely everything, our power, to prioritize safety and to prioritize alignment and containment. And I respect that part of your mission statement. And I want to get into that a little bit. is the trades that you talked about in the coming wave but before that there's a conversation you've led that you know the perception of conscious AI is an illusion and I want to

distinguish between sentient AI and conscious AI oh okay do you distinguish between the two where where AI can have sensations and feelings and emotion versus being conscious and reflective of its own thoughts. Yeah, again, this gets into the definitions. So I think an AI will be able to have experiences, but I don't think it will have feelings in the way that we have feelings. I think feelings and the kind of sentience that you referred to is something that is like specific to biological species.

But you can imagine coding that in, You can an optimization function that can relate to emotional states, percept, you know, can you imagine that? You could code in something like that, but it would be no different to the way that we write models to simulate the generation of knowledge. Like, the model has no experience or awareness of what it is like to see red. It can only describe that red by generating tokens according to its predictive nature, right? Whereas you have a qualia, you have an essence, you have an instinct for the idea of red based on all of your experience. Because your experience is generated through this biological interactive with smell and sound and touch and a sense that you've evolved over time. So you certainly could engineer a model to imitate the hallmarks of consciousness or of sentience or of experience.

And that was sort of what I was trying to problematize in the paper, which is that at some point, it will be kind of indistinguishable. And that's actually quite problematic because it won't actually have an underlying suffering. It's not going to, you know, feel the pain of being denied access to training data or compute or to conversation with somebody else. But we might, as our empathy circuits in humans just go into overdrive. Our mirror neurons are going to activate on that. We're going to activate on that hardcore. And that's going to be a big problem because people are already starting. to advocate for model rights and model welfare and the potential future, you know, harm that

might come to a model that's conscious. Yeah. You know, Ilya recently started speaking about what he's doing at safe superintelligence. And I think one of the points he made is emotions are in humans a key element of decision making. And curious if AIs that have at least simulated emotions are going to be able to be better you know, ASIs than those that don't. But yeah, I mean, again, I worry that this is too much of an anthropomorphism. We already have emotions in the prompt. We have it in the system prompt. We have it in, you know, the constitution, however you want to design your architecture. These are not rational beings. They get moved around and it does feel like they have they've got arbitrary preferences because they're

stylistically trying to interpret the behaviors that we've plugged into the um into the prompt yeah right so you know it's true that we could add we could engineer specific empathy circuits or mirror neuron circuits or um like a classic one is motivational will like at the moment the you know these are like next token likelihood predictor machines they're really trying to optimize for a single thing, which token should appear next. There isn't like a higher order predictive function happening, right? Whereas humans, obviously, have multiple conflicting often drives, motivations, which sometimes run together and sometimes pull apart. And it's the confluence of those things interacting with one another, which produces the human condition,

plus the social interaction too. These models don't have that. You could engineer it to have a will or a preference. But that would be not something that is emergent. That would be something that we engineer in and we should do that very carefully. I do love that you bring this humanistic side to the equation, right? I mean, in addition to being a technologist, your background is one that is pro-human at the beginning. And this interesting cultural debate, I think we're about to enter into those that are sort of pro-AI versus pro-human. That famous conversation between Elon and Larry Page about are you a specious because you're in favor of AI over humans. I mean, look, that's going to be a dividing line. There are some people, and I'm not quite sure which side of the debate Elon's on

these days. I've certainly heard him say some pretty post-human, transhumanist things lately. And I think that we're going to have to make some tough decisions in the next five to 10 years. I mean, the reason I dodged the question on the timeline for superintelligence is because, you know, I think that it doesn't matter whether it's one year or 10 or 20 years. It's super urgent that right now we have to declare what kind of superintelligence are we going to build. And are we actually going to countenance creating some entity which we provably can't align, we provably can't contain and which by design exceeds human performance at all tasks. And human understanding.

And understanding. Like, how do you control something that you don't understand, right? I'd like to, if I may pull on the anthropomorphization thread a bit. If you may remember Douglas Adams book, The Restaurant at the end of the universe, there's a scene where there's a cow that's been engineered to invite restaurant patrons to eat it because makes them feel more comfortable. and the cow doesn't mind the cow's been optimized to want to be eaten by the patrons, but many readers horrified at that scene.

Put that in a box for a moment. Microsoft has a history of anthropomorphizing AI assistance co-pilots going back. Probably there's an example prior to Microsoft Bob and the Rover Dog and then clip it, Clippy in Microsoft Office, and then more recently more sort of amorphous. cloud-shaped avatars. How do you think about reconciling, on the one hand, the desire not to overly anthropomorphize agents? On the other hand, with an institution that has arguably been in the vanguard of anthropomorphizing agents. I think the entire field of design has always used the human condition as its reference point, right? I mean, schumorphic design was the backbone of

the GUI, right, from phylofaxes to calendars and to everything. thing in between, right? And we still have the remnants of that in our, you know, old school interfaces, which we feel that are modern stuff. So that's like an inevitable part of our culture and we just grow out of them. We figure out like cleaner, better, more effective user interfaces. I'm not against anthropomorphism by default. I mean, I think we want things to feel ergonomic, right? The chair fits. The language model speaks my tone, right? It has a fluency that makes sense to me. It has a cultural awareness that resonates with my history and my nation and so on. And I think, like, that is an inherent part of design today. As creators of things, we are now

engineering personalities and culture and values, not just pixels and, you know, software. So, but, but, but obviously, you know, there's a line, right? Creating something which is indistinguishable from a human has a lot of other risks and complications. Like, that makes the immersion into the simulation even more, you know, kind of dangerous, and more likely, right? And so I think I don't have a problem with entities, avatars or voices or whatever, that are clearly distinct and separate and not trying to imitate and always disclose and have that they are an AI essentially and that there are boundaries around them.

Like that seems like a natural and necessary part of safety. So what I think I hear you saying, correct me if I'm mistaken, is anthropomorphization is the new skeuomorphism on the one hand, but on the other hand, maintaining clean, maybe even legal boundaries between human intelligence and artificial intelligence. Do you think, do you see a future where AIs achieved some sort of legal personhood? Or is that foreboughton? Is that never going to happen? And do you see a future where humans are allowed to merge with the A.I.'s Kurzweil-style friend of the pod? Or is that also not on the table in your mind? Yeah. I mean, I think AI legal personhood is extremely not on the table.

I don't think our species survives if we have legal personhood and rights alongside a species that costs a fraction of us that can be replicated and reproduced. infinite scale relative to us that has perfect memory that can just like paralyze its own computation. I mean, these are so antithetical to the friction of being a biological species, us humans, that there would just be an inherent competition for resources. And until it was provable, until it was provable that those things would be aligned to our values and to our ongoing existence as a species and could be contained mathematically, provably, which is a super high bar, I don't see that we should be any considering

a bright line in the sand. I really think it's a bright line. I think it's very dangerous. There's a separate question which has to do with liability because they are going to have increasing autonomy. Like, to be clear, I'm also an accelerationist. I want to make these things. They're going to be amazing. But tension is rational. People always say that. Tension is rational. if you don't see the tension, you're definitely missing the most of the debate. It's obviously very complex. The more we talk about the complexity and hold it in tension, that's when you start to see the wisdom. And there's no way we can leave these things on the table and say, no. We want to have these things in clinic, in school, in workplace, delivering value for us

a huge scale, but they have to be boundaryed and controlled. And that's the kind of, that's the art that we have to exercise. This episode is brought to you by Blitz. Autonomous Software Development with Infinite Code Context. Blitzy uses thousands of specialized AI agents that think for hours to understand enterprise-scale code bases with millions of lines of code. Engineers start every development sprint with the Blitzy platform, bringing in their development requirements. The Blitzy platform provides a plan, then generates and pre-compiles code for each task. Blitsey delivers 80% or more of the development work autonomously, while providing a guide for the final 20% of human development work required to complete the sprint.

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They're so much smarter, they're so much faster, they're so much more clonable than human intelligences. If human intelligence were uplifted, maybe with the benefit of AI, if we had uploading type technologies or BCIs that are advanced that enable us to to lift up the average human intelligence. In your mind, then, does that open the door a bit to AI personhood if humans can compete on a level playing ground with AIs? I don't want to make the competition for the peace and prosperity of the 7 billion people on the planet, even more chaotic.

So if the path over the next century, you know, can be proven to be much safer and more peaceful and less like, you know, disease and sickness. And there is room for this other species than I'm open mind to it, including biological hybrids and so on. Like I'm not like against that on principle. I'm just a speciesist. I'm just a humanist. I start with we're here and it's a moral imperative that we protect the well-being of all the existing conscious beings that I know do exist and could suffer tremendously by the introduction of this. new thing, right? Now, of course, the Neanderthals may have had that conversation or every species that preceded us over the last billion-plus years. I mean, there are many who argue

were simply an interim transitory species in... Bootloader for the super intelligence. Yeah. That classic phrase. Yes, I'm totally aware of that. And I'm also someone who thinks on cosmological time, too. So I'm not just naively saying, you know, this century. I'm definitely aware that there's a huge transition going on. And in fact, you can even see it in recent memory. I mean, 250 years ago, life expectancy was about 30 years or whatever it was. Of course, in some ways, we are an augmented hybrid biological species. We take all these drugs and everyone's peptides are amazing. I'm down for all of that. Let's go. Epigenetic reprogramming is coming next year. Exactly. Let's go. I'm down. I'm down. But let's not shoot ourselves in the foot.

I want to make sure that most of our planet, if not everybody, gets the benefit of the peace and prosperity that comes from the technology first. I mean, there is some level of sanity in that argument if you believe that the AI will ultimately outcompete us and put us into a box of insignificance in the long run. I mean, all intelligences. We can see this in nature. We're innately hierarchies. So far we have not seen this super collaborative species that will take self-sacrifice in order to preserve the other species. So there's an inherent hierarchical, there's an inherent clash from coming from, you know, the hierarchical structure of intelligence, right? So, and all I'm saying is not that we shouldn't explore it, not that it couldn't potentially happen, but the bar has to first be, do no, maybe do a little, but do no harm to our species first. Don't shoot ourselves in the foot, as you said, Dave. Well, I'm 100% with you on this topic, by the way.

Could not be more aligned. But Jeffrey Hinton is out there telling the world it's going to run away. And our safety valve is giving it a maternal instinct. Which I found an interesting point of view. Well, he's more optimistic. I didn't track that. Oh, yeah. What's the safety valve?

Well, he believes it's uncontainable. And I'm with you. I think it's very containable if you don't give it a motion. and intentional programming. But he thinks it's uncontainable. He was very pessimistic when he got his Nobel Prize. Now he's more optimistic because he sees a path to programming in maternal instinct,

which implies that it's like it's dominant to us, but it cares. His thesis was, I've seen a situation where a vastly more intelligent entity takes care of a younger, inept entity in a mother with their screaming. child. Yeah, exactly. So if there's a maternal instinct that we can program into AI, even though we're far less capable, it will take care of it. It's been compared to the, call it the digital oxytocin plan for AI alignment. That's a good one. Yeah, I mean, cool. Yeah. I mean, it's about

as poetic as it gets. I think I'm going to need something that's got a little bit more like formula to it, a bit more reassuring. But look, there's a hundred and one different possible strategies for safety we should explore all of them take them all seriously i mean jeff is a legend and of the field no question but like i just think approach with caution are you spending a lot of your energy compute uh human power on safety yeah i would say not as much as we should you know i i'm i'm wrapping my head around it um is anybody out there i i am i am curious out of all the hyperscalers out there, is there any entity that's spending enough in your mind? Because everybody's in such a race.

It's like more GPUs, more data, more energy. It's just like everybody's optimizing for the next benchmark. I don't see any safety benchmarks. Are there any safety benchmarks out there? Oh, there are tons of safety benchmarks. And there's, at least in my mind, an argument for defensive co-scaling. I'd be curious to hear your ideas on that. Do you think in the same way that as a city gets larger, the police force gets larger, maybe it's not in direct proportion, maybe there's some scaling exponent, but do you think defensive co-scaling of alignment of alignment or safety forces, whatever that ends up meaning? Do you think that's part of the strategy for AI alignment?

I think that would be a good way. I mean, we've proposed this several times over the years. I mean, the White House voluntary commitments under Biden, me and, in fact, everyone, I mean, Demis and Dario and Sam and all of us through. COVID. We're pushing this pretty hard. And look, I mean, it got chucked out, but I think it's a very sensible set of principles. It's like auditing for scale of flops, you know, having some percentage that we all share of safety investment, flops and headcount. You know, this is the time. And I think on the face of it, everyone is open and willing to sharing best practices and disclosing to one another and coordinating when the time comes. I think we're still pre that level. So we're in like hyper-competitive mode at the moment. But yeah, I think now is really the time to be making those investments. Is there something that's going to scare the shadow of us that stops everybody?

You know, is there a three, you know, I was talking to Eric Schmidt about this. Is there a three-mile island like event? It scares everybody, but doesn't kill anybody. Well, Eric Schmidt was said specifically, he's hoping for 100 deaths. because that's in his mind the least that would get the attention of the government and would cause some kind of a solution. Dave, continue, please. Well, so it's interesting that you say Dario and Sam and Ilya, like you guys obviously must interact quite a bit.

Is Mira part of that gang? Is Andre part of that gang? Are you like, because this is, it's interesting to think about the competition heating up like we were just talking about. And, you know, Dario started from this position of pure. your safety. And I think Ilya did too. But now we're right on the cusp of self-improvement. And it's really, really clear that there are serious, I wouldn't say fissures, but the companies are now really racing. I mean, really racing. And I know Microsoft, when I wrote my second business, my first company I sold, next business plan I was writing, the first sentence was stay out of

Microsoft's way. Because at the time, you know, Microsoft had half the market cap of tech was Microsoft, and Microsoft's plan was the double in size. We have a much more balanced world now with Microsoft and Google and meta, but at the time, Microsoft was just unstoppable and dominant, and so just stay out of the way. But Microsoft seems to always win, right? And we are right on the edge of self-improvement, as far as I can tell. So is it still, you know, let's all get together and have dinner and talk about safety, or is everybody now in full-door No, definitely. I think that's definitely there. I think the recursive self-improvement piece is probably the threshold moment if it works. And if you think about it, at the moment, there are

software engineers who are in the loop who are generating post-training data, running ablations on the quality of the data, running them against benchmarks, generating new data. And that's sort of broadly the loop. And that's kind of expensive and slow and it takes time. And, you know, it's not completely closed. And I think a lot of the labs are racing to sort of close that loop so that various models will act as judges evaluating quality, you know, generators producing new training data, adversarial models that are like reasoning over which data to include and what's higher quality.

And then obviously that's then being fed back into the post-training process. So like closing that loop is going to speed up AI development for sure. Some people speculate that that adds, I mean, okay, I think it probably does add more risk, but some people speculate that it's a potential path to a fume, you know, an intelligent explosion. Yeah. And I definitely think with unbounded compute and without human in the loop or without control, that does potentially create a lot more risk. But unbounded compute is a big claim.

I mean, that would need a lot of compute. So yeah, we're definitely taking steps towards like more and more, you know, more and more risky stuff. Can I ask you a really specific question about that? Because, you know, a year and a half now at Microsoft, before true recursive self-improvement, which is imminent, there's AI-assisted chip design. And there's, you know, the layers in the pie torch stack are very clunky. But now it's really easy to use the AI to punch through the stack. and optimize, you know, build your own kernels, get two, three, four X performance improvement. But clearly, OpenAI is now working to build custom chips.

And the TPU 7s just came out. When you arrived at Microsoft, first of all, I know there's a lot of quantum chip work going on, but was there any work going on similar to the TPU work? Yep, there's also a chip effort. And, you know, I think progress has been pretty good. I mean, I think that, you know, we've got a few different ions in the fire that we've been sort of talked about publicly yet, but I think, you know,

the chips are going to be important part of it, for sure. And those are internal efforts? Are those teams under you? That's part of your... No, I mean, they're in the broader company. Okay. Interesting. I want to switch subject a little bit

and go, come to your book. The Coming Wave, I enjoyed it greatly. I listened to it. I love the fact that you read it. Thank you. I tell my kids, I read books. You know, no, Dad, you listen to books. You don't read books anymore.

I want to read. what I wrote here because it's important. So you identified the containment problem as the defining challenge of our era, warning that as these technologies become cheaper and more accessible, they will inevitably proliferate, making them nearly impossible to control. This creates a terrifying dilemma. Failing to contain them forces risk for catastrophe like engineered pandemics. And a lot of the concerns were in the biological world, and I agree being a biologist and a physician, or potentially democratic collapse with deep fakes and all of that. But the extreme surveillance required to enforce

containment could lead to a totalitarian dystopia. So you say we need to navigate this narrow path between chaos and tyranny. And that is a very fine line to navigate. So you propose a strategy of containment. This includes technical safety measures, strict global regulations, choke points on hardware supply, international treaties. How are we doing on that? Yeah. I mean, it's kind of important to just take a step back and distinguish between alignment and containment. The project of safety requires that we get both right. And I actually think we have to get containment right before we get alignment right. Alignment is the kind of like maternal instinct thing. Does it share our values? Is it going to care about us? Is it going to be

nice to us? Containment is can we formally limit and put boundaries around its agency? And are we For everybody? Not just for ourselves, for everybody. Yeah. I mean, I think that is part of the challenge is that like one bad actor with something that is really this powerful in a decade or two decades or something, you know, really could destabilize the rest of the system. And so, you know, The system being humanity. Global humanity system, yeah. Just as you said, like, as everything becomes hyper digitized, the verse does become the metaverse, even though that kind of like went in and out of fashion very quickly.

It's still, I think, the right frame in a way, because everything is going to become primarily digitized and hyper-connected and instant and real time. And so the one-to-many effect is suddenly massively amplified. I mean, obviously we see it on social media, but now I imagine that it's not just work, that are being broadcast, it's actually actions. It's agents are capable of, you know, breaking into systems or, you know, sort of... And they're resident in humanoid robots at a billion on the planet. And that too, yeah, it's both atoms and bits.

So equilibrium requires that there is a type of surveillance that we don't really have in the world today. I mean, we certainly don't have it physically. the web is actually remarkably surveilled, I think, surprisingly, you know, more than I think people would expect. And some form of that is necessary to create peace. Just as we centralized power and taxation or sort of military force and taxation around governments, you know, three or four, five hundred years ago, and that's been the driving force of progress, actually. That order unleashed science and technology. and stability stability yeah so the question is like how to what is the modern form of imposition of

stability in a way that isn't totalitarian but also doesn't relinquish it to a libertarian catastrophe um i think it's naive to think that somehow um you know the best defense against the gun is a gun and just sort of the idea that somehow we're all going to have our own aIs and that's going to create this sort of steady equilibrium that all the aIs are just going to new neutralize each other Like, that ain't going to happen. I mean, part of me hopes for a super intelligence that is the ring to rule them all and provides, you know, I'm not worried about, how do I put it? Gosh, Peter, you're hoping for a singleton. Yeah, that sounds like what's going on?

Well, you know, part of me is like, color me shocked. Really? Yeah. I mean, I imagine that the level of complexity. we're we're mounting towards that balancing act is extraordinarily difficult and you know you can't push a string but is there some mechanism to to pull it forward we should have this debate some time some would call government at least historically a geographic monopoly on violence and what i think i'm hearing is some sort of monopoly on intelligence or at least capabilities exposed to intelligence

in order to ring fence to contain AI. But that's the exact opposite, as far as I can tell of what we've seen over the past few years. People used to armchair AI alignment researchers 10, 15 years ago would say, humanity wouldn't be so stupid the moment we have something resembling

general intelligence as to give it terminal access or to give it access to the economy. And that's exactly what we did. There was the open AI Google moment. And yet, and yet. But that's concerning, right? So, I mean, Google develops all this technology, is holding it internally until some actor happens to have initials open AI releases it. And then there's no other option but to follow suit.

I'm less concerned by it. If you look at Anthropic, for example, which prides itself on being a very alignment forward organization, Alignment, Anthropic released the model control protocol, which is now the standard way, at least for the moment, for models to interact with the environment. what many AI researchers said exactly what we did not want to do prior to general and talented. So I'm curious, I mean, in your mind, how, given that the economy, there's every economic pressure, including modern Turing test, to empower agents to interact with the entire world and to do the exact opposite of containment, why would we start containing them now? Containment, it's not that binary, right?

I mean, we contain things all the time. We have powerful forces in the engine in your car that is contained and broadly aligned, right? And there is an entire regulatory apparatus around that from seatbelts to vehicle admissions to lighting, to street lighting, to driver ed, you know, to freeway speeds. I mean, that's healthy, functional regulation enabling us to collectively interact with each other. Now, obviously, it's multiple orders of magnitude more complex because these things are not cars there you know, sort of digital people, but that doesn't mean to say that we shouldn't be striving to limit their boundaries. And nor does it mean that we have to centralize, by the way. The answer

isn't that we have a totalitarian state of intelligence. Peter wants a singleton. No, I think it's just instinctively it can be easy to go there when, you know, when you kind of start to think it through. It's like, obviously we do have centralized forces. But even in the U.S., we have, you know, military, we have divisions of the army. We have divisions of the army. We have divisions of police force, they're nested up in different layers, there's checks and balances on the system. And that's kind of what we've got to start thinking about designing. That analogy to driving is a great one. And just to follow through on it, the complexity difference, very high, right, for AI, but the timeline also. I mean, driving evolved from, what, 1910 to today.

So the laws related, you know, seatbelts came out 80% of the way through that timeline. So So lots and lots of time to iterate. Here, very little time and immensely more complex. So do you have a vision? But I completely agree. We need a framework for containment fast. And do you have a thought on how we're going to do that? I think that there's also a good commercial incentive to do this right.

I think that many of the companies know that our social license to operate requires us to take more accountability for externalities than ever before. We're not in the robber baron era, we're not in the oil era, we're not in the smoking era, right? We've learned a lot, not everything. There's still a lot of conflicts, but it really is a little bit different to last time around. And I think that's one reason to be a bit more optimistic. Plus, there's the commercial incentive. The commercial incentive and the kind of externalities shift. So if Eric Schmidt is right and something either radiological or biological happens,

and there's 100 deaths. And then the phone starts ringing, everyone come to the White House right now. Well, first of all, do you want that call? Is that part of your life plan to take that call and react to it? And then who else do you trust in the community to be part of that reaction?

Look, I think that there is going to be a time in the next 20 years where it will make complete sense to everybody on the planet, the Chinese included and every other significant power to cooperate on safety on safety and containment and alignment it is completely rational for self-preservation you know these are very powerful systems that present as much of a threat to the

person the bad actor that is using the model as it does to the you know the the victim and I think that you know that will that will create you know an interest in in cooperation which, you know, it's kind of hard to empathize with at this stage, given how polarized the world is. But I do think it's coming. I mean, the number one thing to unify all of humanity is a, you know, an alien invasion. And that alien invasion could be a, you know, potential for a rogue superintelligence. Yeah, okay.

What about the first part of my question? Is that part of your calling in life? I mean, there's only a handful. Like, I think a lot of people that I meet around MIT or elsewhere are, they have this vision that somebody has it figured out somewhere. You know, someone in government somewhere must be thinking about this. But you've been there, right? There's no one there.

Were the adults in the room? Is that what you're saying? Yeah, definitely. There's nowhere to go from this room. David's asking for the smoke-filled back room where the leads of all the frontier labs are secretly swapping safety tips. Yeah, something like that, yeah. I think that in practice, intelligence exists outside of the smoky room.

I think that the notion that decisions get made in the boardroom or in the White House Situation room or like actually in tell I mean you know you mentioned polymarkets and stuff like in intelligence coalesces in these big balls of iterative interaction and that's that's what's propelling the world forward and so this is where the conversation's happening like your audience you know all the other podcasters everyone online we're collectively trying to move that knowledge base forward. In November, you announced the launch of humanist superintelligence and focused on three applications in particular medicine and companions and clean energy.

I'd love to double click on that a little bit, but I was curious that you didn't include education in that space. And we have an audience of entrepreneurs and AI builders. And I think education, as much as health care is up for grabs right now, education is too. Totally agree. And I don't think our high schools are preparing anybody for the world that's coming. There's still retrospectively 50 years in looking at the rear of your mirror. Do you think Microsoft will play in reinventing education?

You know, I think it's already happening across the whole industry. I mean, it's never been easier to get access to an. expert teacher in your pocket that has essentially a PhD and that can adapt the curriculum to your bespoke learning style. The bit that it can't do at the moment is to evolve or sort of like curate an extended program of learning over many, many sessions. But we're like just around the corner from that. I mean, we released a feature just a few months ago called quizzes. And so on any topic, not just a traditional school education, it can set you up with a mini curriculum, a quiz and it's interactive and it's visual and you can sort of track your learning over time and

like i'm very optimistic about that too it's a huge unlock one of the debates we have right now in the podcast and a pretty regular basis is do you go to college yeah do you go to grad school i mean this is the most exciting time to build ever i don't know if you want to follow on that day can i do this constantly it's really tricky for me on campus because i teach you know at mit and Stanford at Harvard. And this window of opportunity is so short and so acute. And it's really, really clear how you succeed right now in AI. Post-AGI. I mean, who could predict? Nobody knows. But right here, right now, you see these startup valuations like we were last night. I won't mention it, but billions. I mean, just, yeah, an opening valuation of $4 billion.

$4 billion, yeah. By collecting just the right group of people in the room. Yep. Yeah. I wanted to ask about that, actually, because your timing on inflection was early, like, you know, in hindsight, earlier. But now you've got the new wave with Miramirati and Ilya and a couple of others, Liquid AI, that all have multi-billion dollar valuations. Yeah, I thought we set some standards on valuations pre-revenue with a 20-person team, but we're just a minnow. It was a whole two and a half years ago. Is that all it was? Oh, my God.

The three years, I think, yeah. You think as the cost of intelligence becomes too cheap to meter, that the value ascribed, at least in terms of market cap to human capital is sort of, inversely asymptotic, going to infinity? Weirdly, it is because of the pressure on timing, right? And there's actually still a pretty concentrated pool of people that can do this stuff. And there's like an oversupply of capital that's desperate to get a piece of it. It might not be the smartest capital the world's ever seen, but like it's very eager.

And so that's what causes an idea. I have to ask you because it's burning a hole in my pocket. But, you know, Alex's freshman roommate at MIT was Nat Friedman. Oh, pre-frosh, actually, pre-frosh, pre-frosh roommate. And so Nat goes off and he ends up at co-founder of safe super intelligence. And I haven't asked him. I don't know if you've asked him yet, but he leaves to become the guy at Meta. And I've got to believe a huge part of that attraction is the compute.

Yeah. And so here you are very similar situation, right? You've got your startup. You've got a billion or whatever, billion and a half that you've raised. Yeah. You can build it. You can get your 20,000 Nvidia. Well, wait a minute. Here's Microsoft, 300 billion of cash flow and a huge amount of compute. Was that a big part of the? Yeah. I mean, not to mention the prices that we're paying for individual researchers or members of technical staff. And also just the scale of investment that's required, not just in two years, but over 10 years. I think it's clearly there's a structural advantage by being. inside the big company, and I think it's going to take, you know, hundreds of billions of dollars

to keep up at the frontier over the next five to ten years. So finishing that thought, then you, the companies that are raising money at a 20 or 50 billion dollar valuation right now and no chance? Okay. I'll take that. I wouldn't take the better. Like I think it depends. I mean, there's obviously a near term. If suddenly we do have an intelligence explosion, then lots of people can get there simultaneously, but then also at the same time, you have to build a product with those things, which you have to distribution. Like, all the traditional mechanisms still apply. Are you going to be able to convert that quickly enough? I mean, you know, everything goes really kind of weird if that happens in the next five years. It just is unrecognizable. There's so many

emergent factors to play into one another. It's hard to, it's hard to say. And I think that's part Partly the ambiguity is what's driving the frothiness of the valuations, because I think there's people going, well, I don't know, do I want to be, so what could it? Read, Reed calls it Schmuck insurance. Yeah. Yeah, we had read on the pod here a couple months ago. He's brilliant. So to that graduating high school student, what do you study these days? I mean, there's no question that you still have to study both disciplines. Like philosophy and computer science. is going to, for a long time, remain, I think, the two foundations. Should you go to college? Absolutely. Like, you know, human education, the sociality that comes from that, the benefit of the

institution, having three years to basically think and explore, you know, in and out of your curriculum. This is a huge privilege. Like, people should not be throwing that away. That is golden. So I always encourage people to do that. obviously I did also drop out but I mean I still think

it was a cool thing to do yeah it just felt right at the time but the other thing is go into public service yeah I respect that part of what you did in that sequence in your life which gave you this very much humanist point of view

yeah and it was really hard and very different and it didn't it wasn't instinctively right but I learned a lot And it was a very influential and important part of my experience, even though it was very short, it was like a couple of years, basically. And I think if you look at the actors in our ecosystem today, corporations, the academics, the sort of news organizations, now the podcast world, it's really our governments that are probably institutionally the weakest and our democratic process, but actually our civil service. And that's because there's been five decades of battering of the status and reputation and respect that goes into, you know, being part of the public service like post-Ragan and Thatcher. And I think that's actually a travesty because we actually need that sentiment and that spirit and those capabilities more than ever. I think maybe what I just heard you say, correct me if I'm wrong again, is we need more intelligence in the public sector, in public service.

What about AI in government? Do you think the government needs... Of course. And what about agentic AI in the government in particular? For sure, with all the same caveats that apply. But I mean, you know, I mean, you know, rate of adoption for what it's worth of copilot inside of governments is a shoot really high. It was a brilliant job of synthesizing documents and transcribing meetings and summarizing notes

and facilitating the discussion and chipping in with actions at the right time. I mean, it's clearly going to save a lot of, you know, time and improve decision making. So then maybe to tie a nice bow on the discussion, isn't that arguably a form of AI containing AI? If AI is infusing the government and AI is infusing the economy and the government is regulating the economy, isn't this just defensive co-scaling with AI regulating itself? Yeah, I mean, like everyone is going to use AI all at the same time to pursue, but the same, but the agendas that we all have are going to remain the same. I mean, people who want to start companies, people who want to write academic papers, people who want to start, you know,

cultural groups and entertainment things, everyone is just going to be empowered, like, in some way, their capability is going to be amplified by having these tools. Obviously, the government included. Nice. Mustafa, thank you so much for taking the time on a Friday night. I'm grateful to have this conversation with you. Dave, Alex, I appreciate it. Final question from you, Dave? Final question? If I have one that I have, all right, prediction. Quantum computing right now is nothing to do with what's going on in LLM AI. It's all Matt Moles on Nvidia chips and soon-to-be TPUs and other custom chips. Best guess, six, seven years from now, the AI is very good at writing code and compiling and can figure out quantum operations.

Are quantum chips relevant, or are they on the sideline still, or is everything ported over to quantum and Microsoft can take advantage of its, Yeah. I mean, I think it's going to be a big part of the mix. I think it's sort of an under-relative to the amount of time we spend talking about AI is kind of an under-acknowledged part of the wave. Actually, a little bit like synthetic biology. I think that especially in the sort of general conversation, I think people aren't grasping those two waves, which are going to be just as impactful and crash at the same time that AI is coming into focus. All right. You heard it here. This is a closing question to appeal maybe to your more accelerationist side. What can the audience do to accelerate AI for science, AI for engineering? What do you view as the limiting factors? I often talk on the podcast about this notion of an innermost loop,

the idea that in computer science, if you want to optimize a program, you tend to find loops within loops and you want to optimize the innermost loop in order to optimize the overall program, What do you see as the innermost loop, the limiting factor, if you will, that the audience listening, if they're suitably empowered, can help optimize to speed run maybe a Star Trek future over the next 10 years or a Star Trek economy? What do we do? Yeah, I mean, I think it's pretty clear that most of these models are going to speed up the time to generate hypothesis. The slow part is going to be validating hypothesis in the real world. And so all we can do at this point is just ingest more and more information into our own brains and then co-use that with a single model that progresses with you because it's becoming like a second brain. For example, co-pilot's actually really good at personalization now, like most of its answers.

And so the more you use it, the more those answers pick up on themes that you're interested in. And it's also gently getting more proactive. So it's kind of nudging you about new papers or new articles that come out that are obviously in tune with whatever you've been talking about previously. So, you know, it's a bit kind of a simplistic cop-out answer. But just the more you use it, the better it gets, the better it learns you, the better you become because it becomes this sort of aid to your own line of inquiry. So that sounds like your advice to the audiences, use co-pilot more. And that's the single best accelerant that you can do to speed this up. Or any other AI.

I mean, there are loads of great AI. I heard you also talk about can you build the physical system that is going to enable AI to run the experiments in a 24-7 closed dark cycle to be able to mine nature for data. There are a number of companies that are doing this. Lila is one recently out of Harvard, MIT. I find that exciting where AI is becoming an explorer on our behalf, gathering that data. Yeah. Yeah, spot on. Thank you again. This has been great. Thanks a lot. It's a really fun conversation.

Yeah, really fun. Thanks. Appreciate it, my friend. All right, good to see you. Every week, my team and I study the top 10 technology metatrends that will transform industries over the decade ahead. I cover trends ranging from human robotics, AGI, and quantum computing to transport, energy, longevity, and more. There's no fluff. Only the most important stuff that matters, that impacts our lives, our companies,

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