[0:00:00] 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 Saliman 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
[0:00:25] Is from a world of operating systems, search engines, 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 is like not quite the right metaphor. As we know, technologies and science and knowledge proliferate everywhere, all at once, at all scales, basically simultaneously. Are you spending a lot of your energy computing?
[0:00:55] Human power on safety. Yeah, no, I mean... Everybody, welcome to Moonshots. I'm here with DB2 and AWG and Mustafa Suleiman, the co-founder of DeepMind, 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.
[0:01:29] And it's hard to believe that Microsoft is 50 years old. 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 from 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.
[0:01:57] 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 within the Microsoft 365 ecosystem.
[0:02:26] Is the goal in the next couple of years maximum users? Is it data centers? Is it cloud? How do you think of what you're optimizing for? I mean, it's a good question. So we are on any given day a $4 trillion company with almost $300 billion of revenue. That's incredible. It's just...
[0:02:50] Surreal and very, 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 CPU and AI accelerators of all kinds and enabling that to be available to the market. APIs on top of that.
[0:03:17] 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 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.
[0:03:44] 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.
[0:04:07] Using assistive coding agents to both debug their code and also generate large amounts of code, just as we used 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.
[0:04:36] is completely focused on. After seeing five decades worth of transitions, I think the company is super alert to making sure that we're best placed to manage this one. Do you see yourself providing 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.
[0:05:03] I think realistically- 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, Microsoft is really a platform of platforms. I mean, 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.
[0:05:25] And so we will always have masses of APIs that turbocharge 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.
[0:05:50] Trust. I mean, that is actually in many ways the strength of Microsoft. And that's one of the things that attracted 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...
[0:06:17] 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 deliberate, customer-focused patience. There's not the same anxiety and somewhat sclerotic nature that comes with being an insurgent.
[0:06:47] There's some downsides to our position. 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. We're seeing in this hyperscaler war, I mean, literally, a week by week, everybody outdoing each other in this insane period of everybody coming out with the new benchmarks. Do you miss...
[0:07:14] 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.
[0:07:36] Really? There were some amazing papers. 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 I've always taken a multi-decade view.
[0:07:56] 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?
[0:08:19] Well, there were more than three of us, but I mean, for the decade between 2010 and 2012, sorry, 2020, I mean, there were just like so few.
[0:08:28] Successful commercial applications of deep learning. I mean, there were plenty behind the scenes. There was image recognition, improvements to search. Not a huge market for- Commercial, yeah. Play and go, not a huge market, 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-
[0:08:57] We hit an inflection point. And I think that is very, very different to the grind of training tiny models with very little data and very small clusters back in the 2010s. Every week, my team and I study the top 10 technology meta trends that will transform industries over the decade ahead. I cover trends ranging from humanoid robotics, AGI, and quantum computing to transport, energy, longevity, and more. There's no fluff.
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[0:09:53] Why it matters and how you can benefit from it. To subscribe for free, go to dmandus.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 ImageNet, 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.
[0:10:22] 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 agents. 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...
[0:10:49] 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. It's true. That many of the field now were at 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 a surreal moment, actually. It's like a Sylmar right before it all happened. Yeah.
[0:11:19] Yeah, totally. And 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.
[0:11:47] 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.
[0:12:02] An intelligent knowledge worker or a project manager or 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 what would be the first model to make a million dollars? Given, as I recall, $100,000 in starting capital? That's right.
[0:12:30] Yeah. 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? Right, yeah. I mean, it kind of has been passed. No one's really done a big, you know, alpha... Alex mentions it all the time. The Lobner Silver Prize wound down before we breezed past Turing. Yeah.
[0:12:58] And no one celebrated it. Where was the big Kasparov 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...
[0:13:13] 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? We're spoiled. Where's my Microsoft Logner prize for the modern Turing test? Right, exactly. Yeah, you know, like someone said to me earlier on, but you know, this AI thing, it's still in its infancy, isn't it? And I'm like, man, if this is infancy, wow.
[0:13:37] I can talk to my computer fluently in real time. Star Trek is here. Yeah, exactly. 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.
[0:14:00] Can we get together again after the modern Turing test has been passed and just to celebrate, recognize it? And drink virtual glasses again? Absolutely. Hopefully we can pop a champagne or something. I think we should. We'll have an optimist pop the cork for us or something. Yeah, exactly. Exactly. Dave? I want to flesh out that backstory a little bit more too. It's such a cool story. But I remember really clearly after DeepMind got acquired by Google. What was the price tag on that deal? It was like half a billion dollars. Yeah, $650. $650. What a deal.
[0:14:29] 650. What year was that? 2014. 2014. I remember reading maybe a year or two later that...
[0:14:36] Google justifies deal by having DeepMind 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. The data center thing was pretty cool. We did actually reduce the cost of cooling the Google data center fleet by 40%. It's so funny because I read it at the time and I was like...
[0:15:00] 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. That's it. At the time. That's right. But you were talking about the flat part of the exponential. And you think about, OK, all of this R&D, which is so close to becoming AGI.
[0:15:22] is tuning the air conditioning. 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 general purpose nature of the models. And I think like...
[0:15:50] It's so easy to get caught up thinking five years is a long time. It's like a blink of an eye. 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 do. And I think historically, before digitization, we had much more of a natural intuition for...
[0:16:14] The movement of the landscape and the seasons and 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 a lot of people, including this group, that are operating around the clock every day just because when we do a Moonshots podcast week to week just to celebrate and talk about what's just happened.
[0:16:41] It's insane on a week-by-week basis what's going on. Yeah. And Peter's always saying people are very, very bad at exponentials. 100,000 years of evolution has us predicting tomorrow will be like yesterday. But you're one of the few people who, 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.
[0:17:10] And so you're one of the few people who, you know, having seen it before can say, yeah, here it 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 doubling, you know, like that's the bit is that when you're like, oh my God, this, like, I remember this.
[0:17:37] The MNIST image generation thing. For sure. I worked on that. Generative models. 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 DeepMind, Dan Bistra, this like awesome Dutch guy out of EPFL.
[0:18:04] I was like, man, that is amazing. It's learned something about the idea of seven. It's got a concept of seven. How cool is that? I got the highest score on MNIST ever in 1991 when it first came out.
[0:18:30] Three years old, right? Yeah, that's nine. Nine years old, okay. Yeah, and actually that's the same data set that's now in PyTorch that people benchmark off of. Pretty crazy. It's incredible. How often are you surprised by what you're seeing? I mean, how often is there like a Move 37, you know, sort of like aha moment? Is it happening more frequently? I was absolutely blown away by the first versions of Lambda at Google.
[0:19:00] This was like maybe 12 people working on it, led by Noam Chazir and Daniel DeFritis and Kwok Lee. And I got involved later, maybe three or four or five months after they'd 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, they'd produce an answer and have a prompt and blah, blah, blah. But they were really the first to push it for conversation and dialogue.
[0:19:28] And just seeing the kind of emergent behaviors that arise in yourself, like things that you didn't even think to ask because you know there'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 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 Noam left.
[0:19:58] And David Luan left to do Adept. And we were all like, okay, this is the moment. And so 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, going back to your earlier days, did you anticipate the kinds of capabilities that have resulted?
[0:20:25] 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 really awesome people on an LP.
[0:20:50] Deep learning paper at DeepMind 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 the way the models now work.
[0:21:12] And, you know, it was like a pretty big contribution. It was a good, well-cited 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 like hindsight bias and say, well, it was all very predictable, but...
[0:21:35] 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.
[0:21:51] Yeah, we'd love to maybe pull on that theme a bit. So you mentioned how surprising your generative seven 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.
[0:22:14] 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 US dollar return on investment. I'm curious about the next surprises to come. AI for science. Microsoft Research has an AI for science initiative.
[0:22:35] Do you have timelines in your mind for AI solving math, which we're seeing a whole bunch of startups right now tear through Erdős 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...
[0:22:57] 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 a logical reasoning path, and then can basically apply that to many, many other domains.
[0:23:22] 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...
[0:23:41] Mathematical theorem solving or new scientific challenges because that's basically what humans do all the time. We sort of combine 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.
[0:24:04] 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 10x-ing of return on investment? It's going to be harder because I think a lot of the training data, if you like,
[0:24:29] 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...
[0:24:51] You can have reinforcement learning in that category. Yeah, where a human is participating in steering the reinforcement learning trajectory. In a novel domain where it really is inventing completely new knowledge.
[0:25:07] That's kind of more happening in a very abstract sort of vector space. It's unclear yet how the human is going to intervene in the theorem solving problem. Obviously, everyone's working on this, particularly in biology, synthetic materials, and stuff like that. It's already giving humans a better intuition for where in the search space to look for new hypotheses for drugs, for example, or for materials. 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 don't
[0:25:36] We actually ran the experiment. You know, we pipetted 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 at light speed.
[0:26:05] 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, it's how-
[0:26:34] How cheap it is, how accessible it is. 100%. That's 1,000x over two years. So can I do that again? Or was that a one-time gift? I think it's like 100x. The inference cost has come down. A single token inference cost I think has come down 100x in the last two years. There have been competing estimates. Some estimates measure intelligence per token per dollar. There's an estimate that it's 40x year over year, but that's for certain weight classes of models. I've seen 1,000x for some.
[0:27:02] Classes of models. It's craziness. 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 so that like when we founded Inflection, you know, and this was like maybe nine months or maybe a year before ChatGBT was released.
[0:27:28] Yeah, we started doing fundraising a year before ChatGPT was released. We basically raised a billion and a half dollars with a 25-person team to build what at the time was the largest H100 cluster with Nvidia and CoreWeave. We were CoreWeave's first AI customer. Interesting. And they were previously in crypto, and we were like their first AI customers working with them to build our data centers. And obviously, Nvidia got behind us. I think we built...
[0:27:57] Cluster at the time was about 15,000 H100s growing to 22,000. And then obviously that year, ChatGPT came out and a few months around that time, Lama came out. And so we were like, oh my God. Our entire capital base of our company has just been undermined by the fact that open source.
[0:28:23] 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 OpenAI as an API and all the other APIs. And so then they had a much, much lower 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.
[0:28:51] Demodernization, 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.
[0:29:11] And obviously, that's going to have massive labor displacement effects. But it's also going to have a weirdly deflationary effect. Because 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 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.
[0:29:41] Which, by the way, is what we started to talk about a little bit earlier. I mean, I posit that in the long term, there's an extraordinary future for humanity, right? Where access to food, water, energy, healthcare, education is accessible to every man, woman, and child. And it's the shorter term that is challenging, right? The two to seven year timeframe. Does that fit your model too? Yeah. The short term, I think, is going to be quite unstable.
[0:30:10] The medium to longer term, it's pretty clear that these models are already world class at diagnostics. 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...
[0:30:27] Try and take a set of rare conditions from the New England Journal of Medicine, rare cases that can't be easily diagnosed, that the best experts do a kind of weak job on. And it's like four times more accurate, roughly. It's about 2x less the cost in terms of unnecessary testing. There's a study that came out of Harvard and Stanford looking at, in this case, it was GPT-4.
[0:30:56] A physician by themselves, a physician with GPT-4, and GPT-4 by itself. And it was incredible that if you left the AI alone, it was far more accurate in diagnostics than the human. We're biased in our...
[0:31:09] And 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?
[0:31:39] Just a year and a half. Year and a half. So you're, but you're, you feel like you're part of the, you're indoctrinated. So what's the, what's the mandate from Satya? Is it win AGI or is it be self-sufficient or, or what is the, what's the target? I don't think there's really a winning of AGI. I think this is a misframing 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.
[0:32:08] 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 once, at all scales, basically simultaneously or within a year or two. And so my mission is to ensure that we are self-sufficient.
[0:32:34] 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 superintelligence team inside of the company. I'm also responsible for Copilot. So this is sort of our tool for taking these models to production in all of our consumer surfaces.
[0:32:52] So just to clarify, so when we look at Polymarket, 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. Right. So now there will be, I assume. Yeah, there will be. Yeah. Next year.
[0:33:10] 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, DeepMind or OpenAI, 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.
[0:33:38] So when you arrived, so if we go back to inflection, the thesis there is 18,000 H100s. 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 OpenAI 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.
[0:34:07] What it looks like and how it arrives in a building that 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. Yeah, I mean, phone calls every day from all the CEOs to all of the other people. So it's this constant battle.
[0:34:35] And yeah, I mean, we're really building out the team now from scratch. And that's pretty much how it's been. 10,000 employees under you now? No, no. I mean, so the core super intelligence team is like a few hundred. I mean, that's really the number one priority. And the rest of that is Copilot, the search engine. Along that line, I just have to ask, because the terms AGI and ASI, super intelligence, start getting thrown around in a very interesting fashion. Do you...
[0:35:03] Do you have a internal definition of AGI versus digital superintelligence here? Yeah, I mean, I think very loosely. These are just points on a curve. Are they interchangeable in your mind, AGI and ASI, or 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, it'll be blurred, and we'll have recognized it in retrospect.
[0:35:33] Yeah. Roughly speaking, at the far end of the spectrum, a super intelligence 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.
[0:36:01] I don't know. But it is close enough that we should be doing absolutely everything in 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. The trades that you talked about in the coming wave. But before that, there's a conversation you've led that, you know, the...
[0:36:27] 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 AI can have sensations and feelings and emotions versus being conscious and reflective of its own thoughts? Yeah, again, this gets into the definitions. So I think...
[0:36:57] 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 specific to biological species. But you can imagine coding that in an optimization function that can relate to...
[0:37:21] 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. 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...
[0:37:47] 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.
[0:38:07] 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 feel the pain of being denied access to training data or compute or to conversation with somebody else. But we might, as our...
[0:38:32] 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 harm that might come to a model that's conscious. You know, Ilya recently started speaking about what he's doing at Safe Superintelligence. And I think one of the points he made is,
[0:39:02] 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 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.
[0:39:27] We have it in the system prompt. We have it in the constitution, however you want to design your architecture. These are not rational beings. They get moved around and it does feel like they've got arbitrary preferences because they're stylistically trying to interpret the behaviors that we've plugged into the prompt. It's true that we could engineer specific empathy circuits or mirror neuron circuits.
[0:39:56] Like a classic one is motivation or will. Like at the moment, these are like...
[0:40:02] 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.
[0:40:30] 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 it's interesting.
[0:40:57] 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 speciesist? Because you're in favor of AI over humans.
[0:41:14] 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 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.
[0:41:43] 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?
[0:42:03] I'd like to, if I may, pull on the anthropomorphization thread a bit. 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 it makes them feel more comfortable. And the cow doesn't mind. The cow has 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.
[0:42:32] Microsoft has a history of anthropomorphizing AI assistants, 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?
[0:43:01] 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, skeuomorphic design was the backbone of the GUI, right? From filofaxes to calendars and everything in between, right? And we still have the remnants of that in our...
[0:43:25] Old school interfaces, which we feel that are modern stuff. That's an inevitable part of our culture, and we just grow out of them. We figure out cleaner, better, more effective user interfaces. I'm not against anthropomorphism by default.
[0:43:42] I think we want things to feel ergonomic. The chair fits. The language model speaks my tone. 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 that is an inherent part of design today. As creators of things, we are now...
[0:44:05] Engineering personalities and culture and values, not just pixels and software. But obviously, there's a line. Creating something which is indistinguishable from a human has a lot of other risks and complications. That makes the immersion into the simulation even more dangerous and more likely.
[0:44:33] 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 that they are an AI essentially and that there are boundaries around them. 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
[0:45:02] Yeah, I mean, I think AI legal personhood is extremely not on the table.
[0:45:30] 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 at infinite scale relative to us, that has perfect memory, that can just paralyze its own computation.
[0:45:55] 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 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.
[0:46:20] I don't see that we should be considering giving legal rights. 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. To be clear, I'm also an accelerationist.
[0:46:36] I'm hearing real tension there.
[0:46:55] And there's no way we can leave these things on the table and say, no, like we want to have these things in clinic, in school, in workplace, delivering value for us a huge scale, but they have to be boundaried and controlled. And that's the kind of, that's the art that we have to exercise.
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[0:48:06] Ready to 5x your engineering velocity? Visit blitzy.com to schedule a demo and start building with Blitzy today. It sounds though, if I may, the primary rationale that I'm hearing for why not.
[0:48:22] AI personhood has to do with the inadequacies of the human form as currently constructed. I heard you say, well, they'll outrace humans. 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 lift up the average human intelligence, in your mind then,
[0:48:48] 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 can be proven to be much safer and more peaceful and less disease and sickness,
[0:49:12] And there is room for this other species, then I'm open-minded to it, including biological hybrids and so on. I'm not against that on principle. I'm just a speciesist. I'm just a humanist. I start with, we're here.
[0:49:28] 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. 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 we're simply an interim transitory species in...
[0:49:55] Bootloader for the super intelligence. 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, right? We take all these drugs and everyone's peptide.
[0:50:24] 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. I mean, there is some level of sanity in that argument, if you believe.
[0:50:50] That the AI will ultimately out-compete 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 hierarchical.
[0:51:07] So far, we have not seen this super collaborative species that will take self-sacrifice in order to preserve the other species. There's an inherent clash coming from the hierarchical structure of intelligence. 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.
[0:51:34] 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 Geoffrey 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. What does that mean? I didn't track that. What's the safety valve? He believes it's uncontainable.
[0:52:00] And I'm with you. I think it's very containable if you don't give it emotional 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 dominant to us, but it cares. His thesis was, I've seen a situation where a vastly more intelligent entity takes care
[0:52:29] 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 us. It's been compared to the, call it the digital oxytocin plan for AI alignment. I like that. That's a good one. Yeah, I mean, cool.
[0:52:53] 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 formula to it, a bit more reassuring. But look, there's 101 different possible strategies for safety. We should explore all of them, take them all seriously. I mean, Jeff is a legend of the field, no question. But I just think approach with caution. Are you spending a lot of your energy, compute, human power on safety? Yeah, I would say not as much as we should.
[0:53:22] I'm wrapping my head around it. Is anybody out there, 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.
[0:53:48] 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 forces or safety forces, whatever that ends up meaning, do you think that's?
[0:54:16] Part of the strategy for AI line? 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 that me and, in fact, everyone, I mean, Demis and Dario and Sam and all of us through COVID were 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.
[0:54:38] Having some percentage that we all share of safety investment, flops and headcount. 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.
[0:55:05] Is there something that's going to scare the shit out of us that stops everybody? I was talking to Eric Schmidt about this. Is there a three-mile island-like event that scares everybody but doesn't kill anybody? Eric Schmidt 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.
[0:55:30] 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 safety. And I think Ilya did too. But now we're right on the cusp of self-improvement.
[0:55:58] 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, Microsoft had half the market cap of tech was Microsoft.
[0:56:24] And Microsoft's plan was to 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.
[0:56:45] Is it still, you know, let's all get together and have dinner and talk about safety? Or is everybody now in full bore competition? 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.
[0:57:11] Running them against benchmarks, generating new data. And that's sort of broadly the loop.
[0:57:17] And that's kind of expensive and slow and it takes time and 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, 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.
[0:57:47] Closing that loop is going to speed up AI development for sure.
[0:58:04] 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 more and more risky stuff. Can I ask you a really specific question about that? Because you did a year and a half now at Microsoft.
[0:58:29] Before true recursive self-improvement, which is imminent, there's AI-assisted chip design. And the layers in the PyTorch stack are very clunky. But now it's really easy to use the AI to punch through the stack and optimize, build your own kernels, get 2, 3, 4x performance improvement. But clearly, OpenAI is now working to build custom chips. And the TPU7s just came out.
[0:58:58] 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 irons in the fire that we haven't sort of talked about publicly yet, but I think, you know, the chips are going to be an important part of it, for sure. Those are internal efforts. Are those teams under you? That's part of your...
[0:59:26] No, I mean, they're in the broader company. Okay, interesting. I want to switch subject a little bit and 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. 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.
[0:59:53] 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 your concerns were in the biological world. And I agree, being a biologist and a physician.
[1:00:21] 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...
[1:00:49] Regulations, choke points on hardware supply, international treaties. How are we doing on that? 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.
[1:01:10] 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. I mean, I think that is part of the challenge is that like-
[1:01:35] One bad actor with something that is really this powerful in a decade or two decades or something really could destabilize the rest of the system. The system being humanity. Global humanity system, yeah. Just as you said, as everything becomes hyper-digitized, the verse does become the metaverse, even though that kind of went in and out of fashion very quickly.
[1:01:58] I think the right frame in a way, because everything is going to become primarily digitized and hyper-connected and instant and real-time. The one-to-many effect is suddenly massively amplified. Obviously, we see it on social media, but now imagine that it's not just words that are being broadcast, it's actually actions. Agents are capable of breaking into systems.
[1:02:24] 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, more than I think people would expect.
[1:02:52] Stability. Stability, yeah.
[1:03:15] So the question is, 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? I think it's naive to think that somehow the best defense against a gun is a gun. The idea that somehow we're all going to have our own AIs, and that's going to create this steady equilibrium that all the AIs are just going to neutralize each other. That ain't going to happen.
[1:03:44] 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 it's 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-
[1:04:13] That balancing act is extraordinarily difficult. And, you know, you can't push a string, but is there some mechanism to pull it forward? We should have this debate sometime.
[1:04:44] 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, our 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 OpenAI Google moment. But that's concerning, right? So, I mean, Google develops...
[1:05:12] All this technology is holding internally until some actor happens to have initials OpenAI, 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...
[1:05:32] 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 intelligence. So I'm curious, I mean, in your mind, how, given that the economy there's...
[1:05:50] 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.
[1:06:08] 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 street lighting to driver ed to freeway speeds. I mean, that's healthy, functional regulation enabling us to collectively interact with each other.
[1:06:29] Now, obviously, it's multiple orders of magnitude more complex because these things are not cars. They're, 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 that oversees everything.
[1:06:49] No, I think it's just instinctively, it can be easy to go there when you kind of start to think it through. It's like, obviously, we do have centralized forces. But even in the US, we have military, we have divisions of the army, we have divisions of the police force that nested up in different layers. There's checks and balances on the system. And that's kind of what we got to start thinking about designing. That analogy to driving is a great one. And just to follow through on it, the complexity difference.
[1:07:19] Very high, right, for AI. But the timeline also. I mean, driving evolved from, what, 1910 to today. The late 1800s. So the laws related, you know, seatbelts came out 80% of the way through that timeline. 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...
[1:07:44] 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...
[1:07:56] That our social license to operate requires us to take more accountability for externalities than ever before. We're not in the Robert Barron era. We're not in the oil era. We're not in the smoking era. 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.
[1:08:26] 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
[1:08:56] 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. These are very powerful systems that present as much of a threat to the bad actor that is using the model as it does to the victim.
[1:09:25] I think that that will create an interest in cooperation, which is 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 an alien invasion. And that alien invasion could be a potential for a rogue superintelligence. Yeah, okay.
[1:09:54] What about the first part of my question? Is that part of your calling in life? I mean, there's only a handful. I think a lot of people that I meet around MIT or elsewhere, they have this vision that somebody has it figured out somewhere. Someone in government somewhere must be thinking about this. But you've been there, right? There's no one there. We're the adults in the room. Is that what you're saying? Yeah, definitely.
[1:10:17] There's nowhere to go from this route. 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 actually, I mean, you mentioned polymarkets and stuff. Intelligence,
[1:10:45] And that's what's propelling the world forward. And so this is where the conversation's happening. Like your audience, 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...
[1:11:11] 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 healthcare is up for grabs right now, education is too. Totally agree.
[1:11:38] 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 in the rear view 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.
[1:12:06] 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...
[1:12:33] Track your learning over time. And I'm very optimistic about that, too. It's a huge unlock. One of the debates we have right now in the podcast on 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, Dave. God, I do this constantly. It's really tricky for me on campus because I teach at MIT and Stanford and Harvard.
[1:13:00] 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, yep. I wanted to ask about that, actually, because your timing on inflection was early. Like, you know, in hindsight,
[1:13:30] But now you've got the new wave with Mira Maradi 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 all two and a half years ago. Is that all it was? Oh my God. 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?
[1:13:59] 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 it. 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.
[1:14:29] Oh, Prefrosh actually, Prefrosh. Prefrosh roommate. And so Nat goes off and he ends up at co-founder of, say, 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.
[1:14:57] You can build it. You can get your 20,000 NVIDIA... Oh, 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. Also, just the scale of investment that's required, not just in two years, but over 10 years. I think it...
[1:15:24] 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 10 years. So finishing that thought, then you, the companies that are raising money at a 20 or $50 billion valuation right now, and no chance. Okay. I'll take that. I wouldn't take the bet.
[1:15:53] It depends. I mean, there's obviously a near term. If suddenly we do have an intelligence explosion, then lots of people didn't get there simultaneously. But then also at the same time, you have to build a product with those things. 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...
[1:16:22] It's hard to say. And I think that's 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... What do you call it? Reid calls it schmuck insurance. Yeah. We had Reid on the pod here a couple of months ago. He's brilliant. So to that graduating high school student, what do you study these days?
[1:16:48] I mean, there's no question that you still have to study both disciplines. Philosophy and computer science is going to, for a long time, remain, I think, the two foundations. Should you go to college? Absolutely. Human education, the sociality that comes from that, the benefit of the institution, having three years to basically think and explore.
[1:17:15] In and out of your curriculum. This is a huge privilege. 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 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.
[1:17:44] Which gave you this very much humanist point of view. Yeah. And it was really hard and very different. And it wasn't instinctively right, but I learned a lot. And it was a very influential, 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.
[1:18:12] 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 being part of the public service, like post-Reagan 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.
[1:18:40] 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... And what about agentic AI in the government in particular? For sure. With all the same caveats that apply. But I mean, you know, rate of adoption for what it's worth of Copilot inside of government is actually really high. It's a brilliant job of synthesizing documents and...
[1:19:07] Transcribing meetings and summarizing notes and facilitating the discussion and chipping in with actions at the right time is clearly going to save a lot of time and improve decision making. So then maybe to tie a nice bow on the discussion.
[1:19:22] 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, everyone is going to use AI all at the same time to pursue, but the agendas that we all have are going to remain the same.
[1:19:42] I mean, people who want to start companies, people who want to write academic papers, people who want to start cultural groups and entertainment things, everyone is just going to be empowered 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. Grateful to have this conversation with you. Dave, Alex, I appreciate it.
[1:20:11] Final question from you, Dave? Final question? If I have one? All right. Prediction. Quantum computing right now has 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.
[1:20:40] Are quantum chips relevant? Or are they on the sidelines still? Or is everything ported over to quantum and Microsoft can take advantage of its lead? 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.
[1:21:09] 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?
[1:21:32] 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?
[1:21:58] 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...
[1:22:25] 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.
[1:22:51] 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 audience is use Copilot 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 AIs. I heard you also talk about, can you build the physical system that is going to enable AI to run the experiments?
[1:23:19] In a 24-7 closed dark cycle to be able to mine nature for data. And there are a number of companies that are doing this. Lila is one recently out of Harvard and MIT. I find that exciting where AI is becoming an explorer on our behalf, gathering that data. Yeah, spot on. Thank you again.
[1:23:44] This has been great. Thanks a lot. It was 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 meta trends that will transform industries over the decade ahead. I cover trends ranging from humanoid robotics, AGI, and quantum computing to transport, energy, longevity, and more. There's no fluff.
[1:24:03] Only the most important stuff that matters, that impacts our lives, our companies, and our careers. If you want me to share these meta trends with you, I write a newsletter twice a week, sending it out as a short two-minute read via email. And if you want to discover the most important meta trends 10 years before anyone else, this report's for you. Readers include founders and CEOs from the world's most disruptive companies 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.
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