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

Summary

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Podcast Summary: Moonshots with Peter Diamandis — EP #216

Title: Mustafa Suleyman: The AGI Race Is Fake, Building Safe Superintelligence & the $1M Agentic Economy
Date: December 16, 2025
Host: Peter Diamandis (with Dave and Alex)
Guest: Mustafa Suleyman (CEO, Microsoft AI; Co-founder, DeepMind and Inflection AI)

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Overview:

This episode features a deep-dive conversation with Mustafa Suleyman, tracing his journey from DeepMind and Inflection AI to leading Microsoft AI. The dialogue spans the so-called “race” to AGI, how the agentic paradigm will transform tech, the critical need for safety and alignment, economic benchmarks for autonomy, new frontiers for AI’s impact, and provocative visions for humanity’s co-evolution with superintelligence. Suleyman is direct: there is no “race” to AGI, but we are in the midst of an exponential shift toward agentic systems. The conversation is dynamic, humanistic, and candid, shot through with both optimism and sober warnings.

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Key Topics & Insights

1. The “AGI Race” is a False Narrative

• Mustafa Suleyman on AGI:
  “I don't think there's really a winning of AGI. I'm not sure there's a race.” (00:03)
  “A race implies it's zero sum. It implies there's a finish line… Technologies and science and knowledge proliferate everywhere all at once, at all scales, basically simultaneously.” (00:21)

• On Microsoft’s real goal:
  “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.” (32:02)

2. The Paradigm Shift: From OSes and Search to AI Agents

• Transition from classical interfaces:
  “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.” (00:21, 02:41)
  “All of these user interfaces are going to get subsumed into a conversational agentic form… you're going to do less and less of the direct computing.” (02:55)

• Microsoft’s agent vision:
  “Maybe in five years time... we’re selling agents that perform certain tasks that come with a certification of reliability, security, safety, and trust. That is actually in many ways the strength of Microsoft.” (05:07)

3. Safety, Alignment, and “Containment”

• On Safety Efforts:
  “I'm wrapping my head around it...I would say not as much as we should.” (53:18)
  “It's close enough that we should be doing absolutely everything in our power to prioritize safety and to prioritize alignment and containment.” (36:01)

• Definitions:
  “Alignment is...does it share our values?... Containment is can we formally limit and put boundaries around its agency?” (60:57)

• The containment dilemma:
  “Failing to contain [these systems] forces risk for catastrophe, like...engineered pandemics… But the extreme surveillance required to enforce containment could lead to a totalitarian dystopia...We need to navigate this narrow path between chaos and tyranny.” (59:40)

• Transparency and investment:
  “Auditing for scale of flops, having some percentage that we all share of safety, investment, flops, and headcount… Now is really the time to be making those investments.” (54:19)

4. Economic Benchmarks & The Agentic Economy

• Modern Turing Test:
  “The modern Turing Test was something I proposed…making a pretty simple prediction. If the scaling laws continue...what would be the first model to make a million dollars?” (11:18)

  • Goal: An AI agent that can 10X $100,000 in starting capital—a truly economically autonomous agent.
  • Impact: “We breezed past the Turing Test, right? I mean, it kind of has been passed… agents don't really work yet. The action stuff is still progressing…but it's pretty clear that in the next couple of years those things come into view.” (12:36–13:43)

5. The Exponential Moment: Acceleration & Surprise

• The exponential curve:
  “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—yeah, that's the bit.” (17:19)

• Cost Collapse for Intelligence:
  “The inference cost…has come down 100x in the last two years.” (26:49)
  “The cost of accessing knowledge or intelligence as a service is going to go to zero marginal cost…that’s going to have massive labor displacement effects, but it's also going to…have a weirdly deflationary effect.” (29:06)

• Democratization:
  “It's not really about performance, it's just cost…Open source is going to [thrive].” (28:50)

6. AI’s Next Frontier: Science and Knowledge Discovery

• AI for Science:
  “It’s blown my mind…fact that these methods could learn from one domain—coding, puzzles, maths, the essence of logical reasoning—and then can basically apply that to many, many other domains.” (22:48)

• Challenges:
  “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.” (24:23)

• Acceleration lever:
  “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.” (82:01)
  “Just the more you use it [your AI agent], 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.” (83:01)

7. Anthropomorphism, Legal Personhood & Societal Shift

• Cultural & design considerations:
  “Anthropomorphization is the new skeuomorphism… but obviously there's a line. Creating something which is indistinguishable from a human has a lot of other risks and complications.” (44:52)

• On legal personhood for AI:
  “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...(etc).” (45:24)

• Human-centrism:
  “I'm just a speciesist...I start with we're here. 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.” (48:54)

8. Recursive Self-Improvement & Risks

• Threshold moment:
  “The recursive self-improvement piece is probably the threshold moment if it works… we're really building out the team now from scratch.” (56:51, 34:30)

• Containment and global cooperation:
  “There is going to be a time in the next 20 years where it will make complete sense to everybody...to cooperate on safety, on safety and containment and alignment. It is completely rational for self-preservation…” (68:49)

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Notable Quotes & Memorable Moments

• On “winning” AGI:
  “I don't think there's really a winning of AGI. I think this is a misframing that a lot of people have… I'm not sure there's a race.” – Mustafa Suleyman (00:03, 31:54)

• On the new paradigm:
  “All these user interfaces are going to get subsumed into a conversational agentic form.” – Mustafa Suleyman (02:55)

• On near-term shocks:
  “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 world class at diagnostics.” (30:06)

• On anthropomorphism and AI rights:
  “Anthropomorphization is the new skeuomorphism…AI legal personhood is extremely not on the table… 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.” (44:52, 45:24, 48:54)

• On surprises:
  “I was absolutely blown away by the first versions of Lambda at Google… seeing the kind of emergent behaviors that arise in yourself, like things that you didn’t even think to ask…” (18:54)

• On democratization and open source:
  “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.” (27:03)

• On the future of education:
  “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.” (71:54)

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Timestamps for Key Segments

| Time | Segment / Topic | Speakers | |--------------|-------------------------------|-------------| | 00:03-00:21 | "Is there an AGI race?" | Suleyman | | 02:41-04:47 | Microsoft’s Agentic Vision | Suleyman | | 10:54-13:43 | The $1M Agentic Economy, Modern Turing Test | Alex, Suleyman | | 24:04-25:42 | AI for Science & Math, Limits | Suleyman | | 29:06-30:06 | Deflation & Labor Displacement | Suleyman | | 32:02-35:09 | Microsoft’s AI Mandate | Suleyman | | 36:37-38:36 | Sentience vs. Consciousness | Suleyman | | 44:52-46:38 | Anthropomorphism & Personhood | Suleyman | | 53:13-55:05 | Safety Spending & Co-scaling | Suleyman, Peter, Alex | | 60:57-63:32 | Containment vs. Alignment | Suleyman | | 67:49-69:41 | How Industry Will Self-Regulate| Dave, Suleyman | | 71:54-72:38 | The Future of AI in Education | Suleyman | | 82:01-83:01 | Star Trek Future & Innermost Loops | Alex, Suleyman | | 83:01-83:47 | Closing advice: “Just use it.” | Suleyman |

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Takeaways

• The AGI “race” doesn’t exist the way the media frames it; progress is exponential, diffuse, and collaborative—though competitive.
• Agentic systems are the paradigm shift, shaping how we interact with tech, work, and the broader economy.
• Safety is both urgent and underfunded: more attention, investment, and global cooperation are needed, even as the field accelerates.
• AI’s societal and economic impact is both deflationary and disruptive, promising abundance but also significant turbulence and transition shocks in the near-term.
• Anthropomorphizing AIs creates usability and societal challenges; granting personhood is dangerous and off the table—for now.
• Containment and alignment are twin challenges: boundary-setting and value-sharing are both required for safe superintelligence.
• The exponential cost collapse in AI access is astonishing even to insiders; open source and democratization are driving new business models.
• Advice to entrepreneurs/learners: Engage deeply with AI tools—they compound personal and organizational capabilities, and agents will increasingly become personalized knowledge work leverage.

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Final Thoughts

Mustafa Suleyman offers a rare blend of technical realism, accelerationist optimism, and hard-nosed humanism. This episode is an unmissable primer for anyone who wants to understand the coming decade of AI: its economics, its dangers, its opportunities—and why there’s no simple finish line for the “race.”

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Transcript

[Dave]

What's the mandate from Satya? Is it win AGI?

[Mustafa Suleiman]

I don't think there's really a winning of AGI. I'm not sure there's a race.

[Peter]

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.

[Mustafa Suleiman]

Now, 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. 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 just like not quite the right metaphor. As we know. Technologies and science and knowledge proliferate everywhere all at once, at all scales, basically simultaneously.

[Peter]

Are you spending a lot of your energy compute human power on safety?

[Mustafa Suleiman]

Yeah. No, I mean. Now that's a moonshot.

[Dave]

Ladies and gentlemen.

[Peter]

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.

[Mustafa Suleiman]

Thanks for having me. Yeah, I'm excited to do this.

[Peter]

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. 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 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 providing open access to AI and they're doing sort of a land grab. Try and get as many users as possible. You've been building sort of within the Microsoft 365 ecosystem. 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?

[Mustafa Suleiman]

I mean, it's a good question. So we are, on any Given Day, a $4 trillion company with almost $300 billion of re.

[Peter]

It's incredible.

[Mustafa Suleiman]

It's just 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, you know, CPU and AI accelerators of all kinds and enabling that, you know, to be available to the market. APIs 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, seven 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. 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, 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 placed to manage this one.

[Peter]

Do you see yourself providing sort of an open source AI like the other players out there, or do you think you could keep it contained within Microsoft 365?

[Mustafa Suleiman]

I think we're pretty open minded. I mean we've got some pretty small open source models. I think realistically when I say open.

[Peter]

Source, I really mean open access, if you would.

[Mustafa Suleiman]

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. I mean being a platform and being a great provider of the core infrastructure that enables other people to be productive is, 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. And 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, the slowness or the friction is, 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.

[Peter]

Is it like the old adage, you can't go wrong buying IBM in the old days?

[Mustafa Suleiman]

I think you just, there's a, there's a steadiness about us which I think is reassuring to people and there's a kind of like deliberate customer focused patience. You know, 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.

[Peter]

One more question before I turn over to Alex. You know, we're seeing in these, in this hyperscaler war, I mean literally, you know, a week by week, everybody outdoing each other in this, this insane period of everybody coming out with, with the new benchmarks. 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?

[Mustafa Suleiman]

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 in. 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, the most wild transition we have ever made as a species.

[Dave]

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?

[Mustafa Suleiman]

Well, There were more than three of us, but I mean, for the decade between 2010 and 2012. Yeah, sorry, 2020. I mean there were just like so few successful commercial applications.

[Dave]

Yeah. Of.

[Mustafa Suleiman]

Of. Of deep learning. I mean there were plenty behind the scenes. There was image recognition, improvements to search.

[Dave]

Not a huge market for commercial.

[Mustafa Suleiman]

Yeah. Playing go. Not a huge rock. Exactly. So I think whereas now, I mean you 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, of like training tiny models with very little data and very small clusters back in the 2010s.

[Peter]

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 humanoid 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 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 2 minute read via email. And if you want to discover the most important meta trends ten 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, why it matters, and how you can benefit from it. To subscribe for free, go to dashmandis.com metatrends to gain access to the trends 10 years before anyone else. All right, now back to this episode.

[Alex]

Yeah, so when last we spoke, circa 2015, I think that was perhaps 3 years post Imagenet 5 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 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? Or why are we stuck with benchmarks like vending bench rather than Microsoft leading the way with Microsoft's economically autonomous benchmarks for its agents?

[Mustafa Suleiman]

Yeah, I mean it's probably just worth adding the context that we met in 2015 in Puerto Rico at the AI Safety Conference. True that many, many of the field now were at. At the same time.

[Alex]

Seminal moment.

[Mustafa Suleiman]

Yeah. Was it the day after New Year's Eve or somewhere around New Year's?

[Alex]

Was pretty cold out everywhere except Puerto Rico.

[Mustafa Suleiman]

Yeah, exactly. It was pretty cool. It was quite a surreal moment, actually.

[Alex]

It's like a silomar right before it all happened.

[Mustafa Suleiman]

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 wa, 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 or 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. 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.

[Alex]

Million dollars now given, As I recall, $100,000 in starting capital.

[Mustafa Suleiman]

That's right, yeah. Which model could turn it into a million dollars?

[Alex]

10X return on investment by an agent.

[Mustafa Suleiman]

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.

[Alex]

Know, Alpha Loebner Silver Prize wound down before we breezed past Turing.

[Mustafa Suleiman]

Yeah. And no one celebrated it. Like, where was the big, like, you know, Kasparov Deep blue moment?

[Alex]

Can we clink virtual glasses right now and celebrate that we won? It happened.

[Mustafa Suleiman]

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?

[Peter]

We're spoiled.

[Alex]

Where's my Microsoft Loebner Prize for the modern Turing Test, Right, Exactly.

[Mustafa Suleiman]

Yeah. Someone said to me earlier on, but 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.

[Peter]

Star Trek is here in real time.

[Mustafa Suleiman]

Yeah, exactly. So 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.

[Alex]

Can we get together again after the modern Turing test has been passed and just to celebrate, recognize it?

[Mustafa Suleiman]

Virtual glasses again?

[Alex]

Absolutely.

[Mustafa Suleiman]

Hopefully we can pop a know champagne or something.

[Alex]

I think we should.

[Peter]

We'll have an optimist pop the cork.

[Dave]

For us or something. Exactly, exactly.

[Peter]

Dave.

[Dave]

Hey, I want to flesh out that backstory a little bit more too. It's such a. 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.

[Mustafa Suleiman]

650.

[Peter]

What a deal.

[Dave]

650.

[Ad/Announcer]

What.

[Dave]

What year was that?

[Mustafa Suleiman]

2014.

[Dave]

2014. I remember reading maybe a year or two later that Google justifies deal by having DeepMind tune the air conditioning in the data centers.

[Mustafa Suleiman]

Yeah, right.

[Dave]

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.

[Mustafa Suleiman]

But I mean, the data center thing was pretty cool. We did actually reduce the cost of cooling the Google data center fleet by 40.

[Dave]

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. And it was actually a lot more complicated than the news made it sound. That's it at the time.

[Mustafa Suleiman]

That's right.

[Dave]

But you were talking about the flat part of the exponential. And you think about like, okay, all of this R and D, which is so close to becoming AGI, is tuning the air conditioning. But that's the nature of exponentials. They sneak up on you like this.

[Mustafa Suleiman]

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 an eye. It's a drop in the ocean. And 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, well, it's not coming quick enough. It's like, dude, it's going now pretty quick.

[Peter]

We've shifted to A24, seven 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 moonshots 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.

[Mustafa Suleiman]

Yeah, yeah.

[Dave]

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. 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.

[Mustafa Suleiman]

Before, can say, yeah, I just got very lucky. I mean, we were very lucky to have an intuition for the exponential.

[Dave]

Right?

[Mustafa Suleiman]

And like that's, that, 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. Yeah, you know, like that, 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. There's like, these are like, I can't remember, maybe 256 by 256 pixels. Yeah, you know, black and white, handwritten digits. 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, Don Vistra, this like awesome Dutch guy out of EPFL was generated like the first number seven that was provably not in the training set. 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 7. How cool is that?

[Dave]

You know, I got the highest score on mnist ever in 1991 when it first came out. When you were three years old, right?

[Mustafa Suleiman]

Yeah, nine.

[Dave]

Nine. Nine years old. Okay. Yeah. And. And actually that's the same data set that's now in Pytorch that people like bench. Benchmark.

[Mustafa Suleiman]

Pretty crazy.

[Dave]

Incredible.

[Mustafa Suleiman]

Yeah.

[Peter]

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.

[Dave]

Moment.

[Mustafa Suleiman]

Yeah.

[Peter]

Happening more. More frequently.

[Mustafa Suleiman]

I was absolutely blown away by the first versions of Lambda at Google. This was like maybe 12 people working on it, led by Noam Shazir and Daniel DeFreitas and Quockly. And I got involved later maybe three or four or five months after they've been going and it was just breathtaking. I mean it obviously everyone at that point had been playing with LLMs and they're like one shot that produce an answer and you know, have a prompt and bl 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 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. 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.

[Peter]

And the scaling laws have delivered such unexpected performance. Right. I mean, was going back to your earlier days, did. 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?

[Dave]

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.

[Mustafa Suleiman]

Yeah, I mean, we. In 2015, I collaborated with a bunch of really awesome people on a NLP 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 that went the way the models now work. 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. 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. Can we add more data to this? Can we clarify our prediction target and can we add more computers and broadly speaking, that's what's delivered?

[Alex]

Yeah, 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 Lamda 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 by 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. Do you have timelines in your mind for AI solving math, which we're seeing a whole bunch of startups right now tear through erds problems. AI for physics, chemistry, medicine, material science. Material science. What do you think happens and when?

[Mustafa Suleiman]

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, 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 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.

[Alex]

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?

[Mustafa Suleiman]

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 that I 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.

[Peter]

Effort between AI, reinforcement learning in that category.

[Mustafa Suleiman]

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 the human is going to intervene in the theorem solving problem. Obviously everyone's working on this, particularly in like biology and 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, that feed that back to the model, then obviously go and test it in silico and be like, oh, we actually ran the experiment, we pipetted a bunch of stuff and then feed that back into the model to improve the search.

[Alex]

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?

[Peter]

Arguably that would be one of the most impactful things for humanity. That would just fundamentally move everything at light speed.

[Mustafa Suleiman]

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 fast, 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 ever.

[Peter]

Imagined it would be so cheap.

[Mustafa Suleiman]

That bit I also totally got wrong.

[Peter]

It's like biggest surprise for me isn't that we're getting this level of capability, it's how cheap it is, how accessible it is.

[Mustafa Suleiman]

100%.

[Dave]

I mean, that's a thousand X over two years. So is it going to do that again or are we. Was that a one time, Is it a thousand?

[Mustafa Suleiman]

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.

[Dave]

Last two years.

[Alex]

Okay, there, there have been competing estimates. Some estimates measure intelligence per token, per dollar.

[Mustafa Suleiman]

Right.

[Alex]

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, for some classes of models. Craziness.

[Mustafa Suleiman]

Oh, wow, that's. That's wild. Yeah, no, I mean, I, yeah, that's actually a good point. I got that totally wrong because I, 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. Like, and, and so much so that like when we founded Inflection, you know, and this was like maybe nine months or maybe a year before ChatGPT was released. Yeah, we started doing fundraising a year before ChatGPT was released. You know, we basically, 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 Core Weave. We were Core Weave's first AI customer.

[Peter]

Interesting.

[Mustafa Suleiman]

And you know, 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 cluster at the time was about 15,000 H1 hundreds, growing to 22,000. And like then obviously that year ChatGPT came out and like a few months around that time Llama 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 Llama knowing that they could depend on llama and obviously OpenAI 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.

[Peter]

Abundance, baby demonetization, democratization of the most powerful tools in the universe, our universe.

[Alex]

Hyper deflation, if anything.

[Mustafa Suleiman]

Hyper deflation, yeah, I think that's a really important point. The cost of accessing knowledge or intelligence.

[Peter]

Or capability, intelligence as a service.

[Mustafa Suleiman]

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 what, what is going to happen? Like we 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.

[Peter]

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, 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?

[Mustafa Suleiman]

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 world class at diagnostics. We released a paper maybe four or five months ago now called the MAI Diagnostic Orchestrator. And essentially it uses a ton of models under the hood to try and take 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 4 times more accurate, roughly it's about 2x less the cost in, in terms of unnecessary testing.

[Peter]

There's a study that, that came out of Harvard and Stanford, looking at, in this case was GPT4, a physician by themselves, a physician with GPT4 and GPT4 by itself.

[Mustafa Suleiman]

Yep.

[Peter]

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, in our thoughts, in our, what we saw yesterday, our recent diagnoses.

[Mustafa Suleiman]

Yeah, actually we got a lot of feedback after we released the paper because we 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, 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.

[Peter]

Dave, what are you thinking?

[Dave]

Oh, so much. So Microsoft, you've been here how many years now?

[Mustafa Suleiman]

Just a year and a half.

[Dave]

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?

[Mustafa Suleiman]

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 it's zero sum, it implies that there's a finish line and it implies that there's like medals for 1, 2 and 3, but not 5, 6 and 7. And it's just like not quite the right metaphor. As we know. Technologies and science and knowledge proliferate 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, 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 Copilot. So this is sort of our tool for taking these models to production in all of our consumer surfaces.

[Dave]

So just to clarify, so when we look at Polymarket, which we do a lot on the podcast, you know, the, 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.

[Mustafa Suleiman]

Right.

[Dave]

So now there will be, I assume.

[Mustafa Suleiman]

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, 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, takes many years to build. But yeah, we're absolutely pushing for the frontier. We want to build the best super intelligence and the safest super intelligence models in the world.

[Peter]

Yeah, nice.

[Dave]

So when you arrived. So if we go back to inflection, the thesis there is 18,000 H1 hundreds. We're going to build a big transformer. We're going to take a transformer architecture build. So is I assume now you've got all the OpenAI source code and that was your. You probably looked at it a year and a half ago on day one when you arrived. Just like start scrolling I guess. I don't know, trying to visualize how multi deca billion dollars of R and 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 or did you bring in your team?

[Mustafa Suleiman]

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. It's kind of unprecedented how that's working out.

[Peter]

Crazy.

[Dave]

Yeah, yeah.

[Mustafa Suleiman]

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.

[Peter]

10,000 employees under you now.

[Mustafa Suleiman]

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.

[Peter]

Along that lines, I just have to ask because you know the terms AGI and asi, you know, super intelligence 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?

[Mustafa Suleiman]

Yeah, I mean I think very loosely. It's. These are just points on a curve.

[Peter]

Are they interchangeable in Your mind, AGI and asi, or are they different?

[Mustafa Suleiman]

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.

[Peter]

It'll pass by and it'll be blurred and we will have recognized it in retrospect.

[Mustafa Suleiman]

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.

[Peter]

So I have to ask your question.

[Mustafa Suleiman]

When it's very hard to judge. I don't really know. I can't put a time on it.

[Peter]

Max.

[Mustafa Suleiman]

Pardon?

[Peter]

A min. Max.

[Mustafa Suleiman]

It's very hard to say. I don't know.

[Peter]

Okay.

[Mustafa Suleiman]

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.

[Peter]

And I, I, I respect that part of your mission statement and I want to get into that. That a little bit is the trades that you talked about in, 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.

[Mustafa Suleiman]

Oh, okay.

[Peter]

Do you distinguish between the 2? Where, where AI can have sensations and feelings and emotions versus being conscious and reflective of its own thoughts?

[Mustafa Suleiman]

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.

[Peter]

But you can imagine coding that in. You could an optimization function that is, that can relate to emotional states percept, you know, do you, can you imagine.

[Mustafa Suleiman]

That you, you could code in something like that? But it would be no different to, to the way that we write models to simulate. Sure. 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.

[Peter]

In humans just going overdrive, are going to activate on that.

[Mustafa Suleiman]

Right. 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.

[Peter]

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 asis than those that don't.

[Mustafa Suleiman]

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. When 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. Into the prompt. Right. So, you know, it's true that we could add, we could engineer specific empathy circuits or mirror neuron circuits or like a classic one is motivational will. Like at the moment, 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 part. And it's the confluence of those things interacting with one another which produces the human condition, plus the social, you know, 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.

[Peter]

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 specist because you're in favor of AI over humans?

[Mustafa Suleiman]

I mean, look, that's going to be a dividing line. There are some people and like I'm not quite sure which side of the debate Elon's on these days. Like 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 is super urgent. That right now we have to declare what kind of super intelligence 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?

[Peter]

And human understanding.

[Mustafa Suleiman]

And understanding. Like how do you control something that you don't understand? Right.

[Alex]

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, of anthropomorphizing AI assistants, copilots going back probably. There's an example prior to Microsoft, Bob and the Rover Dog and then Clipit, 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.

[Mustafa Suleiman]

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 to everything in between. Right. And we still have the remnants of that in our 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 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, the, 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, that they are an AI essentially, and that there are boundaries around them like that seems like a natural and necessary part of safety.

[Alex]

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 achieve some sort of legal personhood? Or is that forboden? Is that never going to happen? Do you see a future where humans are allowed to merge with the AI's Kurzweil style, friend of the pod? Or is that also not on the table in your mind?

[Mustafa Suleiman]

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 at 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 bright line in the sand. I really think it's a bright line. I think it's, 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.

[Alex]

Going to be attention there.

[Mustafa Suleiman]

But tension is rational. People always say that tension is, is rational. If you don't see the tension, you' definitely missing the most of the debate. It's obviously very complex. Like 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. 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 art that we have to exercise.

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[Alex]

It sounds though, if I may, the primary rationale that I'm hearing for why not 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 cloneable than human intelligence is. 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 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.

[Mustafa Suleiman]

To make the competition for the peace and prosperity of the 7 billion people on the planet it 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 then 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. Aha. 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?

[Peter]

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.

[Alex]

Bootloader for the super intelligence.

[Mustafa Suleiman]

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, I'm definitely aware that we're, 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 a augmented hybrid biological species, right. We take all these drugs and I know everyone's peptides are amazing.

[Dave]

It's super.

[Mustafa Suleiman]

I'm down for all of that. Let's go.

[Peter]

Epigenetic reprogramming is coming next year.

[Mustafa Suleiman]

Exactly, let's go. I'm down, I'm down. But let's not shoot ourselves in the foot. Like I want to make sure that, you know, most of our planet, if not everybody gets the benefit of the peace and prosperity that comes from the technology.

[Peter]

I mean there is, there is some level of sanity in that argument if you believe that the AI will ultimately out compete us and, and put us into, into a box of insignificance. I mean in the long, in the.

[Mustafa Suleiman]

Long run, I mean all intelligences, we, we, we can see this in nature. We're innately hierarchical. So far we have not seen this supra collaborative species that will take self sacrifice in order to preserve the other species. 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.

[Peter]

Don't don't shoot ourselves in the foot, as you said, Dave.

[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. Our safety valve is giving it a maternal instinct and which I found.

[Peter]

I found an interesting point of view.

[Dave]

Well, what does that mean?

[Mustafa Suleiman]

I didn't, I didn't track that. Oh, yeah, safety valve.

[Dave]

Well, he believes it's uncontainable. And I. 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 like it's dominant to us, but it cares.

[Peter]

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.

[Dave]

Yeah.

[Peter]

So if there's a maternal instinct that we can program into AI even though we're far less capable, it will take care.

[Alex]

It's been compared to the. Call it the digital oxytocin plan for AI alignment.

[Mustafa Suleiman]

That's a good one. Yeah. I mean, cool.

[Dave]

Yeah.

[Mustafa Suleiman]

I mean, it's about as poetic as it gets. I think I'm gonna need something that's got a little bit more like formula to it, a bit more reassuring. But look, there's 101 difference. 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.

[Peter]

Are you spending a lot of your energy compute human power on safety?

[Mustafa Suleiman]

Yeah, I would say not as much as we should. You know, I, I'm. I'm wrapping my head around it.

[Peter]

Is anybody out there? 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?

[Alex]

Oh, there are tons of safety benchmarks. And there's at least in my mind, an argument for defensive co scaling. I 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 part of the strategy for AI alignment?

[Mustafa Suleiman]

I think that would be a good. 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. 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, 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.

[Peter]

Is there something that's going to scare the shad 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 scares everybody but doesn't kill anybody.

[Dave]

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 would cause some kind of a solution.

[Peter]

Dave, continue please.

[Dave]

Well, so it's interesting that you, 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, and 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 and it's really, really clear that there are serious, I wouldn't say fishers, 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, was stay out of Microsoft's way. Because at the time Microsoft had half the market cap of tech was Microsoft. 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. So is it still, you know, let's all get together and have dinner and talk about safety or is everybody now in full bore?

[Mustafa Suleiman]

No, definitely. I think that's, 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 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, 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 foom, you know, an intelligent explosion.

[Dave]

Yeah.

[Mustafa Suleiman]

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 more and more risky stuff.

[Dave]

Can I ask you a really specific question about that? Because a year and a half now at Microsoft 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, 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?

[Mustafa Suleiman]

Yep, there's, there's also a chip effort and you know, I think progress has Been pretty good. I mean, I. 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 important part of.

[Dave]

It for sure, and that those are internal efforts. Are those teams under you? That's. That's part of your.

[Mustafa Suleiman]

No, I mean, they're in the broader company.

[Dave]

Yeah. Okay, interesting.

[Peter]

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.

[Mustafa Suleiman]

Thank you.

[Peter]

You know, I tell my kids I read books. You know, dad, you listen to books. You don't read books anymore. I want to. 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, you know, engineered pandemics. And a lot of your 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?

[Mustafa Suleiman]

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, Yeah. I mean, I think that is part of the challenge. Is that like. 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, just the system being humanity. Global humanity. System. Yeah. Just as you said, like, as everything becomes hyper digitized, the. 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 time. And so the one to many effect is suddenly massively amplified. I mean obviously we see it on social media, but now imagine that it's not just words that are being broadcast, it's actually actions. It's agents are capable of, you know, you know, breaking into systems or you know, sort of.

[Peter]

And they're resident in humanoid robots at a billion on the planet.

[Mustafa Suleiman]

And that too. Yeah, it's both atoms and bits.

[Peter]

Yeah.

[Mustafa Suleiman]

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 centralize power and taxation or sort of military force and taxation around governments, you know, three or four or 500 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, 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, you know, the best defense against a gun is a gun. I mean just sort of the, 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.

[Peter]

I mean, part of me hopes for a, a super intelligence that is the ring, to rule them all and provide. You know, I'm not worried about, how do I put it? I'm worried about Peter.

[Alex]

You're, you're hoping for a singleton.

[Mustafa Suleiman]

Yeah. That sounds like.

[Dave]

What's going on?

[Peter]

Well, you know, part of me is.

[Alex]

Like, color me shocked.

[Peter]

Really?

[Alex]

Yeah.

[Peter]

I mean, I imagine that the level of complexity we, we're, we're mounting towards, that balancing act is extraordinarily difficult. And you can't push a string. But is there some mechanism to pull it forward? We should have this debate sometimes.

[Alex]

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, 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.

[Peter]

The OpenAI Google moment.

[Alex]

And yet.

[Peter]

But that's concerning, right? So, I mean, Google develops 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.

[Alex]

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, 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 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?

[Mustafa Suleiman]

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 seat belts to vehicle admissions, to lighting to drive, you know, 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, 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 totally Terran state of intelligence.

[Alex]

Peter wants a singleton.

[Mustafa Suleiman]

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 US we have, you know, military, we have divisions of the army, we have divisions of the 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.

[Dave]

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, seat belts came out 80% of the way through that timeline. Yeah. So lots and lots of time to iterate here. Very little time and immensely more complex. So do you have a vision? But, but I completely agree. We need a framework for containment fast. Yeah. And do you have a thought on how we're going?

[Mustafa Suleiman]

I think that there's also a good commercial incentive to do this. Right, Right. I think that like the many of the companies know that they. 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.

[Dave]

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?

[Mustafa Suleiman]

It.

[Dave]

And then who else do you trust in the community to be part of that reaction?

[Mustafa Suleiman]

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 victim. And I think that, you know, that will, that will create, you know, an interest 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.

[Peter]

The, 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.

[Dave]

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, someone in government somewhere must be thinking about this. But you've been there, right? There's no, there's no one there.

[Peter]

Were the adults in the room, is that what you're saying?

[Dave]

Yeah, definitely. There's nowhere to go from this road.

[Alex]

Dave is asking for the smoke filled back room where the, the leads of all the frontier labs are secretly swapping safety tips.

[Dave]

Yeah, something like that.

[Ad/Announcer]

Yeah.

[Mustafa Suleiman]

I, I think that in practice, intelligence exists outside of the smoky room. I think that, that the, the notion that like decisions get made in the boardroom or in the White House situation room or like actually intel. I mean, you mentioned poly markets and stuff. Like, intelligence coalesces in these big balls of iterative interaction 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 basically space forward.

[Peter]

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 I, you know, 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. And I don't think our high schools are preparing anybody for the world that's coming. They're still retrospectively 50 years in looking the rearview mirror. Do you think Microsoft will play in reinventing education?

[Mustafa Suleiman]

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 is 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.

[Peter]

One of the debates we have right now in, in the podcast on a pretty regular basis is, do you go to college?

[Dave]

Yeah.

[Peter]

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]

Dave, wait. God, I do this constantly. It's really tricky for me on campus because I teach 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 last night, I won't mention it, but yeah, billions.

[Peter]

I mean just. Yeah. An opening valuation of $4 billion by collecting just the right group of people in the room.

[Dave]

Yep, yep. I wanted to ask about that actually, because your, your timing on inflection was early, like you know, in hindsight earlier, but now you've got the new wave with Miram Moratti and Ilya and, and a couple of others, liquid AI that all have multi billion dollar valuations.

[Mustafa Suleiman]

Yeah, I thought we set some standards on valuations pre revenue with a 20 person team, but we're just a minnow. Two and a half years ago.

[Dave]

Oh, is that all it was? Oh my God.

[Mustafa Suleiman]

Three years. I think.

[Dave]

Yeah.

[Alex]

You think as the, the cost of intelligence becomes too cheap to meter that the, the value ascribed, at least in terms of market cap to human capital is sort of inversely asymptotic. Going to infinity.

[Mustafa Suleiman]

Weirdly it is because of the pressure on timing. Right. And there's, there's actually still a pretty concentrated pool of people that can do this stuff and there's like an oversupply of, 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 to ask you because.

[Dave]

This is burning a hole in my pocket. But you know, Alex's freshman roommate at MIT was Nat Friedman, Pre frosh, actually pre frosh, pre frosh, pre frosh 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.

[Peter]

Yeah.

[Dave]

And so here you are, very similar situation.

[Mustafa Suleiman]

Right.

[Dave]

You've got your startup, you've got a billion or whatever billion and a half that you've raised. 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.

[Mustafa Suleiman]

Yeah, I mean, not to mention the, the prices that we're paying for individual, you know, researchers or members, technical staff. And like, I mean, just the, also just the, the scale of investment that's required not just in two years, but over 10 years. I, I think it, it's. Clearly there's a structural advantage by being inside the big company. And I think it's going to take. Take, you know, hundreds of billions of dollars to keep up at the frontier over the next five to 10 years.

[Dave]

So finishing that thought, then you. The, the companies that are raising money at a 20 or $50 billion valuation right now and no chance. Okay, I'll take that.

[Mustafa Suleiman]

But like, like, I think it depends. I mean, there's obviously a near term. If suddenly we do have an intelligence explosion and then lots of people can 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 gonna 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 partly the ambiguity is what's driving the frothiness of the valuations. Because I think there's people going, well, I don't know. I don't. Do I want to be. So what do you call it? Reid calls it schmuck insurance.

[Peter]

Yeah, yeah. We had, we had Reed on the pod here a couple months ago. He's brilliant. So to that graduating high school student, what do you study these days?

[Mustafa Suleiman]

I mean, there's no question that you still have to study both disciplines, like philosophy and computer science is, Is gonna 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, I still think it.

[Peter]

Was a cool thing to do.

[Mustafa Suleiman]

Yeah, it was just. It felt right at the time. But the other thing is go into public service.

[Peter]

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.

[Alex]

You.

[Mustafa Suleiman]

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, important part of my experience. Even though it's very short, it was like a couple 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 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.

[Alex]

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?

[Mustafa Suleiman]

For sure, with all the same caveats that apply, but I mean rate of adoption, for what it's worth of copilot inside of governments is actually really high. Does 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. It's clearly going to save a lot of time and improved decision making.

[Alex]

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?

[Mustafa Suleiman]

Yeah, I mean everyone is going to use AI all at the same time to be pursue, but the same. But the agendas that we all have are going to remain the same. I mean the 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 at their, their capability is going to be amplified by having these tools, obviously, the government included.

[Peter]

Nice. Mustafa, thank you so much for taking the time on a Friday night. Grateful to have this conversation with you, Dave. Alex, appreciate it. Want a final question from you, Dave?

[Dave]

Final question if I have one that I have. 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 configure out quantum operations. Are quantum chips relevant or they're on the sidelines still or is everything ported over to quantum and Microsoft can take advantage of its lead?

[Mustafa Suleiman]

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, in the sort of, you know, general conversation, I think people aren't grasping those two waves which are going to be just as, as, as impactful and, and crash at the same time that AI is coming into focus.

[Dave]

Yeah. All right, you heard it here.

[Alex]

This is a closing question to appeal maybe to your more acceleration 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 speedrun maybe a Star Trek future over the next 10 years or a Star Trek economy. What do we do?

[Mustafa Suleiman]

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. Like for example, Copilot is 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.

[Alex]

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.

[Mustafa Suleiman]

Or any other AI. I mean, there are loads of great AI.

[Peter]

I heard you also talk about, 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, yeah.

[Mustafa Suleiman]

Spot on.

[Peter]

Yeah.

[Mustafa Suleiman]

Thank you again, this has been great. Thanks a lot. It's a really fun conversation.

[Dave]

Yeah, really fun.

[Mustafa Suleiman]

Thanks.

[Peter]

Appreciate it, my friend.

[Mustafa Suleiman]

All right, good to see you.

[Peter]

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 humanoid 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 and our careers. If you want me to share these metatrends with you, I write a newsletter twice a week, sending it out as a short 2 minute read via email. And if you want to discover the most important meta trends ten 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, why it matters and how you can benefit from it. To subscribe for free, go to dashmandis.com metatrends to gain access to the trends 10 years before anyone else. Alright, now back to this episode.

[Mustafa Suleiman]

So you're about to make a trade based on a friend's text, but which.

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[Mustafa Suleiman]

We could buy a house in Tulum, get optioning those options. We could lose everything. Or let's do a little research, get your head in the trade and make the investment decision that's right for you.

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[Mustafa Suleiman]

Org Tradesmart.