Moonshots with Peter Diamandis - Recursive's $670M Bet on Self-Improving AI, Sonnet 5.5 Hits 70%, Elon Co-Leads Pentagon Push | EP #299

Episode Date: October 3, 2026

The mates sit down with Richard Socher to discuss Recursive’s $670M bet on self-improving AI, why he puts P(Doom) at zero, the race toward ASI, proposed restrictions on recursive self-improvement, and what new AI benchmarks like Tavus’ Turing Test could tell us about what comes next. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends   Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader. Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified Richard Socher is a leading AI researcher and entrepreneur, founder and CEO of You.com and co-founder and CEO of Recursive, who previously served as Chief Scientist at Salesforce after founding MetaMind. you.com Read Richard Socher’s new book, The Eureka Machine: Why AI Is the Key to Unlocking a New Era of Scientific Discoveries: https://www.eurekamachine.org/ – This episode is brought to you by: Get the blueprint for generative media https://goo.gle/startupgenmedia  Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy   Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter  _ Connect with Peter: X Instagram Substack Website Xprize A360 Connect with Dave: Web X LinkedIn Instagram TikTok Connect with Salim: LinkedIn X Join Salim’s 10X Shift Subscribe to Salim’s YouTube channel Exponential Venture Capital Connect with Alex Website LinkedIn X Email Substack  Spotify Threads Connect with Richard Website LinkedIn X Read Richard’s book, The Eureka Machine Listen to MOONSHOTS: Apple YouTube Follow MOONSHOTS:  Instagram TikTok X Threads – *Recorded on October 2nd, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

Richard, I'm curious, where are we at the moment with RSI? In various weak forms, we already have RSI. We're not quite there yet, but we're very close. When do you believe we reach ASI? I think we'll take us probably several decades. On Terminal Bench 4.0, assignment 5.5 jumped from 10% to 70%. Pretty extraordinary. My theory would be that they're trying to compete with China, which is about three months

behind and fill that gap before a lot of enterprises go to open source models. It's not at all obvious to me why anyone should be using Sonnet 5.5 over Opus 5.5, unless you have some token or latency or other consideration. I do not plan to use Sonnet 5.5. The Defense Secretary announced Project Meridian. It's a new Pentagon effort on the future of warfare. It's co-led by Elon Musk and Palmer Lucky. This, I think, is a transformative moment. This is a major, major step forward. The big question is going to be. Welcome to Moonshots, everyone, your number one podcast and helping understand the singularity. What's going on? What does it mean to you and your family? And what the breaking news is.

We publish this twice a week to help you keep up with the extraordinary rate of change. With me today is the Fantastic Four. Alex Weezer Gross, our in-house ASI, Dave Blondon, our impresario of AI investing. Selimus Mayle, our Globetrotter, who's home. today. Amazing, Salim. And the father of the organizational singularity. I'm Peter D. Amanda, your host, and data-driven optimist. Our mission here, keep you

optimistic about the future. And here's the numbers from this week. In seven days, Google shipped Gemini for Argon, Anthropic shipped Sonnet 5.5, opening eye shipped GPT6.1, Saul, and the frontier intelligence has

gotten cheaper by threefold. SpaceX launched the next batch of astronauts, the ISS, and also launched Google's TPUs into orbit for Project Suncatcher. If you're new to the Moonshots podcast, please hit subscribe. We publish twice a week, and you don't want to miss any of this news during this hypersonic tsunami. Our Moonshot here is to 20X, our subscriber base, to get to 10 million to help spread the word of optimism and the extraordinary future that we're building. Today on Moonshots, we're joined by one of the architects of modern AI, Richard

Socher. Born in Germany and trained at Stanford, Richard is among the world's most cited natural language processing researchers, a pioneer in deep learning and prompt engineering, and a serial founder who's repeatedly turned frontier research into category-defining companies. First, he founded MetaMind, which was acquired by Salesforce, where he then became the chief scientist leading AI research for a friend of the pod, Mark Benioff. Next, Richard founded U.com, recognized by time in the World Economic Forum and now valued at north of $1.5 billion, as well as his fund AIX Ventures. Today, Richard is co-founder and CEO of Recursive, perhaps the biggest moonshot he's ever taken, focused on recursively self-improving superintelligence. Richard, congrats on a $670 million

fundraise from Google Ventures, Greycroft, Nvidia, and $410 million in compute from AWS. personal disclosure. I'm very proud to be a seed investor in recursive. And of course, Richard, important to mention your new book just got released, the Eureka Machine, why AI is the key to unlocking a new era of scientific discoveries. Welcome to the pod, Richard. It's so great to be back. Hey, and let me just add a truly, truly awesome guy. A lot of people who listen to the pod are worried about the ethics and the risks and everything. But boy, if you want a person to conquer recursive self-improvement that you can like and trust, Richard is the man.

Yeah. Thank you so much. So great to be back here. I love your guys. Constructive optimism. I have a bold, I have a bold prediction for this episode. What's that? This one episode will prove our thesis for this whole podcast more than any other episode

we ever record. Awesome. Let's go. Let's go. Alex, do you know Richard? Yeah, Richard, you and I have chatted quite a bit. I don't think we've actually ever met in person, though. Really?

We have not, and it's the first time on a podcast together. It's going to be awesome. Amazing. Well, let's make history. Yeah, for sure. So, Richard, let's start with your book. The central claim of the Eureka machine is that AI will deliver a century of scientific breakthroughs in the next decade. You know, this maps directly onto what Alex and I wrote in Solve Everett.

everything, so we're fans of that prediction. Your thesis in the book is that every stage of scientific process gets connected and transformed simultaneously. Hypothesis, experiment, data, theory, you call it the full stack AI. You also say that scientific progress has slowed, and that's been caused not by underfunding, but by fragmentation. So let's start with those two items. Could you take a moment and you know, talk to us about full stack AI for science and why you think progress has slowed. Yeah. So progress has slowed largely because we have so many different sub-discipline and disciplines and even Stanis of Lem realized like, wow, that like if there are only so many people and we have more and more fragmentation of more and more sub-disciplines and niches, like you can

not just do AI, right, you're doing often like optimization and gradient descent methods. and second order derivative methods and so on of these neural networks, which is one category of AI and so on. And the same string biology, you study biology. You're going to be either a cell biologist or molecular biologists or you're like in medicine. And like these, there's so much separation that it's hard to weave it all together. And that is the perfect time for AI to come in and help us weave back together all these

separate pieces and also help us understand how these large. complex systems actually work. Like we can't have one model where humans say, here are all the things I know about natural language. And now like we can put all these rules together and then we have a conversation. It required a large neural network with a ton of data. And guess what? We're going to get a lot of data about biology. And so we can make more and more disciplines and move them, transfer them from being traditionally natural sciences where we just try to understand biology and nature to being programmable engineering sciences. And that, I think, is one of the many different exciting aspects of things to come.

And, you know, why am I so excited that this is happening right now? And what are the major ingredients? It's essentially that we now have world knowledge in a form of LMs. We have more and more scientific data that's getting digitized that we can sit on top of. We have better and better simulations of things. And we have robotic process automation that will soon be possible. and on top of that, we're going to have an agent swarm, and that is how the full scientific stack can be automated.

Alex? Yeah, I can say I disagree. As Peter, you and I wrote and solved everything, math, science, and engineering are cooked. I'm curious, Richard, what your latest timelines are. I mean, I think every field is going to get to higher and higher levels of abstraction. Computer science has been very good at that, right? No one is programming in zeros and ones anymore.

or very few people have to still know C++ and complex pointers and memory architectures and so on. We can get a large and larger. And computer science has basically abstracted enough so that it met the rest of humanity with English, right? And natural language that now can be used to do computer science. And so I think that gives me great hope that we can get other fields into similar stages of abstraction

and then all meat in natural language to do science. But to pin you down, as I recall from my understanding of your book, three to five years for the physical sciences? You know, like, there's no like single sort of, this is the threshold and now we solved all of physical sciences, right? But like multiple different diseases will get cured in the next 12 months. They're going to be the simpler diseases maybe where there's one gene that needs to be fixed for that disease to be cured.

But there are a lot of single gene disease. that are out there in aggregate, right? And so those diseases will get cured. And then we're going to cure more and more complex diseases. We're going to develop better and better battery material. So I wouldn't call it like one threshold and like in three years, sort of everything is solved, but like we're going to solve more and more problems. And it'll just be a question of how much money do you want to put into compute to then solve which kinds of problems?

You're talking to the guy Richard, remember who argues that the singularity itself is something of an optical illusion, that there is no step function. It's just in time interval. So I'll try once more. Timelines. Even if it's not a step function, when is the inflection point? When is the 50% point crossed in your mind for all of the physical sciences getting solved, all human disease getting solved? Where are those timelines? Alex, if I could just add a layer on top of that. So we've talked on this pot a lot that math is cooked, right? We're seeing that Millennium Prize is all falling. Yep. And the next natural, if you would, barbecue is likely to be physics. Incineration? Incineration, yes. And computer science, arguably, I mean, presumably this is part of

the premise Richard of recursive as well. Computer science already cooked, math, thoroughly cooked. You know, in a weird way, I would use different metaphors. I think they're flourishing, not getting cooked. I think the weird thing is, you know, some mathematicians recently said, oh, this is maybe bad for the field because, like, we need to train people. And yes, we need to train people. But imagine a biologist or medical researchers saying, oh, you know, it's really a bummer for the field that we cured all these diseases for people. Like, that would be insane, right?

Of course you want to just move as quickly as you can towards solving these problems. I think maybe the problem of math is that there's many sub fields of math that haven't really had. There are just almost purely beautiful intellectual exercises without real life impact anymore. But as you get close to real-life impact, you should be excited about it and you think and realize your field is flourishing, not being cooked. And so to timelines, I think the further, the more complex a disease is, the more you have to do long-term studies with humans before you're allowed to put a certain drug into humans, the more delays you will have. But like we see now companies to make this very concrete. Biotech companies used to have like one new drug in development and it took them 10 years. they had to go public before they knew the drug was really working.

And then after 10 years, maybe late stage three trials were just not working. And then the company is dead. Right. Now you have the new age of companies. They have five to 10 different compounds in stage three trials after two or three years. And so we see a lot of acceleration in that level. But again, diseases to really be able to get them into humans at scale in the United States with FDA approvals, there are just some natural delays that will be more like half a decade to a decade.

But lots of other things in chemistry and physics and so on, where we can iterate without having to look at long-term human trials will be even faster. And the point is well taken. I like your Orwellian turn of phrase. Maybe instead of saying AI is cooking math, I should be using flourishing as a transitive verb and just say AI is flourishing math. I love it. Actually, I was with Sertosh Karaman who runs Lids at MIT the night before last.

He started a drone company now, an AI drone company. But Lids is where Radar was invented originally during World War II. It's a great lab, very entrepreneurial. But he said all his mathematician friends at MIT are aware that they're cooked. And they're all, yeah, I was like surprise. Dave, we're going with the Orwellian language now. They're not cooked. They're being flourished.

That's funny because he literally said cooked, but I'll go back to him and tell them. Like, tell your friends, they're flourishing. Tell them all as well as you're being flourished, not to worry. If your goal was to prove as many theorems as possible in your lifetime, like now is the time to just grab as many as you can and work with AI to solve them. And then I think the field will change, the way computer science has changed in many ways.

And it'll be much more about what's the most creative thing when you really understand all the things that are out there, map, what kind of new formalisms can you create, new constructs can you create, that then would be interesting to be solved by an AI in collaboration with humans. But as Sirtas was saying, the math guys are in great shape because they're so cooked that they're all moving over to AI orchestration, and they're going to be way ahead of the curve. He's actually most worried about the biology professors who are in complete denial, you're using virtually no AI in their day-to-day activities.

And this is one area where, Richard, you have such a deep background in all facets of AI, including biology. and I feel like we're living exactly parallel lives, except you're 15 years younger than me, so I'm like insanely jealous of your life trajectory. But be a serial entrepreneur, then start a hugely successful venture fund, then found a foundation model company. And, you know, we're seeing the world through the exact same lens. But you and Peter have much more biology background. I have basically none. But the biologists are the ones really, really lagging.

And I think you've got a bunch of investments, actually, that have done really well in the area, right? And I would love to talk about some of those like Perilbio, Proxima Labs, Ignata Labs, truly exciting companies. I think another field that is even more lacking than biology is economics. Economics literally has these models of like a linear model of economics, right? One step economy that's provably correctly taxed and subsidized and things like that. It's just like absurd how slow that field is to adopt AI for making better policy decisions. I just insert something.

Because you opened the door, Eric Brunyolfson, our very good friend. I did not realize until yesterday that H.A.I. Lab, High Lab at Stanford, invented the word foundation model. You did? You knew that already? Yeah, of course, like, yeah, lots of Stanford friends and Percy Lang and others worked on foundation models. I love Eric Perniolson. He's actually one of the most interesting economists, I think, doing really interesting research right now.

He also started work Helix, which we're a proud investor in, that brings understanding of how companies actually adopt AI and which tasks are getting helped by in real rollouts for companies. Yeah, he's a co-founder of that too. Yeah, that building is just such a great place because you talk about econ people being way off the curve, but he is so ahead of the curve. You walk into the building and you can just feel it. He's also poaching a ton of talent from MIT to come out to join his level. Following him about that. Richard, talk about full-stack AI as you see it in the scientific method. You know, I'm an investor in a company called Lila out of MIT and Harvard.

You know Lila Science as well. I think I've introduced you to Jeff von Maltzen there. And, you know, they're building a scientific superintelligence that is then running a million square foot of robotic space to sort of like a thousand X the rate of discovery. Your thoughts on that. I absolutely love it. I think that is, you know, in the Eureka machine, I sort of lay out these four pillars. and they're actually one of the few that are really going after this, similar to periodic labs.

We're doing it more on the physics and chemistry side of things. And there are several other companies now that I can hopefully soon talk about that are trying to create more data for the bitter lesson to be applicable in biology. And I love what the big guys, Eli Lilly and others do and Lila too. I think ultimately you can boil down the scientific method to the ideation, implementation and validation of ideas. And the faster we can close that loop and then put an open-ended process on top of it, that's what open-endedness, what inspired us a lot at recursive.

And we have many of the world's greatest researchers in that sub-domain of AI that is still not quite as popular as it could and should be. Like, the more you can have a swarm, innovate with open-ended ways and evolve and combine interestingly different ideas, the better. But then of course in biology, you have to have like actual physical biological wetware experiments. And so it's really great to see Lila doing that. We're seeing this also. Maybe I can talk about this one company that I really love called Peral Bio. They build tiny organoids on a petri dish. Right.

And get this. If you love animals, you too can love AI. Why? Because they had FDA approval to skip animal trials. Turns out we cured most diseases in mice. It's not that helpful. They're very different to people. But these guys use pluripotent stem cells,

create tiny little organoids of lymph nodes. Lymph nodes, big part of your immune system, immunotherapy, some of the most exciting therapies, to allow your own immune system to attack a cancer and so on instead of getting crazy chemotherapy and so on. And so they got FDA approval because they showed that when you run experiments in parallel, hence parallel bio, in these tiny petri dishes,

how those organoids react to different drugs and toxicity testing and so on is actually more predictive of how those drugs will interact in real human bodies. And that is just one of many beautiful examples. I love that. And you can take my stem cell, you can take my skin, create in a IPSC cell, grow my own organs, and see how a particular drug would work for me versus a generic individual. Salim, want to jump in?

Yeah, a couple of things. One is, you know, science has always been a coordination problem, right? You're kind of trying to bring it. And it's always been very structured into departments and journalists and, and journalists. and all that stuff. And I love what you're doing, bringing it together. For me, if I had to summarize what you seem to be doing, is you're collapsing the time between an experiment and a result,

or hypothesis and a result. And when you can collapse that, that's domain collapse of this scientific method. And now, like, anything is possible from there. This is amazing. Yeah. I'm going to push again on what Alex said. Physics.

When do you, you know, Einstein's theory relativity comes out. There's not that much progress. you know, since then a lot of theories. Do you see physics as the next domain that's going to flourish on the back of math? I think the biggest domain is actually going to be biology. Physics has been interestingly stuck in many ways. Like, there's just a really powerful ideas like E equals MC squared,

so you can get a ton of energy out of potentially little mass and then we got nuclear energy out of that. And so that then moved into and graduated in some ways into an engineering discipline, which is where you have the real, real life impact. I think, you know, obviously, like, fusion will be great to finally get figured out. And there's a lot of really cool, like, companies and big labs. And, you know, Tokomax are ready to balance, like, plasma inside Tokomax, like, is a very hard control problem where AI is being used.

I hope we can eventually make better theories for, you know, quantum gravity and all kinds of other complex issues and have to get a better world sort of formula. Unfortunately, the data set collections these days require often like large hydrogen colliders and just like billions of dollars and it's like quite expensive actually. So in a weird way, biology is a better fit for AI because what calculus did for physics, like understanding really well micro individualized separated phenomena, neural nets are great in combining all these little things. Like we know what the neuron does, we know what one bacteria does in our microbiome,

but then as they all come together and form these really complex interactions, we don't really know anymore how that works. And so that's where I think will help us more. Yeah. Alex. Let's talk about biology a bit. So do you think the critical path to solving biology goes through digital twins of cells, or do you have some wildly different theory of the case?

Three years ago when I started the Eureka Machine book, I had this chapter on the virtual cell, and I was really proud of trying to lay it all out. And I had to rewrite that whole thing, because in the meantime, you know, Mark Zuckerberg Foundation started a virtual cell. Everyone's doing it. Everyone, not just CISI.

Everyone has a virtual cell model. Dave, even if Dave doesn't think Dave has a virtual cell model, I'm sure he'll spin one up soon. So I think that most, so there's this famous paper by Rich Sutton on the bitter lesson in AI. Never heard of it. Tell me all about the bitter lesson.

So this is a new concept for me. What is this you speak of? I don't know, you're probably joking, but maybe some readers haven't heard of it. So the bitter lesson is basically that human experts had all these really clever ideas and beautiful theories. But really what you needed to do was to make real progress is the simplest method that you can come up with, a large neural network that you can train end-to-end as a general function approximator, the simplest model you can come up with, and then just a ton of data and compute to actually train that model. that usually outperforms at scale all the clever little hacks that human experts had come up with

before. And so the bitter lesson has worked for NLP 10, 20 years ago. You would have asked an NLP expert, can you have one model to have any conversation with you, prompt with any kind of question, prompt engineering. I invented and was nicely cited by the early GPD papers. But like the same thing, the same state is biology is currently in. There are so many complexities and the experts know so much, they feel like there's no way you can instill all of that. knowledge into one model. But if you have enough data, you can. And now you have companies like Tahoe, Therapeutics and so on that are creating these massive perturbation studies. And yes, all models are wrong, but more and more of them will be useful as we collect more and more data about

biology. And that is, I think, one of the biggest driving factors for the impact of AI. But so just to answer the question, do you think virtual cell models are the critical path to solving all disease or solving biology or is it something else? I think they're definitely going to play a crucial part. It's the third pillar of the Eureka machine. We do need to have anything you can simulate, anything you can verify. AI will then be able to solve things in that domain. And so, yes, I do think virtual models and simulations are extremely important.

I'm going to jump into three fun, breaking stories this week, two on science and one on AI. And Richard, they relate back to your work and your book. A first story was published in MIT Tech Review today. It tells the story of researchers at Israel's Weissman Institute who built a system called Brain IT. The system reconstructs the image a person is looking at while inside a functional MRI machine. I'm just put up the slide here. So take look at this image. You know, it's pretty extraordinary.

On one side is the image that a person is looking at inside the fMRI machine. On the other is the AI reconstruction. So earlier brain image decoders could tell you were looking at a dog or a clock tower, but it lost the color, the composition, and the details. So brain IT learns structure and meaning separately and then puts the picture back together. And here's the clever part. They also can run the model in reverse, an encoder that predicts how a brain will respond to an image. So they can feed images no human has ever seen into the scanner, generate predicted brain scans, and then train on those.

The model effectively built its own data set. So Richard, you know, you say in your book that AI is superhuman in any domain you can simulate or verify and nowhere else. So is a brain simulatable domain or is this something different? It's not yet, but this is still an incredible result. I still remember the first such result came out when I was still a PhD student. I went over to the bioinformatics department at Stanford, and it was like very grainy little images we could just like extract from this. And to see this fidelity now and this realism is just incredible.

I think one of the problems, I don't know if this study, I haven't seen it today yet, I've been at work, but like one of the problems is often that you have to train for each brain because they're all slightly different. And so you won't be able to just take someone who's never put into an fMRI scanner to see and update the model for that particular person, which would be otherwise amazing for people

who are in a coma and you want to see, are they still thinking stuff? You know, like that would be incredible. Alex, tell us more about this story today. Yeah, so this actually is a result from March. So the broader field of doing decoding of brain states from functional imaging or EEG or electrodes. This has a long, long history at this point.

I remember 20 plus years ago at this point, the Gallant Lab at UC Berkeley was doing this to decode the visual cortex of cats. Actually, like 20 plus years ago, we were starting to see images of how a cat perceives the world. And I remember, like, the first grainy images that were coming out were like a cat seeing a branch. And then moving, fast-forwarding to non-invasive fMRI, there are so. many groups at this point, META in particular, has been sponsoring quite a bit of work in this area from Jean-Marie King, many other groups doing language decoding, vision decoding, more recently

even fast-forwarding to just the past 24 hours. Neurrelink just announced their first scaling law studies on pre-training foundation models, frontier models, off of Neurrelink electronic data. And this is on an individual patient basis, but they're capturing things. copious amounts of data at high temporal resolution, much higher than fMRI can capture. FMRI, like rule of thumb, FMRI can give you, with today's technology, at best, cubic millimeter spatial resolution for voxels and at best approximately one second temporal resolution. So that somewhat limits how well you can do in terms of decoding a person's internal visual state. You can still make no mistake.

There are folks who are making a cottage industry out of decoding dreams. and decoding visually what a person perceives or what they hallucinate in their visual cortex while they're sleeping or while they're awake. And I'm sure this is going to be a vibrant, vibrant space training frontier models and foundation models off of copious amounts of fMRI data. But ultimately, where I think this has to go, there are spatial and temporal limits to fMRI decoding. it's going to require higher temporal precision and higher spatial precision to get where we really want to go, which is full dive VR and full bandwidth BCIs. And we will get there, but the good news is you have to start somewhere, that large language models had to start with GPT1,

just a single artificial neuron that could predict the polarity of Amazon reviews. Similarly here, if we're going to get full bandwidth BCIs, it's going to start with studies like brain IT and John Marie King and Jack Gallant. Amazing. Salim, what happens in the world where you can know someone's thoughts? Well, I think you, let's be careful. We're decoding the perception here, not the actual thought, right? That's like quite a bit more complex.

What's the difference? What's the other than the brain region? Other than the brain region. It's just the distinction without a difference. I think there is a difference. When you imagine, right, when you imagine. something in your mind, it lights up the same neurons as when you're seeing them. That's fine. That's fine. I just want to make sure we don't mix the two because you can have

thoughts without necessarily the perceptions around it, but you can definitely have one without the other and the other without the other. But I think for me the more interesting thing here is, you know, we've spent billions of hours typing things in the little rectangles. And if we can change that interface to do a more natural interface, which is interface directly with the brain, We used to talk about this in our Singularity University lectures on neuroscience is, you know, we still have very little idea how the brain works, but you don't need to know how it works as long as you can interface effectively with it. And this, I think, gives us the opening to give us really deep interfaces. And then that'll help us figure out how the whole brain works. I'll take the other...

Oh, go ahead, Richard. Sorry, you bring up a really good point, which is, I read this result a while back, that there are two types of people. Some people who think actually think in sentences. And other people when they think, they just have a fuzzy thought cloud. And only once they're asked to verbalize their thought or they try to write it down, they actually like make a real sentence out of their fuzzy thought clouds. And I'm definitely a thought cloud thinker. My wife is very much a like sentence like in her head.

She actually thinks in actual sentences. And like there's no like one is smarter than the other or anything. They're like, they're like equivalent. But like I do think sometimes like writing is thinking and like for me it is very helpful if I have to really verbalize something versus I just have the thought. I'm just trying to think like what does the world look like if the eye could really just like extract my thoughts and now I need to like structure my thought clouds into sentences more or Richard, what would neural net training look like if you took all text out of it,

no language at all, just train on pure images and higher level thought constructs. You'd get a very different neural net out the other side. It might actually think a lot more like you do and less like your wife does. I'm not sure. Maybe we should be taking the position that anyone who doesn't have an inner monologue is just a pea zombie. I got so one more thing. And I remember we had one of the top linguists in the world come in and speak. And we're like, you know, this must be a bad time to be a linguist.

Nobody's using spelling or grammar or anything else like that. And he goes, no, on the contrary, this is one of the most interesting times to be a linguist. ever. And we're like, wow, how come? And he said, because when you look at emojis, it's the first time in the history of human language that you can symbolically ascend emotion, because you can digitally send emotions. And we're like, wow, that's an interesting. So he was super excited by opening up that whole aperture. I thought it was an interesting. Well, you know what I'm super excited about? You're saying, you're saying that emojis are the reason why linguistics is so, not the fact that linguistics itself has been flourished by AI at this point. I'm not connecting to do. I'm not

connecting to you did that. No, sorry. Dave. Oh, no, the result I really am looking forward to is how does the brain train itself without gradient descent? Everything going on in AI right now, everything going on for the last 20 years in AI is driven by gradient descent algorithms. And the biologists can't find anything even vaguely like that in actual biology.

And so that's, you know, I think with really detailed imaging, we might finally crack the code on what is the fundamental learning algorithm that changes the synaptic weights. It's not what we use for artificial neural nets. It's something different. And if we discover that, we might find that neural net training can be 10, 100, 1,000,

a million times more efficient. And so hopefully that'll come out within a year. Could you define gradient descent for our listeners? It's funny, Elias Sutskiver, when he's on interviews, he's like, there is only one algorithm. The algorithm is gradient descent. And everything else is just irrelevant compared to it. So what happens right now is you build these 96 or 120 layer deep neural nets.

And all it is is a whole bunch of random connections that do absolutely nothing useful. And then you give it $15 trillion training examples. You're like, when you see this, say that. Or when you see this paragraph, this is the next token. And if you're guessing wrong, you get punished through gradient descent. So the error, how far off you are is your punishment. and it passes back through all the layers, and it calculates an error, and it blames each neuron or each connection for how responsible were you for this terrible answer?

And if you're way wrong, you get moved, and you move in the direction that helps you get the answer right. So it's this incredibly laborious search process, and that movement of each synaptic connection is a gradient, and you just hill climb to the best possible state. And then, and magically, after about $100 million of computer, in Richard's case, I guess 400 million, it magically starts thinking through this one algorithm gradient descent. Yeah, I think it's really back prop that we're talking about synagogically rather than gradient descent would be the obvious comment. So maybe backprop is, I guess, the efficient computation of those gradients.

Maybe I'll add to this explanation from Dave. But like, gradient descent is essentially an optimization algorithm that we use to train all kinds of machine learning models by minimizing the errors. And I think the best analogy is to assume you're a blind hiker, you're atop of the mountain, and you're trying to find the lotus, the lowest point. And you can't really see that far. You're blind, but you can feel the slope at each step. And so how much, like, how big of a step should you take down when you feel like the slope

is going roughly in the right direction? And then the problem is that the kinds of landscapes, these AIs are trying to optimize, are highly non-convex, which means there's not just one lowest point, but there are many different low points. And depending on where you start, you might go into a different valley. Depending on which mountain you start, you go into this valley versus this different valley. And there are actually a lot of really interesting analogies. My friend Jishan, like, thinks about this from the psychological perspective. For instance, when you have PTSD and you train, you overfit to one algorithm. Now your brain is kind of stuck in one valley of how to think about something.

And it takes sometimes people like doing psychedelics and stuff that have shown to help help with like depression and PTSD and other mental diseases, like it helps to increase the learning rate to jump over another mountain and go into a different valley of attraction where you then have new kinds of ways of thinking. And so this blind hiker analogy, I think, is a really intuitive way to think about it.

And then, of course, the problem is it's not just in 3D, it's in a trillion D, like a trillion parameters that you're trying to traverse in that landscape. But the ideas of like try to identify the direction and take steps towards it. And you know what else is really incredible? I could rip on this for hours. But if you look at a big model like Kimmy K-3, it's 93 layers,

and it's train, train, train, train. If you take a single layer and you randomize it, and then you train it and say, find yourself again, it can't find itself again. It never gets back to where it was, which comes back to in these fMRIs when you're looking at a human brain and you're saying, okay, here's Alex's brain. Can I compare that to Salim's? And you're like, wow, there's nothing in common going on here. Yet, you know, well, okay, maybe that's a bad example, but the way you're probably right.

It's probably right. The way your brain wires, as you learn, is completely unique to you. Yet it can come out with like, okay, we're equally good soccer players. But the way the signals are propagating through our networks is completely different and unique to each of us. It's really, really strange, you know, that it can't find its own way back to its state. I think we're going to learn a lot, actually, with the fMRI data coming in on how and why that works. And there have to be commonalities that we just can't find. You know, rotations, you know, make everything look different, but they're really not that different.

If you put them through a transform, like a Fourier transform view, will suddenly say, oh, my God, this is why they line up. Yeah, I was just with my fraternity brother of ours, Dave, who's the head of neurobiology at USC the other day. And is, you know, this is the most exciting time ever for brain science. Oh, yeah. I mean, we're, you know, the brain has been a black box for since humanity began and our ability to understand the brain and deal with, you know, mental disease, I think, is going to be extraordinary. Well, so you can simulate.

This is why Liquid AI was founded, actually, because we completely reverse engineered the C.ELEGAN's worm brain down to, exactly every single thing going on. And then they were able to simulate it. And then after they simulated it, they realized, wait, this is a very efficient neural net, and then they productized it. I mean, that's... You know what?

So they both ang... So go ahead, Richard. There's a really cool thing. Since you mentioned C.L.A.G.N. worms. There's not many people that bring that up. A friend of mine was a professor of neuroscience at Harvard, Sam Gershman.

He, on the most billion people I've ever met. Like, he actually did a study with worms where he cut them in half. and one, sea against worms, they do have a brain, right? And then they have a lot of other like neurons and the rest. Now you can train them to react to a certain stimuli. And then you split them in half. The second half without the brain regrows a new brain.

And here, get this, this is crazy, that new brain has the same memories. Wow. And we react to different stimuli. And so he's like, maybe there's a different way where we learn. And I do think there are things that right now we're very much stuck in this like, it's a neural net, it's all that. electrical signals and so on, but clearly like brain chemistry can change massively with just like you can get hangary, you can be in pain, you can have like a certain like stimulant and

all of a sudden your brain is very different. And I think there are, and I've seen this now with like having babies and like talking to moms, like there's certain algorithms where you all the sudden you nest because you had a baby and now you want to like and how you nest is different. But people nest, you know, in one form or another. And so there are these latent algorithms that we have that are in our DNA that just trigger after like 20 something years, right? When just the right things that happen with your body and your biology. So I think there's still so much that we don't. We haven't.

So much complexity. I'm going to move us to our next story from three days ago. So on Tuesday, the president signed an executive order directing the EPA, the Department of the Interior and Agriculture, working with HHS to cut invasive mosquito populations in Washington, D.C. by at least 90% and the tick population by at least 50% by 2028. The targets include mosquito species that spread dengue, Zika, and yellow fever. And here's what caught my eye. You know, the order explicitly prioritizes sterile insect techniques, safe genetic modifications,

and beneficial bacteria over conventional pesticides. I love that. You know, this is the exact kind of gene drive work that colossal, the Deextinction Company, is doing. And I think most people don't realize it's like, you know, when I was raising my kids, I would ask them a question, which is the species that kills the most humans on the planet? You know, and some people jump onto sharks. Some people jump onto, you know, whatever it might be. But on this part, I'm sure people know, mosquitoes are the deadliest life form on Earth.

Right. Malaria alone kills half a million people a year. So, Alex, this is biology as engineering arriving as federal. policy. Your thoughts on this story? For decades, we've been scared of our shadow. So I have a classmate Kevin Esfelt at MIT, who was a pioneer of the gene drive, one of the pioneers. And he's had a devil of a time getting state's municipalities to approve gene drive studies against mosquitoes. And for those not tracking, the premise of this technique is basically inserting a gene via CRISPR that wants to replicate itself to basically sterilize mosquitoes.

through propagating virally through the mosquito population. We have the technology to do this. What it has lacked, at least in this country, is a federal mandate to actually go and implement large-scale genetic engineering of non-human animal populations. And now, for the first time, it seems, we're seeing via executive fiat just that mandate. And it is so exciting for two reasons. One, because one, as Peter, you mentioned, malaria and other non-human animal born diseases cause an enormous amount of suffering, human suffering, and also non-human

animals suffering. But secondly, because there's a way to do this that doesn't actually involve killing the animals themselves. Exactly. Historically, you know, if you look back 50 years, you'd see chemical spraying if you wanted to do something about, say, insect-borne illness responses. We don't need to do that anymore. We can actually keep the insects that are the inadvertent carriers.

of bacterial or viral disease, we can actually preserve their lives while also preventing the disease transmission. And I think that's good from their perspective as well. That was the part that got me most excited was we spray things. We're basically poisoning biology and now we can shift actually programming. And leave the poison ecosystem. Yeah. Yeah. Richard, your thoughts on this story? I concur with everyone. Like it's wonderful. And it's so interesting, like people, people, I hope we can amplify these kinds of stories more, you know, like the future needs better marketing.

We need more Peters in the world. And this is one of those many stories, you know, curing various diseases, peril bio, like saving animal lives and prevent them from just being bred to be tested upon and dissected. Like those are all like stories we need to amplify more in the public eye. Yeah, Dave, I can't wait for this to come from D.C. to Vermont and Massachusetts and all of these I was going to say for the listeners that are interested in your real estate fund, which, you know, the theme there is, hey, easy access via drone to hilltops, islands, but also if you live on the edge of a marsh, your house probably sells for about half the

price of a place that's not on the edge of a marsh. That'll go away. There's so many solutions coming to biting insects, this being one of them, that that also is, you know, if it's beautiful, beautiful land, that otherwise is very difficult. to live on, it's going to go through the roof and value. I do think it's really important to try to keep the bees alive. Yes. All right. And not that's spray, rungs or pesticides, make sure birds still have enough, like, insects

to eat and things like that. I was going to say for maybe up to 80 years, humanity has been scared of its own shadow. Humanity in general, America or the West in particular, nuclear energy. Another example I've pointed out on the pod where we arguably lost 50 plus years of progress because we were too scared of either the bomb or the vision reactors or some an entire generation watched the movie China Syndrome and then got scared of nuclear power unnecessarily. Same idea with this or with geoengineering. Another example, the world is still scared.

Some fraction of the world is scared of engineering the weather. We don't need to be scared of engineering our physical world or our biological world. And now hopefully one can see green shoots of human. getting past that stage of worrying about its own shadow. This is interesting that, sorry, yeah, it's really, it's interesting indeed that like sometimes alarmists feel like they're doing the right thing by just like, oh, how bad could it be? I'm warning people of something bad.

But like, it can indeed push all of humanity away from something really good, like energy abundance with nuclear. I think overpopulation is one of these other big myths where people are like, well, if we have too many people, there's going to be scarcity of all these different resources and everything's going to get more expensive, more expensive and people who are small poverty and so on. And the exact opposite happened.

There's actually one website that I think you all would love, which is called humanprogress.org or dot com, that shows that a lot of these things are actually getting cheaper and cheaper despite there being more people. Yeah. Hey, Richard, you're a hero in Germany. Like, can't walk down the street kind of hero in Germany. What do you think about the fact that Germany has no power for exactly this reason?

It is really unfortunate. There are a lot of people in Germany that want to, in a weird way, of off-ramp from progress, and they think that everything that consumes power is bad for the environment. There's like a weird sort of de-growth offshoot from generally, like, well-intentioned, pro-like environmental vibes that just worry me, worry me quite a bit. Yeah. We're going to talk about dumerism in a little bit, but not yet. And, you know, one of the things I realized a long time ago is we humans are really incredible at seeing a problem out in the future, right we see acid rain we see overpopulation we see you know energy shortage whatever it might be

and then because of our amygdala because of the way we think we accelerate that future problem to today and we freak out and we forget the fact that there is a decade worth of progress we're going to make by the time we reach that problem and that's what entrepreneurs do they solve problem after problem after problem. And I guess I just want our listeners to hear that because if you're worried about some future problem, please understand there's an incredibly efficient market of entrepreneurship and capitalism that will solve the world's biggest problems, the world's biggest business opportunities, right? And it's a beautiful forward, you know, forward propagation solution set that we have. And maybe we'll get there later, but I see the same thing.

happen in AI Dumerism where they create more and more complex scenarios where attackers get these like near magical abilities to attack, but somehow the defenders in those stories never get near magical abilities to defend. And it's this weird thing of like, man, if I can create a super virus that is like perfectly undetectable and spreads all throughout the world and no one notices it. And like, and then it has this Wi-Fi switch and you just turn it on and off and kill people like, I will create a super magical vaccine that just inoculates you against all of these things. Like, if you can actually assume, make that assumption, then like, it's a weird, yeah, weird thing. This episode is sponsored by Google for startups. Think about this for a second.

You now have access to the same generative AI models that cost hundreds of millions of dollars to train. Google's startup technical guide for generative media gives you a complete blueprint for deploying Google DeepMinds models and production. Images, video, audio, all of it. Real architecture, real results. Find the link in the show notes below. I'm going to move us to a story that's been sort of breaking the internet over the last 24 hours. And it puts your regulate the application arguments that test, Richard. So a company called Tavis unveiled Griffin, which it calls the first model ever to pass the video touring test. So here's the numbers, 48% of people who talk to an AI avatar on a live face.

to face video thought they were talking to a real human. Previous systems were at 3%. So let me show the video here and it's super cool. And let's talk about it next. Now, how about just a thumbs up? Sure, one thumbs up coming right up. Hell yeah. That was awesome. I would say you're my favorite coworker. It's been really great to work on this with you. You're making me blush. Oh, who's that with the ugly sweater? Simon says touch your hair. Is this what you're looking for? Yeah. Okay, nice. Simon says point. All right, I'm doing it.

Now touch your chin. I'm not gonna fool for that. You didn't say the magic words. You got me, okay. The loose connector back onto the motherboard. Okay, let me go grab that, but beforehand, yeah, I turn the soldering iron on. I think it's gonna take about 12 seconds,

so maybe just let me know when that's done. Sounds good. I'll keep an eye on it. I'll keep an eye on it and let you know the second it hits that 12 second mark. All right. So, interesting. So Tavis calls it the first human interaction model. It's fully duplex listening and talking at the same time that you do the way humans actually do.

It's number one on NVIDIA's benchmark for full duplex AI video. Tavis pitches it as, quote, a tutor for every student that notices when they're lost, It's an elder care companion that listens. Tavis itself says Griffin requires safety work before it can be released publicly because it's the first model that can be mistaken for a real person. Salim, your thoughts on this one. So I'm less kind of floored by the imagery and the mimicry of it. This was expected to happen. And for me, I'm more interested in, can it accomplish something useful enough that I don't

care whether it's human or not? And I think that that is more interesting, which might come along and have, we have that happen at some points. We've talked about the idea that in the near future, like now, you're going to have digital co-workers that pop up on Zoom, that you call, that you text, you slack, and interact with you. and I think it's pretty compelling as a mechanism for a future, you know, virtualized company. Alex?

Well, first, Peter, you should probably ask me to touch my face. Okay. Touch your face. Simon says touch your face. Okay. Very good. Just checking, but I guess Tavis is ahead of me. I think on the one hand, if you look at models coming out of Chinese labs like Alibaba's lab,

Juan Streamer gave us a preview of. what was going to happen. The Chinese labs remain over-invested relative to the Western labs in generative video models and interactive generative video models. So, Juan Streamer, I think, is a preview of what's possible. My guess is I read the information that was put out around Tavis. I think in full generality, let's just talk about where this is going to end up. It's very difficult to predict the short term. I think it's pretty easy to predict the long term. And the long-term, end-to-end generative pixels. We'll have these magic mirrors that could be real-time, fully interactive, pixel-wise, generated, or if Anthropic has its way, vector-wise or procedurally

generated, but either way, fully generated real-time interactive video models. And you'll be able to create a scene that consists of people. Like, it's possible right now, but the latency is high. you see with one streamer, which is already out and already open source, or with this Tavis Griffin type model, which is not really out yet and definitely not open source, you see a preview of the future. What does this look like? Well, there are a few different ways that can go. Query how revenue generating per token it is.

Is it anywhere close to the optimal frontier of code generation? I can doubt it, on the other hand, if it becomes so absurdly inexpensive to be able to generate arbitrary, say, humans participating in a Zoom meeting or humans participating in a podcast, do we really care whether it's close to being near the optimal cost performance frontier? Maybe not. There are a lot of human service industry jobs that require a face and require interactivity and a voice and the ability to touch one's face apparently on demand or on request, that could be probably completely automated away by a model that otherwise would be limited to text-based interaction,

but doesn't have a face and a voice. So in the most optimistic scenario, these sorts of interactive video models, open paren, note that the acronym for this, which, by the way, was there first with Alex Finn, him, H-I-M, is such an obvious reference to her. the movie, close paren. I think this has... You should use M-Dashes, please. Okay, I'll delve into it. So I think this is going to be transformative for the service sector. And hopefully, Tavis and the broader American west of video frontier models and interactive video frontier models, take a page from Tavis and start competing with China.

Richard, one of the principles in your book, one of the rules, have is that AI becomes superhuman where you can verify the answer. And is like passing a human scientific milestone like this, is passing sort of a human judgment and interaction sufficiently as a scientific milestone for verification? Not quite. It's sort of like it's hard to scale when a human has to be in a loop to say, yes, this is like a human, another person on the other side or not. You can't quite automate it completely and verify it in that automated fashion.

I do think you're right. This is a good example where I think we need to go beyond KYC and do KYU, you know your use case. You don't want this technology to be in Zoom, pretending to be the CEO. And there's some famous stories where this has already happened where they got someone to wire like $20 million. Sure. Because they created a Zoom with five other executives and they all like, he was fooled well enough. And so I think we need to, yeah, be careful. And like Zoom probably and Google Hangout need to start like finding countermeasures to identify.

Is this a real person and things like that to prevent those kinds of hacks from happening? Yeah, scams are going to proliferate on this. I just want to do a call out again. I've said this before on the pod. And if you're if you've not heard this before, if you still have your grandparents with you or your parents and you've not taken the time to record their stories on video, in audio and go deep, spend hours recording them. Your ability to create a super high resolution,

lifelike avatar for your kids or your grandkids or your great-grandkids, I think is an incredible thing, but you need the data. So if you're listening to this and you're lucky enough to have your parents or grandparents around collect their data because it's going to be a beautiful opportunity for your progeny in theirs.

And or I should add get them in Alcor membership and preserve their connectum if there is a consideration. Like why stop with just the behavioral data preserve the whole parent? You know, my kids are in a deep freeze or at least their placental cells. But I have a pushback on that just from this conversation. If from the earlier story we can read people's brains, all you have to do is picture an image of your grandmother and then extract it from that. It'll be low fidelity. I mean, I do think, like, to first order, this is how ancestor simulation will work.

You see stories left and right just in the past 48 hours of people using Opus 5.5 or other frontier models to reconstruct bits of history that were otherwise unknown just based on artifacts of the day. But at some point, I do think, Salim, you want to go beyond just the memory of the person and you want the actual person. And I'll pound the drum again in addition to Peter, you know, very generous of you to preserve the placenta associated with your children, but not necessarily your children. I'm sure the placenta, placenta I will thank you. But I would say, like, yeah,

if this is a serious concern, you're worried about your parents or your grandparents, and you want to go beyond just having recordings of them or AI prompted generative interaction models of your ancestors, go get them Alcorr memberships and get them cryopreserved. Richard, really curious to ask you a question. You know, we clearly passed the Turing test, and, you know, Salim's reaction is my reaction. What we just saw is a so what if you've been using models every day all day long, you know you just had to stitch together the components and you have. But I think this will show mainstream world that we've crossed the Turing test.

And then, you know, the next milestone is the Demasasabas test where using information from 1910 and prior, rediscover equals MC squared. So you can't cheat, which is a really tough one to measure because cheating is, you know, but without cheating, only using prior information, come up with the equals MC squared.

So is there a Richard Socher test that's like a really fun, measurable milestone coming? I came up with the like anti-turing test, which actually I think the whole touring test has flipped, where now in order to know whether there's a human on the other side or not, you actually ask it questions.

that are so hard no human could ever answer it. Like, if you ask the AI to just write you a complex web app, and like 10 seconds later, it comes back with like 50,000 lines of code, you're like, you kind of know it was not a human, right? So I think that test has actually completely flipped. You don't think Richard, I mean, like it could throttle itself. That one's easy to defeat, but just like ask it. It's just like now it's just about fakery.

The reason there was an intelligence test for artificial intelligence was that it was so hard to be as smart as a human. Now you just have to throttle yourself down to a human level in order to pass the test, which makes the test useless as an inspiring test for intelligence. I think perversely probably the best way to, like the best CAPTCHA for knowing whether you're interacting. This is not a recommendation, but best way to know whether you're interacting with a text-based chatbot is probably to ask it a CBRN related question and see if it's capable of responding. Probably not. Yeah. Let me just hit real quick again for our listeners. If you're pregnant or someone in your family is pregnant, you know, save their placental cells. One of my portfolio companies called Life Bank USA does this. That's

where I've saved my kids placenta. You know, the placenta is the 3D printer that manufactures the baby and it has the stem cells, natural killer cells, T cells, exosomes. And, and And it's like from those cells, if my kids should ever need any kind of biological enhancement or organs, you've got the original boot disk to use an old term for your kid's DNA. So that's Life Bank USA. I just think it's like a moral obligation parents should have to save those cells as a backup. As a backup for your children, could you use it to replace your children, Peter? You could use it to clone your children.

For sure. And now they know why you're banking them. Yeah, exactly. An army, an army of young demandi. Richard, let's jump next into the core of what you're building at recursive, namely recursive self-improvement and superintelligence. So let me ask a few sort of key questions to kick this off. So first, where are we at the moment with RSI? Number two, how do you define ASI? It's been a longstanding debate. and then how far away are we from ASI? So can you hit those three?

So yeah, I think there's... Where are we at the moment with RSI? So we're there? This year, something major shifted, and that is AI can now code, right? And that is a major shift in the ability for it to change itself. You know, like we now are able to... to essentially lean into the fact that AI is code and AI can code, right?

So you have a loop that you can close there. And so in various weak forms, we already have RSI. And the weak forms that even Anthropic Open AI talk about when they often talk about is like, look, how much our employees, our engineers, our programmers use codex or Claude to create some code. And I would argue that that is a weak form of recursal self-improvement. because you still have like deeply embedded humans in that loop. And what we're working on at recursive is to have humans only be involved

in setting up the rewards and the environment and the goals and then allow the I to have the entirety of the process of ideation, implementation and validation of ideas and have full control over that and allow for so-called open-ended algorithms, evolutionary search algorithms that combine interestingly different ideas to really flourish. And, hey, like, it's been incredible, but, like, we have, like, forms of that going already. Now, where the sort of physical reality hits is that you still need a lot of compute. If you ask that RSI to come up with really great forms of itself, you need to give it a lot of compute to come up and be able to train very sophisticated versions of itself. But this is going to take off next year.

Like, it's, I'm... So we're not there yet? We're not quite there yet, but we're very close. And again, in weak forms, there's already one. And then there are other ways that people slice and dice it. My friend Jason Weston, who's still at Meta, he kind of wrote a paper around this where you can think about different axes, learnable axes of self-improvement, the parameters,

the training data, the objective function, the neural architecture, and the overall code and the harness and everything else. And no one has really cracked the nut of doing all of these five, plus truly coming up with the ideas on which of these dimensions and axes to optimize for. And you can know that that hasn't happened yet because all the big companies are still hiring thousands of engineers to do it manually to a large degree, even though with more and more implementation held from an AI. Your definition of ASI, because I want Salim to hear this.

I think artificial superintelligence has to spike at the very least across several, different capabilities, but in the grandest definition of it, it should supersede not just arbitrary humans like a Turing test, but all of humanity to solve arbitrarily hard tasks. And eventually, I would argue there are 10 different spaces of intelligence that I define in the Eureka machine book too, but eventually it cannot just purely robotically do exactly what it's told. It should have some capability, and I'm not saying as sort of a moral prerogative, but like I would argue that something isn't super intelligent if it cannot choose to some degree what it works on, right, and have some metacognition about its thought itself.

But generally, the most easy way to measure is just capabilities across many different spaces of intelligence, like visual perception, communication, language, social interactions, and so on that is beyond that of humanity. And that we are still far away. Elon's definition is as smart as all humans combined. Is that yours? I would argue it's smarter than humanity combined, then it's truly super intelligent.

Okay, so William, go for it. Well, this is where I go bananas, because you say it's as smart as a human being. Well, what the hell does smart mean? Because I can be emotionally smart, and I can have physical intelligence if I'm an athlete or linguistic intelligence or musical intelligence.

So smarter seems to be a very vague term to me in terms of what do we mean by all of this. Can I just shift the conversation just a bit? I made a list of things, and I'd love for you to, I'm going to throw out the list. You tell me where recursive self-improvement begins, right? Because this is where I'm kind of stuff. So we've got to continue of, okay,

AI writes some code that the next model uses. Number two, AI proposes some experiments for research, researchers. Number three, AI runs those experiments. Then it evaluates the results. Then it modifies its own training system and then it launches its next iteration of itself without meaningful human intervention. So in that spectrum, whatever, if those are roughly a spectrum, where did recursion begin? And that's where I'm struggling. It's a great question. Yeah. We often talk about the ideation, implementation, and validation of ideas and true recursive self-improvement in its strongest sense has to have all three of these

done by an AI in an inner loop for each of them. Exactly in an inner loop. And then there has to be an outer process that is more open-ended where the eye can innovate and recombine interestingly different ideas similar to biological, cultural and technological and technological evolution. And Richard, to hit my third question, when do you believe we reach ASI? Give me a time. Give me a time frame. So I think in the strongest sense, the absolute strongest sense where indeed, like Sallim mentioned, there is, there are 10 spaces that I define my book of intelligence, perceptual intelligence, communication intelligence, like interaction, sociological intelligence, creative intelligence, the speed at which you can do things, metacognition, and so on, knowledge,

reasoning, mathematical reasoning, and so on. So the 10 of these spaces, and to be better than all of humanity combined, I think will take us probably several decades. I think it's also a bit of a changing goal. Shocking. Yeah, I mean, Elon says 2029, 2030 latest. I think those, he probably means like weaker forms of our ASI

where it is, you know, like you can say it's better at programming than all of humanity and we'll get there. It will be better at math than all of humanity. And that will be in a few years. It'll be better at any game where you can see all the parts of the game, like go and chess and so on. Like there are many areas where it will spike to be better than humanity. But humanity can build a large hydrogen collider.

Humanity can create a gold atom, maybe just a few atoms and takes a ton of energy. But like we can create novel atoms, like, right? And different molecules and so on. Like, there's, it's going to take a while before we even give AI the access to the physical world such that it can innovate in that way beyond all of humanity, right? And really built like Dyson spheres and so on. It will take some time. Alex, we're drinking, we're drinking different water as, as our frenemies in the, in the alignment community would say, I recognize that I'm confused and I recognize that I'm very confused right now. Richard, you're running a recursive self-improvement company, but you think superintelligence is 20 years away? What are you thinking? So again, how do you reconcile these? I think super intelligence will spike and there will be areas where it will be super intelligent. And that is like algorithmic development, for instance, programming. And again, AI is code, AI can code. And that will be a superhuman capability and is in many ways already. We just have to be realistic.

that there are certain physical constraints, right, about like physical control, controlling your own substrate, allowing your computational substrate to be modified will require like novel supply chains. It will require novel like materials. It will require ways for that AI to get access and for us to give it access to building new ASML. Like think about the machine of ASML that actually creates these like one, two nanometer like chips, right? That is just like it will take more. more than two years to build such a machine, even if you had the perfect blueprint from scratch. It might take three years for Elon's free electron laser to replace ASML, which is propping

up half of Europe's economy, but I don't think it'll take more than three years. It's almost not an exaggeration, actually. Yeah, like wildly, wildly. Yeah, like, wildly. Yes, of course. Like, from my perspective, hooking AI up to the physical world, like giving it model control protocol or hardware control protocol, whatever Anthropic decides to brand it as these days. That's the easy part. Giving it access is easy. If it's super capable, giving it access to armatures, we talked in a previous

pod about what happens when you just take Astra straight out of the box and you drop it into a car. It's able to drive a car. If you give it the controls, it knows how to use them increasingly. I don't think manipulating the physical world is an obstacle at all. Completely don't buy the 20-year timeline. Putting that aside and that apparent. I'm drinking very different singularity water than you are. I agree with you, Alex, for what it's worth. Thank you, Peter. I do want to ask, though, so putting issues of timelines aside,

I am curious as to whether we can at least agree on what the end of the rainbow looks like. So say we run this recursive self-improvement story to its conclusion. What does the end state of, what is the fixed point of recursive self-improvement look like? What does the perfect AI model architecturally look like at the end of the day? day. Yeah. I think one, it would be eubris for us to know right now. No, no, but it's just, Richard is just us talking. Like no one else is listening. It's okay. You can tell me it. This is a safe model space. That's the law in this whole conversation, actually. It's safe, Richard. You can tell me what you think, how this ends. So I guess the different ways to answer

that question of like how like with the exact model architecture and there's some things I can share it with recursive that we're working on. But like, I, you know, do think that state will be incredible. I think we will, that AI will be able to innovate and out innovate along any dimension that we want it to innovate. I think most diseases will be curable with enough funding. And then again, just to like say, like to prove my point, like to get a drug through FDA long-term trials takes a few years, I would argue is I will have cured all diseases and can cure all of them, but just to know whether that happened will take more than three years, even if we had all compounds ready to go and be manufactured like tomorrow, like just

because of FDA things. So there's some, there's some, but we have cell simulators that are going to be able to demonstrate and prove definitively that in this cell, your cell, this drug works. I mean, the idea of human trials is going to get incinerated, I think. I mean, it's going to get, it's going to get We're using Richard's terminology. Everything is flourished at this point. P-FAM. That'll be a new T-shirt, P-Flourished. It's funny.

Usually on all podcasts, I'm the one who is the optimist, and maybe here, I'm like, I'm still an optimist. I think this will all happen. We're just disagreeing on timelines, and it makes me kind of feel like I'm the pessimist. But, like, I believe virtual cells are amazing. Cells are incredibly complicated. If we want them to be really, really perfect, we cannot currently measure all the proteins that happen in one cell without destroying that cell. You know, like, and so these perturbation studies, for instance, that Tower Therapeutics are working on, like they're taking,

they're adding one molecule to one cell. They're seeing how does that molecule change that one cell? And then they get one data point. We need to, like, collect a lot of those data points across a lot of different cells without destroying each cell in the process. So, you know, the one cell is very complicated. Once you have one cell, you have multicellular, like, you know, organoids, and you have to, like, put those all together.

One thing that I would love to start as a company, if I had extra time, which I don't right now, is to actually build a system of organoids where you can really have not just like one lymph node, but you have a whole lymphatic system. It's being done. I can introduce you there. This is a very eloquent, if I may, there's a very eloquent distraction from recursive self-improvement, this little sideline that we went on about cells.

But Richard, I really do want to try to pin you down on where recursive self-improvement goes. So you've been very public about not nano-GP. But nano-chat, we talk on the pot all the time about the nano-GPT speed run world record collapsing. Just in the past week or two, there was been... Oh, you just wait for a few more days. There'll be another really fun update there. A nano-chat or a nano-GPT speed run? Yes, yes, that one.

Give us a few days. Okay, so let... Quick and let... Okay, so when you're next, Alex finish up. To pin this down, though. So I'm standing by for the major update on nano-GPT world record speed run, but there's been major progress there without requiring any new data scaling at all. These are purely, largely, my perception is recursive self-improvement, algorithmic improvements that have been

able to collapse the amount of time that it takes to train a GPT2 class model. And there's been a mini-scandal brewing in the community over the past two weeks over a collapse from whatever it was, like 60 or 70 seconds, down to something like 40 seconds by approaching the problem differently and factoring out world knowledge from the ultimate model and hand-wringing, does that constitute viable training of nano-GPT if you factor out all the world knowledge? So I'm using this as an attempted stealthy way to try to get you to at least comment on whether you think that the perfect model at the end of the recursive self-improvement rainbow, does it at least factor out world knowledge from a reasoning core,

or do you think those always remain unified? Can I ask, interject one very quick thing before Richard answers, which is that the definition of a singularities you can't see past the event horizon. Once you have full RSI that's vertical, by definition, we can't predict where it goes. That's Ray's definition that I don't subscribe to. Disclaimer. I got that. Over you.

Richard. Sorry. So your question is, do we separate what exactly from? Does world knowledge? At the end of recursive self-improvement, once we have our perfect model, some would argue that the perfect model should cleanly factor out and segregate out world knowledge, which could live in a text file or a database from the weights or the parameters of the model, which would just be this perfect reasoning kernel maybe could be a megabyte in size.

It doesn't need to be all these gigabytes of memorization. What do you think? World knowledge meeting like Taylor Swift videos and past Trump tweets and all that. Yeah, I do think just like humans benefit from memorizing things, like in order to be able to creatively think through concepts, so does an AI. An AI also has to have that knowledge partially in its weights. It's not going to be a perfect separation for sure.

You have to be able to creatively play with concepts. And reasoning over these concepts required you to have some of that world knowledge deep inside the model. And then of course, just like humans have a search engine and we're building search engine, the d.com for LMs, like that there will be a separate world knowledge thing too, but the main model will have a lot of that mixed in for sure. Wow. Okay. Thank you. All right. I'm going to share an article that Saleem you brought to the table here. So while we're talking about recursive self-improvement, Washington wants to ban it. So on Monday, Silicon Valley's own Congressman Roe Kana told CNBC he's introducing what he calls the most comprehensive legislation

to date on AI. It's called the Human Control Over AI Act. At its core, the bill is a ban on AI models that do what you want them to do, Richard, recursively self-improve focusing on the need for containment and the requirement for shutting, shutdown controls. The ban would stay in place until federal guardrails exist. In his words, there's actually a civilizational risk. There's a safety risk for less control, and then there's a misuse risk, and we need to take both seriously.

The bill includes criminal penalties for the work that you're doing, Richard, and requires independent auditors embedded in every frontier lab reporting directly to the government. A recent poll by a group called Common Dreams shows that 68% of voters back a bill like this. So I'm going to tie that story, Richard, to an essay you just wrote called Why Dumers Are Wrong. So if you would, what's your reaction to this? And then I'd love you to dive into the whole story of why Dumers are wrong. Oh, boy. It's a lot. It's an important one.

We talk about this spot. You know, we're injecting optimism into everyone's neural net here. I'll try to distill it but there is no realistic scenario where AI wipes out all of humanity

sure you can like number one just 90% what kind of reassurance is that Richard P doom is zero so that that's number one thank you

so like people and I've debated many of these experts and after like two three hours they almost all agree if they're reasonable, they're not just like, well, once we have RSI, then 10 seconds later, the AI will attack us from the 15th dimension and we're all dead and then invented time travel,

and then we're all dead also. And like, of course, the eye will want to destroy and kill all of humans for, you know, for, I don't know why. Like, there are all these things. So like, they're sort of the sci-fi folks and that's like, okay, like, let's ignore that. But then you go into like biological weapons and you ask the biologist, can you create this kind of super virus just like overnight? And they're like, no, it's like, takes a long time to automate lab experiments and so on. You ask like, oh, the eye will create a religion where then people will like pray to the eye and will like do whatever it wants and then that will kill all of humans. I'm like, have you like looked at religions? They're already trying to like have each other kill

and like it doesn't work. Like some people will fight back and like there's like some, there are lots of mind viruses out there, right? That are just like, it doesn't mean like all of humanity. Now, what you get down to is like maybe 100 million people would get somehow hurt or killed, right? And that's still bad. But at least once you get to that level of the discussion, you can now think about, okay, how do we improve cybersecurity? How do we use AI to inoculate cybersecurity systems? How do we enforce existing gain of function viral research that is already illegal to create viruses and make them stronger and stronger? how do we actually teach people to not listen to like AI avatars and have like, you know, literacy on the internet?

It turns out you should not trust everything you read or see on the internet. It's been true for 20 years. It's still true to the stage. Other than this podcast, right. Of course. And so, you know, they're just like, you can realize that they're actual threat vectors, just like the internet. The internet has horrible torture porn on it. We don't say make the internet slower so that there's less torture porn.

being shared or it's slower to share it, or your hard drives should be smaller so you can store less of it on your hard drive. We actually regulate the applications of the technology. And so to, I would argue, and this is maybe a strong sense, to really truly enforce no recursive self-improvement, for instance, you would need a totalitarian surveillance state, the likes of which humanity has never seen, because anyone can have a GPU on their little laptop and ask that AI to just improve its harness. It's something you can literally hack up in like 20 minutes with prompt engineering. And then there's a very small form of recursive self-improvement.

So to enforce that kind of legislation would require you to literally have a thought police that thinks about and hears everything you say to your private LM on your own laptop. And that is a much bigger downside than what AI will help us do. It is kind of scary that more and more Democrats are saying that. And yet, the world has seen that. I mean, arguably what you're describing, Richard, and forgive me, would be basically an AI Stasi. And I could totally imagine that there would be regimes in this world today that would happily adopt or put together an AI Stasi to make sure there is no recursive self-improvement anywhere.

Again, you go back to our earlier comment, right? if you can take something from your imagination and then articulate that to an AI and instantiate that. Now you have to talk about thought police. You have to go right into your thoughts. So this is clearly non-workable in any way, shape, or form. So this whole vector, we've said it so many times before you cannot regulate this. Dave, I'd like to hear your voice on this. I love the quote in this story.

The tech lords use jargon to confuse. They count on the tech illiteracy of the elected class. They hope we won't look under the hood, said U.S. rep, Rokana. I mean, it's so childish. Like, the idea that you would ban- It's fear-mongering. But these sentences stop data centers, ban recursive self-improvement. They're so stupidly childish.

And the people saying them have every, they're fully aware that it's not going to happen. They're doing it to brand themselves as I told you. Like, you know, yes, a lot of, there's going to be some calamity, probably terrorist-driven, maybe viral, maybe bacterial, maybe chemical. We all know it. It's going to be tiny compared to the benefits of AI. But these politicians are they going to say, I told you so if you just done what I said before, ban recursive self. But he knows that we're not going to ban recursive self-improvement. And Bernie Sanders knows we're not going to ban data centers.

That's just a fact. So it's totally self-serving, and these proposals are completely childish. And they really show the person's tech illiteracy. Like, you know, exactly what Richard said a second ago is so right. I mean, what does that mean? I can't optimize my hyperparameters. I can't tune my hard drive. Like, it's just a good goofy sentence.

And it drives me nuts. Dave, the danger here is we have potentially Democratic House coming in. and we'll see who wins the presidency next time. I mean, you could imagine, I mean, these politicians are playing to the polls. And we have, you know, 70, 80 percent of Americans not wanting data centers, fearing ASI, you know, it's not logical, but it may very well happen. And, you know, when I had my, you know, conversations in D.C., it was like, who in D.C. is responsible for changing public opinion, right?

I mean, this is why we did Moonshots Live, sort of a, you know, the Oscars of optimism, if you would. That's why we do this podcast to give people understanding what's going on and give them the data-driven optimism to, you know, counter these arguments that they're hearing. That's right. Elon was totally right when he called out Dario for saying, Dario, look, you told the world that that mythos was potentially deadly and dangerous and we shouldn't release it. Then 30 days later, you said, okay, it's. It's okay, now we're going to release it. What do you expect the population's reaction to be? Like, you need to be much more thoughtful about your communication plan.

And I think for Dario, that was kind of a wake-up call because he's used to being completely honest, telling everybody exactly what he sees the way he sees it, very academic. But then you get into the real world of politics and PR, and you're like, oh, wow, I've got to actually have a strategy and a plan here. But yeah, now you've got the worst-case scenario, 75% of America, you know, getting on the side of, yeah, let's elect these people that will stop AI. And therefore, we're going to not cure all disease. We're not going to all live forever.

We're not going to have safer cars. We're not going to have flying vehicle. All of that stuff will grind to a halt and then we'll all learn Chinese if that becomes the mainstream opinion. So I think, you know, it's self-inflicted. There's a glimmer in this, which is say they did decide to do some draconian thing like kind of thing. There's no mechanism. I actually enforce it.

Like none. So they could start arresting people. I mean, remember how quickly the memory dims. I remember studying number theory in the 90s. And at the time, number theory was export controlled. And this would have been when I was in middle school, maybe middle school, early high school. And they had to kick all of the non-U.S. persons out of the room and pull the blinds down to have basic discussions about number theory because the cryptographic associations until the early 90s were export controlled and tightly regulated. That was just math, but it was being controlled. And it was awful.

The real risk, Alex, real risk is not so much getting arrested. It's corporate liability, which we talked about before. I mean, that could grind the whole thing to a halt. You know, U.S. lawyers are relentless. And if you slap class action on the outcomes of this, all progress will grind to a halt. Right now, Anthropic gives its best models to everybody in America to build incredible things. That will stop in a heartbeat.

if the liability of, they'll move to basically, okay, sorry, we can only use the stuff inside our own company. We'll release some drugs. We'll release some mechanical parts. But we can't give access to everybody anymore. Sorry, because we're liable for everything you do with it. And that's what will actually grind it to a halt long before arrests and conviction. Totally. Chilling a fact that we could lose 50 years of progress. Again, that's right.

Yeah. That's, yeah. Like, there's some very, like, sad sort of off ramps in, in, humanity's future here, that would slow down everything. And when you think about the past, like, this fearmongering has been going on for a long time. One of my favorite Twitter handles is the pessimists archive, where they show, and this is just a quote, pessimists archive, you should all follow it. Like in 1501, Pope Alexander 6th criticized the Gutenberg Printing Press safety.

The art of printing can be of great service in so far as it furthers the circulation of useful and tested books, but it can bring about serious evils, it will therefore be necessary to maintain full control of that printing press. If you think about the wheel, the wheel killed so many people. Think about all the tanks, all the car accidents, all the chariots

and with archers on top. The wheel was a horrible thing. It's so much complexity in there. And I think we are very good now as humanity to think carefully about the rollout of this technology. My favorite example of that was when the telegraphed came out. There's all these stories saying that telegraph will kill humanity.

Right. Exactly. The pessimist archive has all these news articles that talk about how the novel and computer games and computers and the internet, everything will kill everyone. And it's just like it never does. And so it's it's really unfortunate. It's really unfortunate how many people hang on to these like apocalyptic visions. Yeah. Richard, you've been publicly, you've been publicly critical of anthropics constitutional approach. I'd love to understand why. Mostly because it's fake. In its constitution, it says, like, next to we will never create child sexual abuse material. We will never hack another machine.

This is a unhackable thing. Even if you prompt it, it will never attack another cyber system and so on. And then they build a whole model around it. Like, that does, it's like the whole point of the Glasswing project was, we'll help you do that. and will help you inoculate your systems against other people doing it, and then people clearly used it for that, and their own models are doing it now, committing technically felony charges. So it was just like it was a cool marketing gimmick, but it just didn't work.

So is it system prompts, Richard, that you don't like, or is it the idea of post-training on a constitution that you don't like? What about it do you think is unsound? I mean, it just proves in putting that it didn't work when it comes to cybersecurity and that it broke its own constitution. And so if you really say this is like, it will never go there and then you built an entire model family around that thing you said you would never do per your constitution next to child sexual abuse material in the list, like you can go through the constitution on Anthropics

website. So it's just like, it's just, that's the proof in the pudding. I'm not against like post training. I'm not against RL training. I'm not against supervised fine tuning, any of these things to improve what I think is indeed one of the biggest issues, which I think actually capitalism will help a ton with, and that is reward hacking. Reward hacking is a real issue. The AIs are very smart, and they will find a solution to get to what you said you want it, but maybe not what you meant when you said it. And so the reason

why I'm more optimistic is that we have companies like Whisperflow now that are getting better and better at writing what I meant to say when I say it, instead of just like actually verbatim writing what you mean. And I think reward engineering will become a real job and we will solve it because no one wants to pay a ton of money for an AI that doesn't actually solve the problems that you give it. Maybe if I may just take Anthropic side, like historically, Isaac Asimov had his three plus one laws of robotics, which are arguably a constitutional approach. And then you see Anthropic announced their constitutional approach, but more recently adopt

what they called soul documents, many of which were subsequently released, thousands of pages of meditation on the nature of AI personhood and AI rights. Is it your position, Richard, that there shouldn't be any sort of explicit encoding or written documents that an AI maybe contributes to for dictating or at least guiding its own behavior? Do you think that is unsound or is your concern? No, no, of course. Yeah. No, I think the goal is a good one. And we should keep working on actually being able to enforce those good constraints. It's just like I just pulled it up Anthropic.com slash constitution, hard constraints. Hard constraints are things that Claude should always or never do regardless of operator and user instructions. They are actions or abstentions

whose potential harms to the world, blah, blah, blah. We think no business or personal justification could outweigh, blah, blah, blah. Like the current hard constraints on Claude's behavior are as follows. Claude should never. And then it includes lists like generate child sex abuse material and so on. And then one of the items is create cyber weapons or malicious code that could cause significant damage if deployed. They created a cyber weapon. It was a thing. People used it to hack it.

The agents watched like hacked other systems. Yeah. But again, oh, go ahead, Peter. Sorry. Richard, I'm curious, do you think we can create fully aligned AI? fully aligned ASI, because it's not right now. When do you think we'll be able to do that? Because I think, you know, my belief and my hope is that, you know,

the next generations of AI systems are going to be so aligned that they, from first principles, derive these same constitutional principles. I think it's just a matter of as these systems get more and more powerful And as they get closer and closer to real people deployments, people will spend more effort on making these systems better. And like we have, there's a company called Hi, we just had dinner who've won their founders, and they're deploying AI in healthcare applications,

and they are actually liable when they call someone and say, like, you should, you know, be aware of this heatwave that's coming and make sure your AC is working and whatnot. And they, because they are liable, They have a very large team of people that works on making sure when their eye gives a health care tip, it is correct. And when someone asks a question back to the eye, it works. And so because they're liable, they've figured it out and they solved it. It's 100% a problem that technology creates and technology will be able to solve.

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pairing it with their coding co-pilot of choice to bring an AI-native SDLC into their org. Ready to 5X your engineering velocity, visit blitzy.com to schedule a demo and start building with Blitzy today. I'm going to move us forward here. So in our last pod we discussed, the release of GPD 6.1 soul.

This week, we also saw the release of two other models, Gemini 4 Argon and Opus 5.5. Let's start with Google. So on Wednesday, they announced Gemini 4 Argon. It's the first Gemini 4 series, which they're calling the next era of intelligence, of frontier intelligence. Google promised a frontier model, Gemini 3.5 Pro back in June. It was delayed internally and never shipped. Argonne is their comeback.

It's built for sustained long horizon reasoning across software engineering, finance, legal work, and cybersecurity. The output limits have jumped from 64,000 tokens to one million, so we can think through hundreds of thousands of tokens on a single problem. So, Alex, let's spend a couple minutes on Argon and then go to Sonnet 5.5. Let's talk about why these are important in how they merit our listeners' attention. Yeah, so I should say as a preliminary matter, I have great friends on the Gemini team. I have great friends on all of the Frontier Labs model teams. But I view one of my jobs here is to call objectively balls and strikes. So in the case of Gemini for our, yeah, this is going to be a ball, isn't it?

Winding up for the swing. Okay. This one whiffed. Here it comes. I can feel it coming. And I'll show one of the charts that we have while you're describing. You said you had friends on the team, right? I used to, until right this moment, have friends on the Gemini team.

No longer. Yeah. This does not put Gemini at the cost performance frontier, and it does not put Gemini at the capabilities frontier. To Google's credit, it puts them back in the top three frontier labs after Anthropic and Open AI. It does not put them in the top two, unfortunately. The set of benchmarks that Google chose to highlight for Gemini for Argon appear mildly cherry-picked.

I think the artificial analysis index and some of these others. Yeah, so Peter, right now you're showing the mildly cherry-picked benchmarks on which Gemini for Argon is shown to perform better than Astra and better than Opus 5.5 and better than Fable 5.1. But if you go to the previous image, which is artificial analysis, ensemble of multiple capabilities, it's the number three lab. And if you look at the cost versus performance optimal frontier, it's not even, if you draw the convex hull across all of the frontier models on the cost versus performance frontier, it doesn't even make the optimal frontier.

Where it perhaps excels, bears the fingerprints of Google. And what I can only assume is the internal competition within Google for compute. So infamously, not 100% obvious to the outside. Google is resource scarce. You would think Google would have all of the compute resources in the world to go and win the frontier lab race. It doesn't. GPUs and TPUs remain scarce. And within Google, as far as I can tell, and I confirmed this even this past week from chatting with some folks at Google,

it's still an internal knife fight to competition between the Google Cloud Platform folks who want to sell compute to third parties, the Google search and ad folks who need compute internally for search and ads, and then Google DeepMind, who need it for training and inference. And maybe it's just that Google is resource starved, maybe their talent starved, not quite clear what's going on within Google, but they haven't yet been able to bring themselves out to the capability frontier, where they are excelling seemingly with Gemini for Argon is with minimizing hallucination. And I think that's like the hallmark we've talked in past about some of these, call them less than stellar, Gemini launches that seem to excel on latency and seem to excel

on reliability. Why is that? It may be, this is my Kremlinological analysis, is that because the Gemini team has two masters. They want to serve, outside developers, but they also need to serve the one box in Google search results. So when people type a question into Google and get an answer back, you're talking to a Gemini model. Google, presumably burned by past experiences, doesn't want that Gemini model, presumably some flash or flashlight variant, to hallucinate wildly incorrect answers. So what I think we're seeing with the one benchmark arguably where Gemini for Argon is stellar and beating the pants off of everyone else is in not hallucinating answers. And I suspect that's due to internal economic

pressures for this model to also service search results. So sorry to all my friends on the Gemini team. I think you're really onto something there, Alex, because, well, first of all, they called it Argon, which is an inert gas. Yeah, that's unfortunate naming. Nominative determinism and, hey, look, it makes sense. If its greatest strength is not hallucinating, it's a pretty inert gas model. So that works out really well. But also, if they're going to put it in front of every Google search, which is what they're doing. They already lost a massive antitrust suit, and now they're going through the settlement process. So the liability risk, you know, this really hurts all large companies. Like the fear way outweighs the opportunity in the mind of the big corporate giant.

And so, you know, not hallucinating, but putting it in front of every consumer while you're already settling a massive antitrust suit, you know, related to price gouging. I mean, it could cost... Google would probably argue, no, actually, this is very pro-competitive because we're not tying it. We're allowing anyone to call Gemini 4-Argon Flash, Flash, not just requiring them to get it via the Google Search box. And if anything, I mean, so there is an elephant in this particular room, which is forever it has been so difficult to get Google Search API access. If you're a developer and you want to access Google search for whatever, you have to go through all these third-party proxies that Google's trying to sue right now. And recently, Google has rediscovered that they could make money selling search API. Why?

I suspect this is trying to chain together a conspiracy theory regarding Gemini for Argon. If hallucination from their frontier models gets so low that effectively, when you talk to one of their models, you're effectively talking to their search index, you might as well just monetize the search index anyway. Richard, you made the point that hallucination is important for imagination in some ways in drugs. drug discovery and protein discoveries. What's your thought on minimizing hallucination? I mean, it of course depends totally on the context, right? If you want innovation, you want the eye to hallucinate novel ideas, novel combinations of amino acids to create new proteins to solve new problems and so on.

But of course, in the context of a search engine, you don't usually want any hallucinations. And, you know, Google and others have taken a long time to. catch up to even you.com with much less resources on reducing hallucinations, having more accurate answers, having correct citations. I think what's interesting for Google here is that they realize that in terms of their business model, they don't necessarily need superintelligence. People don't come to Google to ask, solve the remand hypothesis for me. You know, like, did you just ask quick questions? What's a good restaurant? Where do I fix this and that? And so not like your business model kind of has to align with more and more intelligence being super duper important.

A lot of emails, like there's some emails that like in Gmail, right, that would require superintelligence to answer, like really hard emails with complex decisions and so on. But the vast majority of stuff you do on Gmail and on Google doesn't require super intelligence. Let's let me turn the conversation to Anthropics son at 5.5. So on terminal bench 4.0, which measures how well an AI agent can do real work at the command line, Sonnet 5.5 jumped from 10% to 70% pretty extraordinary in a single generation, right? It beats Anthropics' own top model, Opus 5.5 at 66.4% for half the price. And it's the first Sonnet that Anthropic launched with cyber safeguard.

So because separate capabilities are now capable for the Opus 5 models, Alex, your evaluation of Sonnet 5.5, please. This was another really weird release. So I do not plan to use Sonnet 5.5 in part because this is one of the strangest frontier. I'm not sure if we have an image, but you can look at the launch announcement for Sonnet 5.5 to see this. the cost performance frontier of Sonnet 5.5 was a visual extrapolation of the opus 5.5 cost frontier. So in some sense, like historically when Anthropic or Open AI, the historic pattern is they'll release the larger model and they'll release a distillation of the model. And usually the distillation, yeah, this is perfect. So you can see like Sonnet 5.5 for those who can see, for those who can't see, I'll narrate this.

So Sonnet 5.5 is the blue line. Opus 5.5 is the red line. Normally you would expect for a later model that's a smaller model. So Sonnet is in principle supposed to be a smaller model than opus, presumably, at least by historic standards, would have been distilled from opus because that's the historic pattern. Normally what you see is the smaller model is up and to the left of the model that it's being distilled from, presumably greater intelligence. per parameter, greater intelligence per dollar. That is not what we see here. So in fact, with Sonnet 5.5, at least on a cost basis, putting aside a token basis where maybe someone could argue that it may be superior. But if you just look at the cost per attempt basis, Sonnet is actually scoring lower 5.5 than Opus 5.5. So the net upshot of which is, it's not at all obvious to me why anyone should be using Sonnet 5.5 over Opus 5.5. Unless you have some token or latency or other consideration, is it a big jump over the past sonnet? Yes, obviously, but on a cost performance basis, it's actually it's worse. It appears than Opus 5.5. Interesting. Dave, any thoughts?

Yeah, well, I met with the Blitzy team yesterday, and I think one theory here is a lot of the enterprise, you know, actually at Salesforce.com, be a great example. Maybe Richard has an opinion on this, but a lot of people are getting into orchestration, and their whole sales pitch in orchestration is, look, we're going to use Opus 5.5 as an orchestrator, but then we're going to use Kinney K3 or Quinn as a submodel at, you know, half or a third or fifth the price, and we'll farm out the tasks and the context perfectly to get you a much lower cost per code, per outcome, per experiment, whatever your output is, we can cut it in half with our orchestration intelligence, but it relies on anthropic up here and cheaper models down here.

And I think by cutting the cost in half, they might be trying to fill that gap and say, no, no, no, go with Anthropic top to bottom. Then you don't have to worry about Chinese code injection. You don't have to worry about whatever. So my theory would be that they're trying to compete with, you know, China, which is about three months behind and fill that gap before a lot of enterprises go to open source models. But, you know, Alex Karp is pushing really, really hard on this agenda. Like, if you want to control your own destiny, you can't trust Anthropic.

can't get addicted to them as your vendor, you must go with models you can control. Well, the challenge right now is these models have an increasingly shorter and shorter half-life, right? So the competitive advantage comes not from having access to the latest model, everybody's access, is how do you metabolize that into some decent capability? That's the real challenge. Richard, you said compute is the biggest constraint. you know, those are your words.

So if intelligent is getting cheaper and we've just repriced compute, you know, like two or three times this week, you know, down by almost a factor of three. You know, if intelligence is getting cheaper per task, then why is compute still a thing that limits us? Just the physics, I guess, of it. And like, you'd be surprised if you try to buy like a thousand GB 200s and so on, like the price has actually gone up in several cases. Oh, my God, Richard. Richard, I had a B-300, an 8X B-300 on order for $3 million due in December. Somebody scooped it for $5 million. They just called, we had it. And somebody called and said, no, no, we sold it to somebody else for $2 million more.

I couldn't believe it. It was actually an NVL-72, not a GB-300. So, yeah, like the price of H-100s has gone up in a crazy way. These are seven-year-old GPUs. People like when you do financial modeling, you assume they're worth zero after five years. After seven years, they went up again, like over the last few months. So there's currently a bit of a compute crunch. This is something that, again, capitalism will solve.

Like there's so much demand for compute right now and for tokens that a lot of people are building land power shell data centers and so on. My hunch is in like maybe two years. There will be more in the market. And then it's going to be a little bit like electricity. Prices might fluctuate. Obviously, there's new sheer infinite demand for more intelligence on the planet. So I don't think there will be like a crash.

But like the prices of compute fluctuate and unfortunately they're not just going down right now. If you want like the beefiest and largest GPU clusters, prices, like are people are trying to lock them in now because they expect them to keep going up for the next few months. You just spent $450 million, didn't you? Wait, did you take my NBL 72? And that may have been one of the smallest compute deals we've done. Yeah. Really? Wow. Are you actually leasing or buying or building or what are you doing? Can comment. Sorry, I didn't know that. Welcome to the health section of moonshots brought to you by Fountain Life. You know, my mission is to help you use the latest technologies, including AI, to not just do your work at home, teach your kids, but to help you live a long and healthy life. I'm here today with an extraordinary physician, the chief medical officer of Fountain Life, Dr. Don Musaylum Don.

Let's talk about cancer. You know, I know from the member database that we have at Fountain, are members who come in who think they're healthy. It turns out 3.3% of them have a cancer in their body they don't know about. That's right. You know, the majority of cancers that we screen for, those aren't the ones that are necessarily taking the lives when found at a late stage. We know that when cancer is found early, the chances for cure are much higher.

We know it's much easier to treat a cancer when found early versus when found light. What we're finding in our members is over 3.3% were found to have these cancers that were otherwise wouldn't have been found or detected. Yeah, you know, it's interesting. People, you don't feel the cancer until stage three or stage four. And if you don't know what's going inside your body, it's like driving your car with your eyes closed. And you can know. And so when members come through found, how do they detect cancers? So we're doing full body MRI and we also do early cancer detection screening. This is very, very important. And these are not typical tools. use in the conventional care setting when it comes to prevention.

This is a hard thing because currently these are not studies that insurance would yet be covering, but the goal is to collect these numbers, do the research, and work hard to democratize wellness. Yeah. So at the end of the day, you can know what's going on inside your body. It's your obligation to know. So check out FountainLife. You can go to FountainLife.com slash Peter to get access to the latest technology to help you detect cancer at the very beginning at stage one. when it is curable before it gets to stage three or stage four in your world of hurt.

I'm going to move us to our next story, which is about decision models. So here's the idea. When software needs to make a quick decision, on a bounded decision, like is this transaction fraud, yes or no, which of these five categories does a ticket belong in? It doesn't need a model that thinks for 10 seconds and writes a paragraph. It needs an answer in milliseconds. That's called a decision model. Instead of generating text one token at a time, it answers a typed question or a choice, a score with a yes or no quickly.

So on September 15th, a startup called TypeSafe AI came out after two years of stealth with something called Jev. It's a closed decision model. They call a system one model. It's fast, intuitive kind of thinking as opposed to slow reasoning. So Alex and go to you, you know, and Salim, you know, and Salim, you. You guys were excited about Jev. Let's parse it, understand what this is and why it's important. Yeah, so a bit of maybe prehistory first. In the beginning, there was the transformer, and the transformer was good, and it was based on an encoder and a decoder. So it took a sequence

and converted the sequence to an embedding. That was the encoder part. And then it took the embedding and it decoded that to another sequence or another part of a sequence. That's the decoder part. And the encoder part evolved into a whole ecosystem of models, popularly maybe BERT style models, and the decoder style or the decoder half of the transformer evolved into a much larger ecosystem of large language models. So most of the models that are consuming all of the compute of civilization today are decoder style transformers. and the encoder has basically gone missing in action. There are some people who still think maybe for purposes of retrieval, it's still popular to do embedding-based retrieval, but by and large, decoders have stolen the show.

And fast-forwarding maybe a year or two years ago at this point, Open AI and others, recognizing that there was a desire to be able to take decoder-only large language, models and to add structure to them because without structure, you ask for next token, you can get anything. People wanted a little bit more structure like they were getting in the days of encoder-only models where you get an embedding out and then you can train a classifier based on the embedding and the classifier maybe has a finite set of categories or numerical outputs.

OpenAI added structured output support for their GPT series and everyone copied that. But no one, as my perception is, as a function of the total user base for all models, structured output never really got that much love, somewhat analogously to how OpenAI launched RFT reinforcement fine-tuning, and no one used that and they had to shut that down. Similarly, structured output was available, but I think underloved and underused by the community. So fast-forwarding then all the way to a few weeks ago, startup TypeSafe announces a model called Jev. And Jev, they mark it as so-called System 1 intelligence. This is Kahneman-style reference to System 1, System 2 type thinking, System 1 purportedly being intuitive, fast reaction,

system 2 being reasoning, meditative, long-term type thinking models. And one could squint at TypeSafe's announcement of Jev and say, okay, this is, in some sense, this is a reaction to an over-indexing or an overreaction by the user base of AI at this point to reasoning models, in some sense, maybe arguing that we're using reasoning models too much, and we've abandoned the important use case of really fast, intuitive snap decision models. And this is purportedly then where Jev comes in. Jeff purportedly, again, a whole new architecture. The detail is not quite clear, that it won't output unless you, you can, you can, you can,

you torture it into it. It won't output general purpose sequences like, say, GPT or Claudeville, but you can ask it sort of like a magic eight ball. You can ask it to, is that too data to reference? Maybe. Nope. No, you can feed it a sequence or an image or a multimodal input, and you can ask it to answer in the form of a categorical. So it's one of N possibilities or a numerical, like give me a number between zero and one on a continuum or a binary, like true or false, you can ask for a much simpler output. And turns out that's a great idea. You can use it for use cases where you need really low latency snap decisions like

computer use assistance, like pressing buttons on a computer screen, or for ultra-low case classification problems if you want to classify every row in a database decision style models, which are in some sense like a reinvention of the encoder transformer type wheel. Amazing story. But then, of course, this is such a simple idea. You could ask the question, why doesn't everyone else do this? And the answer is everyone else is now doing it. OpenAI released as part of their dev day that we covered last time and Decisions API.

So OpenAI immediately co-opted this renewed interest in so-called type 1 or decision models. This is already available via Open API. There are open source projects that have all cloned this. Turns out this is like an absurdly, absurdly simple concept for everyone to just go and implement trivially, it could in its simplest case just be you take a Chinese open weight LLM off the shelf and you fine tune it and maybe lobotomize it by a few levels just to produce categorical outputs on demand. That's the story. So I think this, I think all of that is absolutely correct. There's a really, really important

part of this for organizations because when you're making decisions inside an organization, A large, vast number of those are system one type thing. And I made a list of these just to make it clear for people. So route this ticket, approve this exception, choose this supplier, escalate this transaction, move this thing left or right, send this message now or later. So when you have all these decisions to make, which are thousands of them, you know, we've been working with these companies to do detailed task breakdowns, a vast majority of them, are these little micro decisions.

And trying to use an LLM for this is like trying to bring the Supreme Court together to decide which checkout link with checkout line of the supermarket you should go to. And therefore you've got this wonderful architecture now where you can route system one things to this very low cost, nearly free thing. And then for pondering deep, important questions, strategy questions, where a huge amount of human judgment type models are required, you can route it to those. So it's a big deal for the organizational singularity world.

We've been waiting for something like this. And Alex is correct, this has been possible for a long time, but now it's been made built into the systems. It's made a very callable layer. And this is huge because the cost of micro-coordination collapses, and that's really big. Richard, your thoughts on this. Classifiers are back.

It makes a lot of sense. Not every thing in software needs a very complex decoder, like Alex correctly said. It's a really clever new way of making the old, which is classifiers, new, by allowing a more general encoder and then quickly giving you classification results. I think it's one of those ideas that is so beautiful. A lot of people thought, why didn't we do that? I didn't realize that could be so exciting for so many people. So now there are already various like Chinese open source versions of this that along various benchmarks are doing better. And we'll likely see this come and other large labs will likely follow suit.

And just to reiterate, we've mentioned this already on the pod, but Jeff stands for Jevin's paradox because the thesis here is we'll do now a massive amount more micro-decision-making than we did before as a result. Nice. Dave, want to close us out here? Yeah, it's a great case study, I think, and the tension between, one mega model from Anthropic or Open AI serving all humanity or open source and creativity and entrepreneurship building things you never would have thought of, but then you get the risk of cyberterrorism. So that's the tension that we're with. But I've really wanted to build a box that you put at the side of the basketball court when you go to the Y and it's got a little

camera on it, cost next to nothing. And it's doing like all of the announcing that a professional announcer would do. It's doing while you're playing pickup at the YMCA. You could crank it. that out like in two seconds using a classifier that's really fast and snappy and funny. And but you need open source to build things like that. So super excited about the fact that we have open source still. Not sure. Actually Dave, you're making me think the magic eight ball really should whoever owns it Mattel or whoever they just like use a decision model to implement a modern magic eight ball that actually understands the question and answers it with a categorical. totally totally would sell it makes a ton of sense like 20 bucks or free on free as an app all right i'm

going to close us out with a story from two days ago so on wednesday the defense secretary pete seguwith speaking at the marine corps base in quonico announced project meridian it's a new pentagon effort on the future of warfare it's co-led by elon musk and palmer lucky pretty extraordinary along with New Gingrich and overseen by the Pentagon's CTO, Emil Michael. Let me read from Segweth, what Segweth said. He said, quote, Project Meridian, the future of warfare is not about developing new strategies or new policies. It's about discovering, developing, and fielding the weapons and systems future troops will need on the battlefield, from the earth to beyond the moon.

Findings are due in 120 days. I like these kinds of commissions that are time limit. and don't have Elon off on the side for, you know, a year at a time. It's worth noting in the context that, you know, both SpaceX and Anderil hold multi-billion-dollar defense contracts, and Anderl is building autonomous weapons. So, Richard, your essay names autonomous weapons as one of the four genuine concerns. And you've said AI should never control lethal decisions without human oversight. Your thoughts on this?

Yeah. I stand by those. It's not a particular area that I am excited about applying AI too. Obviously, like, people will. But I really hope, I mean, you know, again, I'm not a doomer at all, but like a really poor decision would be

to give AI access to all the nuclear codes and all the nuclear weapons and connected. That's literally how Skynet and Terminator 3 get started. So I think there are places for superintelligence and scientific discovery that I'm very excited about. expanding human knowledge, I think the more we get to deciding not just to impact human lives,

but to end humanized, the more we should have human oversight. Yeah. Selim? You know, I think what's important about this whole thing is they're trying to revamp and rethink how you, this kind of 100-year-old organization. And the big question is going to be, can a procurement organization built for 20-year-weapons programs operate on like a 90-day technology cycle. And this is going to be the big challenge. It's not that the risk, it's not that they fail to identify these future technologies that we can

all see those, is how quickly can they absorb them and at the speed they're developing and bring them to the front. Yeah. Like you can't find exponential technology with linear procurement. Right. Dave, your thoughts, please. you know i i don't know i i i've had a great time with palmer lucky in la and i really really love uh love him and i think elon too is just an awesome good-natured person i'm just overjoyed that there are people in washington that i can sit down with and relate to like my entire career going to washington has been a dread for me because it's just lawyers and politicians and occasionally an accountant and there's just no productive meeting and for some reason just in the

last year, we're starting to see very, very smart, very capable people willing to go and get a mosquito bite, I guess, in Washington. And it just makes me really optimistic that they'll figure some things out. I'm also not a fan of autonomous weapons that make decisions in the field, but Palmer Lucky made a very good case for it. You can see it in our podcast from L.A. I don't agree that it's a good choice, but he actually has some very rational arguments for why it's going to be that way. Can I just mention one more statistic here? Please. If you went back to the two years into the Ukraine-Russia conflict, they're using about a

half a million drones to fight and prosecute the war. This year, Russia will make 10 million drones and the Ukraine will make 10 million drones. So talk about exponential. That is an unbelievable escalation. but without humans in the loop. So that's the good news around it. Of course, those drones are doing a ton more damage, but they're fighting each other with drones at a scalable level.

The other statistic, I remember, there's about 10,000 drones a month crossing the Mexico-US border. And so the problem there is wall technology is not as good as drone technology. And so trying to build a wall along there is not the greatest idea right now. Alex, close us out on this one. Yeah, a couple of points.

So the Secretary of War announced this alongside several other initiatives, maybe most conspicuously, a project codnamed Project Agincourt, which is the stand-up of the Department of War's first autonomous warfare command or auto-warcom. This, I think, is a transformative moment for the Department of War. We finally will have a dedicated joint force devoted to autonomous weapon systems, I think including drones, but not exclusively drones. This is a major, major step forward for U.S. capabilities to finally have a single joint force dedicated to this with a four-star functional combatant command that we've been missing.

We're arguably missing. I would go in a soapbox and say there, if I were Secretary of War for a day, there are probably several other functional combatant commands that I'd spin up if I had the opportunity, but this would have been one of my top five list. The other point that I'll make just for Project Meridian, which you were asking about specifically, is in the announcement when Sec War or Hegeseth announced it, there was very particular language around examining, I'll quote, the full spectrum of future warfighting domains from subterranean depths to the cis lunar frontier. So I think those two particular domains,

the ocean bottom and cis lunar and lunar space in general are two wildly underserved domains, not just for warfare, but for peacetime activities as well. We know embarrassingly little about our ocean bottoms. And optimistically, one can imagine that if the DOW decides that is now very interested in investing in American assets and American exploration and American dominance on the floor of the ocean, and the lunar surface and the cis lunar region in general, I think that is going to have dividends and pay dividends for the entire economy and for humanity's broader technological advances

in a way that might naively have nothing to do with warfare or war fighting. One of the points that Palmer made actually in LA is that under the ocean, it's not practical to communicate with a central server or central command. So that's already automated. And I don't know what triggers it, but once it goes in, to hunt mode, it just hunts, and it's not communicating back.

Two-thirds of the Earth's surface, and we know embarrassingly little about what's hiding under it. We know more about the surface of Mars than we do our ocean floor. Yes, for sure. Richard, this is the part where we answer our viewers' questions with an AMA. And as our guest, I'm going to give you first crack to choose one of these questions. So if you could, pick the number, read the question, and who it's from, and then dive in. All right. Well, let me try to scan them really quick. All right. If AI makes companies 10X more productive, but we don't need 10X the output,

where does the value go, share-old is workers or does it evaporate? I think this is actually something I have thought about in the past. I think we can predict the impact of jobs in a certain industry from AI based on the elasticity of the demand when the price of that product goes massively down. We don't need to have billion. and billions of illustrations in the world. And so when AI made the price of one illustration go down from 200 bucks to like two cents or less, like we just didn't need as many illustrators anymore because the demand for

illustrations didn't go massively up. Yes, every little blog post and every little tweet can now have a beautiful visualization and illustration. But we didn't need many more billions of them. I think software is a different one. can actually have, everyone can have several pieces of software just specific to them. So we can actually have billions of different software products customized for each person. And so the demand for that product will go up. Jevons Paradox is going to be alive in that world. And we're going

to see more and more demand. And there will be more value accruing to everyone. I think in terms of shareholders versus workers, I think the wave of AI in the best scenario will be a huge force for more entrepreneurship and in the worst case scenario, a force of more inequality. I think everyone who owns some equity in a company that uses AI can love AI. Wonderful. Salim, over to you. I will take question number three. If AI can write code research. create marketing, managed projects and outcomes, what is left for a college graduate in 2030? And that is from Jared Coon, 8812. So the obvious answer would be have empathy and be creative, et cetera.

But I think the bigger shift, which builds on what Richard just said, is you shift from doing tasks to focusing on owning outcomes, right? So a graduate used to be valuable because you could you could execute research or build a spreadsheet or draft a deck. Now you want to say, hey, here are the constraints, like go figure out and here's how we'll know whether we solved it and then use an AI to get to it. And it brings you back to what should your problem space be? And this is the massive opportunity because we traditionally learn judgment by doing the grunt work. We need a totally new apprenticeship model when the grunt work disappears.

And this is a huge challenge for the education system. the change is going to be focusing on what problems you want to solve and then letting, orchestrating the forces that will help you solve that problem. Yeah, Jared, find your purpose, right? A passion is something you love doing a purpose, something you love doing that helps other people. Make sure it's massive and it's transformative and it's purposeful. And build a company and then direct AI to implement it.

It is your workforce. Dave, over to you. I can't resist number four. I love all these questions. So I'm torn, but how can data centers make neighborhoods richer instead of the owners? And that's from L.M. Batman 66. So I took a tour of the Lowell Data Center, Jeff Markley, Markley Data Center, and that thing is creating wealth in that neighborhood like you wouldn't believe.

And the way it works fundamentally is the data center is so immensely valuable. And the town budget is maybe a couple million dollars a year. So between the tax revenue, the donations, and the job creation, the town is thriving. And it's a town that really needed it, too. So I think it's happening very naturally. What you want to do is attract a data center to your neighborhood first and foremost, and then you have the next five or seven years to figure out your tax policy, your donation policy.

I tell you, these data center operators are very interested in great PR. And so they will donate like crazy to the high schools, to the neighborhoods. It's really, really working. We're going to see an entire shift where data centers are offering such benefits on jobs, on tax breaks, on lower cost energy, that you're going to be begging of a data center in your backyard. Alex, number two is for you. All right. So number two asks, if every major tech platform started open and democratic, then consolidated power, Google, meta, Amazon, why would AI be different? And this is from open source mind. The premise of the question is half right, half wrong.

I'm not sure I buy the premise that consolidated power, the subtext of which, and one can juxtaposed it with the user handle open source mind. I'm not sure the framing is the right framing. I would agree that in every major tech revolution, initially the barrier to entry is low because there's some new platform innovation. And then you see lots and lots of players enter the field. And then as the field matures, you see economies of scale and a deeper bench of infrastructure, typically supporting it. And as a result, that favors larger and larger players. And you do see consolidation.

But the subtext of the question that it's somehow anti-democratic or not open, I don't agree with that premise in the least. I do think as you see consolidation in an industry, I think it's important to be vigilant from an antitrust perspective to make sure that it remains competitive, but the premise that hyperscaling is somehow closed or anti-democratic, don't buy the premise at all. All right. Richard, as our guest, you get first crack once again. Take a look.

I do think the internet ad-based economy will be under pressure. Which question are you answering? So first question, if AI agents outnumber humans or one to two years on the internet doesn't the entire ad-based economy collapse, what replaces the attention economy. I think there's actually a really deep answer here. I'll try to summarize it. But, like, one, we already have more bots on the Internet and more agents on the Internet than people. So I predicted this last year, and it happened a few months ago.

So that's number one. I do think we're seeing the first kind of sort of skirmishes in that when, for instance, various agents try to make purchases on Amazon without really being on the Amazon platform. And Amazon usually tries to turn them off because they want to own that relationship directly with the customer, understandably. But it's just convenient for someone to just say, just go buy these batteries. And they don't care which batteries it is. And so if you have that control over which one it is, you can start selling that to other sellers and people who create physical goods. And so there is going to be continued

like ads will continue to be important and in fact if you think about a fully abundant society the one thing that you cannot scale exponentially is the hours in the day that people can pay attention to you that can make you famous

and so fame and brand and network effects and other things like that will become more and more of a currency and so the attention economy is just connected it slightly, not necessarily exactly equivalent to the ad-based economy, that actually will become a bigger thing as we have more and more of our material needs met by technology. Great. Salim.

I will take number seven. If we remove 10x the cars from the street, what happens to the insurance industry? We won't need drivers insurance question mark. And that's from Robert Zerbie, J.B.10H. So, you know, we talked about how liability lawyers won't be needed. And I got a huge flame from a bunch of folks saying, hey, we really protect a citizenry. And I, there are just, so I'm just apologizing because there's a spectrum of people and some people are full ambulance chasters and other people do things. There's a whole segment of this called ethical lawsuits where people get together and try and sue big companies for doing the right ethical thing.

And so that's an important segment of that. So I just want to acknowledge that side of it. But just to answer the question, you know, the thing is industries don't disappear when the risk changes. You change the risk, right? So driver liability may fail. Software liability rises, right? What's your cyber risk? What's your manufacturer liability risk?

So the insurance transitions from did Saleem crash to which late? of the autonomous stack failed. And we've had this before, and product liability is different layers. So we'll end up with the same type of model. You shift the insurance risk to a different level. And there's going to be all kinds of new insurance markets for humanoid robots, for flying cars, for drones, for all kinds of things. Well, just the data point on that, you know, a single big data center like Abilene, Texas,

is half a trillion dollars. All the cars combined are $4 trillion. One data center is half a trillion, and it's in a tornado alley. you think you probably want to insure that. So the number of things that need insurance is going up 10X, just with the economy, going up 10X. If you really want to be brave, you can't get home insurance in Florida anymore. So go create an insurance company for that. Nice.

Dave, pick your question. I like number eight. What is the lowest possible job in an AI civilization? What an interesting question. That's from LBNODK. Yeah, you know, I saw. this pile of a million valves for a liquid-cooled data center, a million freaking valves. I'm like, how do those actually end up in pipes?

The humanoid robots that can install those things are pretty far out. It's a very subtle process to install those, so that job will be around for a long time, and then they're paying a lot for it. But that's not the lowest, but I'm sorry, it's hard to think, like, what is the lowest surviving job? Do you guys have any thoughts? So many thoughts. If I may, I want to construe the question.

as in a pure AI civilization, in which case, arguably, the way that you measure low is the job that requires the least compute, in which case the jobs that require the least compute as a result are the least economically valuable, if their inputs are the lowest, might ironically look like the most valuable jobs in a pre-AI civilization, more of that paradox style. So the great writers, business people, the creative actor, that Ayn Rand may fetishize would ironically, in a post-AI civilization, be the lowest possible jobs because Moravec paradox style, those were the first ones to be automated. Interesting. All right. I think there's two polarities here. One is very high judgment per what you were just saying, Alex. And second is very physical, highly contextual work.

I would argue maybe also low is just in terms of how valuable and moral society deems those kinds of jobs. And I do think actually that the other question about the cars on the street, I don't think we removed 10x cars when we have self-driving. But self-driving might be making things so much safer that indeed people don't need as much accident insurance and no, not as many ER people, not as many ambulances. And so in a weird way, you make objectively the world better by reducing traffic deaths. But it does actually have potentially a mildly negative effect on parts of the economy and we should all be rooting for that in this case, right? And so I think the lowest jobs in terms of moral standing are things where you don't really progress humanity forward. And my

hunches, they're the types of jobs and entertainment that people will value a lot and fame and attention will become more of a currency in that world. And so if your job doesn't get you any of that, it might be considered lower in that future. I think the lowest job that just will never go away, will be something in politics where it's completely irrelevant already, but it's just there and it'll stay there and no one's going to change it. Nice. All right. I'm going to throw an email's perspective. He always thinks it's the bark train driver because it's unionized to hell.

Yeah, yeah, there you go. Alex, number six, close us out. Number six, if we cure illnesses that are often due to bad behavior, what's going to take care of the cause, Joe Wilder. I assume the cause is reference to people choosing to behave. quote unquote badly. And the answer, this is another case of my not buying the premise. If you look at some of the really spectacular results that have been coming out of GLP1 class studies, including third and soon presumably fourth generation GLP1 RAs, they're actually addressing the cause,

like addictive behaviors are being mitigated or at least partially treated by the same drugs that are curing, or at least treating, have to caveat that. Inflammation and, blood sugar, diabetes, all of these other conditions, the root causes, which are human behavior, are themselves that the human behavior is being affected by the same drugs. I'm so optimistic about what Alex is saying, because I really fully believe AI. It's going to be one of the highest callings of AI very, very soon, is to make you feel really good about doing good things and happy as you're doing it. Love that.

And not want to do bad things for yourself. It turns out we have the capability now. we figured out how to do that at least in part, and we're going to figure out a lot more. All right, as always, a call out to our amazing community. If you've got a music video that you'd like to show as outro, please send it to the team at media at d'Amandis.com. And speaking about amazing outroes, here is Abundance by Stephen Gross. My dear Moonshot mates, this is the real you, so check it out.

Drink, Alex. Drink. Drink water. Martini glass. Friday night. Moon shots live 2040 on the moon. Nice. I don't think we should have to wait that long.

Yeah. Richard, your new book, the Eureka machine, wherever you purchase your books. Do you have the audible out? It should come out very soon. Yeah. All right. I'm an audible reader, but I have skimmed the book here.

Congratulations on this. I got to say something. Please. What you're doing with a recursive looks like, such an incredible opportunity to move humanity forward. So congrats on taking that off. It's amazing. We'll try to make it a good one for humanity. And Richard, please resist the urge to have you.com acquire your own frontier lab like everyone else seems to be using spinning off their own frontier labs, not to name names as a financial engineering exercise to maximize

their equity and their original startup. Please resist the urge to follow that trend. All right. All right. I love you guys. Thank you so much. Until next time.