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

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

• 0:00Speaker 00

  Richard, I'm curious, where are we at the moment with RSI?

• 0:04Speaker 01

  In various weak forms, we already have RSI. We're not quite there yet, but we're very close.

• 0:10Speaker 00

  When do you believe we reach ASI?

• 0:14Speaker 01

  I think it will take us probably several decades.

• 0:18Speaker 00

  On Terminal Bench 4.0, Sonnet 5.5 jumped from 10% to 70%, pretty extraordinary.

• 0:24Speaker 02

  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.

• 0:34Speaker 03

  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.

• 0:45Speaker 00

  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.

• 0:54Speaker 03

  This, I think, is a transformative moment. This is a major, major step forward.

• 0:59Speaker 02

  The big question is going to be...

• 1:03Speaker 04

  Now that's a moonshot, ladies and gentlemen.

• 1:08Speaker 04

  This episode is brought to you by the Abundance Summit and Link Ventures.

• 1:14Speaker 00

  Welcome to Moonshots, everyone, your number one podcast in 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 Weiser Gross, our in-house ASI; Dave London, our impresario of AI investing; Saleh Missmael, our globetrotter, who's home today. Amazing, Saleh.

• 1:42Speaker 05

  Woo-hoo.

• 1:43Speaker 00

  And the father of the organizational singularity. I'm Peter Diamandis, 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 4 Argon, Anthropic shipped Sonnet 5.5, OpenAI shipped GPT-6.1, Sol, 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 Sun Catcher. 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.

• 2:26Speaker 00

  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, Marc Benioff.

• 3:07Speaker 00

  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, Grey Croft, Nvidia and AMD, 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

• 3:52Speaker 00

  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.

• 4:00Speaker 02

  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.

• 4:15Speaker 01

  Yeah. Thank you so much. So great to be back here. I love your guys' constructive optimism. So important in the world right now.

• 4:22Speaker 02

  I have a bold prediction for this episode.

• 4:24Speaker 00

  What's that?

• 4:25Speaker 02

  This one episode will prove our thesis for this whole podcast more than any other episode we ever record.

• 4:31Speaker 00

  Awesome.

• 4:32Speaker 01

  Let's go.

• 4:33Speaker 00

  Alex, do you know Richard?

• 4:36Speaker 03

  Yeah, Richard, you and I have chatted quite a bit. I don't think we've actually ever met in person, though.

• 4:42Speaker 01

  Really? We have not, and it's the first time on a podcast together. It's gonna be awesome.

• 4:46Speaker 03

  Amazing. Well, let's make history.

• 4:48Speaker 00

  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 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.

• 5:31Speaker 00

  Could you take a moment and, you know, talk to us about full stack AI for science and why you think progress has slowed?

• 5:38Speaker 01

  Yeah, so progress has slowed largely because we have so many different sub-disciplines and disciplines, and even scientists of them realize, like, wow, that 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. In the same stream biology, you study biology, you're going to be either a cell biologist or a molecular biologist, or you're like in medicine, and there's so much separation

• 6:22Speaker 01

  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 gonna 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.

• 7:04Speaker 01

  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 the 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.

• 7:36Speaker 03

  Alex? Yeah, I can say I disagree. As Peter, you and I wrote in Solve Everything, math, science, and engineering are cooked. I'm curious, Richard, what your latest timelines are?

• 7:46Speaker 01

  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. Very few people have to still know C++ and complex pointers and memory architectures and so on. We can get larger 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 meet in natural language to do science.

• 8:29Speaker 03

  But to pin you down, as I recall from my understanding of your book, three to five years for the physical sciences?

• 8:37Speaker 01

  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 gonna 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 diseases that are out there in aggregate, right? And so, those diseases will get cured, and then we're gonna cure more and more complex diseases, we're gonna 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 gonna solve more and more problems, and it'll just be a question of, how much money do you wanna put into compute to then solve which kinds of problems?

• 9:19Speaker 03

  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 a time interval? So, I'll try once more, timelines. When, 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?

• 9:46Speaker 00

  Alex, if I could just add a layer on top of that. So, we've talked on this pod a lot, that math is cooked, right? We're seeing millennium prizes all falling.

• 9:54Speaker 03

  Yep.

• 9:55Speaker 00

  And the next natural, if you would, barbecue is likely to be physics.

• 10:02Speaker 03

  Incineration?

• 10:03Speaker 00

  Incineration, yes.

• 10:04Speaker 03

  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.

• 10:11Speaker 01

  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 researcher 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 wanna just move as quickly as you can towards solving these problems. I think maybe the problem of math is that there's many subfields of math that haven't really had, they're 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

• 10:55Speaker 01

  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.

• 11:39Speaker 01

  And so, like, 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, in physics, and so on, where we can iterate without having to look at long-term human trials, will be even faster.

• 12:02Speaker 03

  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.

• 12:15Speaker 02

  Actually, I was with Surtash 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...

• 12:36Speaker 03

  No, no, no, Dave, we're going with the Orwellian language now. They're not cooked, they're being flourished.

• 12:42Speaker 02

  That's funny, because he literally said cooked, but I'll go back to him and tell him, like, tell your friends they're flourishing.

• 12:47Speaker 03

  Tell them all as well you're being flourished, not to worry.

• 12:50Speaker 01

  If your goal was to prove as many theorems as possible in your lifetime, like, now is the time to just grab, like, 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 in math, 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?

• 13:21Speaker 02

  Well, Surtash 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 gonna 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.

• 14:02Speaker 02

  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?

• 14:12Speaker 01

  And I would love to talk about some of those, like Peril Bio, Proxima Labs, Ignota Labs, they're 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? A 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.

• 14:39Speaker 02

  I just want to insert something, because you opened the door. Eric Brynjolfsson, our very good friend, I did not realize till yesterday that HAI Lab, HiLab, at Stanford invented the word "foundation model." And he sent me the whole, you knew that already?

• 14:55Speaker 01

  Yeah, of course, like, lots of Stanford friends, and Percy Lang and others, worked on foundation models. I love Eric Brynjolfsson, 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, like, understanding of how companies actually adopt AI, and which tasks are getting, you know, helped by, in real rollouts for companies. Yeah, he's a co-founder of that, too.

• 15:21Speaker 02

  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 lab.

• 15:34Speaker 00

  Following in his path. 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, 1,000x the rate of discovery. Your thoughts on that?

• 16:01Speaker 01

  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, who are 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, you know, the big guys, Eli Lilly and others do, and Lila, too. I think, you know, the, 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,

• 16:45Speaker 01

  that's what open-endedness, what inspired us a lot at recursive, and we have many of the world's greatest, like, researchers in that subdomain 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, like, 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 Peril Bio. They build tiny organoids in a petri dish.

• 17:26Speaker 00

  Right.

• 17:27Speaker 01

  And get this, if you love animals, you too can love AI. Why? Because they got 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 pluripotence themselves, 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 Peril 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.

• 18:10Speaker 00

  I love that. And you can take my stem cell, you can take my skin, create an iPSC cell, grow my own organs, and see how a particular drug would work for me versus a generic individual. Saleem, wanna jump in?

• 18:25Speaker 05

  Yeah, a couple of things. One is, you know, science has always been a coordination problem, right? You're kind of trying to bring, and it's always been very structured into departments and journals 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 a domain collapse of the scientific method, and now, like, anything is possible from there. This is amazing.

• 18:55Speaker 00

  Yeah. I'm gonna push again on what Alex said. Physics, when do you, you know, Einstein's theory of 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 gonna flourish on the back of math?

• 19:15Speaker 01

  I think the biggest domain is actually going to be biology. Physics has been interestingly stuck in many ways. Like they 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 already to balance like plasma inside Tokomax like is a very hard control problem that where AI is being used.

• 19:57Speaker 01

  I hope we can eventually make better theories for, you know, quantum gravity and all kinds of other complex issues and have get a better world sort of formula. I 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 this micro individualized separated phenomena. Neural nets are great in combining all these little things like we know what a neuron does, we know what one bacteria does in our microbiome.

• 20:39Speaker 01

  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.

• 20:47Speaker 03

  Yeah, Alex.

• 20:50Speaker 03

  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?

• 21:01Speaker 01

  Three years ago, when I started the Eureka Machine book, I had this chapter on the virtual cell and I was really proud of like trying to lay it all out and I had to rewrite that whole thing because in the meantime, you know, Mark Zuckerberg Foundation started virtual cell.

• 21:12Speaker 03

  Everyone's doing it, everyone, not just us. Everyone has a virtual cell model. Dave, even if Dave doesn't, even if Dave doesn't think Dave has a virtual cell model, I'm sure he'll spin one up soon.

• 21:24Speaker 01

  So I think that most, so there's this famous paper by Rich Sutton on the bitter lesson in AI.

• 21:31Speaker 03

  Never heard of it. Tell me all about the bitter lesson. This is a new concept for me. What is this useful for?

• 21:36Speaker 01

  I don't know. You're probably joking, but probably maybe some, 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 the sim like 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 it's just a ton of data and compute to like actually train that model. And 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 of

• 22:18Speaker 01

  any kind of question, prompt engineering I like invented and was nicely cited by the early GPT 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.

• 22:54Speaker 03

  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?

• 23:03Speaker 01

  I, 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.

• 23:22Speaker 00

  I'm going to jump into three fun breaking stories this week, two on science and one on AI. And Richard, they, you know, relate back to your work and your book. Our first story was published in MIT Tech Review today. It tells the story of researchers at Israel's Weisman 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 post, put up the slide here. So take a 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.

• 24:04Speaker 00

  You know, 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 it images no human has ever seen into the scanner, generate predicted brain scans and then train on those. The model effectively builds 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.

• 24:48Speaker 00

  So is a brain simulatable, is the brain a simulatable domain? Or is this something different?

• 24:55Speaker 01

  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 they, you know, it's 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 at the network, but like, um, 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 like in a coma and you want to see, are they still thinking stuff? You know, like that would be incredible.

• 25:41Speaker 00

  Alex, tell us more about this, about this story today.

• 25:43Speaker 03

  Yeah, so this actually is a result from March. So this, 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 Galant lab at UC Berkeley was doing this to decode the, 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

• 26:27Speaker 03

  King, many other groups doing language decoding, vision decoding. More recently, even fast forwarding to just the past 24 hours, Neuralink just announced their first scaling law studies on pre-training foundation models, frontier models off of Neuralink electronic data. And this is on an individual patient basis, but they're capturing copious amounts of, 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.

• 27:09Speaker 03

  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 have, 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

• 27:51Speaker 03

  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 GPT-1, 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 Jean-Marie King and Jack Galant.

• 28:17Speaker 00

  Amazing. Saleem, what, what happens in the world where you can know someone's thoughts?

• 28:23Speaker 05

  Well, I think you, you, let's be careful. We're decoding the perception here, not the actual thought, right? That's like quite a bit more complex.

• 28:30Speaker 01

  What's the difference? What's the, other than the brain region, other than the brain region, this is a difference without a difference?

• 28:35Speaker 05

  I think there, there is a difference.

• 28:38Speaker 00

  Well, no, when, when you imagine, right? When you imagine something in your mind, it lights up the same neurons as when you're seeing them.

• 28:46Speaker 05

  That, that's fine. That's fine. Just, I just want to make sure we don't mix the two because you can have thoughts without, without necessarily the perceptions around it, but you can definitely have one without the other and the other without the other. So, 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, how the brain works.

• 29:31Speaker 01

  I'll take the other. You bring up a really, oh, go ahead, Richard. Sorry. You bring up a really good point, which is, I, 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, there's no like, one is smarter than the other or anything. There's 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

• 30:15Speaker 01

  something versus I just have the thought. I'm just trying to think like, what does the world look like if the I could really just like extract my thoughts? And now I need to like, structure my thought clouds into sentences more and more precisely.

• 30:27Speaker 02

  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 and might actually think a lot more like you do and less like your wife does. Cause.

• 30:42Speaker 03

  I'm not sure. Maybe, maybe we should be taking the position that anyone who doesn't have an inner monologue is just a pee zombie.

• 30:48Speaker 05

  I got one more thing. And I remember we had one of the top linguists in the world come in and speak. And we were 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, cause 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 that was an interesting.

• 31:24Speaker 03

  Well, you know what? You're saying, 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.

• 31:35Speaker 05

  I'm not connecting to you to that. That was not me.

• 31:37Speaker 02

  Dave. Oh, no. The, 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, that's, you know, I think with, 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, it's not what we use for artificial neural nets. It's, it's something different. And if we discover that, we might find that neural net training can be 10, 100, a thousand, a million times more efficient. And so hopefully that'll come out, you know, within a year.

• 32:22Speaker 03

  Dave, can you, could you define gradient descent for our listeners?

• 32:24Speaker 02

  It's funny, Ilya Sutskever, when, when he's on interviews, he's like, there is only one algorithm. The algorithm is gradient descent. Everything else is just irrelevant compared to. 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 trillions, 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, how responsible

• 33:09Speaker 02

  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, you just hill climb to the best possible state. And then magically after about a hundred million dollars of compute, or in Richard's case, I guess 400 million, it magically starts thinking through this one algorithm gradient descent.

• 33:37Speaker 03

  Yeah. I think it's really back, back prop that we're talking about cynic doctically rather than gradient descent would be the, the obvious comment.

• 33:44Speaker 01

  So maybe, yeah, back prop is, I guess, the efficient computation of those gradients. Maybe I'll, I'll add to, 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 at the top of the mountain and you're trying to find the lowest, 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

• 34:26Speaker 01

  not just one lowest point, but there are many different low points. And if you, 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, 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, to help help with like depression and PTSD and other mental diseases, like they, it helps to increase the learning rate to jump over another mountain and go into a different valley of attraction,

• 35:10Speaker 01

  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.

• 35:29Speaker 02

  And you know what else is really incredible? I could rip on this for hours, but if you look at a big model like Kimi K3, it's 93 layers and it's trained, trained, trained, trained, trained. 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 FMRI's, 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, yet, you know, well, okay, maybe that's a bad example, but.

• 36:03Speaker 03

  You're gonna take that sitting up, Salim?

• 36:06Speaker 05

  He's probably right.

• 36:08Speaker 02

  The way your brain wires as you learn is completely unique to you, yet, 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, which is really, really strange, you know, that, that it can't, it can't find its own way back to its, 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, 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, if you put them through a transform, like a Fourier transform view will suddenly say, oh my God, this is why they line up.

• 36:45Speaker 00

  Yeah. I was, I was just with my fraternity brother of ours, Dave, who's the head of neurobiology at USC the other day. And he's, you know, this is the most exciting time ever for brain science. Right? I mean, we're, you know, the brain has been a black box for, for, since humanity began and our ability to understand the brain and deal with, with, you know, mental disease, I think is going to be extraordinary.

• 37:15Speaker 02

  Well, so you can simulate, this is why Liquid AI was founded, actually, because we completely reverse engineered the C. elegans 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 so they both. So go ahead, Richard.

• 37:37Speaker 01

  There's a really cool thing. Since you mentioned C. elegans worms, there's not many people that bring that up. A friend of mine is a professor for neuroscience at Harvard, Sam Gershman. He, one of the most brilliant people I've ever met, like, he actually did a study with worms where he cut them in half. And one C. elegans worms, they do have a brain, right? And then they have a lot of other like neurons in 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.

• 38:12Speaker 00

  Wow.

• 38:12Speaker 01

  And reacts 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 electrical signals and so on. But clearly, like, brain chemistry can change massively with just like, you can get hangry, 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 of a 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

• 38:52Speaker 01

  that we have that are in our DNA, that just trigger after like, 20 something years, right? When just the right things then happen with your body and your biology. So I think there's still so much that we don't, we haven't.

• 39:04Speaker 00

  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 DC 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. Right? I love that. You know, this is the exact kind of gene drive work that colossal, that

• 39:48Speaker 00

  the extinction company is doing. You know, I think most people don't realize it's like, you know, when I was raising my kids, I would ask them a question, who, which, 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 pod, I'm sure people know mosquitoes are the deadliest life form on Earth.

• 40:08Speaker 02

  By far.

• 40:08Speaker 00

  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?

• 40:22Speaker 03

  For decades, we've been scared of our shadow. So I have a classmate, Kevin Esvelt at MIT, who was a pioneer of the gene drive, one of the pioneers. And he's had a devil of a time getting states 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,

• 41:05Speaker 03

  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 is, Peter, you mentioned malaria and other non-human animal borne diseases cause an enormous amount of suffering, human suffering, and also non-human animal suffering. But secondly, because there's a way to do this that doesn't actually involve killing the animals themselves. Historically,

• 41:31Speaker 00

  Exactly. It's humane.

• 41:32Speaker 03

  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.

• 41:58Speaker 02

  That was the part that got me most excited was, we spray things, we're basically poisoning biology, and now we can shift actually.

• 42:05Speaker 00

  Poison ecosystem, right?

• 42:07Speaker 02

  And leave the poison ecosystem. Yeah.

• 42:09Speaker 00

  Yeah. Richard, your thoughts on this story?

• 42:12Speaker 01

  I concur with everyone, like, it's wonderful. And it's so interesting, like, people, I hope we can amplify these, 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.

• 42:42Speaker 00

  Yeah, Dave, I can't wait for this to come from DC to Vermont and Massachusetts and all of these.

• 42:48Speaker 02

  I was gonna 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, 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 in value.

• 43:19Speaker 01

  I do think it's really important to try to like, you know, keep the bees, like, alive, right? And not that spray a bunch of pesticides, make sure birds still have enough, like, insects to eat and things like that. Yeah.

• 43:30Speaker 03

  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 fission 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.

• 44:12Speaker 03

  And now, hopefully, one can see green shoots of humanity getting past that stage of worrying about its own shadow.

• 44:18Speaker 01

  This is interesting that, sorry, it's, 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, and people are, it's more 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, like, that that shows that a lot of these things are actually getting cheaper and cheaper despite there being more people.

• 44:58Speaker 02

  Yeah. 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?

• 45:09Speaker 01

  It is really unfortunate. There are a lot of people in Germany that want to, in a weird way, sort 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 decrowth offshoot from generally, like, well-intentioned, pro, like, environmental vibes that just worry me, worry me quite a bit. Yeah.

• 45:33Speaker 00

  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

• 46:17Speaker 00

  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.

• 46:34Speaker 01

  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 wifi switch, and you just turn it on and off, and then 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. And like, it's a weird, yeah, weird thing.

• 47:13Speaker 00

  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 Startup Tactical Guide for Generative Media gives you a complete blueprint for deploying Google DeepMind's models in 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, it puts your regulate the application argument to the test, Richard.

• 47:52Speaker 00

  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. Forty-eight percent 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.

• 48:18Speaker 01

  Now, how about just a thumbs up?

• 48:22Speaker 06

  Sure, one thumbs up, coming right up.

• 48:23Speaker 07

  Hell yeah, that was awesome. I would say you're my favorite coworker. It's been, it's been really great to work on this with you.

• 48:30Speaker 06

  You're making me blush. Ah, 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.

• 48:44Speaker 06

  Now, touch your chin.

• 48:47Speaker 06

  I'm not gonna fall for that. You didn't say the magic words. Uh, you got me, okay.

• 48:53Speaker 08

  The loose connector back onto the motherboard.

• 48:57Speaker 09

  Okay, let me go, let me go grab that. But beforehand, yeah, I turned the soldering iron on. I think it's gonna take about 12 seconds. So maybe just let me know when that's done.

• 49:07Speaker 08

  It sounds good. I'll keep an eye on it and let you know the second it hits that 12 second mark.

• 49:14Speaker 00

  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, 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. Saleem, your thoughts on this one?

• 49:51Speaker 05

  So, I'm less kind of floored by the imagery and the mimicry of it. This was expected to happen. 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 point.

• 50:16Speaker 00

  We've talked about the idea that in the near future, like now, you're gonna have digital coworkers 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?

• 50:37Speaker 03

  Well, first, Peter, you should probably ask me to touch my face.

• 50:40Speaker 00

  Okay, touch your face. Simon says, touch your face.

• 50:45Speaker 03

  Okay.

• 50:45Speaker 00

  Very good.

• 50:46Speaker 03

  Just checking, but I guess Tavis is ahead of me. I, I, I think, on the one hand, if you look at models coming out of Chinese labs, like Alibaba's lab, Wanstreamer gave us a preview of what was going to happen. The Chinese labs remain overinvested relative to the Western labs in generative video models and interactive generative video models. So Wanstreamer, I think, is a preview of what's possible. My guess is, I, I, I read the, the information that was put out around Tavis. I, I think in full generality, let, let's just talk about where this is going to end up. It's, it's very difficult to predict the short term. I think it's pretty easy to predict the long term. In the long term, end-to-end generative pixels will have these magic mirrors

• 51:31Speaker 03

  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 Wanstreamer, 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 it can go.

• 52:13Speaker 03

  Query how revenue generating per token it is. Is it anywhere close to the optimal frontier of code generation? Doubt it. On the other hand, if it becomes so absurdly inexpensive to be able to, 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, 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, on request, that could be probably completely automated away

• 52:57Speaker 03

  by a, 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, the movie, close paren. I, I think that this, this has, this has-

• 53:25Speaker 00

  You should use, you should use M-dashes, please.

• 53:27Speaker 03

  Okay, I'll delve into it. So I, I, I think, I, I think this is going to be transformative for the service sector. And hopefully, Tavis and broader American West of video frontier models and interactive video frontier models, take a page from Tavis and start competing with China.

• 53:46Speaker 00

  Richard, one of the principles in your book, one of the rules you have, is that AI becomes superhuman where you can verify the answer. And, you know, is, like, passing a human scientific milestone like this, is passing a sort of a human judgment and interaction, you know, sufficiently as a scientific milestone for verification?

• 54:08Speaker 01

  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, in that automated fashion. I do think you're, you're right. This is a good example where I think we need to do, be, go beyond KYC and do KYU, know your use case, right? Like, you don't want this technology to be in Zoom pretending to be the CEO. And like, there's some famous stories where this has already happened, where they got someone to wire like $20 million because they created a Zoom with five other executives and they all like, it was fooled well enough. And, and so I think we need to, yeah, be careful.

• 54:51Speaker 01

  And like Zoom probably and Google Hangout need to start, like, finding countermeasures to identify, is this a real person and, and things like that, to prevent those kinds of hacks from happening.

• 55:02Speaker 00

  Yeah, scams are gonna proliferate on this. I, 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 and audio, and go deep, spend, you know, hours recording them, right? Your ability to create a super-high-resolution lifelike avatar for your, 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 gonna be a, a beautiful opportunity for your, for your progeny and theirs.

• 55:46Speaker 03

  And/or, I, I should add, get them an Alcor membership and preserve their connectum if there is a, a consideration, like, why stop with just the behavioral data? Preserve the whole parent.

• 55:59Speaker 00

  You know, my kids, my kids are in a deep freeze, or at least their placental cells.

• 56:06Speaker 05

  But I have a pushback on that just from, I have a pushback on that just from this conversation.

• 56:11Speaker 00

  Please.

• 56:11Speaker 05

  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.

• 56:18Speaker 03

  It'll be low, low fidelity. I mean, I, I do think, like, to, 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, 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, I do think, Saleem, you want to go beyond just the memory of, of the person and you want the actual person. And I'll pound the, 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, I'm sure the placenta, placenta eye will, will thank you.

• 56:55Speaker 00

  Yeah, go, go ahead, finish.

• 56:57Speaker 03

  But I, I, I would say, like, yeah, if, if, 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 Alcor memberships and get them cryopreserved.

• 57:16Speaker 05

  Yeah.

• 57:16Speaker 02

  Richard, really, really curious to ask you a question. You know, we, we clearly passed the Turing test and, you know, Saleem's reaction is my reaction. The, what we just saw is a so what if you've been using models every day, all day long? You, you know, you just had to stitch together the components and you have. But I, but I think this will show mainstream world that we've crossed the Turing test. And then, you know, the next milestone is the Demis Hassabis 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, is, you know, but without cheating, only using prior information, come up with the equals MC squared.

• 57:56Speaker 02

  So is there a Richard Socher test that's like a really fun, measurable milestone coming?

• 58:03Speaker 01

  I, I came up with the, like, anti-Turing test, which actually I think the whole Turing 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 asked an 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.

• 58:34Speaker 03

  You don't think, Richard, I mean, like, it, it could throttle itself? That one's easy to defeat, but just like, ask it?

• 58:39Speaker 01

  It's just like, now it's just about, it's just about fakery. It, like, the reason it 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 human level in order to pass the test, which makes the test useless as an inspiring test for intelligence.

• 58:59Speaker 03

  Yeah, 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.

• 59:17Speaker 00

  Yeah. Let me just hit real quick, again, for our listeners. If you're pregnant or someone in your family is pregnant, you know,

• 59:26Speaker 00

  Save their placental cells. One of my portfolio companies called Life Bank USA does this. That's where I've saved my my kid's 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 it's like, from those cells, if my kid 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. Just, I just think it's like a moral obligation, parents should have to to save those cells as a backup.

• 1:00:04Speaker 03

  As a backup for your children, could you use it to replace your children, Peter?

• 1:00:09Speaker 00

  You could, you could use it to clone your children, for sure.

• 1:00:13Speaker 03

  And now they know why you're banking them.

• 1:00:15Speaker 00

  Yeah, exactly. An army, an army of young demand die. 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?

• 1:00:47Speaker 01

  So, yeah, I think there's a

• 1:00:50Speaker 00

  Where are we at the moment with RSI?

• 1:00:52Speaker 01

  So

• 1:00:53Speaker 00

  We're there?

• 1:00:54Speaker 01

  This, 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 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, OpenAI talk about, when they often talk about is like, look, how much our employees, our engineers, our programmers, use Codex or

• 1:01:35Speaker 01

  Claude to create some code. And I would argue that that is a weak form of recursive self-improvement, because you still have like deeply embedded humans in that loop. 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 AI to have the entirety of the process of ideation, implementation, and validation of ideas, and have full control over that, and allow for the 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 the sort of physical reality hits is that you

• 1:02:20Speaker 01

  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

• 1:02:37Speaker 00

  So we're not there yet?

• 1:02:38Speaker 01

  We're not quite there yet, but we're very close. Like, 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 is 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, like, truly coming up with the ideas on which of these dimensions and axes to optimize for. And you can know that that hasn't, hasn't happened yet, because all the big companies are still hiring thousands of engineers to do it manually, to a large

• 1:03:23Speaker 01

  degree.

• 1:03:24Speaker 00

  And

• 1:03:25Speaker 01

  More and more implementation help from an AI.

• 1:03:26Speaker 00

  Your definition of ASI, because I want Salim to hear this.

• 1:03:29Speaker 01

  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 Uyghur 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 a sort of a moral prerogative, but like, I would argue that something isn't

• 1:04:10Speaker 01

  superintelligent if it cannot choose, to some degree, what it works on, right? And has to have some meta cognition about its thought itself. But generally, the most easy way to measure it 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.

• 1:04:35Speaker 00

  Elon's definition is as smart as all humans combined. Is that yours?

• 1:04:41Speaker 01

  I would argue it's, it's smarter than humanity combined, than, than it's truly superintelligent.

• 1:04:48Speaker 00

  Okay, Salim, go for it.

• 1:04:49Speaker 05

  Well, it's, I, 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 be, 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 gonna throw you out the list, and you tell me where recursive self-improvement begins, right? Because I'm, this is where I'm kind of soft. So we've got a continuum of, okay, AI writes some code that the next

• 1:05:29Speaker 05

  model uses. Number two, AI proposes some experiments for researchers. Number three, AI runs those experiments, or 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.

• 1:05:57Speaker 01

  It's a great question, yeah. I, 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

• 1:06:11Speaker 05

  So you have an inner loop for each of them?

• 1:06:13Speaker 01

  Exactly, in an inner loop. And then there has to be an outer process that is more open-ended, where the AI can innovate and recombine, interestingly, different ideas, similar to biological, cultural, and technological evolution.

• 1:06:26Speaker 00

  And, and Richard, to hit my third question, when do you believe we reach ASI? Give me a time, give me a timeframe.

• 1:06:36Speaker 01

  So I think in the, I think in the strongest sense, the absolute strongest sense, where indeed, like Salim mentioned, there is, there are 10 spaces that I define my book of intelligence, perceptual intelligence, communication intelligence, so like, interaction, sociological intelligence, creative intelligence, the speed at which you can do things, meta cognition, 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.

• 1:07:14Speaker 00

  Really?

• 1:07:15Speaker 01

  I think it's also a bit of a

• 1:07:16Speaker 00

  That's shocking, shocking. Yeah, I mean, Elon says 2029, 2030 latest.

• 1:07:22Speaker 01

  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's, it will be better at math than all of humanity, and will, like, that, 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, 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 it takes a ton of energy. But like, we can create novel, like, 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

• 1:08:07Speaker 01

  physical world, such that it can innovate in that way, beyond all of humanity, right? And really build like Dyson spheres and so on. That, it will take some time.

• 1:08:16Speaker 00

  Alex, I

• 1:08:18Speaker 03

  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?

• 1:08:37Speaker 01

  So again, the superintelligence, I think, I think superintelligence will spike, and there will be areas where it will be superintelligent. And that is like, algorithmic development, for instance, programming. And again, 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, like, 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

• 1:09:21Speaker 01

  new ASML. Like, think about the machine of ASML that actually creates these like, 1, 2 nanometer, like, chips, right? That is just like, it will take more than two years to build such a machine, even if you had the perfect blueprint from scratch.

• 1:09:34Speaker 03

  It might take, I mean, 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.

• 1:09:45Speaker 01

  That's almost not an exaggeration, actually.

• 1:09:48Speaker 03

  Yeah, like wildly, wildly.

• 1:09:50Speaker 01

  Itself?

• 1:09:51Speaker 03

  Yes, of course. Like, I, 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. Like, 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 apparently I'm drinking very different singularity water than you are.

• 1:10:34Speaker 00

  I agree with you, Alex, for what it's worth.

• 1:10:35Speaker 03

  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?

• 1:11:02Speaker 01

  Yeah. I think, one, it would be ubiquitous for us to know right now.

• 1:11:09Speaker 03

  No, no, but it's just, Richard, it's just us talking. Like, no one else is listening. It's okay. You can tell me. This is a safe model space.

• 1:11:17Speaker 02

  That's the flaw in this whole conversation, actually.

• 1:11:19Speaker 03

  It's safe, Richard. You can tell me what you think, how this ends.

• 1:11:23Speaker 01

  So I guess there are different ways to answer that question of like, how like with the exact model architecture, there's some things I can share it with recursive that we're working on. But like, I 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 ASI 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

• 1:12:08Speaker 01

  things. So there's some, there's some.

• 1:12:10Speaker 00

  But we have, 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.

• 1:12:25Speaker 03

  It's going to get, it's going to get flourished. We're using Richard's terminology. Everything is flourished at this point. That'll be a new, new T-shirt, P. Flourish.

• 1:12:32Speaker 01

  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 then 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, each cell in the process. So, you know, the one cell is very complicated. Once you have one cell, you have multicellular,

• 1:13:17Speaker 01

  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.

• 1:13:33Speaker 00

  It's being done. It's being done. I can introduce you there.

• 1:13:36Speaker 03

  This is a very eloquent, if I may, this is a very eloquent distraction from recursive self-improvement, this, 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 NanoGPT, but NanoChat. We talk on the pod all the time about the NanoGPT speedrun world record collapsing. Just in the past week or two, there was been.

• 1:14:03Speaker 01

  Oh, you just wait for a few more days. There'll be another really fun update there.

• 1:14:08Speaker 03

  On NanoChat or NanoGPT speedrun?

• 1:14:11Speaker 01

  Yes, yes.

• 1:14:12Speaker 03

  Both of those?

• 1:14:13Speaker 01

  Give us a few days.

• 1:14:14Speaker 03

  Okay, so.

• 1:14:17Speaker 00

  So you're next. Let Alex finish up.

• 1:14:19Speaker 03

  To pin this down, though, so I'm standing by for the major update on NanoGPT world record speedrun. But there's been major progress there without requiring any new data scaling at all. These are purely, largely, at my perception, is recursive self-improvement, algorithmic improvements that have been able to collapse the amount of time that it takes to train a GPT-2 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

• 1:15:00Speaker 03

  training of NanoGPT 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, the, 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?

• 1:15:22Speaker 05

  Can I ask, interject one very quick thing before Richard answers?

• 1:15:27Speaker 03

  Please.

• 1:15:27Speaker 05

  Which is that the definition of a singularity is you can't see past the event horizon. Once you have full RSI that's vertical, by definition, we can predict where it goes.

• 1:15:36Speaker 03

  That's Ray's definition that I don't subscribe to.

• 1:15:39Speaker 05

  Disclaimer. I got that. Overdue, overdue, Richard.

• 1:15:45Speaker 01

  Sorry. So your, your question is, do we separate what exactly from?

• 1:15:50Speaker 03

  Does, 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?

• 1:16:16Speaker 02

  Well, world knowledge, meaning like Taylor Swift videos and past Trump tweets and all that, like.

• 1:16:21Speaker 01

  Yeah, I, 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, 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 that you dot com for LMs, like, that there will be a separate world knowledge thing, too. But the, the main model will have a lot of that mixed in, for sure.

• 1:17:05Speaker 03

  Wow. Okay. Thank you.

• 1:17:08Speaker 00

  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, Rohana, 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

• 1:17:52Speaker 00

  a civilization, civilizational risk. There's a safety risk for loss of 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 doomers are wrong.

• 1:18:36Speaker 01

  Oh, boy. There's a lot.

• 1:18:37Speaker 00

  It's an important one. We've talked about this a lot. You know, we're injecting optimism into everyone's neural net here.

• 1:18:45Speaker 01

  I'll try to 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%?

• 1:18:57Speaker 03

  What kind of reassurance is that, Richard?

• 1:19:01Speaker 01

  P doom is zero. So that's number one.

• 1:19:03Speaker 03

  Thank you.

• 1:19:05Speaker 01

  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 ASI, 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 AI 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 it'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, this like takes a long time to automate lab experiments and so on.

• 1:19:46Speaker 01

  You ask like, oh, the AI will create a religion where then people will like pray to the AI 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, 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

• 1:20:29Speaker 01

  viral research? That is already illegal to create like 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 this

• 1:20:51Speaker 00

  Other than this podcast, right.

• 1:20:53Speaker 01

  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 drive 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 stance, 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

• 1:21:33Speaker 01

  little laptop and ask that AI to just like 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 LLM 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.

• 1:22:09Speaker 03

  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.

• 1:22:29Speaker 05

  Again, if 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.

• 1:22:55Speaker 00

  Dave, I'd like to hear your voice on this.

• 1:22:58Speaker 02

  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 US Rep. Ro Khanna. I mean, it's so childish, like, the idea that you would ban recursive-

• 1:23:14Speaker 00

  It's fear mongering.

• 1:23:15Speaker 02

  But like, 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-improvement"? But he knows that we're not going to ban recursive self-improvement. And Bernie Sanders knows we're not going

• 1:23:57Speaker 02

  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, you could, what does that mean? I can't optimize my hyperparameters? I can't tune my hard drive? Like, it's just a goofy sentence, and it just, it drives me nuts that-

• 1:24:23Speaker 00

  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% 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 DC, it was like, who in DC is responsible for changing public opinion? Right? I mean, this is why we did Moonshot's 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

• 1:25:07Speaker 00

  to, you know, counter these arguments that they're hearing.

• 1:25:10Speaker 02

  That's right. Elon was totally right when he called out Dario for saying, "Dario, look, you told the world that Mythos was potentially deadly and dangerous, and we shouldn't release it. Then 30 days later, you said, okay, 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 the, you know, what he sees, the way he sees it, you know, very academic. But then you get into the real world of politics and PR, and you're like, oh, wow, I gotta, I gotta 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

• 1:25:55Speaker 02

  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, you know, all of that stuff will grind to a halt, and then we'll all learn Chinese, if that, if that becomes the mainstream opinion. So I think, you know, it's self-inflicted.

• 1:26:12Speaker 05

  There's a glimmer in this, which is, say they did decide to do some draconian thing like Ro Khanna's thing. There's no mechanism actually enforce it, like, none. So they're-

• 1:26:23Speaker 03

  Sure, there is. They could start arresting people. I mean, remember how quickly the memory dims. Remember, 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-US 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, and it made no sense.

• 1:26:56Speaker 02

  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, US lawyers are relentless. And if you slap class action on the outcomes of this, all progress will grind to a halt. And 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'll actually grind it to a halt long before arrests and convictions.

• 1:27:40Speaker 03

  Totally. Chilling effect, and we could lose 50 years of progress.

• 1:27:44Speaker 00

  Again.

• 1:27:44Speaker 01

  That's right. Yeah. That's, yeah, like, there's some very, like, sad sort of off-ramps in like humanity's future here, that would slow down everything. And when you think about the past, like, this fear mongering 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-

• 1:28:07Speaker 05

  Amazing.

• 1:28:08Speaker 01

  Pessimists' archive, you should all follow it. Like, in 1501, Pope Alexander VI criticized the Gutenberg printing press's safety. "The art of printing can be of great service insofar as it furthers the circulation of useful and tested books, but it can bring about serious evils. I, it will therefore be necessary to maintain full control of that printing press." If you think about the wheel, like, the wheel killed so many people. Like, think about all the tanks, all the car accidents, all the chariots and with archers on top. The wheel was a horrible thing, it killed lots of, like, there's like, 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.

• 1:28:47Speaker 05

  My favorite example of that was when The Telegraph came out. There was all these stories saying that Telegraph will kill humanity.

• 1:28:54Speaker 01

  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 really unfortunate, it's really unfortunate how many people hang on to these like apocalyptic visions.

• 1:29:12Speaker 00

  Yeah. Richard, you've been publicly-

• 1:29:14Speaker 05

  The amygdala.

• 1:29:15Speaker 00

  You've been publicly critical of Anthropic's constitutional approach. I'd love to understand why.

• 1:29:21Speaker 01

  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 an 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, is like, the whole point of the Glasswing project was, we'll help you do that. And we'll 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. Like, so it's just like, it was a cool marketing gimmick, but it just didn't work.

• 1:30:04Speaker 03

  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?

• 1:30:14Speaker 01

  I mean, it just approves in a pudding 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, unlike, 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 Anthropic's 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, and AIs are very smart, and they will find a solution

• 1:30:58Speaker 01

  to get to what you said you wanted, 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.

• 1:31:25Speaker 03

  Richard, maybe if I may just take Anthropic's side. Like, historically, Isaac Asimov had his three-plus-one laws of robotics, which were arguably a constitutional approach. And then you see Anthropic announce their constitutional approach, but more recently adopt what they call Seoul 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?

• 1:32:04Speaker 01

  No, of course. 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/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. 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 use,

• 1:32:48Speaker 01

  could cause significant damage if deployed. They created a cyber weapon. Like, it was a thing. It was like, people used it to hack it. The agents watched, like, hacked other systems.

• 1:32:59Speaker 03

  Yep. But again, oh, go ahead, Peter, sorry.

• 1:33:02Speaker 00

  Richard, I'm curious, do you think we can create fully-aligned AI?

• 1:33:08Speaker 01

  Hundred percent.

• 1:33:09Speaker 00

  Fully-aligned ASI?

• 1:33:11Speaker 01

  Of course.

• 1:33:12Speaker 00

  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.

• 1:33:29Speaker 01

  I think, 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 Hippocratic AI, we just had dinner with one of 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 heat wave that's coming and make sure your AC is working and whatnot. And they, because they're liable, they have a very large team of people that works on making sure when their AI gives a healthcare tip, it is correct.

• 1:34:13Speaker 01

  And when someone asks a question back to the AI, it works. And so because they're liable, they've figured it out and they solved it. It's hundred percent a problem that technology creates, and technology will be able to solve.

• 1:34:26Speaker 10

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• 1:35:06Speaker 10

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• 1:35:27Speaker 00

  I'm going to move us forward here. So in our last pod, we discussed the release of GPT-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. Argon is their comeback. It's built for sustained, long-horizon reasoning across software engineering, finance, legal work, and cybersecurity.

• 1:36:10Speaker 00

  The output limits have jumped from 64,000 tokens to 1 million, so it 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 important, why these are important and how they merit our listeners' attention.

• 1:36:32Speaker 03

  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 4 Arg-

• 1:36:49Speaker 02

  This is going to be a ball, isn't it?

• 1:36:52Speaker 03

  Winding up for the swing.

• 1:36:53Speaker 02

  Okay. This one whiffed.

• 1:36:55Speaker 03

  Here it comes.

• 1:36:57Speaker 02

  You can feel it coming.

• 1:36:58Speaker 00

  And I'll show one of the charts that we have while you're describing.

• 1:37:02Speaker 02

  You said you had friends on the team, right?

• 1:37:05Speaker 03

  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 OpenAI. It does not put them in the top two, unfortunately. The set of benchmarks that Google chose to highlight for Gemini 4 Argon appear mildly cherry-picked. Yeah, I think the artificial analysis index and some of these others, yeah, so, Peter,

• 1:37:45Speaker 03

  right now, you're showing the mildly cherry-picked benchmarks on which Gemini 4 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

• 1:38:29Speaker 03

  internal competition within Google for compute. So infamously, like, 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, of 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.

• 1:39:12Speaker 03

  And maybe it's just that Google is resource-starved, maybe their talent's 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 4 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 Kremlin-ological analysis, is that because the Gemini team has two masters.

• 1:39:53Speaker 03

  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 4 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.

• 1:40:32Speaker 02

  I think you're really on to something there, Alex, because, well, first of all, they called it Argon, which is an inert gas.

• 1:40:38Speaker 03

  Yeah, that's unfortunate naming.

• 1:40:41Speaker 03

  Nominate a determinism.

• 1:40:42Speaker 02

  But 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 them.

• 1:41:20Speaker 03

  Google would probably argue, no, actually, this is very pro-competitive because we're not tying it. We're allowing anyone to call Gemini for Argon, flash, 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

• 1:42:05Speaker 03

  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.

• 1:42:14Speaker 00

  Richard, you made the point that hallucination is important for imagination in some ways, in drug discovery and protein discoveries. What's your thought on minimizing hallucination?

• 1:42:25Speaker 01

  I mean, it, of course, depends totally on the context, right? If you want innovation, you want the AI 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 Udot 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 realized that in terms of their business model, they don't necessarily need superintelligence. They just, people don't come to Google to ask, solve the Riemann hypothesis for me.

• 1:43:09Speaker 01

  You know, like, did you just ask quick questions? What's a good restaurant? Where do I fix this and that? Like, 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 superintelligence.

• 1:43:38Speaker 00

  Let's, let me turn the conversation to Anthropic's Sonnet 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 Anthropic's own top model, Opus 5.5, at 66.4% for half the price. And it's the first Sonnet that Anthropic launched with cyber safeguards. So because separate capabilities are now capable for the Opus 5 models. Alex, your evaluation of Sonnet 5.5, please?

• 1:44:21Speaker 03

  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, 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 OpenAI, when they, 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,

• 1:45:05Speaker 03

  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,

• 1:45:48Speaker 03

  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.

• 1:46:19Speaker 00

  Interesting. Dave, any thoughts?

• 1:46:22Speaker 02

  Yeah, well, I met with the Blitzy team yesterday, and I think one theory here is, you know, a lot of the enterprise, you know, actually, Salesforce.com would 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 Kini K3 or Quen as a sub-model at, you know, half or a third or a 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,

• 1:47:07Speaker 02

  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 then 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. You can't get addicted to them as your vendor. You must go with models you can control.

• 1:47:40Speaker 05

  Well, the, you know, 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 has access. It's how do you metabolize that into some decent capability? That's the real challenge.

• 1:48:01Speaker 00

  Richard, you said compute is the biggest constraint. You know, those are your words. So if intelligence 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?

• 1:48:22Speaker 01

  Just the physics, I guess, of it. And like, you'd be surprised. If you try to buy, like, 1,000 GB 200s and so on, like, the price has actually gone up in several cases.

• 1:48:33Speaker 02

  Oh, my God, Richard, Richard. I had a B300, an 8X B300 on order for 3 million bucks, 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 NBL 72, not a GB 300.

• 1:48:54Speaker 01

  So, yeah, like, the price of H100s has gone up in a crazy way. There's like, these are seven-year-old GPUs. People, like, when you do financial modeling, you assume they're worth zero after five years. They're, 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 on the market, and then it's going to be a little bit like electricity. Prices might fluctuate. Obviously, there's a 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,

• 1:49:39Speaker 01

  and unfortunately, they're not just going down. Right now, if you want, like, the beefiest and largest GPU clusters, prices, like, people are trying to lock them in now because they expect them to keep going up for the next few months.

• 1:49:52Speaker 02

  Richard, you just spent $450 million, didn't you? Wait, did you take my NBL 72?

• 1:49:57Speaker 01

  And that may have been one of the smallest compute deals we've done. Yeah.

• 1:50:01Speaker 02

  Really? Wow. Are you actually leasing or buying or building? Or what are you doing?

• 1:50:07Speaker 00

  Can comment.

• 1:50:08Speaker 05

  Sorry, I didn't know that.

• 1:50:09Speaker 00

  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. Dawn Musalem. Dawn, let's talk about cancer. You know, I know from the member database that we have at Fountain, our 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.

• 1:50:43Speaker 11

  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 late. 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.

• 1:51:08Speaker 00

  Yeah, you know, it's interesting, people, you don't feel the cancer until stage three or stage four. And, and if you don't know what's going on inside your body, it's like driving your car with your eyes closed. And you can know. And so when members come through Fountain, how do they detect cancers?

• 1:51:23Speaker 11

  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 used 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.

• 1:51:44Speaker 00

  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 Fountain Life and go to fountainlife.com/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, and you're 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, an abounded decision, like is this a 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.

• 1:52:28Speaker 00

  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, I'm going to go to you, you know, and Saleem, you guys were excited about Jev. Let's parse it, understand what this is and why it's important.

• 1:53:03Speaker 03

  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.

• 1:53:44Speaker 03

  So most of the, the models that are consuming all of the computer 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, two years ago at this point, OpenAI and others recognizing that there was a desire to be able to take decoder only large language models and to

• 1:54:24Speaker 03

  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, 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 a front, 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.

• 1:55:08Speaker 03

  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 market as so-called system one intelligence. This is Kahneman style reference to system one, system two type thinking, system one purportedly being intuitive, fast reaction, system two 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,

• 1:55:46Speaker 03

  this is a reaction to an over indexing or an overreaction by the, the user base of, 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, the important use case of really fast, intuitive snap decision models. And this is purportedly then where Jev comes in. Jev purportedly, again, a whole new architecture, the, the detail's not quite clear, that it can, it won't output unless you, you torture it into it. It, it won't output general purpose sequences like say GPT or Claude will, but you can ask it sort of like a magic eight ball. You, you can ask it to, is that too dated a reference?

• 1:56:32Speaker 03

  Maybe?

• 1:56:32Speaker 05

  Nope.

• 1:56:33Speaker 03

  Nope. You, you can, you can feed it a sequence or an image or a multimodal input, and you can ask it to, 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, you can ask for a much simpler output. And turns out, that's a great idea. Like you can use it for, for use cases where, 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 reinvent, reinvention of the encoder transformer type wheel.

• 1:57:18Speaker 03

  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, a decisions API. So OpenAI immediately co-opted this renewed interest in so-called type one 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 Openweight 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.

• 1:58:02Speaker 05

  So I think this, I think all of that is absolutely correct, that 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 or 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're, 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

• 1:58:47Speaker 05

  for this is like trying to bring the Supreme Court together to decide what, like which checkout, which 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, 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, it's made a very callable layer.

• 1:59:30Speaker 05

  And this is huge because the cost of micro coordination collapses, and that's really big.

• 1:59:34Speaker 00

  Richard, your thoughts on this?

• 1:59:37Speaker 01

  Classifiers are back. It makes a lot of sense. You know, not everything 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, ah, 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.

• 2:00:20Speaker 05

  And just to reiterate, we've mentioned this already on the pod, but Jeff stands for Jevon'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.

• 2:00:33Speaker 00

  Nice. Dave, want to close us out here?

• 2:00:35Speaker 02

  Yeah, it's a great case study, I think, and the tension between one mega model from Anthropic or OpenAI 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. You know, so that's the tension that we're with. But I 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 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.

• 2:01:18Speaker 02

  So super excited about the fact that we have open source still. Not sure.

• 2:01:23Speaker 00

  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.

• 2:01:37Speaker 02

  Totally, totally would sell. It makes a ton of sense, like 20 bucks.

• 2:01:41Speaker 00

  Or free on free as an app. All right, I'm going to close this out with a story from two days ago. So on Wednesday, the Defense Secretary Pete Segwit, speaking at the Marine Corps Base in Quantico, 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 Newt Gingrich, and overseen by the Pentagon's CTO, Emile Michael. Let me read from Segwit, what Segwit 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.

• 2:02:27Speaker 00

  Findings are due in 120 days. I like these kinds of commissions that are time limited 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 Anduril hold multi-billion dollar defense contracts and Anduril 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?

• 2:02:59Speaker 01

  Yeah, I stand by those. It's not a particular area that I am excited about applying AI to. 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 connect it. That's literally how Sonnet 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 human lives, the more we should have human oversight.

• 2:03:40Speaker 00

  Yeah. Saleem?

• 2:03:44Speaker 05

  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?

• 2:04:18Speaker 00

  Yeah.

• 2:04:18Speaker 05

  Like you can't find exponential technology with linear procurement.

• 2:04:23Speaker 00

  Right. Dave, your thoughts, please?

• 2:04:26Speaker 02

  Yeah, I don't know. I had a great time with Palmer Lucky in LA and I really, really love, 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 to 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 I just makes me really optimistic that they'll figure some things out.

• 2:05:09Speaker 02

  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, from LA. I don't agree that it's a good choice, but yeah, he actually has some very rational arguments for why it's going to be that way.

• 2:05:26Speaker 05

  Can I just mention one more statistic here?

• 2:05:28Speaker 00

  Please.

• 2:05:29Speaker 05

  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.

• 2:06:11Speaker 05

  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.

• 2:06:20Speaker 00

  Nice. Alex, close us out on this one?

• 2:06:23Speaker 03

  Yeah, a couple of points. So the Secretary of War announced this alongside several other initiatives, maybe most conspicuously a project codenamed Project Agincourt, which is the stand-up of the Department of War's first autonomous warfare command or AutoWarCom. 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 US capabilities to finally have a single joint

• 2:07:05Speaker 03

  force dedicated to this with a four-star functional combatant command that we've been missing, we're arguably missing. You know, 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 SecWar or Hegseth 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."

• 2:07:45Speaker 03

  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 it's now very interested in investing in American assets and American exploration and American dominance on the floor of the oceans 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

• 2:08:29Speaker 03

  in a way that might naively have nothing to do with warfare or warfighting.

• 2:08:34Speaker 02

  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 into hunt mode, it just hunts and it's not communicating back.

• 2:08:51Speaker 03

  Two-thirds of the earth's surface, and we know embarrassingly little about what's hiding under it.

• 2:08:56Speaker 00

  We know more about the surface of Mars than we do our ocean floor.

• 2:08:59Speaker 03

  Yes, for sure.

• 2:09:01Speaker 00

  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.

• 2:09:18Speaker 01

  All right, well, let me try to skim 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? Shareholders, 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 billions and billions of illustrations in the world. And so when AI made the price of one illustration go down from 200 bucks to like 2 cents or

• 2:09:55Speaker 01

  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 beautiful visualization and illustration, but you know, we didn't need many more billions of them. I think software is a different one. You 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. Jevon's paradox is going to be alive in that world and we're going to see more and

• 2:10:36Speaker 01

  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.

• 2:11:02Speaker 00

  Wonderful. Saleem, over to you.

• 2:11:06Speaker 05

  I will take question number three. If AI can write code, research, create marketing, manage projects and outcomes, what is left for a college graduate in 2030? And that is from Jared Kuhn 8812. So the obvious answer would be 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. But 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.

• 2:11:51Speaker 05

  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 groundwork. We need a totally new apprenticeship model when the groundwork disappears. And this is a huge challenge for the education system. But 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.

• 2:12:19Speaker 00

  Jared, find your purpose, right? A passion is something you love doing, a purpose, something you love doing that helps other people.

• 2:12:25Speaker 05

  Make sure it's massive and it's transformative and it's purposeful.

• 2:12:28Speaker 00

  And and build a company and then direct AI to implement it. It is your workforce. Dave, over to you.

• 2:12:37Speaker 02

  I can't resist number four. I love all these questions. I'm torn. But how can data centers make neighborhoods richer instead of the owners? And that's from LM 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.

• 2:13:19Speaker 02

  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.

• 2:13:34Speaker 00

  We're going to see a whole, 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.

• 2:13:48Speaker 03

  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 juxtapose 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.

• 2:14:33Speaker 03

  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 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.

• 2:15:13Speaker 00

  All right. Richard, as our guest, you get first crack once again. Take a look.

• 2:15:20Speaker 01

  I do think the internet ad-based economy will be under pressure.

• 2:15:29Speaker 00

  Which question are you answering?

• 2:15:30Speaker 01

  So first question, if AI agents outnumber humans or 120 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 this, 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

• 2:16:13Speaker 01

  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

• 2:16:57Speaker 01

  and more of a currency. And so the attention economy is just connected 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.

• 2:17:15Speaker 00

  Great. Salim?

• 2:17:17Speaker 05

  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 Zerby, JB10H. 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 the citizenry. And I, there are just, so I'm just apologizing because there's a spectrum of people and some people are full ambulance chasers and other people do things. There's a whole segment of this called ethical lawsuits, where people get together and try and sue the companies for doing the right ethical thing. And so that's an important segment of that.

• 2:18:01Speaker 05

  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 Salim crash to which layer 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.

• 2:18:41Speaker 00

  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.

• 2:18:46Speaker 02

  Well, just a data point on that, you know, a single big data center like Abilene, Texas is half a trillion dollars. All the cars combined are four 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 things, number of things that need insurance is going up 10x, just with the economy going up 10x.

• 2:19:05Speaker 05

  If you really, if you really want to be brave, you can't get home insurance in Florida anymore. So go create an insurance company for that.

• 2:19:13Speaker 00

  Nice. Dave, pick your question.

• 2:19:15Speaker 02

  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. It's hard to think, like, what is the lowest surviving job? Do you guys have any thoughts?

• 2:19:54Speaker 03

  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 actors that Ayn Rand may fetishize would ironically, in a post-AI civilization, be the lowest possible jobs because more of that paradox, paradox style, those were the first ones to be automated.

• 2:20:40Speaker 00

  Interesting. All right.

• 2:20:42Speaker 05

  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.

• 2:20:51Speaker 01

  I would, 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 TANX, the 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

• 2:21:33Speaker 01

  jobs in terms of moral standing are things where you don't really progress humanity forward. And my hunch is there are the types of jobs in 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.

• 2:21:55Speaker 02

  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.

• 2:22:06Speaker 00

  Nice. All right.

• 2:22:07Speaker 05

  Ema, I gotta throw in Ema's perspective. He always thinks it's the Bart train driver because it's unionized hell.

• 2:22:13Speaker 02

  Yeah, yeah, there you go.

• 2:22:14Speaker 00

  Alex, number six, close us out.

• 2:22:16Speaker 03

  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 GLP-1 class studies, including third and soon presumably fourth generation GLP-1 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 in, or at least treating,

• 2:22:54Speaker 03

  have to caveat that, inflammation and blood sugar, diabetes, all of these other conditions, the root causes, which are human behavior, are themselves, the human behavior is being affected by the same drugs.

• 2:23:09Speaker 02

  I'm so optimistic about what Alex is saying because I really fully believe AI, this is 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.

• 2:23:22Speaker 03

  Love that. And not want, 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.

• 2:23:30Speaker 00

  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 [email protected]. And speaking about amazing outros, here is Abundance by Steven Gross. My dear moonshot mates, this is the real you. So check it out.

• 2:24:33Speaker 00

  Drink, Alex, drink.

• 2:24:35Speaker 03

  Drink water.

• 2:24:37Speaker 02

  Martini glass.

• 2:24:38Speaker 00

  That was good.

• 2:24:39Speaker 02

  Friday night.

• 2:24:40Speaker 00

  Moonshots Live 2040 on the moon. Nice.

• 2:24:44Speaker 03

  I don't think we should have to wait that long.

• 2:24:46Speaker 00

  Yeah. Richard, your new book, The Eureka Machine, wherever you purchase your books, do you have the audible out?

• 2:24:53Speaker 01

  Should come out very soon. Yeah.

• 2:24:55Speaker 00

  All right. I'm an audible reader, but I have skimmed the book here. Congratulations on this.

• 2:25:00Speaker 05

  I gotta say, Richard, this, I gotta say something.

• 2:25:01Speaker 00

  Please.

• 2:25:02Speaker 05

  What you're doing with the recursive looks like such an incredible opportunity to move humanity forward. So congrats on taking that on.

• 2:25:09Speaker 02

  Yeah, so cool.

• 2:25:09Speaker 01

  We'll try to make it a good one for humanity.

• 2:25:12Speaker 03

  And, 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.

• 2:25:30Speaker 01

  All right.

• 2:25:31Speaker 00

  All right. I love you guys.

• 2:25:33Speaker 01

  Thank you so much.

• 2:25:34Speaker 00

  Until next time.

• 2:25:48Speaker 12

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