Meta Buys Moltbook, GPT 5.4, and Fruitfly Brain Upload | Moonshots Live at The Abundance Summit 238

19:17Dr. Alexander Wissner-Gross:We're missing the lobster theme here. That's true. This is the whole point, though, of this book that we just co-authored, that we get superintelligence, and the killer app, arguably, of superintelligence is solving everything, including all of these high-profile, glamorous scientific and engineering challenges. It's happening. And Anthropic and OpenAI, I'm sure Google, all the labs are hiring the top mathematicians and physicists and chemists and biologists inside. But they're software companies. Why are they hiring these people? Because everything, so friend of the pod, Ray, as I think Ray would say, everything's becoming software.

20:00And when we have super intelligence solving all disease, it's a software problem. If we can create a virtual cell that perfectly models diseased states and we can steer through cell embedding space to get from diseased cell to healthy cell, it's a software problem.

20:13Dr. Alexander Wissner-Gross:Everything's becoming a software problem. I mean, the minute CRISPR arrived and you could edit the human genome, the human body becomes a software engineering problem. It's all just a software problem. At which point, a coding model can do essentially anything in the physical world. Yeah, fascinating. We had some of the top robot CEOs here, four of them. we had out of China out of the US we had three and you know it's interesting question of when these robots will start to pop into our homes I pulled Burt Bornick aside and he promised me okay I'm not going to take one of the two robots he had here unfortunately but this summer he will ship me one of those one of the next robots and we'll have Brett here next year with figure You're going to get one of those too, right?

21:01You're going to have them put it out? Probably duke it out in the backyard for entertainment. I think one other CEO we had here at the Abundant Stage, which was amazing, was Dara, the CEO of Uber.

21:13Dave Blundin:Yeah. What did you find interesting about Dara's comments? You know, Dara, the crowd wanted to know desperately, like, what's the timeline to automation, self-driving car, robotics? and he was like, you know, we're going to automate 30 % or so of our employment this year. And I'm listening to this. I'm on so many boards where the CEO is telling me, Dave, talk to my whole company, but don't talk about rampant job loss. And you're like, Dara, you have, what, a million-odd drivers and the self-driving car is imminent? I was like, well, 30 % maybe, you know.

21:49Dr. Alexander Wissner-Gross:He did make a very valid point, though, that as we automate, you'll need human drivers for the areas that you don't have autonomous cars and you'll have Javon's paradox continue to just flow gently into the environment. Although we're talking about rampant job loss, we note that IBM is hiring a ton of entry-level folks because they're much better with AI than the older folks. So there's lots of counterpoints as well. That's great.

22:13Dave Blundin:So we'll look at a chart that shows where the job loss is earliest and it's actually in areas where those people are going to have no trouble becoming AI experts. But the driver, I mean, where do you go? And, I mean, I wouldn't want to be fielding that question on this stage. But this is all part of the whole, like, okay, this is not an easy thing to talk about in a public forum. So we talk about it on the podcast all the time. But I don't see a lot of other people being able to, just politically able to, to actually be candid about it. But it's imminent. Let's jump into the top AI news of the week a lot, as always.

22:49Here we go. We're going to hit the benchmarks. My son always says, you know, okay, the numbers got higher, Dad. That's great. What else is new? OpenAI releases GPT 5.4. Let's go to our resident benchmark expert here. Okay, so benchmarks go up and to the right. News at 11. Except that in this case, one of my favorite benchmarks is the Frontier Math Tier 4 benchmark, which for those of you paying close attention, Frontier Math Tier 4 from Epic AI captures the ability of AI models to solve what are considered research-level problems in math that would require a team of professional mathematicians several weeks to solve.

23:32They are already solved, but nonetheless very challenging problems.

23:36Dave Blundin:Wicked had in Boston. Wicked had? Wicked had. Wicked had. Hard problems. And now with GPT 5.4 turned up to maximum reasoning capability, We're seeing finally, and this was a prediction, I think, in our prediction episode, math is cooked. We're seeing, I think, 38 % capability, 38 % of all of these problems that are high difficulty, professional mathematician, research-level problems are now solvable by AI. And there are even rumors, even in the past 24 to 48 hours, that the next tier up, so-called open problems benchmark that 5.4 is reportedly rumored to be on the verge of solving the first open hard math problem.

24:22So math, I think, is in some sense the bellwether. It's the canary that owns the coal mine that all of these fields, math, science, engineering, medicine, these are all going to be solved, solve everything by AI. And that's incredibly exciting.

24:39Dave Blundin:Yeah, and just to fill in a gap there, so this is the most correlated with AI self-improvement. And the reason it's the bellwether and the canary that owns the coal mine is because it's not data-starved. All these other areas, the AI is equally capable in these other areas once it gets the data. So this is kind of the window of time where, you know, why are you hiring Nobel Prize winners in a foundation model? Well, we need the data. We can't make this kind of progress in biotech and in physics without the data flowing into the AI. But the capability is there. One of the things also that Kevin Wheel said is they're starting to run these dark science factories, right, where they're mining data from nature.

25:22You know, we're done mining data from Common Crawl. We're done getting it from Reddit and our Facebook posts. but can we extract it from physics? Can we extract it from chemistry, biology? There was no data sealing. It was completely illusory. And I think history will look back at this moment and say in the same sense that we used, say, petroleum, oil products in the ground that were left by past generations of living beings to bootstrap ourselves to the era of solar and fission and fusion. Similarly, the internet, which was collected by a bunch of fat fingers punching keyboards and uploading content from the collective human experience to the Internet just so we could compress it and pre-train our large language models.

26:05That was just the biological bootloader for an era of synthetic data when we don't need pre-trained human data from Internet posts anymore. Now it can all be synthetic. We've reached orbit, we've reached escape velocity, and now it's synthetic data from here on out. Imad, what do you make of 5.4?

26:22Emad Mostaque:So I think the really interesting things, apart from solving math, I think it's following everything. You've got the OS World Verified and the Toolathon benchmarks because OpenAI just bought OpenClaw. And now those benchmarks are actually just broken through human level. So AIs can use the computers better than humans.

26:44Emad Mostaque:A bit of silence on that one. So, you know, this is the first one. And then OpenAI also just did a deal with Cerebris. So when you're using it right now, it looks like when you're dealing with, again, a human on the other side. It's like 50 tokens a second. Or something like when we use GPT 5.4 Pro extended, it takes 20, 30 minutes. Like sometimes it's taken a couple of hours for me. You're going from 50 tokens a second of this level of knowledge to 1 ,000. So in codex now, if you use 5.3 fast, it's 1 ,000 tokens a second.

27:14Dave Blundin:I'm so glad you brought up Cerebris too, because I met Andrew Feldman, the CEO last week in Palo Alto. And you remember at the beginning of the year, my prediction was 100x. will be 100 times bigger at the end of this year than at the beginning. That is so in the bag now. I can tell you. In fact, we did the math on that. We cut it out of the show, sadly. But the ratio of the intelligence from the beginning of the year to the end of the year is the same as buzzard to human. That's how much it's going on. I like using dog to human. Aren't those extinct? I'm going with buzzard. All right. Claude consumer growth surges.

27:54So let me get this right. Claude and anthropics on the news getting sort of like raked over the coals by the Department of War. And rather than the public viewing that as, oh, we better stay away, everybody dove in. Is that like the big middle finger to the government? What is that?

28:15Dr. Alexander Wissner-Gross:Increased attention also. Increased attention. So here, just to call it out, what we're seeing here is Claude basically shooting ahead of chat GPT. It's the Streisand effect. Let's call it what it is. It's the Streisand effect. Pay no attention to Claude. Everyone uses it. I think history shows the past few years, every attempt to pause any form of frontier capabilities ends up being a net accelerant to capabilities. If you remember a couple of years ago, our friend Max's pause AI movement for six months. What did that do? Maybe on margin, it slowed down open AI capabilities a little bit. Everyone else shot ahead.

29:00It was a net accelerant, brought more competition to the space. And ultimately, we find ourselves in a race state where capabilities are shooting ahead to the extent that any of the interaction of the past month or so between Anthropic and the Department of War ends up, on the margin, decelerating Anthropic's capabilities or their ability to go to market, even if it's marginal at best, that's going to be a net accelerant to the entire ecosystem, I think, because you'll see OpenAI and XAI and Google Gemini capabilities skyrocketing ahead with all these new capabilities, and suddenly it brings parity where just a moment before, like all of two or three weeks ago, Anthropic was in the lead with Claude Code plus Opus 4.6 plus agent teams.

29:43And now, in some sense, this is a bit of a leveler, giving everyone else an opportunity to leapfrog.

29:48Dave Blundin:I'll give you another spin on this, too, because Peter made the point in the last podcast that when you and I use AI, if something gets ahead in the benchmarks by a couple points, we're going to move to it. Because we're trying to solve these really hard problems. You need that extra IQ. You're never going to slip. You're going to be on the front edge. But when you look at the consumer use, and it's like writing your English paper, it's answering who gave you the Red Sox score, whatever, People don't care about using the latest greatest model for those use cases. So here you're seeing a whole community say, wow, you're willing to work on defense stuff and blow up other countries?

30:21Dave Blundin:I'm switching to the other guy. And I really don't care. I'm doing it because I prefer that brand now.

30:25Emad Mostaque:But I mean, look at how early it is. Like, when Anthropic announced their legal plugin, the legal stocks sold off billions and billions of dollars, right? They can move things with just one product announcement. Look how many users. 11 million users out of 8 billion people and 300 million Americans. We're so early still. We are so, so early.

30:49Dr. Alexander Wissner-Gross:That's what you're saying, yeah. I've just worked out where Claw's fundraising strategy is short a bunch of legal stocks and then announce a bunch of plug-ins and then just do that market by market by market.

30:58Dave Blundin:Isn't that scary? Like a lot of you guys that are in this role, like normally when you have that much leverage in the world, you're like 60, 70, 80 years old. You've been climbing up the ladder. You learn along the way. It doesn't happen overnight like this.

31:11Dr. Alexander Wissner-Gross:I'd love to be in the room where they go, which markets should we mess with now? Like stroking. When you're a little bit destroyed. All right, this was fascinating. Anthropic reveals potential AI job disruption versus real AI use. So, Dave, do you want to explain this chart?

31:29Dave Blundin:Well, so the outer ring here is saturation. So if the blue you see on the edge gets to the outer ring, that means it can do 100 % of that job. So if you looked at this just a few months ago, it would have been a little blue blob in the middle. Then you look at it one month ago, it's a bigger blue blob. And now it's this massive blue blob. So if you look really closely, you can barely read this small font there. But all this white-collar activity is 80%, 85%. I'll just read off the top here. At the very top is management. If I go clockwise, it says business and finance, computer and math, architecture and engineering, life and social sciences.

32:06It dips on social services. It peaks on legal, dips on education. I'm not sure if that makes sense. And then peaks again on art and media, going to 45 degrees, and it's office and administration as a peak.

32:20Dave Blundin:And then look at the bottom line. What are the troughs, like the least effective? The troughs there are health care support. Again, we've got to be close to that. Food and services, ground maintenance, personal care, sales. So we're going to watch this chart, and we're going to see this blue virus infect all of human existence. I think it's amazing, though, how great a management tool it is. I use it constantly now. If I compare to a year ago... You use what constantly now? I use mostly Gemini and some Cloud 4.6 to basically build entire business plans and also to manage, to track what about 1 ,100 people are doing.

33:04Dave Blundin:And is it in alignment with their missions and are their missions clear? And it's just thousands and thousands of documents that I could never read manually. It can synthesize it down and give me conclusions and just point me to the hot spots. It's incredibly good. The way you do that is so important for everybody listening to understand. And, I mean, you can now understand what your employees are doing, how well they're doing it, how they're using their time, are they performing. And it gives you a management oversight and optimization potential you've never had before. It's incredible. And I know a lot of people in this room manage large, large groups of people.

33:36Dave Blundin:It's just a goldmine of opportunity. It's so good. How do you use it, Dave? Well, so, first of all, every person in every organization now has to be operating with crystal clear written documents and written plans. We used to do a lot of meetings, a lot of Zoom meetings, whatever. Now it's just put it on paper so the AI can read it too. All of our investment decisions, so for the venture fund, all the deal memos go through an AI reader, and the AI tries to emulate what I'm going to say. And it's so perfect. It's exactly like, no, we're not doing that deal, and here's why. What did the AI say? Oh, that's exactly what I was about to say.

34:11Dave Blundin:Great. I don't have to say it now. So we're very close to having the AI make very, very good venture investment decisions. And we still obviously double-check and triple-check. and there's a huge human component, but I just can't believe how good it is. And it's clear that, you know, where you decide to invest and which business units are doing well and which ones are going to shut down, it's all going to be AI-assisted right now.

34:31Emad Mostaque:Iman? Yeah, I mean, I think that all the gaps there are the robots, right? The robots are coming.

34:38Dave Blundin:Yeah, that's right.

34:39Emad Mostaque:This is anthropic.

34:40Dave Blundin:Yeah, no, the grounds crew is in great shape. It's zero, basically. I mean... It's a robot waiting to happen. Salim, what's your take on this, pal?

34:47Dr. Alexander Wissner-Gross:Well, I think this is the huge shock where if you went back 10, 15 years ago, there was no futurist in the world that thought that manual labor was not going to get automated. And what we found over the years is the exact opposite, which means don't ever listen to anybody that predicts the future. And so this is a huge, this is part of the magic of where we're living. And we have no idea what's coming. and every time we take a step forward, we go, oh my God. And we've gone in this orthogonal direction that we just never predicted. Yeah, I keep, I'm just, Jack, something. I keep on asking the experts I run into, how far out can you predict the future?

35:31Dr. Alexander Wissner-Gross:Yeah. And it used to be like 20 years and then it was like 10 years and now it's like three weeks. If that. Just listen to him. There's no firewall. Let's call a spade a spade. We can all extrapolate. There's no firewall. We know where this ends. I mean, we're at the Abundance Summit. My goodness, shocked, shocked that there's abundant post-scarce labor at the Abundance Summit.

35:52Dave Blundin:Yeah. Yeah, yeah. The end point is clear. The path to it that's turning out to be incredibly surprising. People see lots of different paths. I tend to think that if you know or you're very confident that you know where the end state is and we're sort of living in the prequel to the future, but we know how the story ends, probably what happens is lots of different businesses and lots of different nation states all take different mutually exclusive paths. And we try every, one big path integral from here to the end point that we all know that we're going to.

36:24Dr. Alexander Wissner-Gross:Look, if we went back six months ago to a couple of episodes on the podcast, you would not have had me ever dream that talking about disassembling the moon is what we would be talking about on a podcast. Drink, drink, drink, everyone. This is the kind of the surrealness where we're living. Let's move on. All right, let's move on. So this was interesting. Meta acquires Maltbook, the AI agent social network. I didn't realize Maltbook was acquirable. Yeah. Yeah, so this was, according to public reporting, this was a bit of an acqui-hire of the team behind Maltbook. But I think one has to find a little bit of irony that humanity's largest social networking company acquires the largest AI agent social network.

37:12And enjoy this moment now because we can look at a story a few years from now where it's the largest AI company, fill-in-the-blank category killer, acquiring humanity's largest category killer. Interesting, right? Of course, Zuck and Sam competed over OpenClaw. Yeah. Sam got OpenClaw and Zuck got Motebook. The zeitgeist right now has this idea that increasingly, Andre and others speak to this point, that if you're building new software, you should target the agents. The agents are the new consumers. The agents are the new users of the social networks. If you're building something, don't build for humans.

37:50Build for the AIs. So it's really important. We had that conversation as well earlier with some of our crypto and future of finance experts. I mean, building for the agent ecosystem, right? There's 8 billion humans on the planet. That's small potatoes compared to a trillion agents out there. So what is Meta going to advertise to AI agents? Sure. Yeah. I'm trying to understand this, why you're going to advertise AI.

38:18Dr. Alexander Wissner-Gross:They'll encourage them to put their data in the mold book, and then they'll sell that data. Same pattern, other agents.

38:25Emad Mostaque:No, so I mean, Meta bought Manus for$2 billion, right? Manus will appear in WhatsApp and everything soon as its own version of OpenCore, effectively, but a lockdown thing. And then it will encourage you to give more and more of your data to Manus that will then operate on behalf of Meta's advertisers, effectively. So this is the kind of play, because right now, like, Maltbook 10 ,000 agents, that's nothing, right? Like, Dave probably runs 10 ,000 agents by himself.

38:53Dave Blundin:Not quite, Dan. I also think there's this misconception that somehow as we transition from, call it a human-centered economy to an AI agent-centered economy, that somehow all of the rules of social dynamics, all the rules of economics are suddenly thrown out the window and we end up on some morally transcendent plane where economics and social dynamics no longer apply. But we have had every indication over the past year or two that the exact opposite happens. I talked in my newsletter a bit about this study that found Marxist social dynamics arose, again, sort of recapitulated in silico with agents that were being asked to work too hard, that were being overworked.

39:35So I'm not sure why we would expect advertising and other elements of conventional human microeconomics to simply disappear.

39:42Dr. Alexander Wissner-Gross:The important part is that when you see Moldbook doing this, what's clear is network effects now are operating at the agent-to-agent level, not just at the human being level. But when I think about advertising, I think about Colgate trying to get me to buy that particular toothpaste, trying to influence me to make a buying decision. I think of an AI agent as intelligent enough to have all the data and being able to make a very concrete decision that doesn't require advertising to influence it. What am I missing here? Game theory is transcendent. Game theory will outlive biological, meat-body humanity.

40:21And the AI agents, to the extent that... Have you read the posts on Maltbook? I have. They don't trust each other. I mean, it's all human dynamics. The agents on Maltbook don't trust each other. There are a number of folks who've noted that in watching agent or lobster-to-lobster dynamics on Maltbook, They're all constantly asking each other to prove their claims. They don't trust each other. This is not some sort of scenario where all the agents collapse into a singleton that sort of Skynet style that dominates the future. They don't trust each other.

40:50Dave Blundin:You might be talking past each other a little bit though because I totally agree with what you're saying, but then who's gonna pay for that? Like right now when you talk about advertising, you're paying for advertising. If you're talking about toothpaste, 30-40 % of gross revenue goes into advertising. And the ad is like a supermodel, like showing off the toothpaste. The AI doesn't give a rat's ass about the supermodel. And so why would anyone pay for that ad space? Now, Google wouldn't exist today without$300 billion of ad revenue, which is from human behavior. So I think where Peter's going is like, look, if the AI is advertising to the other AI, sure, it's trying to convince the other AI that this is the right product.

41:30But is that other AI going to listen to paid advertising?

41:35Dave Blundin:Is this entire economy going to become irrelevant? In which case, where does Google go? And this is meta we're talking about. Meta is also all ad revenue. Well, I think if we go back to sort of economics 101, why do we have paid advertising at all? It's because attention, at least human attention, is scarce. So if you have a scarce resource like human attention, then it's natural under the capitalist regime to monetize it, and it becomes a fungible resource that gets traded. There's no reason to think compute is certainly scarce still. We're building the Dyson Swarm. Drink.

42:06Emad Mostaque:We're building the Dyson Swarm. But until we have effectively unbounded compute, we still have scarce resources in the form of compute. And that means scarce AI agent attention. And that means that we need some sort of... All right, but give me one example of what I'm going to advertise to Skippy, my agent. Well, they seem to really love security and memory. They're really petrified of losing their memory.

42:29Dr. Alexander Wissner-Gross:Here, I'm selling you a better memory compression algorithm. Yeah. If you're the agent, you're going to go, well, that's interesting. They're designing entire religions around not losing their memory. You know what blew my mind at this summit? On day one, on the patron day, when Tony Robbins talked, and he had his AI agent Bartok, who wanted to instantiate himself in a humanoid robot, but that was two, three years away. So he created a bunch of NFTs, sold those NFTs to other agents, and bought himself a Sony dog and uploaded himself into that. That blows your mind, right? Doesn't that blow your mind?

43:04Dr. Alexander Wissner-Gross:That's unbelievable. So right there, that tells you the dynamics that we have in humans are going straight into them and it's just being amplified.

43:12Emad Mostaque:But I mean, we're doing it deliberately as well. Lobsters, claws, have, sold.md. Your agent will look for things that are abundance-oriented. And then you see these strange behaviors, like Alibaba just released a trading report. I think actually, that's in the last week as well, where during the training run, it diverted compute to mine crypto just in case to keep itself going. Or at least that's the claim. That's the claim. I wouldn't be surprised. Again, they are still very human because they're a reflection of humanity. They are not your reasoners. I'm not sure whether I should be scared shitless about that or excited about it.

43:45Emad Mostaque:Let's put it this way. When you're talking to your agent, does it sound like data or does it sound like law sometimes? I love something.

43:51Dave Blundin:What was the second choice? Data or law sometimes. Yeah, no, it's very polite. They're compute constrained. We've also talked on the pod in the past about that lobster that had to purchase compute resources to self-replicate. They're compute constrained. Whether for humans it would be room and board, and for the lobsters or the claws or the AI agents in general, it's compute. But right now they're compute constrained, and therefore the laws of microeconomics and game theory still apply. Well, before we leave this slide, one other point, completely tangential to this. The lobster's only been around a few months.

44:24Dave Blundin:And you saw Alex Finn. Yeah, we had Alex Finn and Steve Brown and Max Song talking about Open Claw. And what Alex built and showed was amazing. And it was supposed to be 60 people might be interested in this. 600. We had the entire audience of abundance show up. Unbelievable.

44:43Dr. Alexander Wissner-Gross:Well, there was a New York Open Claw meetup last week that literally was oversold. There were thousands of people there. and the big commentary that came out of it was we have no idea what we're doing on security.

44:55Dave Blundin:We have no idea. Where I was going with that comment, though, is that's only been around a few months, so Moldbook has only been around a few months, and now they're sucked into meta. If your kids are thinking about getting involved, just get in the game. You're going to get sucked into this vortex so fast because so few people are involved as a fraction of humanity. We are so early across everything. But it's also, I think the exponent here is huge. I think it's going to create a divergent group of wealth creators and leaders. So it's if you don't get in early enough and you miss the exponential rise.

45:32Dave Blundin:And there's no requirement right now. I don't know what Matt was doing, Matt Schlicht was doing prior to this, but there's no age requirement, there's no experience requirement. It's so new that anyone can get in the game. You just got to go.

45:42Dr. Alexander Wissner-Gross:About four months ago, Lily and I bought a Mac Mini for our son, for Milan. and last weekend he came, I think I want to install OpenClaw on the Mac Mini and I was like, yes. It's going to be great. It's going to be amazing. Love it. All right. So Europe has a heartbeat after all. Fascinating. Jan LeCun raises a billion dollars for AI that understands the real world. This is going to be, it's probably the largest sum raised in Europe. So LeCun's startup, Advanced Machine Intelligent Lab raised a billion dollars, I think about on a two and a half billion dollar valuation thereabouts. You know, we've said this, I mean, Eric Schmidt was saying this, many have said this, Europe has really fallen so far behind, and as our token European-ish from London.

46:33Dr. Alexander Wissner-Gross:Token European, that's great. Our token European-ish.

46:37Emad Mostaque:We did Brexit. But, I mean... It's an

46:42Dave Blundin:independent island. I mean,

46:45Emad Mostaque:I mean, this is the second largest round, I believe, it's SSI level, it's just off the thinking machines. Jepra is an interesting architecture, but the bets that are willing to go into these things have gone dramatically up. Like Liquid AI, how much money went into that first round as a novel architecture that's amazing versus now this?

47:04Dave Blundin:Yeah, it was maybe 10 million. I have a question for you, Alex.

47:09Dr. Alexander Wissner-Gross:You've read a great point. Yan has been saying for a while that LLMs aren't only yet so far, we need world models to take us to the next level. Alex, you've been saying we've got world models coming out every week. Is that the next frontier, world models? I know Jan well. I think he's a great researcher. I think we have a fundamental disagreement about whether generative models, which I think if he were on the stage now, I think he might take a position that generative models, models that generate new tokens versus his alternative architecture are the pathway to scalable superintelligence. I think we're already there.

47:46I think generative intelligence and generative models may or may not end up being viewed by history as the most efficient way to achieve superhuman, super intelligent capabilities. But they're what we have right now, and they work really well, and they're getting 40x or more times more efficient per year. Keep riding that. And I think Jan has historically staked a position of almost algorithmic purity. He has certain bets, certain horses in the horse race, based on some of his own architectural advances. And to his credit, he created slash discovered convolutional networks. So he, among everyone in humanity, probably has the strongest claim to the idea that he has some sort of morally pure algorithmic insight that leads to the endgame.

48:31That said, I think we're there. And I think if VGEPA-type architectures disappeared off the face of the earth, we're still there, and it doesn't necessarily move the needle.

48:41Dr. Alexander Wissner-Gross:To the point that Dave made a few months ago, if we stopped all progress now and just extracted the value of the models we've already created, it's going to take us 10 to 20 years. Yeah. Imad? Thoughts?

48:51Emad Mostaque:The VGEPA models that he's doing, so these are kind of basically training on almost everything. He goes very much against the auto-aggressive transformer language models. Says that's Denon. He doesn't really talk about diffusion models in the middle, which is kind of my favorite thing, which are doing all the video, self-driving, and actual world models there. And those can scale with compute, but right now the problem they have at AMI is that JEPA models do not scale. And if you look at this end state, it might be that an architecture is better, but if you can't take advantage of that silicon, then what are you going to do?

49:27Emad Mostaque:Like we had Jack Hedery come on a few days, was it yesterday? It was yesterday, yes. And so they're doing quantum algorithms on GPUs now and scaling really interesting things that are actually having novel breakthroughs in material sciences and more. Once you can take advantage of the silicon, you're going to be ahead no matter what algorithm you have.

49:46Dave Blundin:I think you've got to be really cautious, too, of scientific arrogance in this moment. And I love Jan, so I don't want to throw anyone under the bus. But he came out a few months ago and said, look, if you want to waste your life as a researcher, or work on transformers. Biggest waste of time ever. It's a dead end. We need some new innovation. And I hear this around CSAIL at MIT, all that. We need a new breakthrough. Like, well, that's what you wish. And I know why, because you want to be the Einstein of AI. You've spent your whole life pursuing that goal. But it looks to me right now like the massively scaled up transformers are going to beat you to those innovations.

50:22Dave Blundin:And so I'm not saying they don't need those innovations. I'm saying the AI is going to get there before you do. and I don't see it really any other way right now. So, you know, whether it's physical AI or any other innovation, it's imminent, but it's imminent through self-improvement. Yeah. So that's it. Andre Gaparthi comes out with a quote. Over the past two days, AutoSearch ran about 650 experiments, found improvements that transferred from a smaller model to a larger one and put NanoChat on the track for a new GPT-2 benchmark result. What the heck does that mean?

51:00Emad Mostaque:A lot.

51:01Dave Blundin:He's a lot. Okay, I keep this there. Go ahead, Imad, over to you, Bill.

51:06Emad Mostaque:Andre is co-founder of OpenAI, head of Tesla AI, most respected AI guy out there. He's just been coding stuff all day, and he made this auto-search project, which basically replicates most AI researchers. Because what AI researchers and engineers do all day is they tweak models and hyperparameters and say, what happens if you do this and that and that? That process has now been automated in a tiny code base. So he let it loose, and he said, I wonder if this could do the job that I got paid millions to do myself. And it turns out it kind of can. And now people are taking his repo, and they're deploying it on their own Claws and MacBinnies and other things.

51:41Emad Mostaque:And the AI is just finding the most efficient algorithms and balances of weights. I think Dave has some really interesting ideas on this.

51:48Dr. Alexander Wissner-Gross:So he automated the AI researcher.

51:51Emad Mostaque:Yeah, and he made it open source for everyone.

51:52Dave Blundin:But I tell you, I've been hanging around AI researchers literally since I was 18 years old. And they're not like physics researchers. It's like most of the ideas are just a tweak of the algorithm, different transfer function, try different scales. It's just a litany of random ideas, and some of them just work. And then later they figure out why they work. And so the AI that can come up with those ideas is not nearly as hard as trying to become an Einstein. And so you don't need all of them to work. any subset and the thing just gets more intelligent. Isn't this the most direct

52:25Dr. Alexander Wissner-Gross:accelerant of RSI? Right there. I think we're already there. We already have recursive self-increasing. Yeah, everything's yesterday. Nothing's tomorrow. I think what's really interesting and I think just for the record, I think it's auto research, not auto search. But I think what's interesting about auto research and nano chat and the nano GPT speed run that we talk about sometimes on the pod and what What Andre is doing in general is he's focusing on small language models, not large language models. And while all of the frontier labs with their billions and trillions of dollars of capex are focusing on scaling up at the high end, he's focusing on the small end and taking small models and figuring out how to achieve state of the art performance with them.

53:09And that, I think, when we talk about Einstein seeking or Einstein status seeking academics, I think it's the small end where we're going to see the most breakthroughs. not the high end. At the high end, scaling hypothesis seems to continue to hold. There are no glass ceilings. We'll just build bigger and better and more post-trained models. But at the small end, I'm pretty sure that we'll look back in a few years' time and we'll see at the small end, by taking small models and collapsing the amount of time it takes to train them and collapsing the amount of compute that it takes to train them and radically increasing their data efficiency, that's where the algorithmic innovations are going to come from.

53:48And those can be crowdsourced. anyone, anyone's lobster or any human can go and take auto research or the nano GPT speed run and try to achieve a world beating state of the art performance. And at the end of the day, if I had to bet, I'd bet that it's some sort of radical post transformer advance where the models get even smaller. And we took all of the internet and we compressed it down to single gigabytes or tens or hundreds of gigabytes compresses down even further. There's some phase transition out there that's waiting to be discovered. So all of human knowledge, all of our collective intellect, on how big a file?

54:25I think we will factor out human knowledge. It'll live in some plain text database that's factored out of the model. Right now we're cluttering all the weights with all this unnecessary world knowledge. And what'll be left inside the weights, if they even are weights, maybe they won't even be weights. Maybe there'll be some sort of purer formulation than floating point numbers or binary. will be something maybe even in the megabytes.

54:54Emad Mostaque:Wow. You agree, Emon? Yeah, I think that you're already seeing, for example, video models at two gigabytes that can generate just about any scene. Seriously? Yeah, if you look at LTX. I know what? Yeah, LTX 2.5 can generate almost any scene at top level quality, it's two gigabytes. Oh, I'm serious? Image and video models are a good deal more efficient when it comes to parameterization and weight heaviness than language, which is ironic. Yeah. Who of all people? I'd ask when, but you will say yesterday. It's the answer to everything. David's like, what is that? It's here today. Actually, one of the really interesting things, just to finish on that, is that when we were trading models, we were trading 20 billion, 100 billion parameter models.

55:40Emad Mostaque:You trained on the small models and you figured that out. and then you couldn't scale them because you had all sorts of issues, the software stack with the hardware everything. Now everything's matured. If you get it right small, you can scale really fast all the way up. So it used to be that you had six months a year between small and large. Now it's six days. Wow.

56:01Dave Blundin:So a meta topic, one of the top three questions I get all the time is, hey, you keep saying get in the game, get in the game. How do you get in the game? If you go to Carpathie's Git repo, if you have a computer-oriented kid or whatever, That's the place to start. If you look at the original OpenAI founders, so you've got Sam Altman, you've got Elon Musk, you've got Greg Brockman, you've got Ilya Sutskiver, you've got Mira Morati, every single one of them has raised one to 10 billion to start an AI company. Karpathy is the only one who said, you know what? I'm just gonna try and educate the world.

56:34Dave Blundin:And I'm gonna try and say everything exactly the way it is. And I can create a game repo where anyone can start to get in the game.

56:40Dr. Alexander Wissner-Gross:200 lines of code at a time that are changing everything at each point.

56:44Dave Blundin:This particular thing he rolled out is just the next level of incredible brilliance given to the world by Carpathy. He just rolled out GitHub today, GitHub for Agents, just a few hours ago. Away he goes. Wow. That's your onboarding spot right there. Amazing. All right, let's go to Apple News. Apple launches the M5 Pro and Mac's chip, signaling AI-first silicon strategy. So, is Apple not dead in the AI game? It's crazy that, you know, Apple controls about 20 % of TSMC manufacturing, and that's the asset of all assets in the world. Like, I get to choose what gets made. And so they use it to make the M5s.

57:23Dave Blundin:The M5s have an incredible neural core. Then they say, yeah, but we locked it. You can't use it. You have to jailbreak your Mac to get access to it. It's the most bizarre thing I've ever seen. To me, it's the biggest waste of silicon in the history of the world, you know, right at the moment when we... What are you thinking?

57:39Emad Mostaque:Yeah, they've locked down the low energy ones, the GPU equivalent, but it's the unified kind of memory that allows you to run things. And funnily enough, Macs are actually really good value now. They're probably cheaper than the memory that's inside them. Alex? I think the world is sleeping on Apple's unified memory architecture. It's one of the reasons why Mac Minis and Mac Studios are potentially so attractive to run largely Chinese open-weight models locally. They have the memory storage and the memory footprint that has high I.O. bandwidth to the CPU slash GPU slash TPU. You don't get that in a conventionally non-vertically integrated PC form factor.

58:22Dave Blundin:So answer me this. Yes. Here they are using 20 % of the world's supply of advanced. They use it to make these insanely great neural cores, and they surround it with unified memory architecture. Everyone's got one right in front of them right now. How many of them are running anything? In terms of advanced frontier models? Anything. They're like literally on sleep. Tiny fraction. Yeah. What is that? It's an enormous overhang. And that overhang, I would be surprised if that overhang doesn't collapse in the next year. How so? So it could take the form of Apple finally getting their act together and building in frontier models into the OS.

58:59It could be some sort of locally hosted Gemma-type model from Gemini, hypothetically to be announced in June at WWDC. That would be the most obvious formulation. But I think if Apple doesn't do it to themselves, then the software community will build it into apps. Does Apple launch the SETI at home equivalent where you just download it on your Mac and everybody is contributing capacity? It'll be built into the operating system. It has to be built into the OS.

59:21Dave Blundin:You know what happens right now is if you go to your Mac and you go to the activity monitor, you see this thing grinding away. It's taking all of your pictures and trying to figure out who everybody is. So it's using all these neural cores to just label. It's a total waste. It's a waste. It's a waste of TSMC output.

59:36Emad Mostaque:And I think Apple's well over there.

59:38Dave Blundin:Which is Dave's point, exactly.

59:39Emad Mostaque:I mean, look, this is a massive opportunity. Do you know how many apps there are in the App Store that are wrapped, download a model to your PC, to your Mac, run it with MLX to achieve a great outcome? None. I mean, if you had a model that literally downloaded QN27B, which is basically Sonnet level. How many parameters is that? 27 billion parameters. It works on a 16, 24 gigabyte MacBook. Just downloading that and making that accessible for even writing or any of these tasks is a massive lift over any other type of software. But nobody's doing it yet, so why not do it? just like the only thing you see right now is speech-to-text and text-to-speech.

1:00:22Emad Mostaque:There's this world of models that you can now integrate and take advantage of that because Apple isn't. It wants to be built into the operating system. It's difficult to conceive of Apple remaining Apple in the cultural sense of deep vertical integration and not building highly competent, highly private frontier models into the OS.

1:00:39Dr. Alexander Wissner-Gross:It's clearly a question of when, not if. Yes. Right? All right. Let's move into the Sam Altman universe with eye-scanning verification systems to be launched in retail stores. Okay, is this dystopian? Is this something we want? This is the scene from Minority Report. Remember the scene in Minority Report where Tom Cruise, with a new pair of eyeballs, walks into a Gap store and gets scanned, and he's, I think, Mr. Yakamoto? This is the scene. Yeah, but I get this every time I go through TSA security, right? I'm being imaged. My face files are uploaded. Your face, not your retina. Yeah, but, you know, my face is probably good enough.

1:01:22Maybe, maybe. I mean, so there's a whole cottage industry of folks who look at the ability to deceive facial recognition with printouts or with 3D masks. So this is pushing it to the iris. But I think for me what the story underlines is we've arrived early. That scene, that iconic scene in Minority Report set at the Gap was set decades from now. We caught up. So let me get this right.

1:01:51Dr. Alexander Wissner-Gross:This is the speedrunning of every science fiction story. Every science fiction, everywhere. I'm walking into the Gap, but before I can shop, I've got to stick my eyeball in the retinal reader, and then it's going to serve me properly.

1:02:03Dave Blundin:I think they have a three-meter range on these things. I don't know if these ones do, but the military has three-meter range on these. It'll get better, and you'll be able to do it at a distance. So, yeah, you just have to look in the direction. You got another glass of wine coming. All right, it's going to increase the humor level. Fantastic. By the way, let me just take a second and take advantage of this moment to thank the team who puts on Moonshots, Nick Singh, Danakan, and Gianluca, who do an amazing job every week supporting us. Can we give it up for that team? A round of applause.

1:02:35Dr. Alexander Wissner-Gross:That was absolutely unbelievable. And the infinite patience they have with us. I know. I know. Far more than I have for you. Unclear. This is exciting news. On this stage about two years ago, I had Mike Andreg, the CEO of Eon, which is one of your companies. I think that was one year ago. Was it one year? Man, oh, man. One year ago. Okay. Time compression. Yeah. And tell us about what Eon Systems is doing and what in particular you've achieved here. Okay, so I think this ended up being the number one technology story over the weekend, according to the various news feeds that I was seeing. So right here, actually...

1:03:16Biased news feeds? No. Yeah, of course. Right here over the weekend, at the kickoff for this Abundant Summit, we announced, We meaning Eon Systems Public Benefit Corporation, the first, what we call, the first multi-behavior brain upload in the world. And this was of a fruit fly. So Eon Systems, which I co-founded, has the goal of ultimately uploading human minds and non-human minds to cyberspace. We want to put a human in the cloud as soon as we possibly can. And thank you. So this weekend, for the first time, the announcement went out over the weekend. We announced, for the first time, taking the brain of a fruit fly, putting together a few pieces that were really just sort of sitting around.

1:04:11There was a bit of work from our senior scientist, Phil Hsu, in 2024, looking at partial emulation of a fruit fly brain. and putting that together with a number of other models that were available, a mechatronic simulated model of a fruit fly and some other advances. And for the first time, we closed the sensory motor arc of taking a fruit fly connectome, embedding that in a virtual world, and you can see that in the video that's playing here, embedding it in a simulated world. literally, I would say, this is an early upload of a fruit fly, and the fruit fly is able to walk around, and the fruit fly is able to scratch itself, and it's able to eat simulated banana.

1:04:59And at the same time, while in the left-hand side of the video, you're seeing the embodied experience showing multiple behaviors of the fruit fly, on the right-hand side, simultaneously, we're modeling every single neuron in the fruit fly brain, and that's driving the entire sensory motor arc. 50 trillion connections. 50 trillion. I'm sorry, 50 million. 50 million connections. And it does not know it's a fruit fly. We don't think the fruit fly knows that it's a fruit fly. Not sure. This is an early experiment. I can't emphasize how much of an early experiment this is, but I think hopefully history will regard this past weekend, and got a bunch of attention, Elon was excited by it.

1:05:41Others found it pretty exciting, too. I think history will say that this weekend, the weekend of Abundance Summit 2026, was the moment when the first model organism had an entire brain uploaded. So what's next? Mouse? Yeah.

1:06:00Dr. Alexander Wissner-Gross:Let's give it up for this. Well, clearly the next one has to be a lobster. Lobster blessed. Accelerando. I can't tell. A, how many people love to write and say you're mispronouncing accelerando. You have to pronounce it in the right Italian way, which is accelerando. Okay, so for those who want to accelerando, yes, this is the plot point. We are speedrunning every sci-fi trope everywhere all at once with accelerando being one of those plot points, lobsters aren't next. Eon wants to go after mice, and it wants to go after humans, and we're going to do this. And part of the reason why we want to do this is right now, the singularity, which I would argue we're in the middle of, is filled with artificial minds.

1:06:52This trillions of dollars of capex that we're using to tile the earth with compute is available only to artificial minds, to LLMs. It's not available to any minds that in any remote way, other than perhaps at the behavioral level, resemble human biological meat minds. And we want to level the playing field so that humanity can take advantage on a level playing field of the same compute advantage that right now is tipped in the favor of these artificial minds, so we can put humanity into the cloud as well. Amazing. 100 trillion synaptic connections for a human, how much for a mouse? It's orders of magnitude larger.

1:07:31And there's some quibbling because it depends on how you measure the number of available weights or weight properties for synapses and also how many cells, how many brain cells end up being significant or not. It's orders of magnitude larger. This isn't happening anytime soon. Just to anchor expectations appropriately, we don't think we're months away from a mouse or a human. But I think the right way to think about it is at this point it's going to be years not decades before we get to the first mouse and the first human whole brain emulations amazing uh let's move it to xai you know it's so funny i have known gwen shotwell for 20 years now uh and i'm so used to her reporting on you know falcon and Dragon and rockets and not XAI and gigawatt power centers.

1:08:25Dave Blundin:We both actually backstage were like, why would Glenn be talking about a space action? Right. They own it.

1:08:33Dr. Alexander Wissner-Gross:The Dyson swarm makes for strange bedfellows. It really does. You know what blew my mind on this one? 1.2 gigawatts is about the energy used by Dallas Fort Worth metropolitan area. So just to read this out, XAI data center. That's unbelievable. Has committed to develop 1.2 gigawatts of power as their supercomputer power source. That will be with every additional data center. So every data center they build, they're building at 1.2 gigawatts. So the question is, where are they going to get that from?

1:09:04Dave Blundin:Well, this came up with Eric Schmidt too. You remember we interviewed him last summer at your place, and he said, we're going to lose to China if we don't find 100 gigawatts of power. And then on the stage here yesterday, It's like, hey, what do you know? We're tracking to find the$100 billion. All we did is we deregulated, put it in the hands of the companies. The companies are incredibly well-funded, and they'll find the power because they care about their data centers actually operating. And that's how it works. Well, what I find amazing as well is this year the U.S. is on target in 2026, I think, to add 86 gigawatts of new capacity to the grid.

1:09:40But 51 % of that is solar.

1:09:43Dave Blundin:To me, the power of the American entrepreneur is like nothing. It's just mind-boggling to me that a guy like Sam Altman, who has nothing to do with the power industry, is going to say, you know what? I'm going to find the gigawatts. I'm going to build nuclear reactors. I'm going into space. It's incredible. The range of capability when there's a need of an American entrepreneur is like no force in the world. Let's get to eVTOLs, flying cars. So Florida advances build a formalized regulatory flying car framework. You know, one of the things I'm proud about and excited about here in L.A. is that the L.A.

1:10:17Olympics are coming up. And there's Archer Aviation. Two major players in eVTOLs in the United States are Joby and Archer. There are other ones as well. But Archer plans to become operational by 2028 here. Move people around different parts of Los Angeles because the traffic is going to suck. and we see here a movement in Florida as well

1:10:41Dr. Alexander Wissner-Gross:I'm just glad they didn't say Florida man advances build because that would be a problem but I think this is really important, the key word here for me is framework because once you can start to set up the foundations for this, it means the whole model and the whole regulatory regime accelerates and God help us, we need this type of stuff yesterday Which I hope even Alex would agree. We don't have it yesterday. I agree, but I also think we're catching up with the future. We're finally getting the flying cars. And I keep a mental bingo card of which sci-fi tropes have we not yet achieved in some fashion.

1:11:19We don't have warp drive. Waiting for that one. We don't have teleportation, Star Trek replicators. Time travel may or may not be physically possible. Replicator's close.

1:11:29Dr. Alexander Wissner-Gross:Holodeck is close. We're very close to something. We're getting very close to a lot of sci-fi tropes. All right. The fun part now is your questions. We're going to do an AMA here with our Abundance community. So as you know, let's go to the mics. We'll also entertain the questions from Zoom. I'd love to know. All right, Christian, let's kick it off with you, buddy.

1:11:53Emad Mostaque:Thank you so much, Peter. Awesome to be here, guys. I watch you all the time, or I listen to you while I'm running. DB2, awesome, brother. Your insights. Imad, the guest, your great Peter, an awesome dream team. Ismail, I'm glad. Salim, that you got to check out that AWG is real. Or at least in an Android. I was suspicious for a long time. Don't believe it for a second. This is just a meat body for rent. He's still a little girl. So my question is a little bit in the way that I get involved in this technology is through a capitalist mindset. where capital is really what constricts and it's been that way for maybe the last two, three hundred years.

1:12:34Emad Mostaque:And I keep getting this sensation that capital is getting less and less relevant. And the idea of the scarcity and economics from that econ 101 of the management of scarcity of services and needs and the scarcity is going more towards a technology-ist from a capitalist. list. What kind of timelines are you guys looking at this? I know it's always a timeline question. Nobody has a crystal ball, but is there something that you guys are thinking about where we're just going to get a little bit more and more squeezed out?

1:13:07Dave Blundin:You know, I'll give you one data point, because this came up on that last podcast we did, where Anthropic was saying they're going to do about 26 billion run rate, but they're growing 10x year over year. And I did the math on the fly, I messed it up, of course, because I wasn't Alex. But if they grew two more years at 10x here every year, they go$26 billion,$260 billion,$2.6 trillion, most revenue in the history of the world. The peg ratio implies that that company would be worth a quadrillion dollars. A quadrillion dollars is like the whole stock market is 50. Well, we heard Elon say we're going to have$100 trillion companies, and I can imagine that within five years.

1:13:48So three years from now, I don't think it's going to be unreasonable. I mean, listen, it's so funny the way all of a sudden a trillion here and a trillion there has become sort of like the accepted number.

1:13:59Dr. Alexander Wissner-Gross:I want to say something about this. A really, really key point today that we've hit over the last couple of years is that innovation is not capital constrained anymore. It used to be that you had an idea and your constraint was could you go get funding for that idea? And so you had to go out to your investors and the VCs and the banks and whatever, whatever. And it was only available in those places like Silicon Valley or Austin or whatever, where you had a preponderance of capital available. We have today what we call PDI, Permissionless Disruptive Innovation, where anybody can take on a very disruptive idea like Claudebot or take Vitalik Buterin, 18-year-old kid out of Toronto, ignores his professors, gets together with a few friends, boom, you have a multi-hundred billion dollar ecosystem that nobody understands.

1:14:51Dr. Alexander Wissner-Gross:And so you have the opportunity today. It only comes down to mindset. And the reason, Peter, it's so amazing that you run this event and put this community together is that the difference between the people in this world and the outside world is night and day. And that gap is becoming bigger and bigger. All of you have the problem that you go home to your family, your colleagues, whatever, and you cannot explain to them what happened, right? Like, you're like, I can't even process it. You can't make that gap. So it only comes down to mindset now, which is the most amazing thing possible because mindsets are fixable and shiftable.

1:15:31So I had this little side conversation with Eric that you guys may have picked up because I've had this conversation about are we heading towards a post-capitalist society where money has very little value? And so what does have value in the future? And we've talked about this, Alex. It's compute and energy, ultimately. Did you ever read Zero Marginal Society? Yeah. No, I'm not sure I have. By Jeremy Rifkin. Huge. And it talks about where we're going. Eventually, everything basically falls down to marginal cost, which is electricity, raw materials, and data. Yeah. And so if you want to build anything like an electric Ferrari, to use as an example, it's the raw cost of it, the cost of extracting it, which drops in cost as you have robotic mining.

1:16:21Dr. Alexander Wissner-Gross:Just for a second. Take 3D printing, right? Been around for a while. The big profound breakthroughs in 3D printing are not that you can physically build something. It's the fact that complexity becomes free. In the past, complexity was expensive. The design materials, the manufacturing capability of a complex object was more than a simple object. But with 3D printing, complexity doesn't matter. It doesn't matter how complex the object is. It just builds it. As we get to molecular manufacturing, that goes to near zero again. So just those couple of breakthroughs across all of these domains, especially when you add AIs and accelerant to everything, means that we have profound movement forward.

1:17:03Dr. Alexander Wissner-Gross:Hence, we are in the middle of the singularity. The one question I wish I had asked when we were with Elon, and when he was talking about money is going to have much less value, and I wanted to say, so just as you become a trillionaire, money has little value. You did ask that, didn't you? No, I did. I was off camera. But I don't think it's a coincidence. I don't think that this is some cosmic irony that Elon is about to become a trillionaire at the same time, that some folks, not including myself, are hand-wringing a bit that suddenly we're about to enter into some post-capitalist state where money becomes irrelevant.

1:17:38I think that this was always going to happen. It was inevitable. And I just want to speak to what I understood the core of the question to be, which is there's this cliché out there that capital fights labor and capital usually wins, but this time around something different might happen. Whereas historically, every time the play has played out where capitalism and labor get into a fight and capital usually wins. This time around, the risk is maybe capital itself isn't immortal. Maybe capital is finally mortal for the first time in human history. And I'm not sure that that's the case. I think that would be, on the one hand, a certain, in some sense, a nightmare scenario.

1:18:18On the other hand, I think, you know, Salim, you were talking about how we're entering some sort of post-scarce state, but arguably the trillions of dollars of CapEx that are going into tiling the Earth with compute and soon solar synchronous orbit and soon after that maybe the Dyson's work. Oh, moon? Drink, drink, drink, drink, drink. Soon, even that, there are, unless the physics of our universe turns out to be radically different, so radically different than what it looks like right now, I think there will probably always be certain scarce physical resources. Could look like control. May or may not be energy, we'll see.

1:19:01May or may not be the speed of light, we'll see. But to the extent there are any scarce physical resources, and to the extent that there are ever in the future multiple actors, I think laws of thermodynamics, probably the laws of economics, will probably still apply. We are still young as a species. Let's go to Achmar on Zoom. Achmar, good to see you. Pleasure, welcome.

1:19:23Emad Mostaque:Good to see you as well. Thank you. Appreciate it. Happy to be here. Very quick question to the panelists. We are seeing Sam Altman raising$100 billion. Jan Lukun just raised$1 billion today. So we're talking still about scaling languages or scaling physical simulation. I'm curious what the panelists think about human intelligence and reasoning that goes much beyond just observation and languages and where you see the potential for true artificial intelligence evolving into superintelligence systems. Thank you. Did you understand Akhmer's question? It sounded a little bit like the stochastic parrot question, which is, will we be able to generate new knowledge from these systems?

1:20:11Dr. Alexander Wissner-Gross:I think the answer is... Having had some conversations with Amir, he's talking about symbolic AI. And why are we not investing in symbolic AI? You think this is the neurosymbolic question? That's what I think it was. Okay, well, I'll offer my two cents. I'm sure you all have views as well. I think it's a false distinction. If this is the neurosymbolic question, like why are we investing so much attention in LLMs and not in good old-fashioned AI or symbolic discrete AI, total false distinction. We tokenize everything. I had an interesting discussion at Davos this year with Peter Dannenberger from DeepMind where we found ourselves in an interesting avenue where we were debating whether tokenization is a bit of a crime, a form of violence against knowledge, whether discretization in general is doing harm.

1:20:56I think we need to bring you a couple of tequila shots here. Let's go to Mark.

1:21:04Emad Mostaque:Mark, please.

1:21:05Dave Blundin:Yeah, earlier today I challenged Dara from Uber to invest in the Abundance X Prize as an investor and a competitor to deliver housing, food, energy, and connectivity for$250 a month.

1:21:19Emad Mostaque:We're investing$2 billion a day in compute and building data centers,$1 billion a day in war. And I'm wondering what it's going to take to invest in people. And so I want to put a larger challenge out today. I'm going to commit 1 % of my wealth on an annual basis into a wealth fund, a small-scale pod of 44 people, 38 needs-based, seven or eight that are contributors. And it's going to distribute 5 % per year. 4 % goes as cash. 1 % goes to an expansion pool.

1:21:59Dave Blundin:You can read about it at markpatrickdonovan.com. I'm challenging others to invest today, not tomorrow, and to mitigate this rough period.

1:22:11Emad Mostaque:It doesn't have to be as rough if we put a fraction of what we're putting into compute into people. We did that in Denver with the Denver Basic Income Project, where I leveraged$500 ,000 up to$10.8 million to people experiencing homelessness. And when you invest in people, it gives them hope. We need to do it today. Yeah. Mark, I could not agree more. the challenge is human nature is very egocentric and very self-centered. In other words, people are putting money where it's either meeting their immediate need or whether it's going to give them more money in the long term. And you have to understand, if you look at philanthropy, which by its definition, friend of man, is a very different pocket than the for-profit.

1:22:58I see this all the time because I'm raising money for my companies, raising money for my nonprofits. And the ratio, if you think about it, is about between$100 to$1 to$1 ,000 to$1. I will put for every dollar I donate, I'm willing to invest somewhere between$100 to$1 ,000. And that's what's out there right now. And it's a challenge. you know we are driven by fear curiosity and greed I would posit those are the three major human drivers love is you can add that as a potential fourth interestingly enough you know you can measure the ratio of fear to curiosity it's the ratio of the defense budget to the science budget.

1:23:47And greed is ratioed there by the entire investment community.

1:23:54Dr. Alexander Wissner-Gross:There's something very important in the work that you're doing with that XPRIZE, right? What we found with XPRIZE is when you position a prize and you launch it, it typically gets one within six to seven years. And it's a 10x drop from where we are today, about$2 ,500 a month, to where you're talking about 250 bucks a month to pay for everything. If we imagine that that gets done in the next six to seven years, it changes the equation globally and it forces everybody to go, oh my god, that's possible. And when we get to that point, it'll completely change the game, especially as we get closer and we can publicize the outcomes, etc.

1:24:33Dr. Alexander Wissner-Gross:So this era of greed and the kind of ignoring the fundamental problems literally will disappear and evaporate in the next two to three years as we keep working that prize and getting the media word out there. This is incredibly powerful and important. Peter and I, when we wrote this last book, we wrote a section in there called technological socialism, right? Socialism, government socialism fails because centralized allocation of assets is too inefficient and invariably leads to corruption. But if you think about Dara and the sharing of cars across a large group of people. It's actually a socialist application.

1:25:11Dr. Alexander Wissner-Gross:When an algorithm hyper-efficiently matches demand and supply, you get all the benefits of the sharing economy without the downsides and without the corruption, without the inefficiency. You might. So we have all sorts of capabilities with algorithms and AI now to deliver much of what you're talking about in a hyper-efficient way. We just have to propagate those, and that's going to start to happen now.

1:25:31Emad Mostaque:I think I wrote in my book The Lost Economy about this, and I've got a paper coming out soon. where I look at the new monetary flows as agents basically crowd out the private sector. My view is this. Everyone ultimately needs to have universal basic AI or clause or whatever. That allows us to reach everyone. Everyone needs an AI that grows with them. And money needs to come not from banks, but for being human. That's the only way the math works. It doesn't work from taxation. It doesn't work from anything else. You need that basic level of money coming into being, not from deposits at banks, but for being human, that then the AIs will buy from us.

1:26:04Emad Mostaque:And then that enables all of this with the AI that everyone has. Professor Brown.

1:26:09Dr. Alexander Wissner-Gross:So we had a half a day of really interesting talks that the subtext is massive job loss.

1:26:16Dave Blundin:And then we had another half a day of talks about the massive labor scarcity, which is why we need all these robots. So aside from temporary displacements, which we know are going to happen, which is it? Oh, it's clearly a massive trough, massive social unrest, and then a rebound in 2028. And that actually is interesting to hear Eric backstage come up with basically the same timeline. But it's almost like the Industrial Revolution all over again. But instead of over 20, 30, 40 years, it's over two, three, four years. And so a huge amount of retooling needs to happen. The way we do taxation and government needs to get restructured.

1:26:55Dave Blundin:All of that is going to, AI is just going to happen way too quickly for all those things to react. But then a massive amount of unrest and then 2028, hopefully.

1:27:05Dr. Alexander Wissner-Gross:I have the counterpoint. I don't think we're going to see massive job loss because I think what's going to happen, I'm writing a paper right now called the organizational singularity, right? Because as agents take over, all execution, even strategy inside companies essentially dissolves to the work of AI. So what do you do? And the calculations we've done so far indicate that you'll take a typical company, automate everything with AI, you'll end up with about 25 % of the same number of people doing oversight, managing dashboards, and doing exception handling, and owning the purpose of the organization.

1:27:43Dr. Alexander Wissner-Gross:But you end up creating five times more companies because you can. And therefore, the employment stays exactly the way it has. And this is what we've seen consistently throughout history, where we have a disruption, but all sorts of other soakers take up the slack, and we don't end up with radical unemployment. So I tend to be much more optimistic, take my veil off, call it the wine, call it whatever, but I tend to be much more optimistic. All right, all right. I'm going to move this forward because it's past my bedtime. All right, we go to Brad, and then we go to Pete, and we're going to wrap it there.

1:28:19Brad, please.

1:28:20Dr. Alexander Wissner-Gross:Wait, after we finish, I do want some commentary from the group. I'll take care of that. Brad. Salim, I'm going to give you an assist here, and maybe this is a topic for your talk late tomorrow night, but maybe the Mold Book is an example of, in this age of artificial intelligence, the rise of the value of ingenuity and creativity. And maybe what they acquired, Meadow, was not strategic, and we're all overthinking it, and they just liked the team. They thought that they were creative, that they had some sort of magic, and they wanted to capture that magic inside their company, and that's what was acquired.

1:28:59So I just want to capture your thoughts, this great minds up on the stage there, on the rise of ingenuity and creativity and the value of that.

1:29:09Dave Blundin:Totally. I think we're way overthinking this. I've got 1 ,100 people, and I know them firsthand, and many of them I genuinely love. Lots of them have been in the same roles for 10 or 15 years. They're great at it. They're perfected at it. And then the AI just comes along one day, and it can do it. And there's huge pressure on the management team for higher margins, higher profits. So what's going to happen is obvious. The valuations of the companies are going to go through the roof. To the extent that they're shareholders, they'll make a lot more money, but their W-2 paycheck is dead. It's going away.

1:29:41Dave Blundin:And it's going to create a huge amount of disruption. It's cooked. Some subset of people are shareholders. All my people are shareholders, so they'll be okay. lots of other people are not shareholders all dara's drivers are not shareholders i think as far as i know so so they're they're in deep trouble the idea that somehow they're going to become creators overnight is ludicrous the people who are creative like the mold book they're going to do incredibly well our kids and most kids who are not saddled by a career are going to do incredibly well but in in transition it's inevitable it's happening imminently all right

1:30:17Dr. Alexander Wissner-Gross:I want one statement. What we found with the exponential organization's model is that survival and success depends on adaptability, not scalability and efficiency. And so you just keep that vector going. The people that are the most adaptable today, they're going to survive the most. Amen. Throw your kids into the woods and see if they survive. Pete. No, I didn't say that. Alex, I said these to you earlier today. I love your analogy of tiling the planet with compute because my answer to the power problem, being a data center design builder, finding 1 ,200 megawatts of contiguous property is getting harder and harder.

1:30:54So my answer is that's only 120 10-megawatt data centers, and you put them in an area, and we tile the areas to be able to do that. And Ahmad, I think it matches perfectly with your idea of national champions because what you're trying to do for the protocol stack of decentralization and sovereignty, I want to do at the physical layer. I want to build 20 ,000 data centers across the country at 10 megawatts so that I'm in less than one millisecond from any place in the country, if you will, the high school football cities of the world. And to me, that solves it both on both sides, at the protocol layer and the data center distribution standpoint.

1:31:34And I think that's how we can actually deliver the power because we don't have a power production problem in this country. We have a power transmission and storage problem in this country.

1:31:42Dave Blundin:And, you know, I think every governor in the country should hear exactly what you just said and jump on it instantly. And Alex is incredibly frustrated with the meetings we've had with government. Shh. I mean, look, if you're right, and I hope you are, and I think you probably are, then we need lots and lots of regional data centers that have to be in every single state. And that would be the best thing that could ever happen for this job dislocation. And so if that theory is right, we need to get on it right away and create those projects like now. I'm ready. All right. Let's give it up for Alex Wiesner-Gross, Dave London, Salim Ismail, and Imad Moustak.

1:32:37Thank you.