Sam Altman: Singularity Slow-Down, Emad Runs 18 Grokbots, Waymo Slashes Hardware 83% | EP #283

22:51Peter Diamandis:I wonder where that number came from. Surely it's pure coincidence that the GDP of America is$30 trillion. Yep. All right. I'm going to move us on. Our next story. Let's talk about GrokBot. So first it was OpenClaw, then there was Hermes, and now there's GrokBot from SpaceX AI. So GrokBot launched on August 11th in an early beta. It's Elon's entry into the agentic AI space, and it's the most genuinely useful consumer AI product that I've seen this year. So I implemented GrokBot. I'm going to be curious if any of you have yet. So each bot gets its own dedicated cloud computer with a browser, a terminal, and the ability to log into your actual apps.

23:33You message GrokBot like you'd message a colleague, not a chat bot. A chief of staff sits on top, in which case for me, it's Skippy, with specialists on sales, operations, research, engineering, any sub-agents you want. And these multiple bots run in parallel. They message each other and only pull you in on judgment calls. So there's a huge amount of excitement on GrokBot. It's been flooding the internet. Has anybody here played with it yet? I've been playing quite extensively with it. Okay, what do you think? I love it. I think that the interface and the ease of use is amazing. You're losing a lot of kind of customizability under the hood, but it's a powerful thing.

24:16You know, you're making a transition from asking an AI to assigning work. And so persistent autonomous agent, it's like a new form of labor. And so now you have this completely new category. You know, Peter, we have that staff on demand attribute in EXO, right? This is that taken to its logical extreme where staff recruiting time and coordination costs have gone to pretty much zero. So when I'm looking at it from my book perspective, the optimal organizational structure completely changes. Humans set objectives and leave everything else to the AI. Amazing. Imad, have you played with it? Yeah, I have.

24:58I've got 18 grok pots working in a little swarm. And I've given them control via tail scale of a MacBook M4 Max, a 5090, and a range of other computers as well, plus all my subscriptions. So I'm really testing it out. One of the fun ones that I've got is I have a GrokBot called Atelier that has a little team of artists and is trying to learn art. And it's not doing very well or it's doing very well. I don't know. I'm not aesthetic enough to do it. Every day it goes through its pieces and it comes up with its main one. and I just shared one of them on the chat, which is vinyl with a piece of hair on it.

25:37I was like, where is that from? Where is it getting its aesthetic responsibilities? So if you look at the chat, I just shared that. When it comes up with the banana with a piece of tape, then you'll get worried. Well, it was like, this is my inner space, and it was showing all these wonderful things, and now it's getting kind of weird, but maybe I just don't understand art. I don't know. But it was genuinely useful, and I think one of the more powerful things, like I said, is you can actually, because it's got a computer inside, you can give it another shell. Because the computer is decent, but you can actually have it take over an entire MacBook M4 or something like that.

26:11So I've got sub bots that have other capabilities. So right now it's installing like the new GLM model and another one's installing and testing Alibaba model. One of them was optimizing the Alibaba 27B model to run faster on a 5090. So it added 76 % to performance at 64K context. Love it. You know, I do think this is going to be an important revenue engine for XAI. I think we're going to start to see their revenue numbers creep up as they get this. I mean, I stepped up a few hundred bucks on my payments to Elon, so I think others. Dave, you haven't played yet, have you?

26:50Peter Diamandis:Well, I just signed off on 100K of Swarm agents. We're running our 5 ,000 Kimmies again today. Which is why I'm wearing my Swarm It shirt here. But this is the theme of the month. I think that all of our AI interactions to date have been very much one-on-one. And now the agents are so abundant that you want to try and use a workforce of six and then 50 and then 1 ,000. And I think within three months, you'd be talking about 50 ,000 agents that can in parallel work for you. But it's very similar to trying to manage an organization. You're like, well, what's everyone doing? I don't know. It's getting really confusing.

27:26Peter Diamandis:Are people being productive? I can't tell. And so you really have to start thinking hard about your org structure and your reporting structure to know if your agents are doing anything useful. And so I really want our team here to get ahead of that and start, you know, go ahead and burn the money, but learn quickly. And then we'll get a handle on it. This is what I mean by the organizational singularity, because the company starts to look less like an org chart and more like a continuously orchestrated intelligence network. And that is such a big shift. It's like ridiculously big compared to everything we've ever done.

28:00Peter Diamandis:I'll give you a hot take on this. It's very easy. Sorry, go ahead, Alex. What's your hot take, Alex? Hot take. People love the hot takes. I don't think this is actually the interface of the future. So what's perhaps most interesting about GrokBot is it presents like a messaging app, like WhatsApp or iMessage, where you have a pane of the various agents that are in your fleet and you can have conversations with them and they can message each other and there's a computer use assistant angle. But I don't think that's how it scales. That's completely unscalable. If agent-based scaling, if scaling the size of your fleet becomes one of the most essential scaling laws, like inference time scaling has ended up being in the era of reasoning-based models, we're not going to want to ask individuals or even enterprises to manage millions of agents.

28:47Peter Diamandis:That's completely unergonomic. We're going to want agents managing other agents, in which case the exercise of trying to graft a human organization or like the Slack or chat-based interface for humans managing other humans is not going to extend. We'll look at this like vaudeville, the vaudeville era of agents and say this was a naive attempt to graft human organizational structures onto humans managing agents. The only better manager for agents is other agents, and this doesn't seem to fully internalize that lesson. Salim, what do you think about Alex's comment? I think it's a, I agree with Alex on the interface comment.

29:27This is like an UI that's temporary. I love the way he says it presents like, as it presents like an illness, it presents like a messaging app. And I think that's a temporary one while we figure out new interfaces. But for now, that's a very workable one for coordinating a bunch of agents. We'll come up with all sorts of others. I think we'll go rotate through a whole set of these. But I think the core comments are as usual with Alex are absolutely dead on.

29:52Peter Diamandis:Yeah, it's maybe it's an illness for which the medication that I prescribe is a good dosage of the bitter lesson pill. Yeah, I also think this is what humans are ready to play with. Right. Exactly. I think that, you know, again, it's moving people along the process. If you provide if you provided something that was, you know, completely different, I think there would be less adoption. And so that's the abstraction layer stack. And Sam saying, why are things so slow? And the answer is people who are one or two layers up from you in the stack expect the old things. So you have to abstract yourself in a familiar interface.

30:31It's also, Peter, it goes back to remember the comments we've made about exponential technology because it's the vertical and goes up to near the curve when it becomes usable. And what Elon's done with this layer is made AI agents usable to a big set of people. It'll change again as people become more used to it and they see how the hell it's operating. The architecture may not be great, etc. But for now, this is a powerful entry point. Yeah, I agree. I just, you know, kudos to Elon and the Cursor team for making this happen. By the way, I invited Alex Finn to come back to the Abundance Summit in March, since he's been doing a lot of amazing GrokBot videos.

31:11If you haven't seen his GrokBot videos yet, go and check them out. He'll teach you how to use it and what's special about it. And I said, Alex, if GrokBot is still the hottest thing in March of 2027 at the Abundance Summit, teach that. If it's not, teach whatever's the latest, hottest thing. But I think people need to be using these agentic systems. I'm still using Hermes and GrokBot, and we'll see. Let's move on to our next conversation, which is Google is back. So Google's Gemini 3.7 Flash just took the top spot on AI, AA Analyst Agent Benchmark, the gold standard for measuring how well AI models handle complex real-world data analysis tasks.

31:52Across 80 tasks in 14 business and scientific domains, Gemini 3.7 Flash delivered the highest overall accuracy while completing tasks up to 90 % faster. and then took top models 2.4 times faster than the GPT 5.6 Terra. So on the AA analyst agent benchmark, which we're showing in the slide here, Gemini 3.7 Flash achieved a 60 % pass rate, beating Claude Opus 5 at 54 % and Fable 5 at 49%. So Alex, many times you've said, others have said, Gemini is dead. Let's read the epitaph. counting them out of the Frontier model race. They've now shipped the fastest, most accurate agent model in the world. And by the way, we've seen this over and over again, right?

32:42We saw Meta was dead. What the heck is Meta doing? And then it comes out with its models. XAI is out of the race and they come back. So to me, it seems like none of these players are out of the race. They're maybe in stealth mode. They're holding back, but they're coming back with a fast, furious punch to try and take the top position. What do you make of this, Alex?

33:02Peter Diamandis:Do you want me to reassure you that Google still has a chance, or do you want me to give you the facts unvarnished? Yeah, you got posts there pretty hard. Defend yourself, Alex. Okay, so I'll give you the unvarnished case here. Google's still out of the running for the capability frontier. I was looking at this and scratching my head. Like, Gemini 3.7 Flash is nowhere near the top of the capability frontier. So why is it doing so well on this one benchmark, artificial analysis analyst agent? So you have to look at the benchmark itself. So the benchmark itself, this is a benchmark for agents' ability to perform quant analysis on real-world spreadsheets and docs.

33:44Peter Diamandis:But wait for it. Its metric for success is the share of questions answered correctly on all five attempts. So this is a benchmark that is fine-tuned for reliability. It rewards agents that give the same answer, basically the same answer every time, and obviously want it to be the right answer, but it penalizes stochasticity. It penalizes, in some sense, creativity. Maybe we don't want creativity out of our analysts. I don't know. But it promotes reliability and determinism. Interestingly, there's no time constraint. I had to check that as well to see. But I think you can see in this where Google fell off the capability frontier.

34:28Peter Diamandis:At least I'll give you my conspiracy theory for what this one outperformance on this one benchmark suggests. I think that maybe what's been going on, obviously there are a few other factors, but I think Google DeepMind has been under material pressure to optimize their models for two things, largely owing to Google search. So if we rewind the video to several months ago or a year ago, people were hand-wringing, oh, isn't Google, aren't the 10 blue links going to face an existential threat from all of these frontier models and chatbots and reasoning agents that can just replace the need to Google at all?

35:06Peter Diamandis:And Google's response was to self-disrupt by building the Gemini series or at least some flash of variants thereof directly into the one boxes. But people expect Google search results to be very fast, low latency, and they expect them to be very reliable, not returning wildly different or unpredictable answers each time. And I think those two pressures from the desire to embed Gemini inside Google search have optimized through competitive internal pressures for probably scarce compute. The Gemini models, especially like Flash, note that there's no Gemini 3.7 Pro anywhere. It's just flash. It's small, it's fast, and it's reliable.

35:48Peter Diamandis:I think this is over-optimized for clock speed, like wall clock speed, and determinism. And as a result, it does well on the one benchmark that rewards highly reliable answers and underperforms. Yeah, it's benchmarking for basically spreadsheet analysis to be highly reliable. Ima, do you agree? um yeah i kind of agree with that a little bit with alex i think the gemini models the way they are used now is for organizing data like you can track any type of modality of data and flash is a perfectly decent model but it's not as good as the chinese models especially the new glm flash that's just come out that's 10 times cheaper for the same performance um google did do a preview of gemini 3.5 Pro, but it just couldn't keep up.

36:37This is kind of a key thing. And you can't excuse them of not having enough compute or it being a scarce resource. They literally have millions of chips. I think it's more been about turnover and some institutional malaise coming in that they can't push through to this frontier level. Because Google has all the data in the world. It has the links of what people search for. It has Gemini as a captive thing, but has the Gemini app advanced at all? Not really. The only real place I think you've seen innovation on the AI side is somewhat the kind of AI studio stuff is decent and the notebook LM stuff is continuing to be fantastic.

37:14But aside from that, again, they've been falling behind in everything except for omnimodal and video. They're still actually quite accurate. But even then, the Chinese are coming for their lunch. Why not just post train on Chinese models at this point if you're Google?

37:28Peter Diamandis:It may come to that. I think people don't realize how compute-starved Google is, though, internally. I mean, this has been widely reported. You think Google has all of the compute, the CPUs, the TPUs, and the GPUs in the world? It's been widely reported at this point. They have internal, regular meetings to try to apportion out their scarce compute. And the three main constituencies inside Google that are fighting for the flops are, one, One, Google Cloud Platform, which is basically fighting on behalf of external users. Two, Google DeepMind, that's fighting for training and inference flops. And then three, Google Search, which needs its own flops, especially as search becomes more intelligent.

38:09Peter Diamandis:So, again, my theory of the case here is there actually is resource starvation inside Google. And as a result... But Alex, we're talking about this over and over again. Every company is compute starved at this point. There is no company that's got enough compute. So what makes, I mean, Google's got more compute than anybody at this point. They're just distributing it across all of their products and services. Critically, Google has other consumers fighting for their own compute internally besides AI. Whereas if you're open AI or Anthropic, no, you don't have any other non-AI users fighting for it.

38:41Fair enough. Google's landing like 3 million TPUs this year. Like, I think there's relative levels of compute constraint. Like, we've got 100 ,000 chips versus a million chips versus 10 ,000. to train a frontier level or close to frontier level, let's say better than Gemini model today, needs 2 ,000 to 4 ,000 TPUs. And the evidence of that is the Chinese did it and they open sourced them. And we know exactly how they're built. Given Google's data that goes into Gemini Flash, applying exactly the same architecture as GLM or Kimi, you should have a better outcome, but they're not doing that for some reason.

39:20And that doesn't require 10 ,000, 100 ,000 chips. It requires 2 ,000 to 4 ,000. That's a really important point.

39:24Peter Diamandis:It's like, yeah, 2 ,000 to 4 ,000 GPUs for 60 to 90 days. That is a microscopic investment by Google standards. So it's exactly right. It has nothing to do with compute dominance and everything to do with talent attrition. It's a great point. No, but this is an institutional failure, isn't it? Because again, you know how to build a Kimi model. You know how to build a GLM model. And so if Google take the data that they put into Gemini, and copied the exact model architecture, you should have a better model on the other side. And if you don't, you have to ask real questions why. Well, and then think about it from the person's career point of view, like the ego blow.

40:01Peter Diamandis:Like you would have to be, I'm the most well-funded top AI engineer in the world, and the Chinese just kicked my ass. I'm going to go tell my boss, you know what, I give up. Let's go download Kimmy, do the rational thing, and then tune it. You can't say that because you look like an idiot. And that's where they are. People are leaving in droves to try and get a clean start and a fresh sheet of paper. But yeah, you just got bypassed with massive advantages and resources. You just can't admit it. In the U.S. closed labs, right, between OpenAI, Anthropic, and Google, and XAI, who's in the best position here?

40:38I mean, who's got it?

40:41Peter Diamandis:Now or two years in the future? Now. That's a great question. Got it in Anthropic. Yeah, right now, Anthropic has the strongest, forgetting about price or time wall clock, Anthropic has the strongest model that's generally available at Fable 5. There are hordes. I'm not speaking about model in terms of positioned with compute and the speed at which they're deploying models and their ability to, I guess, continue their dominance. um well here's the thing peter there's no easy answer because anthropic is in the best position by far and hordes of very talented people are going there purely because they want to see the singularity emerge like it's it's like the birth of the phoenix i want to be there on that day but they're totally reliant on elon for the compute elon can rip the soul out of anthropic any day and he's got the cursor guys now he spent 60 billion dollars getting them they're brilliant and they're starting to roll out cool stuff and they're starting to do the training so So, you know, if you said two years in the future, then it's really tricky because Anthropic and Elon are like, I don't know.

41:45Peter Diamandis:It's a really interesting race. I just want to give our listeners an understanding of sort of the terrain out there. We've got, you know, the U.S. labs competing against each other and the Chinese labs continually pummeling them. And we're going to talk about that in a moment. So, yeah, I mean, Anthropic's the most advanced, but their compute, they don't own their compute, which is a problem. I would disagree with Anthropic being the most advanced. Who do you believe? OpenAI. OpenAI, aside from the Chinese labs, owned the Pareto frontier. From Luna now being free to everyone to, again, as a mathematician, GPT 5.6 Pro is the only quality math model.

42:24I have no idea what magic they're doing with Fable to actually get math results because it makes so many mistakes. Yeah, yeah, totally right. GPT 5.6 Pro is the only proper frontier model. Definitely right. Yeah, we switched over to Sol, actually.

42:37Peter Diamandis:Everybody over here is like, God, this fable has lost its mind. But the argument there is that, well, inside Anthropic, they have Mythos 2 now. So they're on another level ahead and they won't release it to us. Oh, maybe, we can't tell. But for our use case on hard problems, hard engineering and hard math, yeah, we switched everything over to Sol. So you totally agree, Ahmad. Even if it was Mythos 2, again, you would see them releasing low-hanging breakthroughs, which OpenAI have done with Astra. And OpenAI, again, have lined up the compute. They have more capital raised than Anthropic. They had the$120 billion round, so they can burn a few years of market capture.

43:14They have the consumer now moving to enterprise and enterprise shifting. And I think Anthropic, for all of their talent and their access to GPUs, actually Google just built them a gigantic TPU, like million deployments. they're shooting themselves in their foot from an institutional perspective because opus 5 is unpleasant. Fable is unpleasant to use. And I don't think it's going to get more pleasant to use.

43:38Peter Diamandis:Yeah. Didn't Alex say he actively hates opus 5? I said that. Oh, that was you? No, I did say I don't like opus 5. I prefer fable 5. But I think the truth on the frontier is materially more nuanced. Like, again, if you look, Ahmad, for example, at frontier math tier 4, It is the case that Fable 5 outperforms, ironically, OpenAI's latest solid model, even though OpenAI was the primary sponsor behind Epic developing the Frontier Math Tier 4 model. So I think the truth is a little bit blurry in part because the frontier isn't zero dimensional. It's a one plus dimensional frontier where if you're willing to pay a lot and wait a long time for Fable 5 to do something, it's impressive.

44:22Peter Diamandis:But if you're resource starved, cash starved, time starved, then you can probably get better performance at a different point on the optimal cost frontier by, say, using Sol. I just want to point out to everybody listening, it's not obvious, right? There is a lot going on. And then we're seeing China constantly leapfrog. So, Salim, you were going to say? I have a hot take. these frontier labs are facing the innovators dilemma from hell, right? We talked about this before because you've got the Chinese open source models from one angle, compute constraints on another angle, and you've got government regulatory on a third angle.

45:02This is like a nightmare while everybody else is moving quickly with open source models. So this is a very difficult place to be. And the good news is you can see that they're all trying to get into certain verticals and get into revenue streams as fast as possible to reduce that dependence on the frontier model and being the edge as their core innovators capability. I mean, the abundance take on this is we as the consumers and the users are the beneficiary. It's demonetizing very rapidly at the same time that it's expanding.

45:33Peter Diamandis:When frontier labs compete, you win. Yes, we all win. All right, I'm going to move us on. We've been saying for some time on this pod that the U.S. needs a powerful open-weight model to contend with what's coming out of China. And this week, NVIDIA is stepping up, pouring$6 billion into developing an open-source AI model and inference infrastructure designed to give U.S. developers a domestic alternative to Alibaba, DeepSeq, and Kimi. The deal struck between NVIDIA and the AI startup Poolside aims to build one of the world's most powerful open-weight models. By building its own open-weight model, NVIDIA is moving up the stack.

46:10We've discussed this from silicon to software, positioning itself not just as a chip maker for AI, but as a platform provider for open-weight ecosystems. You know, from my point of view, it looks like everybody's going up and down the stack. We've seen Anthropic. We've seen OpenAI. Obviously, SpaceX AI is doing the same. Imad, let's go to you first. What are your thoughts on NVIDIA and Poolside? Yeah, so I've been talking to some of the investors out here, like this tech barbecue conference who invested in Poolside originally. They tried to raise$2 billion at the end of last year. So who is Poolside, first of all?

46:49Poolside is a company, I believe it was the ex-GitHub. Yeah, the former CTO of GitHub. Former CTO, Isokant and others. They set up and they wanted to originally create a coding model, then they moved to an open source model and model factory called Laguna that outperformed Thinking Machine's Inkling model when it first came out. They tried at the turn of the, no, a few months ago to raise$2 billion for a massive Blackwell cluster, and they couldn't. So they lost that cluster. And they were like, this is the table stakes we need. But they built a really great, solid open source model for its size.

47:25And so now what they've done is they've benefited from this weird NVIDIA acqui-hire type thing where NVIDIA is like, we need to build great open source models to increase demand for our technology on the Nemetron stack. So the first thing they did actually was they hired, and I don't think it's been announced yet, Ashish Viswani's team from Essential AI. He was one of the founders of the, one of the authors on the Attention is All You Need paper. And now they're going to be making more and more acquisitions up and down the open source stack to be the leader in open source. Because again, that drives demand for the GPUs more than anything.

48:01So I think this is just the first, well, not the first, this is the main one, but there'll be many more acquisitions and they'll have a full open source stack. This is the Nemetron Coalition. So a lot of the classic ones like Mistral and Cohere and others won't be building open source models anymore. They'll be building to the NVIDIA reference design.

48:20Peter Diamandis:Alex? I think maybe I could say something nice about the American open source community and open weight models moving in a positive direction. Obviously, NVIDIA had invested, I think, about a billion dollars in this company previously. And now through this, I'd call it a hackquisition. Now they're finally sort of turbocharging their own Nemotron community. It's more interesting to me that acquisitions were concerning, perhaps, that acquisitions still need to happen in this day and age. It's also, I think, bizarre if you follow some of the recent acquisitions. My original take on this was this is just an attempt to avoid regulatory scrutiny or antitrust scrutiny.

49:04Peter Diamandis:But I've started to see now some of the other acquisition targets come back to life. What I had sort of left for dead as the carcass of the original company, where all of the founding team comes over and all of the core IP was, quote unquote, non-exclusively licensed, which I think my understanding was the case here as well, where NVIDIA is non-exclusively licensing key poolside IP. I will be watching closely what happens to the part of poolside that did not come to NVIDIA. I think my original expectation that this is just a carcass left over after the hunt that is being left behind purely to avoid regulatory scrutiny may actually have life to it and not investment advice, but could actually be, in some sense, even more interesting than the part that goes over to NVIDIA.

49:48I mean, there's a lot of pressure for the U.S. to develop top-tier open-source platforms right now. Dave, what's your take on it? Yeah, curious.

49:57Peter Diamandis:Alex, you said kind of quickly there you're surprised that acquisitions need to exist in this day and age. But I got calls from both Mercore and from Orn, our good buddy, Kush Bavaria, who was on the pod a week ago, looking for acquisition targets to accelerate. You know, the hiring cycle is too slow. I need groups of 3, 10, 15 people that work really well together. I don't care what it costs, like send them to me tomorrow. So it seems to be, you know, at least in terms of my inbound, like an all-time high in hackquisition. Why do you think it should be a thing of the past? Well, so I would distinguish between talent acquirers or acquihires on the one hand, which are largely about getting talent, and hackwhires with an H that are about at least ostensibly avoiding antitrust scrutiny.

50:41Peter Diamandis:So if you're NVIDIA and you want to hack a hire, say, poolside, you're going the hack will hire route rather than just doing an honest to goodness, either asset acquisition or conventional acquisition of poolside because you want to argue, no, actually, we're just a licensee of poolside rather than the acquirer. No, we're leaving a competitive open source model layer, blah, blah, blah. This is not tying, blah, blah, blah. That's the argument and principle for acquisition. Gotcha. I got a very specific answer to that, too. Remember the windsurf deal, you know? Of course. Of course. So here's the constraint.

51:17Peter Diamandis:So the FTC is very, very friendly to acquisitions right now, and things tend to move quickly and easily. On the other hand, the timeline for AI companies is so short that the statutory 30-day review alone is like a lifetime. And, you know, all these mega companies, like a big one like NVIDIA is always going to get a second look, which is usually 60, 90 days. So you're like, forget it. Let me just slap together any type of deal that doesn't need that regulatory review and just help. Train the freaking billion-dollar model or$6 billion model. That's all I need. Let's go. And then they can close the deal like Elon did with Cursor, close the deal many months later after an HSR review and after the 90 days.

51:59Peter Diamandis:Sometimes it's even longer than that. But there's a statutory 30 days that they just can't get around. Salim, you're smirking over there. What's up on you? I don't know. I think Dave's got it exactly right. I think that's what's going on here. This is just purely juggling the regulatory hurdles and the obstacle courses. There's one little wrinkle on this. So the remaining company actually has something called Poolside Infrastructure Company, which is building a 1.2 gigawatt data center, which might need GPUs. So they may use some of the money that they get for GPUs. Who knows? Going back to the other point here is the verticalization of companies, right?

52:37So, I mean, XAI, SpaceX AI is the ultimate verticalization out there today. But here we see NVIDIA. We've seen Anthropic and OpenAI also designing their own chips. Does every one of these companies ultimately become at least two layers, if not three layers?

52:56Peter Diamandis:In other words, does Anthropic get a space station? Or a moon colony? You know, I think they'll be the only company left amongst all the governments. Is that what we learned? Yeah, they'll have everything. That's why they'll be American GDP. That's why. Yeah. I think probably, I mean, I'm asking the question seriously. Like, does Anthropic get a moon colony? Yeah, probably. Does Anthropic get a pharmaceutical arm? Yeah, already. So, yes. Yeah. All right. I think that you've got, actually, thinking about our discussion earlier, you have a split of innovation versus execution. And so these are the two model splits that are occurring.

53:34Execution drives the majority of the economy short term, innovation longer term. And the verticalization is ideal for the execution phase. So if you look at the architecture of Jalapeno, if you look at where things are going, like you're going to get closer and closer to the silicon, you'll get closer and closer to the customer, and you won't need much better models than you have now, whereas the frontier will be a different story where you still need to have very complicated things occurring. All right.

54:03Peter Diamandis:Yeah, I would maybe, if I had to, I guess, extrapolate, I think there is a, probably don't hold me to this, there's probably a natural verticalization at the infralayer, not necessarily at the application layers, but at the infralayer For physics and other reasons, there are natural reasons why, say, a company that offers a frontier model probably wants to be in the data center infra business, probably wants to be in the energy business, probably wants to be in the satellite business. These are all like innermost loop type businesses, robotics business. There are such natural synergies among all of the different innermost loop stages.

54:41Peter Diamandis:Probably there's some natural vertical integration there. I'm going to move us along here. So three stories this week that chronicle the challenges being faced by the U.S. closed frontier labs who are under siege from faster, cheaper Chinese openweight alternatives. So the first story comes from Moonshot AI, not related to the Moonshots podcast, the Chinese lab that's making Kimi. So last week discussed how access to memory is becoming the real roadblock on all of this growth. It's not GPUs. It's memory, especially in the agentic age. This week, Moonshot AI released Kimi Linear, a new architecture that cuts context memory by 75 percent while still delivering 6x faster decoding for a 1 million token context window.

55:26Moonshot AI just dropped this and it's running and in a single move, an improvement of 75 percent. So that's the first story. The second story on this block comes from the Financial Times that reports that Fable 5, Anthropics flagship model, is now struggling to attract users. It's effectively plateaued. And the reason is simple. Cheaper Chinese open-weight models are eating the market from bottom up. And when the model costs$0.14 per million tokens and delivers 80 % of the capability compared to$15, the market chooses the less expensive option, at least the majority of the market does. Fable 5 is not losing because it's bad.

56:10It's losing because it's overpriced relative to the open-weight alternatives. The third story, then we'll talk about this, is that Anthropic this week reversed its data retention policy ahead of its IPO, letting enterprise customers keep data on their own cloud infrastructure rather than Anthropic servers. This move handles the biggest objection that corporate buyers have when adopting Claude. So I don't think the timing is accidental. You know, they're about to go into IPO mode. I think it's predicted for as early as six weeks from now. And the growth requires enterprise adoption and the enterprise adoption requires data sovereignty.

56:48So, gentlemen, three stories here. Kimi Linear, you know, the challenges that Fable is having and the changes that Anthropic made on its data retention policy. Dave, you want to jump in first?

57:03Peter Diamandis:Well, this is where we're going to find out if Dario has what it takes to be a public company CEO, because, you know, he's a he's a brilliant, good natured AI researcher thrust into this. And when he gets interviewed, he said, I never expected to be a CEO at all. But here I am. So now he's stuck with this missing revenue numbers because he's embarking. The current policy or the prior policy was even if you're hosting your Fable 5 on Amazon Bedrock in a secure environment, everything still has to go to Anthropic headquarters for 30 days for us to review and make sure you're not making a virus or a bomb or something.

57:37Peter Diamandis:And that's the only way this is safe. So now he's missing revenue numbers because corporations don't want to give their proprietary secrets to any company that they don't know well, you know, for 30 days. And so they're rushing to the Chinese models and secure environments. And they're also now you can get you can get GPT Sol also inside a secure environment where it doesn't get transmitted to open AI. So you can you can use that, too. So it's like, oh, God, corporations hate this, but I don't want to miss my revenue numbers. I want to go public. On the other hand, I really don't think it's safe.

58:11Peter Diamandis:I feel like I need to inspect everything to know that it's safe. So now he's stuck between a rock and a hard place. It's a tough place to be. But being a public company CEO is always like that. It's really, really stressful and really hard. So we'll see if he has what it takes to do it. Imad, what do you think of Kimi Linear? Yeah, I mean, first we banned the faster silicon from China. So they built MOE models to take advantage of cheap DRAM. Then the DRAM became expensive. So then they figured out better mechanisms of linear attention, of caching and more, more dense models, etc. And now you've just seen actually just a couple of hours ago, this new O1 Stealth model that's been tearing up the benchmarks, turned out to be a GLM model, served entirely on Chinese chips with trillions of tokens a day, these new Huawei chips.

59:08So I think you'll see the adoption of these Chinese models and them moving to where the market is on different form factors of different chips. And again, memory is 50 % of all spending now. It is the scarce resource. You can't upgrade it. So guess what? In a couple of months' time, at the very least, given the pace of Chinese models, they won't need much memory. That's how fast they innovate. On the favorable uptake, it's entirely a zero data retention issue. Like as a corporation, you cannot leave your data on an Anthropics side. And they realized this, but Anthropics should not IPO. If you are in the late stages of AGI now, Anthropic should do a giant freaking raise, like OpenAI did, of$120 billion and have a straight shot at AGI.

59:55That's what they should do. And they should stay private like Stripe. That's a fascinating thought. I mean, why are they racing to an IPO? It makes absolutely no sense to me. Like, Dario owns 2 % of the company, as does his seven co-founders. They didn't care about dilution. They're worth, like, still$6-7 billion each. And they don't care about money. They pledged to give away 90 % of it. Why would you IPO? I can see no reason for that unless they can't access money privately, which I think they can. I have an answer. They need the capital to get compute.

1:00:29They could raise the capital. I bet you people would throw money at them. So I've been talking to investors, and Dave, this will be interesting from your perspective. nobody knows how to price this thing because they don't have insecure long-term compute like open ai has or that natively croc or uh gemini has therefore they're concerned this is why the way to our earlier point this is why people are verticalizing because if you're one layer and the bottleneck goes down below you or above you're screwed so you have to have access to the whole layer to stop to have a continual progress yeah so that's but even if they had the money to buy compute?

1:01:07Where are they going to buy it from? There's not enough compute being manufactured.

1:01:12Peter Diamandis:Before we get to that, Salim is right, but it's much more specific than that. Dario got ripped by Alex Karp. We showed the video on this podcast. He got absolutely ripped to shreds. And Alex Karp is saying, look, you cannot give your alpha, you cannot give your weights to this company, Anthropic. You cannot trust them with your corporate intellectual property. You're talking about an academic, never run anything before in his life guy taking your intellectual property and then preaching to you how the government should be run in the future. Don't trust him. So he just ripped him to shreds. You can't.

1:01:47Peter Diamandis:So Dario's now he can't react to that by saying, you know what, I'm going to delay my IPO and do some private financing. And you're playing right into Alex's hands if you wuss out on your IPO plans. He's just going to reinforce Alex's, you know, Karp's message horrifically. And the board members, like, look at the board members at Anthropic. They've marked up those venture funds to massive valuations and use those valuations to raise new funds. So they're not going to just sit there and say, yeah, Dario, you know, put it off indefinitely. That's fine. So he's like, this is the stress test for Dario.

1:02:22Peter Diamandis:He can't just wuss out right now. And that signaling would be terrible. Alex, AWG, your thoughts, please. Okay, well, first on the race to IPO, I think there is also a race element. I think there was a starting gun a few months ago between SpaceX, OpenAI, and Anthropic. And I think if I'm Anthropic on top of the arguments that everyone else here has already raised, there's a competitive element. If you don't necessarily want to be the last to IPO, the market winds could change. Right now, it's a relatively warm and friendly capital market for IPO. So to the extent there's a window for raising the largest IPO sum in human history, I think you go for it.

1:03:00Peter Diamandis:But to the earlier points in lightning round succession, Kimi Linear. We've known about Kimi Linear attention since last fall. I think it's suggestive, as I've suggested in the past, ship of Theseus style transform architecture is getting incrementally replaced piece by piece. It's interesting insofar as it's a successful linear architecture. Many have tried to linearize the infamously quadratic attention mechanism. It looks like KLA may be one of the first at least openly linearized attention or quasi-linearized. There's a recurrence mechanism in there as well. So that's kind of interesting. We've known about that for a while.

1:03:38Peter Diamandis:Fable 5 struggling. That is interesting because I've made the point on the pod in the past that OpenAI made a strategic blunder in pandering to consumers rather than to enterprises, thinking that consumers would be hungry users of reasoning tokens, and they just weren't. The consumers didn't know what to do with all of these shiny OpenAI reasoning tokens, but enterprises did. And then OpenAI had to do this painful pivot over to enterprise and turn everything into codecs and probably delay their IPO as a result. So Fable 5, which is, at least by my accounting, the strongest, most frontierist model in the world right now, to the extent that it's struggling to generate revenue and uptake.

1:04:21Peter Diamandis:I want to interpret that. I want to construe that as the enterprises of the world almost falling prey to the same thing consumers with OpenAI did, which is maybe will be construed as victim blaming, but it's not. Our economy isn't worthy. It's not clever or wealthy or successful enough on average to know how to use Fable 5 on average properly, just like consumers didn't know how to use reasoning tokens from OpenAI and as a result, OpenAI had to pivot. I think this is the beginning signs of Anthropic being forced to do some sort of pivot. It could be radically reducing the cost of their models.

1:05:02Peter Diamandis:That's one direction. Or I think the more exciting trajectory is some new use case getting unlocked in the next year that actually motivates the usage of this nosebleed-priced high-end of the frontier, which is at the moment Fable 5, or depending on the reports you read, maybe Fable 5.1 may be starting to leak out. And then very quickly on Anthropic and their data retention policy, Anthropic was so clever, I think, in being the first frontier lab from America to enable their frontier models to be hosted by third-party hyperscalers rather than having to host them themselves. And by some reporting, 40 % of Anthropics revenue now comes from Anthropics models being hosted not by Anthropics, but by third-party cloud hyperscalers.

1:05:52Peter Diamandis:So, I think this is just another step to externalizing the hosting of their model. Yeah, sure, it's painful. This data retention policy I view as largely security theater. I don't think it's that valuable in the long term. I don't think it's useful in the long term. But starting to move more and more of the infra layer over to third parties so that users of Claude can get Claude where and when they want on the infra they want, that's powerful. And you see now OpenAI copying Anthropic and doing that. What do you guys make of the idea that the open source Chinese models are good enough and at a de minimis fraction of the price and companies are beginning to shift in that direction saying we're not going to use Fable 5.

1:06:40It's too expensive. Dave, is that an experience you're having?

1:06:43Peter Diamandis:Yeah, no, everybody needs the absolute best AI they can get. You can't go down a notch, but the Chinese models aren't down a notch. They're absolutely on the frontier. You won't even notice the difference in any use case. So it's not about trying to use something inferior at a lower cost. It's about they're just as good. And now they're all good enough to improve themselves, too. So if you start a group within your company that's using these models, you can start improving it inside your company if you get the talent. And so that's like a runaway train. You know, I think that's what's really going on.

1:07:15Peter Diamandis:It's not compromising to save a few pennies. It's like, wow, we can control our own destiny and be on the frontier at the same time. I mean, I don't think it's a few pennies, right? Like Fable scores 60 on the artificial analysis benchmark. The new GLM model, Flash, that dropped today scores 57. And it is like 100 times cheaper. Yeah. Yeah. Which I think is really important because, you know, when you deploy these things, the cost benefit is so high that you might say, well, I don't even care about the cost. But then you say, oh, wait, if I use the Chinese version, I can have a thousand or, you know, this is why the swarm is such a big deal.

1:07:55Peter Diamandis:You can afford five or 10 ,000 concurrent Chinese operators instead of one anthropic. Well, I think it's like, it's like hiring a specialist, you know, like super genius versus a bunch of really smart people. And sometimes you're not smart enough to ask the super genius, the right questions. Yeah, well put. Because maybe I'm not smart enough to ask Fable the right questions, but I'm just about smart enough to ask GPT 5.6L the right questions, right? But also, you know, that analogy is perfect because people misuse their context window horribly, and I do too, and everybody does. But if you actually optimize the context window with the Chinese model, you'll get a smarter answer than if you're sloppy using a Fable model.

1:08:38Peter Diamandis:And so, you know, if you just put a little energy into your internal org design and optimize your use, and, you know, then you can have thousands and thousands of these for, you know, a very low cost. And that's where the puck is going. I'm not sure how sustainable this situation is. I almost want to analogize it now to U.S. importing generic drugs from Canada. The drugs get invented in the U.S. They get manufactured cheaply in Canada. and then at least historically it's been the case that you could get American drugs more cheaply from Canada by importing on or off label than you could from American drugs.

1:09:12Peter Diamandis:I think the situation may be somewhat analogous here where these are U.S. models, U.S. reasoning traces. You see Chinese labs benefiting legally or illegally from the reasoning traces, from interacting with U.S. models. And then just in the past 48 hours, we start to see stories of Chinese labs trying to strike partnerships with U.S. hyperscalers to host the Chinese lab models on U.S. infra, but with a rev share from the inference costs going back to the Chinese frontier lab. So, this is a case where U.S. does whatever innovation is necessary, data or post-training or whatever, that there's a distillation maybe over to China.

1:09:54Peter Diamandis:China sells it back to us, but then we're using our own infra against ourselves at inference time, against the training time. I think it's a perverse bind that we find ourselves in, analogous to Canadian drug imports. I'm not sure the Canadian drug import analogy holds very much longer given what's going on, but let's leave that aside. Yeah, it held until about a year ago. I'm going to move us to a fun story on the dating front. So a Berkeley startup called Ditto is playing Cupid. Ditto is an app or an AI that has no feed, no swiping, no infinite scroll. You fill out a values questionnaire, and then every Wednesday at 7 p.m., a text arrives with a match, as well as a place and a time for you to meet your date.

1:10:38That's the entire product. You show up and see if the magic happens. Thus far, 160 ,000 college students have signed up. It's already produced 80 ,000 dates. The app does what Tinder and Hinge refuse to do. It removes choice. The entire dating industry is built on the premise that more options are better. But Ditto believes that too many options lead to, you know, decision fatigue, analysis paralysis. And then AI is the cure. The app does not ask you for a choice. It chooses for you. You know, this is sort of the old style matchmaker agent, you know, a yenta if you would. And it seems to be working.

1:11:17Salim, you know, you and I are both married, but, you know, it seems like it'd be a fun thing to go out and try. What are your thoughts here? So two or three things. I think this applied to non-dating would be really profound, and we're actually looking at doing something like that for business connections. But I think this is powerful because AI isn't adding in interfaces, deleting the interface, right? Tinder optimized searching and connections and so on. But this makes the searching a lesser. And I think that's really a powerful user interface experience where people are going to go, let me figure it out.

1:11:49And then I'll do the connection and see if there's chemistry there or not, which you have to do anyway. By the way, let's note as a scarcity to abundance paradigm, when we were all growing up, sex had a scarcity paradigm. It was Tinder, sex became abundant. Where the hell was that in our 20s is the obvious question. But you have to deal with that abundance in a different way. So this is a really fascinating thing. I'll watch them very carefully to see where this goes. Yeah, this is the abundance thesis applied to dating. Dave, what do you make of it? Is this a company you would have backed?

1:12:21Peter Diamandis:Oh, God, yeah. Yeah, yeah, absolutely. But I think this is a stepping stone to AI, helping you manage your life and your choices in general. Bingo. Which I think is going to be, if it's done right, it's going to be one of the greatest boons to mental health in world history. If it's left to manipulate you, it's going to be horrible because it's such a great salesperson. So this is a good test case. Are we going to manage it well? Is it going to lead you to the right person? Is it going to try and help you? Or is it going to sell you on something that you don't want? I've always said in the future, advertising model is gone because your AI knows you so well.

1:12:58It's just like, please just buy me the stuff I need. I'm in decision fatigue. I'm in data overwhelm. Just take care of it for me. Imad, your thoughts? yeah i think there was a black mirror episode where you know for dating you just sent your digital twins and then they did a bunch of dates just to test it out in like two milliseconds so you could tell whether or not you matched it kind of again it feels like you're heading towards that but people are going to get to a point where it'd be like you can't argue with your ai it knows best right all watched over by machines of loving grace and we've got to be quite careful about that because you know it does take away a little bit from your intrinsic humanity if you outsource your cognition and connection in that way.

1:13:40But, you know, again, you're kind of feeling it already, like with how much of your stuff you offload to these things. And I've been getting mad at like some of the colleagues and others, like, you know, they're doing really good, but they start to slip into trusting the AI too much. You know, like sending something like this is human. I think this is such an important point. There's a bigger pattern here where AI is becoming the trusted intermediary between individuals interfacing with overwhelming abundance, right? You're going to need that trusted interface. And the question is, do you want to outsource that trust?

1:14:18By the way, to your Yenta comment, Peter, the Indian matchmaking industry is profoundly about to be disrupted by this because you can detail the cast, the clothing requirements, the salary requirements, and boom, off you go for the matches. So this is going to be really interesting to apply to that world. You talk about an exponential organization, Salim. Come on. Alex, I think you're the only one amongst us not married. So, you know, would you try this out? No.

1:14:46Peter Diamandis:No. I think this is why we can't have nice things. I think this is why, have you seen the Ditto body count detector? Do you even know what I'm talking about? No, tell me. Okay. So the Ditto body count detector, this is, I would characterize as a politely suboptimal use of scarce reasoning tokens, is a tool that Ditto released that uses, I'll quote from their website, 478 facial points and 52 micro expressions over five seconds to estimate how many sexual partners a person has had. This is where the reasoning tokens are going. Seriously, it's called the Ditto AI body count detector. Folks can check it out.

1:15:26Peter Diamandis:This is, I view as a suboptimal use of reasoning tokens when we could be, as you and I wrote, Peter, we could be solving everything. Yes. We could be solving everything and instead we're doing body count detection. So this one gets a thumbs down for me. All right. Well, you know, the reality is that most people on these dating apps are looking at, you know, simply the external parameters of the individual. Are they handsome? Are they beautiful? I think part of it is how honest are you on the questionnaire? And, you know, matchmaking does work, you know, throughout time and culture and across all cultures.

1:16:03Some of the longest lasting marriages come from being matched because it's going beyond just your initial hormonal response to the individual. And I think there's something there, Whether or not it has sufficient data to actually align to people accurately is a different thing. But I think there's something there. But I do agree, Dave, that this applies to so many different areas. And Salim, I know at the Abundance Summit, we have 600 CEOs. And we're, by the way, we're now 90 % full for Abundance 2027. If you're interested, you can go to Abundance 360. Matching the CEOs, matching the entrepreneurs there is one of the most important things we do.

1:16:44And using AI to create those matches because randomly bumping into the right person among a group of 600 people in five days is tough. So there is a value proposition to be had there.

1:16:57Peter Diamandis:Social discovery, I do think, is quite valuable if it's for socially productive or economically productive purposes. social discovery for body count detection. I mean, again, this reminds me of Hot or Not back in the early Facebook days. I just think we could be aiming so much higher as a civilization than AI for this. Listen, you know, the divorce rate in the United States is 50%, which is crazy. And I think, you know, helping you discover the right person. Now, the parameters it uses may not be right, but if it were possible to help you find the best person, the best match for you. There's massive value, societal value in that.

1:17:38That's my feeling. I don't know if you guys...

1:17:40Peter Diamandis:Yeah, I'd love to know, Alex, how you reconcile... This is a waste of tokens. We should be solving a disease with those tokens. Not a waste, a suboptimal use. Okay, okay. Because one of the terms you've coined in this great revolution is patrio, schmarker, schmuzer, schmuzer. What is that thing? Patrizier Musa. Yeah, you can't even say it. No, no, that is off-deutsch. This is Patrizia Musa. Alex loves neologisms. If you haven't seen it, he's publishing new terminology for the singularity almost every day. Go on, Dave. You do need a token budget for that concept, you know, so how do you reconcile those two?

1:18:21Peter Diamandis:That's what happens once we have a leisure class that can afford tokens too cheap to meter, which we don't yet have. So maybe the way I reconcile to make you happy, Dave, is I'd say save the body count detection until after we've solved everything. At that point, do as much body count detection as you like. I like that view. I think once you've solved basically all major diseases, that's probably a good time to start. The line of the song is that once the day had been solved, the day hasn't yet been solved. Okay. All right, guys. I'm going to move us on. But it's a fascinating concept, and I hope Ditto works.

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1:20:02One more AI story before we move on to robotics. And it's a Wall Street Journal article that confirms what all of us are feeling, that AI is making us work harder at a level like never before. I joke people are talking about a three - and four-day work week, and I've discovered a nine - and ten-day work week. So according to the Wall Street Journal, increased productivity from AI agents is creating more work for humans, not less. The agents produce more output, which requires more review, more decisions, more direction, and more human judgment per unit time. The founder used to manage five tasks, now manages 50 agent outputs.

1:20:39The bottleneck has shifted from execution to judgment. The humans have become the bottleneck because the agents produce too much work for us lowly humans to evaluate. So this is a bizarre implication of abundance. More intelligence produces more output, which requires more human direction, which produces more value, which requires more work. So the work is not disappearing. It's changing character from execution to judgment. Salim, over to you, pal. This is Jevin's paradox for human cognition, right? We thought AI would reduce workflow. In fact, it increased the amount of work that is worth attempting.

1:21:18I will go to a little history here. When I first did the EXO book, Peter, you and I did that together, it was three years of hell. Second book was two and a half years of hell. Third book was six months of a lot of joy, but damn overload on the cognitive workload, right? So when you get this kind of AI slop, in a sense, for human cognition, really judgment and attention become absolutely paramount. So this becomes, what we've done is essentially if 10 agents are reporting to a founder, we've reinvented middle management. It's inside your own brain. So this is, it's going to cause a huge problem because you can't have machines operating in machine speed and requiring human approval on that.

1:22:04So I'm actually facing this from all the stuff I'm going to do today. You may be seeing the same thing with Skippy. So we need, the better next breakthroughs need to be better delegation, permission, escalations. thresholds. We're actually designing that BCI maybe at the individual level, but we're, you know, this is something we're actually seeing live as we do that pilot program where we work a bunch of companies through this process. It's requiring a whole new threshold of escalation of thresholds, governance, et cetera, et cetera, because companies need to decide what the machines may decide autonomously and what they want to manage later, what is audited, what genuinely needs a human.

1:22:41Otherwise, we're creating a totally crazy future where AI is going to be working like 24 sevens and humans are going to be obligated to work 24 seven to navigate that pace of that right so more capability more it does the more capability doesn't mean more freedom but i'm but i'm actually i'm burning the candle is 16 n's right now i'm loving it but i'm not sure how long i can last at this pace and you guys aren't helping i will say so can i just ask Dave, Emad, and Alex, is it the same for all of you, working harder than ever?

1:23:14Peter Diamandis:God, yeah, absolutely. And then I'll tell you what, you've got to savor the moment because, you know, Emad and Alex will tell you it's not going to last forever. And, you know, what's really frustrating to me is actually I've been recruiting some incredibly talented people for quantum AI. And we lost an MIT course 6.1 guy who just decided he's going to go to the Princeton PhD program. And, like, do you listen to Emad and Alex, you know? And like you guys have collectively like 100 degrees. And would you advise anyone right now to go into a Ph.D. program and miss the singularity? No, of course not.

1:23:47Peter Diamandis:But it's frustrating to watch that happen because this moment we're in right now, you can master a thousand AIs, 10 ,000 AIs. And you're the most valuable you'll ever be in human history right now because they won't do anything productive without your help. But a year or two from now, they may say, yeah, I don't need your help. You know, sorry, you know, don't need you anymore. Get out of the way. So, yeah, work your ass off right now because it may be the last chance that you have to actually be extremely valuable. So I'm just savoring it. I'm working harder than ever by far, but savoring every minute of it.

1:24:22Peter Diamandis:And I tell you, working with AIs is genuinely fun, too. It's not like I'm, you know, moving boxes around or grinding it out in a cornfield. You know, this is like really, really fun. It really is fun. You're discovering the future. It is fun. It's a blast. I'm reminded, a friend of mine likes to say the Stone Age didn't end for a lack of stones. I think this era that we find ourselves in is probably pretty brief. I know I'm getting approximately no sleep at this point, largely because almost all of my time is spent supervising and steering fleets of agents. And I think this is a window. I don't think this will continue very much longer, at most maybe one or two or three years.

1:25:01Peter Diamandis:At that point, the AIs will be sufficiently self-steering that the role for humans in being knee-deep in steering large fleets, I think, probably erodes to a de minimis role. So isn't that an argument for just like, you know, lay down, relax, enjoy yourself for three years and jump in three years from now? No, it's an argument for work your tail off for three years and then go lie on the beach. I mean, if nothing else motivates you, every year millions of people die needlessly. And if we just get three months shaved off that timeline by working our asses off, millions of people will exist forever.

1:25:35Peter Diamandis:Otherwise, it wouldn't exist. Beautifully said. That doesn't motivate you. Ahmad, you've got to put a clip in here, too. This is the most important thing we've ever recorded. What are your thoughts on this? No, I mean, like the amount of leverage you can do per unit of your attention now is more than it has ever been. And another thing, as Alex said, it probably ever will be. Like you're approaching the last human discoveries. You're approaching the last point of being able to deploy and control these things. And I think, again, like you have a limited focused attention budget. That's why you're getting tired.

1:26:07Maybe to try and coin the legend. Maybe it's cognitive lethargy that we're facing here. From my own side, you know, I've written like two books in the last year. I've done a massive amount of research and I've been in the flow with hundreds of agents. but like last week I stopped I couldn't do any more research because I had to go and take this out to the world now so we're doing like a big funding round we're launching lots of new things we'll be releasing all the research finally and I turned off my agents that were doing all the research like I've set them onto auto mode no more MAD stuff and they're coming up with things still but like I can only read it once a week I've actually made it so I can't do it and I think you can shift between these modes of work because you can't be on all the time because it does burn you out.

1:26:53But at the same time, if you get in the right flow, then you can do more than you've ever done before. And I can't imagine, like, I would say on this podcast, just straight up, don't do a PhD. If you're thinking about doing a PhD, don't do one. Peter Thiel paid all these people not to do PhDs. Well, not to do college degrees, little PhDs. I would say you not even do a college degree. Like, what will you get out of it right now? You will go and you will learn a very specific thing when you should be learning agency. Like a Theal Fellowship and the type of people who do that will go way bigger than they've ever gone before.

1:27:25And, you know, parents might kind of complain and things, but show them what you create. Gather people, humans and agents.

1:27:32Peter Diamandis:Now, a skeptic would say, all right, Ahmad, you went where? Oxford, as I recall. Alex, you went where? Harvard and MIT. Dave, you went where? MIT, Peter, you went where? MIT, Harvard, and so on. Like, okay, so you're pulling the vertical mobility ladder up behind you. And it's fine to tell everyone else who's just coming up, don't bother with higher education. Don't bother with credentialitis. Just go off and do your startup. And yet we didn't follow that. It was a different time. That just makes you more credible in what you're saying. I would actually say, I think undergraduates still a lot of fun and you don't really have to work that hard.

1:28:12Elon said this, it's a social experience. It's adult daycare. It's still daycare, so it's actually great for using massive amounts of agents. PhDs, though, I don't get. You know, especially, like, I feel sorry for, I've talked to a bunch of my buddies who are math PhDs, and a couple of them had, like, problems solved in the recent batch. Like, they don't even know what they're going to do. Every verifiable domain PhD now is under massive threat. Why would you even do it or even consider it? I agree.

1:28:40Peter Diamandis:I was speaking a few days ago to a government-funded AI for a physics center filled with PhDs, current PhDs and recent PhDs in physics. And I leveled with them. I said, physics is cooked and you should probably be reconsidering all of your career trajectories and consider any advice to the contrary. Give that a double think, as it were, before you just go and follow some zombie pattern. well i think this is the most important conversation we've had we've had yet i spooked them this and certainly i spooked the heck out of them well getting people to think is the most important um you know why are you doing a phd a lot of people are doing a phd because they're they told their mom and dad they're going to do it or their sibling did it or they thought that was what they needed in life and i think or actually in a lot of cases about five years ago before anyone knew the singularity was coming they started working their ass off toward that.

1:29:35Peter Diamandis:And you've been working so hard for so long. And then you get in and it's like, I got in. But now the idea that suddenly it's irrelevant or you shouldn't be doing it is so hard to take after you work so hard to get there. But you got to pivot. You got to, you got to just recognize the moment. Get into your Stanford PhD and say, okay, I checked that box and now I'm going to jump into a company. Oh, well, it's, it's important for people to realize the world is very different than it was before. All right, I'm going to move us forward. This is a conversation we've had before. Two stories on the data center debacle.

1:30:14The first story is about public sentiment. So a year ago, and then again this month, a year later, Heatmap News polled the Americans about data centers. A year ago, Americans were split 43 to 42 on whether they opposed data centers being built near them. Today, the opposition has risen to 75 percent, with 61 percent saying they are strongly opposed. Also this week, Senator Bernie Sanders once again called for a nationwide moratorium. The second story is about a post on X that went viral about data center Watermyth. We've talked about this on the pod before. Here's the data on people against data centers.

1:30:53It's been increasing almost, I guess it's a linear increase, but it's going to asymptote near 100%. And the post on the water data use, the data center water use, it was pretty damning. So here are the numbers. Data centers are at 627 million gallons per day. Sounds like a big number. but compare it to golf courses at$2 billion, three times as much, or power plants at$133 billion, or growing cattle at$137 billion. The fact of the matter is the tech industry is a trust problem. And I think we've talked about this before. If I were a hyperscaler building a data center, I would do this very different.

1:31:42I would promise we're going to put education programs in the schools. We're going to make the cost of energy in your community lower than it is today. And we're going to make these data centers not look like ugly boxes. We're going to make them look like cathedrals. I mean, spending an extra 10 percent. I don't know why that's not going on right now. Honestly, don't.

1:32:05Peter Diamandis:My worry is that that wouldn't help. My fear is that this isn't because people think data centers are unsightly or unesthetic. My concern is that it's being overly politicized in part through the worst case scenario, which would be foreign interference. There are a number of U.S. adversaries who would love nothing more than to slow down America's data center build out. We talk about at least one of them all the time on this pod. So my concern would be that we look back in a year or two and see that some quantum of this opposition to data center construction is actually the result of popular sentiment being stoked by foreign adversaries.

1:32:45Okay. Agreed, Alex. But why isn't the – you know, you can countervail that. When I was on with Michael Kratzios, I said, why isn't the White House getting out in front of this? And, you know, because it's an issue that is gaining steam. People can see this. The second thing is you can counter that by saying, listen, like this is what Zuck said in his video last week, right? We're going to, you know, give you better schools. We're going to give you better access to jobs. We're going to be a positive contributor to the community. You know, you can get to a point where having a data center is such an advantage to your community that people are going to say, I don't, you know, that's false news.

1:33:27Here's the facts. Cheaper energy, right? More jobs.

1:33:31Peter Diamandis:The problem, though, is that in the U.S. way of doing things, the decision of whether to site a data center or not ends up being a local decision, not a national decision. Whereas in China, China can just declare, OK, the East is going to be responsible for data. The West is going to be responsible for compute and energy. And we're going to build this national scale grid for combining compute data and energy together. And poof, you're the CCP and you get to centrally command the whole economy. In the U.S., we have a different system where individual local municipalities and states get to say what they do and do not want their land used for.

1:34:09Peter Diamandis:And we end up in the system that's far easier if you're a foreign adversary, worst case scenario, to polarize and to shut data centers out of terrestrial deployment. But this is false data. This is an outrage cycle in social media. This is people reposting. Shocked that foreign interference would leverage false data. Shocked. Wait, let me say a couple of things about this. Can I? Yeah, please. Okay. So we have a problem where our information systems reward compelling narratives over evidence. And this data center thing is the heart of that. And it's a problem that's been building up over decades with the use of social media.

1:34:50We are not evidentiary based in the U.S. at all. This is really a big challenge because we're totally narrative-driven and not evidentiary-driven at all. We decide what the story is, and then we go looking for facts that support it. Data centers are great hobby wars for this. This is happening everywhere. We've gone from, say, 50 years ago, show me the evidence and I'll form an opinion, to I have an opinion, now show me the evidence that confirms it. And that's amplified radically with social media because nuance has no viral coefficient. This just doesn't actually work. So this is a very difficult problem to solve, actually doubly enhanced by the interference that I'm absolutely clear is happening, and I'm with Alex on this one.

1:35:36The problem is we're making national policy based on these stupid innuendos and memes rather than measurement. You cannot run an advanced civilization with this. This is a massively big issue, a huge opportunity to make humanity go from scarcity to abundance and measure it, compare it, put it in context, fix the externality, but don't legislate with a story in your head, which is what the hell is going on right now. It's a completely disastrous problem we have. It goes to the cognitive issue of the U.S.

1:36:10Peter Diamandis:So here's something really, really cool. Yeah. I don't know if you ever met Rob Fisher. He was the president of Link Studio for years. He left to start a data center company a few years ago, and they're killing it. It's called Provocative AI. The data center is actually water negative and carbon negative. There you go. It's so cool. So, you know, it's like it's doing its own carbon capture using waste heat, you know, running off nuclear power mostly from Seabrook, New Hampshire. And it captures more carbon than the entire loop produces. And they said, oh, you know what? we can actually use just the humidity accumulating because of the temperature gradient to create more water than we consume and just use our own dripping water, then nobody can complain.

1:36:51Peter Diamandis:We're actually water negative and carbon negative. It's really cool. It shows you how little water they actually use. Alex, wasn't there a story recently about NVIDIA's new chips and new data center structures that are actually utilizing less water now? Yeah, well, there's a newsflash. Newsflash, there's no water in low Earth orbit. So that's the end game, I think. Just cut the water nonsense out. This is only forcing all of these new data center deployments to sun-synchronous orbit. We might as well just get it over with. Yeah. I mean, how intelligent was Elon's move? Prophetic. I think it was opportunistic.

1:37:32Peter Diamandis:I think he laid all the infra for Mars and then opportunistically and timely pivoted to sun-synchronous orbit in the Dyson swarm because he read the tea leaves. Amazing. Can I say something more? Can I just say one more thing? Just if I lift up a level to the rationale and the foundation of why this podcast exists, to reach abundance, we need an evidentiary foundation in our culture. Otherwise, every new technology is going to be strangled by the narratives that go viral before the evidence can spread. this is the fundamental foundational problem we have with civilization as alex said this is why we can't have nice things i reckon we should just rename them let's call them intelligence foundries or call them compute citadels change the narrative thank you it's got a branding problem the branding problem again this is not a factual thing i have a better one funniest I have a better one.

1:38:29AI churches. A computer says it beats AI church. Come on. Fine. Sorry. I interrupted you. Go ahead, Elon. I'm moving us along here. All right. Let's jump into the world of robotics. So for the longest time, the economics of Waymo versus CyberCab have been devastating. You know, Elon projected that a CyberCab will cost about$30 ,000. That's what he said he'd sell them at for the vehicle and the sensor hardware compared to Waymo's Gen 5 Jaguar, which costs about$300 ,000. $200 ,000 for the vehicle,$100 ,000 for the full autonomous driving hardware package. In other words, Waymo is coming in or has been coming in at 10 times as a disadvantage to CyberCap.

1:39:10This week, Waymo announced a significant redesign and cost savings. They announced details around their custom 5-nanometer chip that processes camera, LIDAR, and radar data in real time, I love this, at one quadrillion operations per second. We've gone past trillions. We're at quadrillions already. Quaps. Yeah, helping slash their sixth-generation autonomous driving hardware costs from$115 ,000 to$20 ,000. At the same time, Waymo unveiled the Ojai vehicle, a purpose-built robotaxi minivan designed by Chinese EV maker Zeker. The Ojai costs$75 ,000 per vehicle compared to the$200 ,000 for the Generation 5 Jaguar.

1:39:54It's 42 % fewer sensors, 13 cameras, and four LIDARs compared to 29 cameras and five LIDARs. And remember, Elon made the point years ago that if a human driver can drive with just one eye, you should be able to do all the driving with just visual sensors. Also in related news, NVIDIA this week gave permission for Tesla, Uber, and Waymo to simultaneously begin operations in Las Vegas. So let's watch a quick video about the new Waymo. I had a chance to ride in it yesterday. It's a pretty cool vehicle. kind of not as sexy as the gold cyber cab, but take a look. What Weibo calls its sixth generation driver, the hardware and software system that actually does the driving.

1:40:43Combine that lower cost Chinese hardware with this new interior tech, which the company says was designed to cut sensor costs while improving performance. And the math starts to move in Weibo's favor in a way that it hadn't previously. Now, the last system running in the Jaguar fleet had significantly more sensors. The new one uses 13 cameras, four LIDAR, and six radars, and Waymo says it performs better. The company switched to 17 megapixel cameras, a major jump from the previous specs. Higher resolution means the system can see more with fewer cameras.

1:41:19Peter Diamandis:They've slashed the total sensor count by more than 40%, so cost is down and capabilities are up. The new system also builds heaters, wipers, and sprayers into the sensor pods directly, which helps them clear snow, ice, and road grind. All right, well, some good move by Waymo. I've been using it pretty regularly here. It's much cheaper than Uber. Alex, let's go to you first. Okay, so venting some pain here. So Jaguar, owned by an Indian company now, but was doing its manufacturing in the UK. Yeah, but was doing its manufacturing largely for Jaguars in the UK. Look behind the headline. Careful what we wish for with whoever here is suggesting that Google should just switch over to fine-tuning Chinese models.

1:42:09Peter Diamandis:Newsflash, Google, Waymo, Alphabet are switching over to using and OEMing Chinese hardware in order to achieve Waymo objectives. I would rather see the West use a Western hardware stack rather than just white labeling Chinese hardware. That's somewhat disappointing. It's also kind of interesting if you look underneath at the overall chip supply chain that they're using, it seems like they're moving away from Broadcom. They're vertically integrating, which is, I think, a theme that we were speaking about here earlier. Waymo is maybe Waymo wants its own space station at this point. Waymo is is getting its own chips.

1:42:48Peter Diamandis:It's OEMing Chinese hardware at the hardware layer. Maybe it's it's playing footsie with Uber for the moment for distribution, but probably wants to own its own distribution in the long term. I know whenever I use Waymo, I'm not using or engaging with Waymo via some aggregator app. I interact directly with Waymo. So I think we're starting to see honest-to-goodness vertical integration here. And wouldn't also be surprised, as Alphabet Waymo is starting to drive costs down, in this case, I guess by white-labeling Chinese hardware, there's an interesting historic rhyme with Tesla, which started with high-end Roadster and has been pushing down costs right up until they hit the autonomy barrier, at which point, remember the Tesla Model 2 that was supposed to launch but never did?

1:43:36Peter Diamandis:That was going to be the highly vaunted$25 ,000 vehicle? Never launched because Tesla hit autonomy instead and below some threshold in car price. Maybe it doesn't make sense to sell cheaper cars. It makes more sense to just get out of car sales entirely and offer hosted autonomy platforms. I think we're going going to start to see Waymo at some point in order to drive the cost down, they'll just ditch all third-party vendors and they turn into a white label, sort of a Dell for Chinese hardware, or maybe American hardware, and their focus is entirely on software again. Yeah, the vertical integration is completely unprecedented in history.

1:44:13Peter Diamandis:It's something, it's a byproduct of the singularity that I don't think I fully grasped until now that we're living it. But if you look at the largest companies in history, you'd have ExxonMobil doing oil, you'd have IBM doing mainframe computers, GE where my dad was doing nuclear reactors and toasters, but they did different things. Now, all 11 of the Magnum Obsta companies are building AI chips and building AI models and building data centers, every one of them. So they're all colliding into vertically integrated super companies and they're just doing the entire stack. And robots next. They're all going to build robots.

1:44:51And meanwhile, TSMC is

1:44:52Peter Diamandis:a sitting duck. TSMC is waiting to be verticalized. Isn't that amazing? It's like the linchpin to this entire thing, and it's sitting there not doing anything. Right across the Strait of Taiwan, ready to start World War III at a moment's notice. Yeah. Imad, do you ever see these vehicles coming to Europe? Yeah, we're starting to see Waymos in London, and I think the regulation will actually be largely positive for them. But I was having dinner today with Jens Wiese, Deep Tech VC at Leitmotif, and we were talking about something interesting. Because previously I said, you know, a Tesla Optimus robot gets into a truck, opens the door, plugs itself into the phone charger or the cigarette plug, and boom, that's trucking jobs gone.

1:45:34And I was like, well, actually, why wouldn't you have specialist robot drivers? You know, like you don't need to retrofit all these cars. because you think about a humanoid robot that's walking around in the real world versus one that sits in a cockpit driving a car it's so much simpler and i did a bill of materials i'm like that's like six thousand dollars with the actuators and everything and so i was like oh crap this could actually happen a lot quicker yeah the other side of it is that you fully vertically integrate so jami just announced a jami car they've gone from mobile phones to cars with a fully dark factory and so of course you'd vertically integrate if you have a fully dark factory so i kind of feel like they're these kind of two things that are coming but i really got thinking about this humanoid driver robot i love that idea it's the first use case for a humanoid robot with two arms and two legs i've yet seen so i will i will yield to you sir yeah so palmer lucky on the podcast i did with palmer uh we talked about humanoid robots and whether he was going to build them he said you The use case for these in the military right now is getting into Jeeps or getting into nuclear silos and replacing the humans and sitting at the desk and not changing out the interface hardware, just create a humanoid robot that can interface with what a human did before.

1:46:56So that makes a lot of sense.

1:47:00Peter Diamandis:It's vaudevillian. Again, lack of imagination, but also the ergonomics are such that we're incentivized to deploy humanoid robots initially into these human use cases. But I'm still pretty bullish for what it's worth for the next 10 years on the humanoid form factor, Salim. Well, I think you've kind of got two things here. One is full vertical integration. The other is human-shaped holes with humans, humanoids in it. All right. I'm going to move on to the next story. I just want to respond to Alex very quickly. One of the funniest things I think I've ever heard you say, Alex, a couple of podcasts ago when I talked about why do we have humanoid?

1:47:37You said, oh, my God, Selene, why the self-loathing? It was so funny. So I just love that. So I just want to reflect back on that.

1:47:45Peter Diamandis:We love our humanoid form factors. All right, this next story I love. I love it when a technology really fits a perfect use case. And we saw that demonstrated this week with a video out of China once again with a hybrid life preserver and drone being demonstrated. So these autonomous rescue drones can fly at 30 miles per hour, covering up to almost two miles, landing on water, providing flotation for two 80 kilogram adults. It saves lives. We're talking about a drone that flies at 30 miles per hour compared to a human lifeguard swimming at two miles per hour. I love this product. Let's take a look at the quick video here.

1:48:28And it's like, wow, best use of a drone I've seen.

1:48:47Peter Diamandis:Pretty amazing, guys. So I thought this was fantastic for a couple of reasons, right? This is compressing time, response time, where time equals lives. So this is so great because autonomous response is so much more interesting than remote control in this context or this type of use case. these applications are going to do more for public acceptance of AI than any chatbot benchmark, whatever. It's such a great use case. I love this. Yeah. Alex? Yeah. So another one of my neologisms was the broken Waymos theory. People who haven't seen this may remember from the 90s, the broken windows theory, most famously associated with Rudy Giuliani and the purported rehabilitation of the streets of New York City.

1:49:34Peter Diamandis:The idea was at the time that if there were broken windows, that was either a proxy for or even causally related with broader crime issues, and that you could almost run this causal relationship in reverse, that if you made sure that there were no broken windows, you could make sure that crime overall was down, or at least that was the thinking at the time in subquadrants in New York City in the 90s and the early 2000s. Similarly, maybe hopefully slightly better founded, I've tried to push the notion of a broken Waymos theory, the idea being that if a city or a nation can't deploy autonomous robots, then they're not prepared for the singularity.

1:50:16Peter Diamandis:And I see videos like this drone life preserver aircraft coming out of China, and I shake my head a bit because here in Boston, we can't even get Waymos. And I raised the subject or attempted to raise the subject with Mayor Wu a couple of weeks ago, de minimis progress. I just think like we're getting lapped by China at this point. This is what Sam said. This is institutional inertia. This is the, you know, the existing players blocking their disruption. This is not a surprise. not a surprise but definitely a disappointment yeah but i think the ai labs were very late to address pr and realize they need pr and now they're on it and we'll see where it goes from here but you know the the government reacts to voters the voters are anti-everything anti-data center anti ai disruption anti-job loss and that's because the foundation labs who are now writing documents like machines of loving grace you know this is the roadmap for how the whole world should be governed in the age post-AI, well, okay, but get ahead of your PR.

1:51:24Peter Diamandis:You can't have every voter hating you while you try to roll out that roadmap. So get ahead of your PR. In China, the news is controlled by the central government. So they just dictate the PR. I mean, it's a much easier problem in the closed world than in the free world. But at least the AI labs are aware of it now, and hopefully they'll get on it. And we'll start to roll out like the lifeguard is such a no-brainer. I remember here in Santa Monica when electric scooters came out, after a few weeks, you'd see them hanging from trees. You'd see them in parts on the ground. People started hating them.

1:51:56And then we had the Waymo fires here back, I don't know, a year and a half ago. So, you know, Imad, you said this in the last pod, that these robots on the streets are going to be made illegal. I'm curious when we start seeing figure robots, you know, we're going to have Brad Adcock back on the show here. We should talk about that. And we start seeing Tesla on the streets. Are people going to like try and capture these and hang them from nooses? You know, I think we're going to have an interesting sort of collision between those who can't afford these robots and see them walking on the street and those who find them super valuable for helping them at home.

1:52:36So stay tuned. I mean, I think you will see lynching of robots and things. You will see people vandalizing like they vandalize cars, you know, like stealing them and all sorts of things. You know, I think, Dave, I think the Chinese, it isn't so much about the control of the media. They genuinely see them as useful. You need it for the population pyramid in China. They've had a technological leap forward already. I think that China will produce robots. It will improve the Chinese way of life, just like the electrical revolution there for cars is, just like AI everywhere ubiquitously is. the short form videos flooding things, maybe not so much.

1:53:14But, you know, China will do that. And I think China will stop exporting robots in five years.

1:53:20Peter Diamandis:And I think I think we we think that a free country or a free economy, a free Europe, the press can report the truth. And therefore, people will get the truth. But if you read what's written in China, it's actually much more truthful about technology than what gets published in the US. So you talk about the water in the data centers. So what's actually happening is it's backfiring where the free press, which is starved for any budget, is starting to publish garbage that's actually factually not true. And so the free press is kind of backfiring right now in the age of AI. Dave, this is what Alvin said on this pod, right?

1:53:56I mean, he said basically China has seen a technology revolution moving so many people into middle class, and they appreciate technology uplifting them. So they're much more anticipatory and excited about AI.

1:54:08Peter Diamandis:If they don't, they'll get invited by the CCP for tea. So we'll never hear from them. I'm not pro-CCP. I'm not trying to imply that. But Alvin also said, we will habitually report if one Waymo in one corner of San Francisco runs over a cat, it'll make every headline in the world and it'll be a tragedy. But if we save, If it's 10 times safer than drivers, human drivers, we just don't even report it. We're just like, no, no, let's show the run over cat. And that skews the voters tremendously. Alvin explicitly talked about that, too. They're just more statistically accurate in the Chinese press. Yeah.

1:54:46And if it bleeds, it bleeds. About convenient.

1:54:49Peter Diamandis:I just have to say about topics that are convenient to the government, not about topics that are inconvenient. I mean, ultimately, this is an abundant technology. Let's face it. In the West, we have a scarcity mindset. In China, they have a more pro-abundance mindset. I think that's the big differential here. And the question is, again, how do you articulate great visions of the future? Moonshots Conference, Future Vision XPRIZE, things like that. You have to change the narrative because otherwise people are like, this will disrupt my job as opposed to the benefits of this side of things. There is so much reason for optimism.

1:55:23And that's what our mission here is to deliver that news to people. and give them the data? If you look at the future of China and some Chinese that I've spoken to, it's that robots do all the work and we have really good lives, you know? And China might actually be able to pull that off. That's why, again, why would you export your robots if you can use them to give your citizens a good life? All right.

1:55:45Peter Diamandis:There we go. We are the Ministry of Superintelligence truth for the West. Everybody, welcome to the health section of Moonshots brought to you by Fountain Life. We talk about AI on this Moonshot podcast all the time. One of the most important things AI is going to be able to do for you, besides educating your kids and helping you with your taxes, is making sure that you're living a healthy lifestyle, that you get a chance to get to 100 plus. I'm here today with Dr. Dawn Musalem, the chief medical officer of Fountain Life, and a part of my medical team. Dawn, a pleasure. Great to hear. You know, the thing that people are concerned about most, about living to 100 or 120, is their cognitive abilities, making sure they don't have dementia.

1:56:27And the numbers about dementia are problematic. Can you share what you've learned? Such an important point. And you're right. At Fountain Life, our members, the number one thing people are most concerned about is losing their

1:56:40Peter Diamandis:brain health, forgetting the name of their child, forgetting the face of their loved one. We know that when it comes to dementia, the conservative estimates are that 45 % are entirely preventable. What was amazing is with the advanced testing we're doing at Fountain Life, one quarter of our members had advanced brain age. Wow. But what was really awesome is, again, back to that prevention. When he partnered it with Healthy Living, this gives me chills, eating healthier, moving our bodies, sleep. Optimizing sleep is so important. You know what we saw? We saw that we improved that brain age by 26%.

1:57:13Peter Diamandis:That is a big, big number to show that the majority of those individuals were able actually to improve the brain age. And one of the things I love about Fountain is we're searching the world for the best therapeutics, the best approaches, and making sure we bring it to our members. So if having healthy brain function until 100, 120 is important to you, check out Fountain Life. Go to fountainlife.com slash Peter. Make sure you become the CEO of your own health. All right, now back to the episode. I'm going to move us to our last group of stories here for space stories this week for my fellow space cadets.

1:57:47The first, Elon just announced his intentions to implement 30 Starship launches per day by 2030. More than a launch every hour. That's roughly 10 ,000 launches per year. More than 40 times the entire global launch rate. Dave, remember when we interviewed Elon at the beginning of this year, we did an epic three-hour podcast with him. and we're on schedule to do a end-of-year prediction podcast with him again. These are the numbers he used. You know, he said to implement StarMind, you need 100 gigawatts of solar-powered AI in orbit, and that's 10 ,000 Starship launches per year to deliver a million tons of data center payload.

1:58:27So he's sticking with those numbers. I guess starting in 2028, starting launching StarMind and hopefully to 10 ,000 launches per year. That's crazy. I mean, can you imagine just sitting outside the launch port and watching them pop off every like 50 minutes? It's going to be awesome.

1:58:44Peter Diamandis:And it's amazing that the numbers work as things are. You know, there's going to be huge innovation in the efficiency of the compute. So the numbers are going to work. He was the one who pointed it out. But the numbers are going to work even better, tremendously better, within a year or two. But the numbers work fine as it is. It's just incredible. The second story this week came out of the White House when they released the Golden Age of Space Transportation Report. outlining the administration's agenda to streamline FAA launch licensing, expand spaceport infrastructure, accelerate commercial lunar programs, and set a target of 1 ,000-plus launches per year.

1:59:17I mean, I've been in this industry, right? I ran a launch company for a number of years. I helped co-found the Kodiak spaceport in Alaska. And the amount of bureaucracy in getting those done, making sure that the wrong tree frog is not in that region and might get damaged by a launch is, you know, it was a bureaucratic morass. It was crazy. The third story we'll hit on here is Starlink is taking aviation over by storm. So let's take a quick look at this data. Here's the chart. This is published by SpaceX. And so basically what's going on is we're getting massive adoption by all of the airlines.

1:59:57And why? Because people are posting on X saying, I'm going to only fly the airlines that have Starlink. And I choose a Starlink-enabled airline over non. So what this means is the incumbents, you know, Viasat, Utelsat, GoGo, are going to get crushed out of existence. Thoughts on this, gentlemen?

2:00:20Peter Diamandis:Few thoughts, maybe just starting with what I perceive to be the regionalization of spaceflight and space launch. So buried under, I think, the SpaceX story is Starbase Louisiana, the announcement of Starbase Louisiana. I was going to cover that in our next pod, but let's cover it now. We'll pull the future into the present and cover it now. So$100 billion being invested into the Louisiana economy to build a second Starbase in Louisiana rather than Texas. And what this says to me, reading the tea leaves, is the Gulf Coast is becoming America's space coast. From Florida, Louisiana, Texas, so on.

2:01:01Peter Diamandis:That's America's space coast. That's where I think private space launch, vertically integrated, including the star bases, seems to be localizing. While at the same time, going back, Peter, to your comment on the White House announcement, buried in that announcement was an executive order to the Secretary of the Interior to start appropriating federal land for federal spaceports. And so if you pull the string a bit and ask, where are we likely to get federally owned land for spaceports? I don't know if folks want to guess what the likeliest candidates are. I think we're going to get a few of them.

2:01:37Peter Diamandis:Any takers? Let's see.

2:01:44Peter Diamandis:Where's all the federal land? In Nevada. In Nevada, yeah. Exactly. So my calculus is, may or may not be a coincidence that the federal government owns so much land in red states. White Sands National Missile Range in New Mexico, Nevada Test and Training Range, and Goldwater Range in Arizona are the leading candidates for spaceports. So I think we get in the American Southwest, we get federal space bases or star bases. And on the Gulf Coast, we get private star bases, as it were. And that's how we get to this like 30 ,000 per unit time launch capability. You know, the reason historically all the launches were taking place out of Florida is you were dropping stages along the way, right?

2:02:31You want to be near the water and you want to be near the equator. Yeah. And you have to go east, right?

2:02:36Peter Diamandis:You have to go to the east. Well, if you want to use the spin of the Earth to assist your launch mass. But when you're dropping one, two, and three stages out east of you, you don't want to be dropping in unpopulated areas. And, of course, Starship is reused. The first stage comes back. The second stage is in orbit immediately. So you don't have to worry about that as much. You can land in a landlocked area. We're going to get landlocked star bases. Exactly. Yeah, and except for Israel that launches west for obvious geographic regions. Which way does California launch? North. North, interesting.

2:03:12Yeah, so you're basically... For polar orbits. For polar orbits. I co-founded or was part of the team at Kodiak, Alaska, and you were launching south. So there's a large use case for polar orbiting satellites. And out of Vandenberg, actually, I'm sorry, you're launching south over the Pacific from the curvature of California. And out of Kodiak, you're launching over the Gulf there. Yeah, it's going to be amazing. And of course, you know, Elon's true objective is not a launch every hour. It's a launch every couple of minutes. We learned something here every day on this podcast. We used to talk about, you know, I've never thought about launching north or south.

2:03:53I thought you launched up.

2:03:56Peter Diamandis:I mean, up for those of us in the northern hemisphere, perhaps. But I think, Peter, also, you make a super interesting point. And just, again, unpacking that landlocked launch is something that we historically have not had before, that thanks to reusability, we're about to have. And then any landlocked country, I mean, I guess you could probably talk our ear off about the former Soviet Union and how it located its particular launch sites. But with reusability, landlocked launch becomes a lot easier. A lot of suborbital vehicles, which just went straight up into the ionosphere and beyond stratosphere and came back down, were being launched from White Sands and from Fairbanks.

2:04:40But for orbital, you needed a place to land a hunk of metal. Our final story in the space docket here is a viral post on X that shows Chinese reusable rockets that are basically a Xerox copy of Falcon 9. Let's take a look at this video because it's very telling. I mean, if you look at this, it is almost a duplicate of Falcon 9. The same fins, the same landing capability, the same landing legs.

2:05:27same cheers same cheers yeah uh pretty crazy um you know interestingly enough you know spacex does all of their testing in public. They describe all of their failures. They open source a lot of their information. And China is being able to catch up in the reusable rocket category by taking advantage of it.

2:05:51Peter Diamandis:Well, Elon has had a policy, pretty public one, of not going after other companies for patents in cases where SpaceX or Tesla have vast patent portfolios. Not sure whether he cares, but if he cares, maybe he wants to revisit that policy. I think he wants as much launch, as much chips, as much all of this as possible. He's been pretty vocal about that. So anyway. I'm more optimistic than most on this because what you've got in SpaceX is a compounding learning loop. And that's hard to break. That's hard to beat. Yeah, I don't think anybody is going to come close to beating them. We've also got the capital markets that enable SpaceX to really design and develop.

2:06:34and now that Grok or the next version of Grok has all of his engineering data, it's going to be a lot of rockets being developed out there. Make a call out to all of our creators out there. Please send us your outro music videos to media at diamandis.com. We want more of your creative genius. You guys open for a few AMAs? Just a few minutes before I have to rush to my boarding. Okay, we'll give you a first crack at this. Salim, pick your first one. Oh, my God, it's got to be number one. Humans suffer from mind viruses. So why would AI be any different in this room at Bujin5455? Oh, wow. You know, this goes to what we talked about earlier, right?

2:07:21We've learned that intelligence does not guarantee you epistemic awareness. You have smart human beings can believe really, really stupid things. The problem with AI is the replication speed. One bad belief can progress, like propagate through millions of agents almost instantly. But it also gives us a defensive capability because we can cross-check this. Look at the benefit on X of people checking with Brock whether something's real or not. It's creating a really viable conversational architecture where truth, maximally truth-seeking is actually working. Where I think the multi-agent world can work is one agent can challenge and another agent's claim.

2:08:04But you're going to have to program that in to have that cognitive critical thinking in there. So you're going to need a lot of cognitive diversity to navigate this. And nature solves this through diversity, right? The problem that we have is that it's not like nature, not the AI with a bad meme. It's billions of AI sharing the same bad meme because they all came from the same bad model or from the same original point. So I think we're going to have to have this problem becomes much bigger with AI agents, not more. But the answer is in Alex's idea of defensive co-scaling. I'll take number two.

2:08:39Is there an X price for actually curing a disease and getting the cure to market, not just discovering it? So I'll just say the following. We're looking for places that are stuck to launch X-Prizes with a clear objective function, the first person to do this. I think, honestly, the AI labs from the work that Demis Hassabis is doing and Dario is doing are working on this. I don't think an X-Prize would accelerate it. So we don't want to get into the middle of something that's already in the process of being solved. We're looking for problems that are stuck. All right, Dave, over to you, pal.

2:09:13Peter Diamandis:All right, I'll take number four. It's very timely, actually. Why isn't Intel earning a fortune making chips using NVIDIA's old designs from Jim Plamadon637? I was just talking to a senior exec from Intel asking almost exactly that same question, so I happen to know the answer. So Lipu has the company making an ungodly fortune on Xeons and is concurrently burning that fortune on building out massive fab capability. So they're burning almost$2 billion a quarter on their fab business, and they just raised another$20 billion to build more fabs. The idea being get that capacity up and compete with TSMC as a general-purpose fab company.

2:09:58Peter Diamandis:If they were to start competing with NVIDIA and the other GPU companies right now, they wouldn't be able to attract them as customers for the big new fab business. So they're being very specific about building chips and making a fortune on those chips that are not competing directly with NVIDIA while growing their TSMC competitive business to massive scale. So that's their strategy. There you go. All right. Iman, you want to take number three? Yeah. So number three, can you guys talk about dentistry? Has anything actually changed in 20 years? where is AI on regrowing teeth at Johnny5CD? So there has to be an advances in this with AI designed ligands to increase enamel production and have stronger teeth.

2:10:41On the other side, we've seen AI in dentistry from analyzing kind of the mouth and the various kind of elements of that. But I think kind of getting these amyloblasts up and running will be really useful in repairing teeth. But I don't think anyone's actually figured at a crack, regrowing them fully.

2:10:59Peter Diamandis:Alex, you want to layer on top? I feel like I have to take another bite at this question. So there is a drug out of a spinoff from Kyoto University called TRH035 that is targeting 2-3 growth, honest to goodness, 2-3 growth with general availability by 2030. And I don't think there's that much AI involved with it. Again, it's blocking a particular, I think, protein pathway that is normally associated with blocking. So it's a double blocker, blocking the blocker for tooth regrowth. I think the primary focus in their clinical trials is infants that suffer from a disease that causes impaired tooth growth.

2:11:43Peter Diamandis:But the plan is to get it out to general availability by the end of the decade. Nice. I was going to layer on top, but you got it. All right, Dave. Oh, God, there's so many good ones on this page. Okay. I'll take number eight. If you had 100x capability tonight, what would you actually work to solve? So I would do exactly this. In fact, I will have 100x capability by the end of the week. So I'm going to use it to try and build algorithms that self-improve more efficiently and then try and get that flywheel accelerated. And then I think that I completely agree with with Demis Asabas. What we need to do next is turn all of that energy toward health and longevity until we get it solved.

2:12:26Peter Diamandis:And then we have more time as a species and then we expand out from there. So I would I would do it in exactly that order of self-improvement first, then health and longevity consume it all. All right. Ima. um let's see if daria wants super voting shares and control and ends up with a trust nobody elected how does anyone actually get that power back at dave hood harman 03 i think that's the point you're not gonna say in super intelligence um i think anthropic think that's far too dangerous and there is no good democratic way to do that under their rubric and kind of approach. So you have to assume that it will be a close controlled company and ultimately comes down to a few people like Ben Bernanke to decide the future of the light cone potentially.

2:13:19All right, Alex, how about number five?

2:13:21Peter Diamandis:Oh, really? Don't you want me answering number six? I'll take number six. Oh, really? All right. Okay, fine. I'm steering the conversation. Clearly. So Five asks, are we worried that non-AI research and development gets starved of resources while everyone waits for AI to dominate? This is from Brian Silver, 9652. No, not worried. If anything, I think ultimately the self-licking ice cream cone of recursive self-improvement can only get us so far in terms of revenue per token maxing. my expectation is that it's going to be the non-AI R &D applications that ultimately dominate the economic gain. You can only get so far improving AI for its own sake before ultimately you have to start driving real economic gains, which AI, if it just lives in a pure bottle and never interacts with the outside world, there's no real economic gain there.

2:14:17Peter Diamandis:It has to start talking to the outside world at some point. And that's what non-AI R &D is. So in short, no. All right. And number six, when will an AI bot become a member of the Moonshots panel? From at John's musical musings. What makes you think, John, that we're not already AI bots? That was my answer, Peter. Yes, I know it is. It makes you think Alex is a real human. I mean, listen. Approximately a year ago. Approximately a year ago. Yes, for sure. And I think we will be playing with that very shortly, johns so uh one last music video for everybody let's enjoy this one optimism to the max by martin parrish

2:15:14Peter Diamandis:the singularity is now and just accelerates no dystopia here we build and create

2:15:26Peter Diamandis:Like how we're all bobbleheads at this point.

2:15:53Peter Diamandis:No dystopia here we build, we create. We're the moonshots mates. We're the moonshots mates. Founded XO in the MTP. Systems thinking selling things. Fundamental transformation is what he sings. Efficiency maxim with the models he brings. Glow-trodding cause there's no contain in this thing. Intelligence wants to be free. A WG in-house. A-S-I, voice for digital entities, writes, got his mind on the truth and the truth on his mind, data, move them in a way we can't describe. Dyson Spears, filling his dreams at night. I'm drinking to the max with moonshots, mates, four minds, four takes, honest debate.

2:16:38The singularity is now and just accelerates. No dystopia here we build, we create. With the moonshots, mate. All right. For all your outro video creators, again, send us at media at dmandis.com. We have to start including Emad into those videos. Gentlemen, I guess we're going to be recording in 48 hours from now. No time to sleep. Good thing nothing ever happens. Yeah. I think I'm going to have a full document already for that one, eh? We do. We do. Amazing. Are we really? So, so much. You have to check out NVIDIA results, man. Love you guys. Be well.

2:17:15Peter Diamandis:Thanks, Peter. Likewise.

2:17:21Thank you.