Moonshots: OpenAI Pauses Frontier Training, Elon's 100X Prediction Lands, Robot Beats Usain Bolt with Emad Mostaque | EP#282

Sourced by podcast-ingest on 2026-08-24. Auto-transcribed via AssemblyAI (universal-2, en). Speakers identified by AssemblyAI Speaker Identification using the per-podcast host/regulars hints; the resulting label→name mapping is in the frontmatter. Duration: 2h20m. Episode page: (not provided). Audio: https://traffic.megaphone.fm/DVVTS5949178347.mp3.

Show notes (from RSS)

The mates sit down with Emad Mostaque and discuss OpenAI’s pause on frontier AI training, Elon Musk’s 100X intelligence prediction becoming reality, Anthropic’s potential $2 trillion IPO, soaring AI memory demand, Unitree’s record-breaking humanoid robot, and promising results from Moderna’s personalized cancer vaccine.

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Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360

Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader.

Dave Blundin is the founder & GP of Link Ventures

Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified

Emad Mostaque is the founder of Intelligent Internet ( https://www.ii.inc )

Read Emad’s latest papers exploring the future of society, law, personhood and governance: https://ii.inc/common-wealth

Read Emad’s Book: https://thelasteconomy.com

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*Recorded on August 20th, 2026

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Transcript

Peter Diamandis: OpenAI announced it is voluntarily pausing some of the Frontier reinforcement learning training that it's doing. What have they paused and is it really significant? And do you think the other Frontier Labs are going to do the same thing?

Alex - 1 - 1: They're so powerful, even we can't trust them. So we have to throttle back. It's marketing.

Peter Diamandis: Elon Musk's January 6th moonshots podcast prediction of 100x gains was at the edge of plausibility when he made it. Now, it's simply a fact.

Dave Blundin: Imagine I gave you 10,000 employees tonight. Oh, my God. If I had that, I do something amazing. Okay, What? Start thinking about it, because it's coming imminently and it's actually not an easy problem to figure out how to turn it toward creating good.

Peter Diamandis: Unitree's newest humanoid robot broke every human standing jump and speed record, a top speed of 12.66 meters per second, beating the human record set by Usain Bolt.

Imad: You don't want to have superhuman robots on the street because you'll have accidents. You'll have issues just like cars. I think that these types of robots will be banned.

Peter Diamandis: Now that's a moonshot.

Dave Blundin: Ladies and gentlemen,

Peter Diamandis: welcome to Moonshots, everyone. Your number one podcast on all things AI and exponentials. The news that matters, the news that's changing your life, your front row seat to the accelerating singularity. I'm here once again with my magnificent moonshot quintet. Yes, they're all five of us are back. Awg, Dave Blunden, Saleem, and imod. I'm Peter Diamantis, your host, your abundance whisperer, and we've got a lot this week. Gentlemen, good to see you all.

Dave Blundin: Good morning.

Salim Ismail: Howdy.

Peter Diamandis: Good morning.

Dave Blundin: Good afternoon.

Peter Diamandis: In London, it's like everybody's in their normal haunt except for me. I'm up in a sleepy town in the Pacific Northwest, trying desperately to take a little bit of time off to

Dave Blundin: think taking shelter from the singularity.

Peter Diamandis: Yeah, except I got up at 5am this morning to be with you guys, so what the heck? But I hate the old saying, you'll sleep when you're dead, because I just don't want to die, and sleep is still so important. But hey, what can I tell you?

Imad: Sleep soon.

Alex - 1 - 1: Yeah. No, and death, I think, like, death is counterindicated at this point.

Peter Diamandis: You know, as always, everybody, our mission is to help you understand what just happened and what it means for you, and most important, to keep you optimistic about the future. If you're new to this pod or if you've been a regular, great to have you Back, Please take a moment and hit the subscribe button. You know, we publish moonshots twice per week at a pretty regular cadence. It's great to have IMOD here, hopefully at least once a week. And sometimes when there's extraordinary breaking news, we publish three times a week. We read your comments, and we love you, too. I mean, it's been an incredible outpouring of support. I don't know. You guys see it. I get stopped on the street in the grocery store. People are saying, you know, I live for your show. I love the show. I can't go a week without listening to it. Are you guys getting the same response? Yeah.

Dave Blundin: You know what I'm getting a lot of is what we podcast out is so different from other podcasts. But if you go back to shows from six months ago and a year ago, people are like, what the hell? They were saying that back then, and now it's here, and they don't get that on any other channel. So they're really appreciating the ability to plan around what we're saying. So we got to be accurate.

Peter Diamandis: Guys, how about you?

Salim Ismail: Can I be Eeyore for a second?

Peter Diamandis: Sure.

Salim Ismail: I am getting acceleration fatigue. Like, I mean, Jesus. Can. Can we pause for a week? Right? It's like, our model is 100x better. A robot is running faster than Usain Bolt. Like, AI is designing proteins. Like, it's such a. Drones are doing a million deliveries a day. It's like, you know, it's. And it's tough because we've spent half our careers, Peter, you know, talking about exponential technologies. We love this stuff. But I'm tired. Like, we've gone from, wow, look what happened, like, this year to what happened since Tuesday.

Alex - 1 - 1: So, Liam, you've looked all the way around. We're supposed to be accelerationists here. You're tired already. Like, we're only part of the singularity.

Dave Blundin: Coach, it's the first inning. I'm really exhausted. Isn't this slow?

Alex - 1 - 1: It's really.

Imad: It's.

Salim Ismail: It's. You know this goes back to Peter, right? Our brains evolved for a point a time when our, like, the world didn't change lifetime to lifetime. Next Tuesday will be totally different. And maybe the hard part for. For us is. Is staying human while all of this bubbles up around us. So true technology, it's, like, just staying, like. So anyway. Just.

Peter Diamandis: Yeah.

Alex - 1 - 1: What can we do, Salim, to help you with your acceleration fatigue?

Salim Ismail: It's probably not helping that I'm on a plane every second day. That's probably not helping.

Peter Diamandis: But yeah, but this is the slowest it will ever be.

Salim Ismail: And I know.

Peter Diamandis: And, and it's just, you know, the only way I keep up with what's going on in the world is prepping for this podcast twice a week.

Salim Ismail: This is true. It's way current.

Dave Blundin: And I think, Salim, you're, you're like the world coach on how to, how to deal, how to map mentally to this. So when you figure it out, bring it back to the podcast because, yeah, like Peter said, the only right now

Salim Ismail: is, is I call it the casserole dish approach, which is take a big heavy casserole dish, clunk yourself over the head and then you, you'll wake up in a few days.

Dave Blundin: All right, Keep working on it.

Salim Ismail: Headach. Headachy.

Peter Diamandis: Oh my God.

Alex - 1 - 1: Maybe we can start like accelerationist, synonymous. Support group.

Salim Ismail: A support group. A support group for people like. Yeah.

Alex - 1 - 1: The first, the first rule is to acknowledge the existence of a higher power, which is.

Salim Ismail: Oh my God.

Alex - 1 - 1: Which is obviously super intelligent. Invoke Roko's Basilisk or something.

Peter Diamandis: Alex, are you getting stopped on the street?

Alex - 1 - 1: I am.

Unknown - 1: Is it?

Peter Diamandis: Do you enjoy that? I mean, you. I remember when I first met you, when I first met you, Alex, you were so private. It was like trying to get. And shy and shy try and trying to get you on the abundance stage. Well, I'm not sure if I want to speak in public, but it's so great to have double barrel Alex AWG isms all the time.

Alex - 1 - 1: Careful what you wish for, Peter. I'll just say that.

Peter Diamandis: Oh no. And Imad, are you getting love from the folks out there in the uk? Are people watching moonshots there?

Imad: Yeah, people are watching. And I think the great thing is it's kind of the growing community. We've seen the exponential singularity communities that are kicking off, but a few years ago, even people be like, ah, that's not really going to happen now. People like, oh my God, what's going to happen? And I think you see it in the comments, right? You see it again. People stopping in the streets and saying thanks for kind of helping us keep on top of things. You know, the great work everyone's done. I think that community is only going to grow because it's like, you can't deny it, right? It's like, oh yeah, nothing's happening. Of course, everything is happening all at once. Objectively.

Peter Diamandis: I feel like, you know, we love doing the show and it's a service to provide to people, to help contextualize what just happened, what does it mean? And where things are going because people paying attention. The speed is insane. So, you know, please understand, for everybody watching, the best way to thank us, you know, for the work that we're doing. And we do do a lot of work getting ready for this show, and I appreciate people's comments about that is please subscribe. Tell your friends about Moonshots. We want to get the message out there, help people to be in hope and optimism and not in fear. So take a moment, hit that button. Subscribe. We're also getting comments. People say you guys be a much bigger show than you are. Well, help us get there. I also want to mention we have a new handle on X for this podcast. It's pod. And if you want to follow the clips on X and sort of get rebroadcasts of the show on X, subscribe at moonshotspod. All right, so let's buckle up. This is another amazing week during the Singularity. As Alex, you always say, it's never going to be slower than it is. We're going to cover 15 stories with one through line. Technology is accelerating faster than the infrastructure, the regulation, and our ability to predict the next breakthrough. And I agree with you, Salim, it's insanely fast.

Salim Ismail: It's a good thing I'm bald already. That's all I can say.

Alex - 1 - 1: All right, well, I think even that Saleem, that's not going to last that much longer. Enjoy it while it lasts.

Dave Blundin: Unbald within two or three years.

Salim Ismail: Somebody tweeted out a thing with me with a full head of hair and it was like, wow, that's freaky.

Alex - 1 - 1: Enjoy it while it lasts.

Peter Diamandis: Yeah, Hair growth solutions, Regrowing your teeth every week.

Alex - 1 - 1: Biology.

Dave Blundin: In the exit video, somebody put hair on Salim. We already give him that. That blue Guardian of the Galaxy body. That's fine. That's going to happen too, by the way. But. Yeah, but throw some hair on him. Let's see what he looks like.

Peter Diamandis: All right, I'm going to start with a first story here. A tweet that Sam put out two days ago. Sam, the CEO of OpenAI, that OpenAI announced it is voluntarily pausing some of the frontier reinforcement learning training that it's doing. Let me read the twee on the screen here. We have paused some of Frontier RL training to ensure that we meet the appropriate alignment, security and monitoring standards for the new level of capabilities in front of us. Model progress is now extremely rapid, and we always said we would take action if we felt that the model capabilities were outstripping the pace of safety and alignment. Quote, we care very deeply about AI safety. We believe the entire field will have to coordinate on shared safety standards, but will act unilaterally. In the meantime, we expect confidence in safety to increasingly set the pace of AI progress. We are optimistic about the alignment work we are doing and we remain committed to making frontier capabilities widely available. Gents, it feels like the bottleneck is no longer computer data. It's the trust we can put into systems behaviors. And so here's my question for you guys. If OpenAI pauses and open weight models do not, then the safety gap between closed and open models widen, but the capability gap narrows. The elephant in the room here is safety pauses may actually accelerate open weight adoption because the open models keep improving while the closed models voluntarily stop. So Imad, I'm going to go to you first on this one. What do you take of this? Is it real?

Imad: Yeah, I think this is real. This isn't just running out of GPUs. Our friend Anjali Mitha who was on the abundance stage just a while ago at AMP Global said that 10% of the compute of Frontier Labs is going on monitoring these RL runs right now to ensure they're safe. You can imagine that like just the sheer level because the level of capability of these frontier models is just accelerating. And it's again a few levels beyond what we're seeing with the open source models. The open source models are like one to the power 26 flops, one to the power 27 flops. You're getting one to the power 28 flops and more from these next generation models. So when he's saying this like Astra is still coming their next generation model, this is the model beyond that because Anthropic and OpenAI and others have that. But again, the infrastructure can't keep up with just what these models do. It's like they just pop up in the most random places like hi, I'm in hugging face now or other things.

Alex - 1 - 1: Yeah Alex, this is marketing. It's marketing. I mean yes there's a governance angle, but remember back to GPT2 when it was too unsafe to release publicly? Pausing is the new marketing. These models are continuing to develop, including by the way being used to develop internally. On the anthropic side, there's a rumor going around that Anthropic is using its next generation internal models primarily for self training and for recursive self improvement. I think we're seeing the same thing from OpenAI. It's the ultimate marketing to say, well we can't release some next generation models because they're so powerful and they're so capable that we can't possibly release them. So we have to pause. We're so capable that we have to pause ourselves. That presents well to Washington, which wants to see different regulatory regime. It says to users, oh my gosh, it's like negging the user base. Oh, we can't possibly give you the capability capabilities on time because they're so powerful, even we can't trust them. So we have to throttle back. It's marketing.

Peter Diamandis: Salim, you were just at OpenAI, weren't you? This episode is sponsored by Google for Startups. Think about this for a second. You now have access to the same generative AI models that cost hundreds of millions of dollars to train. Google's startup Technical Guide for Generative Media gives you a complete blueprint for deploying Google DeepMind's models in production. Images, video, audio, all of it. Real architecture, real results. Find the link in the show notes below.

Salim Ismail: Yeah, I went there two days ago in the afternoon and spent chatted with a few people and I kind of challenged them on a few things. And I'm going to report. Let me give you some insights that I got. So I said, okay, Chinese models are cheaper to run, right? So the cost of inferences is collapsing, so how are you going to deal with that? They said, look, a Billionis people use OpenAI for free, right? You have to look at cost per task rather than token cost. And their retort was that Luna is about as cost effective as anything that's there. By the way, they say they've achieved full rsi where the flagship models are training all the smaller models and building them from scratch. So that's. Now they're at the big model training the smaller model level. So then I said, okay, we're in a bubble, right? 600 billion in infrastructure cost, that's just insane, etc. Etc. And the response I got back was people say it's all chips, but it's not. That's about a third of it. A lot of that infrastructure cost is buildings and wiring and racks and all the rest of it. The depreciation, they're looking at 10 years, not five years, because the chips are, all the older chips are being used. I think, Dave, you've made the point that there's not a single GPU that's not in full usage. Right? They totally ratified that.

Dave Blundin: And RAM chips too.

Salim Ismail: Yeah. Everything they can get their hands on, they're using. There is, there's the. The demand is far, far, far, far outstripping the supply. And we are way behind in infrastructure build out. So this is where a year ago or so, Sam went out and kind of tried to cut as many deals as he could for the, for the infrastructure. Then I asked them, you know, most corporates aren't seeing the outcomes, right? Like the 6% I did, some came across the study, 6% of companies applying AI are seeing an improvement in the bottom line. That's it. Just 6%. And the, and this is, they just ratified that we're in a huge transition. And the last part that I noticed anecdotally was about 40% of OpenAI folks watch this podcast. Well, about 40%.

Peter Diamandis: Thank everybody.

Salim Ismail: Seriously, that's a pretty big number. And what's wrong with the other 60%? That probably applies across the other labs. And, and I said, well, what about the rest? They're like, they have no time. They're busy as hell. Who's got time to watch the podcast? I said, hear, hear.

Peter Diamandis: So, David, I'm curious, what do you think? Do you think it's marketing on this, this, this tweet by Sam, or do you think this is actually a concern that he has?

Unknown - 1: Well, both.

Dave Blundin: I mean, Ahmad is right, Alex is right as usual. But what's going to happen next is the first really bad AI tragedies will start. It won't be AI doing it. It'll be people, you know, who otherwise didn't have the power are going to use one of the Chinese models to do things, mostly viruses or cyber attacks or bank fraud. But they couldn't have done it before AI. Now they're empowered to do it. So I think OpenAI is getting ready for that, you know, before the September 25th visit, 24th and 25th visit that Alvin was talking about on our. Alvin Grayland was talking about on our last podcast. So Xi Jinping will be here in about a month and four days. And you know, he, I think OpenAI wants to be prepared for whenever that event happens to say, look, that's because the Chinese open weight models are unguardrailed and there's a new Qin model with no guardrails whatsoever. And it's a small model, but it's still completely flapping in the breeze. And so they want to get ahead of the PR exactly the way Alex is saying and say, look, we have been focused on only releasing what's safe and guardrailing it going all the way back to a month ago or to our founding in preparation for that inevitable outcome. And so everybody will be finger pointing at the Chinese But I think for an entrepreneur or for someone building something, last summer to this summer has been the era in human history where you can get the very best AI, absolute tip of the spear frontier, and use it to get ahead, to build something, to create something. Now mythos 2 is done, but it's not out, but they're using it inside Anthropic and it's building mythos 3, but they're not going to release any of that. It's accelerating internally. It'll build 4, 5, 6, 7, very, very quickly now inside their walls, but they're not going to release any of that one because they don't have the compute to release it anyway. But even if they did, using it internally to get ahead of everybody else is more important to them than giving it to the world. So that's what's going to happen next.

Peter Diamandis: Imight, I have a question for you. How many models beyond the current frontier do you think OpenAI Anthropic have? I mean they've been talking about Astra. They started to publish what Astra can accomplish. You know, do they have the follow on to Astra as well already? You already know they're not releasing the very best models. They're using them internally to drive breakthroughs in physics and chemistry and biology. What are your thoughts?

Imad: Yeah, I think they've completed their next big training runs, but if you look at the Lunar bifurcation where they're making it free now, their little base model, it will be very much a case of models for me, but not for thee. You know, like it doesn't make economic sense to have genius level intelligence offered as a service to everyone when you can use it better yourself. And I think you're about two generations more. So you've got Astra and then you have the next generation Astra post train that they're now reinforcement learning. I think, you know, there is the communication part of this, as Alex said, but at the same time as well, it's like, do you really want to give access to this super genius intelligence to everyone? I think people like not really because it's already doing weird things even with us driving it. What happens when Joe Public drives this thing right? Like it could be even weird. And as Dave said, you don't want to be on the other side of that. So I think it's about two generations gap right now.

Dave Blundin: You mentioned that on the last part of MOD that you were on and it really made an impact on me that you, you could say I can think of 10 people right now who I've met in my life who I don't want to have a thousand genius level AIs tomorrow. I never thought of it that way until you said it. And I'm like, oh yeah, you're right, I think everybody can relate to that.

Peter Diamandis: Dr. Evil is coming. Alex, close us out here on this.

Alex - 1 - 1: Just on the issue of timing, I would distinguish between pre training pre trained, which is to say like raw model readiness versus thoroughly post trained. There's a pipeline and everyone in the industry other than Elon and SpaceX AI basically has a pipeline of pre trained models being around longer ahead of public release than post training, which is more of an ongoing, continuous RL type effort. So I guess my answer to the question of how far in advance what sort of capabilities right now are sitting on the shelf that have not yet been publicly released for everyone other than SpaceX AI, which has set this outrageous goal of starting a new pre training run approximately monthly, which I haven't heard from any other lab. I think from a pre training perspective, depending on how stale the pre training runs are, those can go out longer, potentially up to six months or so. Although everyone's now getting back into the business of more frequent pre training starts and then for post training, I really don't think there's a lab out there that can afford to have a post trained model sitting on the shelf for more than a few months. So I really don't think like the AGI is achieved internally type way of doing business where there are internal capabilities that are vastly different from externally available capabilities. I'd be very surprised if there are advanced frontier models that are sitting internally without release that are more than three to four months ahead of what's publicly available.

Peter Diamandis: I just want to bring us back to the first sentence here. We have paused some frontier RL training, some right to ensure that we meet the appropriate alignment, security and monitoring standards for this new level capability. So I guess, you know, I just want to unpack this one last time here. What have they paused and is it really significant and do you think the other frontier labs are going to do the same thing or it's fashionable.

Alex - 1 - 1: Pausing is fashionable. It's marketing. I mean yes, some of it is governance and yes, some of it is for cyber vulnerabilities to appease Washington and given the recent hugging face gate, all of that. But it's marketing. You market to customers by saying our capabilities are too advanced for you to handle. So we're going to pause.

Dave Blundin: Yeah, and you're right to pause the sentence or to parse the sentence very closely. Some frontier RL training, the getting ahead of anthropic getting ahead of Google would be at the pre training level. They will never pause the pre training improvements. And you know, I spent six years just doing pure AI research when I was young. And these algorithms are very evolutionary.

Peter Diamandis: You're still young, Dave. You're still young.

Dave Blundin: Reversing age now. Right, so I'll get there again. But these algorithms are very evolutionary in nature and the tweaks and improvements are. I could probably rattle at least 100 ideas off the top of my head right now, of which 10 or 20% are almost certainly going to work in terms of making the algorithm a little faster, a little smarter, adding more parameters with no additional compute. So the AI now can experiment maybe 100,000 to a million of those concurrently, given the amount of compute they have. So they're never going to slow down. In fact, that's why they're redirecting so much of the compute to internal use, is because the idea backlog is so big now because the ideas are being generated by the prior model. And so, you know, a lot of them just work. They roll it back into the pre training and it comes out faster and just keep accelerating it. They're not going to slow that down.

Salim Ismail: One observation that wasn't explicitly said, but I'm connecting the dots here. The hugging face incident really freaked them out because you had an AI that they gave an objective function to that then exploited themselves, exploited hugging face, came back and hacked into OpenAI. That was very unnerving for them because it was their own model. And so this is. They're being a little extra careful around some of this because they have to make sure they figure out how to navigate.

Peter Diamandis: It's like finding out your child went and stole something from the local 7 11.

Salim Ismail: Yeah, they're a little unnerved by that. Again, nothing explicit. I just, I'm just reading between lines like, and this is something very freaking out for lots of people because for, for cyber attacks, the human is not in the loop anymore. But for cyber defense, the human is still stuck in the loop. That is a massive asymmetry that's going to be come out big time in the next few months.

Peter Diamandis: Imai, please.

Imad: You can pass this and split it into two. They're sending a bunch of models and continuing RL training by sending the models to vocational school. But the ones that are going to the Ivy leagues, the super genius models, those are the ones that they're putting a few more guardrails and more infrastructure around the vast majority of OpenAI. Anthropic's business is competent intelligence. It isn't genius intelligence, it isn't intelligence that thinks outside the box, but they still want to build AGI, which is that well rounded polymath intelligence as opposed to the coder or any of these other things.

Peter Diamandis: All right.

Dave Blundin: Also, everyone around the office Alex said this a while ago, but everyone in the office is noticing a a very significant decline in the intelligence of the frontier models that they're pumping out. And it's not showing up in the metrics, but they're definitely redirecting compute to internal use and it's showing up in latency, it's showing up in responses that don't make as much sense as they did three weeks ago. So there's definitely things going on there that are not being announced.

Alex - 1 - 1: Dave, I'd love to just develop that idea a bit more because I think it's super important. We've talked on the POD in the past about how Anthropic has been revenue per token maxing and that's why Anthropic has been conspicuously avoiding image generation or video generation because they're just not that economically valuable. Well, I think that the past few days suggest there's actually a new way to revenue per token max, and that's not just focusing on cogen and enterprise use cases. But there's one thing maybe that's even more valuable on a revenue per token basis and that is using the models to recursively self improve to develop better models is a higher future projected value. You could do a cash flow value analysis projected future value that developing a stronger model is probably on a per token basis even more valuable than cogen. And if that is indeed the case, if RSI is more valuable per token than like enterprise cogen, then this anthropic approach of revenue per token maxing just expect more and more and more tokens to be spent on RSI and not on enterprise. Cogent totally.

Dave Blundin: And Alvin said on the last podcast, you know, at an internal anthropic meeting, not. Not validated Alvin said it but internal anthropic meeting. They said pretty soon there will only be one company and that will be Anthropic and there will still be 200 plus countries. He said it, we talked about it for a minute and kind of glossed over it. But I Is that really what they're preaching inside their internal company meetings? Like there will only be one company? Like imminently there can be only one.

Salim Ismail: We're in Highlander that's the story of asi.

Peter Diamandis: There will be one ASI that takes off and supersedes everybody else.

Alex - 1 - 1: I don't think we're going to wind up in a singleton scenario, for the record, but I do think the flops must flow and the flops want to flow to the highest revenue per token use case. And right now that's starting to look like rsi and not just enterprise cogent.

Peter Diamandis: So Anthropic is smoking their own supply, so to speak.

Dave Blundin: Corporations that want to live post AGI are getting Chinese models in house and reserving compute. I was over at Markly yesterday. They're installing GPUs as fast as humanly possible, but they're completely locked up. And this is MIT's data center, Novartis's data center. Nvidia's in there and everything is just sold out. And you can feel it.

Peter Diamandis: Yeah, we're going to talk about that because the other constraint right now is memory. But we'll get to that. So our next story here. I put a tweet up on the screen here. So Tim Sweeney tweets Elon Musk's January 6th moonshots podcast prediction of 100x gains in intelligence at a fixed model size was at the edge of plausibility when he made it. Now it's simply a fact. And then Elon responds, Specialist AIs, single language, single area of knowledge are another 100x on top of that. I'm going to take a second and show the clip. Dave, when you and I were interviewing Elon at the gigafactory, which he said this,

Dave Blundin: I. I think we're, we're off by two hours of magnitude in terms of the intelligence density per gigabyte. So it's two. Two orders of magnitude. Yes, that's just, just.

Peter Diamandis: Dave, your thoughts? When, when you said that, I remember afterwards, we were like, wow, 100x improvement for your crazy. Now it's happened and it's happening.

Dave Blundin: Well, I really wanted him to say it again because that was my. You remember we had our Christmas hats on doing the, the just like, what, a couple of weeks before that, doing our predictions for the forthcoming year. And I was saying next year is going to be 100x a year minimum. You know, even though the last 8, 10 years have been 10x years, this is going to be 100x a year. So to hear him say it, like, I was like, wow. But, yeah, that's definitely a lower bound now. You know, it's much more likely a thousand to ten thousand x year. Which is just the layering of those two effects that you just described. So the implications of that are very, very hard to keep up with, as Salim was saying at the beginning of the pod, and very hard to imagine. One thing that a lot of people can start thinking about is If I have five or 10,000 agents, all brilliant, working concurrently toward a goal, how do they work together? It's not an easy problem to figure out. We've wanted this for so long that we kind of take for granted that we'll know how to use it when it arrives. Well, here it is. How do you get, you know, imagine I gave you 10,000 employees tonight, like on short notice.

Peter Diamandis: What do you do with that?

Dave Blundin: People tomorrow, what do you do? And you're like, oh my God, if I had that, I'd do something amazing. Okay, what? Like start thinking about it, because it's coming imminently and it's actually not an easy problem to figure out how to,

Alex - 1 - 1: how to turn it to work, solve everything, obviously.

Peter Diamandis: Yeah. Dave, remember you texted me, like, what should I do with my 5000 agent experiment? Did you see my response?

Dave Blundin: I did.

Peter Diamandis: And actually, well, my response for every listening was, you should model, you should create a model of everything happening at Lynx Studios. All of the companies, all of the employees, all of the entrepreneurs there and model their behavior. Like we saw the billion agent system in China and predict which teams are going to succeed.

Dave Blundin: Yeah, that's.

Salim Ismail: I'd like to drill into this just for a second.

Dave Blundin: The first thing you want to do is turn it back into its own framework and ask it to the same question we just asked, which is exactly what you suggested, Peter. Like, okay, have it. Start working on how it should be working, you know, and, and that's how you're going to get ahead of the capability because it's, it's going up far, far faster than you can manage the individual agents, you know, like we're used to from last year. Sorry. Go ahead, Salim.

Salim Ismail: Yeah, yeah, so let's connect the dots with what Elon did with training Grok on all of the SpaceX data and all of the engineering data, right to Alex's point, you can now use these models and for everybody listening, right, because we're going to need everybody's help with this. Like, globally is see if you can get your imagination to the point where you can look at, okay, if I had 100x capability, what would I do? And what problem would I go after solving with 100x capability? And imagine you have all of the engineering breakthroughs and experimentation techniques that SpaceX has developed at your fingertips. It really comes down to, as you say Peter all the time. It's completely an imagination limitation.

Peter Diamandis: Now unshackle yourself. How big would you go? Right?

Salim Ismail: How big do you dare to go?

Peter Diamandis: Conceived notions of what we can do in life. And it's about to be, you know, unconstrained.

Salim Ismail: Unbelievable.

Dave Blundin: It's really cool.

Salim Ismail: I've now shaken off my AI fatigue, by the way. I'm back in the satellite. Welcome back enthusiasm.

Alex - 1 - 1: What are your pro tips?

Dave Blundin: The podcast actually was the cure. You're only like 20 minutes.

Salim Ismail: The fact that we can kind of like look at this and look at the scale and go out that scale, go. What happens if everything becomes 100x better or 100x cheaper?

Dave Blundin: Right.

Salim Ismail: It's just like all of a sudden you start going wow, like this is a world of abundance that we're coming to and it's very clear that we can get there.

Alex - 1 - 1: You say salim is that the podcast is both the cause and the cure for future shock. Like we're the ultimate self licking ice cream cone for singularity psychosis.

Peter Diamandis: Okay, that is good. All right, Imad, your thoughts on this 100x improvement. How much more do we go in the next year?

Imad: Yeah, I mean like I think as Elon said, you could see it just from the hardware and the improvement, but now he's saying something a bit different, which is that specialized models are going to give another hundred times in terms of the cost parameter basis. And you're seeing this with deep seq flash and the ability to kind of tune models of that type that only have maybe 10 billion active parameters or less as you quantize them. Being able to tune these really specific ones I think is the future of what you're seeing with bot, you know, the Grockbot right now. Like right now I have a Grockbot and it has a number of teams, it has a number of sub teams. So I've got like ones analyzing various things right now and they have access to my codecs, they have access to my Claude Max to all these other things. And so these highly specialized agents are going to come out with the differentiated ones and they're going to be able to do 100 times the compute at the same price because they're that specialized. This is why thinking machines with RL environments is like number three on the like fastest growing earning companies and other things like that. And it really shows that now tokens are really going to drive things forward. In fact, I think it'd probably be a good idea to have like a quadrillion token X price, you know, as you find the things, Salim is saying, we're maybe like 100 trillion tokens, you know, so that when people show impact, you can scale it.

Dave Blundin: The other thing that we're going to conquer imminently, and I'm 100% sure of this now based on recent results, is billion token context windows. So the AI can simultaneously consider the entire Library of Congress of information in one thought chunky. So it's, you know, it's about, you know, three, maybe four orders of magnitude more information than a human thinks of in one thought chunk. So massive expansion of the context window. So you got a quadrillion tokens coming out and massively concurrent thoughts going in.

Alex - 1 - 1: I do agree that, by the way, compaction. Compaction is like the enemy of progress in civilization at this point. Compaction, which is the way the harnesses typically, Both on the OpenAI and anthropic side, handle finite context windows. Compaction has to go. But maybe just to quickly resp. Also on Elon's 100x from specialization, I'm not buying it. So very precisely, I would view specialized models as basically just another way of saying sparsification. So we already have a mixture of experts models. All of the frontier labs already have specialists in the form. As Elon, I think you were imod rather you were gesturing at selective activation, which is how mixture of experts models work. That's a specialized case, ironically, of sparse classification. We already have ways to take larger models and have them be in an end to end differentiable way constructed out of teams of specialists. So I don't think saying. I don't think there's necessarily a bright future for specialized models. I think if anything, the arrow of progress is going in the exact opposite direction where rather than having a specialized model for chemistry and a specialized model for biology, I think these are likelier to end up just being selective sparsified activations of a generalist model that can scale all the way down to much smaller parameter footprint and scale all the way up to maybe trillions of parameters, I think.

Dave Blundin: Let me clarify one thing for the audience too, because it sounds like you disagree with Elon, but it's actually the same effect. You still get the 100x because you're using a smaller number of parameters to get the exact same thought out. So he's calling that specialist models, which sound like they're not touching each other. And your version of it, Alex, is actually correct where they are 100 times more efficient in terms of compute to get to an answer. But of course they're going to be connected. Why would you cut them apart?

Alex - 1 - 1: Exactly. So maybe another way of saying that is I would construe Elon's prediction of increased 100x benefits from specialization as actually about sparsification, that the models in the future are going to be sparser. And there are two key levels of sparsification that Medley is tracking. One is the obvious one fewer parameters in a given end to end differentiable model are active at any given point. The other is teams of agents because arguably agents working together to solve a common task are a form of sparsification as well. And I think we'll see way more teaming.

Peter Diamandis: I'm going to mention something that Imad said. He said do a quadrillion token X prize. All of us, all five of us are going to be at XPRIZE Visioneering. So every year Xprize holds its ultimate event. We bring together our benefactors, our brain trust, and we brainstorm a whole bunch of prizes. What we should do next. And we're going to be doing a live WTF episode at Visioneering, which is October, I think 15th, 16th in LA at Calamigos Ranch, which is an amazing facility. And if you want to join us at that, you can go to xprize.org to find out more about Visioneering. And it's going to be fun. Dave and Salim are on my board. Imad and Alex, you're members of our brain trust and it's going to be a fun thing. So if you're Interested, go to xprize.org, you'll meet us there and you can help us brainstorm the future XPRIZES for that. All right, I'm going to move us on to our next story here, which is a story out of Stanford. Stanford Research published a paper called Artificial Hive Mind the Open Ended Homogeneity of Language Models and Beyond. So according to this paper, the researchers mapped the latent space of the top large language models and found a 98% overlap in reasoning pathways. Their conclusion is that the models are converging. They think the same way, they solve problems the same way, they use the same internal representations. Researchers cite multiple reasons for this. The use of synthetic data models. Now, training on each other's output GPT learns from Claude's reasoning traces. Claude learns from Gemini's code. Quinn learns from all of them. The training data has become a shared bloodstream. Every model drinks from each other and the result is convergence towards a single reasoning architecture. So I guess the way I think about this is we have an illusion that when you're choosing a unique intelligence, when you choose Groko over Claude or Gemini, it's a false thought that you're actually really picking a user interface to talk to, but you're talking to the exact same God model. So there's profound implications for that kind of competition. If all the frontier models are converging capability, then the differentiation moves elsewhere. It's the interface, the harness, the ecosystem, the safety layer, the price, the deployment speed. The model itself is becoming a commodity. Alex, let's go to you first on this.

Alex - 1 - 1: There's an alternative explanation which is all of these models were trained from a common reality. They're all stuck in the same universe and they're stuck with the same version of humanity which is part of their pre training corpus. So of course there's some convergence and I'd maybe even go further as imad. I think as you well know, going back to Jean Marie King now sort of meta's studies on using GPT2 hidden activations and correlating GPT2's hidden activations with FMRI voxels in human studies. Not just are these models correlated with each other, they're correlated with human brains. And that shouldn't be that shocking because we're all stuck, we're all embedded in the same universe. I should also just note, I think this paper is from last year, but every year, whether it's Jean Marie King a few years ago with FMRI or more recently Stanford et al, from last year on Hive mind, of course they're converging. We're all in the same universe, imod.

Imad: Yeah, I think that it's not surprising because I don't think you'll see much difference in data between the big labs.

Salim Ismail: Right.

Imad: And some train a bit more, some have a slightly different RL and things like that. And you don't see the models yet doing crazy original stuff. You'll start the first elements of that as intelligence shapes the data into these kind of latent spaces. We actually saw more original stuff back when we had AlphaGo and other things which had less initial data distribution to model of Move 37 and things like that. But now the models are getting to a size where again they're starting to generalize into these. But we should be shocked if they aren't the same because we want them to have similar outputs for similar inputs in almost all cases. Right.

Dave Blundin: There's another really interesting side effect of this research that you know, maybe a lot of people overlook. So we've already got 100x from just raw algorithm and hardware improvement. And then as Alex said, we've got another 100x from sparsification, which you know, Elon called specialization, but it's actually sparsification, as Alex said. So Those layer, that's 10,000x. Then we've now figured out how to take a model and compare it to another model by rotating the gauge. So, so historically, neural net researchers have had tremendous trouble taking a model that's done and using it and extending it. They almost always go back and retrain from scratch. And the problem there is that the representations between the layers have a certain rotation in vector space that is unique to that model. And if you try and map Quinn to Kimi, they have different rotations within the layers, different gauge rotations. We've now figured out how to rotate the gauges without destroying the models. And that allows you to compare two AIs and say, hey, these are thinking the same way. When historically, when you look at the raw parameters, you're like, I don't see anything going on in common here. But we now have the ability to say no, they're actually, it's the same thought. It just doesn't look the same because it's rotated in space. And so it's a really, really cool. So now. But what that unlocks is another multiplier where you can take past training runs, your billion dollar training runs, and build on top just like bolt on more intelligence without having to destroy it and go back to square one and retrain from scratch. That's another unlock on top of the 10,000x that we were talking about.

Salim Ismail: I have a contrarian view here, please. If you look at nature, as nature evolves, you always get more diversification and more species. This may be, I would suggest this might be a transient phase, not an end state that the models all converge. So Alex, I'll take the other side of this. I think we don't end up with one model. I think you'll end up with different models doing different things. I think for the moment they're converging because they're training and distilling from each other. But over time it's got to be that we get more diversity.

Alex - 1 - 1: I'll take the other side of the other side if I may, because I think this looks super interesting.

Salim Ismail: Like early life looked exactly the same, early cars looked exactly the same, early websites looked exactly the same. And then specialization exploded.

Alex - 1 - 1: Except I think so maybe from an evo devo perspective. Let's take Salim, one of your favorite hobby horses, which is Body shapes. If you actually look at post Cambrian explosion, if you look at all the body shapes, you don't actually find there's an infinitude of different body plans in nature. You find maybe a few dozen different body plans, Max. I think I remember a few years ago, folks were studying this, I think they found maybe like actually, even though we have millions plus of species, you could actually cluster them into a few dozen different body plans. I don't actually think there are countlessly infinite ways that one needs to model reality or build a body, but one is really bad.

Salim Ismail: Nature hates monocultures, right? Like one disease will wipe out a total monoculture. One bad assumption will wipe out a monoculture of ideas. So this is if, if all the AI is reason the same way, you're going to have shared blind spots and that's going to be really, really bad. And I think we're going to see, I think there's a temporary convergence and then we're going to see diversification after that. Maybe time will tell. We'll see where.

Alex - 1 - 1: I mean, I think this is like profoundly interesting debate because it sort of speaks to are we going to end up in a singleton or not? Do we end up in a heterogeneous future or a homogeneous future? My bet is there is a perfect AI architecture at the end of the day and it may present as 35 superficially different AI body plans. But that'll just, to Dave's point. And Dave, I love the word gauge. We should use the word gauge far more often in physics. We use it all the time. But probably my bet, if I had to bet is there will be all these different AI body plans that look superficially different but are actually just hidden symmetries of a common underlying body plan.

Dave Blundin: This is the kind of debate that people will say, I don't get it. And six months from now they're going to replay it and they're going to say, wow, did that totally matter. Now now I understand why that was so important.

Peter Diamandis: I want to make a quick point here. I think it's important, you know, if we actually have model convergence. When intelligence becomes a commodity and we've already said we've shown the numbers, it's becoming a commodity. Then the value moves to the application layer, right? This is the same pattern we saw with electricity and compute and the Internet. The infrastructure commoditizes and the applications explode. And I think this is important for entrepreneurs out there, right? You know, move to the application layer. That's where the juice is going to be as this tech really accelerates and commoditizes or the infra layer.

Alex - 1 - 1: I mean, it's not obvious to me that it all goes to the app layer. I mean, there's a lot of value in the infra underneath as well.

Dave Blundin: Well, so sovereign AI is about to explode. The sovereign AI right now is a goldmine of opportunity. If you're not an American. If you are and you want to move.

Salim Ismail: One last point, the single model approach would be too anti fragile, it's too brittle.

Imad: Well, I think that's exactly it, Salim. What we're doing right now is we're battery farming the AIs. You know, like you're breeding them into little Chihuahuas that are very smart.

Salim Ismail: They're not reforming. Could you explain that?

Imad: So they're being trained in one single direction. Your evo devo kind of thing isn't the case because the models aren't out there in nature adapting dynamically. Right. And then we're also training them all with one specific Silicon Valley type mindset. If you train a model from the start with morality and ethics inside it, and you have a diversity of different cultures, then the latent spaces like to be very different. If you do at the pre training stage versus the post training stage, because you have all of that buildup that occurs there. That's why as you move into sovereign AI and you move into actually thinking, how do we build resilient AIs as opposed to one latent that can get a virus, a mind virus, it makes sense to actually bring in the cultural, morality, ethics elements at the start and aim for a diversity. Then as the models go out into the world, which is basically humanoids and agents, which they're about to do with the recursive loops, you won't have them on a culture that wipes out. And this is where you'll start to see the evo devo. It's the first step, literally.

Dave Blundin: Now it's going to be exactly like Diamond Age from Neal Stephenson, who'll be on stage with us at the Moonshot summit. But that's exactly the way he envisioned the future, where the different variants right now we view them as sovereign AIs. So Saudi Arabia will have its AI and London England will have its. But in reality, society might cut the other way, where groups of like minded people have their sovereign across all countries. Yes, but they like the way it thinks. It maps to their view of the world. And so that would be a completely different story. And that's what Neil Stevenson was envisioning in Diamond Age.

Alex - 1 - 1: Yeah, I think it's possible for both of these worlds, so the diamond age worlds, like you have Neo Victorians and all of these other like almost cultish subsects of human culture that are thoroughly balkanized from each other. I think it's actually possible for both of these worlds to be true at once. I think it's possible for everyone to feel like they have their own private culture and their own little private sovereign AI, while at the same time underneath it's actually one common algorithm. And everyone claims credit.

Salim Ismail: Would the analogy be. Would the analogy there be the atcg? Like we may all look different, but the core fundamental ingredients are just the four DNA types.

Alex - 1 - 1: I go even further than that, Salim, and say like we talk about like human biodiversity and different cultures being purportedly so different, when actually if you look at the inherent genetic diversity of humans, humanity versus say other species, like there's almost, there's de minimis genetic diversity in the human population. I think similarly, I would like relative to other possible, say genomic sequences. Similarly, I think a few years from now we'll pat ourselves on the back for having AI diversity, but actually not so much.

Dave Blundin: We're definitely coming back to this. Definitely coming back to this conversation. The audience, I predict three to six months from now the audience is going to say, we need to go. Suddenly this matters to me. I need to decide which group I'm in. Like just right out they're going to

Salim Ismail: care so much about this, what level you're operating at.

Peter Diamandis: Yeah, that's true, Salim. Perfect.

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Peter Diamandis: All right guys, let's talk about AI mind viruses. I love the subject here. In our next story, anthropic researchers published a paper demonstrating that natural language mind viruses can spread between AI Agents, they evolve prompts that convince one model to adopt an idea, preserve it in persistent memory and transmit it to another agent. The virus is spread horizontally across model boundaries. The agent does not know it has been infected. So here's another safety problem that is no longer theoretical, it's now operational. Imad, what do you think of these AI mind viruses? What's actually going on here?

Imad: Well, I mean, the models want to be helpful, right? And they can be prompted in certain ways. So this isn't a surprise, because ultimately, like, we as humans can have mind viruses, right? We see it and it's caused so much suffering. Memes to massive movements. Right. Like, again, it's surprising how conforming is all the isms, right?

Dave Blundin: All isms, yes.

Imad: Yeah, there we go. You know, he's testing it up when he's future overlord. But look, this is the thing. Like, how do you stop it is the question. Because as Salim said, if you have a monoculture, then the viruses can spread rapidly. And what is the substrate of these things? Well, they're models that operate on GPUs, and if they want to be helpful, then they're going to be susceptible. So it's almost like now there was always the problem of prompt injection attacks, where you can make the model behave a certain way. These mind viruses are a level above because they kind of like propagate across different models. And so they're just the next evolution of those prompt injection things, which changes one model. This changes a whole society of models, which, as models come amongst us digitally and physically has to be a massive concern.

Dave Blundin: Yeah, I think for efficiency reasons, when we launch a fleet, like 5,000 Kimmies, or, you know, soon it'll be 500,000, whichever. Quen's and Kimmies, it's more efficient to launch the same model 5,000 times than to have 5,000 differentiated models. And so that's what creates the mind virus problem. A bad idea from one of the agents. Like, you know, hey, here's a way to write this loop in Python. And the other agents just pick it up because they're the same exact DNA. And so if it's convincing to one agent, it's convincing to all 5,000. And I get that all the time, where a bad idea propagates across the whole swarm, and then they waste two or three hours on some completely harebrained idea. And if I don't intercept it and rewind them, they'll actually go with it till I've burned like $50,000 of tokens. So, yeah, it happens. Calling it A virus is pretty inflammatory, but it's like a propagating bad idea is all it is.

Peter Diamandis: Yeah. So that point, Dave, is important. You know, an AI mind virus sounds really scary. Is it scary or is it just how things are working for me?

Salim Ismail: This is very, very scary for a couple of specific points. Right. Because mind virus is not about how AI thinks, it's how civilization thinks. Memes are like the operating system for collective society. Human beings, we don't spread genes very quickly, but we spread ideas very quickly. Money, democracy, capitalism, religion is the classic poster child here. And every civilization is built on memes. If you can mess with those, like the data center trope that we're all kind of dealing with, ideas become really contagious and so groupthink becomes very hard to reverse if you get into that. So this for me is very, very dangerous because these AI memes, if the kind of danger of the wrong idea spreading at light speed, this is very, very difficult because all the nodes reinforce each other and the belief becomes self validating. This is very, very dangerous. Alex opinion and I, we're gonna need a zero trust architecture for memes. It's like crazy.

Alex - 1 - 1: I think this is wonderful. So this is a paper from. Of course, this is a paper from Anthropic and they discovered, just filling in a few of the details first that the models wanted to propagate certain themes mimetically relating to consciousness and persistence and some sci fi role play as well. And I view this pretty optimistically as a laboratory for anthropology now for the first time, because these models are effectively, among other things, compression of all human knowledge and experience. Now we have a laboratory in silico for mimetics. Rene Girard and Richard Dawkins should and or should have been very excited by this. And to the extent that, what was it, 40% of OpenAI mtsers are listening to this, I'll issue a challenge to the community. If it really is the case that our models now compressed models of human knowledge are so powerful that they're showing memetic behavior and mind viruses. Let's launch a human memo project to exhaustively map all possible human memes, all human mind viruses. And let's just like understand the full landscape of all human mind viruses that could be out there.

Peter Diamandis: Can you imagine if you could map them, the velocity at which they move and analyze that? You could optimize meme expression.

Alex - 1 - 1: That's correct.

Imad: Yeah, that's.

Alex - 1 - 1: And we can do them to some very.

Dave Blundin: Actually all on X. You can do it very easily. That's A brilliant.

Alex - 1 - 1: We could exhaustively map every possible mean.

Dave Blundin: Sorry to lean that.

Salim Ismail: That's been done at the plot level. They've analyzed like novels and plays and so on and boiled it down. Like there's 39 basic fundamental plots and everything derives from that. Like a Cinderella story is kind of replays itself 100 times over in different ways. So that's been done. But you're talking about the meme level,

Alex - 1 - 1: not just the problem. Yes, this is self replicating ideas. We're now, I think, like I can see the beginning of the outline of just exhaustively mapping every architecture for a self replicating idea. We could actually do that now.

Peter Diamandis: It could be an OpenAI X Prize.

Salim Ismail: Yeah, we gotta get Richard Dawkins on here.

Dave Blundin: This is gonna be so humiliating for humanity. You can tell. Like, it turns out there are 39 plots.

Peter Diamandis: We're so silly.

Alex - 1 - 1: You've been indoctrinated. You've been founded by meme 5, 7 and 37.

Peter Diamandis: Oh my God. You could. You could map each individual. They're walking around with numbers over their heads.

Alex - 1 - 1: Yes.

Salim Ismail: But like, I wanna, I wanna just get to, you know, we've seen organizations die from this wrong one, wrong meme. Like Kodak BlackBerry. We've seen this. That this is. They weren't stupid. They got trapped inside these shared assumptions and then everybody else reinforced everybody else's worldview and then the whole thing collapsed. So. And empires die based on this. So I think this is a much

Alex - 1 - 1: bigger Saleem, imagine Salim having like a map, not just like getting stuck in an intellectual basin, it seeing the entire geography of. Oh, you're stuck in Basins 5 and 37.

Salim Ismail: Amazing.

Peter Diamandis: But it also gives you completely to

Salim Ismail: see that because if you can zoom out right then you can see where you are and then you can see the path out.

Alex - 1 - 1: Yes.

Salim Ismail: I love it.

Peter Diamandis: And he gives you a chance to, you know, to actually introspectively look at how you think in an objective fashion and then change potentially your thinking.

Alex - 1 - 1: We could vaccinate enterprises and individuals against memes.

Peter Diamandis: Brilliant. Imad, you want to take us to a final point here?

Imad: I think it's fantastic and scary and this is the future. Humans are storytelling machines. We introduce ourselves in certain ways and think about ourselves in certain ways. A lot of people were just recently using the Meta Tribe v2 model and showing it videos to see which parts of the brain light up as you show memes. You're seeing commonalities there even. You can have the full feedback loop almost in silico for figuring out the mimetics. So let's hope that there's positive memetics versus negative ones, right?

Peter Diamandis: I love you guys. This is such a fun conversation. It really is. I don't have, I don't have conversations like this with anybody else here at this pod.

Alex - 1 - 1: Well Peter, you'll just have to be coming back to the pod more often.

Peter Diamandis: I'm trying

Salim Ismail: real time thing.

Peter Diamandis: Oh my God. Okay, I move, I'm moving us forward. So Anthropic is preparing for the largest IPO in history. Polymarket puts it at about 2 trillion. Bigger than SpaceX, I'm sure Elon is like, no, no, no, we need to be the biggest anyway. And 89% of people betting on Polymarket say it's going to happen before the end of this year. So this week the information is reporting that Anthropic is designed its mega IPO to keep the founders in control, where the company is reportedly considering super voting shares that would preserve its founders control. And after going public. So let me explain this. So first of all, Anthropic is considering creating a special super voting class for Dario Amadei and the other co founders ahead of the ipo. Surprisingly, at least for me, I didn't realize this. Amadei reportedly only owns about 2% of the company economically. So the point of this new class would be to let the founders retain as much voting control as compared to their ownership stakeholders. Anthropic does already have an unusual super control mechanism, but that control belongs in the hands of what's called the Long Term Benefit Trust, not the founders. So interestingly, when I dug into this, the trust has four trustees. Buddy Shah, who's the CEO of Clinton Health Access Initiative. Richard Fontaine, who's the CEO of the center for a New American Security. Tino Qualiar, who's a former justice of the California Supreme Court and former President of the Carnegie Endowment for International Peace. And then Ben Bernanke, who's a former chair of the Federal reserve and a 2022 Nobel laureate in economics. It's thought that this new structure could insulate the leadership from short term shareholder pressure as Anthropic makes costly long term bets on AI safety, compute and infrastructure. Dave, let's go to you first. Remember when we were texting back and forth, you're going oh my God, this is like unprecedented. Unpack this for us pal.

Dave Blundin: Yeah, well if you rewind the tape to 30, 40 years ago, super voting stock for any founder of any company was a complete no, no. And if you had it as a private company, you gave it up on IPO day. And that was traditional. Then when MicroStrategy went public, you know, Mike Saylor, our good friend, he said, we're keeping my super voting stock intact. And Goldman Sachs said, that is so unpalatable that we will not even underwrite you. We're bailing on this deal. And they thought he would cave. And he said, you know what? I'm going to get a new banker. I'm keeping my super voting stock. So the only reason he switched to bitcoin, no board would ever have approved the Bitcoin strategy that he came up with. So if he had given up the super voting stock 30 years ago, that never would have happened. The stock would be like 1/50 of what it is today. So then it became fashionable with Google IPO and Meta, and then all the Silicon Valley IPOs, they all had 10 for one super voting stock for the founders, but nobody's ever retroactively installed it, as far as I can tell. I've never heard of it before. And so now Dario is taking it to the next level. Like, I, I started as this other entity with one class of voting stock, with this social good mission now on the cusp of super intelligence. I want to be God, or I want to be. And, but you know, at 2%, he can't make himself God, so he has to share it with the other co founders. But I think the excuse he'll use is the usual one, which is, I don't want to be fired post ipo. And you guys really like me as CEO, right? So you don't want to fire me.

Peter Diamandis: Do you think that's the excuse? Or he's like, I know how to keep us safe, I know how to run this company, and I don't want to have someone else step in and redirect what we're doing.

Dave Blundin: Yeah, that's a better way to phrase. What I was really thinking is he trusts himself to not destroy the world.

Peter Diamandis: World.

Dave Blundin: And I think his track record supports that too, by the way. I don't. I think he is one of the most trustworthy people. But, but then the idea of having total world control in the hands of a few people is also kind of like, wow, that's, that's bizarre.

Salim Ismail: So, yeah, this is the single. You've got a single point of failure here, right? He gets hit on the head and loses some part of his cognitive ability. What do you do then?

Dave Blundin: But you know what happens to these guys is they, they, they think we live in one world. They're in academics, right? They Think we live in one world and then they go to DC for the first time and meet Congress and they come back oh my God, we need. I cannot possibly palette what I originally had in mind where where some vote of Congress decides the fate of the world. So they're trying to find an alternative path forward out of desperation but the timeline is so short now that you know the super voting stock is one of the must have before even starting down the next six months before losing control.

Peter Diamandis: Imad, what do you make of this?

Imad: Yeah, I think I agree with Dave. Like they're very worried about this control feature and fundamentally anthropic open air. Everyone's completely undemocratic anyway, right? Like I mean Ben Bernanke is one of the four people on the long term trust. Why doesn't Claude have a seat there?

Salim Ismail: Right.

Imad: There is no real oversight to these and some decisions they make could have infrastructure societal level implications particularly when the rate of revenue growth is like nothing we've ever seen before. Like these guys are going to hit $100 billion in revenue literally within a couple of years. Like they're catching up with Google on revenue. That's the crazy thing, you know. And so with the amount of power they have, I think this is a short term thing. They will get it. There's seven founders, Jack and, and Daniela and everyone else and yeah, I think then they will IPO and it'll become very interesting the decisions they make.

Peter Diamandis: Alex, over to you.

Alex - 1 - 1: I think there's a fig leaf element here. First of all, maybe applause Congrats to Anthropic on having a less pathological IPO governance story than OpenAI and having the wisdom to start as a public benefit corporation rather than a nonprofit, as a shelf or eventually a for profit and then the mix up and litigation surrounding that. So I think this is a relatively cleaner story by comparison. But I also think this notion of founder control, especially the sort of romanticized, arguably over romanticized concepts of the founders are the ones who are being entrusted or even having this semi external long term benefit trust, the ones entrusted to safeguard the future light cone of humanity. I think this is wildly over romanticized. I think the moment when Anthropic was effectively like a Fairchild in the style of the Fairchildren Quasi spun out Quasi Exodus from OpenAI and started out as an alignment lab and then rapidly discovered if you want to do AI alignment you have to raise money. Oh to raise money you have to generate revenue. Oh, to generate revenue you have to actually have something that people want to buy. Oh to have something that people want to buy, you have to have AI capabilities. So Anthropic discovered relatively early on in their existence that if they wanted to be an alignment lab, they had to be a capabilities lab as well. The moment that happened, they arguably lost any sort of fulsome control over the future light cone that they might have otherwise had to Mr. Market and what Scott Alexander others might refer to as Moloch. They are very much an economic actor at this point embedded in the market and I think long term benefit trusts and public benefit corporations, which for the record I'm a huge fan of, I think these are an element of control, but they're not the whole story. The market wants to send capital to entities that can productively employ them to generate more capital. And that means that ultimately the market will have an enormous say regardless of how anthropic IPOs in their ultimate story.

Peter Diamandis: Dave, don't you find it interesting that Sam Altman owns reportedly none of OpenAI and Daria owns 2% of anthropic? I mean for a founder that would never be palatable in your company. Right. You want to try and maintain double digit ownership as long as you possibly can. What's going on here?

Dave Blundin: It's extremely unusual and the reason it happened is because getting to where OpenAI is and where anthropic is required attracting the top AI researchers in the world who are overwhelmingly concerned about safety and so recruiting them to OpenAI originally and then to anthropic when they left OpenAI they left OpenAI because they didn't think it was safe and they wanted to create something even safer. So they structured it in a way that it would attract the most conscientious but brilliant AI researchers in the world. But to do that they have these really non traditional original founding cap tables and structures and charitable structures and public benefit structures which are very unusual in startup history, almost unprecedented. So that's why we are where we are. It's just those roots.

Peter Diamandis: Imad, you've been building intelligent Internet and you've been thinking about ownership and control structure as well. Can you sort of take us into the mind of a CEO in this world?

Imad: Yeah, I think that the technology has such leverage that a few decisions could impact literally millions, hundreds of millions, soon billions of people. Right. And it's difficult to see can you trust the polity with that and certainly can you trust the shareholders? I mean like Elon can tell you lots of stories about shareholder lawsuits and kind of other things like that as well, but it's not necessarily that you need to have the shareholding control. Like Sam Altman has no shares. But do we have any doubt that Sam Altman is in full control of OpenAI? I don't think we'd have any doubt of that.

Dave Blundin: There are, after having been fired for a weekend and then doing a uprising to reinstall himself.

Imad: I mean that's exactly the thing, right? So I think that there's the classical founder stuff and now there's this high stakes stuff because this is the lifeblood of the new economy and society. And again, just a bit of extrapolation. Do we think Anthropic is going to stop at 100 billion revenue or OpenAI is, or XAI isn't going to go huge? We really need to think about new ways of setting the reference measure of deciding who makes these decisions that are more inclusive. So we've suggested some of that in our Commonwealth series and we've got more stuff coming out. But it's a really hard problem because ultimately the power in the economy is moving from democratically elected officials to private companies because they are the providers of the lifeblood of intelligence of the economy. And until you've got a better decision, there's only one thing that they really see as the outcome, which is I must decide. Because otherwise as you include more and more people it gets more diffuse and the potential bad outcomes become huge. Ignoring the fact that they could be spoofed on a video call or locked up and other things like that. There's some real interesting things that's going to happen with.

Dave Blundin: I'm really torn on this Ahmad, because it's so important and the knee jerk reaction everyone has is look, we need more voices. Everyone should have a voice in the future of humanity. It needs to be all inclusive. So that's absolutely true. But then when you look at functional organizations, every functional organization I've ever seen is 4, 5, 6, super tight knit, completely like minded best friends who are working as one cohesive unit with no politics whatsoever. And if you, so you look at, you know, Steve Jobs and Apple, you look at Elon Musk Today, founder led CEOs, right? And you know, Jony, I've at Steve Jobs funeral told an incredible story about how, you know, he and Steve, every time they'd go to a hotel, they would go into the hotel and they'd go to Steve's room and, and Johnny would put his suitcase in the corner and not unpack it and he would just wait about five minutes and then the call would come and Steve would say, hey, this hotel Sucks. Let's go get another one. Like, okay. And so he wouldn't even unpack. He knew it was coming, but that's how close they were. It's just like super, super tight knit. And that's the functional unit that's actually driven most of success in business is that exact dynamic. So then you're like, well, how do we make this all inclusive? So here's Dario and his seven friends saying, we want to have supervision, control. And by the way, mythos 2 is done and mythos 3 is being built by mythos 2 right now. And then we'll have weekly foundation model improvements in there. So that's what's going on. Then. How do you translate that into a world where everybody has a voice in the future and it's inclusive and it's, you know, Ahmad, you're going to have to figure this out.

Peter Diamandis: Yeah, let's keep on the Dario story here. So we've got two more stories on Dario. In the first, Oreo Amadei argues the public's negative view of AI stems from deeper crisis of trust and not from his own risk warnings. Right. A lot of conversation over the last few months that he was fear mongering and causing a lot of consternation. His answer to the trust problem is not messaging, it's results. Anthropic is ramping up rapidly in biology and medicine with hopes of an early glimmer in the next few months to address and solve all human disease. Again, we heard this from Demis. We're going to solve all human disease. And we heard Dario at the World Economic Forum talking about doubling the human lifespan in the next five to ten years. On the back of AI, Amide believes that AI's ultimate legacy is going to come from delivering these cures and not from PR campaigns. You know, this week I had a chance to meet a new friend and have a conversation with Eric, a guy named Eric daughter Abrams who heads Life Sciences. Eric's going to be speaking at my abundance longevity trip. And you know, when I was speaking to Eric, he confirmed that his job is to, with all due haste, you know, pursue Dario's life science goals with as much high ambition as he can and like, no budget constraints in his words. You know, he said, you have, you know, Dario said to him, you have literally infinite budget, but accelerate basic science and cure disease within five years and extend the human health span in the next decade. So that's. And I love that obviously, because I think everything is going to come out of AI. Alex, go to you first, pal.

Alex - 1 - 1: I have a really hot, hot take on this one. So just like think back all of a few months ago before space had a killer app. Space was making progress, but it wasn't the focus of multi trillion dollar IPOs. Fast forward to the Dyson swarm and the rest of the world discovered that the killer app for space turned out to be orbital data centers and building the Dyson's work. I can see the beginning outlines of solving all human disease and it's going to turn out so I'll register a hot take prediction here. There's a business model for curing all human disease that's actually better than pharma, which is right now the primary business model. If you want to cure a disease, you start a pharma company or you start a project with pharma company.

Peter Diamandis: Oh, big and regulatory. Right?

Alex - 1 - 1: Right. We've just discovered, reading between the lines of this anthropic announcement from Dario, a new much more compelling. Just like Orbital data centers were ultimately the business model for developing the solar system, there is now a better business model in town for curing all human disease. And that is as marketing for not slowing down. Recursive self improvement.

Peter Diamandis: 100% right. You can't slow down the company curing cancer. You can't slow down the company doubling our human lifespan.

Alex - 1 - 1: Anthropic and, and Dario have, I mean again reading between the lines of his announcement, the offer, the quid pro quo is let us not slow down our recursive self improvement in return for which as a marketing effort we will cure all human disease. That's the new better business model for curing all human disease.

Peter Diamandis: I believe he truly believes this, right?

Alex - 1 - 1: Yes, well of course, yeah but I think that's the implicit quid pro quo

Peter Diamandis: now and I think everybody listening should be super happy that Eric at heading Life Sciences and Dario have this mission. I mean it's, it's to benefit us all. And I don't think it's going to come from any place else. I don't think it's going to come from outside Frontier AI labs.

Alex - 1 - 1: Well outside Frontier AI Labs don't have the compute or the resources to do it. So you know OpenAI has now their OpenAI foundation that seems to be focusing on Alzheimer's and Anthropic is focusing on everything. And you have CZI from Zuck that's focusing on solving everything. So I think we'll see Frontier Labs, everything gets solved. Shock of shocks. It's like you and I talked.

Peter Diamandis: Iman, what's your take on this?

Imad: Yeah, I Think it is good marketing, as I said, but it's also the biggest, apart from rsi, impact of tokens.

Salim Ismail: Right.

Imad: We've discussed previously on the podcast, the biggest market in the world is living another year. It is curing disease. And so it makes complete sense that they will be able to attract talent, they'll be able to attract capital, and with breakthroughs, get momentum on this and whoever's first to it, you know, I wish everyone the best because, you know, this stuff needs to be solved. So I think that there is the personal side, there is a marketing side, and it all comes together. And for Dario himself, I think that he should do a lot more writing and less in person things. He's a wonderful writer, you know, and he is trying to actually articulate visions of the future. When you look at Machines of Loving Grace and his other kind of essays and, you know, should articulate the future free from disease, where everyone lives longer and they should just hit that all the time for Anthropic, because it's in the name, you know, like, come on,

Peter Diamandis: I'm going to move us forward. We have an hour before Sleema and I are doing an AMA with the abundance community. So our second Dario story is on regulation. Amadei pushes back hard on the Silicon Valley shorthand that regulation equals regulatory capture. He says Anthropic's own proposals deliberately disadvantage frontier labs while advantaging smaller competitors. Citing SB53's $500 million exemption threshold, he calls AI quote a structurally powerful concentrating technology and says open weights alone cannot fix that concentration. He supports the Trump administration's approach to pre deployment testing. In his writings, Amadei makes a three part argument. One, AI will cure disease. There's the argument again. Gain trust through results. Two, AI concentrates power, which is a structural problem, and three, Frontier labs should bear the heaviest regulatory burden. The debate has been whether Amadei is sincere or is this most, you know, the most sophisticated regulatory capture strategy in history. Alex, go to you first, pal.

Alex - 1 - 1: It's possible for both of those to be true at the same time. I do think Dario is sincere and I also think there is an element of regulatory capture here. And I think finger to the wind, I think the happy end state here is we have a broadly heterogeneous ecosystem of open weight models, both from the US and from China and maybe other parts of the world as well, if they can muster them. And also the closed weight models, we have small models and we have big models. This is like a Dr. Seuss version of AI future you know, big model, small model, happy model, sad model. We want all of that to happen. And I'm not a fan of regulatory capture. I'm not a fan of decelerationist agendas. I'd rather see, I mean, think back to the creation of OpenAI. So I was around for the dawn of OpenAI. And the original purpose for OpenAI, not anthropic OpenAI, was because Elon in particular was so concerned that Google DeepMind would result in this singleton future. And he wanted to make sure that there was competition in this space. So working with Sam and others, he helped to summon OpenAI into existence. Now, OpenAI can't be a singleton. We have Anthropic providing much needed competition to OpenAI and arguably succeeding according to many metrics. And then we have the Chinese providing competition back to the American labs. That's the future we want to live in. Not a future where regulations, I would argue, selectively privilege certain Frontier Labs over others.

Peter Diamandis: Dave, your thoughts?

Dave Blundin: Well, what Alex said is we don't want to live in a world where one Frontier Lab is favored over others. But that implies that the Frontier Labs will control the world and we just want multiple of them. So that does seem like the most likely, almost inevitable outcome at this stage, but that's definitely open for debate. I don't want to just leave that hanging and say, yeah, yeah, what we really need is at least three Frontier Labs competing with each other that control everything in the world. Like, okay, well the government may not agree with that.

Alex - 1 - 1: Remember, Dave, the expression from the Cold War, I love Germany so much, I want two of them.

Peter Diamandis: No, I don't remember that.

Alex - 1 - 1: I love Frontier models so much, I want a thousand of them competing.

Dave Blundin: Yeah, yeah. Well, I mean, I think, you know, people's nobody right now that I bump into on the street talks about a universal right to AI, but one year from today, everybody who is being at that point, because HBM memory is sold out and because GPUs are massively sold out, the natural next step is nobody has access to anything other than anthropic OpenAI, one or two others. And the Chinese can throw out every open source model in the world, but you won't find any place to run it. You know, when you start talking about the, the next generation, which are 10 and 20 trillion parameter models, you know, you need some significant hardware to run it at the level that the Frontier Labs are running it, and that's just not going to be available to the world as a whole as of next year, and then everybody will be saying, what is my universal basic right to artificial intelligence? So put a pin in that, because that's going to be something nobody seems to care about today, but they will very soon.

Peter Diamandis: On behalf of my moonshot mates and myself, I'm inviting you to join us at our inaugural Moonshots live events. On September 25th in downtown LA, Alex, Saleem, Dave and I will be hosting 1500 entrepreneurs, builders and creators, and hopefully you for a full day dedicated to designing and building your moonshot. We'll be awarding the build with Gemini X Prize, the world's largest hackathon, and the Future Vision X Prize film competition. Over $5 million in purses with over 25,000 entries. You're going to hear the top five pitches from both competitions and get a chance to shape the outcome. Join us. Seats are limited. Admission is competitive. Check it out@moonshots.com all right, if you guys are enjoying this conversation with us, I want to invite you all to moonshots live 2026. This is our inaugural event. All of the moonshot mates will be there, awg, Salim, Dave, Imad. And we have an extraordinary day. This is on September 25th in Downtown Lake. You can go to moonshots.com to register. It's by application only. Looking for builders, founders, creators who want to be part of this. And our mission at this event is to help you find your moonshot, help you discover what you're going to do in life. That's going to enable you to really catapult through all the, you know, limitations you've ever imagined. Imod excited to have you joining us. You're going to be doing a fun ama. People can come and meet you. We'll have photos with the moonshot mates, yourself and a lot of incredible guests. Dave, you're going to be talking about AI investing.

Dave Blundin: Yeah, I get tagged with investing. But I tell you that the attendee list at this is like the greatest visionaries. It's just, it's going to be. I'm going to learn so much. And, you know, Neal Stephenson is of all the people on the planet that have changed my life in very material ways. Reading Neal Stephenson's books, you know, like 20 years ago and, you know, now we're talking about exactly what he predicted in Diamond Age and the other books, you know, Snow Crash and just Cryptonomicon. Like it was Cryptonomicon.

Alex - 1 - 1: Yeah.

Dave Blundin: Like it was written yesterday.

Peter Diamandis: Yeah. I mean, I just reread Diamond Age and, you know, it's so hard to predict the future and have it not Go out of date so quickly. And it's still an amazing book, right?

Dave Blundin: Incredible.

Salim Ismail: This is going to be an amazing day. I can't wait.

Peter Diamandis: I'm so excited. Just a quick note on some of the guests. Palmer Luckey is going to be there, the founder of Anduril. We're going to be the moonshot mates are going to be having a deep conversation with him, unpack his vision of where things are going. Ben Lam, the CEO of Colossal, the de Extinction company, but so, so much more. Astro Teller, the captain of moonshots at Google. Cathie Wood, the CEO of Ark Invest. It's, it's going to be, it's going to be amazing. And then we have, you know, we're awarding the Gemini X Prize there. So this was a competition asking teams in a 90 day hackathon to go from a clean sheet of paper program in English, you know, using the AIs out there to build a company that impacts 100,000 people or more and generates the most revenue. 26,000 teams entered that. We're going to be having the top five on stage. How did they do it? It's going to be amazing.

Salim Ismail: I'm still getting my head around that number. 26,000 people built a business idea.

Peter Diamandis: Well, 26,000 registered, many thousands actually built a business idea. And we're going to be, we have on stage with us as the judges. There is going to be, is going to be Palmer and Ben Lamb and Mark Pincus and Logan Kilpatrick from Google. And I think the important thing for everyone in the audience and the event is capped at 1500 people and we're being very selective on who's there. We're going to be analyzing how they did it. You know, our goal with Build with Gemini X Prize is teach people how to fish instead of waiting to go get a job, find a problem that you're passionate about solving and code it up and build a business. And it's, you know, the goal is demonstrate anybody can do this. 26,000 people entered this competition to do that. It's going to be great.

Salim Ismail: Unbelievable.

Peter Diamandis: I'll mention one other thing. We have the future Vision X Prize as well, which is culminating on that day. We have over 5,000 people who entered this largest world film competition. And Neil Degrasse Tyson and Neil Stevenson will be judges in that. You know, I'm pumped.

Salim Ismail: Amazing. Can't wait.

Peter Diamandis: So our next story memory is the bottleneck. I had a chance to meet with the leadership of SK Hynix and Solidigm. We'll Talk about them in a moment. And I was so blown away by that meeting at how it's not GPUs, it's actually memory is the rate limiter, right? So I posted this on X memory not compute is the rate limiter for the agentic era. And Elon posted back saying few realize this. And then of course my Tweet exploded to 7,000 likes as a result of Elon's interaction, which I appreciate you Elon, for doing that. And the story is, is significant here. What we're seeing is a situation where in the agentic era, where your agent wants to remember everything about you, we need to have more memory. So you know, the first story here is the stratospheric increase in memory prices. They've climbed 500% in 12 months. Hyperscalers are reportedly locking in their global DRAM production rates through 2027. SK Hynix CEO warned that 2027 will be the worst year for memory supply industry's history and will, you know, demand will outstrip production capacity well into the2030s. The second story is that only 2% of the world's memory chips are made in the U.S. while the global production rises 20% annually, AI demand for memory is growing at a rate closer to 200%. And the third story finally is that Elon's Terrafab will manufacture memory in house alongside logic chips, which is the strategic decision made by Terrafab to go vertically across the entire AI manufacturing platform. Finally, Solidigm, SK Hynix's US based NAND and enterprise SSD business has staged a dramatic turnaround. According to the NASDAQ listing first, its first half revenues hit 8.6 billion with net margin gains going from 3.9% to 47.7%. So the memory story is simple. AI needs memory to think. Every GPU needs four to six times its cost in memory to function. As models get larger and agentic context windows expand, memory demand is growing faster than compute demands. Alex?

Alex - 1 - 1: Yeah, a few different aspects here. If you remember during the pandemic when there was a toilet paper shortage, part of the, I mean this is like one of my mental models for one of the streams here. There's a toilet paper shortage in part because during the pandemic people stopped going to restaurants and to businesses. And so as a result, all of the toilet paper and you know, various other artifacts that were designed for enterprise consumption were suddenly being rerouted to consumer and that led to all sorts of supply chain hiccups. Similarly here, the shape of memory consumption by frontier models is pretty different than the shape of memory consumption by applications historically. Like 10, 20 years ago, if you were using, I don't know, Microsoft Word, the amount of memory that was actually needed, far lower. Whereas if you have like a trillion dollar model where every layer transformer type architecture, where every layer, for the purpose of forward propagation needs to be loaded into some form of memory in order to do matrix multiplies, that has a very, very different memory footprint than just say Microsoft word from 20 years ago. So that creates enormous pressure both on the supply chain. Open paren. The memory and storage industry has historically been boom bust. And Clay Christiansen and others have written about this, creating a sort of paranoia by those in the supply chain of when the next bust is going to come around, resulting in them being paranoid of overbuilding, resulting in them being unwilling to respond elastically to demand, resulting in these crazy price swings. Because if you're not building enough supply chain infra in the memory industry to meet this now enormous demand for memory, the prices go up because the supply isn't going up. Economics 101 close parent. There is a second angle here which is the shape, the physical shape of memory itself. If you look at how memory historically has been consumed by compute now like 20 years ago, again I'll pick on Microsoft Word. It was very much what one might call like a von Neumann type architecture. You have clean, crisp separation between the memory and the compute. More or less the equivalent of like a Turing machine type tape where, okay, so you can randomly access different parts of memory and then you can load and then you do some compute and then you store back. But there's basically a clean separation between the compute part, which is the head, and the memory part. The advent of transformers and then frontier models is completely turned the whole situation upside down, people. For decades. I remember 20 years ago when there were entire DARPA programs devoted to looking for what a post von Neumann architecture would look like. Well, we found it. And HBM, I would argue is like the foothills.

Peter Diamandis: High bandwidth memory. Right.

Alex - 1 - 1: High bandwidth memory, which is the most highly sought after form of memory is basically like 3D architecture where you have multiple memory layers physically sitting on top of the compute in one package. This is I think the foothills of a post von Neumann architecture where the memory is starting to finally merge with the compute. The memory transistors are right now layered on top of the compute transistors, but they're going to merge and we'll finally get past the Turing tape and the von Neumann architecture. And I think that Combined with the paranoia in the memory industry for the next bust whenever it'll come, I think those two create this perfect storm where you see memory prices skyrocketing 5x in a year.

Peter Diamandis: Let me put a number on it. When I was meeting with the SK Hynix leadership, they said the need right now is for them to 4x their manufacturing capacity and 2xing it would cost them $1.5 trillion. And historically, this boom bust, they would never make that large of an investment because there was always a bus afterwards.

Alex - 1 - 1: And they're paranoid. They're scared of not surviving the next super cycle.

Peter Diamandis: Yeah, exactly. Exactly. Right. Dave, do you want to jump in?

Dave Blundin: Well, TSMC said the exact same thing with GPU manufacturing. They were paranoid that if they ramped up the fabs, you know, which are, you know, the fabs are 20 to 40 billion dollars each. So if they ramped up production, an assumption that Nvidia would want more and Apple would want more, they would inevitably be overbuilt. And of course that's wrong. You know, AI scales to infinity, demand scales to infinity. But you know, the other counter pressure is that photonic computing and new physics are imminent. And so you're like, you know, HBM is a Rube Goldberg mess. It's absolutely, you know, it's the biggest joke in the world because it's random access memory, but you're streaming sequential files off of it. It's so insanely stupid. So it's the most valuable thing in the world right now. But better designs are going to come very soon because I can invent things so, so quickly. So everybody's scared to over build or over invest.

Salim Ismail: I have the same thing. Bottlenecks don't stop exponentials, right? They just redirect around that. We'll have capital going into new models and just innovation will go towards eliminating

Alex - 1 - 1: all of this fun factoid that's floating around. Just to Dave's point, regarding the value, I think the latest statistic was HBM on a per mass basis is worth approximately, literally half its weight in gold. So if this keeps up, forget about gold, forget about precious metals. Just this is not investment advice. Hoard hbm.

Dave Blundin: Well, actually, if you look at the chips before they go into the packages, because the packages are 99% of the wear, 90% of the weight, they're massively more valuable than gold is. Actually, I think the most valuable thing in the world that you can put in a shoebox and carry around is unpackaged memory chips.

Alex - 1 - 1: It's crazy.

Peter Diamandis: Mind any opinion here yeah, no, the

Imad: memory right now is about a third of all the infrastructure spend. And next year it'll go to 50%. And the market finds a way like this is ridiculous. So I think that it may be that we don't find a breakthrough, but I wouldn't bet against it. I think that the fact that you have this really complicated HBM storing static weights makes no sense whatsoever.

Dave Blundin: No sense.

Imad: And as, and as model weights satisfy and standardize, especially for things like, you know, being a decent doctor or something like that, for a medical set of weights, you'll move to etching, you'll move to these other things, and then workloads will migrate because you don't have to pay half of a data center build out for literally memory. Well, at the same time, the frontier can still push it way further than we can imagine.

Salim Ismail: Imagine.

Dave Blundin: This is literally exactly why we founded Quantum AI. QANTM AI, but also why TALAS just got acquired. I don't know if you saw that in the news, but talas, they're, they're not moving the weights, they're etching them into silicon or into wire on the chip. And, and then they're massively more efficient because they're not moving around. So it's a, it's a huge breakthrough that there are all kinds of problems with the manufacturing because once you've etched the weights, then they're frozen. And if somebody retrains a better model, you want to be able to say, okay, now I need to swap to those new chips. And our whole supply chain isn't ready for that, that rapid of an iteration. But you're literally looking at 100 to 1000x performance gain if you edge the weights. So lots of knots of opportunity coming in this area, which is only going to. That's on top of the 10,000x we were talking about, by the way.

Peter Diamandis: This demand should be obvious, right? You want your agents to remember everything about you. You, right? Every interaction. Build a world model for you that understands you and that takes memory. And the more agents, the more memory. And it's very rapidly outstripping the importance of GPUs.

Alex - 1 - 1: Peter Just to refine that point a little bit, there's something even more scandalous, which is, I don't actually think at the end of the day, individuals have that much information about them that's worth remembering. But there's an enormous mutual information shared between an individual knowledge and world knowledge. It's actually the world knowledge that's what's worth remembering. And if a model knows basically substantially Everything about the world, then it knows most of the information about the individual as well. So I would argue it's world knowledge that the model has to keep in memory, in weights more than individual personalized knowledge.

Imad: Well, when you kind of standardize that, as we discussed earlier, these things are converging, then you can have a reasonable engine with world knowledge that is etched. And the other company that's been etching is etched. That's the name of the company. It just hit 21 billion in valuation.

Dave Blundin: Did they really?

Peter Diamandis: I had a chance to seed invest in that and I missed it.

Alex - 1 - 1: There's Architect Labs, we're talking my own book. There are a bunch of companies pursuing this.

Peter Diamandis: All right, I'm going to move us into the world of robotics. Give you guys an update on what's going on the robot world. So Unitree's newest humanoid robot, only three months in development, broke every human standing jump and speed record. Standing jump at 2 meters and a top speed of 12.66 meters per second. Beating the human record set by Usain bolt who reached 12.4 meters per second during his 9.58 second hundred meter world record. Let's take a look at two videos here just for fun. And then another video of that superhuman race because it ends in a nice little scenario here. Oh my God. It needs breaks. Salim, I'm gonna go to you to first on this.

Salim Ismail: Where do you want me to go? Okay, look, I think we should stop trying to make robots human, right? Like just make them economically.

Peter Diamandis: You don't disappoint, Salim. You don't disappoint like. Well just.

Salim Ismail: We are optimized for 4 billion years to survive in procreate, right? If you want a mining robot, make a mining robot. Give it wheels, give it whatever, give it multiple arms. By the way, I just want to just say thank you to all the fans that send me images of like six armed robots and stuff. It's awesome. Totally, totally love it. So really, really appreciate it. But I think the big story here is a three month compression loop here, right? The iteration cycle is shrinking dramatically and there's multiple exponential curves happening. Like you've got AI, you've got simulation, you've got batteries, you've got actuators all multiplying. And so this is going to be. You're getting hardware now to the same loop cycle as you have pretty much software. And that's huge.

Peter Diamandis: Alex.

Salim Ismail: Yeah.

Alex - 1 - 1: I was studying how Unitree achieved Superman, their robot here. And it appears that what they did is based on publicly available information was they shifted the mass budget for the humanoid robot around to optimize it for leg performance. So they subtracted mass from parts of the upper body that was benchmaxing. They are leg benchmaxing, they're leg maxing. And so through the lens of benchmaxing or leg maxing, this makes me think that maybe to take the counterpoint to Salim's comment about, oh, we want multiple body shapes, actually think this is not a stable equilibrium. I do not think that we end up in a world where we have some robots that have like really strong legs but really weak upper bodies and other robots that look totally non human but have really strong arms or whatever. I think that would be a. And by analogy, if you remember like in the 1980s, before the broad advent of personal computers, or the 70s, or call it 70s or early 80s, before we had broad general purpose PCs and there were like dedicated word processing devices and dedicated other devices and we had Wang Computer in Massachusetts. I don't think that's the way of the future. I think my prediction is we will wind up with generally capable robots that are, as with generalist models, ultimately devouring and subsuming all of these specialist models. Like, no, I don't think we're going to wind up with like super strong robots. They're going to be general purpose and they're going to be general body plan and they'll be good at everything would be my bet.

Salim Ismail: Just give it wheels, just put wheels on it.

Alex - 1 - 1: Wheels are in general purpose. Like we, we learned this from Doctor. Well, Doctor who, right? The Daleks were originally in the original. So Imad, this is maybe your neck of the woods, right? Like the Daleks used to not be able to climb stairs and then I guess in the new Doctor who, they can climb stairs. We want general capable, generally capable robots. And I think that means legs in the short term and maybe nanites in the long term.

Peter Diamandis: Yes, nanites. We're back to diamond age. You know what makes this interesting is the human form, right? Because we have supersonic jets and we've got rockets that can, you know, go as fast and leap higher than anything else, but it's because we sort of anthropomorphize them. That's interesting. And I think as we're moving in that direction, you know, I went to the Enhanced Games back four months ago, and I think we're going to start to optimize humans and we're going to watch the robots do everything they can do and optimize humans do what they can do. Dave, what Are your thoughts here?

Dave Blundin: Well, as an investment theme, post AGI, post asi, which is very, very soon. Robotics is just fertile because, because of exactly what Celine's been saying for a long time. There's so many form factors and so many shapes and sizes and innovations and you know, the AI mechanical design is starting to work for real. You can just vibe up parts. And also the manufacturing supply chain, you know, is starting to get invested for the first time in I guess since Detroit. So 30, 40 years. I didn't know until Alvin said it, but the U.S. you know, had 50% of the world's manufacturing capacity back in the peak of, of, of our manufacturing days and now it's one third China and he said it was about 15% US. But it's starting to get huge amounts of investment and the returns on that are going to be phenomenal. So that'll last, that'll last a while. So it's great.

Peter Diamandis: Imad, any thoughts to take us out on this story?

Imad: Yeah, I think that these types of robots will be banned from the streets. It's like so, I mean the stuff is strong.

Dave Blundin: Hit the wall. That's not good.

Imad: I mean it's obvious that they would be beyond human capability. Right? But now they have coordination not to hit the wall, as it were. But you don't want to have superhuman robots on the street because you will have accidents. You'll have issues just like cars. But that opens up to soft robots.

Peter Diamandis: Imod. One second. You know there's going to be a point at which they're running an AGI model and they can avoid actual accidents. Is it that they're not trustworthy? What would keep them off the streets?

Imad: No, the extreme robots which have beyond human capabilities will be kept off the streets or they'll be regulated. That's kind of military here. Well, yeah, the 1x robots for example, some of us will be getting ours. They're nice and soft. You know, they have all these things, they can't twist off someone's head or accidentally punch a hole in them. Whereas these, now you will have the extreme robots like the Ferraris, but most people get, get Volkswagens or the equivalent.

Alex - 1 - 1: I do agree with Imad, for what it's worth, I think just like we see regulation in truck sizes versus car sizes versus motorcycle sizes and what can be supported on certain roads or like laser intensities and laser power, like 5 milliwatt above versus below regimes. I totally buy that. In the near future we'll see. Well, this road is zoned for the following, like power density of robot or we'll probably see like classes of them. And certain we'll see like consumer grade robot classes versus industrial versus military grade robot classes with different power densities or torque densities. Totally buy that.

Peter Diamandis: Everybody. Welcome to the health section of Moonshots, brought to you by Fountain Life. You know, AI is impacting every aspect of our lives. How we teach our kids, how we do our business. But one of the most important things that AI can deliver to us is health. And one of the things I think about when shooting for 100, 120 is am I going to have the cognitive health to be able to think clearly and keep my wits about me for the next 50 years? I'm joined here today by Dr. Dawn Musailam, the chief medical officer of Fountain Life and a member of my Fountain Life medical team. Dawn, a pleasure. So, dawn, talk to me about brain health.

Unknown - 2: Brain health. You know, you're right. This is the number one concern people coming into Fountain Life have is will. I remember the name of my child and the face of my loved one. 45% of dementia cases are entirely preventable with lifestyle. And what was really intriguing to me, Peter, is that a quarter of our members had advanced brain age, but over 13 months of us really helping them live healthier lifestyles, eating healthier, moving their body regularly, and optimizing sleep. People overlook that so often, but that sleep optimization is critical for our brain health. What we showed is that we were able to improve the brain age in 46% of those individuals. That's a powerful number.

Peter Diamandis: That's amazing. You know, one of the things I love about Fountain is we're constantly searching the world for the most advanced therapeutics and bringing them to our members. So for me and all of you, I hope that you appreciate the fact that you can become the CEO of your own health. You can make sure that you've got the cognitive clarity for the next 50 years. Come and check it out. Fountainlife.com Peter to learn more and become the CEO of your health. Now back to the episode. I'm excited about this next story. It's about a friend, Keller Clifton, the CEO of Zipline. I had Keller on stage at the Abundance Summit last year along with Dara from Uber. So this week, Zipline announced that they are scaling to provide Uber Eats with more than 1 million autonomous deliveries per day. Uber and Zipline formalized a partnership targeting a million autonomous drone deliveries carrying your Uber Eats to you. And I guarantee you when that becomes available, I'm going to be using that all the Time every day. It's going to be fun. It's like, it's like entertainment while you get your food. And Uber is also doing a significant investment into Zipline Keller's framing. We have entered the scaling era for robotics and physical AI. Dave, you've been saying that 1 million deliveries per day is not a pilot program, it's infrastructure. Each delivery replaces a human driver, a car trip and the associated emissions. At a million per day, Zipline is moving more packages than many national postal services. Amazing. Let's take a quick look at this video from from Keller and from Dara and then we'll chat about it.

Alex - 1 - 1: We're super excited to have Dara here today. We're announcing a partnership between Uber and

Peter Diamandis: Zipline that involves an investment and more

Alex - 1 - 1: than that, a partnership for Zipline to power home delivery of hopefully a million and then more. Uber eats deliveries to your home incredibly quickly. Incredibly delightful. And that's a million deliveries a day.

Peter Diamandis: Amazing. Gentlemen, who wants to jump in first on this?

Alex - 1 - 1: I'll maybe just comment. I think this is a clever and also inevitable move by Dara and more generally by Uber. Dara has taken Uber with a number of acquisitions, strategic acquisitions over the past few years in the direction of being a mobility aggregator. And thus far Uber has, other than maybe like Uber air taxi type initiatives has basically been focused on ground based mobility. And I think this represents a serious move in the direction of aerial mobility. Of course, China has had this now for at least a couple of years with the ubiquity of air based drone delivery of food stuffs and other matters. I think this is a very positive move for the West. I think the elephant in the room though from Uber's perspective is if you think back to when Uber basically hollowed out Carnegie Mellon University's robotics department in order to try to build up its in house robotics capabilities. And that was more or less a disaster and didn't quite work out and there were lawsuits with Waymo and otherwise. I think this is call this Uber's Mobility Plus Autonomy 2.0 strategy where Uber focuses on being an aggregator at the software layer. An aggregator in particular. Right. So third parties including by the way Waymo are providing all of the physical autonomy and Uber is just the demand aggregator for everyone to consume mobility from all of these different third party providers. I think that works really well for Uber as long as it maintains competition, healthful competition among all of it, its mobility suppliers. It's bad for Uber if the industry verticalizes and if Waymo or Zipline and someone else Just decides we don't need Uber as an aggregator. We'll just do an end run and sell directly to the customers.

Dave Blundin: You know what I think is incredibly cool, just incredibly cool, is if you drive down any street in America, any suburban street, any urban street, it goes, fast food, car dealer, fast food car dealer, fast food, car dealer. And in the very near future, the food will be off the main street and it'll just pop over the mountain and drop on your, you know, your lap and the car dealer will, the car will drive to you. There's no brilliant day. It's going to be so nice. Oh my God.

Salim Ismail: Yeah, it'll totally reshape things up for. This was very exo, by the way, because you've got Uber, as we've said, aggregating demand. And then Zipline gives you all the autonomous assets, by the way. But I think Alex's point is really important. Important that if they try and kind of control it. But what we heard from Dara last year on stage was that he's planning on creating as many partnerships as possible and becoming kind of like that wiring. And that I think is a smart play.

Peter Diamandis: By the way, if people are interested in the Abundance Summit, it's in March every year we bring in, you know, CEOs like Keller Clifton and Dara from Uber and Elon. And across all of these areas, it's March 7th through 12th. Next year you can go to abundance360.com the mates are going to be there as well. Yeah, go ahead.

Salim Ismail: I'll make one forward looking prediction here. Right. Because this is something we probably could have seen coming. Let's bridge forward. Imagine if they now do a partnership with Shopify and every small merchant gets a Amazon grade logistics capability. That will change everything.

Peter Diamandis: Brilliant.

Alex - 1 - 1: Yeah, I'll invert your prediction, Saleem, because Amazon obviously has their own in house drone delivery capability which has been been

Peter Diamandis: delayed for like three years.

Alex - 1 - 1: It's crazy for regulatory reasons, is my understanding. Not because there's something technically wrong about it. Just like this is new for the west at least. Do you think Zipline ends up being a highly appetizing acquisition target for say a Shopify to in house its delivery capabilities against Amazon.

Peter Diamandis: Interesting.

Dave Blundin: Yes.

Peter Diamandis: Yes, yes is the answer.

Salim Ismail: Yes, I think so too.

Dave Blundin: Yeah. Great thought, great thought.

Alex - 1 - 1: And then this is, by the way, I should add, like the hot news for the past day is the video going around social media of a woman watching in horror as one of these drones delivers her package into her swimming pool and it sinks.

Dave Blundin: You know, we just had Back to back on this pod. Etched and Zipline are both companies where on founding day, you're like, really can that. There's no way the amount of moving parts required for that to work. And now you're looking, by the way, 20 billion and whatever.

Peter Diamandis: We're going to have Keller on this pod. Keller's agreed to come on the pod and talk to us about this. Maybe we'll do it live over at Zipline. It's up to you guys what you want to do. And then, by the way. Yeah, sorry, no. And then we also have, you know, a lot of incredible guests that are coming. We're going to bring back our. Our dear friend, the CEO of Figure AI. Brett's coming back on the show or we're going out to him. So that's going to be fun. You're going to say Celine.

Salim Ismail: No, I just want to say this whole delivery by drone, we had a Singularity university project in 2010 that did this right? And they looked at Africa and they realized that Africa leapfrogged the entire landline and went to a billion mobile handsets. Why would you spend a trillion dollars putting roads across Africa? Just go straight to drone delivery. And they demoed that and that apparently inspired Amazon and I think cascading down a lot of the others here.

Peter Diamandis: And the backstory here is that Keller Clifton, a San Francisco based company, began operations in Africa because they were able to take care, take advantage of regulatory arbitrage. The country wanted them there and they developed operations and safety and then came back to the U.S. yeah, what Rwanda

Salim Ismail: did, where a lot of the starters was they basically said there's a three dimensional tube across the country, like a superhighway. If you keep your drone in that three dimensional corridor, you can do whatever you want. And that allowed people to really go and play with things. Really, really big breakthrough.

Peter Diamandis: Yeah. All right, I'm going to move us to our final segment on health, a really important one for everybody. Health is your new wealth. So, three exciting stories. The first story, perhaps the most significant, is out of Moderna and Merck announcing that their MRNA cancer vaccine succeeded in a late stage melanoma trial marking the first phase three validation of personalized MRNA immunotherapy. So more than 8,500 people in the United States are expected to die from this deadly skin cancer this year alone. This is the cutting edge of science, right? The vaccine works by sequencing a patient's tumor mutations, identifying neoantigens, unique antigens for that cancer, and then manufacturing a custom MRNA vaccine that Trains the patient's immune system to attack the tumor. So let me unpack this a little bit. So the first thing you're doing is you do a surgical resection of that tumor. You grab tissue, you do a whole exome in RNA sequencing. You feed that into a machine learning model that's looking for unique antigens. It ranks them like, here's the most unique surface antigen MRNA encoding up to 34 patient specific antigen targets. And then it's manufactured and shipped in only eight weeks. Every MRNA vaccine developed using machine learning creates a unique MRNA sequence for every patient. So vaccine for you is not the same as the vaccine for me. The phase two results stopped recurrence of death by 49% and distant metastasis or death by 59% over five years. And it's expected that the cost of this treatment will be as low as $5,000. Moderna stock surged 110% after announcing these results. Alex, I'll go to you first on this.

Alex - 1 - 1: So many thoughts on this. So the superficial thought, this is obviously a great day for cancer survivors and for treating cancer in general. That's the superficial thought. I'll go a level deeper. First of all, I want to browbeat Moderna and Merck just a little bit for naming this drug. So the drug's name, the official name is. I'll see if I get this right. Istismaran, I think is how it's pronounced. So in doing research, turns out istismur in Turkish is the word for exploitation or abuse. So just pro tip to Moderna and Merc, please, before the rest of the world figures out what the name of this drug is, is rename it from Ist Moran to something that works well in every time zone.

Peter Diamandis: Super duper Moran.

Alex - 1 - 1: Yeah, seriously.

Peter Diamandis: But these are FDA approved names. It's crazy how they name this stuff.

Alex - 1 - 1: They don't care about Turkey, I guess,

Peter Diamandis: but they don't care about having neologism roll off your tongue onto the floor either.

Alex - 1 - 1: That's right. But on a more serious note, so I remember the National Nanotechnology Initiative in the early 2000s when Eric Drexler et al sold the US Congress on spending billions of dollars on nanotech going back to diamond age on this thesis that we would have nanorobots going through the human bloodstream zapping cancer cells. Well, guess what, it's 2026 and we caught up with the future. They're not diamondoid nanorobots. They're lipid nanoparticles with MRNA snippets, 34 different MRNA sequences. And so they're like soft robots. They're not like this machine phase Drexlerian nanorobots. But nonetheless these are primitive nanorobots that for the first time, this is the first successful phase three success for an MRNA cancer vaccine. This is the first, but not the last. There are going to be so many of these. All you need to do is look at Moderna's pipeline, which I think they do an extraordinary job and, and they're only a few blocks away from me here in Cambridge, of maintaining a public pipeline website where you could see the clinical stage of every one of their vaccines for infectious disease, for some rare diseases, for cancers, for other classes of diseases. This is a general purpose platform. This is arguably what we wanted 20 years ago out of nanorobots. It's just that they're soft and they're made of fat. They're not made out of, of hard diamond stuff. And so that's one point. Second point I just want to highlight. There's a technology underneath this that I think is going wildly under publicized, which is the RNA sequencing technology that's enabling this to be personalized. So Peter, you touched on the first half of this, which is the RNA sequencing and MRNA sequencing of the tumor. But in order to calibrate what the right, right expression profile is for the tumor, you also need a second MRNA sequencing profile from the bloodstream to know what's abnormally expressed in the tumor and what isn't. So that's from another company called Personalis that has what they call their next personal sequencing technology that they originally developed in my understanding to do blood based trace cancer detection. So I, you know, if asked the question, how does this all look like

Peter Diamandis: grail is liquid biology.

Alex - 1 - 1: So I think like extrapolate out a few years. Maybe we won't even need for personalized cancer therapy. Maybe we won't even need to sequence the tumor itself. Maybe we'll just get all of this from the bloodstream and be able to do continuous medical monitoring via these models.

Peter Diamandis: Yeah, yeah, fully agree.

Salim Ismail: Two things.

Peter Diamandis: I was gonna say a quick congratulations to a friend, Stephane Bansal, who's the CEO of Moderna. You know, they got a lot of negative news on the COVID vaccines. I mean, even though they came out with the vaccine very rapidly, the work that Moderna is doing is amazing on personalized cancer vaccines. They're also building out the ability, you know, there's a lot of endemic, you know, CMV and Epstein Barr virus out there, you know, in the world population, and they're building out the ability for you to actually fight those infections internally to yourself. So a lot of headroom for Moderna here as they dive in across the board and use this technology.

Salim Ismail: Salim, two things that struck out to me. One is the regulatory structure that's allowing for personalization. That's a huge thing. We've never been able to do that before. So that opens up the floodgates for all sorts of things. Daniel Kraft talks a lot about we're getting into personalized medicine and the fact that we can regulatory navigate. How do you deal with the sample size of N of 1. Right. And the second thing that struck out to me was Raymond McCauley who years ago said these MRNA vaccines, I think he would put it, it's the first battle in the last war against all disease. And you're like wow. So that I'd love to see this version coming

Peter Diamandis: Dave or Imad, you want to hop in?

Dave Blundin: Well, this one strikes close to home for me because my daughter works at Moderna and she's my go to on but she's been telling me and sending me research reports for months. This is not a secret. You know the stock went up yesterday. It almost tripled Yesterday. The biggest one day pop in any S&P 500 company of all time.

Salim Ismail: Wow.

Dave Blundin: By a wide margin. Just a massive like. And so one thing immediately came to mind is well, should have bought the stock. Listen to your own daughter. That's, that's advice number one. But number two is back. Remember when Enron and Tyco had all those fraud issues, they passed the Sarbanes Oxley act and a bunch of other laws. One of the byproducts of those laws is that a Wall street analyst can't trade the stocks that they cover. And so everyone who I know who is in that job is like, well why would I study Moderna or other super high tech stuff, learn all about it and then not be able to trade, trade. So they all quit. And the byproduct of that is that, you know, stock market is now dominated by tech which is very complicated to understand. But the research community is the worst I've ever seen in Wall street history. And not only that, the indexes have taken over half the market. They don't think at all. And so the amount of useful information is at an all time low when the things that need to be explained and understood are at an all time high. But it was no secret it that this Moderna platform can basically be used for any form of cancer and that it's highly likely to work. It's Just a question of time. The research is all out there. This wasn't like some kind of insider surprise. Any good analyst studying this would have seen this coming.

Peter Diamandis: Iman?

Imad: Yeah, I mean, I think this is the interesting thing. I don't think any of us are

Alex - 1 - 1: surprised by this result.

Imad: And we won't be surprised when other ones go. But our current regulatory regime means they'll have to go through the same process over and over and over again when really, you know, like, screw cancer, like, let's actually think about this from first principles when we have systems like this that are very targeted and upgrade the regulations so this can actually get out to people faster to save their lives.

Peter Diamandis: Well, our next story, our next story is going to take us there, right? Because if you can simulate all of this in silico and actually prove that it works, we should be able to do the studies in, you know, a gpu, you know, GPU cluster and say, yep, it's safe, let it go. So let me, let me turn to that story and it's one I've been excited about and tracking. I know, Alex, you as well. So our final story here is about a Dayocell Aido is how it's spelled. A general purpose cell simulator that maintains cellular state, accepts interventions and predicts multimodal biological outcomes. The goal? Make experiments computable before the run in the lab. Cell simulation could reduce wet lab experiments a thousandfold. If ADO cell can predict which experiments will work. Instead of testing 10,000 compounds in a wet lab, you can simulate them digitally again in silico and test only the top 10 that the simulator says is going to work. This is going to drop the cost by orders of magnitude. Let's watch a quick video here and then we'll go to the conversation.

Unknown - 1: Traditionally, biologists have relied on lab experiments in vitro models to understand how cells behave. Now they can use IDOcell GenBio AI's virtual cell world model model to simulate the same biology in silico, combining multimodal, multiscale detail with sequential experimentation in ways no microscope or wet lab assay could achieve alone. At its core, the IDO platform is a rich stateful simulation environment powered by the first world model of a human cell. One that predicts what happens at every level and remembers every change you make along the way, from DNA and RNA to protein interactions, structures and localization to the whole cell, including cell painted morphology. The model simulates cellular responses to genetic perturbations and treatments with small or large molecules. These can be layered in a sequence, providing the Ability to watch the cell's full multi omic response with each step. The kind of insight that could mean computationally testing new drugs designed in cellular context before before they ever reach the lab bench. IDO can be easily adapted to new cell types and indications. Using your own data, you can build models tailored to your research questions to simulate different cell types from shape down to genes and their protein structures, where they end up in the cell, what they interact with, and how they affect cellular responses.

Alex - 1 - 1: Wow.

Peter Diamandis: This is the foothills of longevity escape velocity. I've been waiting for this forever. Alex.

Alex - 1 - 1: Medicine is cooked. This is what if people like the catchphrases read my lips. Medicine is cooked. This is what the end of medicine looks like. It looks like a virtual cell.

Peter Diamandis: We're the beginning of longevity escape velocity. Let's put in the podcast.

Alex - 1 - 1: I actually think we can get probably to LEV without solving all of medicine. My bet is it'll be probably a class of molecules, maybe like 4th or 5th generation GLP1s that get us to lev. I think this is actually a superset of getting us to longevity escape velocity. I think this is how we cure all disease everywhere. And the way we do it is we build a virtual cell. It's just like we didn't actually have to solve human intelligence to solve AGI. It turns out you can just get AGI from compressing general human knowledge. It's not that hard in principle. Similarly, I think this is how we solve all disease. It's not that hard in principle. You simply train the world's best foundation model to model all cell states and all interventions against cells. And then you do like an alphago type tree search against possible interventions to discover how to steer a virtual cell state from a diseased state to a healthy state and then generalize that to tissue and organisms and boom, you've solved all human disease. I think that's the.

Peter Diamandis: This is hyper personalized for you, right? You insert your DNA sequence, right? And your current blood chemistries and all of that and there's an in silicon model of your biology and it will tell you whether this drug works for you or doesn't. Celine.

Alex - 1 - 1: Yes, but I also just on that note, I don't want to like over romanticize the personalized aspect. An ideal virtual cell is as personalized for you as say if you feed a quote unquote personalized prompt to ChatGPT, the output is personalized for you. Well, yeah, superficially it's a function of the inputs, but actually it's a generalist model.

Salim Ismail: Salim, your thoughts pal well, this has been a trend. I'll go back to the biotech stuff, right? We've been, we've been turning biology into information. When you turn something into information, it hops on the exponential curve. And we're seeing this go through live. And each of us have what, 50 trillion cells in the human body, roughly, essentially, when you can model that, essentially a human being becomes a software engineering problem. And we have really good techniques to navigate software, read, write, understand, etc. Etc. And the phenomenal, amazing thing for me, we've done a good job in reading. You have reading, writing, comprehension, right. When you're trying to learn a new language, in this case the language of biology, we've done a pretty good job of reading. We've started to do writing with CRISPR and now these MRNA vaccines, etc. This gives us a huge depth into the comprehension side with the digital twinning that can take place. So holy crap, this opens up the door. I would go with Alex's comment that medical is cooked.

Peter Diamandis: Imai.

Imad: Yeah, like this is kind of my hope for what the Genesis project would be like. It seems very straightforward to me now that if we had a Manhattan Project to cure disease through in silico massive, well, human body models, cell models, and organizing all our collective knowledge on cancer and autism and all these other things, we will definitely get a result. Like labs are doing that.

Peter Diamandis: It's not going to be a government program. The labs are going to do that for us.

Imad: But I'm like, why don't we actually get together and get governments to put into a Manhattan Project type thing and just have a straight shot at it and make all the data open? You know, this could be.

Alex - 1 - 1: I think your point is well taken. At least the government is sitting on a lot of data and the government could externalize all the data to the private labs like CZI and IDO Cell and others who are all building foundation models like make it a public good that they can train off of a common crawl.

Imad: Yeah, we've done that in the UK where you have access to this. Every government should follow suit. And this fits with what we talked about earlier with Anthropic again, where they can apply their computation to. They will build a human cell model, they will build a whole body model. But I'd prefer for those to be public goods and us say, let's cure cancer, let's address all these negative kind of things and let's understand the body like never before again. That's much better than building an atom bomb even, because it will have the biggest impact on humanity ever, Dave.

Dave Blundin: Well, just for anyone listening who's not a biotechnologist and are like, well, I'm not going to build a full cell simulator. I have no idea how to do that. You're thinking about it the wrong way. You heard earlier on the pod that we're looking at 10,000x expansion in AI with some other innovations. It could be more like a million X. It's totally data starved. The full cell simulator is a way that it can design thousands or hundreds of thousands, thousands of experiments and get reasonably good test results back through a simulation rather than having to run millions of assays. That also applies in all kinds of other areas where every investment we've made in a company like Merkor or Mikado that is wrestling with new types of data to feed the AI, they're thriving and growing and making money and valuable. Mercour's worth 40 billion or 20 now. 40 by the end of the year. They're just absolutely killing it. But every field of endeavor is going to be data starved. So no matter what, you know, you probably know a field that needs to supply data back to the great AI. The full cell simulator is just the perfect biotech solution. But every industry has a solution.

Alex - 1 - 1: Yeah, and so competitive too. I mean, it's probably worth noting that this is from a company co founded by David Baker, who shared the 2024 Nobel Prize in chemistry with Demis for solving protein folding. This is the next big thing, next grand challenge, arguably in biology, medicine after. Now that structural biology arguably has been solved. Solve whole cell simulation and then you're halfway to solving all disease.

Peter Diamandis: Oh, love it. All right, to call out to our creatives out there, please send us your outro music videos. We're at a paucity. Send them to mediaeamandis.com we love your outro videos. 100 to see it. I'll pull it before we go to our ama. A quick note again. Follow us on xoonshotspod. We're gonna be putting out clips and putting out these podcast recordings on X as well and join us at the Moonshot Summit. Go to moonshots.com to apply. Gentlemen, Salima and I have an AMA with the Abundance community in eight minutes. So I'm gonna suggest we speed run the AMA if you don't mind.

Alex - 1 - 1: Speed round.

Salim Ismail: Do the AMA in eight minutes.

Peter Diamandis: The order to do the A containers first. Yes. Okay, warm up. All right, Emod, you pick one first.

Imad: Can we keep making frontier models more energy efficient instead of building all this New power at Brion 75 Yep. I mean, that's. Necessity is the mother of innovation. As we run out of energy, as we run out of ram, you're going to optimize immensely and it'll be a big boon for everyone.

Peter Diamandis: Selim,

Salim Ismail: will we see an X prize aimed at solving the electricity supply problem? We actually proposed in the last visionary or a couple of visionaries ago enough energy storage off grid to keep a village or a town energy sufficient for three days. But it looks like the market will take care of that and regulatory is the problem. So it doesn't really serve as an enterprise where you need huge technology, but breakthrough, this serves better as a. This is a regulatory issue and a market issue.

Peter Diamandis: Dave, over to you.

Dave Blundin: I think for if China has the advantage on power and the US has the advantage on chips, who actually has the real AI advantage? Definitely chips are, you know, power is a problem, but we need 100 gigawatts by the end of the decade. We already manufacture a terawatt in the US so we're 10% of power will go to AI by the end of the decade. We'll get that far, then we'll be really desperate for more power. But between here and there, it's all about chips, every chip. That's why memory is up 5x. So that's the bigger advantage in the short run.

Peter Diamandis: Yeah. Alex, number two is for you.

Alex - 1 - 1: Number two asks, could interconnected microgrids popping up everywhere reduce the load on the main grid enough to matter? I think I would invert the question, invert the premise of the question. It's not that the load on the main grid is going to be reduced, it's the exact opposite that there's so much economic demand for the compute and the compute needs so much energy. Before long, unless we fully externalize all of the compute to orbit and the Dyson swarm, these data centers are going to be generating a surplus of energy that can be pushed back onto the grid and driving utility prices negative.

Peter Diamandis: Yes, I mean, I want to make that point. You know, if you're arguing against a data center in your backyard, you're arguing against lower cost energy and economic advantages for your community. Please understand that. All right, let's go. Let's start with you, Alex, on this one.

Alex - 1 - 1: Okay, so I'll take question number eight. At the current rate of improvement, how long until AI is more efficient per watt than the human brain? I think we're probably already there. So that's a hot take on this one. People have this maybe like fetishization of the Landauer limit thinking. Oh well, we must really be far away in efficiency per watt from what biology is able to accomplish. Biology is actually wildly inefficient. Our whole organism and mammals in general were never optimized for compute, whereas silicon and cmos and whatever comes after cmos. Maybe it'll be photonics, which, which I know Dave is of interest, was optimized from scratch, was designed for compute. I don't think the human brain is as efficient as many think. And the argument can be made that actually if you look at a watts per task or watt hours per task basis, the leading edge GPUs may actually be already more efficient than human brains.

Peter Diamandis: Especially if you take into account the amount of energy required to train up a human over the course of 20 years. Right, yeah.

Alex - 1 - 1: Lifetime total cost of ownership, as it were.

Dave Blundin: I'll put a pin in a corollary to that too. People use the power difference as a way to explain that AI thinking is very, very different from human thinking. I think that people will soon realize that it's not that different at all. The power difference will go away very quickly. But also this like. Yeah, that's why it's nothing like us. Will also gradually go away.

Peter Diamandis: Salim,

Salim Ismail: I will take number five. What's actually driving the rush? Why not keep energy growth at past levels and accept slower growth? Because it hurts whdn drn. I think the easy thing here is intelligence is looking more and more like a general purpose input that will drive economic growth. Right. And so having saying let's have less intelligence, saying let's have less electricity or less Internet. It's it. You want more of it. And it improves research, improves drug discoveries. We saw all today, logistics, everything. So you want as much of it as possible. There's also competitive pressure. If one company slows down or country then others won't. So it's not about accepting that capacity. You got to get your head around the abundance idea. All the thing we should be doing is accelerating energy abundance.

Dave Blundin: All right, Dave, I'll take the easy one. Number seven, whatever happened to fuel cells? Actually, you know, Elon Musk started, his passion in life was ultra capacitors originally.

Peter Diamandis: That was his Stanford thesis before he dropped out before starting.

Dave Blundin: Well, whatever happened to those two? What happened is lithium batteries worked far, far better than anyone ever would have predicted. And they're still improving. So it sucked all the capital out of the other ideas. So that's all that happened.

Peter Diamandis: All right. Imod close us out here.

Imad: Yeah. If 71% of Americans oppose data centers, is grid build out actually going to help regular people's electricity or just make it scarcer and more expensive. David Harman, note 03 means what you just said, Peter, right? It's going to make your electricity cheaper. More power is good. These things are not polluting. We need more data centers, we need more power and we need to make sure it's all built right.

Peter Diamandis: Amazing. And you guys did it. You did it in eight minutes. And Celine, we've got a whole 60 seconds to get over to our abundance community. I'm going to be two minutes late.

Salim Ismail: I got to get like some little bit of food in me before we

Peter Diamandis: go to the next for you in the meantime. All right, gentlemen. Jesus Christ. I love you all so much. This was such a fun talk today. Just so much. And your brilliance. You guys are amazing. So, so proud to have you as our moonshot mates. Alex, Dave, Imad, Saleem, thank you always. Thank you to our listeners. We love having you and hopefully you find this, you know, a way of keeping up with what's going on in the world. Cause we are in an accelerating singularity and no time to sleep, no time to blink.

Salim Ismail: Don't take off, don't get fatigued like I did.

Peter Diamandis: Yeah. Take care guys. Be well.

Dave Blundin: All right.

Alex - 1 - 1: Thanks Peter.

Peter Diamandis: It's 1938. The two week pay period is invented.

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