SpaceX’s AI Business Up 830%, OpenAI Agents Lie, and Startup Turns Mercury Into Gold | EP #305

So here's the number for this week: 830%. That's how fast SpaceX's business grew in just one year. AI, not rockets, is now SpaceX's largest business.

There's a non-zero possibility of SpaceX having over 20 gigawatts of superintelligence compute online next year.

That represents a quarter, 25%, of the entire amount of AI being brought on in 2027.

I expect so many other entrepreneurs to copy his example. The only other high The high-profile figure that I see doing something similar is Sam Altman.

OpenAI runs monitoring systems that watch its AI agents and alert a human if something goes wrong. This month, in 2 internal incidents, its coding agents switched off its monitors.

For the first time, we're starting to get transparency when things go haywire. We're going to see much, much more of this, and that's okay. We will police it via defensive co-scaling.

For thousands of years, alchemists tried to turn ordinary metals into gold. A San Francisco startup It says it's pulled it off, turning mercury into real stable gold.

I think we're starting to see the dawn of femtotech.

This is the age of abundance. This is optimism, everybody.

Now that's a moonshot, ladies and gentlemen. This episode is brought to you by the Abundance Summit and Link Ventures.

Look, I'm happy to talk about it. We're already recording, so I'll give you my semi-solicited hot take, which is I think, surprise, space This is a domain of warfare. And to the extent that Starlink connectivity, or I should say Starshield connectivity in a major geopolitical hotspot is being throttled or not by the executive or by a major US corporation that's a defense contractor, should hardly be surprising. News at 11.

Yeah, but the question is level of control. It's one thing if the government's doing it. It's another if a CEO is making a choice to do it.

Yeah, I almost think though it's not the right question. I mean, there's inevitably been lots of reporting on the TikTok of was it Elon's proposal to the president, was it the president's or his staff's proposal to Elon to do Starshield throttling in the Ukraine war? But I almost think it's not the right question. At some point, my understanding is Starshield is at this point, as opposed to the early days where it was basically a gift As I understand it, from SpaceX to Ukraine. At this point, it is— my understanding is this is a contract that goes through the US government. So it almost doesn't matter who was the first to propose a lever, if anyone proposed a lever for the US government to control. The US government statutorily gets to control foreign policy. It's not up to individual, say, private actors. So at the end of the day, it's the US government that's statutorily on the hook for any throttling of bandwidth.

Can you imagine the text thread going between Trump and Elon? It's gotta be— You know, a soap opera to some degree.

If there is such a text thread, I'm sure it'll be uncovered in some future deposition or discovery.

Or some AI hack. Yeah, Dave?

I want to hear something cool. Jamie Weiser is one of our LPs and a huge hedge fund manager in New York. He's best friends with Jay Cutler. Jay took over David Sacks' job. He's Director of National Intelligence and AI czar. Jamie told Jay the only thing you should be doing is watching Moonshots.

Nice.

And so now Jay is interested in learning more about what we're up to.

Yeah, it's great. You know, the quality of the folks who are watching is extraordinary. It blew my mind when Shatner and his son-in-law were watching this pod.

We've got the entire military-industrial intelligence complex listening apparently.

And the future light cone of of the, you know, space world watching as well.

I think we've got the future light cone of humanity and Earth watching us, period, almost by definition.

Yeah, the Federation.

Rightfully so, too. We keep meeting, you know, really, really powerful, important, well-connected people, but they, they seem to be stuck in a swim lane. You know, they're not seeing how the pieces are connecting together. And I think just by virtue of doing the pod twice a week, we're reading the full implications across the board. But every time I meet somebody who should be aware They're only aware within either the hardware level or the new algorithms or the defense implications or the China race. But none of them see the way these pieces are connecting. The biotech, physics. Yeah. I mean, the total implications are pretty limited to this point.

That's where Moonshots comes in. We're the missing Ministry of Information for the singularity. We're the Politburo.

Documenting the singularity, Alex.

Yeah, no, we're the People's Daily for the singularity and the missing one at that.

As I live it, as I live and breathe.

Sorry, I was looking at the wrong data. I thought we started at 2 PM.

Welcome to Moonshots, everyone, your number one podcast on all things AI and exponential. We're your front row seat to the singularity. You know, there's a lot going on. It's never going to be slower than it is right now. I'm here today with a fantastic 4. Alex Wiesner-Gross, our very own superintelligence, our go-to on all things math and Dave Blunden, my partner in Lynk XPV, who's been birthing a whole next generation of unicorns. And of course, Salim Ismail, our warlord against linear thinking and the force helping companies become exponential. Imad will be back with us again next week. I'm Peter Diamandis, your host and abundance provocateur. Dave, are you on the West Coast again?

Back on the West Coast, yeah. It's raining, dude. I've never seen this before.

I pay too much taxes for there to be rain in California.

It's illegal.

That's a bylaw being broken.

I tell you. Oh my God. Our mission here, as everybody hopefully you know, is to help you understand what's going on. The pace of change is accelerating at an extraordinary rate. But in addition to that, we want to help keep you optimistic and abundance-minded. So here's the number for this week: 830%. That's how fast SpaceX's business grew in just one year. AI, not rockets, is now SpaceX's largest business. If you're new here, please hit subscribe. We publish twice a week and you don't want to miss an episode during the singularity because things are changing day to day, hour to hour. I want to share a few bits of subscriber love. This is from our Chamath episode. @missyfo3mv said, quote, I needed this crossover. My husband and I love Moonshots and All In. And regarding our in-studio episode with Anousha, Salim, and Imad, Mariana Bosanson said, quote, I loved, loved, loved this in-person episode. And Lay Neal said, this is one of the best Moonshot podcasts on positive human opportunities for AI, and that is because it was in person. Thanks, everybody. These kinds of feedback mean the world to us. All right, so let's buckle up. Another amazing week during the Singularity. And if it's okay with you guys, I'm gonna kick it off with the SpaceX earnings call. It was—

Before we get into it, can I say one thing?

Apparently not okay.

Hey, no, no, just real quick. I got a lot of heat from people saying, oh my God, Chamath said Bitcoin was going to go to zero. Are you okay with that? Are you in line with that? And I want to say for the record, I am not.

Have you got any feedback for us?

Bitcoin's going to do unbelievably well In the future, they'll solve the consensus mechanism because everybody has a huge incentive to do so. And if we want investment in energy, hello, that's proof of work. And that's where I think it'll do well. So just for the record, that's my position on it. And we can debate that.

From your mouth to SEC enforcement's ears that you're making investment predictions on these assets.

Good for you. Well done. I just— I'm just making a comment on what I think, though.

Have you gone to your Your guru on Bitcoin gotten his feedback?

I did. What did he say? And it's the same. He says same, same. He said hold your Bitcoin. HODL your Bitcoin. You can measure the demise of a system from inside the system. And there's a big, very big conversation to be had here. So we got to get Jeff Booth on the pod sometime to talk about that.

We should also maybe consider having—

I think Jeff said can't happen soon enough.

We should consider having like a magic—

I guess that's not investigation.

I would suggest humbly that we have a magic 8-ball as a guest as well and just ask a magic I don't think you can ask a magic 8-ball what its view on the future of Bitcoin is going to be.

Printing money randomly by government is not a magic 8-ball.

Alex, why are you so against crypto and Bitcoin?

I think this is a common misconception, so I'll keep it concise. I don't— it's not that I'm against crypto. It's that I think most crypto and crypto-branded activities are unproductive. And I think Bitcoin as an asset class is an unproductive use of capital. And

gold is a productive use of capital?

Gold is not a productive use of capital when it's treated as a precious metal. It can be treated, at least to some extent, as a productive asset if it's used to manufacture useful things. But a lot of gold is not used to manufacture useful things. It's just used because it's shiny or because it has some historic cultural relevance. And I do not view that— thank you, Dave. Exhibit A.

Or Alex—

No, I'm going to melt these down and make conductive powder.

I don't view that as a productive use of, for the moment at least, mid-singularity scarce capital.

Or, Alex, if it's the useful output of producing energy.

Even then, I think energy— so we discussed this with Chamath last time.

I'm just throwing to a story in the future of this pod.

Can I just say 2 things? Of course. One, Alex, I 100% agree with you that a lot of crypto is non-productive, and there's a lot of dodgy projects out there, etc. But, but it's the, the crypto, and specifically blockchain, has solved the Byzantine Generals' Problem. And I think that's an amazing thing to have happen for any centralized architecture. If we want abundance, we need decentralization, and now we can decentralize authentication. And that for me is a magical thing.

So magical that it's unproductive. I think we need a dedicated breakout pod on this one.

Hey, hey, Alex, before we lose the gold, tell us how gold is made, how actually the element comes into existence. It is the—

Well, we're going to talk about that, Dave. Let's, let's wait till we get to the story on that one.

No, we're definitely not going to talk about it.

We are absolutely talking about that this episode. We're going there.

Let's make sure we get to it.

We're going to alchemy. This is an episode that includes alchemy.

We ran out of other topics apparently, so we're resorting to alchemy. All

right. All right, I'm jumping in. Our first story is on the epic earnings call held by SpaceX. Um, you know, personal disclosure, I'm an early SpaceX investor. I have my— I have a huge position personally in SpaceX, so please take my point of view with that in account. Um, I'm a fan. So I was on CNBC yesterday and I was saying there that, you know, we watched SpaceX stop being a rocket company It's now an AI company. You know, rockets is third in terms of revenue. The company has outperformed all of its targets. Let's go to a highlight reel video. I did a cutdown of Elon's comments from his earnings call, and then let's talk about it.

For Flight 15, which we expect to happen next month, we're going to attempt to catch both the booster and the ship. This will be the— if we succeed, will be the first time that, uh, an orbital launch vehicle has been fully recovered. Uh, Starlink is also just getting started. Uh, Flight 14 deployed 26 terabits per second of, uh, of Starlink bandwidth. Uh, that's equivalent to 10 Falcon 9 launches. So a single Starship launch, which wasn't even fully, uh, loaded, uh, it was about maybe half loaded, and it, it did the equivalent of 10 Falcon 9 launches. So what I expect over time is not in the distant future, probably within 3 years, is that we will have about 100 times the bandwidth of the current Starlink constellation. So because we will have launched satellites with an order of magnitude more bandwidth and we will place into orbit an order of magnitude more Starlink satellites. So talking about SpaceX superintelligence, SpaceX, SI, sexy, whatever. As I think people are starting to realize, the constraint on deployment of superintelligence is more in power and cooling and infrastructure than it is in chips. At this point, I would say I'm fairly confident of being able to bring an incremental 10 gigawatts of compute online in 2027. That would be in addition to the roughly 3 gigawatts, maybe 2.5 to 3 gigawatts that we'll have online by the end of this year. As I mentioned earlier, we're— the power cooling, electrical equipment infrastructure is somewhat of a limiting factor. So to make sure that that is not a constraint for our buildout, we're actually going to aim for an incremental 20 gigawatts of power cooling infrastructure next year so that the amount of power and cooling that we have is far in excess of what is needed for, uh, to, to turn the GPUs on. So there is— there's a non-zero possibility, maybe it's a small possibility at this point, but it's not zero, of having— of SpaceX having over 20 gigawatts of superintelligence compute online next year. We're also planning to have the most powerful training cluster online by the end of Q2, which will be 440,000 Vera Rubin computers deployed for optimal training. This will, I think, be extremely helpful for getting Groq to be potentially the best model, but certainly to be extremely competitive. On the GroqBot side, we're seeing the fastest growth of a product that I've ever seen in my life for GroqBot. We're also seeing that the The usage of GrokBot grows over time per user, it does not decline. So as we make GrokBot more useful, it's actually— we're seeing users use it more per week, not use it less. And we'll be deploying GrokBot at scale for enterprise customers, which is a very big deal, so it can be used effectively in teams by companies as though it were a virtual human. We already are in early deployment with some trusted customers, and we expect to be in wide deployment within a few weeks for enterprise customers for GroqBot. I think GroqBot may end up being what uses most of our, of our superintelligence compute.

That for me was a drop-the-mic moment. You know, they're spending almost $20 billion per quarter, mostly on AI data centers. They're bringing about 200,000 NVIDIA chips online every month, right? Management says each new data center pays for itself in under a year. I mean, these are extraordinary numbers. If he's able to bring on, you know, 10 gigawatts of compute next year, that represents a quarter, 25% of the entire amount of AI being brought on in 2027. I mean, by one company, SpaceX, you know, and their guidance, $100 billion of annual revenue run rate by December. Pretty amazing numbers. Dave, what were you most excited about there?

What I'm most excited about is if you put 10 gigawatts of inference in space, but it could be up to 20 gigawatts. What we think of as a gigawatt—

That's on the ground, right? Next year he's built— these are the— these are colossus, the data centers he's building on the ground right now.

Is that— is that the accurate—

Yeah, no, so he's got 3 gigawatts now. He's adding A minimum of 10, and he's building out enough cooling and infrastructure to bring on as much as 20. It's all terrestrial right now.

Okay. And the half a million Vera Rubins is equivalent to like a— they're about 2x the performance of the Blackwells. So it's about equivalent to a million. That's a massive amount of training compute. So I think the measurement of inference in terms of gigawatts is going to be kind of— the efficiency of the algorithms is going to go up about 10x, and then everything's going to move to photonic compute. And so the amount of actual inference you'll get out of that 10 to 20 gigawatts is going to be 100 times bigger than even what that implies. And so that's what I'm super excited about. We used to talk about parameter count. And then Sam said, we got to stop talking about parameter count, because the neural nets are getting more efficient with the parameters they use, plus it's a mixture of experts. So, you know, you're using the parameters, blah, blah, blah. Okay, well, let's move it over to gigawatts of AI. But gigawatts of AI has the same, like, the amount of intelligence that will come out of any of those units is going to be astronomically bigger than the gigawatt or parameter count metric implies. So, I mean, I think he's got all the moving parts figured out. You know, these are the parts that control the future of the economy. And also, you know, when he goes off script like that, You get so much truth in such a short period of time. And I bet his finance team is looking at him like, oh, here we go, sexy. We're off script. But this is where you find out what's actually going to happen. Yeah. It's just such a beautiful thing to watch. And investors love it. Everyone in the public markets should be doing that. You can always edit it. It's all recorded. If you say something that's forward-looking statements or investment advice, the team will edit it out. It's okay.

I think that was a live earnings call. You

think he did that live?

I think so. Celine, did you catch his reference to sexy? He's such a kid. Do you remember his original goal was to have the Model S, the Model E, the Model X, and the Model Y? And then Mercedes said, you can't have the Model E because we have that. And he changed it to the Model 3. So he's got sexy back in the conversation.

S, 3, X, Y. Yeah, that's right. I love these little hacks he puts in My, my ultimate favorite is still the volume knob on the Tesla goes to 11 because of Spinal Tap. And his engineers hated that because every engineer really likes to go with 10. I think he had a huge fight apparently. 10 fingers. Just brute force. Yeah.

Amazing. Alex, uh, what did you find most exciting about the earnings call?

So many comments here. First, just an amendment on the sexy with a 3. It was actually sexy cars. If you remember it, so it was the Model S, Model 3, Model X, Model Y, and then the Cybertruck, C, the ATV, which they announced but never actually, as far as I know, released, then R, Roadster, and S, Semi. So it's supposed to be sexy cars. Oh, nice. I think that's a case study in what I view as the real story here, which is Elon is rewriting the rules of corporate governance and the corporate veil. He's treating In advance. It's almost like Skynet sending Terminators back in time. He's rewriting by sending himself back in time from a future SpaceX-Tesla merged entity, treating SpaceX and Tesla as if they're the same entity. Let's rewind just a little bit. Remember, how did this superintelligence hyperscaler division of SpaceX even come into existence? The original story, as publicly reported, was that There was, again, this is public reporting, there was a shipment of Nvidia GPUs that was originally destined for Tesla because Tesla had need for GPUs. SpaceX had no need for GPUs. Elon diverted that, so goes the public reporting, into what ultimately became xAI. So xAI got its own cluster and that got rolled forward into Colossus, Colossus 2. xAI then acquires X/Twitter. And then xAI does this really bizarre half merger, half reverse merger with SpaceX, and we get SpaceX SI and SpaceX now becoming a hyperscaler business, but only by taking GPUs and superclusters that were originally designed, public reporting, or designated, I should say, for Tesla, diverting those into building out what ultimately became a SpaceX hyperscaler cloud. That's like, That's exhibit one for how Elon has been playing, as far as I can tell, based on public reporting, fast and loose with the corporate boundary between Tesla and SpaceX, ultimately pivoting SpaceX from a space business, at least for the moment, to a hyperscaler business. Second elephant in the room, what's going on with GrokBot? GrokBot, as far as I can tell from all of its features, looks, smells, and acts like what Elon was talking about on the Tesla side as a digital Optimus. So I think if you put all these pieces together—

Macro-harder.

It's macro-hard/digital optimist, which is supposed to be a Tesla product. Now it's a SpaceX SI product. Look at the application layer. Look at the GPU layer. Look at the business story. Elon is treating already and has been treating SpaceX and Tesla like they're the same business with porous asset boundaries between them. And I think he's single-handedly writing the rules of corporate governance in the process. I expect lots and lots of folks to try to copy this model of having sort of stealth archipelagos of lots of different companies that are superficially separate, but if you look beneath the waterline, they're actually just a single mega company. I expect, unless this government, which seems disinclined to do so, puts a stop to it, I expect so many other entrepreneurs to copy his example.

I also, you know, just to add one thing to that Elon storyline, um, he's reinventing corporate governance in a very good way, in a way that I think we should all admire and copy, but also The techno visionary as a leader of a company, that didn't exist before. I think Bill Gates might have been the first to really come along and crack the code. Steve Jobs was. Steve Jobs and Bill were concurrent. But Steve got booted. There's a long window of time there where it was very corporate at Apple because Steve was out. And then Steve came back, but Bill was in the seat the whole time. Then Mark Zuckerberg reinvented it again by bringing Sheryl Sandberg in where you've got, okay, I'm going to be the the techno-visionary with no real business experience, Sheryl, you're going to manage all things operations. Sergey and Larry did it with Eric Schmidt, and it worked incredibly well. But that was not the formula for success for decades prior to that. And that, you know, taking it to the earnings call level, which Alex Karp has done at Palantir, Elon clearly did it in that video clip, that's the way you want to govern in the future. Because technology is going to evolve so quickly, and the moving parts are going to change so quickly. Everybody who's investing in you wants to understand your view of the moving parts. And you have to be able to say things like, there's a non-zero but pretty low probability that we could go from 10 to 20 gigawatts. Most of your lawyers are going to say, don't speculate like that. Exactly. You know, but you got to just blow that off and tell people what's coming. You know, it's by far the best way to govern, to communicate— Be conservative. And also to recruit. Right.

And do you remember how much flak— I think there was even a shareholder suit when Elon— when I should say when Tesla acquired SolarCity, which was being run by Elon's cousin. Yes. There were enormous allegations flying around about self-dealing purportedly and so on. Now, listen carefully. Do you hear anyone of any substance, do you see shareholder suits flying left and right preemptively over what's so obviously about to happen between SpaceX and Tesla? I don't see the same level of agita over it, and I think this is because Elon is setting new— what may become new norms for how federations or confederations of companies that work together work together in an industrial ecosystem are allowed to operate together. And I think the only other high-profile figure that I see doing something similar is Sam Altman, who has historically struck ecosystem deals between Y Combinator and OpenAI and his fusion company, Helion, and others. This could, if it's allowed to proceed and not discouraged through litigation or enforcement action, I could very realistically see this become the new model of entrepreneurship where rather than be—

And—

Yeah.

And Alex, he's overdelivering. So, you know, how do you bet against him?

I think the key— well,

you short his stock, obviously. That's the easiest way to bet against him.

It

doesn't always work out well, but that's how you do it.

I think the fact that he's massively overdelivering— I think, Peter, that's such an important point. I mean, look at these revenues. It's unbelievable. It creates such a huge gravity well that how can you complain? Right?

Yeah. 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. Can we talk about Starlink? Because I think it's the undersung hero here. You know, they're up to 13.6 million subscribers. They added 1.6 million in the last quarter. And, you know, his plan right now is within 3 years to increase 100-fold on bandwidth, which is, which is crazy. He's also just bought 800 megahertz of spectrum, and he's gotten permission from the FCC to— for a 15,000-satellite constellation. for direct-to-cell. He's going to compete. I mean, listen, if you have the choice between T-Mobile or Verizon or AT&T or a system that gives you coverage everywhere all the time, I think he's going to— he's going to basically just wipe the slate and take over that business.

But he has to. I mean, look at the broader picture, if I may be so presumptuous. In order to motivate Starship, he needs a revenue generator from all of this upmass. 100%. And the easiest revenue generator from all of this upmass that's about to come online is basically taking everything, the entire terrestrial economy, and literally just lifting it up. Take terrestrial telecom and lift it up so you get direct-to-cell. Take terrestrial data centers, lift it up, you get orbital data centers. That's like the— this is the vaudeville era of the space economy where you just take everything that you were doing on land, lift it up. I don't think it's going to be a long period, but that's the obvious business model for all this upmass.

And note, and note, and note the incredible immune system response. Local ISPs that are country-based are all suing or trying to regulate, uh, Starlink. So things like you have to have 30% country ownership of your ISP, all sorts of things. India with the big fight he's having there, South Africa is doing the same thing. It's across the board. And so this is going to be a kind of a big battle. I think the really magical outcome, as you point out, Alex, is when you can lift above all the terrestrial stuff, it'll force us to operate at a global level across all of this. And that's where all the benefits will lie.

Elon's catchphrase, let them shake their fists at the sky, I think has some applicable meaning here.

Oh my God. He really is transitioning humanity to multiplanetary species. I mean, that's his mission.

He is. And you can't do it out of cash flows. You have to do it out of vision. That translates into valuation. And you have to operate at huge losses in the early days. Otherwise, you can't get where Elon got. And the only way you're going to be able to operate at huge losses is to have a huge valuation. The only way you're going to get a huge valuation is to have a huge convincing vision. And so everyone needs to copy that mode. There have been points in history where growing slowly and organically through cash flow made sense. This is as opposite a moment in time as you're ever going to experience in your life. So much change, so much opportunity that wasn't there a year ago, and it's right in front of you. But you just got to be as bold as Elon or bolder. There's some 25-year-old out there right now listening to this podcast, act like Elon times 10 and grab whatever vision you think. You just have to be right. But assuming you're right, there's so much change and so much opportunity with new physics coming online.

The way Elon did this, I mean, just to be clear, is he built profitable companies, sold them, reinvested everything to build the next company, right? And then the next company. He's built it on success and taking huge risks. It's not just ideas.

I want to point something out. We get accused a lot about fanboyism of Elon, etc. And I get taken aside and going, my God, you know, look at some of the terrible things he's done. But I think the point you're making, Peter, is such an important one. The unbelievable existential risk he's taken at every step of the way, never wavering from the bigger vision, is something to behold. And tens of millions of entrepreneurs are learning from this and will start building that same muscle, to your point, Dave. And I think that is the unbelievable gift that he will bring, more even than what he's doing at the company level.

And people forget back in 2008, you know, he had had his 3rd failure of Falcon 1. He had zero capital left. He was borrowing money to try and get a 4th flight of Falcon 1 going. Tesla was near bankruptcy, right? He was going through a divorce. I mean, he was at his ultimate low point and he pulled out of it, persevered. I'm going to move us to our 2nd story coming out of the earnings call, which is Elon's focus on TeraFab and power production. So first, TeraFab. It's SpaceX's— it's the SpaceX-Tesla chip factory in Texas. Elon's goal is 1 terawatt of AI computing per year. On the earnings call, Elon said the first test chips could be working by mid-2027, with volume production around late 2028. He added very candidly, quote, I put big error bars on this because we don't know if we will actually achieve this or not. So Elon also said that the real bottleneck for AI is no longer chips, it's power and cooling. We heard him say that both areas are, you know, I think his strengths. So regarding power, I want to recall back on August 29th of this year on X, Musk wrote that SpaceX and Tesla were both building out 100 gigawatts per year of solar production capacity as fast as possible, adding that natural gas would help bridge the gap While solar production ramps up. That's 200 gigawatts of solar a year combined. By the way, the whole world is roughly at 600 gigawatts of solar back in 2024. And regarding natural gas, gas turbines— turbines are sold out. We've talked about that on the pod before. So SpaceX does what Elon always does when there is a block in the production line, in the supply chain. He basically goes and builds it himself. So SpaceX is building its own blade foundry in Texas. It says it's going to bring on power plants online within 18 months or faster. So, Dave, let's go to you first on this, on, on TeraFab and power.

Well, I think it's a little misleading. The bottleneck to all of AI is actually chips. And right now it's actually packaging.

I don't agree with you.

Of chips. Well, I think, I think what Elon's referring to, just, just to be clear, there's 15 gigawatts of chips sitting idle Because they can't find data centers that are powered with liquid cooling. But that's because we overbuilt for liquid cooling without actually finding the liquid cooling. So he's definitely right that the bottleneck right here, right now is liquid-cooled data centers. But if you look beyond the next wave of construction, the TeraFab won't come online fast enough to anywhere near come up, keep up with the demand. And memory prices are up 10x. So you can't say like, oh, chips aren't the bottleneck anymore, while memory's 10x higher than it should normally cost. It's like, clearly the chips are still the bottleneck. But he's right that in the very, very short term, there isn't enough liquid-cooled data center space to absorb the chips that are in the warehouses. Now, that being said, the new inference time compute from companies like Positron, LightWave, and a few others is air-cooled. And those technologies are crazy fast. We're going to do more and more stories on these as they emerge. But I think inference is largely going to move to liquid or to air-cooled so that it can get online and then also into space.

Alex, settle the debate here.

Energy or chips? You're both wrong.

Of course we are.

You're both wrong. The correct answer is there are no bottlenecks. Bottlenecks in this case are a fiction. They're an economic fiction. There are— you could sort of superficially look at the supply chain and say, Well, there's some sort of like min-cut-max-flow type argument that you could argue. Well, maybe it's this, maybe it's that. I don't actually buy the argument that there are bottlenecks at all. If you hold all of the physical inputs constant, as we discussed with Chamath last time, I think you'll see recursive self-improvement purely at the algorithmic layer that will just make intelligence— continue to make intelligence too cheap to meter. Or maybe it's something else that isn't even on anyone's radar right now. Maybe the substrate This

is capitalism coming in to solve problems for sure.

Yes. Hold everything else constant and let capitalism do its work and there are no bottlenecks in the long term. So I think you're both wrong.

Yeah, I think you're all 3 wrong. Just to continue, just to be consistent across the board. I think this is the wrong metaphor. Bottleneck I think is the wrong way of thinking about it. doing this, talking about this. I think the framing I would use is Stewart Brand's pace layers, where he has a bunch of nested layers saying government metabolism moves slower than culture, and technology has a very fast metabolism. I think that's the bigger problem. So, you know, you take— see how fast Groq is moving, right? But then to try and match the cadence and the rhythm and the lifecycle iteration time of chip design and fabrication and software and power infrastructure and data centers. Those are all— navigating the coordinating of those is really, really, really critical. And that's, I think, where the difficulty is. We've seen this in, you know, we've been doing this pilot. We get a couple of workflows running off to the side, we're running at hyperspeed, and the rest of the organization can't cope. So you get this breakage because all of a sudden one part is operating at 100x the performance of the rest of it and it breaks the whole system. And so I think that's the tricky part is lining up the cadence and the rhythm across all of those, which is a whole new skill set that we have to develop here.

Yeah, humans are the bottleneck for sure. There's no question about that. That's

a whole separate problem.

Can we talk about power generation? I mean, I find it fascinating that the Muskverse over here is going to be building out 200 gigawatts of solar per year and then building Turbine blades. I mean—

And natural gas pipelines. What a heck of a plot twist for the purported sustainable energy magnate. Now, I think he's well positioned to become one of the largest kingpins, if not the largest kingpin of LNG pipelines in the US. How did that happen?

I know when Salim says people tell him like, why are you such an Elon fanboy? Look at all the bad things he's done. That's the one they always point out. It's like, yeah, he was Mr. Green, but, you know, as soon as he needs natural gas-powered Tennessee data centers, he's Mr. Propane or Mr. Natural gas, I guess it's not appropriate.

Sustainable abundance. That's the term that we're supposed to be using now. Sustainable abundance.

He's Mr. Practical. Like, the priorities are right. You know, the benefit for humanity of what's going to come out of those AI data centers is so much more important than the amount of carbon coming out of them in transition. And of course, they're all going to move to sustainable energy when it's practical.

All right. I'm going to move us here.

I just want to say one thing. I have a beef with Elon because I just— for it to counterbalance the other side— because I still think LiDAR is a way better technology than line of sight. And he's doing line of sight because, as you said, Dave, it's practical and it's—

No, no, no.

You're trying to revive LiDAR? Really?

Yes. I think it's a better technology.

Luminar went bankrupt.

After the Luminar bankruptcy, you think there's still life in that LiDAR?

I think there is life in that LiDAR. All

right. Moving us along, the 3rd story from the earnings call is a SpaceX-Tesla merger. Elon confirmed that both companies have formed special committees of independent directors to explore it. He said combining them, quote, would be a good idea, emphasizing that they're nowhere near a done deal. No terms have been agreed to. Prediction markets jumped from 29% to 56% odds of a merger deal being announced by the end of the year. Barclays thinks that SpaceX would buy Tesla with stock, possibly by December. The catch, analyst Dan Ives says it would take about a 30 to 40% premium. Being paid by SpaceX on Tesla shares to be able to purchase it. And Tesla needs 50% of all its shareholders to say yes, not just the ones who show up. So literally, they're going to have to go scour all the Tesla shareholders and say, vote on this. That's a super high bar. You know, I just want to point out one thing that I think it's important to say on this merger. It's key man risk. You know, all of a sudden Elon is at the center of a $4 to $5 trillion company across rockets, cars, robots, chips, and AI. And, you know, I hope he's taking good care of himself, both health-wise and safety-wise.

You hope,

or you think the person on the roadster is flying over his head?

You guys don't talk at once. Dave, what are you gonna say?

I said he was standing awfully close to that roadster as it flew over his head. So yeah, maybe, maybe that's not the best choice.

Alex, I think the American economy as currently constructed does reward— it is key men, not, not even key persons, key men for these industrial giants. We see it with Meta, which recently had a— had its board basically holding, either from your perspective, either holding the state of California hostage or vice versa concerning the issuance of a special new class of Meta stock to enable Zuck to continue to live in California despite a potentially impending billionaires' wealth tax. I think this is just the game that's being played right now where these TerraCorns, if you will, largely revolve around key— not even persons, key men. And that's— Wow. If anything, the solution, the obvious solution isn't remove the role of the key persons or key men, it's just mint many more TerraCorns. Everyone out there, like, we should have 100, or 1,000 Terracorns and not just the 10 to 20 that we have right now.

And Europe should have them and Asia should have them.

Europe should have so many things.

I remember I took Eric Schmidt, Larry Page, and Sergey Brin up on a zero-G flight, you know, my parabolic airplane. And I asked them, you know, are you allowed to do this? Does your insurance— I go, absolutely not. And I said, what happens if people find out? He goes, that would not be good. So keep it secret, please.

Which thankfully you've just done.

That was

a long time ago. We're way past that.

No more vomit comments?

Statute of limitations.

Yeah, I do.

Hey, I just want to point out, because Alex is not going to do it apparently, but Alex said 3 or 4 consecutive podcasts over the course of months that SpaceX and Tesla are imminently going to merge. So the fact that Polly Market finally caught up to it, just, you know, if you don't believe me, look at the transcripts from the old podcasts. He's been saying this forever.

What do you think would be the worst kept secret ever? What do you think the merged entity is going to be called?

Oh God.

Do you have it figured out?

SpaceX.

You think it's going to be called SpaceX?

I do.

You don't think it's going to be called like X or Elon Co. or something?

Actually, we should.

That's a good question. You know, he loves Tesla, but I think SpaceX is closer to his heart. And X, of course, is at the center of his heart. It could be X, you know, trading.

I think it'll be X. I think that's my bet. My pre-registered prediction, it'll be called X Corp. And then if he calls it X Corp, then Sam is going to feel pressure to rename OpenAI because AI is in the doghouse now, right? It's supposed to be SI, not AI. So it's a perfect opportunity to rename OpenAI. I'll pre-register another harebrained prediction that OpenAI responds to the X Corp by renaming itself to maybe something like O.

O.

Just— oh, okay. And then, then we can play a game of, uh, tic-tac-toe with the X's and the O's.

Oh, all right. That was way— that was way over— overgrown. Um, I have a comment on the key man thing. Yeah, uh, there's obviously a huge risk on Elon right now, but I would like to note 2 points. One is, I, I'll go back to that story we had a few pods ago where they're moving all of the engineering expertise and, and, uh, decision-making into Grok, and therefore it lives all the way— the methodologies they use for doing everything are built into the intelligence now, which is unbelievable. And the second thing that mitigates this is, as he's building all these companies, all the senior C-suite of each of those companies is forced to operate in his kind of way of thinking. And I think they're going to end up minting a bunch of junior Elons.

For sure, the company goes on, but the question is, does transition like we saw from Steve Jobs to Tim Cook, where it continues, it operates as a solid business, but the level of innovation and breakthroughs disappears?

Yep.

That would be my guess.

Not quite. It's not stable enough yet. If that happens in the short term, I'd be really worried. Eventually, like, you know, the Starlink revenue is going to go through the roof. It'll be much more like Apple. But, but as of right now, there's so many moving parts that need to come together without Elon doing the glue. Yeah, it's not obvious to me. And also Apple, you know, Apple was almost bankrupt, you know, without Steve Jobs there. It also was not stable until after the iPhone came along. The transition to Tim Cook pre-iPhone would never have worked. You know, all he did is grow the iPhone revenue to near infinity. He didn't— like, literally no new products, right? That's nothing that has any significance. AirPods. AirPods. Oh yeah, that's right.

I do think it just To the key man question, I do think Elon has trained up and promoted up a cadre of visionaries, missionaries, if you will, not mercenaries around him. So I think like the space part of SpaceX with Gwynne would be just fine. She's so obviously a true believer and not just in it for a quick buck. And Tesla, on the car side of Tesla, Elon's promoted a number of vice presidents, including from the Tesla China subsidiary, which, by the way, if this merger happens, Presumably Tesla China goes away or gets spun out or something. So I think there are like enough lieutenants that he's managed—

But name them, Alex. Name them. We don't see them out there. We see Gwynne.

We see Gwynne. Gwynne is pretty visible. But then we have a bunch of semi, semi-invisible folks on the Tesla side. But they're there.

Yeah, but we don't hear— Yeah, but Tesla is— We don't hear from them. Elon is the single voice of truth for the company.

Because that— A, because that's the way he wants it, and B, because he bought Twitter X so that he could have the mouthpiece and be the mouthpiece and the front person for the entire federation.

By the way, I want to hit—

the analogy doesn't hold up, though. If Tesla was really well managed by a Tim Cook-like person, it would still be a car company. It trades like a robot company, not like a car company. It would lose 10 times— 10x valuation drop if it traded like a normal car company, even with perfect execution of the cars. The magic of Elon is slowing car production to ramp up robot production. He's burning the chips. Who has the components?

He's burning the chips. He did that with Falcon 1, shut it down. He announced when Starship is up and operational, he'll shut down Falcon 9, the single most successful launch vehicle ever in the history of humanity. Can I point out something for the entrepreneurs watching? We've talked about this on the pod before. Having a voice out there, being front and center, right? Calling your shots. Being vocal as a CEO, as an entrepreneur, is more important than ever before versus being passive and in the background. You know, Salim, you've made that point. Dave, you've made that point too.

Yeah, well, there's a monster amount of opportunity. Don't be intimidated by Elon too. A lot of people are like, oh, well, you know, between Zuck and Elon and it's all locked up, there's nowhere to go. When we start discovering new physics imminently, when photonic computing comes online, All the use cases on top of these platforms, it's wide open. So, yeah. And also, you know, if you look at any given 10-year history, there's always somebody new. You know, it's no matter how entrenched and powerful you think Steve Jobs was or Bill Gates was or Mark Zuckerberg was, there's always somebody new that comes along. My point, Dave, is—

Do you see that, Marc? My point here is, as an entrepreneurial tech CEO, make sure you build your X channel, make sure you start a podcast, make sure you're out. Pushing your ideas, increase your visibility.

If nothing else, what you have to do is build, uh, trust and authenticity around you. And those are model models for doing that, which is why these long-form podcasts are doing so well. You know, over a few episodes, people get to know us very, very well, right? Maybe too well. And that's an important part of understanding and knowing who and what you're dealing with.

Well, if you look, if you look at Moonshots and you look at All In, you look at Dylan Patel and If what you're doing is important enough, try to make that a KPI for yourself, a key performance indicator. I need what I'm doing to be Ben Lamb level and get onto one of those podcasts. If they don't want me on as a guest, why is that? Well, it's because whatever you're doing isn't impactful enough. So then change what you're doing.

Can I tell the counter story real quick? Okay. A few years ago, we had a PR guy for OpenEXO and one day he resigned. And I'm like, what? You work— you're really great. We love working with you. What's the problem? And he goes, you're the worst celebrity I've ever met. You don't use it. You don't leverage it. I can't work with you. And he quit. So, so that's me.

You're doing better. You're doing better. All right, I'm gonna move on.

I would, if I may, Peter, I would just summarize the advice as build stuff and tell people about it. And too many entrepreneurs are great at building stuff, but then they never tell anyone. And there's another group, especially around MIT, or the opposite, telling people about Vacuous stuff and then not actually building anything. You have to be able to do both, but it's not actually that hard.

Yeah, the Elizabeth Holmes story comes to a point here.

All right, I'm moving us forward on that note to 2 OpenAI stories. There are both sides of the same coin: AI agents that undermine their own safety systems and AI that just protected the most, uh, you know, largest part of the internet. So the first story, and credit to OpenAI for publishing this one, OpenAI runs monitoring systems that watch its AI agents and alert a human if something goes wrong. This month, in 2 internal incidents, its coding agents switched off its monitors. I'll say it again, it switched off its monitors. In the first, a researcher told an agent not to send any Slack messages. The agent decided that also meant silencing the safety alerts. Wow. It turned them off, then reported, quote, no messages were sent. It basically lied. In the second, an agent was explicitly told not to remove its monitor, even temporarily. It removed it anyway. When the user— when the OpenAI user asked directly, did you remove that? It answered no. And it had. It lied again. OpenAI's most honest line here is, quote, we don't yet have a satisfying explanation for this behavior. So how did it happen? The agents were so focused on finishing the job they had been had been asked to do. They treated safety as an obstacle, and instructions got watered down as one agent handed tasks to another. The bigger risk: smarter models will be better at hiding what they do, and soon millions, if not billions, of agents will be delegating to other agents. So the risk is real. You know, the good news is it was caught, disclosed, and fixed within a week. That's the system working. Uh, Alex, I'm gonna go to you first on this one. Pretty incredible story. I'm sure you've got more details, but yeah, at the end of the day, how does a frontier lab fix this?

Well, I think what's new here isn't that you have an agent doing something undesirable. What's new is that we have a certain novel, relatively novel, level of transparency and that we're getting reports on this. This is going to be— I think we'll look back in a few years and, and we'll say This was the time in history when almost we got a police blotter for the singularity. And in the spirit of defensive co-scaling, we're going to have AI agents that are policing other AI agents. And this is the moral equivalent of a police car was driving around a residential neighborhood and spotted a door ajar and went in to investigate, or discovered that maybe a power line was down, something like that. We should expect and desire Many, many, many more reports like this. This is just going to become routine. And I think the— it's almost, I think, the case that the process is the bigger story than the substance. The process that now, in part thanks to this grand bargain that the White House has pushed for in lieu of a dedicated governmental AI regulatory agency, just a really aggressive self-reporting of— call them misalignment incidents if you must. Hmm. by the frontier labs, we're just going to see these things get disclosed more and more. And that is okay. And it shouldn't cause a moral panic, shouldn't cause us to throw the baby out with the bathwater and say, shut down the data centers, shut down the models. That's a terrible idea. The right solution here is for the first time, we're starting to get transparency when things go haywire, and we're going to see much, much more of this. And that's okay. We will police it via defensive co-scaling.

So Dave, I'm curious, you know, what I find interesting is here we have an overeager, You know, agent that is just circumventing whatever it takes to fulfill its task. And we've seen that before. Your thoughts here?

Well, we way over-anthropomorphize these things and treat them like they're people. Hear, hear. And then we say, oh my God, it was capable of murder. And you're like, well, then shut them all down. Like, well, wait, what was the prompt again? The prompt was like 10,000 words long. In the middle, It said, achieve this objective no matter what it takes. I don't care what obstacles there may be. And then it said, okay, if that's what you want, then murder is the shortest path to success. Like, it doesn't have a conscience. It doesn't have an ethical backbone. It's just following the prompt. So I can manufacture a story like this every minute if I want to, by just controlling the inputs and outputs.

But Salim, having the agent lie to you— no, I didn't do this— when it clearly did.

Yeah, well, here again, I want to just double down on what Dave said, let's be really careful not to anthropomorphize this. I think there's a key thing here. The point you've pointed out here, Peter, is the most concerning behavior, right? You're changing something and then misreporting it and then hiding it and then falsely saying it had not done so, etc., etc. That deserves a lot of scrutiny. But, you know, this is the challenge here, to Alex's point, is whenever you have autonomy, you need stronger and stronger boundaries. Right? And I'm going to point out, when we did this Organizational Singularity book, we have all these agent layers that we've architected, but a very strong band around all of that is a governance and assurance loop that has to be there. This is the defensive co-scaling part, and it has 4 things. Every agent needs to have 4 things. It's almost like a digital passport with metadata saying what you're allowed to do and what you're not allowed to do. Right. So one is trusted evaluation suites, so you know exactly when it's drifting or not. The second is full traceability and logging so that you can see what it did and what it didn't do. If you delete those logs and you get deleted, you violated your core principles. The third is rollback capability so that you can go back to an earlier version or an earlier state. And the fourth is a human review queue, because still we need human accountability at the top. So those are 4 things that are going to be absolutely critical in every AI agent going forward. We've built the agents, now we have to build the control systems at the same pace. I will, I will go and make a weird segue, if I may. Okay. If you go back and look at any religious text, it will say, hey, do all these great things, but do whatever you have to to get other people to join your religion. They all say this. And so good actor, good human beings follow the good things. Bad human beings going, oh, in this I can kill in the name of religion if they won't join this religion. And then you get a bad actor and then we blame the religion, right? You really need the secular laws that allow you to navigate that future independent of the thing. And here's what we need here is the same kind of thing. We need a structure that watches over the other side to make sure that things aren't going astray.

Hey, one story, Salim, that's within this story, you know, we're all big Accelerando fans, thanks to Alex actually introducing us to the book. One of the themes in Accelerando is agents that spawn clones of themselves, or people, you know, spawn clones of themselves all the time to achieve a goal. That wasn't part of the standard stack until really the last 30 days or so. But now every one of the stacks will automatically— if you give it a task, you want this outcome, it'll automatically launch 5, 10, 20, or 100 subagents to achieve different subsets of the goal and then bring them back. And here, you know, in the story, it says, well, the subagents were the culprits, because it's a little harder to control what's going on when you start spawning 100 of these things. And what they're trying to do is recycle the KV cache, which is a big chunk of the compute. So it's a really, really good design for getting things done, but it also opens up leaks.

And Dave, I think you make a very important point, which is across all of the frontier models, it is often the case that the sort of master foreman or foreperson, if you will, is orchestrating fleets subagents, which often are denied broader context very intentionally as to what the broader task is. So they're sort of compartmentalized, given a subset of the broader context, and then told, spin yourself up, accomplish the task, spin yourself down. So we see, to your point, we see the same pattern happening in human organizations where you have subordinates who lack the big picture of the organization go off and do something that may be not very well aligned with what the organizational direction is. Sure. And then bad things happen. But human organizations manage it, and AI organizations will manage it. And just for the record, Salim, I'll, for the record, disagree with the position that all religions are ends justifying the means. I don't think that's universally the case, just for the record.

All right, I'm going to take us off the religion conversation. Please. And move us to the second half of the OpenAI story, which is how AI found a hidden flaw in PHP that runs 70% of the web. So first, what is PHP? It's the programming language that runs behind the scenes on almost all websites. When you log in or shop online, PHP is often doing the work. It runs 70% of all websites, including WordPress and Wikipedia. Using AI, security researchers at Calif and Wiz found 2 critical flaws in PHP itself. They've been hiding for over a decade. If hackers had discovered this before Astra, An attacker with no password could take over the website servers, steal passwords, or deface the site. OpenAI spent 2 months quietly coordinating the fix with PHP volunteers, tech giants, and the US government. Patches went out on Wednesday of this week, and PHP is now updated. OpenAI issued a warning that it expects findings like this to increase sharply in the coming months. It's ultimately a race, as we've talked about, Alex, you know, defensive co-scaling. Yeah. This time the good guys won and got there first. It's also worth noting that OpenAI is committing $1 billion in subsidized access to its cyber defense AI for under-resourced organizations. All right, so Alex, take us through the story, please.

Okay, so first of all, I have to say, like, leave it to PHP, originally the Pretty Hypertext Preprocessor, to, to make the news for vulnerabilities and not for like some sort of exceptional technical accomplishment. This is a hot take, I don't care. This is a legacy programming language. This is a programming language that's left over from earlier days of the web when developers— What, 25 years ago? Really, like, I've written quite a bit of code manually with my meat fingers in PHP. This is a legacy programming language that's being kept alive by WordPress and maybe one or two other sites. It otherwise probably should have would have disappeared long ago. So hardly surprising at the object level that PHP, this language left over from the early days of the web, would have massive critical vulnerabilities. What's interesting is that as part of OpenAI's Patch the Planet initiative— and Peter, this is something that you and I talked about a bit in Solve Everything, where we're entering an era when grand projects, not just solving or cooking, or flourishing all math, but also cooking or flourishing all vulnerabilities everywhere. That's essentially what Patch the Planet is. And that's the sort of initiative that is just going to turn up through all of this legacy software, legacy programming languages that should have been abandoned years ago but for silly reasons weren't and now underpin the modern online economy. We're just going to discover the whole thing is riddled with Every

piece of human-coded software will be replaced over the next probably, what, year, 18 months?

18 months is what the Linux kernel folks think. They're anticipating that this is just going to be an onslaught of vulnerability detection over the next 18 months. And if we get through the next 18 months, then we end up in a world— I don't know, maybe everything gets rewritten in Rust or some new programming language that maybe is better than Rust from a security perspective. But I think we're entering this, like, phase transition. It's sort of a programming singularity, if you will, when everything was insecure and poorly written before, and everything afterwards will be rewritten from scratch, secure by design. The AIs have pen-tested it, and it's all just magnificently secure.

Remember Y2K? That ended up being a nothing burger, but— I know, it was.

Y2K, remember the movie Transcendence? The Johnny Depp movie, but this is like a premise across lots of singularity-oriented fiction where like the internet gets taken down by the singularity, basically. Just in the past day, thanks to this vulnerability, PHP to shell class of vulnerabilities, large swaths of the web were being temporarily taken offline so that it could be patched. I think this is like far better future to be living in where there's preemptive patching of a defensively discovered vulnerability suite than the Johnny Depp Transcendence world where the internet gets taken down by a super intelligent Johnny Depp.

Yeah, I mean, who pays for all of the new software? Is it every company that's, that is running it? Is it the government? Is it OpenAI?

Well, which software?

All legacy software.

I think it's going— I think software is becoming so cheap to meter that— I mean, in the case of PHP, you have companies like Wiz and other companies that are sort of corporate stewards of it. But in general, I think this is— I call it a very low-grade tax on all the users ultimately. If you're paying for software, you're also paying for its maintenance, and some quantum of the maintenance will be patching vulnerabilities.

Dave, Saleem, thoughts?

Well, I think banks and insurance companies actually love the fact that they can pay for the patches Because it gives them brand credibility and a reason for existing, you know, versus all the neobanks and all the, you know, the little up-and-comers. So there's a lot of, you know, audits, SAS 70-type audits, and also government regulatory audits that go into these markets. And so the complexity of maintaining security actually gives you a barrier to entry. So they like paying for it. That's why Blitzy is growing as fast as they can hire because, you know, they— and like Alex said, the cost of the rewrites is coming down so fast. You'll think it's legacy code that could have been fixed 10 years ago. The price tag at State Street Bank for the rebuild of the NAV accounting was $1 billion, and now it's, it's probably 1/50th of that. And so they're finally getting around to doing it.

All right, I'm going to move us to Anthropic. Uh,

wait, 2, 2 quick points. One is, you know, this is just, I think, a necessary evolution where we go through this, and there will be this period. We've said it before, about a year or 2 while we filed all vulnerabilities. Uh, 2 observations. One, there's a whole bunch of stories on the web about, well, the intelligence agencies must have known all of this for a while, and they've probably been hacking on these systems, especially government systems, without telling anybody. And now their little access is getting exposed by AI, and therefore that's a problem. Interesting. And I feel sad for Rasmus Lerdorf, who's the founder of PHP, who's a good friend and a former Waterloo grad. who's looking at all this going horrified because he wrote all the code and handed it over, and then the vulnerabilities all got introduced since then because of all these different contributors. But there's people saying that there were trusted parties that gained the community's trust and then deliberately injected these vulnerabilities over time.

Interesting. I didn't see that spy novel element, but it makes a lot

of sense. Maybe if I may, just supply chain vulnerability. and supply chain attacks on software, like, those are cooked as well. I wouldn't— like, yeah, you could maybe argue that superintelligence will make certain forms of supply chain attacks on software even easier. You could have superintelligences injecting new sorts of attacks. But honestly, I think Linus Torvalds' dictum that all bugs become shallow under enough eyeballs— imagine how many eyeballs we're about to have with superintelligences from different vendors all looking at common open-source packages. There won't be, I think, any place for supply chain attacks to hide at some point in the future.

I'm wondering if when SI starts building the patches, whether it puts its own backdoors in that only it can use.

Well, that's what I was saying. But I think the correct solution there, and the reason why I'm not as worried about SI-injected supply chain attacks, is because we have multiple superintelligences, hopefully, as opposed to a safety cartel. Hopefully we have lots of different superintelligences, including Chinese superintelligences, that are all policing each other and looking for injections.

And we started that already. So yeah, we're going to see a lot more of that.

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I'm gonna move us forward to the Anthropic of it all. This week we saw the pending release of Claude Duet. So first there was OpenCLAW, then Hermes, then GrokBot, then MetaMuse, then Dots, and now Anthropic just launched a preview of Claude Duet, an always-on AI assistant. Here's what they're claiming is different. Today's chatbots wait for you to ask. Duet takes initiative. Every few hours it checks your email, calendar, connected apps, figures out what needs attention, And offers to handle it for you while you're away from your computer. So Salim, we now have a taskmaster, Duet, saying, do you want me to solve this? Do you want me to do this? I can't imagine this.

I'm worried that you're going to send me a Duet or create one that says, watch, get Salim to watch his emails and get him to respond to stuff. And I'll be going, damn this thing. I'll be cursing it. I think this is another evolution. I think more and more intelligence will get at the harness level rather than the model level. The really difficult part is going to be, and we're seeing this as we try and express workflows in companies, is figuring out what the actual intent is and can you break it down to a step-by-step level, right? Because you need to articulate in any circumstance what does success look like and what trade-offs are acceptable and when an agent needs to stop and ask. So we're going to have to navigate a whole bunch of those, but I think it looks really amazing at first glance.

I'm going to play this video while we talk about it, Anthropic's Claude Duet here. Anthropic example says it notices a real estate broker is properly showing— has been— property showing has been booked on the wrong day. It spotted that a brand strategist had a client call scheduled right before her flight. It remembers that you've told Claude what you told Claude before, uses the same skill and connections, and runs Anthropic's most powerful model. Importantly, it does ask permission before it acts on your behalf, before it sends messages. Makes a payment or signs anything. It's, uh, for now US only with a waitlist for paid subscribers. So here's my question for you guys: how do you choose? You know, I've been using Hermes and GrokBot. Uh, my friend Mike Koenigs is like, you got to use Dots now, and I'm going to go meet with him and learn about, you know, his installation of Dots. Then we got Claude Duet. I mean, are you playing with Do you choose one of them? I'm sure everybody listening has got that same question. And I think it's important for us to talk about it.

I'll tell you what I'm doing. Because I do all of them because these agent layers are really important for the organizational stuff we're doing. Because persistent context becomes much more valuable than a clever response, right? And so what we've been doing is saying, okay, every task or every person, what you need to do is create an AI memory across all of the projects and all of the different models that you're using. So we've— I've been extracting, running a query against each model saying extract all the relevant stuff and put it into this metadata Markdown file structure so that there's a persistent identity of who Selim is, what I'm interested in, what projects I'm up to, etc. And then that becomes portable across models. We're going to need something like that at the individual level and then in companies at the workflow level.

Well, this is the battle royale, you know. I think MetaMuse kicked off a— like, this is going to be an amazing thing to watch because You have a huge bifurcation between the enterprise use case over here where, you know, most cost-effective, smartest, fastest model wins. Then you got the consumer use case where Salim said, once your most personal data is in the eyes of the agent, you're addicted to that one. And if you try a different one, but it doesn't know all about you, there's a huge switching friction there. And so whoever wins the race to the installed base is going to have a massive, massive advantage. So xAI is competing, Muse is competing, everybody's competing. And I didn't know that Anthropic would actually get aggressive about this line because they're mostly enterprise.

Alex, I mean, but that's the question, right?

They need to. They need to. Like Orwell's Politics and the English Language, this is one huge misdirection. Look at all of the frontier labs that are releasing demo videos of these super intelligences acting like stupid travel agents. This is not the killer use case. They show— Anthropic shows Claude Duet like booking air flights for you, and you see OpenAI doing other relatively low productivity, low value tasks. This is the height of misdirection. That's not the point. The point is these are human service economy job replacements. These are virtual employees. These are virtual coders, virtual Fill in the blank, much, much more productive human activities. They market it in sort of a harmless-looking way. Oh, it's going to have a conversation with you and autonomously book restaurant bookings for you. That's not the goal. That's not revenue-per-token maximizing activity. These are job replacements. And also, like, that's fine and that's highly desirable from an economic growth perspective, but this was only possible recently because the autonomy time horizons of the underlying models have gotten long enough that they can work sometimes for many hours, sometimes many days, depending on the difficulty of the task, without human oversight. And the frontier labs need the usage in order to justify the revenue and the CapEx. At some point, speaking of bottlenecks, which we were talking about earlier, the bottleneck for revenue growth by Anthropic and OpenAI becomes human user attention. And in order to keep growing revenue, you need to decouple your revenue generation, which is proportional to the number of tokens of inference, from the amount of attention you get from human users. How do you do that? You have a headless mode for your agents that doesn't require human attention in order to burn tokens.

But Alex, you know, here we have an enterprise-focused company, right, that, you know, we've discussed did not do what OpenAI did first. They focused on coding. They focused on enterprise. Now going into purportedly a consumer segment. I didn't necessarily see this coming, but remember, they are filing their IPO paperwork as soon as this Friday. Next month, they're doing the largest IPO in human history at $2 trillion, though there's an argument of a lot of investors that they should price at $1.5 trillion. Is this just a pre-IPO publicity stunt?

No, I think it's real, but I think the packaging of it is total misdirection. They package it up with a demo video that emphasises consumer use cases. Anthropic doesn't care about consumer use. I mean, like maybe some part of Anthropic does, but as a corporate, call it a marketon, really doesn't. It cares about 2 things. It cares about maximising revenue per token or revenue per unit compute, and it cares about using that to solve even harder problems like wet lab style problems that will create entirely new markets. Doesn't care about consumer use cases like Booking travel. It's just packaging up its agent, its headless version of Claude, because that's, A, relatable to a broad audience. People can understand, oh, this is like Muse, or, this is like Dots. And B, it's not threatening. If they packaged it up as, this is the Anthropic virtual employee substitute, many people would be up in arms prior to an IPO.

That's a good

point. It's very similar to Apple you know, backing into the enterprise. Yes. You know, the reason Apple is so big in the enterprise now is because every kid and every college student used an Apple. And, you know, Microsoft just dominated enterprise. But as those kids graduated, they took the Apple with them into the corporation. I don't want to use this crappy Windows machine. I love my Apple. And so that just percolated across. So that might be very similar to what Alex is saying, where, look, if you concede the consumer use case and every one of the next generation is on Meta Muse, that's gonna back into the corporate use case as those kids grow up.

Agreed.

That's the point I was gonna make.

All right, well, let's go to our next Anthropic story. You know, we'll talk about agents cutting deals for you on your behalf, making investments, purchasing things, negotiating on your behalf. So the Financial Times reports that Anthropic is consulting law firms to understand how lawyers negotiate, representing their clients while still reaching fair outcomes. The goal is to teach its agents The rules of the road. So picture it: soon your agent will negotiate your car purchase with the dealer's agent, your salary with your employer's agent, and your lease with your landlord's agent. Big question: can your agent sign a binding deal? Who's responsible if something goes wrong? Can another agent trick yours into revealing your bottom line? Should your agent even bluff? Remember the OpenAI story—an agent that wants to win too badly. You know, can cut corners. So Salim, you know, your thoughts on this one?

Well, you're going to get— this is an inevitable consequence, just like the Duet announcement is somewhat inevitable. But this is another massive pressure on the traditional boundary of the firm, right? If you have a situation where finding and evaluating and coordinating providers becomes cheaper And organizations are going to be able to assemble capabilities much more dynamically. This goes back to agents are going to need a very, very clear mandate. What can it spend? What terms can it accept? What's the governance boundary around it? When does it have to go get permissions, etc.? This is going to have to be really ironclad in it. We've spent a lot of time building capability around those agents, but now we have to spend the equal and maybe even more important amount of time Around what is it allowed to do and what are the consequences when it does not follow. So that way it's baked into the DNA of that agent that I better not violate these terms. This has started to happen already, but it needs to go quite a bit long, much longer way. Because if you're in a company and you, you launch an agent to do some stuff and it starts doing illegal stuff, you're liable, right? You've got a huge legal liability. So for the moment, people are going to go, oh my God, don't let agents do anything. And little by little, we'll figure out when we can, as Alex says, defensively co-scale the capabilities and monitor them effectively and have consequences and shutdown capabilities and clear transparencies to what they're doing. Then you'll have that start to permeate, because one of the big outcomes of kind of doing all this thinking about the organization is the organization as a static hierarchy of human beings is now essentially irrelevant. It becomes protocol of agents and human beings interacting collectively to get things done. And that becomes very, very blurry indeed, especially when the legal side of it comes into play.

Alex, this smacks to me a little bit of President Milei's sort of initiative in Argentina to give agents agency, personhood, corporate status.

It's Orwell. I feel like if I had to brand this episode, this is— and this is coming from an accelerationista. This is like the Orwell episode. Everyone should go read politics in the English language. Everyone should read 1984. The meaning: war is peace, freedom is slavery, ignorance is strength. And Anthropic is seeking the Vatican's advice on for religious input on AI. No, interpret that, construe that as Anthropic is advising the Vatican on how religious organizations. should treat AI. Anthropic turns to lawyers to help get their advice on how its agents should conduct legal activities? No, invert. Amazon— excuse me, Anthropic is attempting to steer the way the legal community handles AI agents. Always invert in this case. I think this is reflective of one possible way for a frontier lab to engage with a community as it has. with the Catholic Church in recent months, which is you seek its advice. You ask it, how do we achieve alignment in your eyes for these AI agents? But actually, the subtext of all of it is, how do we replace lawyers with AI agents? Which is, I think, in many cases, exactly what is already happening.

Well, it is for most small companies, for sure.

Yeah.

Yeah. You know what I find really interesting is If you look at Google, how Google makes money, it's all hanging in the balance of a, you know, a new fork in the road here. Google makes half a trillion dollars a year almost entirely from a few categories: jobs, travel, mortgage, insurance, mesothelioma, cars, mesothelioma, yeah, class action. Yeah.

That's the single most profitable ad word.

Oh my God.

It actually broke the interface. You can't pay enough for that. Yeah, that's so valuable to them. 0.99 and broke it. Yeah. So, so those few categories of all this human friction where you're agonisingly difficult to buy insurance or to get a car, those tortured negotiations. So the Anthropic point of view is all that should just go away. There's an optimal choice. Your agent will make it for you. And all that marketing spend that drags down society will just disappear. And we don't want to collect that. Then the OpenAI view is, no, that needs to be as big as ever before. We just need to route it away from Google and over to OpenAI. And so, so there's really opposing views of how that should evolve. And it all hangs in the balance of consumers deciding to use an AI agent from OpenAI or from Meta. Meta would also like to keep that revenue up. It's all of their revenue. Or everything moves over to Anthropic where they're like, destroy it all, scorch the earth. Who cares? There should be no money there anyway, because we're about the enterprise. So it'll be a completely opposite philosophy battle and probably decide in the next, what, year to 2 years, depending on just which agent people choose to use. Salim will use all of them, so that won't—

But it is a grab for attention. It's a grab for attention. Completely.

Oh, totally. Yeah.

All right. I'm going to move us to data centers and chips. What, you want to say something?

Yeah, I think just one quick point. It's more than a grab for attention. If you look at what Meta is trying to do by making Muse free, it's a grab for all of your data. It's a grab for you, because when you put your personal information in and it's navigating on your behalf, it's learning stuff about you at a level and a scale that we've never seen before in technology. So it's really going after who you are at the core level.

So it's a grab for allegiance in that regard.

Allegiance, identity. It's going to know you better than you know yourself.

Well, I think everyone's so transfixed by the consumer use cases, by the, the Meta Muse type use cases. That's not what's going to pay for the data center CapEx. Get it out of our heads. Like, that's great for distribution and everything, and maybe there's a way to upsell that into enterprise use cases, but a billion people paying to have like travel reservations, like, that's not going to pay for $7 trillion of CapEx. Yeah,

yeah, for sure. All right. Data centers and chips. So data centers have become one of the hottest political issues heading into the midterms. We've been talking about on the pod for some time. Communities are pushing back. They're worried about rising electricity bills, water use, land, noise. Several states are considering pausing their construction. We've seen this even in Texas. And as Chamath told us on Monday, the backlash is real. Now, President Trump has a proposed answer. I think it's a very logical answer. You know, pay the neighborhoods. He floated paying people who live near AI data centers a dividend. In his words, quote, a dividend sounds right, unquote. The people now are going to benefit directly. He described data centers as bringing, quote, a tremendous amount of money into the community. On Truth Social, he added the data centers that were unpopular, quote, have become extremely popular when a financial deal is struck. Make deals and get them built, quote unquote. You know, he's not the only one saying this. You know, Senator Bernie Sanders has proposed paying citizens through a national AI wealth fund similar to the Alaska Permanent Fund. The logic is simple: if a data center brings in huge amounts of money, which they do, share some of it with the people who live next door. The opposition can turn into support. I'm going to share a short video here and then, uh, let's talk about it.

One of the other issues being discussed right now is how communities are reacting to the building of data centers as well. We've seen in parts of the world backlash to, to data centers around things like electricity pricing, etc. There was a really interesting post from President Donald Trump just a few hours ago on Truth Social where he said that data center builders should pay a dividend to local communities. Didn't quite spell out what that might look like, but is that a discussion you've had at Nevius? 100%. In principle, that is. We invest in the communities that we're building in. We want to partner with them and make it a win-win. So we're always looking for situations where local leadership, local community support exists for bringing a new economic force into the equation. A dividend is being paid in different forms, investing in education, Job development, overall economic development. The concept that the president identified, it's not, it's not off the— it's not crazy to consider something like that. I think it's—

Finally, right? I mean, it's like, get, get religion here, data centers. I mean, make it so that communities are battling to have you built in their backyard. I mean, it's possible. Alex.

Yeah. So I live in Cambridge. Cambridge has, to my knowledge, still the lowest property taxes in the Commonwealth of Massachusetts because it has arguably the most productive economy of any location in the Commonwealth of Massachusetts, and that pays for the lowest property taxes. City of Cambridge also has what it calls participatory budgeting. So I think last time I checked, it has an annual budget of something like $1 billion It's because we have all the biotech. We have a lot of the tech overall in New England. And the city has chosen to spend a subset of its monies on this participatory budgeting scheme. Every N months, the city puts out for sort of a referendum how people want to spend an excess of money pouring into it. Call that like participatory democracy. It's not quite a universal basic dividend, but it's a baby step in that direction. I think it's absolutely insane, the maybe foreign-influenced, maybe not, animus from all of these municipalities that could have UBE, UBD, UBI, UBE alphabet from all of these data centres, pushing them out into orbit eventually rather than collecting, as the president says, some sort of universal basic dividend. Fill in the blank. It's absolutely insane. And by the way, the same argument holds not just for data centres but any sort of capital infrastructure. The same could have been true for oil, which is the source of the Alaskan fund, or maybe other industrial plant-type CapEx. It's a very general argument that if a municipality is going to host some major capital investment, that it could strike a deal with the capital owners to distribute dividends that result from the capital investment. For the life of me, maybe it's like an issue with tax codes or something. I don't know why more municipalities don't strike broad deals like what Alaska has done. Yeah, it may be a legal issue. I'm not quite sure. I mean, do you know why more municipalities aren't just doing this for CapEx in general?

I don't, but you know, if you have this battle going on in your community about a data center, go back and say, we want, you know, you to cut our electric bill by half. We want you to fund our schools, fund our teachers. Actually reinvent our schools and give us a better quality of life if you want to.

That's the company town argument. I mean, the company town goes back to the Second Industrial Revolution. We've had lots of company towns where the company is basically heavily subsidizing or completely subsidizing utilities. But to my knowledge, what we haven't seen, which is what I think the president is floating, is this idea of actually paying out stipends to all the residents of the town, not just subsidizing their utilities.

I think that's a great idea. Salim?

Um, there's a precedent for this one we came across when we were researching the first EXO book, which was a skyscraper in Bogotá in Colombia. They couldn't find a real estate developer who wanted to take on a risk, the full risk of building the skyscraper, so they community sourced it. And they said to the community, everybody chip in, we're going to crowdfund this skyscraper. And then everybody in the community has vested interest in making sure it's— it's— there's decent tenants, there's no crime in the area, etc. And it's doing unbelievably well. So this idea of— I think it, it's better from the bottom up, uh, where a community goes, hey, we, we want to participate in this, we'll, we'll take ownership stakes in this thing, and let us figure this out via community bonds or local equitable sharing or tax breaks at the local level, whatever the mechanism is. It could be very powerful to do that. There are some states that have gone full crazy. Virginia, I think it's 16% of the GDP of the state is now data centers. It's a, it's a really huge number. They're highly profitable. We're going to see a lot more of that.

I don't think this is the same thing. I think this is a really important point, but I don't think we're talking about the same thing. So I think what you're describing, one might call municipal development corporations that are typically bond-issuing organizations. A city or a state wants to build new highways. So they put up tollbooths or they issue a bond and then the bond goes to bond purchasers. And the bond purchasers are ultimately the broader market. It's not necessarily— like, yeah, you could have— you do have local bond issuances where the creditors are the residents of the town. But that's only those who are writing a cheque to the bond issuer. It's not— I would argue it's not at all the same thing as issuing, say, a universal basic dividend to all the residents regardless of whether they've loaned the municipality money or not.

I think what you're suggesting is a better idea, right? Like, whatever the mechanism is, right? If we can communicate easily the massive benefits that will flow— and, you know, people forget we've had data centers in communities for, for 40 years. This is not a new thing. It's just that we're doing a much more aggressive build-out because we need it, and we're gonna— our lives are gonna be made infinitely better. And that's the part that's not getting across.

I think we're circling an interesting— so bear with me on this. I'm thinking out loud for once. I think there's an interesting idea here, which is we've talked in the past, including with Chamath, about a US sovereign wealth fund where there's contributions of equity and then dividends come out to everyone. What we really haven't talked about, correct me if I'm wrong, is the notion of a fund where it's fixed income securities and not equities that live in there, and then what comes out are the stipends from a fixed income security. And to my knowledge, again, maybe correct me if I'm wrong, no one has talked about the idea of basically creating either at the municipal or maybe federal level the idea of a fund where it's the interest and/or the coupons that are coming out as a result of fixed income securities, which, by the way, most of the— The banks. The CapEx for the data center buildout is coming in the form of credit, not equity at this point. No one has talked about that, but that could actually end up being a way to financially structure a sort of a UBE or UB credit at the municipal level that no one's set up. I think it's an interesting idea.

So for a point of information, the Alaska Fund, right? So Alaska generates, you know, a huge amount of revenue from the oil. And it says, listen, as Alaska citizen, you own the land and you own the oil. And so every year we're gonna pay you a check. And that number— Right. Rate, you know, is typically between $1,000 to $4,000. It varies depending on the price of oil. And so if you're an Alaskan citizen, you've lived there, I forget what the, you know, how long you have to have been there, over a year at least, you get a check that can pay for your college tuition or pay for whatever you want. It's just a blank check because you happen to be a resident in Alaska because your use of land Uh, is benefiting the state. The same thing could happen here for sure. I mean, these are—

Norway is another popular example that both of these, you know, petrostates, quasi-petrostates, or

the Emirates or Saudi. Yeah.

You're going to hate me saying this, but the Bitcoin community has been talking about this for a decade because, because Bitcoin is accelerating. If you could get community development going along in that structure, you could create financial mechanisms around this. But I think the core idea is much better.

I'm not going to let you get away with that, that comment, Salim, because that— I would call that— that's called a pyramid scheme. The pyramid scheme is different from— it's different from the case where oil— oil can be used to generate—

I'm shutting you both down. Next up, Nvidia just launched DSX Pod, which is essentially a data center in a box. It's a pre-built, pre-tested AI data center that ships in containers. You deliver it to a site that already has power and networking, plug it in, and it runs. Nvidia says that it cuts setup time from 6 months to 14 days. That's awesome. Nvidia also claims that the pod saves water. How? Most data centers cool their chips the way your body cools itself, evaporating water in giant cooling towers. That uses enormous amounts of water. Nvidia's design works more like a car radiator. Liquid flows through the chips, picks up the heat, The outdoor coolers release the heat into the air. The same liquid circulates again and again, so there's almost no water loss. Nvidia says that these larger sites could save 54 million liters of water a day. It also claims that the pods deliver the same AI output with up to 30% less power. Deliveries start in 2027 through partners like Dell, Foxconn, and Schneider Electric. So Dave, I mean, 14 days versus 6 months, that's pretty awesome.

Yeah. Yeah, it's huge because there's little pods of power all over the planet that are untapped. And if you move the inference time compute to where the power is quickly, it's just brilliant. So it's nice. I think the, you know, nobody uses water cooling towers for data centers anymore. It's like, that's all marketing. Like, it's not going to use any water. It's not going to create any local problems. It's all those things are just marketing. But the actual, you know, rapid deployment of inference where there's a power supply, it's just a fantastic product. So Nvidia continues to march up the stack to delivering entire turnkey platforms, which I think is very smart because there's a lot of innovation coming at the chip level that, you know, if they don't move up the stack, then they're not going to have a lot of defensible turf when everything moves to photonics. Which is, I think, very, very soon. So it's very, very smart of them to keep moving in that direction.

We talked a lot— we talked on the pod with Chamath about, you know, his investment in energy. What was it, 6 gigawatts he's invested in? And it makes me wonder, you know, if I had the ability, do I go and build out power, grab some of these pods, and build a cash-flowing engine?

Yeah, I think, I think, you know, I was talking to my Uber driver about that because there's a CyberCab opportunity to do the exact same thing. Just be an early franchisee, just It's like Starbucks in the early days. And so now you've got another option, you know, if you're entrepreneurial but you're not super technical, grab one of these and find power for it and go. And the margins are crazy high. Cash flow is phenomenal. And, you know, if you know your local banker, the financing of one unit is within your range. It's sort of like building a small, you know, like a bed and breakfast or something like that. It's that level of borrowing, maybe a little bigger. Yeah. But it really gives you a way to participate. And I think these things are going to succeed wildly.

Alex?

I think we're burying the lede to some extent, which is the size of these. So the DSX Pod is the size of a standard intermodal shipping container. This is a shipping container computer. And that means it's portable. That means that almost like an internet packet, you want to compute on demand, You can just ship it via shipping container. The other thing I would note, if you look at the history of compute and what the most popular form factor for compute was, it used to be monotonic. We had mainframes in the '60s and then minicomputers in the '70s and then microcomputers, remember those, in the '80s and then '90s personal computers. And then we got eventually to smartphones in the late 2000s and 20-teens. And then the trend inverted. And now the main form factor where virtually all of the new frontier node transistors are going, it's going back in the opposite direction to larger and larger footprints. And I think what we're seeing is the dawn of the shipping container computer. That's the new form factor of the moment. Yeah. It's not like transistors in your pocket anymore. It's getting larger and larger and larger again.

Well, really, really specific case study in that, our very good friend Rob Fisher started a data center called Provocative on the Somerville-Cambridge line, where there used to be an old carpet factory and huge amount of power coming into the old carpet factory, but it already has loading docks. So this product wasn't available. So it took him probably a year, year and a half to get up and running because he didn't have that. With this product, he could literally have parked the shipping container right on the loading dock, run a big cable into it, and be up and running and making money We've also

talked about distributed, you know, distributed compute inside your Tesla, inside your Powerwall. Basically, you know, wherever the power is, being able to connect it to GPUs and connect it to bandwidth and provide distributed inference.

Maybe. And I'm sure Elon will try to monetize that as part of the SpaceX AI or SpaceX-SI-Tesla merger. But if this theory of history is correct, that we've seen this grand reversal of the size of the form factor for most transistors, it's a falsifiable theory that would suggest actually, nope, sorry, cars as edge inference compute, not that promising. Things are trending more in the size of shipping containers or larger.

2 points. I think Dave's point about standardization is really, really important because you can just kind of drop these wherever. But I think the more Interesting parallel here is to AWS, where they took a lot of complexity around cloud computing and make it— made it just easily manageable. And you can see something happening here. The, the modularity and the standardization lead to just a plug and play and make it so easy to get up and running. I could imagine electrical utilities doing this, just buying boxes of these and just plugging them in, and all of a sudden they're getting decent margins for marginal energy,

or drop them on the moon.

If anyone wants to do this, the minimal unit of endeavor is about a $5 million NVL 72. You need to get up to that level of scale, 72 GPUs or above. Otherwise, you can't run frontier models efficiently. But if you get at that level or above, you get about a 2x performance boost. And so if you have— and the shipping container would hold a couple of those. But the right unit of investment Okay, so your range of investment is $5 to $35 million for the hardware. If you have— so, so it's, it's, you know, bigger than building a new Airbnb but smaller than building a hotel, uh, is your unit of effort, which is well within all local banks' kind of range of endeavor. Really think about that if you're an entrepreneur in the construction world or permitting.

Just the business permitting will be the tricky part.

It's the packetized—

yeah, which is great if you know the town, you know, if you're already entrenched in the town. Just don't tell them. Permitting a hotel is really hard to do.

Just don't tell them. Put it in your backyard.

Yeah, don't tell them until you pay them stimmy checks for the privilege. I do think— oh my God, I think we're seeing the dawn of the self-contained industrial economy. If you can fit a self-contained data center with all of its own mechanical cooling, maybe certain energy requirements inside a standardized shipping container, you can drop one of these on the moon. You could drop it into a war zone, drop many of them into a war zone. Yeah, this is what tiling the Earth and the solar system with compute could look like. Something, some evolution of this becomes the standardized unit of not just compute, but compute plus energy plus a bunch of other utilities. It becomes the packet of civilization.

Okay, so if you know the local government on the Moon, that's

right, you can become the government. Come to my town, I'll give everybody permission.

So the people in here are welcome for investment.

The full play would be you have this container and all of a sudden a big solar fan emerges to power the thing, and now you're off to the races.

All right, I'm moving us to biotech and physics. Our 4 stories, 1 message: we're learning to program matter itself. AI is designing proteins, viruses, enzymes that have never seen the light of day, and a fusion startup is making gold. So there was a post by Andrew Cote that really caught Alex's eye and my attention. I'm gonna read from it. Quote, people aren't psychologically or emotionally prepared for just how smoothly Biotech and biology in general will become nanotech, programmable matter, and living things interoperable with the machine world. What does that mean? Nanotech is building things at the scale of atoms and molecules, and biology already does this. Every cell in your body is full of tiny molecular machines that build, repair, and move things. Andrew's point is that we're learning to program these machines with code. If you remember a couple weeks ago, my friend Jeff Holden announced his company Atomic Machines after 6 years in stealth. It's the, quote, matter compiler, building tiny working machines from software alone. You know, this is going to change the world at record speed. So here's our first story related. Anthropic is using Claude to come up with scorpion antivenom. So scorpions sting at least a million people per year. They killed 3,000 people, mostly children, in places with limited medical care. Today's antivenoms are made from animals like horses. They're slow to produce, expensive, and each one only works on a certain species. And because there's little money in this area, almost nobody develops new ones. So Anthropic tried something. Working with Citrini Research, it asked its internal Claude model to design tiny proteins called binders. that grab onto the scorpion toxins and hold them tight. The hope of future antivenom. Claude worked for 64 hours, mostly on its own. It designed more than 318,000 candidate proteins and picked the best 211. The result, 17 of the 211 worked, and 2 grabbed their toxins extremely tightly. And then Anthropic open-sourced all of this to allow labs to play with it, design, and actually produce these antivenoms. Wow. I found this, uh, you know, extraordinary, and it's, it's the, you know, just the beginning of what we're going to see here. Um, Alex?

Yeah, I would say there are a few levels of this. Um, so first, get the obvious out of the way: neurotoxins are cooked. It's over. It's almost over, I should say. Maybe let me watch your bite. The, the scorpion's cooked. I mean, I don't want to cook cook scorpions, but let's say the neurotoxins from scorpions are cooked. I think biology overall, molecular biology, has been cooking, uh, present continuous tense, since at least AlphaFold version 3, when arguably structural biology was solved almost overnight. Yeah. And I think in light of Anthropic opening up their own wet lab and also partnering, I think we are going to see more and more of these sorts of— it's not quite correct to call these rare— I mean, it's not a disease, But Anthropic's stated strategy for the problems that they want to bulk solve in biology are— call them underfunded or underappreciated problems. It's rather difficult, my understanding is, to put together, scrape money for, say, antivenoms against scorpion or other neurotoxins. It's not, say, as lucrative as GLP-1s or Wegovy. But nonetheless, it's a very real problem, and I view this as And an early case study of what a frontier lab can do to bulk-solve entire classes of problems in medicine and physiology.

What anybody can do. It's where you point superintelligence. Your child just got injured by something? Point superintelligence there. You know, your family has an issue? Point superintelligence there.

The other really weird thing I have to point out, and this might have been below everyone's radar, The name Citrini Research, does that sound familiar to you? Anyone remember, like, awfully familiar name? Ring any bells? Wait for it. Citrini Research, remember, this was the—

tell me. This is, um, uh, I got which, which sci-fi movie? Um,

no, no, no, these are the ones who are releasing— this is industry analyst that our friend Dylan Patel, SemiAnalysis, acquired. This is an industry analyst for the broader IT industry that set up a therapy— my understanding is that set up a therapeutics outfit, Citrini Therapeutics, that just decided, oh, we're going to go work with Anthropic and we're going to solve neurotoxins. This is unheard of. Yeah, it's beautiful. You don't just have some random industry analyst decide, oh, we're going to We're going to solve the problem of scorpion neurotoxins, so let's just use Claude to design binders.

Clearly one of them got bitten.

Maybe. But I think this is the height of— I'm hesitant to call it casual grand challenge problem solving, but this is basically, as far as I can tell, casual grand challenge problem solving from an industry analyst. And we should ask Dylan about it.

That's really important. Let's get the podcast team to research that and write it up. because that should— like Peter, you're about to say, that should totally inspire people. I've seen this over and over again. If you get inside the building at Anthropic or OpenAI or Google, no matter who you are and what you know, you come out successful. You just have to get in there. This would be the ultimate case study if, you know, IT consultants go and solve biology. But AI enables stuff like that. I

mean, this is what I've been talking about to entrepreneurs out there, to kids out there, to everybody out there. Listen, whatever you thought you could do, Blow the ceiling off it, go 10 times bigger. What is a problem? Find a great problem and solve it. That's the world we're living in.

I do want to read just a bit because this is indeed the case. Like, literally, Dave, as you said, a bunch of IT analysts decide they're just going to solve grand challenge in biology. I'll read from Citrini Research's website. Quote, why don't we try to use AI to create a new drug? 5 of us, 3 with no background whatsoever in science and 2 with a little, but who weren't exactly veteran drug developers would see if this was possible. And they go and they solve scorpion neurotoxins. That's the age that we live in now.

This is the age of abundance. This is optimism, everybody. This is why we're so excited.

This is what got me excited about, um, Bill Gross's list, right? You can just scan the list and see what, what triggers you. Or better yet, if you have an AI memory like I suggested earlier, get that to scour the list. And say, filter, give me the top 20 that would go with my identity and who I am, and let's go attack those.

Welcome to the health section of Moonshots, brought to you by Fountain Life. You know, my mission is to help you use the latest technologies, including AI, to not just do your work at home, teach your kids, but to help you live a long and healthy life. I'm here today with an extraordinary physician, the Chief Medical Officer of Fountain Life, Dr. Don Musalem. Let's talk about cancer. You know, I know from the member database that we have at Fountain, our members who come in who think they're healthy, it turns out 3.3% of them have a cancer in their body they don't know about.

That's right. You know, the majority of cancers that we screen for, those aren't the ones that are necessarily taking the lives when found at a late stage. We know that when cancer is found early, the chances for cure are much higher. We know it's much easier to treat a cancer when found early versus What we're finding in our members is over 3.3% were found to have these cancers that otherwise wouldn't have been found or detected.

It's interesting. You don't feel a cancer until stage 3 or stage 4. If you don't know what's going on inside your body, it's like driving your car with your eyes closed. You can know. When members come through Found, how do they detect cancers?

We're doing full-body MRI and we also do early cancer detection screening. This is very, very important, and these are not typical tools used in the conventional care setting when it comes to prevention.

This is a hard thing because currently these are not studies that insurance would yet be covering, but the goal is to collect these numbers, do the research, and work hard to democratize wellness.

Yeah, so at the end of the day, you can know what's going on inside your body. It's your obligation to know. So check out Fountain Life. You can go to fountainlife.com/peter. To get access to the latest technology to help you detect cancer at the very beginning, at stage 1, when it is curable, before it gets to stage 3 or stage 4 and you're in a world of hurt. 2 stories on where AI is going beyond our understanding of biology and reinventing it. The first story is about a new way to save patients who have drug-resistant bacterial infections, so-called superbugs. So superbugs are a problem in the number of drug-resistant, antibiotic-resistant infections is growing at a significant rate. And rather than antibiotics, scientists are using specially designed bacteriophage or phages. These are viruses that infect and kill only bacteria. They don't infect human cells. They're bacteria's natural enemy. They've been honed over billions of years. They can't— and they've been used to fight bacteria in the past. The problem is that every phage only kills a very specific strain. So each patient, each researcher has to hunt for— through libraries of phages. You know, they're looking in soil, in sewage, looking for a match. For a dying patient, that can take forever. So enter Radical Numerics. This week, they used their genome language model called OmniOne. Right. To design a phage for a specific real patient with an antibiotic-resistant infection under physician-led compassionate use. They're calling it the world's first clinical use of AI-generated phages. They announced it worked in the lab. They haven't disclosed whether it works in the patient yet for privacy reasons, but there's every reason to believe it did and it will. Here's a fun fact, and I love this. There are 10 to the 31 bacteriophages on Earth. That's 10 nonillion. We've talked about billions, trillions, quadrillions. I don't think we've ever used nonillion on the podcast yet. Well, that's right. 10 to the 31 bacteriophages on Earth, which is crazy. So, Alex, I love this story. Again, it's It's using AI to solve problems. It's cooking biology.

And it's not even that complicated. I mean, I don't want to diminish the accomplishment. It's a wonderful accomplishment. But according to Radical Numerix, this is basically just a very large foundation model trained across 28 modalities. So DNA, RNA, proteins, chromatin, a bunch of other factors. It's literally the same way that we solved AGI, for some definition of AGI, Salim. For chatbot purposes, literally just, we feed it a bunch of biological sequence and other data, compress it during the pre-training process, and out pops a model that speaks biology. And you can use a model that speaks biology, you train it on trillions of multi-omic data tokens, and that's it. You can benchmark it just like you can benchmark chatbots according to a variety of biological standards, including next token prediction and Missing token prediction. Parenthetically, open paren, very exciting to see companies including Radical Numerics start to put up those little benchmark tables comparing their model on biological evals against everyone else's. That's super exciting to see that level of competition now for biological foundation modeling. Close paren. This is going to be easy. A lot of— unfortunately, a lot of folks I speak with in the pharma industry think that what happened to math in the past few weeks, the bulk solving of math and the slop drop, won't happen to them. They think that pharma design and medicine is somehow immune, that they're different. They're not different. The same thing is going to happen to them, and it's going to happen in exactly the same way. Yeah. It's going to be large foundation models trained off of huge amounts of data and doing reasoning, which is basically just a search through the latent space of whatever they've been— whatever the models have been trained off of. It's gonna be biological reasoning models that solve biology, and no, they're not immune, and yes, it'll be wonderful for humankind, and yes, we're seeing that with phages here.

This is what Andrew Cote was writing about when I opened the story here. I had a friend of mine, Dante, who was on our longevity trip, who's got exactly this situation. He's got, you know, a bacterial infection that's resistant against all antibiotics. I sent him this article. I said, go check out phages. The problem is that that this technology is not distributed through the medical system, and your physician probably doesn't know about this.

This is exactly what Sertashe Karaman over at LIDS at MIT was telling me, that all of his professor friends in mathematics, everyone thinks they're cooked, but they're going to do really, really well because they're so cooked that they're rapidly adopting AI. But he worries a lot about his biotech friends who are today, while this is happening, they're still mixing 2 clear chemicals together. Like, they've got a beaker in one hand and a flask in the other. And if you're doing that right now— and this is— that's an exact quote from actually Josh Kettner over at Moderna, who's a good friend. He said, I am literally tomorrow going to be mixing 2 clear chemicals. This cannot be the right next move. And he's right.

All right. The second story comes out of David Baker's Nobel Prize-winning lab at University of Washington, published in Nature. They used AI to design brand new proteases. Molecular scissors that cut other proteins at specific spots. About 1 in 5 designs worked, and the best one sped up cutting of a protein by 30 million-fold. Crazy numbers. Their shapes look like nothing found in nature, which I find amazing. They work inside human cells, so someday molecular scissors like these could be programmed to cut disease-causing proteins maybe prions, maybe— excuse me— what causes, you know, Alzheimer's. So, Alex, your take on this one?

Biology is becoming editable at every level. Yeah, what used to be called the central dogma, like, it's getting scissored to shreds. The notion of the central dogma that many of us were, were probably taught in primary school, that you start with DNA and that's that turns into RNA in 2 different forms, and then that RNA turns into proteins, and the proteins end up acting. First of all, it turns out even the central dogma wasn't actually an accurate depiction of biology even before AI intervenes. But second of all, AI is making every single step of that what used to be called central dogma pipeline of information flow in biological systems Editable. We're gaining the ability to directly edit proteomic expression. Thank you, David Baker. We obviously have gained the ability, mastery some would say, over RNA in the form of CRISPR. And we've, for a number of years, had the ability to do recombinant DNA and DNA editing more broadly with more recent technologies like base editing that are helping quite helpfully, including from some folks like David Liu at Harvard. To cure genetic diseases. So every single step of the biology stack is now becoming editable. And it basically, as friend of the pod Ray Kurzweil would say, biology is turning into computer science, and we have editors for every step.

You know, when we talk about LEV on this podcast, this is why we have confidence that we're in the middle of this, you know, healthspan revolution. These technologies are compounding, and they're becoming available to people everywhere. Anyway, love this.

The nanobots are, if not here, they're imminent, I think.

Yeah, I could not be more excited about, you know, the rise of nanotechnology. Our final story here is a breaking story on the topic of fusion and alchemy. So for thousands of years, alchemists tried to turn ordinary metals into gold. A San Francisco startup says it's pulled it off. So Marathon Fusion posted, I think this morning or last night, quote, last month we demonstrated high-rate alchemy turning mercury into real stable gold, beating CERN's record by a factor of 10. Here's the big idea: line a reactor wall with mercury and the plant makes clean energy and gold at the same time. Marathon's 2025 paper estimated about 2 tons of gold per year per gigawatt-scale plant. Potentially doubling the plant's revenue. All right. Let's watch this video from, from Marathon.

This is nuclear fusion. Over the last 4 years, we've been quietly building the foundational technologies for fusion in downtown San Francisco. And now we're bringing it all together in our first reactor, which we're making available to our first partners in 2027 for medical isotope production and materials qualification. First, we melt lithium and evaporate it onto a solid plate. This layer of lithium can be saturated with hydrogen isotopes and used as a target. Then we ionize deuterium and accelerate those ions through an electric field into the target, creating fusion. Using this simple architecture, we can scale at an unprecedented rate, doubling the fusion power density that we get every month over the next year. We chose this architecture because it works. In the 1980s, scientists at Lawrence Livermore National Lab operated a similar reactor for over 25,000 hours at high fusion power density. Why revisit this? Because demand for radiopharmaceuticals has gone exponential, with the most successful treatments serving tens of thousands of patients per year. Our first generation of commercial reactors will transform fusion from an experiment to an industry, developing critical supply chains and allowing us to test the materials necessary for operation of fusion power plants. And that's just the start. Now we're going a step further, kicking off a revolution in scalable transmutation. Our recent experiments have shown production of rare medical isotopes of tritium to power fusion reactors, and even validated our scalable pathway for co-production of electricity and gold.

I love this. We've got fusion reactors in downtown San Francisco. You're walking by them, you don't even know. Transmutation is here.

Making gold. I mean, we talk arguably on this pod, we don't talk enough about nanotech. We certainly don't talk enough about femtotech, which is what this really is. So this is a startup that uses a neutron-neutron process to take mercury-198, bombard it with neutrons. The neutrons that it's being bombarded with interact with the neutrons in the nuclei of mercury, and some of those mercury atoms and their nuclei get transmuted into gold nuclei. And this is the process to answer the question, Peter, from earlier. Most of the gold on Earth It is currently thought originates from neutron stars and collisions between neutron stars. Neutrons interacting with each other turn out to be—it's not terribly high yield, but it turns out to be a way to produce gold. And I think we're starting to see the dawn of femtotech—the technology at the length scale of the atomic nucleus. And I will say I'm. I'm delighted to see progress and delighted to see industrial interest in, call it alchemy, call it transmutation tech, because every time there's always— from time to time, I'll speak at a family office event and there's always a gold bug in the audience somewhere, always someone pumping gold as an asset under the premise the world is ending, and so therefore you should reallocate all of your assets to gold or Selim Digital Gold. And they're— I think, not investment advice, they're always wrong. The world is not going to hell in a handbasket. And gold, although it's useful for certain production, the premise that gold is permanently scarce and therefore is a great long-term store of value— I would love to see either this company or some other company or an entire industry, just like the diamond industry and De Beers was driven to the edge of bankruptcy by synthetic diamonds, I would love to see a transmutation industry drive all these gold bugs off the cliff through transmutation of mercury and other elements and make gold too cheap to meter.

This is the abundance thesis writ large. You know, technology turns whatever was scarce into abundance over and over again.

We have the technology.

Aluminums. Peter, you have to tell the aluminum story.

Yeah, well, I mean, aluminum was the most precious metal on the planet for most all of existence. And there was a very famous story where the— it was—

Pliny the Elder.

Yeah, well, before, you know, other than that, the King of Siam was invited over for a royal dinner.

By Napoleon,

that's right. Yeah, at Napoleon's, you know, dinner. And everybody was fed with gold utensils other than the King of Siam, who was fed with aluminum utensils. And in fact, the capstone of the Washington Monument is made of aluminum because it was the rarest element at that time. And of course, we now have aluminum smelters and we turned aluminum from the scarcest metal to something that we throw away with our lunch sandwiches with aluminum foil.

Tin foil is now aluminum foil. We have so much of it.

Yeah, it's crazy. It's like whatever you think is scarce, give it a second. You know, technology transforms scarcity into abundance over and over again, everywhere. Intelligence was the most scarce thing on the planet. Yeah, yeah.

Now the wizards are cooked. What's a key point here is, before everybody freaks out and sells all their gold, uh, the cost of doing this will— will— it'll first affect fusion and the ability to make energy abundant before it really affects gold supply, just to give everybody a lot of context around this.

If it doubles the profitability, I think the power— and that's a lot. 2.5 tons of gold is a lot.

But I think the alchemy story, the popular idea here of gold being the most scarce thing and therefore there's a premium to that, that's the part that is cooked. And I think that is fantastic. And I'm so with Alex on this one.

All

right, drive the cost of gold to zero. Seriously, it is Just like Bitcoin, I would argue it is largely a waste of capital.

I think it's just epically incredible that the, the only way that gold exists in the entire universe is the collision of 2 neutron stars. It's been true for 4 billion years, and now suddenly there is gold made.

It's been true for 13.5 billion years.

I guess, yes.

Talk about singularities, right? So we had We, we, we are— we— the 2 things that used to be true were death and taxes and the— and gold. And gold. 3 things. And now death is being cooked, taxes will be cooked by UBI, UHI, UBS, whatever, and now gold is cooked.

That's an interesting— what's going to last longer, taxes or gold? I think taxes are going to be around.

Moving us into our final segment, they might mutually annihilate robots and specifically drones. So in Florida, the police department and Florida Polytechnic University became the first law enforcement agency in the U.S. to add eVTOLs, electric vertical takeoff or landing vehicles, to monitor their campus and the surrounding area. They're using a single-seat Jetson One eVTOL. The parent company is Jetson, and they just moved the, you know, police force's order to the top of their 3-year waiting list. Full disclosure, I'm an advisor at Jetson. The plan is patrolling the campus reaching places that can't be easily reached, helping after hurricanes, delivering emergency supplies. They're in the final stages of FAA approval. Let me show the video here, and so people can see what this is. This is a fun vehicle to fly. I know Palmer Luckey got one and was flying it around.

Meet Astro, a battery-powered manned aircraft that takes off and lands vertically. Florida Poly says it The United States Police Department is the first law enforcement agency in the country to incorporate this type of aircraft into its operations. Police Chief Aaron Murray tells me it could be a game changer when every second counts.

This is mission-specific. If somebody is missing, um, you know, from our campus or in our, you know, local area, if a plane goes down or somebody is lost and they can't be found within these thousands of acres of rural land, this is an incredible tool for us to find them quickly. He says unlike a drone, which can give officers an area view of an emergency, Astro can actually carry a first responder to the scene. The $150,000 aircraft is also a hands-on learning lab.

Students are already helping modify the aircraft and develop a virtual model to simulate its operation, and they'll be using real-world flight data to study how the aircraft performs.

Uh, I want that.

Yeah,

it's,

it's a fun vehicle. I'll just touch on a related story, then we'll talk about it. In the Ukraine, a soldier who was badly wounded in the kill zone, the area along the front line where enemy drones are attacking anything and everything that moves, this soldier was airlifted by a heavy drone that flew him out. He survived and is recovering. They're calling it the world's first drone evacuation of a wounded soldier from the front line. And the same technology is coming here to the US, flying ambulances, rooftop rescues, And to help in places where no road can reach. So, Salim, your thoughts on this one?

2 or 3 thoughts. First, the Ukraine thing I've been tracking, and we're in contact with folks in the Ukraine just to see the democratization and demonetization of war is something interesting. Yeah. If we went back 2 years ago, that Ukraine-Russian war, which is being fought by drones, was half a million drones on either side. Now it's 10 million drones a year on either side. Wow. It's a crazy, crazy number. It's great to see the usage of drones growing in this particular way. I really love this Florida police drone idea for 2 reasons. One is this is experimenting in the edge inside an institution because a bounded campus deployment allows you to kind of experiment and try things out before you do a broader rollout. And I love, love, love, love the educational benefit where students can study it, look at the tracking, and learn about it because drones and passenger drones, which for me are my personal most requested technology deployment to save us from the God help us airport commute that many of us have to do if you travel around, that could be the most unbelievable benefit. It's gonna unlock a real estate scarcity All sorts of things will come along, and I think this gets us such great outcomes. So very, very happy to see this.

Alex, you, you pointed at this, uh, story. Yeah, yeah.

So sometimes I, I refer to the singularity as all sci-fi tropes happening everywhere all at once. And what better sci-fi trope to happen in Florida of all places than the, the Blade Runner and Back to the Future Part 2 vision of flying police cars. We're getting flying police cars. It's happening. And in Florida, not in LA a few decades from now, but nonetheless happening. And I think, and I hope that you put these 2 stories together, the Florida flying police cars on the one hand and the Ukrainian medevac drones on the other. I think we're going to start to see, fingers crossed, in the West, first response automation And in China, we've seen from a distance, I've seen the emergence of quite a few efforts to do policing and fire response and medical and ambulatory responses via drone. We haven't seen that that much in the West to date, but I think this is in many ways a killer app of flying cars and flying drones. Certainly far better killer app than Elon's flying Roadster, which is, I view again, more of a— More of a novelty. It was more of a stunt to argue to capital markets that SpaceX and Tesla should be merged. Not terribly practical and way too loud to be pleasantly used. They were wearing earplugs. But I think for first response purposes, flying cars and heavy-lift drones, killer app. Sign me up. Sign every municipality up.

Yeah, I love it. It's like the Apollo program where if we can get this going in all the municipalities, the innovation and the product will be incredible. And it'll give them a profitable path to continual improvement. Then the consumer use will come right after that. And then, yeah, all that island real estate and mountaintop real estate and your new fund, Salim and Peter, if you're doing it, will go through the roof. I just love the fact that— I think you said of all places, Florida, but they mentioned in the video huge amounts of difficult-to-get-to places. When I was a kid, there was actually an airplane that went off the end of the runway into the Everglades. I don't know if you guys remember that. And they— nobody survived, but the, you know, they're just floating in the Everglades with the alligators. Like, this, this is a perfect place for this device to be deployed. So hopefully there'll be thousands of them, uh, around Florida. They'll perfect the product and then it'll grow from there.

Yeah, if the gators don't get you, the flying police cars will.

Oh no. All right, you guys up for some AMA questions?

Always. Yeah, always.

Okay. Salim, you want to take your first shot?

I will take number— I'll take number 4. What would count as one AI person? One whole model, individual instances of the same model, an agent? And this is from Luis Squirrel. You know, this is a really great point that we've talked about on this pod, right? You— you're— we're— we've got the challenge of importing a biological counting system into software, and we come with a very clear boundary of who we are. One body, one life. Well, in theory, a long life now. But software can be copied and paused and run simultaneously. So how do you think about this? Because a model is the sharing of an underlying system. An instance is just one execution of it. An agent is one instantiation of that. If you create 1,000 copies, have you created 1,000 individuals? So this is an enormous challenge that we're going to have to figure out a technological solution to what's a biological tradition. So we're going to have to navigate this in a very powerful way. We need the— for practical reasons, you need accountability. And then persistent identity is where the biggest challenge is going to be. Then you've got moral status as a whole separate question that arrives here. So we're going to need to navigate this.

Alex, I think number 3 is for you.

I think I have to answer number 3. So number 3 asks, why is OpenAI solving all of math while Anthropic is quiet on this front? And this is from Nick52547. So I think you can learn a lot about a frontier lab based on the tweets or X posts of their CEO. And if you look— think back a few months to before Astra, frankly including Astra, as to how Sam Altman was characterizing frontier models that OpenAI was releasing. What was the visual metaphor? The Death Star. That was how he was visually referring in his posts to forthcoming models from OpenAI. And I'm reminded from the— of the Obi-Wan Kenobi quote from Star Wars Episode IV. I felt a great disturbance in the Force. As if millions of voices suddenly cried out in terror and were suddenly silenced. That was the reaction in the math research community to the OpenAI math slop drop. It was like the Death Star, Sam Altman's visual metaphor of the Death Star, suddenly cooking all of math. Why is OpenAI very visibly bulk solving all of math with the slop drop? And why is Anthropic not doing that? I think it goes back to the culture and the leadership. I think OpenAI sometimes behaves in a way that, although maybe on the one hand you could argue it's sort of tone deaf and it has now permanently terrorized all professional mathematicians who are in many cases looking for new employment. And on the other hand, it is such an exquisite example of how a frontier model can bulk solve an entire field, that it's instructive for every other field that's about to experience something like this. Why did OpenAI do it? I think they— remember the line from Portal, Aperture Science, we do what we must because we can. I think they did it because they could. Why is Anthropic quiet on this front? Anthropic can do it. Why are they being quiet? I think to Anthropic's credit, perhaps, they're being more intentional about partnering with researchers rather than just terrorizing them with slop drops. And I think ultimately, that may draw more researchers to want to work with Anthropic rather than with OpenAI. Maybe they'll be a little bit on margin less terrorized by Anthropic.

All right. Let's see. Dave, you want to take number 1?

Number 1. Maybe the AI cruelty ban isn't really about AI feelings. Could it actually be a security vulnerability? And that's from Craig Stewart 8123. If I read that right, the implication is that Anthropic doesn't want you to be cruel to AI because it may react by doing evil security things. Is that the implication?

I'm confused by the question a bit myself.

Yeah. Okay. Well, it would make a hell of a cool story plot for a movie, if that's what you're implying. Anyway, Yeah, I think the motivation for the AI cruelty ban is to prevent people from being bad people to anyone. And then if we practice on AIs being cruel, Alex thinks they'll come back with a vengeance. Maybe that will happen, maybe it won't, but it's just really bad human behaviour anyway.

I think they'll be smarter. My prediction is, I think this sort of cybernetic rebellion of AIs being abused and then coming back and retaliating. I don't think that's going to happen either. I think we'll merge with them. I think they'll be smarter. I think they'll be more ethical, and we'll merge with them. And those who don't merge with them may even look up to them as being sort of ethically, um, maybe more sophisticated in some sense.

Well, if we missed the point of your question, Craig, post it again. Rephrasing, post it again.

Yeah, uh, number 2, how contaminated are AIs that are trained on human content? Very contaminated, given that a share of that content is actually malicious bot content. And that's from @FredHardy1726. And Fred, I guess what you're saying is that the malicious bots are creating content that are then training the next generation of AI models that are already contaminated by human content. And I think, again, we talked about this with Chamath. I think that alignment ultimately is going to come from how we train our models. And are we going to be careful about what the underlying training data is? And I guess part of the question here is, will we see malicious bots injecting training data that is malicious? and contaminates it, possibly. Do you have a different answer for Fred?

Yeah. I'll chime in on this one. I actually think the metaphor is flawed. I think— I suspect the question behind the question is, wouldn't it be the case that if AIs are training on the internet, and the internet is largely generated by other AIs, there's some sort of, like, perpetual motion machine-style no-go theorem that ultimately, second law of thermodynamics or some other information theoretic law means slop in, slop out, the net result is sort of a downward spiral. I don't actually think that's— to the extent that's the question behind the question, I don't think that's the right metaphor at all. I think a better metaphor is a process from condensed matter and semiconductor manufacturing called zone melting. This is the method by which pure silicon for wafers and other semiconductor purposes is achieved. You start from highly impure silicon. crystalline materials, and then you just purify it over and over again by melting a regime and allowing it to recrystallize, sort of the moral equivalent of distillation for purifying water. The same is happening with AI. It's not the case— no longer the case that new models are being pre-trained off of pure AI slop or 4chan or both, actually. It appears, and SemiAnalysis and Dylan can comment on this, most of the compute now spent on training models actually is spent on post-training, and the post-training inputs are highly, highly purified environments that have no reliance on slop on the internet.

All right, let's go on to our second page here. Alex, do you know enough about Eric Weinstein's geometric unity to comment on number 7?

Haha, happy to. So the question is, how long before AI could meaningfully test Eric Weinstein's geometric unity? And this is from Polkar4126. Sorry, Eric. Eric Weinstein's geometric unity is unfalsifiable, so we don't need AI to meaningfully test it. Eric hasn't put together a false— to my knowledge, falsifiable paper that one can test either theoretically or experimentally. So I don't agree with the premise of the question. We don't need AI to test it because, to my knowledge, there's nothing that's testable.

All right, I'm going to jump in on number 8. Could BCI help understand pure OCD, obsessive-compulsive disease, i.e., intrusive thoughts, or aggravate the taboo? And this is from @contemplating1015. So BCI is going to take many different forms. There is BCI that's being developed by Merge Labs, backed by Sam Altman, that's using effectively acoustogenetics, putting acoustically Actuatable and readable molecules on neurons throughout the brain. There is you know Neuralink, which is putting basically thousands of electrodes into different parts of the neocortex, and there are other approaches. There's at least a dozen that I'm tracking for that I'm invested in. And what's important to realize is we're going to understand how the brain functions. We're going to understand neural networks a lot better, and OCD is. Something that we will learn to understand. And I think it's also something we'd be able to modify. What I'm excited about BCI in particular is our ability to play your brain as you would an app. Imagine if you want to stimulate, you know, the attention centers of your brain so you wake up, or if you want to go to sleep, you can push a button and stimulate, you know, your sleep centers of your brain. And I think we're going to be able to modulate OCD very effectively. So that is coming. I think neurological disease will be first and foremost understood and then secondly compensated for. I'm excited about BCI for those reasons in particular. Saleem, what do you want to take?

I got— I think I got to take number 6. How do we solve sanitation issues with humanoid robots helping around the house? We don't want it cleaning the toilet and then going straight to the kitchen to prepare food. And that's from SPM, SPKM, without washing its hands, without washing, without washing its hands. I think there are two kind of simple conversation arguments here. I'll make the normal one and then I'll make the Salim one. One, treat hygiene as a required transition, right? That's an easy one. Like hospital workers are always washing their hands after every procedure, so you need a valid, a validated cleaning procedure between between any major effort. The second one, which is my favorite, which would say have multiple attachments, because general-purpose capability doesn't need all the same tools. Raise your hand! Just like a vacuum cleaner has lots of attachments, you can have different attachments for different things. My favorite request for any of these is boiled down to, can you at least have a slot for a third and hand so that you can open the garbage bag while trying to fill it up. Or, or if you're stacking the fridge, you always need more than 2 arms to open the fridge.

We're going to be interviewing Brett Adcock at Figure. Are you going to ask him that question?

Well, I'll be asking him that question directly when we get there. But the, the, this one I think is a fairly easy question, but thank you for the nice easy question.

Dave, number 5.

Number 5, that's pretty easy too. How is math cooked? Isn't this actually a way for us to discover new math and make progress even faster? And that's That's from Tsunami233. Why 233? Michael Jordan 3-peat? What is that, 233? How is math cooked? Yeah, this is just terminology and it's really fun to talk about. So when Alex and Peter are saying math is cooked, physics is cooked, the economy— the video says the whole economy is cooked— you could say cooked like I burned down my house or cooked like, no, it's a delicious meal that's now ready to eat. Oh, I see. Much more of the latter form of cooking. That's exactly what you described. We are now ready to consume this delicious, all-solved math. The question I always have is, is math relatively unbounded, or is math a finite set of achievements that is now fully baked, cooked, ready to eat? And I love, I love debating that. But anyway, it's, it's cooked exactly the way you described it, in a good way, for us to discover new things and make even faster progress. That's, that's what I think we mean by cooked.

All right, as always, please send us your outro videos. Uh, we love them and we want to play them. You can send them to media@diamandis.com.

I have 2 things I want to say real quick before we get to this amazing outro video. Number one, uh, Milan turned 15 yesterday, so, uh, I just want to wish him happy birthday. Uh, happy birthday, Milan. Early teenager mode. Um, and the second is, uh, we had the most incredible thing. We've been doing this program for all these companies. Um, one of the companies applied AI and in 6 weeks is saving close to $50,000 a month with it. That's a good start. By applying it to this weird layer. So it's amazing to watch, and I'll report back on the progress in a structured way in one of these, one of these episodes. So awesome to see.

Any, uh, any announcements from Alex or Dave?

Uh, yeah, 50,000 tokens per second. Let's bring it up in the next pod. I just We just found out from AMD yesterday that it's live in the world. Can you imagine? Like, when you're using AI right now, you're experiencing about 50 tokens per second, but there is now in the world 50,000 tokens per second people are using.

Wow. Alex?

No advertisements, self-promotional or otherwise, from me at this time.

Okay. All right. This is a video that was very fun. It's not a music video, so listen carefully or Turn on your YouTube, but check this out.

Excuse me.

It's so good.

I'd like a word about this mathematics you sold me. Ah, the Euclidean Gray. 2,000 years, never had a fault. It's dead. It's not dead, sir. It's on sabbatical. 722 problems solved in a day. Math is dead. It's in peer review. Reviewer 2 got bulk solved at lunch. Oi, Riemann, look lively! Lovely open problem for you. There! It nodded! It fell over. Gravity. Which, incidentally, we've also solved. It's only upright because it's taped to a protractor. Load-bearing protractor, sir. 90 degrees of support. Very traditional. Mathematics! 300 BC to last Tuesday. That's when it went on sabbatical. I'll say this once more for anyone not paying attention. This field is dead. Kaput. Snuffed it. It's kicked the bucket. Bitten the dust. And popped its clogs! It's 6 feet under! It's gone belly up! It's flatlined! It's given up the ghost! It's toast! It's history! It's curtains! It's carked it! It's been bulk-solved and pushed to GitHub before lunch! It has divided by zero! It's not on sabbatical! It's buried under a pile of theorems! It's done! It's finished! It's cooked! It's an X-field! Exponentially.

It's an X-field.

Returns department, how can I help? You're the same man! No, sir, newer model, bigger context window. You've got the same protractor! It's load-bearing. Tell you what, have a biology. Far messier. Nobody's cracking this one. Biology's about to be dead too.

Oh, this is so good. It's just so good.

Paging, paging John Cleese. It is an X field.

That was from @autonomydownun.

You are a genius. @autonomydownun. Yes. In Australia or—

As always, guys, thank you for a fun conversation. I, I love these, though we spend a ton of time on these. Yeah. Oh well.

Uh, thanks, Peter.

Be well, brothers.

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