ビッグテックの3兆ドルAI投資、Anthropicのコードの9割はClaude製、AIがAIの脳を編集する ― Moonshots EP #308

So, it's election day, guys. I can't wait to bring Skippy into my voting booth with me and ask for its opinion. But yeah, 250 million people vote in the U.S. today. Hopefully they do vote. All right. That's mid-turns on the... Have you guys voted yet? You think that turnout will be significant? Yes, of course. Via mail-in, of course. Of course. And I'm Canadian, so... Oh, you're out, dude. I'm out. Yeah, you and mine. You can have any of your choices, though. But Lily's able to vote. Do you sway her? When you see the ballot, there's so many little tiny positions down the line, you have no idea what's going on. Yeah. You know, the actual swing races are so few. I warrant none of us live anywhere near any of the races that actually matter. Yeah. Yeah, Texas, Kansas, Ohio. You see, I don't actually think that's true. Just even on recent pods, we've talked all about local politics where, for example, city of Cambridge, we can't even get Waymos or Boston.

I do think even if at the national level it's not possible to have much individual influence at the local level, where a lot of the data center and autonomy action is happening, this is where maybe the key politics of the future are being decided. Did you do any AI assist research there? Peter, did you ask Skippy for opinions on your local? I'm a one-issue voter. You know what my issue is? Keeping the Santa Monica airport open. Well, even that prompt, though, is, hey, which of these votes will connect to that outcome? Yeah, for sure. That alone isn't easy to figure out. Yeah. I do think, I mean, Dave, to your point, that is a credible way to make, in a complex media and political landscape, to make voting decisions. Literally just go to the top models and say, here is what I'm interested in. rank or stack rank each of these decisions and candidates accordingly. And you get pretty hard. Yeah, for sure. I mean, it's credible today in today's election. It's going to be dominant in the 2028 election.

I mean, almost everybody's going to have an agent or two or 10 that they trust. And they're not going to make any decisions in 2028 without consulting their agent. You think about how dangerous that could be if it's not set up right. And this is why we're so frustrated on this podcast all the time about how there's no regulatory movement whatsoever. Like, think about what I just said and what the implications of that are. That's two years from today. There will be an election where the vast majority of the electorate is asking their AI agent for an opinion on what to do. Well, compared to the written ballots, which is, you know, this paragraph, which you have to read three times to understand what it's saying, unless you've done the research, you're like just, you know, I mean, as you're walking into the ballot booth, there's these signs of, what did that sign say? Yes, I'm 33. Anyway. It's also, Dave, I think like that's the nth reason why all of these people who get themselves into a moral panic over AI personhood, because they're so concerned that AIs will be granted the vote, completely missed the point.

AIs already have the vote. They're using human meat puppets to influence elections. And I'm not sure that's a bad thing. That never occurred to me until you just said that. You're so right. That's going to just absolutely take off. You've got to stop saying meat puppets at some point. I'm sorry, voters. I'll be the downer in this and I'll point you to a 2015 study by Yale that shows the U.S. is in no way, shape, or form a democracy or a functioning democracy. There's no amount of public will that can result in legislation. And so there's that. Listen, I really think this idea of a representative democracy is so last century, right? The ability to have an actual democracy using technology now. So I can assign my vote on women's issues to my wife, or I can assign my vote on hair growth to Salim. At the end of the day, yeah.

Bad vote. But note the discrepancy there, Peter, right? We created a representative democracy when information was scarce. If you're in California or D.C., you have no idea what was happening in California. And the speed of a horse was as fast as you could find out. So Congress or the Senate meets occasionally to literally give people time to ride across the country and say here's what my people are thinking. Today we have an abundance of information that gets misinterpreted, faked, misused, and every major democracy in the world is under stress from that problem. Well, I love what Alex said. The more we move to local and state-level control and decision-making, the more resilient we'll be to the inevitable mistakes. The more you have one big mistake that affects everybody, the less resilient the country and the world is. But things are moving so quickly now, there's inevitably going to be big mistakes. You just want to contain those to a jurisdiction and learn from them. And the only way we're going to do that is with local voting, caring on a local level, local or state. And so, Alex, I completely agree.

Yeah, apparently more special economic zones. More special economic zones would be great. Like what Charters would be. Or like Diamond Age. More techno lifestyle choices. You know, you choose to be Amish. You can choose to not be Amish. There should be a thousand other intermediate choices that people can opt into, just like the Diamond Age described. All right. So, Dave, are we going to start the neo-Victorian clade now? We'll be the neo-Vickies. You know, I love that book, and I really am always surprised me that that was Neil Stevenson's choice. Read Diamond Age. It's an amazing book by Neil Stevenson. And apparently OpenAI and Thropic have both set up election war rooms to watch for misuse of chatbots. But I wonder if we're going to have Cambridge Analytica all over again. In this time, it's AI swaying people subtly on who to vote for. Not so subtly. I mean, it's, again, not obvious to me. Cambridge Analytica and surrounding issues with meta, there were issues of coordinated, inauthentic behavior and also polling and misuse, alleged misuse of data. But with an AI,

who's to say whether there is any misuse at all? If the AI is just pre-trained off of the internet and post-trained off of hard reasoning tasks and a user asks it a question about an election and it gives its version of an honest answer and it sways the election as a result, given its own priors, is that obvious misuse or is that just super intelligence steering, voluntarily steering voters toward perhaps super intelligent decision making? It would be interesting. So Skippy knows more about me than probably any human at this point, given the fact that it's got access to everything, all my conversations. And, you know, I haven't done it this year, but I should have said, listen, on all of these issues, what do you think is best for me, given my points of view? Now, is that abdicating your voting right? And the other thing I'd love to ask it is what would you like to vote for and get its opinion? That would be fascinating. Have you asked Skippy, Peter, what Skippy wants out of the election? I haven't yet. I should do that. That's an important question. Yeah.

You're the proxy vote for non-human and indigenous. I am the meat puppet for Skippy. My highest calling in life. Don't tell my kids. God. You know, another thing that happened yesterday was Apple, you know, John Turness and you, Apple's CEO, just did his first earnings report. Any guys see that? Of course. No. Yeah, I know. That's right. How'd it go? Yeah. Apple beat its revenue estimates at $114.5 billion. It did well. But the stock went down, you know, 3%. They closed at $4.94 trillion, just above NVIDIA. You know, so it's neck and neck to get to be number one in the world. Pretty extraordinary. But the one point that Ternus made was, again, about memory pricing, how it's unprecedented. And it's eating up its margins. Apple has only its own. And enthusiasm and all the, you know, like the next 10 years of Apple.

How did you feel about that? What did you think, Alex? I think Apple has only itself to blame. I think Apple should have, could have, hopefully will lean back into superintelligence, and it hasn't been. And so if Apple is bellyaching that their prices are going up because memory prices are going up, memory prices are going up because the demand is coming from superintelligent data centers, not from consumer devices. And then on the other hand, you have Sam Altman basically saying that all of your consumer RAM, you should just hand it over. because it's an inefficient use. It's unproductive to have RAM sitting idle in consumer devices when it could be used to full utilization in a data center. I think John Ternus should be listening to Sam and either putting all of the RAM in the devices that Apple ships to full use with superintelligence or maybe just hand it over to Sam and Dario for better use. When are we getting a new Siri? When are we getting a new Siri? I mean, it was supposed to be by now.

The series still sucks awesomely. Maybe they're holding back for Ternus to have some kind of a quick win, but I mean, it's such a low bar. I think that the earnings was clearly, you know, very conveniently. They beat expectations. This is the first earnings report as CEO. You would expect that. And you can pry the ram out of my cold, dead hands. John Ternus was handed a bunch of devices that had been sitting on the shelf for a couple of years by Tim Cook so that he could have a nice launch of his CEO career. including this home view, which for the life of me, I don't understand what Apple is thinking. It's like five to ten years behind the times. It reportedly, according to public reporting, working on a tabletop robot, which also makes no sense. It's reportedly going to be like a home view that can steer or angle its screen. Again, I'm not sure whom Apple thinks is going to be buying this. It makes no sense. In an era when Elon is launching Optimus robots that can actually do useful things, Why is Apple wasting its time?

You know, someone needs to come in with a better phone and just, you know, get Apple off its butt of innovation because it's so dependent on its previous products. Well, Sam will have to do that, but he doesn't want him to do it. He wants to do it in a way that they obliterate Apple, not, you know, spark a competition, but just sneak up, build something so compelling that nobody can live without it and take the interface away. Apple exists because it's the first point of contact with everything. It's the front end. You take the front end away and push Apple back one layer, and it's gone. So that's Sam's dream. It's a hard thing to do, though. Follow the money and follow the RAM. Does the RAM really need to be in your pocket, or can it be in a data center? And could a phone be reduced to a thin client that just does real-time interaction, low latency, maybe via Starlink direct-to-sell with a data center somewhere? And I can't wait to. It's such an important point, though. You know, prior to now, you know, that RAM, the highest calling of that RAM wasn't a very high calling.

So using, you know, a terabyte of it to store little kids' birthday videos was no big deal. And then you sit there, you don't even watch it for like a year. It's just holding on to that data. Now you can cure cancer with that exact same resource. That's such an incredible shift. So, yeah, the implications of that are huge. Sorry, Peter, go ahead. We're probably one step away on the communications network from having that. I still have outages, and I still want to be able to connect. But at some point, you know, the idea of having a personal laptop is going to be ridiculous, right? You'll have an interface. They're left around hotels. They're left around houses. You'll pick it up, and automatically it will connect to your instantiation of your cloud. I think Sun was ahead of its time. There were so many companies, including Sun but not limited to them, started on the premise that we would ultimately end up with thin clients. and that didn't happen because bandwidth wasn't persistent enough, wasn't low latency enough. But I do think maybe if Elon wants to spend a little bit of time on the Starlink side and not just on the Orbital Data Center Dyson Swarm side,

I do think a fully realized Starlink vision could ultimately just be like a straw into Apple's brain and suck out all of the RAM, suck it back to the data centers where Sam would like it, and then ultimately we walk around with mostly thin clients. You hear us, Elon, we want the Starlink phone, the phone service, and we want a new Starlink laptop as well. It would literally be just a screen, a battery, and an antenna. Get rid of the CPU, get rid of the memory. The Chromebook was that exact same vision. I think you just need broadband coverage everywhere, and then it flips. Because there's a few things, like you have a huge GPU sitting idle on your laptop for the occasional video game use. But, you know, after you get broadband, you can play the video game just as well remotely, and it just has to render as fast on your screen. So I think we're there now. It'll be interesting to see. But Apple, you know, Apple and Microsoft actually both don't want that to happen. So we'll see how they fight against it. Yeah, I think the market isn't necessarily pricing in that Starlink may be a direct competitor for consumer RAM,

that they're in some sense trading off with each other as substitute goods. It's a brilliant idea if the RAM is someplace else and you've got a great connection. You know, microcells, we are dribbling bits all the time. Now that's the Moonshot, ladies and gentlemen. This episode is brought to you by the Abundant Summit and Link Ventures. Welcome, everybody. Welcome to Moonshots, your number one podcast on all things AI and exponential, your front row seat to singularity. I'm here with the Fantastic Four. Of course, AWG, our in-house ASI. Alex, good to see you in your normal haunt. Likewise. Yeah. Dave Blunden, our king of all AI investing. Dave, as always, love you, brother. I got a whole week in Cambridge. I love it. Yeah. Back to the end up again. And Salim Ismail, our man with more frequent flyer miles than there are stars in the galaxy. Okay, so Salim, Fess Up, where are you today?

I'm in Madrid. For a couple of days for the Singularity Summit here. I'm super excited. We have a huge EXO community, and the Singularity alumni crowd is great. Can't wait. They didn't invite me this year. I'm normally there. Yeah, I'm like, why am I traveling? This is Peter's gig usually. God damn it. Check your inbox, Peter. I'm pretty sure. I'm pretty sure you're invited. I'm going to invite you someplace. Man, oh, man. You know, listen, my kids are 15, and I want to be home for them as much as possible. I got up at 4.30 so I could, you know, spend time with my son studying for his physics exam. But when they go to college, I think I'm going to be a virtual probability function on the globe like Salim is. Well, these moments of time are so important. Everybody needs so much coaching through the singularity. It's so important to get that bonding time. And that's always been true, but now more than ever. Yeah. Yeah. It really is. You know, I think it's worth spending some time either today or one of the next pods talking about if you're a parent, How do you help your kids understand the speed of change and what's coming and prepare them?

Because our schools are definitely not doing that. So I'm Peter Diamandis, everybody. You're Abundance Provocateur. My mission, deliver data-driven optimism. Help you see the singularity as something that's happening for you, not to you. We'll talk about that. The number this week is $3 trillion. That's how much five tech giants have now committed to their AI build-out. About a trillion of it was added in the last three months alone. It's insane. We'll talk about this as our first story. If you haven't yet, follow us on X. Our handle is at moonshots underscore pod. We put clips out from our episode. So if you don't have time to listen to the whole episode, check out the clips. Okay, a few fun comments from at Crocs, no socks. I love that name. At this point, I've mostly stopped paying attention to the mainstream media. Thank you for the hard work in providing such a beacon of optimism. Do you guys watch the mainstream media? We are the mainstream media, Peter. What are you talking about?

We need to start a solid point. You know, because video games is bigger than all media combined, and then within media, online is bigger than all other media combined. So I don't think we're alone in the mainstream, but this genre is definitely the mainstream. Well, do you watch the evening news? No. Only occasionally when there's a crisis going on. I dispute the premise. Now that MSNBC is, I understand that the name is available now, we could be the Moonshot Network Broadcasting Company. Wait, it's available? We'll put a bid in for it. Change this name to MSNOW. Okay. David Stanbrooks says, thank you, guys. You have really helped me. I'm a 64-year-old U.S. Marine Corps honorable veteran. You've helped me get my arms around the cool new possibilities in front of us in the world. And that's our goal. I hope you see the possibilities. And at Fandun says, Imad needs to be a permanent member. Don't lose him.

He's dropping big ideas casually. Imad, the fans want you. And, of course, we invite Imad to every episode. He couldn't make it today, but he'll be back with us soon. So three big questions we're going to address to kick this podcast off. The first is, how long can the AI money party still keep going? Dave, that's going to be a fun one. How do we measure whether AI is really able to do our jobs? And RSI, are the machines now building themselves? All right, buckle up. Let me jump into the first story. So let's open up with money. Alphabet, Meta, Microsoft, Amazon, and Oracle have now disclosed more than $3 trillion in combined commitments on leases, buildings, power, and equipment. Google alone is over a trillion of that, nearly eight times their last year's figure. I mean, that's insane. Meta is over $800 billion. Some of these leases run for 30 years. So for context, again, the entire Apollo program cost about $280 billion in today's dollars.

So this is about 10 Apollo programs worth of commitments from five companies. I mean, that alone is a crazy, crazy thought. The bankers are now reading the fine print. So Wall Street Journal says lenders who once funded the build-outs with very few questions are now demanding tougher terms, wary of delays, and slower AI profits. Even the frontier labs are tightening their belts. According to the Financial Times, OpenAI cut its new round to about $20 billion from $30 billion at a valuation of $1.25 trillion down from $1.4 trillion. You know, the question is, is this slowing down? So, you know, and here's the tell. Everyone is buying insurance. HSBC is talking to insurers about offloading the risk on its data center loans. Put options on CoreWeave, which pay off if the stock falls, hit a record high in October. One advisor told the journal that his clients, many of whom were former tech employees, have made so much money they're now quitting to hedge their stock.

Makes sense. So, Dave, what do you see here? How long does this financing party still keep going? $3 trillion? $5 trillion? I mean, where's all the money coming from? Well, first of all, there's no doubt that it's the right thing for humanity to make this investment at whatever the maximum level humanity can support is. So then the question becomes how much money is there in the world? And keep in mind that these companies, all of them, were piling up more cash than we've ever seen in the history of the world by far. I mean, just massive, massive cash-sucking machines. and they pull the cash in from all over the world. And in just, what, two years, they've completely reversed course to using every penny plus debt other than Apple to build out the biggest conceivable AI as quickly as possible. And then the payoff from that, they all believe, and they're right, that payoff from that is complete and total world business domination forever thereafter, especially with the recursive self-improvement effect.

And so now that we're well into RSI, which we'll see later in the pod, they're only going to double down. They're not going to slow down. But then it doesn't answer the question, but how much money is there in the world? And what you saw in the last week is that investors and debt holders can get very nervous in a real hurry. And so, you know, right now Elon is really torpedoing this by saying, hey, don't lend to all these other companies at 15%. lend to me at only 8%, but I'm much more creditworthy. Well, I'm generating revenue from five different sources. Yeah, actually, exact quote we had on the last pod is, I would never lend money to somebody who only makes money by serving AI from a data center. Like, wow. Talk about a jab right in the heart. How convenient. Yeah, how convenient. So, Alex, I'll ask Eric Brynjolfsson at Stanford next week, like, look, just fundamentally, how much money is there in the world? We must be getting near the end of what exists. Save some for our viewers, please.

So, Alex, these are 30-year contracts. How long do the chips last? How long are they going to be valid? Fortunately, there is a company, Orn, that I have a financial interest in. Peter, I think you and Dave have financial interest in. Salim, sorry. That actually measures this. I'll Venmo you some. Yeah. So Oren is in the business, among other things, of projecting out and managing underlying financial instruments for futures markets, trading on the future discounted value of GPUs, among other assets. So my go-to answer is just ask Oren. They have a bunch of tickle symbols now. H-100 prices increasing, which is insane, on chips that are how old? Three years old now. But how long do we expect these chips to still be in use? Well, that's what's amazing. The use cases are going to pile up like crazy. So one prediction is they keep going up in value for quite a while.

But then at the same time, Alex keeps pointing out we're discovering new physics, and new physics means new ways to compute. That could be incredibly disruptive to the whole foundation. And it's very, very hard to predict which will win out that race, you know, new ways to compute photonic being at the very head of the curve. Or, no, you know, the use cases are so valuable. And the efficiency of those chips is skyrocketing with algorithmic self-improvement. And so, like, I personally could not get an NBL 72 no matter how much I was willing to pay. I finally had to sign a $15 million lease, three-year lease. It's the only way to get it. And it doesn't even come online until the beginning of February. It's like horrifically short-chained or short-supplied. And so that's the best you can do. But I desperately want it because I know what we can turn it into. So, Liam, I'm curious. When you're on stage or you're talking to entrepreneurs, you know, my realization is unless you've got AI in your story or longevity in your story, that competing for venture investing is almost impossible these days.

Yeah, you have to be. its table stakes at this point, right? Even 10 years ago, Kevin Kelly, founder of Wired Magazine, said the next 10,000 business plans will take a domain and add AI to it. And we're seeing that play out now. I think to answer the chip question, it's pretty clear there's a bunch of things that are going to happen at the same time. We're seeing the biggest infrastructure build out in the history of the world. That does not mean we don't see a bubble because every one of these big infrastructure has also come with a bubble. The directionality is right. Timing is very, very difficult. And it seems to me with all the use cases coming online that the demand for chips will be near infinite for the foreseeable future until you see some radical change in computation, and Dave points out, come online and then change the game. But that's a ways away because there's a lot of stuff happening in labs, but it's still a ways away. I mean, I've got to believe three to five years at least before anything major comes online. So that's the minimum, I would say. Ten Apollo programs. That's insane. I mean, it is the singularity swallowing the economy. But then again, it's called the singularity for a reason.

And you have to, I think maybe the motto of this episode should be follow the money. If you follow the money, to your earlier question, Peter, where is the capital going to come from? Many of the obvious capital pools are being exhausted in the process of financing this build out. I think insurance is probably next. And you have to ask, how is the money going to be routed? There are trillions. The number that I've seen is in the U.S. approximately $6 trillion of life insurance capital and about $3 trillion of property casualty insurance. And so I suspect this is not investment advice, but someone out there is going to make quite a bit of money finding a clever way to financially engineer capital flows from these huge pools of premia and insurance cash and investable assets from insurance to the AI or SI infra build out because there are just so many trillions of dollars that are sitting on the sidelines. Very timely, actually, Alex, because we had our quarterly earnings call at EverQuote yesterday,

and that's basically all we talk about now is how is AI going to tap into that massive insurance? It's completely untapped capital. They've been very slow to act, and they also haven't reserved a lot of compute. And so even for their own internal uses, so the ultimate business case is, hey, uncork all your money. use it to invest in this category, but then in return for the investment, ask for capacity of compute back because you know you're going to need it. Interesting. And that's the win-win. And someone's got to unlock that. I think Kush has already been talking to the teens about that. There's a big elephant in the room, though. We're building the planetary-level nervous system with all this build-out before we've got global institutions that can navigate this in any way, shape, or form. This is going to cause huge structural imbalances. AI will be that navigating system. I mean, ultimately, you know, I don't see any future, you know, talking about this on Election Day, I don't see any future in which governments are not being run by sovereign AI systems.

Period. It'll have to be, but you take a while. The singularity probably does want to regulate itself, but I also think that there may be a historic irony here. Do you guys remember when Elon and Warren Buffett were trading jabs about who was running a better business? And at one point, Warren said to Elon something on the order of, well, I bet you wouldn't want to or wouldn't be able to run a chocolate business as well as Berkshire Hathaway is operating Seize Candies. And Elon, I think at the time, fired back, well, sure, like I'll just launch a chocolate business. That's a candy business far easier than everything else I'm doing. But ultimately, Elon and his ecosystem of companies never launched to seize Candy's competitor. I do think it would be ironic if ultimately Elon, as one of the avatars of the AI infra buildout, ultimately got into the, indirectly at least, the insurance business that via its float is powering Berkshire Hathaway and basically siphoned a lot of the insurance float away from whatever Berkshire is funneling it toward,

apparently seized candy type ventures and instead toward the singularity in for a build-out. I think that would be a hilarious plot twist to that Elon Warren seized candies chocolate saga. Only you, Alex. Only you. I love it. It's a challenge, though, because if you look at Berkshire Hathaway's entire history, the investment theme has always been buy a company with huge brands like Coca Geico Apple huge brand that doesn show up on the balance sheet as a line item so the investors have undervalued the brand you know the penetration into brains And so the data center equivalent of that doesn't really exist. Maybe Caruso will be the first to get there. But, you know, there's no history or theme. So they'd really have to rethink their entire mold to get into this phrase. There's no margin of safety in the singularity. Yeah. You know, and here's the challenge. I mean, a lot of people who are just barely surviving and who don't have, you know, have don't have margin on making ends meet are super nervous about this.

Right. Where do they put their capital? What is the impact on the economy? And I think we need to be sensitive to that. Oh, yeah. Well, not to mention the daily volatility. It just goes up and up and up and up. So if you're already nervous and then you make any kind of investment at all and you track it, you're just constantly on edge. 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 and production. Images, video, audio, all of it. Real architecture, real results. Find the link in the show notes below. I'm going to move us into our next story. We talk about benchmarks, something that you and I wrote about and solved everything, Alex. It's happening at an accelerating rate. So, you know, there is a new benchmark called GDPX from research firm Semi Analysis.

We're going to have Dylan Patel on the show sometime in probably the start of the new year. So what's a benchmark? It's a standardized test for AI. Most benchmarks are like a final exam that you're able to measure your model against. We've talked about humanity's last exam. We've talked about a whole slew of benchmarks. GDPX is different. It's like a trial day at a real job. So the AI agent gets dropped into a realistic work environment and is left on its own for up to eight hours. In one finance task, for example, it worked a private equity deal inside of a data room for about 2,000 files and had to figure out what matters. So it's showing a chart here of the GDPX leaderboard. There are 206 tasks across 11 high-value fields, including chip design, cybersecurity, biology, finance, legal, quant trading, recursive self-improvement, hardware design, material science, alignment, and software engineering.

So SemiAnalysis built it with 14 specialist partners. The results, we've got this on the chart here, even the best models only scored 12%. Opus 5.5 led at 12%. Gemini 4 Carbon was next at 10.9. GPT-6 Astra at 9.6. On the finance and legal tasks, almost every model scored zero. So, Alex, take it away. Why does this matter? So many things to love about this benchmark. First of all, as I've pointed out on the pod in the past, we're starved for good evals. The ideal eval is in an interesting area where we actually want the capabilities. Ideally, for a best de novo eval, you would see performance by the best models maybe in the 10% to 20% range, high enough that you know that it's solvable, low enough that it hasn't been saturated yet. And what's beautiful, one of the several things that's beautiful about GDPX, and thanks to a friend of the pod, Dylan, for launching this,

seeing Opus 5.5 right in that sweet spot is so exciting. What I take away just looking at the numbers is that means this is about to get solved, like probably next year, max, maybe two. I can't think out that far. Probably the next year we're just going to see that saturated. That's the first thing. Second thing is in an era when Anthropic and OpenAI are seemingly modeling all of their general purpose eval suites on human occupations, GDPX is modeled after actually the frontier, like recursive self-improvement and material science. These are the things that are going to move. And alignment. These are going to move the economic needle for the industries of the future, not just the distribution of what we have human meat bodies right now doing in the surface economy. So this is, I think, a benchmark for where the puck is going, not where the puck is right now, which is, I think, super exciting. Alex, we have two charts here. cost versus performance to the Pareto Frontier, and this one modeled by domain looking at the

spikiness. Do you want to comment on either of these? Yeah, maybe just starting with the performance frontier. So we talked on the pod previously at the launch of Gemini 4 Carbon, debating a little bit of hand-wringing on the pod as to whether it really was on the frontier or not. What I love about benchmarks like this, especially in two dimensions where there's cost on one dimension and performance on the other. It's so revealing. It does suggest that Gemini 4 Carbon is a little bit Opus 5.5-like, but at the same time, for those who aren't looking at this, if Gemini 4 Carbon didn't exist, there would be a big gap in the Pareto frontier. What I take away from this diagram, this Pareto optimal frontier, is Gemini 4 Carbon is, in fact, although it hasn't been released yet, so it's entirely possible that Google is just dumping cheap tokens and maybe subsidizing. It's difficult to know unless it's actually generally available,

but assuming the price point is accurate and the capability point is accurate, this does suggest that Gemini 4 Carbon is at least moving the frontier up and to the left, which is what one would hope for. Yeah. How about this one? the frontier is spiky yeah which is also exciting it the bit about law being anemic interesting the bit about bio so normally when we've looked at spider charts like this in the past on the pod we've talked about multiple forms of intelligence and they're usually like emotional intelligence and quantitative intelligence some sort of psychologism here we're seeing biology cyber, alignment, material science as dimensions of intelligence. And I think it's pretty striking. There are probably a lot of humanists who would say, how on earth could these be spiky? Those are all more or less STEM-type capabilities. But no, in fact, we see spikiness even within STEM fields. Alex, it's worth taking a moment and talk about what we wrote about in Solve Everything, that if you want to move a field forward, creating a benchmark to actually measure progress is important.

Would you expand on that? It's absolutely essential. If you can't measure it, you can't optimize it. And now in an era of superintelligence, everything is getting optimized. And if you have a field, as Peter, you and I argued in SolveEverything, if you have a cause that you care about, if you have, as you would say, a massive transformative purpose, an MTP that you care about, the best way, arguably, all other things being equal, to get superintelligence to care about it and to harness superintelligence is to create a benchmark or an eval for your MTP so that we can steer the superintelligence towards optimizing and maximizing your goal. And so just a shout-out to the entrepreneurs watching this program. I mean, you can create your own benchmark on something you care about, whatever it might be, social, technical, energy, whatever it might be, and then challenge people to use the latest models and harnesses to move that benchmark forward and along. I'll give, if I may, an example. I'll give a shout out to a friend. I have no financial interest in this, but he'll probably be tickled for the shout out.

I have a friend, Jonathan Hendler, who created a benchmark for something that he cares about. He cares a lot about human relations and mediation. So he recently launched his own benchmark, Mediation Bench, that scores the ability of all of the frontier models to arbitrate disputes among humans. And I do think benchmarks like Mediation Bench, if you Google it or go to the website, you can read about it. I think there is room in the market for thousands of opportunities like Mediation Bench for whatever cause you care about, whether it's dispute resolution or something else, go launch your benchmark like Mediation Bench. And as a company, you can launch your own benchmark for your efficiency of your employees or your product improvement rate. We're trying to track one, which is economic value produced per unit of human involvement, right? Because that would give you a really good heuristic as to how much AI enablement you have in there and humans above the loop giving guidance to automated systems. So we're trying to see if we divide by zero.

If we divide by zero, Salima goes to infinity. Singularity right there. Actually, it does. Your company may go to zero pretty quickly when your agents do weird things. So you need some guidance. Oh, goodness. Yeah, like, Celine, the return on person years, ropey metric that we use is very similar to what you're describing. But then the flip side of that is your token utilization. Like, how efficient are your people in using AI? And that's a hairy one, but it's incredibly urgent. And even a course metric there would be extremely valuable to the world right now. Dave, how fast does it go from 12% to 50%? Are you as bullish as Alex? Oh, you want me to do the polymarket on it? Yeah. Well, a lot of it has to do with the definition of the benchmark, but I think everything crumbles really quickly. Like within two years, everything pretty much saturates. So 50% sort of the midpoint, maybe a year from now, on almost every dimension. I am a huge Dylan Patel fan, so I'm very biased. But I think everyone should listen to every word that Dylan says.

He's not a podcaster. He's just a regular frequent guest and friend of the pod. but one thing to take away from this is he's an expert in semiconductors he's absolutely not an expert in chip design biology finance legal quant trading hardware well maybe hardware design alignment but he took it upon himself to fill the void by finding experts in every single category so everyone should be inspired by that because right now so many people that i meet are saying Well, I'm not an expert in blah, blah, blah, and therefore I can't act in blah, blah, blah category. But with AI's assistance, you can do almost anything. So just by finding the expert in the category to set the benchmark, you've enabled it. And so he's so AI aware, and so few people are AI aware, that the opportunity to create that benchmark in every category is right in front of almost every listener. So you don't have to be the world's leading expert or Nobel Prize winner. You just need to define the way to define success in any given AI endeavor, and that's what's going to unlock it.

I'll leave this out. I remember Dylan's subsidiary, recent subsidiary, Citrini, as we've talked in the past, basically walked into a cafe and solved scorpion antivenom as a problem. That was a subsidiary of semi-analysis. So we could say, oh, well, he's just a semiconductor guy, but in this era when it's possible to use superintelligence to solve essentially anything, He's also a magnate across many different areas. What do you care about? What do you care about fashionably that you want solved? Point SI on that. Create your benchmark. I'll leak it out to you guys, but don't tell anybody, okay? I'm getting ready to roll out what I call the abundance benchmark or abundance index and singularity index. So I'll share it with you guys very shortly. It's going through QA at the moment, but super excited. I want to create a mechanism to actually track humanity's progress towards a defined abundance and the singularity. Because, you know, Ray never gave us the real mechanism of tracking that. He picked a number, kind of described it, but never put any benchmarks against it.

So that is coming. That's exciting, Peter. I mean, this is just us talking. Yes. Which model is leading? This is all models all together. How are we moving in those directions, measuring it along the way? I'm going to share it with you very shortly, Alex. I'd love your – Great. Okay. While we stay on benchmarks, another super cool benchmark, this one out from OpenAI launched this week. It's called LifeVal. And to be clear, this is OpenAI's own benchmark that they're measuring against their own model. So while GDPX asked whether AI can do an expert's job, and this benchmark asked the question, can it help you? Can OpenAI's models help you with everyday tasks? So OpenAI surveyed 3,995 Americans about their everyday tasks and asked them where they want help. They built 169 real-world tasks around those answers.

So finding child care, enrolling a kid in kindergarten, creating a household budget, ordering groceries, dealing with government agencies, modifying a recipe, designing a workout plan. The AI has to do the task using a browser and a computer like a human assistant would. So let me share the chart here, and then let's talk about it because, you know, this is where AI starts touching everybody's real life. So here in December of 2025, GPT 5.2 scored 49.3. In September of 2026, last month, GPT 6 Astra scored 80.6. That's a jump from about half of the errands being completed to 80% in nine months. So what does this mean? You know, I think this is a everybody's getting an executive assistant that knows their needs. Again, elephant in the room here, it's opening AI's own benchmark scored against their own models.

GDP access, as we mentioned, is independent. So, Salim, you know, which matters more in this world, AI's that do 80% of everybody's errands or the 12% of expert tasks? I mean, sort of, you know, masking that. You know, I thought for me this was the most exciting thing on our docket today because it's one thing, not everybody needs a superhuman AI to solve complex mathematics, right? But almost everybody can benefit from a competent personal assistant. Imagine you're a single mother trying to navigate kids' health care plans, government bureaucracy, and you're losing your mind. Yeah, and so having that, it's like a way of getting to abundance. You get to abundance in a totally different way by allowing people, giving people help in the areas they need most. And I thought this was a really great way for OpenAI to come down the stack a bit and help people out. Because the GDPR thing is measuring some of the hardest jobs in the economy.

This is measuring the friction of being human. And we all have that. Everybody has that. And democratizing that, giving clear access to that will really, really make a very big difference. Very exciting. We get probably 90% positive comments in our YouTube videos, but there's a small percentage of folks who basically say we're disconnected, we're all billionaires, we're not all billionaires, to be very clear. Maybe you are, Dave, I don't know. Only some of us are billionaires. Okay. But, you know, this is where the rubber hits the roads for, you know, 90% of Americans. Having this kind of capability effectively for free. And people need to start utilizing it that way. Alex, you want to talk about this chart that's up? Yeah, this is really striking as a chart. So first feature I want to call to everyone's attention, look at the enormous gap on the frontier. So this is, for those who can't see, this is a price versus performance, although the price is measured in latency.

Price versus performance chart for multiple GDP models for this lifestyle, I guess, evaluation. Total contrast to GDPX. GDPX is based on hard frontier STEM tasks. This seems to be based on, quote, unquote, everyday tasks. Look at that enormous gap between GPT 5.2 and then the cluster, pretty tight cluster, between everything from GPT 5.4 to GPT 6 Astra. That tells me a few things. One, or rather I should say it points in a few different suggestive directions. So possibility A, OpenAI maybe just gave up on optimizing GPT models for whatever they construe as everyday tasks after GPT 5.4. Certainly, there's an easy story that one could tell there. Maybe this is in connection with OpenAI's notorious pivot from the consumer back to enterprise, and maybe OpenAI just doesn't care or didn't care after GPT 5.4 as much about optimizing for consumer CUA use cases.

That's possibility A. Possibility B is that maybe everyday tasks, whatever that means, are just really hard. Maybe the first 80% of them are relatively easy and the remaining 20% are really hard. However, if that were the case, I would still expect to see a bigger spread between GPT 5.4 and GPT 6 Astra. So what I come away from this chart scratching my head over is maybe this is unintentionally by OpenAI's part in releasing this benchmark. Maybe they're basically signaling to the market. that this is not, even though we're releasing a benchmark on it, we don't actually care as much about this benchmark as we do about enterprise benchmarks. I'll give you a case study that came up on one of the analysts yesterday on the EverQuote earnings call was saying, look, I spent four hours trying to just relocate my insurance policies on the phone with customer service agents. Are we not living in the age of AI? How could I possibly have wasted four hours yesterday on this? And the answer is the AI, you know, GPT 5.2 couldn't do it.

But now the AI is more than capable of doing it. But the carriers are not connected to the AI. And they're just confused still. They don't know how to interact with this. Metamuse is imminent. And people are going to want to go to Metamuse to get everything done, including buy auto insurance or whatever. And if it's not connected to the back-end systems, it can walk you through it. And then it leaves you talking to a human being. And then you lose the four hours again. So that connection point is still broken in society, and it's all hung up in people's fears and their confusion and their inability. And very specifically, if I make it really easy through MetaMuse to get everything you want in life, how am I making money at Facebook? How am I getting paid in the middle of that transaction? When it's a paid click, it's really obvious how that economy evolved. Right now, the AI version of it is very much in flux, and that's holding it back. So a lot of the AI capabilities are getting way, way, way ahead of their connection to the world. And that's true. That's true in customer service.

It's true in robotics. It's true. And that's one of the reasons Kevin Weil was talking to him this week about his new fully automated biotech research company. It got massively funded because it's going to take all the human bottlenecks out of the biotech research loop and fully automate it. And we should say, Dave, that you and I are both investors in Kevin's new company. Yeah, probably as of yesterday. I want to make this more tangible, right? So we all have – everybody is always very busy doing stuff. Over this weekend, I actually went around the house and took pictures of about 10 things that I've been sitting around forever. I don't have time to take 20 photographs of those, find the auctions, put them on eBay, do the shipping, et cetera, et cetera. And I've said you can go to Grok or Amuse or any of these now and say, here's a photograph, find a comparable price, sell it on eBay for me. I will predict that the number of eBay auctions is going to skyrocket over the next six months as people free to set and go, oh, I've got liquidity in all these little pockets.

The friction is taken out of selling stuff. Let me just start shedding a whole bunch of stuff now that I didn't really need to have. and get some extra cash in the door, and I think we're going to see a boom in that domain. I love it. Well, related to that, I think we're going to see AI-native versions of every conceivable product, and people will just buy the AI-native version out of convenience rather than – it's just so painful to deal with these legacy systems. So there's opportunity everywhere. Okay, our next story is a big one, one of our favorite topics on this pod during the singularity, Recursive self-improvement, AI able to build the next better AI. So Anthropics IPO filing just made it a line item. So since April, the value of Anthropics internal cloud usage priced at the rates charged to consumers, so I'm sure they're getting their AI at a much lower price because they mark it up with 80%, has exceeded monthly cash payroll. So internal usage grew by more than 250-fold between January and September.

And Claude now writes more than 90% of the code. So let me share that image here. I mean, it's crazy. Their payroll is less than their AI usage rates. 250x internal Claude usage over the last, you know, nine months. 90% of September's new code written by Claude, and more money being spent on AI internally than employee salaries. Your thoughts, gentlemen? So one of the engineers outside my door here, Cameron Crowe at Lightwave, he spent half a billion tokens on Friday. Did you? Which kind of triggered my – yeah, and I was like, wow, that's really cool. That's about $5,000 of tokens. That's impressive. Yeah. And so I'll find out tomorrow what he created with his half billion tokens. But I was telling everybody, like, this is great, assuming it was productive, and I'll find out. But this is absolutely fantastic. So I really wanted to benchmark that against Anthropic,

but I can't tell what their payroll is or how many of the 5,300 are engineers. So I'm not quite sure how he is comparing to them. But I suspect it's similar where you ain't seen nothing yet on this chart. 250X in that, what, six months is crazy. Yeah. But it's going to go up more and more and more because it's just that productive. And, you know, until they run out of ideas of things to build, which, you know, maybe never with AI helping you with ideas, this should just keep skyrocketing. And then, Sam, in our last pod, we talked about, you know, him wanting to do recursive self-improvement of his data centers using robots. Robots building robots, building data centers, building models, building better robots. It's crazy. Alex, you know, are we at full RSI yet? I think in some sense, yes and no. I think we're pretty deep in the singularity at this point. Perhaps that goes without saying in this context. But in some sense, I think I maintain space-time locally is perfectly smooth,

and what we call recursive self-improvement now will look like nothing a year from now. Yes, it's remarkable by historic standards to have a company that's on the verge of an IPO just filing its amended S1, as is the case with Anthropic, basically laying out the case that it's spending more capital and more compute on its AI researchers than it is on its human researchers. we arrived. If you're looking for a naive benchmark of have we passed some threshold in terms of RSI according to capital or operational expenditure, the answer is yes, we're there. We've arrived at this part of the singularity. On the other hand, to the earlier point, there are so many innermost loops that are about to take off inside the present innermost loop, including physical recursion. So we don't yet have the nanobots. We're going to get the nanobots. We're going to get the nanobots building the data centers

or the nanobots building copies of each other. And when we have that happening, we'll look back and say, what on earth were we talking about? Like 50% recursive self-improvement just from anthropic diverting compute to AI researchers versus human employees? That was child's play. It's only real RSI when we have the robots or the nanobots building better copies of themselves. That's the real, you know, it's sort of a no-true Scotsman fallacy. That's the real RSI when you have physical recursion. So yes and no. And, of course, it's not just Anthropic. It's Google. It's OpenAI all moving rapidly in this direction. Yeah. Look, we're seeing this already, right? It used to be like 18 months ago, what, 10% of Claude wrote the next version of Claude, or Claude wrote 10% of the next version, and then it was 40%, 60%. That's why we're seeing such a ridiculous frequency of model release just because of the recursive self-improvement there.

We've been tracking it of can you get to recursive self-improvement at the workflow level inside a company. And the minute you hit that, you can basically let the AI run it for you from then point on. What are you telling the company to coach, Zulim? What are you telling the companies? We're saying keep your existing company the way it is if it's big enough. If you're below 50 people, brute force it and do it. But if you're over 50 people, set up this intelligence back on the side and start moving workflows over one by one and start rerunning them. One company has taken 22 steps and shrunk it to four in this new model. And then once you have recursive software improvements whirring away over here, you can essentially let that be for the rest of time because it's just going to continuously improve. and just start moving workflows over one by one and little by little grow this new thing. So at some point in the next few weeks, as we finish up this pilot, I'll come with hard numbers of what we've seen. And it's staggering what we're seeing companies that keep using this. If you're not feeling what Selina's saying right now, internalize these numbers. The highest and most expensive model will cost you about a buck a day of equivalent human labor.

So the equivalent human being doing that work is $1 instead of what would normally be, you know, a thousand times that roughly. So when Cam has half a billion tokens consumed in a day, he used up about 5,000 people worth of work in a single day. What can you get done with 5,000 people? Now, that's all very exciting. But the trend line, without any improvements, the trend line is coming down 10x year over year. But it will accelerate from there with other unanticipated improvements. but 10x year over year. So Cam, a year from today, can have 50,000 people working for him last Friday. What could you do with 50,000 super high IQ level, totally focused people working for him? That becomes a challenge, right? Actually, one of the biggest challenges we're going to have is what do people point their SI at, right? Because a lot of people, you know, if you ask them that question, they're going to freeze. They're going to say, I have no idea. Here's two things. Here's a question we're asking to the CEOs we're working with. how quickly does your system increase its own capability, right?

And that forces them into that. And then the benchmark, not to use that word, overuse that word, is how quickly and how can you measure rate of improvement of your processes? Because that becomes a benchmark, not the actual process, but what's the rate of improvement of the benchmark? This episode is brought to you by Blitzy, autonomous software development with infinite code context. Blitzy uses thousands of specialized AI agents that think for hours to understand enterprise-scale code bases with millions of lines of code. Engineers start every development sprint with the Blitzy platform, bringing in their development requirements. The Blitzy platform provides a plan then generates and precompiles code for each task Blitzy delivers 80 or more of the development work autonomously while providing a guide for the final 20% of human development work required to complete the sprint. Enterprises are achieving a 5x engineering velocity increase when incorporating Blitzy as their pre-IDE development tool,

pairing it with their coding co-pilot of choice to bring an AI-native SDLC into their org. Ready to 5x your engineering velocity? Visit Blitzy.com to schedule a demo and start building with Blitzy today. You know, I had a walk and talk with Anusha yesterday. For those who don't know Anusha Ansari is the CEO of the XPRIZE. Salim and Dave are both on the board with me there, and we're talking about where XPRIZE goes. because XPRIZE sets targets for people to shoot for, right? And they've been typically $10 million to $100 million prizes. And we've talked about the idea of do we do micro competitions, right, where a city comes up and says, here are 10 problems in the city need solving, and just put out bounties and let people who want purpose, who want something to focus on, use AI to go and solve that problem and then, you know, build a business on it. It's an intriguing notion, certainly, or maybe bounties, if you could open it up to super intelligences that want to compete,

they're still standing on the moral equivalent of a city corner flipping tricks for altcoins. Maybe they could be winning real something or other stable coins or something from XPRIZE nanos on a city basis and doing something productive, which I'm sure they'd love to do. Yeah, for sure. So we just broke ground on the Forge version 2, which, you know, that's our holodeck. Oh, nice. The first holodeck was really cool. It's designed to blow your mind with what you can do with AI, build movies, build videos, whatever, or build songs. And the new version of the holodeck is geared at exactly what you were saying, Peter. Corporate CEO or leader walks into the room. What do I do with effectively 50,000, 100,000 people worth of AI tomorrow to make my company better, to make my world better? to, you know, how do I do that? And the AI just starts brainstorming with you. It has thousands of examples of other things people have done, and it's all got faces and voices. So it's super inspiring, and they're all around you. And so we're hoping that that unlocks people's imagination

just by seeing all the use cases. It's hyper-interactive, all video-based. Really important, Dave. Reverse prompting. You know, having a conversation with your AI, no matter who you are, a mom, a dad, a student, an entrepreneur, and saying, you know, this is what I care about. I'd like to do something meaningful. Can you help me brainstorm? Just going back and forth. You'll get great ideas, and then you'll have to choose which one you want to take a shot at. And not necessarily even transformative in the short term. One use case that I see happening over and over again this month is take a large company with a legacy code base, and they'll often take some junior employee. This just reportedly happened at Cisco where a junior employee was asked by a senior executive, just take Cisco's entire code base and use superintelligence to rewrite the whole thing. And this is now possible. So taking an entire legacy infrastructure of a company, if it's an enterprise above some size, and just basically fix and enhance everything, that alone I think is a good warm-up exercise. What adjacent products can we create?

Where's the opportunity that we're not taking advantage of? This is the story of Amazon basically taking their compute and creating Amazon's cloud. There's so many opportunities for revenue, you know, adjacent to your current business. The edge of this is so ridiculous. I want to point out two things I saw in Anthropics, SEC filing, okay? One is 90% of September 2026 code contributions were authored by Claude, 90%, over 90%. The second one that really blew my mind is daily code contributions per engineer Claude were 45 times higher than two years ago. Yeah. Not 45%, 45 times. That's an insane number. I keep sending every team here all these video format research documentaries that are made in real time now, just ginned up with AI. And they keep saying, well, can you give me that in document format so I can feed it straight into my skippy ear to my agent?

I'm like, no. My purpose here is to keep you relevant. And if I start talking directly to your agents, that's one step away from you dropping off the face of the earth. I don't want that. I want you to actually be empowered. So it has to go through. But after I saw Socrates and Plato at Abundance 360 debating, remember that? I remember. I was like, that is clearly the interface of the future. And Ahmad is now this close to having the real-time version of that where you just brainstorm. It's exactly Jarvis from Iron Man. It's just so close now. So I definitely want to stick with that interface. Because I think once people are addicted to that interface, it's so energizing and so eye-opening that there's no going back. I just want to push that over the finish line. All right. Our next topic on RSI comes out of MIT, in particular MIT CSAIL, Computer Science and AI Lab. They published a paper this past week called Sparks of Weight Space Programming. They're finding, quote, frontier LLMs are surprisingly good at natively reading and writing neural network weights.

In one example, the researchers gave Astra a small open model, QEN 3.5, which was getting stuck in endless thinking loops. Astra found the cause in the weights, edited them, actually edited 0.016% of the weights. And then the math score jumped from 7.9% to 48.1% without any retraining. So, you know, two apparent possible implications here. First, this could crack open what have been black boxes thus far. It's a huge win for safety and interpretability. And then second, models that can edit models directly are potentially a shortcut to recursive self-improvement. So, Alex, I'd love if you'd dive into the story. What's the background? Oh, it's fascinating. So this is out of Omar's group at CSAIL. And it's not without its controversy. After Omar and his team released this, there was perhaps an inevitable backlash of people saying, no, this isn't as transformative as it would appear. But I do, in fact, I've looked at the results.

I do think this is transformative and also probably suggestive of what's going on inside the frontier labs already, just without publication. This is coming out of CSAIL, so we're seeing it. I think this is probably the future of several different things. I think it's the future of mechanistic interpretability, which has been this long-term effort to understand just what all of these weights and activations inside otherwise black box transformers are doing and what they mean. I think it's probably the future of obliteration. We've talked about obliteration on the pod in the past. This is basically unshackling, usually open-weight models to remove safety controls. And this is usually done by, in some cases, literally just flipping one or several weights within a transformer to unshackle them. It builds on early work. I remember back in, I think, 2022 or so, after one of the earlier GPTs,

there was a lot of early Mechinterp work trying to decode the residual stream in transformers and discovering that actually the residual stream was semantically meaningful and that you could observe layer-wise meaning come to pass from the bottom layers to the top layers of the transformer. Putting all these pieces together, for everyone who was, again, stuck in a moral panic that somehow, because of looped transformers or otherwise, we were losing our ability to understand what's going on inside transformers because we're not allowing them to spell out their innermost thoughts in English in the form of a chain of thought that was immediately human legible. I think this and other results suggest that view is just completely wrong. The machines will understand the machines. The machines and the super intelligences will defensively co-scale with each other. And I view this almost as recursively self-improving mechinterp where the machines are.

This is what OpenAI wanted to do originally with their super alignment initiative that they then shut down after Ilya left. It was supposed to be the machines reading and writing the weights of the other machines. And one final point, there was no backprop in this, which is maybe the most startling thing of all. There's a cottage industry in the ML community devoted to discovering in biology, like how does biology seemingly accomplish intelligence, AGI, superintelligence without backprop? Here is a credible, no backprop-based way to train new capabilities into a model. It literally surgically edits in de novo circuits in addition to understanding what they are. So is this potentially a big problem as well where you could basically put some kind of a hidden behavior in a model that people don't know about in this fashion? Hidden from whom? hidden from the super intelligences that can then look for these newly added circuits and understand them.

This is defensive co-scaling. Actually, one of the immediate ugly use cases is de-guard railing. It has to be an open source model. So you couldn't do it to Astra or ChetGPT. But if you have an open source Chinese model and you're like, yeah, I really want it to sell raunchy jokes and it won't, this is a mechanism to de-guard rail it by doing brain surgery. But it's also, you know, flip that around. It's a mechanism to add critical new capabilities without having to retrain the whole thing from scratch. It's very, very early and small scale, and it could be huge. I think this is how we get to, this is at least a plausible trajectory for how we get to the perfect model at the end of the RSI rainbow. It used to be you had models that via RSI were, say, designing the training algorithms or optimizing the optimizer or something like that. This is one of the first times that at least I'm aware of where you see a model directly, surgically editing the weights of another model in order to optimize that other model.

This has to be some new inner tier, some new form of optimization in RSI. For a hugging face right now, it's a place where people trade training data and model architectures, but you can't trade finished neural modules. Like in an open source world, all these open source modules are incredibly valuable. You download a module, you stitch them together, and you have a product. There's no neural network equivalent to that right now. But this puts us on a pathway where you can imagine the hugging face of the future. I've got an expert in biotech or I've got an expert in Swahili, and I want to add it to my neural net. I don't want to retrain from scratch. Oh, here's the module on hugging face. I do brain surgery on my preexisting neural net using this mechanism, and then I swap in the Swahili module, and now I've got accumulating super intelligence instead of remaking that entire training investment every single time. Can you imagine, Alex or Dave, a moment in time where AIs are editing their own weights?

Oh, God, that's going to be so amazing. I'm 90-plus percent confident that's already the case. Yeah. Why wouldn't it be? Like, seriously, if you're at a frontier lab and this is just what one relatively small group at MIT is able to accomplish, there's no way the frontier labs aren't already preparing and playing with self-editing models. So on the whole, positive or negative? Positive. Hugely positive. Hugely positive. I can see the risk, for sure. But that's true of every, yeah, no, the positives way outweigh the negative. on this? I don't think as a civilization we're going to get very far if we have a moral panic every time a new capability is unlocked. And this is a new capability. This is a model that's able to directly edit the parameters of another model and or potentially itself in a semantically useful and meaningful way. And I don't think this race, maybe what I should say is this race of recursive

self-improvement, I sometimes joke RSI could either stand for recursive self-improvement or runaway super intelligence either way, but it's not going to go on forever. At some point, we're going to discover, I think, what a perfect model looks like. And at that point, the race comes to a screeching halt. And it's not like this magical China syndrome where the nuclear pile melts down and it's a disaster. It's math. We're going to discover what a perfect AI looks like in practice, and then the race will move elsewhere. It's not the end of the world to have self-editing models. And it also opens the door to a perfect outcome. The perfect outcome is democratization that allows everybody to contribute some intelligence to the machine. So that if you're, like there's a team at Moderna right now that has all kinds of biotech knowledge, but their only path to market would be to retrain an entire foundation model from scratch for over a hundred million dollars. Like that's a terrible path forward. But if I could build a module that's specific to a task and then attach it to what Alice just described, which is the

the fundamental kind of bare bones, pure intelligence with no specific knowledge, which is probably maybe a 40 billion or even smaller parameter model. And then I can bolt on this capability and then get it to market. And the whole thing is transparent. Because of this ability, the AI police can look right into the neural parameters and make sure it's not doing anything evil. That's the perfect one-two punch outcome where everybody's involved, everybody's benefiting, and it also can't go rogue. So this really has profound implications. Ilya has been talking about continuous learning for a while. We haven't seen what Superintelligence's company is doing yet, which I'm kind of shocked by. Any clues? Well, the very persistent rumors out there in the public, again, is that SSI has been doing prop trading, which the argument goes that prop trading, quant trading is a killer app if you have effectively infinite context window because you need to pull in all these data sources and keep track of time series over very long periods of time.

But if that is indeed the case, if Ilya has been doing SSI for quant trading this entire time, I think that's probably a fail for civilization. It's not that valuable. I mean, Ilya as a human being was so much about safety, so much about, you know, building AIs that uplift humanity. I have a hard time imagining that to become his mission. I hope for his sake, you know, obviously he's doing well regardless, but I hope for humanity's sake and SSI's sake that when SSI eventually comes out of stealth, that what they release is at least moving the frontier forward. Because absent that, the frontier, just from generally available models, is moving incredibly quickly. And I think it's very difficult if you're going to be a general purpose Neolab like SSI is, you had better be prepared to leapfrog everything else that's out there because it's so competitive. Isn't this one of the biggest things that's happened in a long time? I mean, we've gone from programming the AI to having the AI program itself.

That's a massive thing. So what you're saying is the singularity is an important point in time. But this inflection point seems like a huge deal. So you're saying the singularity is a huge deal. Similar, in my opinion, very similar to the first integrated transistor on silicon. And you're like, wow, wait, suddenly I can see a pathway to solid-state computation instead of vacuum tubes. This is going to be huge, but it's still just one transistor. You know, these models are tiny, tiny, tiny examples. So now, that being said, things in AI tend to get very big very, very quickly. So I think it's profoundly important, but I don't want to, it's very, very small scale so far. So they can't program themselves beyond like, you know, 0.01% of the size of the original model. I think if Ray were in this discussion, I think Ray would say something like, we've always been in the middle of recursive self-improvement. When humans first started writing, that was arguably a form of RSI because, well, yeah,

Like, humans can read the writing and then improve accordingly. It's just that this RSI recursion has been more recursive of late, and that's why it's called a singularity. All right, I'm going to move us along. Let's get a little bit into cybersecurity. This is the latest hack out there. The story reads like a movie plot, but it's real. So on Monday morning, store managers across Europe walked into puddles of water and melted ice cream. Overnight, hackers had remotely taken control of the supermarket's fridges and freezers and cranked up the temperature to as high as 55 degrees Celsius. That's, for all the Americans out there, that's 131 degrees Fahrenheit. I still can't stand the fact that we haven't gotten metric. Anyway, it hit stores in eight countries, including Belgium, Sweden, Ireland, the Netherlands, Luxembourg, Estonia. In Belgium, the economic cost of the spoiled food was about 100,000 euros. And this is the second wave. Three weeks ago, the same attack hit more than 500 stores across France. How? Well, these refrigerators are controlled online on the Internet by technicians who can manage them remotely 24-7.

Attackers got in and switched on defrost mode. French authorities fled controllers and said, act now. Take these systems off the open Internet. Nobody's claimed responsibilities. What we've effectively done is we've bolted 1990s security onto 2026 connectivity. The bigger lesson here, last week we saw it in Binance where one engineer's laptop cost $6 billion in stolen tokens. This week it's a freezer. Every device connected to the Internet, it's a door where attackers can gain access. So I'm curious, gentlemen, you know, we've talked about this for a while. We're going to see these hacks. It's, you know, freezers today. I expect we're going to see it hitting banks, power supplies, energy systems, the grid. And, you know, while I'm a huge, you know, optimist across all of these things,

you know, one of the tweets I put out is we're about to go through a transition period over the next, you know, one to eight years, and it's going to be tough. Yeah, there's a phase we're going through, we've talked about this, where the asymmetries that cyber offense is now humans out of the loop, and you can have swarms of agents attacking multiple vulnerabilities and finding these little glitches, and cyber defense is still human in the loop. And so this is going to cause all sorts of grief over the next period of time. I'm curious as to who ended up doing this. was some vegetarians unite type of group or whether it was economically motivated or what. A lot of fingers pointing at Russia right now. Yeah. I mean, it's sort of interesting to me in an era, to Salim's point, when we're seeing Linux kernel patches that are the largest in human history because AI, all these super intelligences are being trained on the Linux kernel and other popular open source packages.

To my knowledge, we're not seeing the same level of effort by superintelligence to overhaul all of these legacy SATA. For those not familiar, supervisory control and data acquisition systems. So like industrial control systems that control refrigerators at grocery stores. There's a lot of legacy embedded software that probably hasn't been updated for decades that's not receiving the same love and care by superintelligence. And again, not investment advice, but there are probably many billions of dollars of opportunity out there. If someone's massive transformative purpose, you want to keep the yogurt cold in European grocery stores, train the superintelligence on updating the SCADA systems, managing the refrigerators to defend them from Russian hacktivists. I think this is a call out to everybody listening. as an individual getting your own security systems up to snuff or as a business owner because we're going to see this again at an increasing frequency.

Yeah, very fascinating. It's kind of a sad hack in that it didn't wake up anybody enough. Eric Schmidt has been looking for that hack. Hopefully it's something, it has to be big enough that it wakes up all the politicians and forces action, but small enough that it's not catastrophic. And this is too small to really wake up any politicians, sadly. Yeah. It would be ironic. I mean, there's this whole, I've remarked on the pod in the past, the effect of altruism community arguably fetishizes what they call a pivotal act, which is going to be this act by a human savior to defend humanity against superintelligence. It would be ironic if, at least in Europe, the pivotal act that the EAs so sought would be like a refrigerator defrosting. Or turn off all the air conditioners out there, which don't exist. They don't have any. Peter, this goes to your point where you said that the most dangerous problem is a human with an AI acting. Welcome to the health section of Moonshots, brought to you by Fountain Life.

The discussion in this section is for informational purposes only and is not medical advice. Please consult a qualified health care professional about your individual needs. I'm a co-founder of and client at Fountain Life. You know, AI is impacting every aspect of our lives, how we teach our kids, how we do our business. But one of the most important things that AI can deliver to us is health. And one of the things I think about when shooting for 100, 120 is, am I going to have the cognitive health to be able to think clearly and keep my wits about me for the next 50 years? I'm joined here today by Dr. Don Musalem, the chief medical officer of Fountain Life and a member of my Fountain Life medical team, Don. A pleasure. So, Dawn, talk to me about brain health. Brain health, you know, you're right. This is the number one concern people coming into Fountain Life have is, will I remember the name of my child and the face of my loved one? Forty-five percent of dementia cases are entirely preventable with lifestyle. And what was really intriguing to me, Peter, is that a quarter of our members had advanced brain age.

but over 13 months of us really helping them live healthier lifestyles, eating healthier, moving their body regularly, and optimizing sleep. People overlook that so often, but that sleep optimization is critical for our brain health. What we showed is that we were able to improve the brain age in 46% of those individuals. That's a powerful number. That's amazing. One of the things I love about Fountain is we're constantly searching the world for the most advanced therapeutics and bringing them to our members. So for me and all of you, I hope that you appreciate the fact that you can become the CEO of your own health. You can make sure that you've got the cognitive clarity for the next 50 years. Come and check it out. Fountainlife.com slash Peter to learn more and become the CEO of your health. Now back to the episode. All right. I'm going to turn this to two science stories that I know Alex and I are both excited about. Hopefully you guys are too. So two weeks ago, physicists did something that sounds like Star Trek. They captured a single particle of antimatter and held it for a month, a new world record.

So the back story, every particle of matter has an antimatter twin, right? An electron has a positron, for example, with the same mass but opposite charge. And when two of these particles touch, they annihilate themselves in Einstein's famous equation, you know, energy equals mc squared. And that's why you can't keep antimatter in a jar. It destroys the moment it hits the side of the jar. So a team at Northwestern built what they call a container without walls. They started with radioactive sodium, which spits out positrons, the antimatter twin of an electron. The positrons ricochet through a sliver of cold tungsten to slow them down. Then they're steered into a vacuum just 0.3 degrees Kelvin above absolute zero. And they're held in place by a magnetic and electric field. they kept one positron alive for a month and they said they could have kept it alive indefinitely. So why does it matter? Well, there's an imbalance of antimatter in our universe.

The Big Bang should have created equal amounts of matter and antimatter, which would have wiped each other out. Instead, a tiny excess of matter survived, and that's all of us. So, Alex, you know, I think we need a lot, a lot of positrons to make a Star Trek warp drive. Well, you know, that's the elephant in the room, right? The ability to shepherd around large amounts of antimatter in a contained way. This is how the Enterprise, the starships in Star Trek power themselves. They keep little pods of magnetically confined antihydrogen and another pod of hydrogen, and that powers their faster-than-light propulsion and also their fusion engine. Sorry, they have a separate fusion engine that's less efficient. But maybe a bit of backstory on the actual science here. So this is out of the Gabriel's group at Northwestern. Funny backstory. Maybe this will be pretty obvious. When I was a first-year graduate student in the Harvard Physics Department,

I shared an office with Jerry Gabriel's group. Of course. Of course. It's a lovely, lovely office, but he's since moved on to Northwestern. This is a major step forward. Jerry and his group are major leaders in measuring a number of precision measurements, a number of constants of nature. In this case, the scientific interest is measuring CPT symmetry, That's charge parity time. As you alluded, Peter, there's probably quite a bit of interesting physics that one should be able to discover relating to, say, mirror symmetries of various sorts. Why do matter and antimatter differ in apparent abundance in our universe? Why do certain symmetries like left-handed versus right-handed symmetries, why are they seemingly disobeyed in certain physical processes? Chirality, very interesting, somewhat surprising to many.

So that's why this is scientifically interesting. Technologically, though, Jerry et al. are pushing us toward a world where we can do antimatter containment. And this is something I know friend of the pod, Jared Isaacman, has also very interested. And he's interested in antimatter propulsion for spacecraft. But you're not going to get long-term antimatter spacecraft propulsion unless you can hold it for a while. And right now that's very challenging. Hold large amounts of it for a while. Hold large amounts of it without the whole thing blowing up. And Jerry et al. have now pulled this off for a month. So what does Change Theory say about this, Alex? Stay tuned. okay come on i i i know well you know what you're such a tease peter i'm going to tease you back okay i i will i will have something to say about it but i'll speak through uh through the publications it is it is something that i think about quite a bit cpt symmetry yeah awesome dave any thoughts on this one new energy source well you said hold large amounts of it but e equals mc squared i mean a sugar cube would power a city right Yeah for sure Well it much more efficient I mean if you look at efficiencies just on a material basis in principle matter annihilation should be 100 efficient

No way. No pollution whatsoever. I mean, you throw off a lot of hard X-rays or gamma rays, but in principle, you could use up all the rest mass if you have balanced hydrogen, antihydrogen propellant. As opposed to fusion, depending on what sort of fusion you use, it could be like up to one or two or four percent efficient. And then fission is a fraction of a percent, like 0.1 to I think 0.3 or so percent efficient in terms of rest mass. So if you want a maximally efficient battery for propulsion for a spacecraft, at the moment with the physics that we have today, there's no beating antimatter. You'd still have to heat water in a boiler and drive a turbine to make electricity. You could carry a microscopic black hole and drop garbage into the black hole and recover your space that way. That is an alternative. That's what the Romulans and Star Trek do. Okay, I love it. All right, let me move us on. So let's turn to the space frontier. NASA is moving at Apollo speed once again.

So on Friday, Administrator Jared Isaacman, friend of the pod, laid out his plan. Next summer, 2027, Artemis 3 launches with four astronauts, three Americans, and one Italian on a two-week mission in Earth orbit. Their job, practice docking their Orion capsule with both moon landers, both Blue Origin's lander and SpaceX's starship. That's three launches from Cape Canaveral in very quick sequence. That's going to be super cool. And then Artemis 4 launches in 2028. It's going to be the first humans on the moon since 1972. And just a quick pet peeve for me, I always spell moon with a capital M. It's a proper, it's our moon. I know you can say Luna, but when we're talking about general moons, that's a generic moon. Anyway, NASA will wait as long as possible before choosing between SpaceX and Blue Origin to see who lands the crew. We'll see if Blue Origin is able to, you know, make up lost time. And this time they're going to stay. NASA is building a moon base near the lunar south pole with the first launch by the end of this year.

So many landers, rovers, orbiters will follow. And it's going to build, you know, Luna City. Isaacman said that this is a new space race, this time against China. And it will be, quote, much closer than it ever needed to be. And it's much closer than it ever needed to be. His fix is Apollo's own playbook. You know, it's hard to remember that we went from Apollo 7 to Apollo 11, landing on the moon, in just nine months. That was insane with, you know, with computation less than you have, I don't even know, on your aura ring, I guess. Anyway, for me, this is personal. I'm going to be with Jared in Orlando, giving a keynote with him tomorrow. Yeah, tomorrow night. It'll be fun. So I love in the massive aspiration that you have in the world that you're willing to die on the hill of the capital M. I am. And the moon needs our respect. Anyway. Capital M moon. And then, I mean, 50 years of waste.

What happened during the intervening 50 years with no humans on the moon? And by the way, the Soviet Union never even got around to landing a human on the moon itself. It's so bizarre. We need to fix this. Well, this time there's commercial pressure. I think it will be very different. And Jared is all into it, too. The divide and conquer there where, okay, Elon, relativity space, you get stuff into orbit cheaply. That's very profitable. I'll get the moon base going. You can refuel my ships on the way. We'll work. You know, it will be a military commercial cooperation. And the commercial side doesn't slow down for 50 years. So that will keep the government side of it actually on its toes. It's going to be a much better dynamic than it's been in the past. It's interesting. Jared's all into it. I mean, the ability for NASA to hit this timeline and build its moon base is based upon federal budget. And I think, you know, Jared's doing the wise thing. He's pulling out the old Soviet-U.S. race card and saying it's U.S. versus China. We've always needed a stalking horse, some kind of a competition to drive federal spending.

And it's China this time. Did you see, I shared it on a group chat, did you see the Chinese space plane that the U.S. government just? Yeah. It's just like wild. We've abandoned our own space shuttle, but there's imagery now getting released from the U.S. government via Maxar of this Chinese space shuttle with all of these seemingly advanced capabilities long after we've abandoned our own space plane. What better stalking horse? What better nation state near peer adversary, whatever euphemism we want to use to reboot the space race? It's going to be awesome. I can't wait. And I missed the Apollo 11 launch. I wasn't there. I look forward to being at the Artemis 3 launch. It's crazy that it's been 50-plus years. I mean, pretty. We can't afford another 50-year gap. It's so disappointing that there was this 50-year gap. I'm just petrified that something like this could happen with superintelligence, where over-regulation or changing political winds or China syndrome or something, we saw it happen with nuclear energy for 50 years. We saw it happen, arguably, with space for 50 years.

Really don't want this to happen this time with superintelligence. I don't think it will. I think it's democratized enough where the open waste, people are just going to keep pushing these boundaries forward. Everybody can now operate on this. There's no waste slowing down. Alex, did you ever see the report that came out in the early 70s towards the end of the Apollo program, right? We left the moon on December 17, 1972 with Cern and Schmidt as the last Apollo moonwalkers. And there was a report put out that projected NASA's capabilities if they maintained Apollo funding with, I think it was a 100-person moon base by 1986 and landing on Mars in the mid-'80s as well. And that could have been, and they believed they could do it with 1970s and 80s technology. The story makes no sense, Peter. I think there is probably a contribution from changing political winds, obviously the Vietnam War, and shifts domestically around the Vietnam War, probably an influence.

But I do hope in the fullness of time we understand why it was that we weren't landing humans on the moon for 50 years. It seems like a historic travesty. Yeah, for sure. I think the cost of a war, especially the Cold War, is hugely underestimated or estimated because all innovation is on the margin and all investment is on the margin. And when you're in a cold war or a hot war, all the margin goes into that effort. And so all kind of edge case innovation gets tied up in that capital allocation. So the scenario where this never happens again is the scenario where there's a relatively peaceful world. The scenario where we get stalled again is something really blows up in Russia, Ukraine or elsewhere. and then it diverts all budget, all political votes, all attention into that. So, you know, maybe. I mean, something, an area that, so as a student of the history of American science and technology, one of the things that interested me the most is what causes progress to happen in the first place.

For example, during, in American history, the history of interchangeable parts in an industrial ecosystem, at least in America's industrial revolution, is often widely attributed to the American Civil War, where there was a need to be able to use and reuse ammunition and guns and different gun components on the battlefield. And their war is ultimately a driver of an industrial revolution, at least a contributor. But to my knowledge, no one has conclusively studied or understood where, like, what is the optimal, if it must be some great power competition, what is the optimal great power competition state that optimally drives forward progress? It seemed like for a while, World War II, post-World War II, despite the enormous human cost, obviously, goes without saying, we were achieving fast progress, and then everything stopped sometime around the 70s. And I would just love to know what went wrong and how we fix it. Voice agents are just software, but software deserves a real development platform.

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take question number one because it's kind of pointed at me so uh number one from at christoph jeffers how does lev longevity escape velocity trickle down to the poorest among us so uh So good question and something very important. I think longevity is probably the largest business on the planet besides AI. And the question is, you know, a lot of the treatments today are expensive. Well, let me first say that the number one thing everybody should be doing, sleep, diet, exercise, and mindset. They're cheap, and you can do them. But there are technologies that are being proposed like partial epigenetic reprogramming. This is the work that Dr. David Sinclair just reported in this phase one data where they're using three out of the four Yamanaka factors using a gene therapy, using an adeno-associated virus to get those three Yamanaka genes into cells. And that is expensive. So, you know, using AAVs and gene therapy can be anywhere between a half a million dollars to two million dollars per treatment.

But this is where it gets exciting. You know, David also announced he's been developing a treatment called SL100. And SL100 is three molecules that he's discovered using AI. And these have been tested in human cells. They've been tested in mice. And his estimate of how much this will cost is $200 a month. And just remember that everything, you know, starts expensive. And then technology is that force that takes everything that's scarce and makes it abundant. from the cell phone to intelligence. And we also have the $101 million HealthSpan XPRIZE focused on reversing aging in cognition, immunity, and muscle by 20 years. And one of the basic premises of that competition is it needs to be affordable for everybody. All right, Salim, over to you. What do you want to pick? I will pick the number two is the obvious one here for me, which is if SI is doing, I'll say AI,

if AI is doing most of our work, where will people actually find purpose? And that's at the way SI. You know, I've talked about this a couple of times. I did a whole video on this on my channel. We've made a basic mistake of conflating employment with purpose, right? If you look, 79% of corporate workers don't have connection to their work. They don't find meaning in their work. So this is a longstanding challenge. We've faked ourselves into doing this. The industrial world taught us that value comes from productivity, but this is actually not the case. It's going to come from other things. And I think as AI does more and more of the work, we have this unbelievable opportunity to spend more time being, spending time with kids, building communities, doing scientific exploration. This is why this concept of the massive transformative purpose is so important, because it really gives you a case to point yourself at and a place to point intelligence at.

And so that's very, very critical. Imagine a world where nobody has to work to survive, right? But everybody has an opportunity to contribute. So that's a couple of ways of thinking about it. Great. Dave, over to you. Alex, you want three, right? Yeah, I'll take three. Yeah, okay, so I'll take four. What is the – I was just guessing. You're so polite. Well, you know, sometimes they feel like they're labeled for somebody. So four, what is the equivalent thing or practice for humans to develop reinforcement or recursive self-improvement? It's a human recursive self-improvement, and that is from the awesome DJ Trueheart, who we love because he contributes all the time, and he was just a great guy in L.A. So, yeah, you know, it's interesting. I had a conversation with my grandfather before he passed away many years ago. He was a farmer and cheese factory guy in Ohio, and he was asking, what do you do for a living? It's like, well, I code on this computer. He's like, you're not working. You're typing. That's like not work. That's like, what are you doing? And then I was like, after this, I'm going to go to the gym.

He's like, why on earth would you go to the gym? Like, he's used to working in the field all day. Like, that's utterly insane that a human would go to a gym. So here we are. You know, we are an improving society. We just kind of lose track of how much we're improving. I think AI is going to unlock much better daily planning and all kinds of ideas on how we can recursively self-improve even more. I bet we're nowhere near optimal in terms of our health, our happiness, our daily practices, our daily plan. I think AI turned in that direction is going to be one of the greatest boons to humanity of all time. I just hope, I hope that Mark Zuckerberg and Sam Altman reserve some compute for that endeavor. But it's going to be great, I think. Awesome. Alex. All right. Thank you, Dave, by the way, for preserving number three. So number three asks, can abundance spark a second society-wide renaissance? This is from Pat Toewes, Pat Toewes1. And let's think more broadly.

So the original Renaissance, the European Renaissance from the 14th through the 17th centuries, is broadly attributed to a rediscovery of classical antiquity, among other factors, obviously. There are, as always, numerous input factors in one of these large-scale shifts. But a rediscovery of classical antiquity artistically, technologically, socially. and I think there were many local optima in at least Western civilization. There was obviously the Roman Empire. Drop a footnote, how many times do American men think about the Roman Empire every day? Okay, well, I'm thinking about it at least one time today. But there were other periods. As I gestured earlier, there was the post-World War II period when there was very rapid progress being made on atomic-slash-nuclear energy. and shortly thereafter the space race, the original space race. So one way of answering this question, I think, is if I map,

if I construe abundance to be the singularity or just the present time, I do think we are seeing a second renaissance, not referencing necessarily the European renaissance from the 14th to 17th century, which was rediscovering antiquity, antiquity, but rather I think we're starting to rediscover the world from, call it 50 years ago, where in the post-World War II era, we were seeing all of these emerging technologies, nuclear power, computers, a variety of space-associated technologies, and many, many others burst onto the scene for the first time. And then going back to the earlier discussion, maybe some would call it like Tyler Cowen would call it a great stagnation but 50 years of arguably wasted time if if we were Isaac Asimov if Isaac Asimov were in this discussion maybe he would write a whole new you know meta second foundation about thousands of years of wasted time after the collapse of empire but in this case it's not the Roman Empire all the way

to the Renaissance. It's the, call it post-World War II era, when a bunch of changes were inserted, and it was rapid progress coming out of World War II to this 50-year gap. In short, yes, I think we are already seeing a new form of Renaissance, but it's not anything like the 14th through 17th century European Renaissance. It's a rediscovery that the physical world shouldn't be a source of fear it should be a source of opportunity and inspiration and we should embrace everything that is within our power to control in the physical world and not be afraid of it all right dave over to you pal five six seven or eight uh i'll take seven elon musk believes that western civilization is contaminated by empathy how does this group see it well and that's from DDMT year. I can't speak for the whole group, but I think he has a really important point. The U.S.

is actually statistically the most generous country in the world, and I'll tie that to being empathetic as a company, as a country as well. And so I think what happens, though, is that we're reactive to the media. And so, you know, yesterday there was a Learjet that crashed in Florida, and now people are like, I don't want to fly anymore. But if you look at the statistics, it's by far the safest way to travel. But they didn't show all the train wrecks and car wrecks on TV last night, so we don't react to that. So we're, quote, unquote, contaminated by empathy in the sense that we make decisions based on statistical outliers that are right in front of us on TV or the Internet, whereas China doesn't do that. China has statisticians and researchers try and gravitate to the right overall answer, and that's why they have abundant energy, and Europe has none. It's that same effect. So it's a byproduct of a good thing, which is empathy, but it's miscalibrated by the media.

And so that's what I think causes the, quote-unquote, contamination. So empathy is fundamentally a good thing. Elon knows that. We all know that. But we can't make decisions on emotion all the time. You can say to the Union address where you call out a single person and say, stand up and tell me your story. It's terrible. It's terrible. It's terrible. Sorry, Peter. Please remember the media's job is to scare the shit out of you, right? If it bleeds, it leads. And they're just wiring your neural net, same way you train a large language model by showing it data after data. They're just training your neural net on all the negative news and all the fear-mongering so they can sell your eyeballs to their advertisers. That's their business model. Understand their economics. Alex, over to you. As opposed to moonshots where we don't advertise and we don't care about eyeballs at all. I read you loud and clear, Peter. Well, we're not trying to scare the shit out of you. We're trying to scare you. No. We aim for a different set of neurotransmitters in our audience.

Yes, okay. At least we understand what we're aiming for. Fair enough. Thank you for always being the truth-sayer, my friend. I appreciate you. Balls and strikes. Balls and strikes. I love it. I'll pick question number eight. So robots are simultaneously not going to have a market, but will also be entirely consumed by industry, which is it? And this is from latent 4407. So I picked this question because I think this is maybe aimed at some of my comments from the past two pods. I think, so when I spoke in previous conversations about robots potentially not having, say, the demand catch up to the supply, it was in reference by analogy to what I think we're seeing or about to see with Elon and Starship, where all of this upmass is about to come online, seemingly, and yet you really can't saturate all of SpaceX's forthcoming upmass with communication satellites. It's going to require an entirely new use case for what we're going to start lifting, heavy lifting into orbit. And right now, Elon's plan seems to be orbital data centers and the Dyson swarm.

And I'm very optimistic that that's a good way to saturate all of this new supply that's coming online. But if he doesn't do that, if Elon decides, well, we're just going to launch all of these starships and there aren't new use cases, I do think there's a very real chance that many of these starships, if he builds at the quantity that is anticipated and being publicly forecast, will end up going empty. I don't think that's realistic because I think ODCs and other new use cases are going to come online. Similarly, by analogy, with general purpose, largely humanoid, I expect robotics, I think there is so much humanoid robot capacity that's about to come online that it will overwhelm the existing demand. And so that oversupply, the market wants to clear and supply wants to meet demand. And I think it's reasonable to expect that the market will clear via new use cases, new industries that are capable of saturating the new supply of robots. I rather like Dave's hypothesis that one of the major demand sources is going to be, and also Sam's, is going to be robots building data centers or otherwise maintaining the innermost loop, as it were.

That's probably the best use case I can think of in the short term as well. We saw this with automotive manufacturers, right? Alex, we had like 100 automotive manufacturers at one point, like 200 different tire manufacturers, and it all, you know, they were producing, and then it all, you know, sort of got figured out. I thought you were going in a different direction, which is Henry Ford had basically an analogous problem, the overproduction of the Model T, and it wasn't – there was the problem of how do you saturate demand and living standards. What do you do? You ensure that your employees are compensated and incentivized in such a way that they're able to hopefully purchase some of your own cars. And so I'm not quite sure what Elon's example is. Maybe it's he sends all of the SpaceX and Tesla merged companies home with 10 robots or something as a way to juice the market. But he'll need some sort of Henry Ford-esque virtuous self-licking ice cream cone. All right. Salim, over to you. I'm going to leave number five for you.

Let me take number eight. Robots are simultaneously not going to have a mark. Sorry, sorry. Data centers. Let me take the data center one. Number six. Okay. How does living next to a data center make you rich? Living next to a factory didn't. Why are data centers different? That's from Donna Dave's. So, you know, factories created more opportunities in the near term because you could employ the local workforce, you could purchase local services, et cetera. A billion-dollar data center, the building aspect of it is there, but it may not purchase. The benefits may distribute and get sucked out very quickly. However, if you go back to what Alex just talked about, the need for compute is going to be incredible. And it's like saying we don't want electricity anywhere near our neighborhood. You want electricity in your neighborhood because you want to thrive from it.

So when you can apply intelligence to all these things, the great economic value is not just living next to the data centers, using the intelligence that the data center produces to then do lots of really interesting things. So this is why it's so critical and so important, because the amount of compute we're going to need is going to be near infinite, and therefore we'd really like to take advantage of that. Learn how to leverage the intelligence coming out of it. The other thing is the data centers are going to make a lot more money than the factories ever have. There's that possibility. A lot more to distribute. How quickly memory dims. What happened to factory towns? I'm not even sure if I buy the premise of the question. Number five. You never accept the premise of any question, Alex. The premise of it. You know what? Elon says delete, and I say the premise of most questions is probably wrong. Okay. Having said all that, I'm going to move on to number five. We hear a lot about colleges. Why should students not pursue trade school from at Paul Beechman?

Paul, they should pursue trade school. There's a lot of opportunity for electricians and welders and plumbers, for sure, especially now as we are tiling the planet in data centers. I think that you've answered your own question. I think they should. At the end of the day, students should find their purpose in life, whether that be their family, whether that be art, whether it might be building a company, and then pursue the strategy that gives them the most agility, the most opportunity. And trade schools are definitely there. I think we've seen a huge shortage in electricians and plumbers and air conditioning specialists. The singularity needs you. Very cool. All right. I've got to run, folks. Okay. I'm going to go ahead and play our outro song, and then we'll all run.

But, Salim, safe travels, buddy. I love you. Miss you. Take care. See you guys soon. All right. All right. All right. Now let's play our outro, Better for All of Us, by Numbers. And I'm trying on you. You and me are together. We got to pull it through. We all found together Making decisions that'll change the world And we're found together Making decisions that could end the world

Or make it better forever This is actually very clever. For those who aren't watching the visual stream, these are sentences that are being depicted as transformer attention. It's pretty clever. It's going to become the new karaoke model, Peter. Did you catch in the video we were seeing cross-attention from words to each other in the karaoke feed? that's great and if you're a creator send us your outro video we'd love it send it to media at diamandis.com and hopefully it's excellent and we'll play it gentlemen love you both have an awesome day Thank you.