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I don't know that it matters too much, but it's certainly fun to dig into. And so I wanted to give a little bit of a brief history of the AI wars, because yesterday we did an interview for French television, and it was absolutely hilarious because they were obsessed with the current thing from two months ago, the AI talent wars. It was actually like, really, really nice team. We had fun, and I'm sure it'll be cool to their audience. Yeah, totally. But it felt like Europe got off of summer break, totally missed the talent ors, and become absolutely obsessed with them. People talk about, oh, LinkedIn's gonna find out about meta poaching, like, next week when X is talking. I was looking back at the dates, and I was like, okay, we talked about this on June 1st. France is, like, gonna get to the bottom of it. October 8th. Yeah, but I mean, they said a whole video crew. It does take time to do those types of productions, but they were obsessed with the numbers. Summer break, like, Jordy kept giving more context on, like, okay, well, there's like, a power law and, like, you know, acquire. Doing an aqua Hire buyout of something like a scale AI to get Alex Wang on the team is wildly different than just, like, a database engineer. How much money did this engineer make? Yeah, they wanted the number for everything. They would have been so happy. They were doing the how much gram? How much money? They wanted you literally just to say, like, like, Steve, $40 million, this person $60 million. They wanted, like, finite dollar amounts on everyone on the Metis list. Nothing would have made them happier. And you did your best. And, yeah, I tried to explain that in America, we don't have to open source all the know exactly what each offer was. And so there's like, a few leaks here and there, but it's mostly, like, directional. But, yeah, July really was the AI talent wars, now that I reflect on it, like, what was the main story of July?

One place develop and fine tune models with serverless GPUs and on demand clusters. There is so much AI news it is we didn't even get to the XAI Nvidia deal. There was an interesting debunk. We were talking about US electricity prices and A says it's come to my attention the general public is uniformly blaming this on AI and so I wasn't uniformly blaming it but I pulled some statistics and it sounded like 70% of the increase in electricity prices was due to AI. It certainly lines up with the rise of AI. So it's easy just to put two charts next to each other. Say hey there's a correlation, there must be causation. But Egg breaks it down a little bit says there's a couple big big factors in Egg's mind. Thank you Egg for the breakdown. The big factors in my mind are general inflation, increase in the money supply. We're seeing that with gold, bitcoin, everything else moving inflation asymmetrically affecting the material supply chain more severely. Supply chain issues caus aging infra being replaced with more complex DG ready networks shuttering cheap fuel based energy before equivalent renewable energy is online causing a wholesale shortage. Higher consumer expectations on outage restoration time especially after storms and the There's a couple of people here that says I'm a power trader, I don't have the energy to tell people otherwise about this. So interesting extra context there. I do. I did see someone quote tweet one of those viral posts about like oh I'm so glad I could see Stephen Hawking at the X Games because my power bill went up 70%. Like it is important that that tech companies build new energy infrastructure and I agree with that. Tech companies should build more energy infrastructure. But Cain, friend of the show was quote was was quoting that and saying like well we've been trying to and there's been a bunch of stuff that's been blocked. There's been new power plants that have been tried to come online and they got blocked. Like the famous one is like Meta was trying to build a big energy power plant and got blocked because it was going to put a bee in danger. And that went super viral. And so you can't be both a NIMBY and also complain about restricted supply potentially. Like those are somewhat incongruent. Totally. This note from Jensen on the OpenAI and AMD deal was notable. Jensen said, I saw the deal. It's unique and surprising considering they were so excited.

You know, 40 portfolio companies. Can you actually give accurate coverage on a market? Right. If you're talking about a category and you have a horse in the race, you can provide a little coverage. Well, I mean, to be fair, we have a horse. We certainly have a horse in the race. So we. We have Capex. They're ramping a lot of people with. It's not that much. Some people are buying GPUs. You guys are buying horses. Yeah, no, people. People would say that. A lot of. They would critique journalists for being horse people. And we came to their defense. Right. We said horses should be celebrated. They're incredible animals. And so this is in some ways a monument to technology. A lot of the early brand was just like, what's the opposite of tech branding? Well, it's like old money equestrian. You're like, 70s Miami. 70S. Exactly. TPPN is a lifestyle. Exactly. It's a lifestyle brand. And the horse is like a perfect example of that Fun. Do you think that venture capital firms will eventually advertise on podcasts?

Even in Intel. We're not going to do intel, we're going to do tsmc. Exactly, exactly. Yeah. Favorite history book of all time. What you got? Ooh, I know. You're going to say seven powers if I ask for Brooks Broadband. It's hard to argue a Shoe Dog. Shoe Dog. So compelling. Shoe Dog is really well made in America or made in Japan and Made in America. Akio Morita, Sony, and then Sam Walton's Walmart book. Just really, really excellent ones we've read. Yeah, Shoe Dog is a fantastic book. It's so readable too. I don't know, it's just like I feel like every, every founder at that level should want a shoe dog. And like, I think the guy, the ghostwriter who wrote Shoe Dog wrote Open. Agassiz. Yeah. Open. Yeah. Is it good? Absolute page. It's good. Open is incredible. I guess I skipped it because it's not so much about business, but I feel like if you're. It made me a tennis fan. If you're a hundred billion dollar CEO, like you need to call him up and get your version of Shoe Dog. But maybe you don't have as much of a. Or if you're Prince Harry. Yeah, yeah. Prince Harry did it too. Right? That's funny. There are a lot of great ones out there. What about, what about Daily Routine? Is there anything special that goes into.

On the drive down. Oh, really? Yeah. I mean, last night we were giving each other, like, real good criticism. Long walk. Is it year 8 now? Year 10. Year 10. We just hit our 10 year 15th. We just hit our 1 year anniversary. Do we need a gong? No, it's too direct of a copy. You need to be inspired. Oh, well, we'll. We'll send you. We'll send you guys one. Just. Yeah, just to have handy. I don't think my wife would like that. We don't. We don't. We record in our homes, you know, not in. I would imagine it develops into, like, a library of all the books that you've sourced and all the photos and stuff. I actually do have a question about that. But first, I mean, I want to start with the story of the research process for the very first episode, and then I want to hear about the most recent research process because I imagine it's very different. But take me through, was it a Google Doc that you were just throwing notes in? LLMs didn't exist. One of our secrets is we've never shared. Okay. Our research process. Oh, never. People ask us, like, sounds like you guys get on the show. And like, you're like, you must be really good actors because you're pretending like you don't know what each other's gonna say. We never share with each other. We genuinely don't know what each other is gonna say. And was it like that at the very first episode? Okay, the first episode. Ten years ago, we were both working out of Madrona's office in Seattle because it's where we worked at the time, side project. And we got together after work and we were like, all right, we're gonna record the Pixar episode. And Instagram. Pixar. Well, Instagram was the pilot, and then Pixar was the first one we released. Those are both, like, huge stories. That's not something you just walk into and, hey, let's just freestyle for 37 minutes. We did. So we were like, okay, we're gonna record. What do you think? Like, start in an hour. And so then we both, like, went and scrolled the Internet for a while and we were like, all right, are you ready? I'm ready, I'm ready. And now. But I don't think we had, like, one Google dot. I think we had our own. I think we've always had seven separate. You know, it's not like we have one document that we're both putting stuff into. Cool, cool. And I think by the end of year one, we had about 400 listeners. Wow. Let's go hit that gong. Which I think people don't realize how. John dropped the method. Yeah, that was how we felt, too. Yeah, I think. Yeah. We were kind of talking about this offline. It's interesting.

Chance of winning the biggest weekend. Well, speaking of markets, Anthony Pompliano hit the Timeline says this is a somewhat crazy idea, but I believe it would be incredibly popular. Open should create a way for people to wager in a prediction market on the price a home will sell for. Everyone has looked at a listing online and said that home is overpriced or that home, that house is a steal. Keith or Boy chimed in, Great idea. EB on X said this is a somewhat crazy idea. How do we add gambling to literally every life interaction? And again, I can see why people are. Get ready to gamble, buddy. I can see why people hate this idea. At the same time, I genuinely think that this is probably a hit product. Whether or not Open should be the one to build it is another. Well, he probably partnered with someone, but yeah, but still, 13 million on that poly market about which movie is going to pop and you can imagine scrolling Zillow and saying, you know, yeah. Or scrolling open and thinking, you know, oh yeah, that house down the street from me, it's a dog, it's going to zero. It's not going to sell for a dime over 500k. Yeah. So anyways, I think this is unfortunately a hit. If we could get every person in a neighborhood, betting on the markets would. Be pretty thin, liquidity wise, I would imagine because there just aren't that many people. But I mean some of the Wall Street Journal mansion section, that's where I want to put some money down. That'd be fun. We review some $20 million mansion and we're like, yeah, we think this is drastically underpriced. It's a steal at 25. Yeah, I think liquidity would be the big problem. And yeah, obviously there's strong argument for why Opendoor, whose mission is to increase home ownership, should probably stay focused on the mission and not not get into gambling on the mission. But who knows? Who knows. Orlando Bravo. It's a long.

Spend, Spend. Even more interesting. Yeah, There's a question in the chat. When will a Theranos FTX slash Enron of AI be in the headlines? If it's 1998, we would expect that to happen probably two years from now if we're mapping this perfectly. That's obviously ridiculous. What's interesting is that Theranos was, I think under $10 billion, FTX was $32 billion and Enron was $70 billion in market cap. Those all look tiny by comparison to some of the big companies. And also those companies weren't. None of them were actually the thing that was driving the market at the time. Like FTX was important, but Coinbase was still bigger and Coinbase made it through. And Theranos was big. But there were plenty of other 2012 era startups that were in that crop of decades. Decacorns that did fine. Airbnb, Doordash. We've talked to the founders of these companies like they made it through. It wasn't all frauds And Enron the same thing with the banking crisis and even Lehman Brothers, Bear Stearns failed. B of a, Morgan Stanley, J.P. morgan, Goldman Sachs. Those companies all continued to get through the trough. Some of them needed Warren Buffett to show up with a blank check, but they did make it through. And so I would be very surprised if everything goes. Remember there was a meaningful gap between FTX and svb. Right. Which it was. Wasn't a. It's not like the collapse of FTX directly caused the collapse of svb. They had their own sort of duration mismatch issue around a bunch of their balance sheet being extremely. And so I wouldn't be surprised if, if there's a, an AI Decacorn that blows up or maybe just winds down. I don't even know. It would be a pure fraud. Just the basic venture math right now would be if you're betting on, you know, 10 different AI growth stage companies in the multi billions, you'd expect one of them to go down and you would still underwrite that as a fund. If you're in. If you're in a bunch of them. I keep going back to this interview.

Evolve and improve the product as the underlying infrastructure improves as well. There was a time when basically every company that I would talk to in your world or in the, I don't know, growth stage doing AI seriously, but in a practical way was very model agnostic. They're an open router, they just kind of use the cheapest tokens and balance the Pareto frontier, have some internal benchmark where with the agent workflows, with browsing standards and agentic browsers and computer use. Is any of that calcifying and is it harder to maintain foundation model company agnosticism or is it still basically the same as 2023 from your perspective? I would say what makes it harder is the rate at which new models are being launch. You have very little bit of time to sit and think about optimizing. Once you figure out that, you know what, we could probably use the cheaper model for this use case, let's go and do it. A new model has come out. So it's really a lot more about keeping up with the new models and making our own opinion. Because you'll hear lots of thoughts on Twitter and opinions like, this model is so much better for XYZ and the reality is it's going to be very different for every company. And we tend to adopt new tools and new models very quickly and generally they perform better, broadly. I mean they could be worse in some tasks, but we have a pretty sophisticated suite of tests that we run and we get a quick benchmark and also things that we're not and I don't think anyone is really great at measuring. There's an element of taste that is also starting to come out that some people prefer a model and you show them all the benchmarks and like, well, you know what, like, I'm used to the way that this model fails. You might tell me that it fails a little bit more often, but I know exactly when and how it's going to fail. And I can't quite put it into words exactly. But like, I can give you a couple examples and I think the level of change and chaos is more like just trying to keep up with the new models and capabilities as opposed to, all right, cool, let's just optimize and go for lower cost models. But we are as a company still relatively model agnostic. So while we are, I guess, in the trillion dollar token club, I will say that we're probably at a lot more than that. Just broadly. Yeah, exactly. What are the risks when building a product like this? We had.

So he says. He says, by the popular conception, we passed the Turing Test. And so I wanted to have a little bit of a debate a couple. Nights ago talking about, have. Have we. And so. And so I ran. I ran the Turing Test, and So I asked chat GPT, GPT5. We haven't. Do you think we passed the Turing Test? And I asked Tyler the same question. And so one of these answers is from Tyler the human, and the other is from ChatGPT. So. So I'll give you answer A, and then I'll give you answer B, and you'll have to detect which one is AI. Is it A or B? AI. So answer A to the question, do you think we passed the Turing Test? That's a fascinating question and one that doesn't have a single yes or no answer, because it depends on how you define passing the Turing Test or whether you think that test is still a useful benchmark. But I'll walk you through the terrain, then give you my verdict. What is the Turing Test, and what does passing mean? Alan turing proposed in 1950 that instead of trying to define thinking, we ask, can a machine imitate human conversational behavior well enough that a human evaluator can't reliably tell the machine from a human in a textual dialogue? Over time, people have realized many problems. The test doesn't measure understanding, consciousness, creativity, or reasoning depth, only imitation of conversation behavior and emotional stylistic cues. So that is answer A, and then answer B is, I asked, do you think we passed the Turing Test? And the answer, yes, we did that a long time ago. So which one do you think is AI? Which one do you think is Tyler? Wait, what was the. Sorry. The first. The first one was. That's a fascinating question and one that doesn't have a single yes or no answer. Because it depends on how you define passing the Turing Test or whether you. And then the second one is just, yes. I asked Tyler, do you think we passed the Turing Test? Of course. Yes. Of course. And it was just so funny to hit him with that. And it does clearly illustrate the difference in how that even though you can't tell it's speaking English well, it still has a very specific style to it, whereas Tyler just says, yeah, yeah, we passed it. But in doing so. Yeah. What's your reaction to this? Okay, Tyler, you should have answered.