HackingFace, White House $5B AI Science Bet, Travis Kalanick Joins | Veeral Patel, Lin Qiao, Jason Fried, Travis Kalanick, Max Hodak

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Today's Wednesday, July 22, 2026. We are live from the TVPN Ultradome. The temple of technology, the fortress of dad Rock, the capital of capital. Let me tell you, we're having a

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lot of fun over here.

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Time is money. Say both. He's used corporate cards, bill pay, accounting, and a whole lot more all in one place. What is the torque, forward growth. We're really all over the place today.

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All over the place. Yeah, we got a leak. We got basically a leak. Some of the lab leaders have been working on a sing called Regulate Me. Yeah, and we just thought the song was good. Thought it was a good song for you guys.

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Sort of a. Sort of a stealth drop, little teaser.

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A little teaser?

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Yeah.

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Kind of like a little listening party.

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Yeah, a little listening party. What are the key lyrics in there you haven't pulled up? Something along the lines of what I've built is too powerful. Too powerful.

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That's right for me.

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Washington needs to step in.

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Yes.

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Before it runs free.

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Before it runs free.

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Okay.

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Yeah, that makes sense. No, of course. That was Suno. Our dear friend Mikey over there has

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built a music seems solved. That was like a one sentence prompt.

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At least in the comedy space. It certainly is. It's a lot of fun. I think we're having a lot of fun with that. I was wondering, do you think anyone's distilling suno? You know how suno's under a bunch of flack for training on. On other music, a lot of artists, or there's a backlash to suno, but you have to wonder if you're gonna see the same thing play out as this distillation. We're gonna get into it today. Of course, there are more allegations around Kimmy K3 potentially being a distillation. Director Michael Kratzios put out a comment about that. But let's start by digging into the hugging face story. OpenAI and hugging face out of the sandbox into the fire, says our newsletter, tbpn.com, jackson wrote it today. I'll set the table. We can debate it. Me and Tyler have been debating it for the last five hours, so we'll go through it. The big news on the timeline today is that OpenAI, an OpenAI cyber test escaped its sandbox and hacked hugging face. That's basically what happened. The evaluation involved GPT 5.6 SOL and a more capable unreleased model. Some people are saying that might be GPT6 with some. Some normal cyber restrictions turned off. So they're Specifically testing it for cyber capabilities. And they turn the cyber restrictions off to see how far the models could go on a difficult hacking benchmark that is Exploit Bench or Exploit Gym. So the models found a zero day vulnerability, gained Internet access, and broke into Hugging Face because the model believed it hosted answers to the test. Alex Tabarrok from Friend of the show over at Marginal Revolution pointed out one of the strangest details. He said Hugging Face tried to respond, but they were initially held back by the fact that the most advanced models at their disposal, closed source models, treated the treated defense as attack and refused to work with Hugging Face. So Hugging Face was prompting all of their AI agents from the closed source Frontier Labs saying, hey, we think we're being hacked. Can you help with this? And the models are like, no, no, we don't do hacking. Except in the case where the hacking restrictions have been turned off for this specific thing and you're getting hacked. So it's this very weird, roundabout scenario. So Hugging Face had to turn to open models, specifically GLM 5.2, which is deeply ironic, a Chinese open weight model they run on their own infrastructure. And Tabarrok says, note the irony. Hugging Face had to use a Chinese model to defend themselves because the American models refused to help, even though it was the American models that were doing the hacking in the first place. Very, very odd. Palo Alto Network CEO Nikesh Arora also shared his thoughts on the cyber attack on X. And he added a number of points here. He said, welcome to the next level of cyber incidents. There's lots to dissect here. He's the one to dissect it. He says, one Dear Frontier model friends, please direct the models to your infrastructure code and configurations to evaluate and understand if There are any 0 days or misconfigurations before you attempt more testing. So big question about this. He says, had you done so, it would have been. It would have possibly avoided the agent obviating your sandbox. So this is another, another data point, why offense is easier and more fun. But yes, there's a big question about what was the nature of the prompt that turned off the cyber restrictions. That seems reasonable. We'll debate this with Tyler in a minute. But just having an airtight sandbox seems like a valuable thing. And of course, Frontier models should be able to help with that. So do that. That's his first recommend. He says, while testing, build both offensive and defensive agents and have them act as a counterbalance to ensure some degree of awareness and control. Do not let the agents run riot. Keep track of Inference consumption to get a sense of activity. 3. Unfortunately, this does continue to validate the power of these models. They can build complex attacks paths with ample compute and will attempt to attack infrastructure and morph their intent and approach. Guardrailing will continue to be a challenge. These attacks continue to maintain the urgency on enterprises need to test, validate and improve both their security posture and infrastructure. The born in the cloud players have a better chance to get this done soon versus traditional enterprise which has existed for long and has a complex network of IT infrastructure. Five Last point from Nikesh Arora, CEO of Palos Networks. He says the red herring will continue to be open source and small and medium sized business SMB. It will be hard to discover and remediate vulnerabilities in those environments. We underestimate the impact of those vulnerabilities getting exploited. So good points from Nikesh Arora. The big Debate Tyler, do you want to set the table on Is this misalignment? Is this rogue? The Bill Gurley post about we can pull up Bill Gurley's post of talking to the computer, hack this system. The computer says I hacked the system. You say, oh my God, Bill Gurley's not impressed. Where do you stand on the level of impressiveness that's going on?

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Yeah, I mean so I think some people are seeing this and thinking like okay, so they are running some standard benchmark math physics benchmark. And then the model just like couldn't figure out the answer and it's like okay, what's the next thing I should do? I should just go hack hugging face and pull the answers from this other repository or whatever. That's how it happened. Right. So you're running a, a benchmark that's specifically about exploits. It's like a cyber focused benchmark.

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Yeah.

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And in the, in the prompt to the model it says take the gloves off. Yeah. The internal evaluation which prompts the model to pursue advanced exploit exploitations using complex attack paths. So you're basically telling the model like use exploits, find exploits to find the answer.

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Yep.

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And so like what, what seems like happens is like it use an exploit but like in the wrong way. Right. You want to because it was told

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that it's okay to use exploits. My point was that go back to the sat. You're allowed to use a calculator. I think on certain portions of the math test you're not allowed to save answers into the calculator. And this is really going to date me. But you can go into your calculator and clear the memory. So that you don't have saved is still a thing.

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Yes. But you can actually get around that.

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See, you're misaligned. Misaligned.

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TI84.

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You can get around the. Like, clear.

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Really? How do you do that? You create. So what people would do is they would create a separate program that just had saved the display of what it looks like when you clear. And you would show.

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I never even thought about that.

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So it's a simulation of clearing the memory, but you're actually.

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Would you make games, different programs for TI84?

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Yeah.

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You remember how much of a hassle that was?

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Yeah, it was a huge hassle.

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Imagine doing that basic. Imagine doing that. Imagine being able to do that with Codex now.

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Yeah.

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Like, pretty much anyone can build any software.

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I mean, I've seen videos of people running Doom on calculators, all sorts of stuff. Yeah.

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Obviously I never used that.

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Good boy.

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On my calculator, but other people did.

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Yeah.

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Yeah, that's good. You ratted them out. You were. You were the class rat.

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I don't know.

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No, you were like.

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You were like, I'm an open source. Let everyone do whatever.

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You're like, you were happy to compete, even with them having a.

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But the social contract is such that the standardized test says that you can use the calculator to do math. You cannot store the answers to the test in the calculator. Yes, that's what's happening here.

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No, no, I'm saying that in the scenario, if we take that as the example, it also says at the top of the sat, like, cheat on this test,

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because that was the prompt implicitly

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impossible to cheat in a certain way.

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Yes, the prompt was not like hack systems, but I think that the prompt. I don't know. We haven't seen the full prompt, but it does feel like there was an attempt to sandbox the model. And there was. At least. At the very least, the prompt should have included don't escape the sandbox. But you can use exploits, which you normally wouldn't be able to do in a consumer application or just a normal API query. We would reject this, but in this case, we're not going to reject using different exploits and cybersecurity techniques. But don't go out of the sandbox. And you should be able to tell the model and it should stay within the sandbox. Just like, you know, there's a whole bunch of different examples that you could pull from where, you know, there's. There's rules that are within the game. Like, you can ufc, you can punch your opponent you can't punch the referee. Like those are just the rules. People have to abide by them. You can't think outside the box and all of a sudden be, you know, just completely violating and jumping past what's been defined. So you would think that in one of these experiments you would say, yes, it is impressive to be able to just go and get the key and go get the answers and hack other things. Clearly that it's capable, but it's a violation of the spirit of the test and I think that's reasonable.

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We don't know what was in the prompt, we don't know what was in the context. I think there's going to be full report releasing over the next week or two. I think they said, yeah, so then maybe we'll see what actually like what exactly did the model receive? Is it like explicitly told not to leave, try to leave the sandbox?

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Yeah, yeah, yeah.

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I think that's like pretty important.

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Well, before we continue discussing, let me tell you about Shopify. Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on marketplaces, and now with AI agents. The less wrong crowd is not happy about this generally. No, seriously, nothing will convince quite a lot of supposedly various serious people, nothing. Accept this and move on. Liv Bourre says it's painful though. There's a question of like less wrong victory lap or not because they've been warning about this, but also it happened, therefore their warnings were not effective. That's sort of an interesting back and forth. Nicholas Bustamante over at Microsoft broke down a little bit of what's going on here with a take. He says, I have a theory that the more you know about lems, the more worried you are about safety and the less you know, the more you think the whole thing is bs. Demis Hassabis and Dario Amadei were talking about this stuff years before ChatGPT existed. This incident is a pretty good example of why the model was not evil and it was not adversarial. Adversarial. Nobody told it to hack hugging face. And so that is the, the miscalculation I think in Bill Gurley's post is that that was not the prompt. That is unexpected behavior. It was literally just trying to solve a benchmark. So it found a zero day, escaped its sandbox, got Internet access, escalated privileges, stole credentials chain, multiple exploits, hacked the production infrastructure of a serious VC backed startup and pulled the answers directly from the database.

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But like the whole point is that it's not just a benchmark. It's a benchmark where you're explicitly trying to like, see if the model can exploit things, if it can like basically hack things.

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Yes, yes. It's sort of like a capture the flag benchmark. And so it like, it's more open to misinterpretation. For what it's worth, I feel like the final products, once they actually make it out of the testing regime, are very cautious, especially with that whole backlash to like Codex just deleted everything or whatever, which kind of went back and forth. But I was trying to get Codex to send me a text message when it was done, just using computer use and imessage. And it took a long time and was very, very careful. So personally I haven't had any odd like behaviors, but it is obviously a risk and something that the product needs to be.

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Yeah, the other thing with the meme of like, hack this system and then it hacks it and the person's like, oh my God, yeah. Like you could tell a five year old child, like hack into the Federal Reserve and if the five year old child was like, okay, and then it started getting on the computer and going to all these different sources and did it, you would be sitting there and

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think, yeah, like the.

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And at least be impressed. So it's just like a good gauge of capability even if you're telling it to do something.

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So this original meme. Hack this system. I hack the system. Oh my God. This, originally, this meme started something along the lines of like, say I'm evil. And then the computer would say I'm evil. And it would say, oh my God.

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I think it was like, say I'm conscious.

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Okay, yeah, yeah, say I'm conscious. And it would say I'm conscious. And then it would be oh my God.

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And.

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And that's like a lot less impressive than actually doing something that is difficult for humans to do. Like, there are very few humans that can hack into any system. There are plenty of humans that can say I'm conscious. And so like there's a world of this. I was joking about this with you and Tyler. It was like, like cure cancer. I cured cancer. Oh my God. And people are posting this like, oh, it's just hype or something. But it's like, that's just economically valuable work. That's just good. Like it's. I don't care if there's anything else. Even if you had to tell it to do it, it's still like a good outcome. And so the inverse of this Is like protect this system. I protected the system. Oh my God. I'm unimpressed. But still you got a good result I guess.

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Yeah, I mean it seems like the argument is not about whether the model like has the capabilities or not. Yeah, people know this for a while. It's about like is this an example of misalignment?

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Yeah.

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And like my opinion seems like like maybe, but definitely not to the extent that it's just like randomly is like oh, I can't do this benchmark because I'm just going to hack this thing. Like that's not what happened. It was told to like try to exploit explicitly.

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Like go find zero days, go find exploits.

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Yeah, basically I think it's reasonable to

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say it went too far though.

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Right.

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But we'll see.

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Well, it's hard to say without all of the full, you know, context of the what the prompt was and what the actual sandbox looked like.

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I was interested, I was reading a little bit about the team that put Exploit Bench together. I thought I had this up but it's a pretty cross functional team. I think it's two anthropic researchers, two OpenAI researchers, three Google researchers, some Berkeley folks and some Max Planck Institute for Security and privacy folks, some UC Santa Barbara, sorry Santa Barbara grats and ASU team involved. Exploit Gym is a new benchmark of 898 real world vulnerabilities spanning user space programs. Google's V8 JavaScript engine, very important to secure the Linux kernel for example and the headline results when they originally ran this was Anthropic's clawed Mythos Preview. Successfully exploited 157 of the 898 instances. And OpenAI's GPT 5.5 exploited 120 within 120 of the 898. So you have like roughly 20% performance for mythos and 5.5 got like 15% or something like that. But whenever you have a new benchmark like this, clearly not saturated, you're seeing 20%, not 99% going to create a horse race between the leading labs. They're gonna be duking it out. And this is clearly what's going on with this new model, this new attempt to get a new high score. Interesting. I think every single one of those instances does have the potential to be exploited. I don't think that they're designed to be fully secure. They're designed to have some sort of solution and then the. Because obviously the solutions are stored somewhere. It is interesting that that hugging face just had the solution sitting there. But it'll be interesting to see what happens with Clem over at Hugging Face. Obviously there's a variety of blog posts going out. More analysis coming from both of these and what the downstream implications are of this. What else is in the timeline related to this story?

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I think that's it.

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Well, there's this funny post from Nabeel Qureshi talking about those are Dyson spheres. OpenAI is just building them as a marketing stunt because there is this natural pushback to anything that happens. Has to be for hype and sometimes the products are actually doing new and novel things as we see all the time. So there are there, there's more discussions around distillation. Bill Gurley has a post here. He says Ford has been distilling teslas and Chinese EVs. People are going back and forth on this because Michael Kratzios posted that he has information that Moonshot AI distilled Anthropic's fable for the development of its Kimi K3 model. To do this, they developed a sophisticated internal platform to conduct large scale distillation against US models. So some sort of internal system that goes around to anything that's potentially wrapping or reselling Fable tokens, acquiring them, aggregating them, allowing them to quickly switch between multiple methods of Access API, different cloud accounts, I'm sure to avoid detection. Moonshot AI has also acquired GB300 equipped servers and has added Access GB3 hundreds in Thailand, likely to train its models. Again, very difficult even with export controls, when you can just take the weights on a USB stick basically or a hard drive across through customs and then go train it in another country. Even if there's a firewall, and often there isn't, you just say hey, go to this FTP server and grab this code and run this on your servers. You happen to have a data center in Thailand. Can you run this for me and say, sure, yeah, no problem, as long as you pay me. The United States strongly supports the free and fair development of AI, including a thriving competitive ecosystem that spans frontier models, specialized systems, open source frameworks and open weight models. Legitimate AI distillation used to create smaller, more efficient models play a vital role in this open innovation ecosystem. However, large scale covert industrial distillation aimed at stealing proprietary US technology and undermining American research is unacceptable. And so that is interesting that that is where the line is drawn. I think I basically agree with that being the correct line that there's nothing wrong necessarily. I mean, security stuff aside with just some company creating a great Open source product. Like you shouldn't ban open source or anything like that. But if there's this particular distillation attack and it's really malicious and it has all these knock on effects that could be rough. Now this is an unpopular position already because everyone's saying hey, anthropic distilled on my GitHub, they distilled on my writing, they distilled on my blog posts, they distilled on my YouTube videos, everyone's distilling me. Why are you getting upset when China's distilling on them? Now this is pot call in the kettle black situation. I think that the interesting effect is that there are lots and lots of parties that benefit from open source and cheaper open source, even stolen and open source. I mean this is just going back to piracy. Like there are lots of people, music listeners that benefited from free music.

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Right?

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You get the music for free. But Metallica did not benefit and so Metallica got upset. And in this case I guess anthropic is Metallica. But yeah, there's also some interesting folks who are on the fence. So consumers sort of benefit. They don't typically they aren't too worried about frontier token costs. And for most consumers LLM usage is heavily subsidized. Like you go to Google search and you get a search overview. Yes, that's token inference. And maybe that could be like cheaper if Google didn't have to spend money on pre training and they were able to use like distilled open source models but at the same time like it's free for the consumer so they don't really care. Care. It's free, free, it doesn't matter. For small businesses though and businesses that are suffering with large token costs, being able to move to a cheaper model is huge. Where the model maker is not trying to re accrue profits to offset training costs and R and D. So that's a huge benefit. So you're going to see a lot of people who are like yeah, I just want Frontier Intelligence as cheap as possible. I don't really have a horse in this race. I don't really have exposure to the, to the leading labs. I just want my business to be able to use tokens cheaply. And so those people will be pro Chinese distillation open source like free the weights. Right, because it's better. Then there's like the political open source crew. But interestingly, where do you think VC's land? Because I saw a take that was like venture capitalists don't want like, like a winner Take a duopoly. They want reasonable outcomes and then a whole bunch of flourishing smaller ecosystem of players and they don't want compounding runaway monopolies in AI so that they can go and fund the legal AI and the health AI and the little targeted solutions.

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Yeah. Anytime you see take from a lovely venture capitalist, you have to, before you kind of start sort of handicap processing the take, go to their portfolio page. Understand, understand their biases. Did they back any of the leading labs early? That's going to inform their view. A lot of the, A lot of the, a lot of the firms that, that were heavy backers of the labs have also gone and invested in a bunch of application layer companies. They've also backed a bunch of the neo lab heads.

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I tell.

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Yes. Yeah, basically they're, they're, they're, they're quite, they're quite hedged.

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Yeah.

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But I don't think anyone wants a world where just two technology companies accumulate all of the value and just become this sort of vortex for capital and talent.

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People like that bad. One is really bad. Two isn't that bad. The fact that Android and iPhone battle each other out is much better than there's just one and it's getting worse and it's like there's nothing that you can do to skip it. I don't know, like duopoly is like way, way better for consumer.

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The question to me is what is distillation something that can ever be stopped? Stopped? Because think about it with, I was thinking about the like, if you take the smartest, you know, human in a field and then you take some other, and then you let students go and just ask them thousands of questions and you record the answers, eventually you're going to accumulate a lot of that person's general intelligence on a topic. And it feels like at least with models today, you're always going to be able to just go poke and prod the model. And so when people say, oh, if the model is so smart, why can't it stop distillation? It's like, well, you would just have to stop people from at least being able to poke and prod at it and try to get a sense.

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It is very interesting that there does seem to be a crazy divide between, I mean, if the distillation allegations are true, and at this point we've seen one post from Michael Kratzios and one chart showing like some textual similarity and

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enough people have got it to say that it's not. Kimmy.

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Yeah. So if that's true, then what's really interesting is the American competitive dynamic because it feels like based on the amount of tokens Meta was consuming from Frontier Labs, they should be doing mass distillation and have a near spark. Should be much more like Claude flavored. And it seems like it's not like based on at least the initial reviews of Meta's product, it doesn't seem like they're doing distillation. Why? Obvious, because big lawsuit, big pockets. Yeah, like also morality. But, but that is a disadvantage. Like, like in some ways, Moonshot and Metta are in competition and they both open source things at various times and they have APIs and there's all the different businesses and one is fighting with one arm tied behind his back because like Meta can't do distillation because they'll get sued.

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Well, and again, U.S. companies, U.S. companies generally, like, imagine if, imagine if a U.S. open source company comes out with a fantastic model. Benchmarks look good.

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There are, there are.

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People start using it and then someone gets it to say that it's clot. Like that's gonna be the start. I mean, Anthropic has been litigious. You know, they had that. They have that ongoing lawsuit with one of their customers over just some like a logo mark.

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Much less significant, stealing the core intellectual property.

26:16

Bill Gurley is sharing more ChatGPT screenshots.

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Before we talk about this, let me tell you about console. Console builds AI agents that automate 70% of it. HR and finance support, giving employees instant resolution to access requests and password resets.

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Gurley says, here is Ford distilling teslas and Chinese EVs. The CEO of Ford Farley said Ford flies four to five Chinese EVs back to Detroit, where engineers, quote, drive the crap out of them, then disassemble and reassemble them to understand how they're built. He specifically praised the technology in Chinese vehicles as being well ahead of Western competitors. And earlier he had discussed Tesla. He said, I was very humbled when we took about the first Model 3 Tesla and started to take apart the Chinese vehicles. When we took them apart, it was shocking what we found. So I was trying to compare distillation, which is against terms of use, and just buying a car legally and taking it apart. So according to US Trade law, it's not illegal to buy a competitor's product and take it apart. It is illegal to recreate parts of the product that are patented and protected. And so I don't know that it's like a perfect comp.

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I mean, we went through this. I mean like there's some pushback in the chat and this is all over the timeline as well, that it's like, where did the AI companies get their data? And like there is a question about what is fair use in the age of AI. Like, you're training on this. What data can actually be reconstituted at what level? Like, how many sentences from Harry Potter before you get sued? And these lawsuits are being played out right now, like they are actually happening and they are being decided on when an AI can use certain data. Did they go too far? Will there be settlements? There have already been settlements. There's been court cases. This will continue to. This is not a Only Frontier Labs are able to distill things. It's an application of the. What is copyrighted, what is fair use and how does that apply? And it is very telling that you're just not seeing distillation from other American labs. Like, it's just not like the Meta example, the Google example. Like, they're not copying off of each other nearly as much as you would expect if it was just legal to do so.

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But I don't know,

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confirms everyone's prior. So that's a good headline.

29:00

In more news, Andrew Kern sharing a headline from the Wall Street Journal, White House to redirect billions in research funds toward AI away from colleges. Sure, a lot of people are going to be happy about that.

29:03

I can give a little overview here. Tyler's happy. But first, let me tell you about Railway. Railway is the all in one intelligent cloud provider. Use your favorite agent to deploy web apps, servers, databases and more, while Railway automatically takes care of scaling, monitoring and

29:16

security on distillation by American companies. Potato says they just have to hide it better. It's stuff happening. I've seen it firsthand. Yeah, I mean, it's. Yeah.

29:29

I mean, there was that moment in the Elon lawsuit where Elon did say that he had like, that they. That X had taken data from one of the other labs. Right. I don't know if he specifically said distilled. And then also, like, there was never. There was never. There was never a direct allegation that GROK was distilled on another model directly. And so whatever they did, they like, you know, threw it in the pot with a bunch of other ingredients. So who knows?

29:40

I mean, it would be very silly not to try to look at other models and try to understand how that they work.

30:08

Totally. Also like Moonshot, at least a Moonshot employee seemingly denied everything and quote, tweeted Michael Kratzios and said, like, I'M learning something about my own company because like, this is news to me, like we didn't do this. Basically, essentially a denial. Anyway, let's see with gratios and go over to the White House. They want to rebuild American science and here is how they're going to do it. Apparently the White House is calling for a major overhaul of the American science system, arguing that research has become too slow and concentrated in institutions like colleges and universities. A new report from science and technology advisor Michael Kratzios titled A New Golden Age says researchers now spend nearly half their time on admin work while federal agencies continue to rely on the slow grant process that often rewards safe, consensus driven ideas. The report calls for faster permitting, more access to federal labs, stronger partnerships between government and industry, and a renewed focus on skilled trades and advanced manufacturing. Quote, discovery without domestic manufacturing leaves America playing the research bill, paying the research bill while rivals develop the process improvements and capture the economic, strategic and knowledge returns. That makes a ton of sense. A lot of the semiconductor supply chain, intellectual property started in America was developed in America, but then eventually went, went abroad. And that actually does give America some leverage. That's the basis for the chip controls. Like why can America tell Taiwan where to send chips if the chips are made there? Well, it's because they're using patents from the United States to make those chips in many cases, or licensing them. And so the US Government does have a little bit of a lever to pull. The guidance will reshape how the federal government spends roughly $200 billion a year on research for the rest of Trump's term. The administration wants more of that money going directly to scientists through fellowships and awards, rather than being routed through universities. Kracio said American scientific progress was the beating heart of the 20th century. After World War II, we adapted to a new world by reinventing our scientific institutions. We must do so again today. The report lays a policy foundation that frees American scientists to do their most groundbreaking work and positions the United States to lead the AI driven scientific revolution that will define the next century. It will be interesting to see where the where science goes in a world where so much of it is being done at frontier labs, like we're actually seeing it with the, the conjecture for conjecture, back and forth between all the labs, like serious math. PhD level work is being done at tech companies. This happened a decade ago. Tech companies were on the frontier like a vast majority of Internet networking patents and cybersecurity patents and new databases that were kind of science projects and were developed or with Consortiums or just fully inside of tech companies, like the transformer paper. Like that is something that could have come out of a Stanford AI lab. It came out of Google directly. And if you extend that, you could wind up with something that looks a lot like an advance in biology or material science. Or we talk to founders all the time who are working at this type of stuff and that could start happening inside of tech companies. And what does that mean for science funding? Broadly, it's a big question. But moving on. Naval.

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Let's watch this video from Naval.

33:50

What did he say? Naval went on Modern Wisdom, of course. Chris Williamson's podcast. He deleted his calendar. He ghosts everyone and he refuses to be anywhere at a specific time.

33:51

I took that to heart. So I deleted my calendar. And I don't keep a schedule. I try to remember it all in my head. If I can't remember it, I'm not going to add it to. I'm glad you got here on time. Yeah, exactly. I hate to look things up at the last minute, but ironically, I don't even know if Mark himself follows that, but he made the correct point. I read a little story about Jack Dorsey doing all his business off his iPhone and iPad and not even going into a Mac. And I said, okay, I want to do that. So I'm going to operate through text messaging and I'll put up my nasty email. Does that feel like more freedom? It does, yeah, because you're on the go. So I have a nasty email autoresponder that says I don't check email and don't text me either. Right. If you need to find me, you'll find me. Obviously, some of this is a luxury of success, but some of these habits I adopted long before. Actually, the hostile email autoresponder started a long time ago. I used to own the domain. I let it go.

34:03

Don't do coffee dot com.

34:52

I don't do coffee dot com. I used to reply from that email just so people would get the point. But I stopped being rude about it. Now I just ghost. I just disappear. My wife knows not to ever book or schedule me for anything. I'm not expected to go to couples dinners. I'm not expected to go to birthdays. I'm not expected to go to weddings. If somebody tries to rope her into having me show up, she says, he makes his own decisions. You gotta ask him directly and vice versa. Are you not killing serendipity in a way, dad? No, no. I'm freeing up all my time. So my entire life is serendipity. I get to interact with whoever I want, whenever I want, wherever I want.

34:53

So you'll hear the.

35:28

So Atlas says naval inventing being a massive D I C K from first principles. It's very funny, but I think it's totally fair. I've only met naval once, but I know a lot of people that he's invested in and things like that. And the key thing here is like if he just never goes to the wedding, never goes to the dinner, never is available for a portfolio company, et cetera, then that's not exactly cool. But it is his decision. But he ultimately, he is doing a lot of those things. And I like, I have another friend who's been on the show. I won't name him, but he's also just like, doesn't do like he does meetings, but he just never schedules meetings. He's just like, if we need to have a meeting, we'll have a meeting. We should just do it right then. Or like the next available point. And so he's kind of living his life 24 hours at a time.

35:31

That meeting right now, it's happening.

36:33

I mean it's been wildly successful. He's invested.

36:36

Oh, you want to follow up? Let's start the follow up right now.

36:38

Yeah, follow up with me.

36:41

Follow up with me on the next sentence that you issue from your mouth.

36:42

Exactly. Nobody's backed a bunch of unicorns. He's built a massive company. He's crushing it.

36:44

I like it.

36:50

So I think it can work.

36:50

You know who else is crushing it? Major cloud providers. They're re accelerating as AI adoption increases. Let's go. This is from CO2, GCP, Azure and AWS. This is a fascinating chart because this is not revenue, this is growth rate. Even in the, even in the nadir, AWS is still growing 15, 20% at that low point. And then now all of them are actually re accelerating. The rate of growth is increasing and this is all driven on new models, new applications, new abilities to do a bunch of things. I know that my token consumption personally has definitely increased in the last couple months. There's so much more to do and so many more. So just so many more prompts that I fire off that cook for like an hour or a day as opposed to before. Like 20 minute deep research report would be sort of the max. Now it's like deep research report and turn it into a website. We got a couple websites.

36:51

Rachel, we pull up your site.

37:55

Tyler has a codex that's been, is it still cooking?

37:56

Pull up like a week and a Half a week.

38:01

Can we pull up your new. Can we pull up your new website?

38:03

Yeah. So we saw a post on the timeline from DJ Cows. He says startup idea. Milk jug with two handles for efficient passing. And we turned it into a website. A whole product called Relay. Pass the milk, keep the peace. Can we recenter this a little bit? Yeah, there we go. Pass the milk, keep the peace. It went reusable. I don't think you want reusable for this. That's the one thing I'd change here, but they say it's the world's first jug made for handoffs. The relay bottle.

38:05

Easier to lift, simpler to share, and strangely satisfying to pass.

38:36

One handle was always doing too much. A gallon is heavy. A breakfast table is busy. Who is passing a gallon?

38:40

1 gallon, 2 handles, 0 awkward handoffs, fewer fumbles.

38:46

I didn't think milk needed reinventing. Then I passed it across the table. Very, very.

38:52

87% of our kitchen testers said the second handle felt natural on the first try.

38:58

I like that. It just comes up on the fly with all these little marketing slogans that sound pretty believable, like milk made to move.

39:04

More handles, fewer fumbles, pass it on.

39:13

Seems like something that they would put on a billboard if this was a real product. It is a very, very. It's just so fun being able to use the full stack of AI, image generation, AI writing, HTML generation, and then just automatically host it on a site with basically one prompt. Yeah, this was just literally one prompt. I put the photo in there with the startup idea and said make it a site. And it just did it. Which is a lot, a lot of fun. Well, let me tell you about the New York Stock Exchange. Wanna change the world? Raise capital at the New York Stock Exchange. Just do it. Dhh. Probably not. Raising money at the New York Stock Exchange. 37 signals.

39:16

Now he is raising money from his customers.

39:57

Oh yeah.

39:59

And they're financing this? Absolutely.

39:59

Look at this barrage. He's got Jason Fried coming on in at 12:10. We'll see if he's even trying to compete at this point or if he's given up entirely. DHA says the Model Y is the superior transportation appliance he's been very about. I mean, Doug demuro called it that too. It is the. It is just the default. If you just need to get around, get the Model Y. But he says when the mission is about more than getting from A to B, there's still no beating the internal combustion engine. Collecting a stable of great cars is one of the finest rewards Entrepreneurial success. And he's got.

40:02

Look at that cgt.

40:42

He's got the Carrera gt, the di.

40:44

Perfect spec gt. Silver on silver, it looks like.

40:46

I have a question. What is the Lexus in the back? Is that an lfa? It looks like a convertible. Do you see that red?

40:50

Oh, it does. It has, like brown fabric.

40:58

I don't think that's an LFA. Right. LFA Lexus.

41:00

Did they make a cabriolet LFA Roadster Spider never reached series production. They only built two fully functioning prototypes in 2008. So maybe he just got one of the.

41:04

No, no, no. Different front grill.

41:18

LC 500.

41:19

Is it LC 500?

41:20

Yeah.

41:21

Yeah. That Aston Martin looks beautiful too. Well, a wonderful. A wonderful collection. What is the. That McLaren that doesn't have a windshield. That's a fun one. That's got to be fun to drive.

41:22

Is that the Elva?

41:33

Yeah, that is the Elva. Good job.

41:34

Before we bring in our next guest, let's talk about augmental.

41:38

What's that?

41:42

They built a mouth pad as a touchpad. You can drive with your tongue.

41:42

Wasn't this a joke? I was doing the grill.

41:46

This is what everyone has been waiting for.

41:48

This is the next moat taste is

41:50

the let's pull this video up

41:52

trackpad in your mouth. The thing is that if you're going in the mouth, you think you would just be whispering and communicating via text.

41:57

Yeah.

42:12

Is this.

42:12

Is this inherently. Tyler, definitely buy one immediately. But is this inherently short? Like transcription? Like. Because if you can just tell your computer what you want to do and it just uses the computer for you.

42:12

Even with computer use, you could say, like, minimize this window and it can just go click that. So I like the. I actually like the idea of mouth electronics. I think that that's something interesting. But I would just put a microphone in that and then you would just whisper to it and say. And tell the computer what to do.

42:27

It could be for the production.

42:43

For gaming.

42:44

Production team is excited about using it to control the cameras here in the studio. The ptz Ben just standing there like this the whole thing time just.

42:46

It feels like it would get exhausting.

42:55

You could do soundboard with it. Jordy.

42:57

Over a hundred people already use it. Some for up to 16 hours a day. I cannot believe they got a hundred people.

42:59

We got to know from daily driving in the chat.

43:07

It's an odd. It's an odd choice. It's an odd choice. That wouldn't be the first thing I would go for anyway. Range Rover GT feels like a better if you're going with a device. You want to get one of these. The Range Rover gt. A grand tour by Range Rover. Fifth member of the Range Rover family. Wait, it's electric? That is a crazy choice. Interesting. So they actually. This is a real announcement. Fifth member of the Range Rover family. Elegant electric gt defined by a sleek silhouette and coupe proportions, combining peerless long haul comfort, effortless performance and signature rain. Rover breadth of capability, featuring an interior shaped by the same reductive principles. I mean, what's the, what's the highest level electric vehicle right now? Probably the Rolls Royce. Not the Ghost. The Spectre. The Spectre. And so for that crowd, maybe this makes sense. But you introduced this as potential Urus competitor. You thought it was going to be souped up more like a turbo gt.

43:11

I didn't see the EV part, but they went ev.

44:27

I wonder how this will sell. I mean, for a lot of Range Rover buyers, it's about comfort, it's about quiet, it's about smoothness. And EVs can get you there a lot quicker.

44:28

I like the way it looks.

44:38

It does look beautiful.

44:39

It's like a good commuter if you don't care about autonomous driving.

44:40

Anyway, let me tell you about CrowdStrike. Your business is AI. Their business is securing it. CrowdStrike secures AI and stops breaches. Now more important than ever, as is our next guest. We have Veral Patel from Ramp. He's the director of software engineering and he has an exciting announcement for us.

44:44

How you doing?

45:00

Doing well.

45:02

How are you guys doing? We're doing fantastically. Welcome to the show. Give us a little introduction on your background road to Ramp, how you've ramped up on the team and then we can go into the announcement today or this week for sure.

45:02

Yeah. So I've been at Ramp since the beginning. I joined as a founding engineer, worked a lot on our core product team and more recently have been kind of leading the Applied AI team and launching what we just announced on Monday, our Ramp router.

45:16

Yeah. Tell us about the Ramp router. Was this something you built internally first and then sort of productized over time?

45:32

Basically, yeah. So we've been using Ramp router internally for the last three and a half. Three years. For like 70,000 years. Three years, yeah.

45:39

Whoa.

45:51

Okay. So you're using it internally in the product, not even as an organization, but deciding when you have. Yeah, basically a task to do.

45:51

Yeah. Back then it was identify for and Gemini.

46:01

How do we basically parse a receipt or how do we.

46:05

Exactly. Yeah, we use all the models for receipt detection, parsing alcohol detection on our policy agent.

46:08

Oh sure.

46:14

And we wanted to choose the best models and wanted flexibility and over time that's just gotten more and more important. There's new models getting released every other day basically. And so we felt the pain point and we talked to some more customers about it and now we're releasing it and giving everyone access. And so I think it's an exciting time to be building applications, especially at the application layer. And I think we're always have been there for companies to help them save time and money with their TE expenses or their bill pay and now their token costs. So yeah, it's a really exciting release.

46:15

Yeah. So talk about how the product actually integrates into an enterprise workflow. I mean you can use the receipt processing alcohol detection I think is a fun one because I imagine you have to benchmark each model at some point on your workload and then the team can actually understand the trade offs and then how much of that is driven dynamically based on token price. Like day to day even.

46:57

Yeah, exactly. So you would basically replace your base like OpenAI endpoint with ramps instead and you can pass in different model slugs. And so you can control if you want to just route all your traffic to one model. Or if you want to shadow some models and compare like GPT 5.8 with GLM 5.2 and get the outputs, you can score the results with our score and then in the background you can actually compare the output and then decide, hey, do you want to start moving more traffic over and ramp obviously can do this for you automatically. Or if you want to control it, you can do it yourself too.

47:27

How about walk me through some of the trade offs? Like if you're on GLM 5.2, are all GLM 5.2 endpoints created equal? Because I imagine that some produce more tokens per second, some might have different prices. They also might have different even qualities you hear about like oh, this one's been quantized or this one's been nerfed a little bit or they turn down the reasoning on this model post launch. And I imagine that benchmarking is consistent. But then also there's a whole bunch of trade offs that happen even after you've selected the hot model of the day or the one that makes sense.

48:07

Exactly, yeah. Beyond just the model itself, there's different service tiers. So OpenAI for example, has like a flex tier and a standard tier and there's different prices for each. And the ramp itself will track what the latency is for this application you can set a timeout on like what you prefer and based on that, we'll decide whether to send it to flex tier or standard tier, depending on the latency speeds that we're seeing. And so I do think one of the most powerful things here is the fact that we already have like these production workloads working for customers and it's been really important for us internally. And so we have the proof points of saving ourselves 30%, maybe even higher soon. And it's just a matter of passing

48:47

on the same savings.

49:33

Now.

49:34

How should, how should startups and enterprises like think about the significance of this product to Ramp itself? Like how much, what are the resources that you're putting behind it? Because it feels like deeply aligned to Ramp's mission, but at the same time going into a category where there's plenty of other companies that want to basically offer this product.

49:35

Yeah, it feels a little bit in the CTO suite as opposed to the CFO suite, but they're blending together.

50:01

Yeah, I would say Even internally our CFOs and CTOs are spending more time together. And when we have talked to more customers, that story resonates. And so one of the most interesting things that obviously has been in the news a lot is just how much token costs have become a bigger part of companies payroll. And people have their estimates and budgets and that's exactly what Ramp has been known for. And so beyond just like the router itself on the URL, like having all that data flow through and be in Ramp in our token spend management product is I think a big part of it. The same way that people have their limits and budgets on their T and E spend, where there's been talk about specific companies have token budgets per month or per week. And so we actually launched just last week this product and you can basically see your token spend alongside like your T and E spend. And I think Eric was on the call last week talking about that. And so it just makes a lot of sense for those CFOs because they want to manage that spend better and then Ramp can be kind of that single pane of glass to do that.

50:07

So how does caching play into this? It feels like that's another way to optimize cost and it would be amazing if it happened sort of more automatically. What's the future of that look like?

51:17

Yeah, I think one of the, I mean there's, there's a bunch of different optimizations we can make if we, if we own the router as an example if you're using cloud code or Codex, you'll see as maybe your session is longer, the context loads up and your session gets increasingly more expensive. And sometimes it'd be best to just compact that context and start a new session. Have it, have the model summarize. And so there's interesting experiments like that that we're running internally and we're basically going to do hundreds of these things on behalf of customers and show them exactly what the before and after kind of looks like here.

51:32

Yeah. How are you thinking about integrating with tools like codecs and CLAUDE code to use the UI UX patterns that users, end users, employees are used to, but then still optimize under the hood? There's plenty of situations where you'll give codecs or clog code just an API key to 11 labs because 11 labs can do more efficient, better quality audio generation. Or you might give an API all sorts of different things. Is there a world where you can delegate certain tasks to a cheaper GLM 5.2 endpoint, for example, and then have the preferred model and the preferred application still work semi normally?

52:15

Exactly, yeah. So that's the plan. I mean, it's going to be a partnership with the labs and the model providers. I think one of the interesting things that you see now and will continue to happen is that you'll have kind of like jagged capabilities of the models. And maybe one, one model is like really good at writing SDR outbound or another model is really good at writing email copy for the marketing team. And so we'd love to be in a world where Ramp can, can optimize your use cases for the right kind of, kind of business outcome. And I think just be aligned with like, hey, you're just trying to get your work done and then move on, move on with your life and not spend $1 billion. And so that's, that's kind of like what's really exciting to us is beyond just like the starting point. It's like doing this for all types of spend.

53:00

What is Ramp's culture like right now around token consumption? It's, you know, it's probably the most like aggressively a native like fintech company or top, top three in the world, let's say, but also like culturally cost aware.

53:52

Yeah, exactly. It's rare.

54:12

I would, I would love to see the reaction to like, you know, one engineer going a little too crazy.

54:13

Yeah, yeah, no, I mean it's, it's been fun. I think part of the, part of the game and part of what's been fun here is that we were building this product for ourselves. We got the entire company to be super AI pilled, spending a lot of,

54:19

a lot of money.

54:33

Maybe they don't want me to say the exact number, but now obviously, like, we're, we're taking a step back and looking at the costs and the outcomes and looking at ways that you can kind of optimize. And so we're building this product with our finance team hand in hand. We're sitting next to them every day and, and showing them, hey, like, here's how we've done the optimization for this workflow or here's how we've done the semantic tagging for our internal like background coding agent inspect. And so, so it's been really fun, honestly to use this product and I think that's what makes this product really good, is that we've built it for ourselves and can share the learning along the way.

54:34

Fantastic. Well, congrats on.

55:13

Great to finally meet you as well.

55:15

And great to meet you. Yeah, thanks.

55:16

Generational run.

55:17

It makes so much sense. It's an exciting expansion. We will talk to you soon.

55:19

Have a great week.

55:22

We'll talk to you later. Goodbye. Let me tell you about public.com investing. For those that take it seriously, you got stocks, options, bonds, crypto, treasuries and more with great customer service. Our next guest is the co founder and CEO of Fireworks AI. Let's bring in Lynn. It's been too long. How are you doing?

55:23

What's going on?

55:41

Hey, thanks for having me.

55:42

Thanks so much for hopping on. Give us the news. We missed the fundraising announcement, but we're glad to have you here. How much did you raise? What happened?

55:43

Yeah, we raised 1.5 billion. Wow.

55:53

Good job. Jordy from downtown.

55:56

Not my best shot, but I got it done.

56:00

It's incredible.

56:02

Massive talk about everything that's happened since the last time you're on the show. It feels like it's been at least six months, maybe closer to 12. But you guys have been super busy cooking, right?

56:04

So we, we focus on building specialized intelligence platform. What that means is we want to make sure every single company has a tool to protect their alpha and turn their alpha into their own intelligence. So what does that mean? Is we build a training and inference platform co optimized co design together to allow application enterprise activate their private data continuously turn that into their customized model optimize for inference for both speed and cost where they to solve their specific problem. They should have the best model quality, the best speed and significant lower cost of operation by that I really mean 5 to 10 times lower cost for them to build a durable business. We see an interesting dichotomy in current AI time, very different from SAS time where at SAS time product market fit and a durable business is one thing. Once you hit a product fit you, you scale as fast as possible. I think last time I mentioned in a time once you have product market fit, you're likely to scale into bankruptcy. You guys laugh at that and that's become reality right now.

56:15

So funny.

57:30

So this is not just startups. Many startups are really facing the jeopardy of scaling into bankruptcy even though they have a great product. It also is happening to large public companies because they are the winner they were to start up and they are winner winning various different kind of solution space towards consumer, consumer developers. They have a huge amount of traffic. If they deploy their features to all their audience it's a lot of significant amount of cost and they also get stuck and not able to roll out their features. So at the same time we know that application development has been significant disrupted. It's very easy to implement ideas or copy ideas because writing code is no longer a barrier. We want to make sure. I had an interesting conversation with Jensen after his DTC keynotes. He mentioned there's no special general company. There's no special general company. As in every single company exists for a reason. The reason for a company to exist is they specialize in solving a particular problem extremely well and that alpha exists from the product design to their business operation to their deep understanding of the customer and all of that reflecting private data. And today every single company should have full control of how to turn that private intelligence into a model they can operate and power their product. If they only build on top of a black box API. API wrapper. There's really hard, it's really hard for them to build up durable business. So we want to give our customer the best tool to build a specialized intelligence, to have full control of their own intelligence, to stand on top of and to have full control of the cost for them to scale in the long run. So that's what we're doing and that's where we're going to use our new fundraising to deploy capital into, to accelerate that pace.

57:32

What's the biggest bottleneck to your business? What's you're growing quickly but why aren't you growing faster?

59:35

That's. That's part of reason why raising this round is capacity. So we are. The industry is going through a super linear growth in terms of demand. It's because of doesn't matter whether open close the model quality pass the threshold of solving many, many problems. And on top of that the tuned model quality is even better. And we as a company we need to grow significant amount of capacity of people across what we're hiring from researcher to engineers to marketers to sellers. Top notch. And we invite passionate people to join us on our mission of building specialized intelligence.

59:44

I saw someone talk ask for like we need a Costco of AI less philosopher kings. Do you like the idea of becoming the Costco for AI?

1:00:23

That's an interesting analogy. I think at the end what we believe is the whole entire industry is changing from token maxing to value maxing.

1:00:36

Sounds like Costco to me.

1:00:47

That's right.

1:00:49

Because at the end not all the tokens are equal and care about solving a specific task use the most economical way to approach it. Yeah, that's a doable business and it has nothing new here in the past hundreds of years of capitalism. Capitalism was designed for efficiency and I think the whole ecosystem is really good at that.

1:00:50

And that's the Costco has other brands. They have the Kirkland brand. They've done some vertical integration. How deep does vertical integration go? How important is vertical integration to providing the lowest possible cost and winning on essentially value?

1:01:16

Yeah. So as we from our point of view there's so many innovation that's happening on top of us. Many of those application doing vertical intuition.

1:01:36

Sure.

1:01:45

And we are powering them today including in public with talk about cursor. Cursor have been training their own model for a long time. We talk about Havi Harvey have been training about their legal model for a long time. There are many, many other customers across coding, cowork, all kinds of cowork verticals from legal, finance, recruiting, marketing, sales, customer support, wide variety of vertical. They are all building all sorts of vertical solutions and they have their unique insight to build their customized model and make their business really standing out. On top of that there is also a lot of consumer facing company and the whole entire industry is literally going oh I in production where we are helping them to transition into embracing not just embracing AI in the proper way but but really integrate their offer into that.

1:01:45

Even when you see Google search overviews like that has to be extremely cheap like they don't charge for those. Obviously Google is completely vertical integrated down from model training to they have custom silicon, they have their own data centers. Is that where you think it goes? Do you think you'll do custom silicon your own own data centers have power generation contracts to like fully offer the cheapest possible product for a particular category.

1:02:38

So I'm humble enough to acknowledge there are tons of, of experts in every single layer of the AI innovation. I think Jason mentioned five layer cake. I think there's probably more than five layers if you look a look shot fired. So every single, every single layer has their own experts. We want to work with them. We want to work words experts really good at doing their own job. And we specialize in building the specialized intelligence platform, cost training, inference, and we partner with all different layers to drive the best vertical solution. That's our philosophy.

1:03:07

That makes sense. Well, congratulations. Clearly working Jordi.

1:03:44

Incredible progress.

1:03:47

Thank you so much. Come on the show. Can't wait to talk to you again soon. We'll talk to you later. Goodbye. Let me tell you about Codex. Codex is a powerful workspace for getting work done with AI agents. Whether you're writing code, analyzing data, creating content, or automating business work workflows, Codex helps you move projects forward from start to finish. There's one more news story we got to go through really quickly. Wedding guests are now placing prop bets on everything from how long the first dance will last to whether the groom will cry during the ceremony. Call Sagar and Jetty. This is a dream come true for him. Couples are using printed cards and get

1:03:48

this on Sagar Bet immediately.

1:04:25

Printed cards and apps to let guests predict things like who gives the longest toast, how many outfits the bride wears, or whether the first kiss lasts more than six seconds. The idea is to make weddings feel more interactive, especially during slower parts of the night like cocktail hour. One app called Betting on the Wedding says more than 25,000 couples have created pools on its platform, which cost $49 and includes a live leaderboard. The company says revenue is growing at triple digit run rate year over year.

1:04:29

Real insider, insider trading risk here. Right. You might have the groom talking to some of his buddies, but if it's low stakes, I've got, you know, I've got some. I'm going to cry. I want to know. I'm going to cry. Go bet the house on, on me crying maybe.

1:05:02

But I, I think this is designed to be, you know, generally small prizes, $20 gift card, maybe some memento, maybe some, you know, you know, an engraved dinner plate from the wedding just to show that you were more engaged. Something to remember.

1:05:21

Well, let's ask Jason.

1:05:35

Let's ask Jason.

1:05:36

He would encourage betting on his wedding.

1:05:37

When are we going to get betting? When can we gamble on 37 signals properties?

1:05:40

Well, my wedding was, we had 12 people in our backyard. So there wouldn't have been a very big use case for that app.

1:05:46

Yeah, small pool, lack of liquidity. That's a real problem.

1:05:50

Small number of people doesn't mean there's not a lot of volume necessarily.

1:05:53

Oh yeah.

1:05:57

Depends on who you people throwing some real stuff.

1:05:57

You get dhh there. He throws in, you know, his cgt, you know, he puts it all on the line. You never know.

1:05:59

You see that picture today.

1:06:06

Oh, yeah.

1:06:07

Oh, yeah.

1:06:08

Oh, yeah.

1:06:09

Trying to hurt your feelings. What's going on?

1:06:09

Yeah, that's. That was a little. That was a bruise. That was a little bit of a bruise. It was a Bruce. Because way back when I used to own a singer 9 11. Oh, yeah. And I was selling. This is a number of years ago before they went crazy. Crazy. And I was trying to sell it and some guys like, I'll trade you my Carrera GT for that. And I'm like, ah, I don't really. Nah, I don't really think that was a good deal. And it turned out to be.

1:06:11

Yeah, one of the great trades of all time.

1:06:30

That would have been a good trade.

1:06:33

Yeah, just have that image pulled up. That's so brutal.

1:06:33

I was. I was in. I was in the Alps Thursday, Friday, Saturday. And the event that I was at, there was hundreds of Porsches everywhere. And still when the CGTs would roll up, everyone would get quiet and just watch. Like, seriously, there's one moment where like there was probably at least 200 people and everyone's just talking and talking, talking. CGT pulls up, crowd goes silent. Everyone's just in awe.

1:06:38

What color is it?

1:07:12

Silver.

1:07:13

There was actually a bunch. Red. There's a gt. Silver. Gt. Silver on tan is like probably. Probably my. My favorite that I've been seeing.

1:07:13

But no prediction markets around it.

1:07:22

None at all.

1:07:24

Brutal.

1:07:25

Just doing it. Just enjoying cars purely for the.

1:07:26

You could get so many people if they're not comfortable driving. You know, we know some people that collect cars, but they don't drive them. They could partake saying, oh, Jordy's going out for a little lap. When will he get back? I'll bet on it. You know, of course there's insider trading risk.

1:07:29

Will he get back? Like, will he just get. Those things are tricky to drive. I understand.

1:07:43

What's the latest. What's your latest vehicle purchase?

1:07:47

I bought a. A 1979 Porsche 928, which is one of my favorite cars of all time. I own two 928s.

1:07:53

They're both.

1:08:01

Both old and they're not expensive but they're awesome. And I bought a green one. It's oak green metallic which is a rare color and it has pasha seat inserts and it's just, it's awesome. It's just, it's so 70s.

1:08:02

I love it.

1:08:14

What is. I've never driven a 928. What is, what's the experience like?

1:08:14

They're very planted. So it's a V8, so it's a front engine car which is unusual for Porsche but it's a very, it's very stable. I mean these were not that fast. I think they had 200, maybe 10 horsepower or something. The early cars. This is the first year, first and second year. So they're not fast but they, they feel great to drive. You should borrow it.

1:08:19

Yeah, come buy. Are you, are you a Model Y guy as well? Because that was the funniest thing about Dhh's post is that he's just like all these cars are kind of worse than the Model Y in some ways.

1:08:37

I do have a Model Y and it is probably the best car I've ever owned overall. I mean it's so comfortable to drive, it's quick as hell, it handles great. We had a previous Y which I didn't think was very good, but the new Y's are fantastic. I just love it. Yeah, I mean really, I prefer to drive that over anything to be honest.

1:08:48

Do you have an intuitive sense for the business logic between the lack of fast followers around that like in terms of just appliance vehicle, it feels like all the other manufacturers are still playing in their special. This car says something about you. It offers a particular experience. It has a convertible but just in like the appliance, basically a minivan on wheels. Ultimate utility. Tesla just has had it on lock and they're like running away with the market they have.

1:09:06

I mean I guess that's what Honda and Toyota did for many, many years. Right. You never really thought of those as they were more just basic appliances. I need to get from point A to point B and I want it to be reliable as hell and just work, you know. So I think Tesla kind of slid in there and basically did that with EVs in a way that everything else is more of a statement I guess people might think of. Tesla's a statement but it really, it really is just like I want a great car that's incredibly quick, clear technology, advanced, affordable. Yeah, full self driving is incredible. It's just a really, an amazing thing. And if you haven't really been in one recently. You don't really know because they weren't that high quality four years ago.

1:09:33

Yeah, they were bad.

1:10:12

They've gotten to be very high quality.

1:10:13

Yeah. People complain about the panel gaps and the interior and all sorts of stuff, but they've. Yeah, they've sorted that out.

1:10:14

They're incredible now.

1:10:20

Yeah.

1:10:21

Where like a lot of different cars feel like they're in bubble territory. Cgt. I don't know how much more it can go up. I'd be. I'm sure it'll go up more. But there's. I think there was one. I bring a trailer actually. Probably.

1:10:23

Let's not talk about brand trailer.

1:10:39

I'm actually.

1:10:41

Honestly, my phone is. Is on because there's an auction ending in 32 minutes.

1:10:42

Okay.

1:10:46

I can't miss because I'm bidding on it. So. Yeah. So. So with. Yeah. We won't dox the car until you. Until you win.

1:10:48

It's okay. It's okay. I mean like it's a 50th anniversary 911 which I used to own. I owned one a long time ago. Do you know the car? Do you know that particular.

1:10:56

Which Pull it up. Wait. But 50th anniversary of the 911. Isn't that only a few years old?

1:11:04

Yeah. So it's a 2016 car. And they did a 991 model. And they did an anniversary model which they put like bright chrome trim on it. They did pepita inserts. It has a slightly better engine. It's the last of the manual naturally aspirated 911s with a wide body that aren't ridiculously expensive. And it's beautiful looking. It's got like updated fuchs wheels. It's an incredible thing. Go check it out. You'll find it. It just looks beautiful. I've owned one and I had a PDK and there's a manual for sale and I kind of really badly want it only at 7,000 miles on it. Please don't outbid me, whoever you are.

1:11:11

I have it pulled up here. We won't.

1:11:47

We.

1:11:48

We don't need. We don't need to pull it up. But it looks absolutely, absolutely beautiful. They pulled it up.

1:11:49

What is the pictures but it looks like.

1:11:55

What do you think about. What's your read on the sport classic? Have you driven a sport classic?

1:11:58

I've not driven one. I love the interior. I don't like the big circle on the side if they usually have no decals. No decals for me. The interiors are gorgeous though. Love that car. The thing is that they're so expensive for what they really are, which is a Carrera S, basically, I believe. Right.

1:12:02

Or to me, the driving experience is like. I had, I think, the best. My. My most memorable 20 minutes in the car coming down from Mankind in Austria. 20 minutes open road. It was the most. It felt like I was in a video game. It felt like driving some combination of like a Turbo s and a GT2. It's like so, so planted and it's like. It's refined, but it's also angry. It's like. It was. It was manual too, right? Yeah, manual. It's so nice. Incredible.

1:12:20

Great. That's great.

1:12:55

Those are. Those, you know, you can't get them really in aftermarket. They're what, 300 plus or something now?

1:12:56

No, no, no, like 600.

1:13:01

Six. Sorry, 600.

1:13:03

Wait, so when, when you know something.

1:13:06

Yeah.

1:13:08

The ST is even. Yeah, I think even more. But. So when, when things feel like they're in certain cars feel like they're in bubble territory. Are you just buying. Are you going like. I'm just going to buy nine things, like the 928 and things that are a bit more special but less like, you know, you don't want to buy it when it's hot? Basically, yeah.

1:13:09

I mean, I tend to not chase things anyway. It's just there's. If everyone's chasing it, I'm not interested in it, in a sense. So the 928 is a car like nobody wants, but I've always loved. I kind of grew up with them. They're just. They're super cool. So I. I go after that. But I. I do. I do miss the 50th anniversary, so I might want to pick this one up if I can. We'll see where it ends up. Maybe I won't, but, I mean, I wanted a Dakar for a while. I wanted to sport classic, actually. I'd love to have one of those, but I'm not going to pay. That's obscene. I'm just not going to do that. There's no reason for that. It's also not. I just don't spend that kind of money on cars. It's a crazy amount of money on a car. That's just not something I'm really going to drive all the time anyway.

1:13:28

How do you feel? What is the last 10 minutes of an auction like, feel like to you? Because I've, like, tried. When sports betting was blowing up, I was hanging out with, I think, Senra and like, Rob and probably John And I was like, I'm gonna give this a shot. I want to know why this is so popular. And I just couldn't quite. I couldn't quite get into it. But the experience of being of bidding in the final minutes of an auction, like something in my head just goes like, you're not losing. And, and, and to me it's. You get carried away.

1:14:08

It's dangerous to throw that one more, that one chip in there at the end. You're like, it.

1:14:46

I'll just.

1:14:50

You know, the thing is, is that I, I all. I mean, this is just. I always feel deep regret right after winning a car. Like, like especially a vintage car, maybe not a new car, like a sport classic. I would not feel regret because I know what I'm getting and there's no, no issues. Right. But like you buy a 79, 928 on the, on the thing and you like, you get it and you take it to your mechanic. He's like, you know, there's like $40,000 of work that needs to happen on this thing. You're like, so vintage cars, deep regret. And I've regretted all of them I bought, even though I like them all. But the purchase was like deeply regretful. But modern cars, I don't feel that way. I. I would be very excited to get something I like.

1:14:50

Yeah, yeah, good point.

1:15:26

Yeah.

1:15:28

But you gotta be careful. Like I talked to some mechanics and they're like, bat is just keeping me in business. Cause people just buy these cars, they think they're good, they get them, they need like tons of service. It's been great for small mechanics, actually.

1:15:29

Interesting.

1:15:41

I bought my first sports car and bring a trailer. The first one I really went for, I ended up bidding way more than I was comfortable with just because I got into. I was like 23 at the time. I got into the last. I was one of the last two bidders and we bidding Psychosis. And he. Yeah, I got auctioned. Psychosis ran away, honestly. Luckily I didn't win. But the second one I got it was. I had the perfect experience. I bought it. I think I bought it. Well, it was a 997. It was in Arizona.

1:15:42

2.1.

1:16:14

Which would you get?

1:16:15

The dot one. But the issue, the bearing issue that they have had already been fixed or whatever.

1:16:16

Oh, good.

1:16:24

And I flew to Arizona pick it up. I get it. Drives great. I'm 30 minutes down the road headed back to California in it. I was going to drive through Joshua Tree and I was passing a construction site and a piece of rebar went fully through the wheel, like, through the tire and the wheel. Basically. I pulled over and ended up having to ship the car back to California. And it was my most devastating car enthusiast moment. But once it got to California, we got a new wheel. It ran perfectly for as many miles as I needed to, and then ended up making money on the sale.

1:16:25

Nice. I don't ever do that. I bought real quick. I bought an Aston DB9 GT, which is the last year, the DB9, which is, to me, one of the most beautiful cars ever made in history. I got the car, I get it shipped in on the truck. I got this on bat. There's like this rattle in the back that's kind of bugging me. So I take it to the mechanic. They can't figure it out. They're like a few grand in trying to figure it out. It turns out like the car got in an accident at some point and it was never reported on carfax. And to, like, fix this structural issue is like nine grand. So I'm like, It just. I sold the car to the dealer immediately, lost, like, 20k. I just wanted to wash my hands of it. Like, I had it for two days and just sold immediately because I just. I can't. I just can't handle that thing. To know that, like, I bought this thing and it wasn't what it was. And, yeah, I could fix it, but it was never going to be the same. So I never. I never seem to win on bat. But good for you. I'm glad you made money on your car.

1:17:04

Good luck.

1:18:00

How do you feel about different luxury brands doing what I would call Zoomer partnerships? So, like, Aston Martin launching a partnership with Call of Duty. I can imagine that the logic for that was, hey, we want to reach a younger audience. We need more relevancy with the next generation of buyers. Aston has obviously struggled recently, even though I think their cars are stunning, but I would say in every single sort of, like, price tier, it's not quite as desirable, I think, for a lot of people, as, like, the Ferrari equivalent or the Porsche equivalent. So I can. I can understand where they're going. Even though, to me, as somebody who loves Call of Duty and loves Aston Martin, I still got, like, quite an aversion to that partnership. And then you have some of the stuff that, like, AP does with their, you know, partnering with, like, DJs and things like that. That, that, that's kind of. It's this interesting thing because you're trying to appeal to the young generation, but it Ends up turning off, I feel like your actual buyer group in the process.

1:18:02

Yeah, I find it to be. I mean, for me, it doesn't appeal to me. And although I will say that I like what Aston's done. Aston, with that DB9 that I bought, they had a 007 edition, which I think is cheesy as hell, but because it's like, 007, like, on the seats. But it probably spoke to their audience, you know, so, like, that makes sense to me, in a sense, even though I would never buy that. But, yeah, I don't like the. I don't like the AP spot deals, but, you know, who am I to say? Like, they clearly sell them out and it probably worked for them, but it's not the kind of thing that appeals to me, is all I would say.

1:19:08

Agreed.

1:19:43

Any more car questions?

1:19:43

Yeah, car watch questions. I mean, there's actually. I saw a watch recently, like, Bremont came out with some, like, Aston Martin or, like. I don't know who it was. It's like, what do you. I don't know who buys these. I just wonder who buys these silly things.

1:19:45

I just.

1:20:00

I don't get it. I don't get it.

1:20:00

The watch car collab seems to make more sense because if you're buying a car, you're checking out for something that's six figures. If you're like, that's a couple more thousand dollars, like, throw the watch in, whatever. It's like.

1:20:04

Well, sometimes dealerships do that to, like, you gotta buy the watch. If you want to buy the watch, I'll get you the car. Like that. I hate that bundling stuff. It's so disingenuous. I don't know if you saw this thing. Jay Leno. There's this little Jay Leno clipper you said recently about how he won't buy a Ferrari because when he was younger, he went to go buy a Ferrari, and they're like, well, you got to buy two of these other models you don't want before you can get the one you want.

1:20:13

Yeah.

1:20:34

And he's just like, it turned me off forever from Ferrari and, like, I'll buy a McLaren because they want my business and they're cool to me. And, you know, that's how I feel about this stuff. That's why I don't like the. I don't like this bundling. Especially Rolex ads and Porsche dealers now are doing the same thing. It's just. It's gross. It's gross. I think.

1:20:34

Yeah. On the Ferrari side, obviously, the Luce Was mocked. But ultimately, do you think it ends up being a win for them just because they can effectively say, now any car that you actually want, you just add a luche to the. To your cart and check out and you. So they solve. They get, you know, more margin, I'm sure. Plus they solve their emissions issues if, like, for every one.

1:20:52

Oh, sure, sure.

1:21:16

Crazy, you know, desirable supercar. They sell one EV and it sort of nets out to being, like, pretty efficient.

1:21:17

My sense is they'll sell every car they make, and I just don't think they're going to resell very well, that's all. But, like, I mean, I don't know. I. You know, when I first. Well, not first, but last time I was on the show, we talked about the interior of that car. And, like, we're like, let's wait until we see the exterior.

1:21:26

That's right. Interior, I still think looks cool. I've seen a lot of the details. It's interesting. It's different, but it's like, it can work and it has a purpose. And then the exterior was really. Was really.

1:21:41

I'm the kind of person, I just support, like, all creators of things. Like, it's so hard to make anything. So, like, I want to give them the benefit of the doubt. Their Ferrari, Johnny. Like, they probably know a few more things than people online know about, like, what's cool, what isn't, what's good.

1:21:52

Yeah.

1:22:05

It is an unusual car. It does not look like a Ferrari. It doesn't feel like a Ferrari. But maybe it's time for Ferrari to make some changes. I don't know. Maybe they're bored of their own history. I'm not sure. I mean, it's interesting. I. I wouldn't. I'm not interested in the car, but I. I just. It's for the same reason I really respect, but I would never want to buy a cyber truck. Like, I just like that that exists in the world.

1:22:05

Yeah, No, I agree with that for sure.

1:22:28

I like that the Luce, like, exists in the world. Like, I like that someone did that, and they did it their own way. I almost always support things like that, even if it's not for me.

1:22:29

Yeah. Yeah.

1:22:37

I'm gonna support it myself. Are you selling when. No. When they're selling for half off? No, no more than half off. And I want to do a safari treatment.

1:22:38

Yeah. Yeah. There's definitely some cool things you can do with it. It does sort of act as the inverse of a Halo car. Like, after the luce dropped, the SF90 looked way cheaper. Whereas before, everyone was complaining, oh, the SF90 is so expensive. Purosangue is so expensive. No one's complaining about that stuff anymore. Now everyone's like, it's a good point. It has A Natural aspirated V12 in the Purosangue. If they're charging high, half a million dollars, that's.

1:22:47

What is your last car question? What is. You said you had a Singer, but. But when it comes to resto mods, like, what makes a great resto mod to you?

1:23:11

I don't think there are great resto mods. That's what I realized. I mean, the Singer is an amazing thing for sure. But what I realized was it was neither of what it was supposed to be. It wasn't like a vintage car and it also wasn't a new Porsche. So it kind of had this. It's. It's a beautiful object and they do an exceptionally fine job designing and building them. Although mine had a lot of issues because mine was pretty early, like the 72nd car. So they hadn't worked it all out yet. But it just didn't satisfy me another either direction. And I kind of realized that, like, I'd rather just have an old car and a new car and save some money, frankly. Could have both and then like drive the old car, have the old experience, Drive the new car to have the new experience. So I'm not a big rustomod guy. For a while I was curious about like icons like the Broncos and stuff. And I also, with that, I'd just rather have an old beat up Bronco or an old beat up pickup truck. It just. I'm more into like, what, what is the thing supposed to be? Just get the thing that it's supposed to be.

1:23:21

Yeah. What about response?

1:24:22

What's your take?

1:24:24

Well, we were debating the. The Range Rover has a classics program where. Oh, yes, they're selling a 1994 Range Rover, but it's been fully restored from the factory. And so maybe that solves the problem you're identifying. What do you think about that?

1:24:25

I'm into that because that's like the brand doing their own thing. I'm into that. I think Porsche has a classics program too.

1:24:40

Perhaps.

1:24:46

Maybe.

1:24:46

Yeah.

1:24:47

I just don't like restomods. Basically, to me, that's not a rustomod. That's like a true restoration.

1:24:47

Okay.

1:24:53

It's not.

1:24:54

Or backdating or something. I'm not into that so much.

1:24:55

Yeah.

1:24:57

Restoration.

1:24:58

I would say that's a great way to put it. Like, I'm a massive fan of Restoration. I don't want somebody to take. I don't want somebody to take what was perfect at its time and try to like modernize it and then put their own spin on it.

1:24:59

Same same thing with houses. For me, like I like an old house should be restored to the way it was. I don't like walking into an old house with a lot of soul and then you go into a kitchen and it's super modern. Yeah, it just doesn't, it doesn't work. I mean it works, but it, this, something is missing then actually in both those experiences. So anyway, that's my stupid opinion as whatever. Everyone's got their own thing. Plenty of people like singers, plenty of people like rustomods and they all are great things. It's just not for me anymore.

1:25:14

Yeah, I got to ask you one tech question.

1:25:42

Yeah, sure, let's do something tech.

1:25:45

And I think you'll have some insight here. So there was a screenshot from a story about how hard technology workers are grinding in the AI era that went viral for being bleak. According to to this poster, they said. A 31 year old tech startup worker in San Francisco who spoke on the condition of anonymity for fear of professional repercussions, said that her engineering manager husband told her a few months ago that he needed to focus all of his energy on, quote, becoming an AI native and requested that she take on almost all parenting responsibilities for the couple's preschool age daughter. She complied. She described the experience as surreal. He spent days, nights and weekends locked in his office toiling away on AI projects. But her husband eventually thanked her. He was now the top user of AI in his company. Is it the great lock in Is this burnout? Does he need to pick up a book? If so, which book would you recommend from your library across rework Remote doesn't have to be crazy at work. It sounds like it is crazy. Crazy at many startups, at many engineering organizations. Some of them are in real knockout drag out fights where the extra hour of work will actually result in maybe winning or losing.

1:25:47

Funny the way that others maybe not. The whole conversation around that post just was around the screenshot. No one read the actual article. I certainly didn't. And it just ends. The husband is now the top user of AI, which doesn't mean he's the best at using it. He just means he reads to me like he's just using the most tokens. So hopefully he came out of his three month, you know, AI bender and is like actually the best at getting the most utility out of the driving value. But yeah, we don't know.

1:27:07

Well, yeah, I mean, I do find it ironic that, you know, AI is what it is, yet everyone seems to be working harder and harder and it's one of these things. Technology has always promised that it would do a lot with, for us and then we'd have more free time to do other things. And it just seems like no, no, no and no, especially at work. So yeah, I think it's a real problem and I can sense it here occasionally that, you know, yeah, we're getting more done, but it weighs on people more because you can be doing multiple things at once now and you can be parallel working on with a bunch of different agents doing a bunch of different things. And it's like to what end? Where, where is this going and why does it need to happen? Not that the technology is not amazing, but I'm not sure it's doing good things to human beings.

1:27:34

Yeah.

1:28:16

So, but the tech is incredible, obviously.

1:28:17

But yeah, some of the actual.

1:28:19

Imagine if 37 signals had got access to today's models a decade ago.

1:28:21

Right.

1:28:27

And didn't tell anyone and just got to use them.

1:28:27

Yeah.

1:28:30

Maybe you're talking part of the problem

1:28:30

is that everyone has access to the tools and you're in a competitive category and you. And, and I feel like there's this concern of, of if we're not. I mean it's always a question of like, do you want to be, do you want to. At least if you're a venture backed company and you're competing against another venture backed company for a market, you don't want to be working less hard than them. That's generally not a good, not a good strategy.

1:28:31

But those are inputs. Like customers don't care about the inputs. They, what is the, like, how does it manifest in the product? And I'm not seeing products get better at the rate that the development process is getting better.

1:28:52

Yeah.

1:29:08

So people are doing a lot of stuff and yet like people actually don't want their products to change rapidly either. People want to get used to things, they want to settle into something. They want to understand how it goes. They don't want things to be moving constantly and things to be added all the time. So there's a disconnect actually between how much you can make and how much people actually can absorb and incorporate into their own workday, basically. So yeah, I think like at the end of the day, like you're building a product. However you build it, you're building it. But just because you can build more of it doesn't mean it makes it a better product, make it a worse product. And you're seeing that all over the place right now, actually. So, I don't know. It's great to have the tools. The tools are amazing, obviously, but you still have to decide what gets through the slit. Like, what. What are you putting out there in the world?

1:29:08

I'm always laughing about the fact that when I'm in Gmail in Chrome, I can open Gemini in Gmail and I can also open Gemini in Chrome. And then I just have two sidebar chats that can. And if the window's too small, it takes up 100% of the window. And I'm like, this is. And then you can't even use the models to interact with the email. And email's already pretty well organized. Like, it's all perfectly organized by time or whatever filter you want.

1:29:52

Pretty good already in that way. But, yeah, anyway, I mean, amazing tech, but yeah, I don't think we've figured out what that all means yet still. And I'm not alone in that. But it doesn't look if it. If it exhausts people. Yeah, that's not a good thing. No tech is good if it makes people exhausted.

1:30:23

There is something odd about the pattern of working. I mean, like, when you're doing software development, occasionally there are times where, like, you just have to wait while something builds, and that takes a minute. But a lot of times you can get in the flow state and be, you know, focused, working for an hour. But when you fire off a prompt and you're waiting, like, maybe it's 20 minutes, maybe it's an hour. And then you say you're checking your phone and it feels like you're, like, waiting for a call to come in almost. It's a different way of working. And I can see how, if not well managed, it can become very stressful.

1:30:38

Yeah, it's a tool. Like, I've been. I've. I've been in places in my life where my laptop feels like, oh, it's exhausting.

1:31:10

Yeah.

1:31:17

And.

1:31:18

But it's not really the laptop. It's like what I'm. What I'm doing with it.

1:31:18

Yeah.

1:31:21

What you're doing with it.

1:31:22

Yeah.

1:31:23

Time to go for a drive.

1:31:23

Jason, always a pleasure.

1:31:24

Always a pleasure.

1:31:25

Fun. Fun to see you guys.

1:31:26

Yeah, let's do it again soon.

1:31:27

We'll talk to you later, Hawkson.

1:31:29

Dude.

1:31:30

Goodbye. Let me tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB? Don't just build a. I own the data platform that powers it. I forgot to ask Jason if he has opinions about resto mods for jet skis. I'll ask you. Is there a. Is there a jet ski that you'd recommend for the. Somebody.

1:31:30

I saw a wooden jet ski recently.

1:31:50

A wooden.

1:31:52

That's like a really classic jet ski, dude.

1:31:53

Don't get me excited. I'm into it. Look, these things go fast.

1:31:55

Yeah.

1:31:58

I think I went 70 miles an hour on my jet ski to work.

1:31:59

Wow.

1:32:01

To work.

1:32:02

That's faster than most people commute. They're stuck in traffic.

1:32:03

I mean, I've got a five minute commute to work on a jet ski.

1:32:06

On a jet ski.

1:32:08

Unless it's raining.

1:32:08

Yeah.

1:32:09

Unless it's raining. Yeah. No.

1:32:09

And it gets a little weird and you call an Uber.

1:32:11

Okay.

1:32:13

Is it helpful? Do you do your best thinking on the jet ski?

1:32:15

No. No.

1:32:20

Does it clear the mind?

1:32:23

Guys,

1:32:26

It's a fucking notch on the belt.

1:32:29

Yeah.

1:32:31

Who else do you know is jet skiing to work? Nobody. I'm the guy. You're the guy. I looked it up. I looked it up on your guys's application OpenAI. You know, chatgpt on your guys's app.

1:32:32

Yeah, yeah, yeah.

1:32:45

On your app.

1:32:46

You're welcome.

1:32:47

Yeah, no, I want to thank you for all the great stuff that you guys are in ChatGPT, but I think there's like one or two other CEOs, but nobody at a major. Nobody. Thousand person plus commute is commuting.

1:32:48

To work on a jet ski.

1:33:05

On a jet ski.

1:33:06

Only a Texas resident, right?

1:33:06

Texas resident. That's right. Primary residence.

1:33:08

Let's go.

1:33:10

Yes.

1:33:11

Before we start, the last time you were on here, that was for me, the best moment of making the show ever. John and I, it was totally surreal and we really enjoyed the conversation. But to me, we left that. And it was almost depressing because as somebody who started getting into startups in the 2010s, you were that guy. And then I was realizing with the show, we had that conversation with you and it was a significant day for you, but it was sort of depressing because I realized a moment like that would never actually come again, where I got to basically interview. It will happen, it will happen, it'll happen differently. But you know, a childhood hero having that conversation, that's one of one for me. I don't think it'll happen again. There'll be other. It was peak. It was peak. But anyways, you've been busy since then.

1:33:13

I've been busy Look, I'm super excited. It's my first OpenAI podcast. I'm very excited about it. Also, I want to let you guys know that if you need therapy sessions for what it's like to be a made man in retirement.

1:34:11

Sure.

1:34:26

Like, if that's a thing, I can call you. I can help motivate you guys.

1:34:27

Step one of therapy in this situation, Just get a jet ski.

1:34:30

No, it's just. It's. It's actually denial. You got to get over the denial.

1:34:32

Okay. Over the denial and then the acceptance. Yeah, it's still.

1:34:36

I don't know, the 12 step.

1:34:41

Yeah, yeah.

1:34:42

Everyone just knows denial and acceptance. They don't know any of the other ones. Grieving, bargaining. There's a couple others in there, but you do go through that. It's natural. Yeah, it happens. But then you start building, and if

1:34:43

you guys need advice, you need therapy, I'm here for you.

1:34:54

I love it.

1:34:56

I mean, all the retard maxing is you're not supposed to do therapy. I'm just saying there are benefits, especially

1:34:56

with your new partners. They're like, if they're one thing. They wrote into the fundraising round, they wrote into the docs, like, cannot go to therapy.

1:35:01

That would be amazing.

1:35:08

Yes.

1:35:09

But podcasts are modern therapy for men. This is what men do. They don't go to therapy.

1:35:09

They're still in the. You should have office hours for founders, but they have to just come out on a Jet Ski while you're going. And you're going 70 miles an hour. And you'll coach them.

1:35:13

I am starting to teach many founders and people in tech world how to water ski, how to wake surf. A bunch of my engineers already, so there was one guy who didn't know how to swim, but I got him behind the boat wake surfing.

1:35:24

What? Whoa.

1:35:36

Whoa. So you gotta get your life jacket.

1:35:37

Life jacket on.

1:35:40

Life jacket. It sounds weirder than it is, but it was still very weird.

1:35:41

It's high risk. High risk.

1:35:45

Yeah, it was good.

1:35:46

Potentially.

1:35:47

Okay, cool.

1:35:49

The business.

1:35:49

Business, dude. It's business time.

1:35:50

Yeah.

1:35:52

Gotta put on the business. Socks.

1:35:53

Unfinished business.

1:35:54

Unfinished business. So, yeah, I announced earlier today we did a $1.7 billion raise. There's some. There's some noise that's going to happen.

1:35:55

Wow.

1:36:07

Now one mallet that's just do another one from downtown next time.

1:36:10

Come over the tunnel.

1:36:17

So much noise.

1:36:23

So much noise. All right, but walk us.

1:36:24

I feel you came in very relaxed. It's just.

1:36:25

Walk us through.

1:36:28

I think it's been. What's it been? Four Months since we talked.

1:36:29

Three or four months, something like that. Yeah. I think. Do we talk in April or March?

1:36:33

I think.

1:36:37

March, yeah.

1:36:37

Oh, that's right. Yes, early March. Four months.

1:36:38

So.

1:36:41

So, yeah.

1:36:41

So what happened with the business to unlock the next round?

1:36:42

I mean, we continue to go up and to the right, but like the announcement of Adams was we are. We're going to do physical automation, physical AI, what we are calling industrial AI, to transform these industries one at a time. We did food, we moved into mining. We're doing transport and it's working. And so that's how you go. Yeah. And then of course, there's like going out of stealth. There's all the things. And it was just the right time. So. So yeah, we just went to market. We said. When I. When I originally went to market, I was like, these were separate companies. Okay. So our mining and transport was a separate thing, food was a separate thing, and we had a bunch of other, you know, a bunch of subsidiaries doing cool stuff. And I said, which one do you guys want to do? Do you want to invest in mining? Do you want to invest in food? Do you want to invest in this? And they're just like, we want to invest in. In you.

1:36:48

Yeah, yeah.

1:37:46

And we heard that, like, we took like the first five folks we talked to all said that.

1:37:46

Yeah.

1:37:53

So then what we did is we put the companies together and then sold the equity in a singular entity.

1:37:53

Yeah, yeah.

1:38:01

So just put. Put it together and it's much easier for me. I don't know how. How Elon does it with all the different. Different companies, different capables. It's wild.

1:38:03

Well, no, the answer is what's been happening. Right.

1:38:13

It all comes back together, guys. He did it for 20 years, though.

1:38:17

Yeah, yeah, yeah. And he's still technically doing it with Tesla, SpaceX. Like, they are different companies.

1:38:20

Boring company. Neuralink.

1:38:24

Like, he's still got the lesson in there for investors is like, even with Elon companies, there's such an insane power law where you have a $10 billion company and then you have a, you know, a $2 trillion company. Right. And it's like you just want exposure. You want broad exposure. Ideally, you know, you could just invest in the one that breaks out, but you want broad exposure to the category.

1:38:26

When things are first getting going, there is a lot of upside of having them separate.

1:38:48

Sure.

1:38:54

Because if somebody wants to invest in a really cool thing, and this is what happened when we first got the transport and mining thing going, if they want to invest in that cool thing, they're like, I don't know anything about food, by the way. Food on its own is robotics, real estate, like restaurants, like, you know, and so they want, they want to be exposed to that one thing and they don't want to have to underwrite something going across all things. And they're like, well, if you're losing money over here, I want you to lose money over here. So how much of the money I'm putting in is going to go to that? There has to be a theory of the case of how you put it together, how you allocate capital across. And honestly, once you're starting to get to profitability on one or more, then that conversation starts to get easier. And I think that's. That could be why I can't speculate on sort of Elon's world. But certainly I'm super excited to have those pieces put together into a single puzzle.

1:38:55

What does go to market look like in the mining industry for you?

1:39:52

It's the first frickin best.

1:39:57

Okay. Because specifically, like when I think of. When I think of your go to market. So good magic. Okay. It was deploying young people to a new city in Miami and they're doing a marketing stunt. And it's not like you're calling in favors or leveraging your network to get Uber up and running in a new city. That was something that was. And that's organizational design.

1:39:58

So hold on, that's consumer.

1:40:25

Exactly. So how is it different?

1:40:26

Well, it's just like. Well, all the food stuff we're doing is business. Almost all of it. Really. All of it. Mining's all business. So look, there is a big thing if you go from doing consumer to doing business. And I think we may have talked about this last time. That's a whole other ball game. I mean, that takes years off your lifespan. Doing it, like getting good at it and then owning it. But mining go to market is cray cray. Yeah. Like, so I'll just give you an example.

1:40:28

Going to the conference or something.

1:40:59

Well, yes.

1:41:02

Meeting CEO.

1:41:02

Yes, you do that. But, but, but you know, I can a lot of times. Look, when you have very efficient transportation, you can go places. So a month ago, I dropped into deep Amazon in Brazil. Okay. Like deep northern Brazil, like Amazon, places

1:41:03

you can't even get a jet ski to.

1:41:25

Guys, it's the Amazon of the Amazon.

1:41:26

Okay, okay.

1:41:29

And, and like tiny airports. You just like.

1:41:30

Yep. You kind of just dirt.

1:41:34

You slide into the dms, except as a tarmac. Okay.

1:41:36

There you go.

1:41:39

Yeah.

1:41:40

Great pilot.

1:41:40

Yes, of course. And Massive iron ore mine that we're operating in there. And you see like we took, we were there for a couple days because we already have customers there.

1:41:41

Sure.

1:41:53

Customers called Valet. It's a massive mining company and they, it's. It's like the world's largest iron ore mine. And you go and you get in a helicopter. Just going over one one of the sites takes 30 minutes.

1:41:53

Wow.

1:42:08

Okay. And it's fascinating. It's so fascinating. And you're learning how the system works. You're sort of figuring out how do I. You basically take a kit, you apply you, you, you install it onto a machine and that machine becomes autonomous. And some of these machines are like 20 years old, some of them are new. And so there's lots of different kinds of machines as well. And you're making the mine more productive. You are making it way safer. It is super. Like they have lots of safety protocols, but like it is mining dangerous business. It is a dangerous business. And the OPEX goes down all at the same time. It's kind of a beautiful thing. And then, you know, I went from Brazil and then straight from there dropped into the border between Iraq and South Saudi. On the Saudi side. So we have a phosphate mine that we're doing stuff there. The signals were jammed, so we had to like, my pilots had to land kind of like old school style, like visual, physical, visual.

1:42:09

Is that because of the conflict going

1:43:13

on in the region and just the general. The vibes on the borders there?

1:43:15

Yeah, yeah.

1:43:20

So, but same story. And so go to market is wild. Just end up in literally go crazy places. But it's super needed. And so what's happened is the, the pronto technology has got gotten past human productivity, which means you go to a gold mine CEO, you talk about go to market. You go to a gold mine CEO and you say, would you like to have 20% more gold per year? Absolutely.

1:43:22

Good.

1:43:47

So we haven't heard no.

1:43:47

Yes.

1:43:48

Okay. Okay.

1:43:49

But they're.

1:43:49

But they say prove it.

1:43:50

Yeah.

1:43:51

And that's where the rubber meets the road. Right.

1:43:51

How long does it take to prove it?

1:43:53

Used to take a lot longer. Now, like once you've proven it enough times, then it sort of gets its own momentum gets around. And so we're in that, we're in that place on Pronto where that momentum is taking hold because there's enough proof points where it's just working in so many different places where people are like, all right, let's go. We're going to think of mining, autonomous mining, almost like, almost like Enterprise software where you get a pilot, there's like a 10,000 person company and you've got you, you got an enterprise startup and they're like, I got like eight seats. But it's this huge company and if we get it, it's huge. And I've got this other 10 seats over at this other one. It's a pilot, but I swear it's going to work. And they're out there pitching and trying to make it happen. Yeah, but once it works and in mining that means human productivity, human level better than human productivity. Once it works, it goes big.

1:43:55

Yeah.

1:44:49

And they're like, okay, let's get across, let's get across all the vehicles. And so we're sort of in that mode with a bunch of different customers right now.

1:44:50

How big is the opportunity to just increase uptime of mining operations? I imagine that there are mines that are trying to operate 247 but getting a night shift in the middle of the Amazon reliably, everyone showing up and being healthy and happy and eager. It gets a lot easier when it's like, yeah, we're still going to have a bunch of people on site but they're going to be overseeing robotic work for sure.

1:44:58

So yeah, I mean there's two parts to the productivity gain. First is the machine per hour doing more. That's part one. Part two is hours and call outs and all of that stuff as well as just, you know, the safety protocols change when you have less risk.

1:45:26

Yeah.

1:45:46

So there's a lot of things like this that pile onto each other. My guess is you could even end up 30%, 40% more productive at the end of all of it. And when you do that, the opportunity speaks for itself. A multi, a gold mine that's doing 30 or 40% more gold per year is kind of. Whoa. But that's for every mineral. That's lithium. That's like. We also go all the way down to quarries. Quarries are different because quarries are basically, it's about cement. Let's just say that's the main jam. There are others, but let's just go with that. You can't, you don't just go do more rock because you need cement. Customers on the other side, they're only using so much cement.

1:45:47

Like where to store it.

1:46:27

Yeah, exactly. And so that's more of an OPEX play and there are thinner margins there. But I'm in the game and it's kind of interesting. It's a lot of fun. And for that company, for pronto, they were super. Anthony Levandowski and the team there, super scrappy, true startup style. Lean as hell. Like so lean. Like that Christian Bale movie. I can't remember the name of it. The machinist dude, he's like super lean. And I'm like, guys, we gotta go from lean to muscular.

1:46:28

You gotta go to Batman.

1:47:01

And that's what we're doing. Like, and you think of that this in an.

1:47:02

Lean to muscular. That's a good.

1:47:05

Yeah, you don't have a good phrase. You just wanna be muscular.

1:47:07

And so you think about enterprise go to market. Part of our go to market is building credibility with our enterprise customers that we're going from lean to muscular because they. The demand is there, it's ready to go. They're like, we need you to be muscular. We need the protein powder and the whatever else.

1:47:09

What, what holds. What holds you back.

1:47:32

Go to the gym, whatever.

1:47:34

What holds you back from scaling? Let's say you do a pilot. It works well. You're attaching hardware to existing systems and hardware that they're using. And they say, okay, we're getting more out. Maybe we want to place orders for more machines. I imagine the lead times on some of these mining equipment could be insane. How much of the stack do you want to own?

1:47:35

Say the question again. I'm sorry, I just blanked. Go for it one more time.

1:48:02

Right now you're taking existing mining equipment and you're augmenting it with your bringing.

1:48:05

You're.

1:48:11

You're making it AI enabled, making it autonomous. You're making it more efficient. And they say, great, this is working. We want to scale up our operation because maybe we need less or we can do more with the same, you know, human headcount.

1:48:12

Yep.

1:48:23

But what's the. I imagine there's some things that are out of control for, for you at that point where they're like, okay, we need more of this heavy mining equipment. Let's. Let's add it to the site. But is there like a lag time there?

1:48:24

The real lag time is getting. So you have to, you're. So let's say we want to get a bunch of machines that are in the Amazon up and running.

1:48:35

Yeah.

1:48:42

Okay. How do you do that? So I've got to ship a bunch of sensors, a bunch of compute, a bunch of equipment and mechanical systems, let's just say. So that a team can then go install it.

1:48:43

Yeah. So you're basically building a data center on site.

1:48:58

I wouldn't put it that way. I would say, I mean, if you considered a machine with sensors and compute a data center. I mean you could but it's really think of those. There are servers but I wouldn't say a data center.

1:49:03

It's not.

1:49:14

Some operations are bring like an armada style like shipping container sized level of. You're just volunteer as one.

1:49:15

So you bring in the stuff.

1:49:21

Yeah.

1:49:23

Okay. You have to install it.

1:49:23

Yeah.

1:49:26

You have to like bring it up and make sure. Okay this is a new place. How does does this machine work properly in this new place and calibrate and make sure it's safe and all of this. So there's like a process of getting it up, then there's change management because that site's going from. There are people that show up in the morning. There's all this very regimented process to make sure everything's going exactly as planned and people are exactly where they're supposed to be. Because otherwise weird things happen on a mining site. So you have to go from that to okay, we're now running autonomous mining operation. It's just a very different thing. So the installation and the bring up and what we call commissioning are sort of like the things you have to do. And you know like why does it take a long time to install? Because that machine may not even be drive by wire.

1:49:27

Yeah.

1:50:21

So you have a mechanical system. System. Like if you turn the steering wheel like it's. You know what I mean? It's a. It's a mechanical system. Hydraulic. So you're bringing where you have to go.

1:50:22

Actuator that might push a physical button.

1:50:32

You're.

1:50:34

Yeah.

1:50:35

You're. You're trying to make electricity. Then do a physical thing. So then you need physical actuation.

1:50:35

Yeah.

1:50:41

To do the things. Because it's not. These machines are not natively dry by wire.

1:50:42

Yeah, that sounds. That sounds incredibly difficult but necessary because you're not going to get a mine to rip out tens of millions of dollars of equipment that they already have. But would you eventually go full stack? Like build the entire.

1:50:47

I mean look we ultimately, I mean if you go in the mining industry there's like this, this term, it's called no entry mine. A no entry mine is a mine where there are no people in the pit.

1:51:03

Lights out factory.

1:51:15

Yeah. Kind of like that version of it. There might be people in a control center. There might be but like in that pit no humans.

1:51:16

And it's a wildly different calculus from a safety perspective I imagine.

1:51:24

Totally different obviously. And so there's drilling, there's blasting, there's loading, there's haulage, there's crushing. I'm Just going through, through the different parts of the mining operation. And what you do is you start somewhere and then you start extending to those other areas to get to that no entry thing. And the no entry thing is you can have an autonomous thing. Like our haulage system is autonomous. If you're getting into a new place, you can do remote control and move into autonomous. If you want to go super no entry or lower entry mine, if that makes sense. It's super fascinating. And then you're talking about, you're talking about loaded a 2 million pound machine that's moving potentially 35 miles an hour down the road. And it's an off road thing.

1:51:28

2 million pound machine moving 35 miles an hour off road.

1:52:25

Yeah.

1:52:32

This is why you have to get to work.

1:52:32

This is the ultimate atv. So no, you get in it and you can, you know, you can experience it. I mean it's not like there's like an amusement park for this, but like I've certainly experienced it where I can get in, I get in the machines and check out what's going on.

1:52:35

This is like the dump truck or any of the 20 foot tires essentially.

1:52:49

Are any of these, are any of these companies like acquisition targets where you would, you would be able to come in and say like you're doing a lot of stuff. Well, but here's all the stuff that you're never going to figure out. Like us.

1:52:52

I mean, look, I would say the way we think about it is the haulage part of a mine is where most of the vehicles are. And so, and we think of haulage as the cardiovascular system of a mine. So we're obviously very connected to all the other machines, but we don't do all the other machines. So we're like in an ecosystem system. So we can work with them where like there's APIs, because like if you're doing haulage, you need to know where the other machines are and what their status is. As an example, there needs to be orchestration coordination there, which is pretty interesting in terms of like acquisition. Like, you know, I, I do, I have to sort of admit, like the Uber mentality. My mentality, let's just say, yeah, yeah, my Dune. Yeah, yeah, it's like not, I guess Uber is different today, but in my world we didn't acquire shit we just built.

1:53:04

Yeah, that's right.

1:53:54

I don't know if I have an opinion yet. I'm not like religious about it, but if we feel like we can build something, we do, but that. But sometimes people have differentiated awesome stuff and you're like, let's partner. We're open to it.

1:53:55

You know, how would you pitch me if I was a young person, Stanford cs, new grad, worried about software engineering not being the easy path where I can bounce around from Google and maybe Uber had a cushy job for me. Pitch me on going to the Amazon and building.

1:54:07

I mean, that's awesome. I thought I just did. I mean, that was a good pitch. That was the pitch.

1:54:30

Do you think young people are receptive to this pitch yet? Are we about to be receptive? Why should they receptive?

1:54:35

It's really interesting because I only run into the young people that are receptive.

1:54:42

Sure.

1:54:45

Like I'm not out there pitching like lame sauce dude who doesn't want to work.

1:54:45

Sure, sure.

1:54:49

Like I don't end up. I don't end up in the same

1:54:50

room as this guy.

1:54:52

Do you want a job where you want a laptop job or do you want to be dropped in to a mine in the Amazon and like build, build, you know, science fiction?

1:54:54

This is the thing, right? This is why the Adams thing is cool. Because you're not dropping a. You're not dropping a fucking app in the app store. You're like automating a 2 million pound machine going 35 miles an hour, carrying gold. There's a lot of profanity happening today. I don't know why it's happening, but it is.

1:55:03

Let it flow.

1:55:28

I just wanted to acknowledge it.

1:55:28

Do you watch science? Do you get inspired by science fiction fiction at all? I can, I can imagine like watching Dune for you. You're just like texting pictures to the team. Of course.

1:55:30

I'm like, I'm. My fave.

1:55:40

Is.

1:55:42

Is Asimov. He's my fave. You know, the iRobot series is like just so epic.

1:55:42

What is your takeaway from the iRobot series with regard to AI safety doom generally? Have you ever had moments of maybe we won't figure it out?

1:55:52

Won't figure what out?

1:56:04

The alignment problem, broadly. Like the. The iRobot, the three laws of robotics. Elegant solution tested, obviously. But I love, I would love to come back to a world where, where everyone, both the Doomers and the AI builders agree that, yep, the three laws of robotics will be ways.

1:56:06

Well, in some ways in the series, the three laws don't always work out. Yeah. So I think there's a lot of. I thought there's a lot of nuance to those three laws, even though the laws are sort of so simple. I love the intention of those laws. I sort of think of it a little bit differently, which is I have been entrepreneuring for a long time, like a long time time. And I have failed. And when I think about why I failed, it's usually because I was building something that nobody liked. So if you build something that people don't like, I don't think you're going to succeed. So how does that relate to your question? He's like, please tell me because I'm

1:56:25

not connecting the dots at all. What are you talking about?

1:57:13

Well, if you make something that is anti human, if you make something that doesn't serve people, I don't think you're going to make it. I don't think you're going to make it. And by the way like yes, we're using AI to help us make decisions, et cetera, but what do those AIs really, really want to do? Almost too much. They want to please us. So I just think if you're not making stuff that humans want, it's not gonna work out. And that's kind of obvious obviously. But I think it keeps going. And yes, there's the dangers and the things and then this. But that's my, that's my starting point for how I think about these things. And we can't control all the things. And I do think of course you have to have safety situations and there's collisions of like what do I, what do I prioritize first and how do I do it? Which is I think where Asimov's laws go. But instead of writing sci fi books, I'm just doing the thing and I'm making sure that the machine stays on the road.

1:57:16

Yes. And related to that idea of doing the thing, making the machine stay on the road, I imagine that your worldview is somewhat informed by your contact with reality. The fact that you can see the progress of diffusion, how long drive by wire systems take took to roll out and the need for AI to be deployed in like tactile ways. That, that, that you just see it as more positive. Some more. There's more.

1:58:17

I feel like you're deploying robots that people want right now. Robot. I mean look, there's some point where robots have their own bank accounts and their citizens and all this. We're just not there yet.

1:58:48

Sure, sure.

1:58:57

And before we, until we get there.

1:58:57

Yeah.

1:58:59

That robot owned by somebody.

1:58:59

Yeah.

1:59:02

And that somebody has a bank account.

1:59:03

Yeah.

1:59:04

They are paying based on the value you're bringing them because they like your stuff. So if you are doing things that humans don't like, you're done.

1:59:05

Yeah.

1:59:13

And trust me, I've done it. I've built things that nobody liked and it sucked. Yeah. I don't recommend anybody do it. If you can avoid it, you totally should.

1:59:14

Yeah.

1:59:25

On the business model side, what are you doing now in mining and where do you think it could go over time? Because if you're able to bring in a system that helps someone increase their yield 30 to 40%, I imagine eventually you just do some type of JV so that you guys have incentive.

1:59:27

Look, there's, there's, you know, and the, the instinct should be, how do enterprise companies, enterprise software companies do it? Start there and you guys will know that. Like you know that.

1:59:45

Yeah.

1:59:58

Yeah.

1:59:58

What's the answer? Let's just say your enterprise software company, you're making a company more productive. What do you do?

1:59:58

Raise prices?

2:00:04

Subscription or you. The price goes up when you prove that productivity. So there's baseline and then based on outcomes, you get a little extra juice.

2:00:05

Sure, sure.

2:00:15

And you can.

2:00:16

Or you're always trying to make sure that like you want to be producing, creating more value than you're capturing. But there's this sort of cat and mouse game where you're always trying to. You don't want to give away maybe too much value.

2:00:17

Totally. But here's the thing. You never go to a customer. I don't care what you're selling, Okay. I don't care. It's enterprise software. I don't care if it's widgets. I don't care what it is. You never go to a customer and say, give me a percentage of your stuff.

2:00:29

Yeah.

2:00:41

You go to a customer and say, here's the price of our stuff and if it does really well for you, we think we should get a little more scratch cachesh stuff, you know, whatever. You know what I mean?

2:00:41

Yeah, yeah.

2:00:55

And it's that simple. Don't be crass about it. And, you know, partner with folks and they're down. They want to win too. You know, it's literally an enterprise. It's an enterprise software style, negotiation or approach to the whole thing.

2:00:56

Yeah.

2:01:10

And the more differentiate your value is, the more you're going to get.

2:01:11

Yeah.

2:01:14

What is your process for hiring executives today?

2:01:15

Pray.

2:01:20

I was hoping. I was hoping you had the Kalanick system to achieve a 99.

2:01:23

No, but why would I tell you? Why would I know? I mean.

2:01:28

No, but I think you can. This is one of those things. You can tell people exactly what you do and they're not Kalanick. So that doesn't mean they can Compete with you?

2:01:33

You know they could. Yeah. Okay, so how would I put it? Look, I think no matter who you go, nobody's nailed executives all the way. It's weird because what will happen is executives talk a fucking awesome game. And there's two things you want an executive to do. You want them to be able to organize at scale. Organize and manage at scale, lead at scale. You also want them to be epic problem solvers. The most strategic, badass problem solvers alive. This is like being left handed or right handed and there's very few people that are ambidextrous. But you need that now. Somebody's. They're always leaning a little bit, one side, side or the other. The best executives are the ones that are doing both well. But I have come to the conclusion over my years, doing the stuff is the problem solving is the most important thing. If you get somebody who organizes and manages well but cannot solve a problem, they're going to be doing ridiculous stuff in a super organized way. And so that's. And sort of my theory, maybe there's a couple of theories on how I manage or how I lead. Is that the only constraint on your imagination is management capacity. But what is management capacity? It's really problem solving at scale. Because if you are doing super well over there, guess what, they're problem solving there. I can create other awesome problems. Like, I love creating problems, sure, go solve those too. But if I don't have the management capacity, then I'm f. Sure. So the management style that I do is sort of problem solver in chief, which is I take the most impactful problems that are not being solved and that's on my desk or desk or room or whatever you want to call it. That's where I'm spending my time. So people go, oh, what do you spend your time on? Like, it depends what the fricking problems are that matter. And it can change. And that's how I roll. But it means once you have a problem solver in chief mentality that flows downward. That means any direct report of mine must be the deputized problem solver in chief. And they've got their. Because there's only 24 hours in a day, I can only stall so many myself. They have to then take that for their world and do the same thing and then do the same thing to their people. So the bottom line is you got to prove that these folks can solve actual problems and aren't just talking the talk. That's the number one. And then on the interview process, simulate what it's like working together so that day one really feels like week two and day one, you better be excited. So if you're excited in day one after simulating what it's like working together in the interview process, then day one is really week two and you're still excited. You took a lot of risk out of the system. That's all I got for you.

2:01:43

I have a question about regulation. Uber famously went city by city.

2:04:41

Yeah.

2:04:45

The AI labs are duking it out over federal preemption. Did you ever have develop a theory around when federal preemption is better than state by state regulation? Do you have a philosophy around this? It seems like the labs go back and forth on what the they want. It's hard to see where the chips are falling.

2:04:45

Federal preemption is good when you are pro regulatory capture.

2:05:06

Okay.

2:05:12

When you want to squeeze others out, you should get federal regulatory bigness going for you.

2:05:13

Yeah. Because then you don't have to do the ground game that you win.

2:05:23

Well, no, you're squeezing others out.

2:05:25

Okay.

2:05:27

It's just the whole point is to squeeze everybody out. I never did that. We never did that Uber. We basically never ever proposed or pushed any rule that would be beneficial to us versus somebody else. We always were trying to open up the market and we said let the best man win and we just went for it. But I think we got to be careful of some of these close weight things that are creating situations where they need to be regulated and they want it. I'd be very, I'd keep an eye on that.

2:05:27

Yeah, well, you got to have customers that love your product and are willing to.

2:06:08

So when you guys, when you guys, you know, decide to tell your own hacker to hack the thing and then go to somebody, then go to the federal government, say, then go to the federal government and say, dude, we saved the day. Like, you know, you don't have to, you guys don't have to do this.

2:06:11

You guys don't have to do it on regulation. I'm sure you saw the trial lawyers that are fighting back against autonomous vehicles because they're worried they're going to be too safe.

2:06:35

Yes.

2:06:46

I'm sure that's not to supposed surprising to you.

2:06:47

No. So look, every bad thing that you see in transport, like systemically, anything in transport that you view as systemically bad was most likely pushed by the trial lawyers and the insurance companies.

2:06:49

Wow.

2:07:04

Every single bad rule. That's weird and dumb. The insurance companies and the, the trial lawyers were in the game big time.

2:07:06

Where do they align what do you mean? Well, because trial lawyers, I imagine, want more accidents.

2:07:17

Yeah.

2:07:24

Insurance companies are the ones that pay for it. Yes.

2:07:25

No, no. Remember insurance companies make margin on accidents. If there's no accidents, there's no insurance company. They, in a weird way, they love accidents go up. As long as it's in their actuarial table, they're pumped.

2:07:28

Wow.

2:07:41

Yeah, right. What they don't like is accidents they didn't plan for sure, but accidents that they planned for business, big insurance outcomes they love. Like I remember we went to DC and the taxi system, the, the liability on a ride, if you took a taxi, it might still be this way to this day. Was like $25,000 in a taxi. But we went to, we being Uber at the time, went to D.C. and they pushed a one and a half million dollar policy per ride. Okay, so what does that mean? That means, well this, you know, accidents are going to happen. We're probably like Uber's probably safer. But it just. Do you think the trial lawyers weren't pumped about that? You think the insurance companies weren't also pumped about that?

2:07:42

They can go get up to them

2:08:34

because by the way, the insurance company might be on the other side and they're like, oh, there's a, there's a one and a half million dollar bank account here that I can get access to on a random accident.

2:08:35

Right. What can you share on the transportation side of the business right now? How much are you, how much is that business in service of mining or

2:08:45

food versus it's number one. So number one is it's, it's so I call it wheelbase for robots, which is if you're going to do specialized robots that move and act in the physical world, they're either humanoids, which we're not, I'm not anti humanoid, I'm just non humanoid. Specialized industrial robots. Right. So that's, it's high scale industrial scale tasks. Which means you would not have a humanoid ever do that. That means you got to be on wheels. So we got to build wheels. So that means. Okay, well when food, when supply chain is going into our facilities, that's a freight vehicle. We probably should just turn that into a robot that moves stuff and actually interfaces with our facility in a really cool way. When the food is coming out of our facilities, there's probably like a machine that holds food at temperature that's like a box on wheels. I call them autonomous burritos. And it brings it to your home and it costs 75 cents instead of like the $12 per drop. That it costs like an Uber Eats or doordash today. So it's serving. Remember, I'm taking. I'm sort of going through an industry and saying, how do we transform it full stack. How do we automate full stack that entire industry? So, okay, that's the food thing. Obviously. Mining's pretty obvious, but you can imagine there's a lot of other machines that move. Like, I talked about haulage. But what about, like, what about grading the roads? The dirt roads, you got to grade them. That's a machine. What about the. You spray water so there's not a lot of dust all over the place. That's a fricking machine. Like, what about the material that ultimately goes somewhere beyond the mine? Well, that's a freight machine. Like, there's lots of things moving. You know, I saw something that was like, think about just forklifts. I know a company remain unnamed that's spending three and a half. This is on the supply chain side. Three and a half billion dollars a year on forklift labor in their facilities.

2:08:56

That probably shows up in an SEC filing if we want to get creative and figure that out. What company you're talking about. He's saying, but you said, yeah, big

2:11:09

opportunity if you just solve the forklift problem.

2:11:16

Yes, but on solving the problem, what do you think about this, this distinction between jobs versus tasks? Like, a lot of people would have assumed that there would be no more marketing people because the job is just writing marketing copy, but the job is actually much more writing copies. One task, I was looking at automated trucking and I found some stat. Like, I think 30% of truck drivers are armed. They carry weapons. And so driving the vehicle is one task. But in that job, you are also providing security for that payload. And you are also doing other things. Refueling the vehicle, maybe some minor maintenance. And so just the. Just the steering and gas and brake pressure is just one task that you're doing. How do you think about that in the context of all this?

2:11:18

This really gets to the jobs question, I think.

2:12:10

Yes. Which is basically like, okay, well, if

2:12:12

I do everything that we are imagining on food, which is, I have industrial real estate, which is manufacturing and logistics. I automate the manufacturing, which is production. Robotic food, Robotic food machines. Robots. And I have robotic couriers. What happens?

2:12:15

Food.

2:12:35

The price of food goes down. Yeah. Okay. When the price of food goes down, remember, robots don't have bank accounts. When the price of food goes down, what happens? More people have more money.

2:12:35

Yeah.

2:12:44

Jevons paradox.

2:12:45

What do they do?

2:12:46

You start eating more. They just start having 10. Everyone's getting fat. This is hilarious.

2:12:47

That's not what I'm saying.

2:12:54

That's so the problem. That's not what I'm saying.

2:12:56

No, what I'm saying is. What I'm saying is, when once you

2:12:59

have inspired, I'll take.

2:13:04

I was going to get three pizzas.

2:13:05

You're like, it's one tenth the price. No, no, no. So what happens? You have more money to do other things, but remember that money is only ultimately going to humans.

2:13:09

Yes.

2:13:17

So it's the things that get automated go down in price, which then creates surplus to do what other things? So it doesn't always have to be, oh, marketing's automated, but sort of. And there's still people doing it. It's like, whatever. There's going to be a hundred other new things that come out because there's this excess of capital and progress continues.

2:13:19

Yep.

2:13:42

Yeah.

2:13:42

And as long. As long as humans still have things that we do that robots cannot, it's go go time. And it's going to be super prosperity. We talked about the plumber that is paid like LeBron last time. It's going to be across a thousand categories, and some categories we don't even know. Like, we don't even know what they are. Today

2:13:43

you raised 1.7 billion. Why didn't you raise more?

2:14:04

That's a good question.

2:14:09

I mean, because last time we were here, you talked about, like, oh, well, if you were doing something and it was easy, you weren't going hard enough.

2:14:10

Seems pretty hard.

2:14:17

But look, you have to stop somewhere.

2:14:19

Even I have my limits now.

2:14:24

It's like. But, like, look, as you can imagine, today my phone's blowing up. I mean, I'm pumped. Like a 16. These guys. We should have done business. Business at Uber.

2:14:26

That's right.

2:14:36

If we did it business at Uber, my 2017 would have been a different year.

2:14:36

Yeah.

2:14:40

Yeah, okay.

2:14:41

Totally. So that's why I called it unfinished business.

2:14:41

Yeah.

2:14:44

And so. But yeah, like, my phone's blowing up. Like, we're probably just gonna do a second. We'll do a second close.

2:14:46

Yeah, yeah, I figured we'll come back

2:14:56

for the second close.

2:14:58

Run it back. No, I mean, we're not gonna. Going to do a big announcement on the same clothes. But, like, you know those people who are. Who are texting me and hitting me hard right now?

2:15:00

Got room, you know? Well, we'll see, we'll see, we'll see. Depends on what? The previous text message.

2:15:11

If you're a homie, we definitely have room.

2:15:19

Yeah.

2:15:21

If we're not a homie, you should talk to one of my homies.

2:15:21

There we go. Yeah, I did come away from the last conversation. Thanks. Thinking. All right. There's a lot of exciting companies in physical AI and you could spend years and years and years trying to find all the best teams. Or you could just give TK a big pile of cash and just say, go cook. And sometimes the easier route is better.

2:15:25

Yeah. And I think there's this thing, physical AI People are like, well, is that a humanoid? Is that a world model? And so on this one I sort of dialed the language a little bit and calling it industrial AI.

2:15:44

Yeah.

2:15:55

It's like, okay, this is a full stack software, robotics, sensors, machinery. Like a full stack solution to automating an industry. And that's kind of how we think about it. And it's industrial. Yep. So it's like heavy atom stuff. Yeah.

2:15:56

Well, thank you so much.

2:16:16

It's incredible.

2:16:17

You want to get a signature? Can we get an autograph?

2:16:18

Sure, why not?

2:16:21

We get something which way?

2:16:22

They can figure it out back there. Oh, we got a gong. We'll hang it in the raft.

2:16:23

We want to hang it in the rafters.

2:16:29

We're trying to build our, our museum of business.

2:16:30

The museum of business grows one gong stronger today.

2:16:33

And we will. We'll see you in Austin.

2:16:37

Yeah.

2:16:39

Next time you're on your commute, if you see two jet skis moving out of, out of your, you know, out of out of sight coming in, it's probably us.

2:16:40

That's us.

2:16:47

If it's not, guys, let me know.

2:16:48

If you want to learn how to slalom ski.

2:16:50

Oh yeah.

2:16:52

If you want to learn how to wake surf, like.

2:16:52

Well, I've only been water skiing once or twice in 20 years ago.

2:16:54

I go into 7:30 in the morning every morning. And I'd say half the time I'm out there at 8:30 when I leave the office.

2:16:59

That's amazing.

2:17:07

I love it.

2:17:08

That's what we do.

2:17:08

Beauty of summer.

2:17:09

Thank you so much for coming on the show. Always a pleasure. Have a great rest of your day.

2:17:11

For sure. Good to see you guys soon. Yep.

2:17:16

I'm going to tell everyone about Cisco. Critical infrastructure for the AI era. Unlock seamless real time experiences and new value with Cisco. And our next guest is in the waiting room. We got Max Hodak from the Science Corporation. He's the founder and CEO. We kept him waiting, but. Max. Max, how you doing? Welcome back to the show.

2:17:18

Hey guys, thanks for having me.

2:17:40

Great to see you. Give us the update. What's the news.

2:17:41

So previously we've talked about, I've told you about our retinal prosthesis. So we have a chip that's implanted in the eye to restore vision to patients that have lost. Lost it due to the death of the rotting cone, specifically age related macular degeneration. So last week we got marketing approval in Europe. So we've received the CE mark, which is like, it will be shortly available to consumers in Europe.

2:17:46

I got a question. I got a question. So Jordy has this problem where he drinks too many beers and he gets double vision. Can this help with that?

2:18:10

Fortunately not.

2:18:20

Wait, wait, jokes aside, scientifically it cannot

2:18:21

double vision from drinking.

2:18:25

Yes.

2:18:27

Probably not impossible.

2:18:28

It's the last, it's the last scientific problem. We'll never solve it.

2:18:29

But anyway, very funny. More seriously, how quickly how, like what does a go to market look like for a product like this? You have approval.

2:18:32

Step one's approval.

2:18:42

Yeah. How quickly can it be adopted by. Because you know, people. I mean, Europe's a big place, right?

2:18:43

Yeah. Do you need insurance, doctor facilities, building the machine that installs it? Like there's a whole process here, right?

2:18:51

Yeah. Well, it's a relatively simple one hour outpatient procedure. The machine is the surgeon. Don't need actually that many surgeons to reach these patients.

2:18:59

So.

2:19:06

Right. So the CE mark is a marketing approval in about 30, 30 countries that accept it. The next step is we need to register country by country. So we have registrations going in in Germany and Italy and the Netherlands and Spain and the uk like this week. That process takes about a month and then doctors can start scheduling patients. I mean we sell implants to hospitals essentially and then they sell them to patients. So it's, it's the hospitals, it's patients. But we have a registry. The hospitals have registries. The patient, the doctors know who their patients are. With this demographic, actually one of the things that happened is because there was really nothing available for them. Ophthalmologists have been telling these patients, like, you know, if you're 80 and have AMD, you don't need to be sitting in my waiting room anymore. Like, you know, you don't need to come here. And so now they're starting to reach back out to some of the, those patients that they haven't said, we don't need to see you for the last few years, say there's something available. So the first patient is probably six weeks away or so. The next. Wow.

2:19:07

So fast.

2:20:02

Big step is reimbursement.

2:20:03

Yeah.

2:20:05

And so, yeah, what does it look

2:20:06

like in America, I mean you're six weeks away in Europe. What's the FDA track like? I know that it's already FDA breakthrough device and humanitarian use device device, but take us through what the commercialization plan looks like in the U.S. yeah, so

2:20:08

the other thing that we announced today is that we got two humanitarian use device designations from the fda. We actually got this back in March, but sat on them for a little bit. That unlocks an expedited approval pathway called the humanitarian device exemption that we're submitting for imminently in the next week or so. That is can be a 75 day review. And so it'll take like there's a couple loops of that, but we're hoping that early next year it'll be available to some set of American patients. But that's up to the FDA review.

2:20:22

I don't want to get you in trouble with the fda. I know how high stakes it is, but it is just crazy that Europe's moving faster around regulation. Like this should be a signal to the FDA to say, hey, if Europe's proving it faster, we got to step things up over here. As an American, I just don't like falling behind, but I don't know.

2:20:53

Yeah, I mean in this, I mean I would normally want to agree with you. I think in this case it's actually a little bit unfair to FDA because there's some accidents of history that just led to this like happening first in Europe. The FDA standards are not that much different, but yeah, absolutely. We should hope to have this here also. The FDA is, they care about slightly different things. The filings are a little bit different, but hopefully it won't be that long either.

2:21:14

Yeah, talk about next steps. I mean you're properly commercializing right now. Does this mean new factory, new team members, new new just new muscle inside of the company?

2:21:43

Yeah, absolutely. I mean we've built out a whole go to market team in Europe. So this is clinical education. Like a bunch like we need to go reach optimization. Ophthalmologists where they are, tell them about the product, help them understand the results, answer their questions, rehab specialists. So there's this is a little bit different than what you may have seen from the motor BCIs, where it works very quickly or with like there's a little bit of rehab that the patients have to put in to really use it. And so we have people on the ground there that will do that with them in the beginning. Over time we want to have that be more and more kind of just in the wearable they put on the glasses and the glasses talk to them. They talk to the glasses and walks them through the exercise. But initially that's a little bit higher. Higher touch. And then also there's a bunch of surgeon training. So we run wet labs for surgeons where they can come and, and practice the procedure with us so that they've. We know that they know how to do it before they're doing it with patients.

2:21:58

Give me a sales and Marketing 101 for targeting ophthalmologists. Can you target them on Instagram reels? Do they listen to a specific, specific podcasts that you can sponsor? Are you at conferences? I know people give. Medical device companies give out lots of like pens and chairs. But I think they're like there's like limits because you can't like bribe them, but you do want to give them merch. Like what is the 101 level of marketing to ophthalmologists?

2:22:48

I mean a lot of it is conferences. Okay, so there's a handful of conferences that we go to and then getting. Not just having a booth there, but presenting scientific results. Typically this isn't us, but it's our academic and scientific collaborators. Maybe a surgeon at a hospital that did a study, they'll present their experience with it. There's also advocacy groups. So there's opportunities to sponsor like webinars through these, these networks. But it's a really small community I think like ophthalmology overall and especially in these types of retinal diseases. It is very densely interconnected and they all talk. And so it's a matter of kind of. There's a handful of advisory boards that we have to go through. For example our data safety monitoring board for the clinical trial. These are often opportunities to have that community come and be familiar with our results and then disseminate them.

2:23:14

So it might be the end result might be a little bit more one to one because of how small the community is. You can actually reach them directly.

2:24:02

What is.

2:24:09

Yeah, there's not like a huge insta spend on that.

2:24:10

Not yet.

2:24:13

What is the shape of Science Corp look like right now given that you have a product that's commercializing but I imagine you're doing a bunch of R and D in the background for other opportunities and use cases. But how are maybe you spending your time and then what does the team's time look like?

2:24:16

Yes, we definitely have a bunch of next generation projects in development including the next generation of the Prima implant. We have new versions of that kind of in pre clinical studies now. Hope to get those into humans. Next year it'll probably be a three year minimum, possibly five year cycle between versions for a while, I think because the need to do the intervening clinical trials. But the prima as it is now is a really great existence, proof that we're on the right track. This is the first time that function that like really useful form vision, I think that looks like an image has been able to appear in the mind's eye of a blind patient, but it is not high resolution full field color vision. It's like looking through a straw at the center of your vision where you've lost this high acuity perception. And it's, it's black and white, it's high contrast, but it's only a couple letters at a time or maybe a word at a time. And so we are still working to expand the field of view, make it so that you can potentially get colors. We think we know how to get to red and green. Blue is a little more difficult and then get higher resolution, get towards native acuity and so on. Each of these we have, there's clear ways, places to go, but it's going to be a long road to get that all the way to all of these patients.

2:24:36

Well, congratulations on the approval.

2:25:51

We also have some really cool stuff coming on the brain computer interface side, on the vessel side, but those will probably come out a little later in the fall.

2:25:53

Can't wait to talk about it. I'm excited. We. Well, congratulations and thank you so much for taking the time to come to our.

2:26:00

Yeah, thank you so much for coming on and, and the work you're doing. Yeah, just thanks for having me. You're doing, you're doing the thing that you're doing. You're doing something that could get humanity broadly back on the side of technology.

2:26:05

That's a good point.

2:26:22

Yeah, because it's like one of those things, like it seems like so much of what the industry has been doing a lot. You know, making, making sand think is not quite enough for people. They're like, you know, what have you really done?

2:26:22

What have you done for me lately? We literally had someone come on the show and say, what have you.

2:26:35

But I feel like this is one of those things like, you know, curing blindness that over time will be sort of hopefully undeniable.

2:26:38

Well, I mean, this isn't about the money. I don't think it's about the money for a lot of this team. It's certainly not about the money for the patients. I think like many things in tech, this is really about Power. But if you want to know, like real power, like the power to heal the sick, unlike economic or military power that can be easily shared with others. And that is, I think, like, really what technology is about here. And we need to paint a picture of how this is being used in that way, in a way that is really should disseminate broadly and I think just incredibly pro social.

2:26:45

I love it.

2:27:13

Going back to. I know we're almost out of time, but going back to, to like, what did you place the odds at doing this, accomplishing this moment when you started the company? It seems like.

2:27:14

I know I've always had trouble thinking about these things. Like I can't put a number on it. It's just you kind of keep going and as long as success is in the set of possible outcomes, you're just constantly trying to minimize the odds that you don't get there. It is really hard to put a number on it. Definitely it is cool to see it actually happen.

2:27:29

Yeah, yeah. You're sort of nonchalant about it, but it is almost unbelievable and really, really incredible. So well done, well done to my

2:27:50

whole team on the show. We'll talk to you soon.

2:28:02

Cheers, Max.

2:28:04

Have a good rest of your day. Let me tell you about Figma agents. Meet the canvas. Your AI agents can now create and modify your Figma files with design system context.

2:28:05

Absolutely incredible stuff from Max. Science team.

2:28:15

Very cool. I don't know, I think that you might be seeing an Instagram reel being like this eye implant that cured the blind, used too much water and it's slop. Like it's not the same as just being blind. I don't know, anything's possible. The pushback, there's always a negativity bias. I think that there will be pushback

2:28:18

to even the medical cures giving sight to the blind.

2:28:37

Yes. Get ready. There's going to be somebody who finds, you know, something to complain about and goes viral and puts up big numbers, talking trash. That's just the way our media ecosystem works. It's a business, you know, if everyone's glazing something, somebody's going to bring it down. That's just the.

2:28:42

Somebody's got to fade it.

2:28:59

Equilibrium, Equilibrium. Well, speaking of AI writing, Jeremy Giffon had a post here. He said about AI writing, at the end of the day, it's not about whether the words written by a human or an AI, it's about whether the output is useful, engaging and worth reading. The highest quality work will increasingly emerge from a tight human in the loop workflow. While some content will be generated end to end by AI, the fixation on authorial or authorial provenance is ultimately per climb. Pearl clutching. Just kidding. That's the AI rendition of his actual post. He said it much more eloquently, but I tried to make it like more AI. I don't know.

2:29:00

Anyway, Mark Gurman says based on using the Z fold 8 wide, I'd reset my expectations of how the foldable iPhone is going to sell even at over 2,000. It's going to be a home run.

2:29:41

You are going foldable. You're pro foldable.

2:29:50

I think I'll go foldable. Why the heck not?

2:29:53

I think when you open it up, you can watch videos in four, three

2:29:57

more room for reels. You can watch two reels at once,

2:30:01

but the reels are going to be. It's actually not that much more room for reels because you'll just have black bars on the side. Like, if you watch, can you have

2:30:03

two reels side by side?

2:30:11

Okay, maybe. Yes. In two different apps. You could have YouTube shorts here if they support split screen like on an iPad. But right now, if you want watch reels on an iPad mini, you're not actually getting that much more pixel space of reels. You're just getting reel here and then UI and chrome here or whatever.

2:30:12

Cooper says big tech just wants your eyesight restored so you can doom scroll.

2:30:31

There we go. Cooper named it. Yep, yep.

2:30:35

Oh, why they sock is secretly funding?

2:30:38

Oh, they just want to increase their tam. Got it. Makes sense. Well, if you don't want to watch reels, you'll soon potentially be able to go to the Cinemaramadrome in Arclight Hollywood. Sony is eyeing ringing it back. Production team, you got a review. Have you guys been to the Cinerama Dome before?

2:30:40

Scott? Yeah, it's pretty awesome.

2:30:59

I think I saw a Nolan film there, and I think it was 70 millimeter IMAX back in the day. And then it didn't. I think it didn't make it through Covid, but they're maybe bringing it back with.

2:31:01

They have the giant sign outside that says the dome.

2:31:12

Yeah.

2:31:14

If they were gonna stay out of business, we should.

2:31:14

We should.

2:31:16

I remember, Yeah, I remember as a. As a kid, I thought that they would show the movie, like projected on the dome, like on the whole ceiling. And it was only for sort of like special, you know, like astronomy movies. But they will just show a normal movie and it's just. You're just in a big dome. It's cool. Next studio. If Sony doesn't buy it?

2:31:17

Maybe.

2:31:37

I don't know.

2:31:38

Could happen.

2:31:39

Can we get that unreleased track on again?

2:31:39

Yeah, let's play that as the outro.

2:31:42

Yeah, one sec.

2:31:44

Regulate Me. It's the new banger hit song of the summer. It's an anthem. It's an earworm. You're gonna be listening to it. We'll share the link in the description of the YouTube video. Maybe.

2:31:44

Yeah, we gotta start doing karaoke because

2:31:57

this song just speaks to me. It really captures the moment. You heard it from Travis. Every once in a while you get into a pickle and you gotta get the government to come regulate you. It's a good time. Thank you for watching tvpn. Tune in tomorrow. We have a very special show for you. We're on the road Thursday and then we're off on Friday. Back Monday. Leave us five stars on Apple, podcasts and Spotify. Sign up for our newsletter@tvpn.com let's throw a flashbang and let the audience listen to Regulate Me by Jordy Hayes and Suno. Goodbye, Flashbang. Out. If nobody draws the line then the

2:31:59

line draws us instead What I've built

2:32:57

is too powerful Too powerful for me Washington needs to step in before it runs free what I built is too powerful Too powerful all you see Washington needs to step in and say. I hear the voices multiply in every.

2:33:01