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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
You're welcome. Today is Thursday, 10/08/2026. We are live from the TBPN UltraDome, the temple of technology, the fortress of finance, the capital of capital. Let me tell you about ramp.com. Time is money.
Speaker 1:Save both. Easy use corporate cards, bill pay, accounting, and a whole lot more all in one place. How many days until Christmas?
Speaker 2:I have a better question. How many hours? Oh. Oh.
Speaker 1:Oh. We're counting down the hours now. Christmas. Okay. There are one thousand eight hundred and sixty two hours until Christmas.
Speaker 1:Get ready, folks.
Speaker 2:Yep. That's right. We're counting... We've moved from days until hours. Yeah.
Speaker 2:That's how close we are.
Speaker 1:That's how close we are. It is October.
Speaker 2:We're just hours away.
Speaker 1:It is early October. We're hours away from Christmas. We are technically hours away from Christmas. That's correct. Yes.
Speaker 1:Anyway...
Speaker 2:We're only eighteen hundred hours away from Christmas.
Speaker 1:Yeah. It's it's the apocalypse day. There's there's so there's so many apocalypses. Mathpocalypse, there's Crypto Apocalypse going on. Although, the market is reacting in a very moderate way, but the timeline is being dramatic, so we gotta break that down.
Speaker 1:Ethereum founder, Vitalik Buterin, and others in crypto are sounding the alarm that AI driven math could threaten blockchain security. And so crypto is going bunker mode. That's the term that they're using. So we've all seen the progress in AI driven math, solving new proofs, solving Navier Stokes. There's a lot of impressive progress in math that maybe has applications in physics, maybe has applications in engineering, fluid dynamics.
Speaker 1:A lot of these things are further out. And if they wind up being useful, they might increase efficiency here.
Speaker 2:Not as far out as you think.
Speaker 1:But the big thing is that crypto is here today. And if there's an advance in the underlying understanding of how cryptography works, it could really reshake... It could really shake up how the crypto industry works and how these products are secured. So are they in danger?
Speaker 3:Rising.
Speaker 1:It's the it's the Ralph Wiggum meme, I think. I'm in danger. Seeing all this, Matthew Green, who teaches cryptography at Johns Hopkins. Johns Hopkins put the situation in stark relief. He said, I think we might lose public key cryptography, which is a which is a huge statement from an expert in cryptography.
Speaker 1:It would require a pretty massive restructuring of many security protocols, but also the crypto industry. At the moment, cryptography has... As a discipline, has been exempted from the list of published papers related to AI enabled math breakthroughs. So there are hundreds of papers, and it's very clear that either the agent swarms that were sent to go solve math problems, they said, hey, don't even bother with any cryptography stuff. Or if they did, they didn't publish those, and they're burying them for now until there's a way to actually patch those.
Speaker 4:Or maybe the cryptography is just too strong.
Speaker 1:That's true. That's a that's a third option. The crypto industry is a little skeptical about it. They think, yeah, maybe there might be some flaws. Of course, they've been preparing for things like this before.
Speaker 1:Quantum computing is potentially existential to many blockchains. And so there has been the concept of Q Day and how they work backwards from that to make sure that they are quantum resistant.
Speaker 2:The interesting thing is, like, historically, when the crypto industry has faced, you know, headwinds Mhmm. The strategy has been hodl.
Speaker 1:Hodl.
Speaker 2:And that's not necessarily gonna be effective for this next chapter.
Speaker 1:That's true. Yeah. That's a good twist. Of course, there are very weird dynamics where if you break all the Bitcoin keys and all the wall wide open and you completely erode all the trust that's been built up in that in that virtual currency, yeah, you stole it all, but it's worth nothing. And so the game theory there of no one would ever wanna steal all the Bitcoin.
Speaker 1:Right? So it's more like, can someone go after long tail...
Speaker 2:I'm sure there's some people that would do it just for the
Speaker 1:love of world burn, joker mode? Yeah. That's probably possible. We could be seeing a a jokerified... Oh, you have the joker ready to go.
Speaker 1:Yeah. A jokerified AI agent swarm would be a bad situation Yeah. For sure. But there is some cause for optimism, you know, much like the broader secure... Cybersecurity scare that happened back in March of this year, there's a gap between closed source and open source models.
Speaker 1:Most people say it's around six months. You know? The capabilities catch up in certain areas. It seems like it's faster. Sometimes it's three months, but six month is sort of a useful heuristic I have found.
Speaker 1:And that actually maps out pretty pretty pretty perfectly with the most recent news. So if you remember, Mythos preview was dropped by Anthropic on 04/07/2026. It's the fourth month of the year. And then CrowdStrike announced that a 26 year old in China used a open source Chinese developed AI agent to execute cyber attacks against South Korean banks on October 7, like the same... I I think that the report came out October 7.
Speaker 1:So it's basically just exactly six months later, you see, like, a open source model be used in a cyber attack. Now fortunately, the the the the banks have, you know, a ton of redundancies, the ability to roll back transactions. They store things to tape. They store things on paper sometimes. There's a lot of different ways to secure a traditional bank that is not purely digital, even though they might have a digital front end and a lot of the money might be stored digitally.
Speaker 1:And the interesting thing is that when I read at least about one of the South Korean bank cybersecurity incidents, it was a pretty small amount of user data that was stored. It was like names and addresses and, like, phone numbers of, like, it was a significant number. It was, like, tens of thousands of clients or something like that. But it wasn't like they robbed the bank and they got the money or they got everyone. Somehow they got into, like, one system for information.
Speaker 1:So it could have been like they they hacked into, like like, their paperless post account. That was like the... What the bank used to send out invites to clients or something like that. Like, there are a whole bunch of different systems and and where... At what level did they get in is is important, but it's clearly happening.
Speaker 2:They got into the client gifting software. They've been they've Potentially. Everyone chocolates.
Speaker 1:Maybe. Maybe. And so, obviously, you don't want hackers anywhere. And so cybersecurity is more important than ever. And so the crypto community does have, you know, a couple months to go bunker mode as they are putting it.
Speaker 1:And it feels like there'll be an incredible amount of progress on these, like, AI proofing systems, but it's an entirely new challenge. It's very it's less... Much less understood than quantum computing, which has been theorized mathematically and sort of formalized for years before the capability came. This is different because we're getting this capability before we've really characterized the shape of the threat. Like like, they know with with quantum, like, there's something called Shor's algorithm, and, like, they gotta fix that.
Speaker 1:So they gotta make sure that Shor's algorithm doesn't work to break Bitcoin while it's open. They know the mathematical problem. They got to solve that. In this scenario, it's like there might be a different algorithm that runs on commodity hardware that works. And how do you know when you don't know the shape of the threat that's coming?
Speaker 1:So it's it's a crypto apocalypse potentially, but the market doesn't think it is. Bitcoin's only down 3% today, and it's still up 3% for the month. And so the the market is, you know, digesting this and optimistic. There's still, you know, lots to do if you work in crypto. And potentially, even if you only own some crypto, there's things to do.
Speaker 1:Vitalik gave some recommendations on, you know, don't panic, but also maybe start thinking about this more intently. He said, I don't recommend anyone scramble to move their funds to new wallets today, but we should take the risks of cryptography from AI accelerated math seriously and minimize our exposure to not just quantum vulnerable cryptography, but also AI vulnerable cryptography. The new core area of risk from this viewpoint is unfortunately MLDSA, FAG, lattices. And this is also another reason along with quantum why e... ECDSA might fall even faster than expected.
Speaker 1:Hence, the fresh address recommendation. So far, people have been in the mode of thinking elliptic curves broken, hash is safe, lattice is safe, but there's a good chance that in the concrete secure... That the concrete security of lattices will take serious hits from the next two years of AI math. And so some people were were posting Scott Lou here was saying, oh, that's weird. The AI math god is somehow no better at cryptography than than a twenty twenty six human.
Speaker 1:We got super intelligence in math, but not cryptography. What's going on? He thought for a minute. This is oh. And I think the interpretation of this, and you can tell me if you disagree, is yeah.
Speaker 1:It's because the AI math god is run by a American company that has no interest in destroying
Speaker 2:In like total chaos.
Speaker 1:Yeah. But in the open source world, it's a very different game. Yeah. And so still still interesting thing about the still interesting to think about the the game theory even in the open source world, but I think that will all be downstream of a whole bunch of changes. And then there is the question of, you know, how how does how does crypto handle this?
Speaker 1:Like, the cryptocurrency industry has been libertarian. They've been against the government in many ways, small government. I wanna own the money. Well, now you're facing a threat from open source, from a non government regulated entity. And so while a lot of people in the AI safety community have said, this is the moment for big government.
Speaker 1:This is the moment to register large clusters. This is the moment to track all the data centers, to to have licenses for Yeah. Inference and training and and reporting to the government. That is a solution. Like if you lock down open source and you say, yeah, you can...
Speaker 1:Like, if you're gonna spin up a million agents and and try and do a bunch of inference to try and break Bitcoin wallets, like, we're gonna find out about it. We're gonna arrest you. Yeah. And it doesn't matter that you're using an Yeah. Model.
Speaker 1:That's a very different psychological frame of mind for a Bitcoiner.
Speaker 2:Yeah. The the other thing here is that there's obviously bio risk with model advancements. But it is gonna be wildly different for somebody to use an obliterated model to try to create a bio weapon and then actually sort of distribute that out into the world than like using a new model to try to like mess around with cryptography. Yeah. That feels squarely in the hacker camp.
Speaker 2:Totally. Like there's people that would just be kind of messing around. Yeah. And then accidentally have a breakthrough or accidentally Yeah. Or maybe intentionally cause financial chaos.
Speaker 2:Yeah. But that's purely digital.
Speaker 1:Yeah.
Speaker 2:And that's the kind of thing that millions of people have been doing for a very long time. Yeah. Just generally kind of hacking around. Wildly different than the kind of person who would go out and try to create, you know, a new pandemic. Yeah.
Speaker 1:Then also... Yeah. And then also another layer will... Of this will feed into like the whole AI doom loss of control scenario because you get an AI agent that's hyper intelligent, and it's also able to break Bitcoin wallets. It breaks a couple open, then it has capital.
Speaker 1:With capital, it can marshal more compute, hire people to do things in the real world, etcetera, etcetera. And so the the the the the doom scenario becomes, like, way more human enabled if you are able to amass capital pretty quickly as a rogue agent. Do you have a take on this, Tyler?
Speaker 4:Yeah. I mean, I I feel like the crypto industry broadly is, like, fairly good at self governance. Right? You've seen, like... Yeah.
Speaker 4:Even if you're talking about, like, Bitcoin, you know, there's, like... They can actually, like, change the protocol. You know, like Bitcoin Cash, which is the the shoot off stuff like this. Yeah. I think the Bitcoin Policy Institute, like, I don't know, maybe maybe a month or two ago had this open letter about how, you know, the solution is we we gotta build up defenses much more and we gotta use...
Speaker 4:You know, spend a lot on inference to try to like figure what what is the right path moving forward. It's... I I think most people are very against, like, limiting open source models, stuff like that.
Speaker 1:Totally. Yeah. It'll be it'll be interesting to see how these how these, like, worlds collide and and where things go. I had some other take, but let me tell you about Codex. Codex is a powerful workspace for getting work done with AI agents.
Speaker 1:Whether you're writing code, analyzing data, creating content, or automating business workflows, Codex helps you move projects forward from start to finish. Thank you for the continued clapping.
Speaker 2:Ben, I don't know if we we... You included this earlier, but he says, lucky coincidence, the labs are only successfully cracking esoteric math problems and not the problems where a solution would give the winner an asymmetric commercial advantage. Again, this is what I've said a bunch of times on the show now. Math problems are the perfect category of problems where you get a result and you're incentivized and excited to share the result. There's no immediate opportunity to commercialize a lot of this stuff.
Speaker 2:Yep. And so, for every for every breakthrough in math, you should assume there's... And the reason this should be getting everyone excited is you should assume there's a bunch of other breakthroughs in other fields that have more commercial applications where the person that has... And it's not just the labs. It's people using the current frontier models to find breakthroughs in their own field.
Speaker 2:And so, again, I'm expecting a real explosion of progress, you know, especially as we enter the later half of this year
Speaker 3:Yeah.
Speaker 2:Or the or the sort of The of the end of
Speaker 1:the year. I I believe Will Dupu posted about this. That's what I was thinking of. Will Dupu posted, I think there was, like, a a slight Bitcoin sell off, like, a while ago, like, maybe around the midsummer time period. And he sort of took a victory lap or something or was, saying, like, yeah.
Speaker 1:In hindsight, once we... Once, like, the AI community built, like, the hack machine 9,000, it should have been obvious that, you should get out of Crypto. And then what was odd was that, Crypto rallied after that. So, like, he was kind of a little bit too early with the take, but now it feels like, okay. Everyone's pieced it together a little bit more.
Speaker 1:But, also, there's so many other factors where you could see this narrative dying down, and all of a sudden, there's some other narrative that's driving crypto and people are piling in for whatever reason. But it was... Yeah. Maybe, like, you know, even in a... Even it's been, you know, pitched as an inflation hedge.
Speaker 1:There's always been people who have said the the restricted supply is valuable. You could have a view on inflation that becomes really popular, and then all of sudden people are piling in. There's a whole bunch of reasons why the market can move. It's not purely driven by this, but it was it was funny that he he he was posting about this, like, months ago. Anyway, moving on.
Speaker 1:Console.com. Console builds AI agents that automate 70% of IT, HR, and finance support, giving employees instant resolution for access requests and password resets. So OpenAI's drop of hundreds of new mathematical proofs is now being called the Mathpocalypse. There's some white pills. There's some black pills.
Speaker 1:People are all over the place. Obviously, we talked about the crypto knock on effects. Says mathematicians, AI is plagiarism, and we don't want to know the answers anyway. Artists, AI is plagiarism, and the art sucks anyway. Chess players, I wonder if I can use AI to increase my rating because there are some people that are just delightful with...
Speaker 1:Delighted with advances and not really, you know, frustrated by it. There's actually a Yeah.
Speaker 2:How do you how do you prevent against, like, botting in online chess? Like, couldn't you... Can't you just be, like, running your own... Like like... I don't know.
Speaker 2:And I'm I'm comparing that to, like, let's say, somebody using, like,
Speaker 1:and and fake.
Speaker 2:But not even that, but basically just running like an AI copilot.
Speaker 1:Yeah. No. You definitely do that.
Speaker 2:But... So unless you're playing chess physically in front of somebody and watching them, you know...
Speaker 1:Yeah. And then and then and then... What what do you think?
Speaker 4:Well, I mean, if you're like elo is like 800 and then you start making like, you know, 2,000 rated moves, like, it's pretty easy to tell that stuff. Yeah.... I I think like, if you if you're on chess.com or whatever, it's like... I think it's fairly hard to cheat.
Speaker 1:Hard to cheat? Or or people just don't cheat because it's like, what's the point? And like... It's like, okay. Yeah.
Speaker 1:I played against somebody who was like smurfing and they had an 800 elo account, but they were clearly playing a 2,000 elo. Like, okay, I lost that game. Let me queue again. Okay. I got someone who's actually 800.
Speaker 1:And I'll just keep playing until I get someone who's actually my
Speaker 4:Yeah. Game. That's probably true.
Speaker 1:And But And and maybe they're using
Speaker 4:Yeah. Like an engine. It's like, I think it's not that hard to figure that out.
Speaker 1:Yes. But like, this is gonna change. Like, because like the models will be so good
Speaker 4:It's over.
Speaker 1:That... I mean, the models will be so good that that you'll be able to say like, I'm I'm Elo 800. Help me play at eight fifty and also really mimic the human experience. So don't make don't...
Speaker 5:But yeah. Agree.
Speaker 4:There's like little incentive to actually cheat on chess.com because...
Speaker 1:Yeah. I I...
Speaker 2:Gold says you can tell if they're bots if they statistically always or nearly always make the optimal...
Speaker 1:Yeah. Exactly. Like, there's no fun. And you can always just go play against the best stock fish model if you want. Like, you can just max it out if you just wanna play against the best computer in the world.
Speaker 1:Nobody does. And so there's not that much value in it because everyone would just be like, cool. Yeah. You just ran the model that everyone else has. Like, do you wanna play against me or not?
Speaker 1:Like, if you wanna just be the model, just go. And you don't win anything. So there's not any incentive, I think. I think that's how it works out. I don't know.
Speaker 1:I do think, yeah. I mean, in the future, like, you will be able to...
Speaker 2:I mean, you you in the gym have been accused of using, you know, robotic assisted lifts. Right? Yes. Yes. Yes.
Speaker 2:You'll wear like big sweat sweat pants Oh, yeah. On the dead lift. Can you imagine having And you have your exoskeleton, you know, putting up, you know, 800 pound dead lifts. I mean Yeah. People, you know, know you're strong but Yeah.
Speaker 2:Not that strong.
Speaker 1:They're skeptical. Yeah. I don't know if you'd get a lot out of that. You could bring fake weights. That's that's one way.
Speaker 1:I mean, people do stuff, but then why not just Photoshop it? Why not just go and Photoshop a screenshot of you on your your profile on chess.com with a 4,000 elo? And then share that on X and be like, I'm the best chess player ever.
Speaker 4:But if someone fact checks it, then you just look like Nate.
Speaker 1:Yeah. And so eventually you have to get down to like fact check by walking through a metal detector and going to an actual chess match because, like, oh, you're really better than Magnus? Let's see if you're better in person. You know? That will...
Speaker 1:That... That's what will happen. And so there's, like like, you're gonna wind up you're gonna be wind up in the in the in the Faraday cage with with Magnus eventually. I don't know. Anyway, let me tell you about Shopify.
Speaker 1:Shopify is the commerce platform that grows with your business, unless you sell, insecure, online, in store, on mobile, on social, on marketplace, and now with AI agents. There's there's a very interesting post in the in the Wall Street Journal about a former NFL lineman who solved a decades old math problem. John Urschel gave up his career with the Baltimore Ravens to study math at MIT and he has
Speaker 2:Got this guy on immediately.
Speaker 1:He he's so jacked. It looks like... Yeah. He's he's definitely... Doesn't need the exoskeleton in the gym.
Speaker 1:He's a former NFL player, John Urschel. Decided to do some light reading this week. And for him, that meant leafing through the latest math papers from OpenAI about topics such as the Riemann hypothesis. Oh my lord, he thought. I was absolutely floored.
Speaker 1:Good rhyming. Ursula was blown away by the findings because he happened to understand the hundreds of proofs better than nearly any human on the planet. Nine years ago, he traded the National Football League for the the Massachusetts Institute of Technology. Crazy that you can take, like, that... I...
Speaker 1:The the NFL has gotta be an incredibly demanding schedule. Like, you can take, like, thousands of hours off of your, like, math career and then, like, go back in the game and not be...
Speaker 2:Well, not to mention getting just slammed in the head for Yeah. Yeah.
Speaker 1:Oh, yeah. They want you to believe in CTE. Counter example.
Speaker 2:Make it make sense.
Speaker 1:One of one sample. Counter.
Speaker 2:No. He's possible. He's actually the smartest person to have ever walked the earth. Oh. So even after getting...
Speaker 1:Yeah. Or or it could be reversed. You get hit in the head and like makes you even more of a genius or something.
Speaker 2:Yeah. It unstuck something.
Speaker 1:Yeah. Sort of like a like a
Speaker 2:What a legend.
Speaker 1:Magical realism.
Speaker 2:Tyler, is this gonna make you get into watching football?
Speaker 1:Yeah. Maybe you should play some future Maybe you should play some full contact tackle football. Might make you better at math. You never know. Ursula was blown away by the discoveries.
Speaker 1:And Monday, instead of studying abstract play designs, he was publishing some impenetrable language of his own to prove a decades old math conjecture. Urschel's paper titled On the Growth On the Growth Factor of Random Matrices is one of a deluge of AI assisted proofs that mathematicians have achieved in this new era when they can combine their genius with supercomputing power. But within this community of brainiacs, there isn't a single person quite like the 35 year old Urschel. After three years, the Baltimore Ravens, the offensive lineman who is listed at six foot three, three hundred and thirteen pounds ditched his helmet and became a professional egg head in 2017. He hasn't regretted it for a moment.
Speaker 1:He says, I feel like I bet on myself in a big way. I'm living my best life right now. You'll love to see it. Long before Urschel became an assistant professor at MIT studying matrices, spectral graph theory, and other subjects that sound like gibberish to just about everybody, Tosh shots fired by the journal. But I agree.
Speaker 1:He was an overachieving kid attending Penn State while playing for the Nittany Needle Lions. He did more than pursue an ambitious major by studying math and win, and he won the prize known as the academic Heisman. By the time the Ravens drafted him in 2014, he already had earned his master's. But a year after protecting Joe Flacco, Urschel started to miss his life crunching numbers. While he felt his analytical background was useful to him as a football player, for instance, it's a...
Speaker 1:He says it helped him learn the playbook. It didn't quite scratch his intellectual itch. That's when he decided to apply to become a doctoral student at MIT, the school he had always dreamed of attending. He began moonlighting there in 2016. And so...
Speaker 2:I know I know what this is all setting up.
Speaker 1:What is this?
Speaker 2:He is gonna he is gonna star in the next Terminator movie. Oh. Football player math genius fights back...
Speaker 1:Triple threat. Yeah. This is exactly...
Speaker 2:Takes on the clankers.
Speaker 1:Yeah. I would love to see it.
Speaker 2:I mean, you would hate to be a clanker Yeah. With this this guy walking you down. Yeah.
Speaker 1:For sure. But he seems to be he seems to be a friend of the clankers because he has actually been using AI in to assist him in his research. So his acknowledgments in the paper that he published cites the help of open AI models and says that he welcomes any question about the role of AI which is upending his entire field. He says, but supercomputers haven't dented the passion that this football player turned mathematician has for math. He compares it to his enjoyment of chess.
Speaker 1:He also likes chess. The computers are better than the humans, yet he stills enjoy... Yet he still enjoys the pursuit of finding the right move. I really want to understand the why of things, Ursula says. It doesn't ruin the why.
Speaker 1:AI doesn't ruin the why. That's a very interesting thing. And I feel like, I mean, just in knowledge retrieval over the past three years, I have felt that. Like like, I know a lot of obscure factoids about Silicon Valley lore. You know?
Speaker 1:But when Chad GPT came out, I wasn't like, oh, no. Like, the computer knows more about Silicon Valley history than me. They're like, oh no, like the computer knows more about the, you know, the random...
Speaker 2:Basically one of the top guys in your field.
Speaker 1:I mean, I did spend like five years making YouTube documentaries about these companies and like trying to learn a lot and like piecing it all together. And like ChatGPT can do that but like it's been such a benefit because I I wanna know. So I'm looking up stuff all the time and I'm like I wanna go on the journey. I wanna gain that information and it gives that to me. It's weird.
Speaker 2:Very very very... We benefit from having knowledge and understanding within us. Like, it's nice to go through life Yeah. And be able to understand things in real time versus reaching just because you have complete ax... You have complete access to Yeah.
Speaker 2:All the information you need and...
Speaker 1:Yeah. Yeah. I mean, there's still obviously the jobs question. How do you monetize that if your job was just solving math problems or your job was just, you know, assembling, you know, sloppy research reports. You you you might be going through a process now.
Speaker 1:But
Speaker 6:I don't know.
Speaker 1:I I'm still I'm still optimistic. I I I still I still feel like we're gonna be hearing from this gentleman, John Urschel, for a very long time. Yeah. Maybe he's like, you know what? I'm going back to I'm going back to football.
Speaker 1:Math has solved. I'm going back to football.
Speaker 2:No. Don't... Yeah. When when... I wonder when he actually gave comment for this article.
Speaker 1:This was like yesterday. Oh, okay. I mean, this literally posted today at 8AM. So I imagine that he was on the phone with The Wall Street Journal over the past like two days. Because it's a pretty detailed profile.
Speaker 1:We should get him on the show. I I I'd be fascinated to hear about what he thinks. Yeah. I mean I I think people will be continuing to study this stuff for for a long time and and and learning it to apply it for their for their for themselves. Anyway, Crypto is meming it.
Speaker 1:He says, OpenAI, we literally solve math. Him, he says, nice one. Now please use the DoorDash CLI to order me some orange chicken. Speaking of ordering food, Starbucks is thinking about buying Chipotle or they thought about buying Chipotle. The headline is Starbucks has explored taking over Chipotle according to a Financial Times report.
Speaker 1:This is a scoop in the Financial Times. Let's see. They've explored merging the two companies in a mega deal. It would be a $41,000,000,000 market value for the burrito chain, and it would rank as the largest restaurant acquisition of all time. So the world's leading coffee shop chain has worked with advisers in recent months on a takeover proposal for Chipotle, which I believe had McDonald's as an anchor investor a long time ago.
Speaker 1:I don't know how how divested they are at this point, but it's interesting to see that they're they're finding another dance partner. A burrito chain with a $41,000,000,000 market cap. The status of Starbucks' takeover plans and whether the company has submitted any formal offers to Chipotle could not be determined. A transformative deal of this size might struggle to get off the ground, the people warned, and a tie up may never materialize given the complexity of combining two consumer giants. The early stage plans would also reunite Brian Nichols, Starbucks chief executive with the fast food chain where he made his name over six years since as a CEO.
Speaker 1:So he was... He left Chipotle August 2024, went over to Starbucks from Chipotle. Chipotle has lost half of its value in that time. So he was, you know, performing well there, but maybe it's an opportunity to put them together. The combined market cap would be like 1 or 2,000,000,000.
Speaker 1:And It's also just what what
Speaker 2:you know, fast casual had been on a crazy run across the board. So I don't think it was I don't think it was him leaving that that caused the decline. But Yeah. He clearly loves the brand.
Speaker 1:Do you...
Speaker 2:Man, it it is so crazy to think about there was a time in my life where I was actually averaging one visit to Chipotle every day.
Speaker 1:Every day.
Speaker 2:You know, some days I wouldn't go, but other days I'd go twice.
Speaker 1:You ever driven Starbucks?
Speaker 2:And now I'm not even averaging once a year.
Speaker 1:Woah. Yeah. Just moved on. What about Starbucks? Have you ever been a Starbucks guy?
Speaker 2:Nah. So so bad.
Speaker 1:You don't like Charbucks?
Speaker 2:That's
Speaker 1:the Starbucks, whole the the the coffee is, like, burnt often or, like, it's a little bit more towards burnt, so they call it Charbucks. But it's... I think it's because people at the time liked it, so that was more popular. And then they add so many things on top with, like, caramel and whipped cream that like you can offset the the harshness of the raw black coffee ingredient.
Speaker 2:We got mosquitoes on the set.
Speaker 1:Lock in. Get the get the flyswatter.
Speaker 2:No. Do some damage. I'm shooting it away.
Speaker 1:Mike Isaac says coworker who clocks in to work carrying a venti whole milk quad latte in a double steak burrito bowl and head straight to the bathroom. Lots of jokes like that. PepsiCo says some really bad things. Joe Weisenhower shared PepsiCo CEO, we don't feel good about the beverage business. PepsiCo CEO, we're putting all the urgency to improve sodas.
Speaker 1:Yeah. Should they add alcohol to the sodas? Topo Chico, the sparkling water brand, created a alcoholic seltzer. Do you think there should be Pepsi? Pepsi Zero?
Speaker 1:Diet Pepsi? Pepsi Max? Pepsi spiked? What do you think?
Speaker 2:I think they should be optimizing for Pepsi Pepsi Pro Max Ultra.
Speaker 1:Pro Max Ultra. Yeah. It is interesting that, you know, Coca Cola never did like a rum and Coke. You could just do that if you were trying to just skew max and get the most number of stock keeping units around. Yeah.
Speaker 1:I don't know.
Speaker 2:They should make a... The the Pro Max Ultra should be like a magnum size Yeah. Model of soda. Yeah. That's where things are headed.
Speaker 1:Okay. Well, let me tell you about Railway. Railway is the all in one intelligent cloud provider. Use your favorite eight inches your boy web app service databases more while Railway automatically takes care of scaling monitoring and security. The last apocalypse, the ARR apocalypse.
Speaker 1:Or no. No. That's... This is the third of fourth of four apocalypses. We got a housing apocalypse coming up.
Speaker 1:But OpenAI reports revenues differently than Anthropic. And so there was a leak that said OpenAI was at almost 70,000,000,000. And then there's another report today that says they're almost at 50,000,000,000. And it's very clearly clear that like the numbers are very closely held. And then there's a game of telephone happening with investors and LPs and other investors.
Speaker 1:And then by the time things make it to the press, it's all over the place and it sort of shifts and people, you know, true up the numbers to try and create an apples to apples comparison. If you're not following the... It's like on AWS, if you buy an OpenAI model, OpenAI keeps that dollar as revenue. Anthropic gives some of that to to Amazon. They both probably give some to Amazon, but Anthropic counts the part that they give to Amazon as a cost.
Speaker 1:And so they they have a higher gross revenue number. And so making these companies apples to apples, it seems like that's what someone tried to do a little bit, but I don't know. It seems like they did it
Speaker 2:sloppily. Yeah.
Speaker 1:It seems like they did it sloppily.
Speaker 2:Still very unclear. And yeah. It's unclear why it didn't get corrected faster.
Speaker 1:Yeah. So...
Speaker 2:But but I mean, like, extremely different difficult environment to be operating in because part of the benefit of being a private company is that you... I mean, that like one of the biggest benefits. Yeah. You don't have to be on this constant reporting schedule. Yeah.
Speaker 2:But at the same time...
Speaker 1:You're like moving
Speaker 2:yeah. Because you're... Because these companies are at a scale
Speaker 1:Yeah.
Speaker 2:Where they... So much public market activity is predicated on Yeah. On their metrics
Speaker 6:Yeah.
Speaker 2:It's sort of you you at time, you know, it's prudent to actually share these numbers at at different times. But there's also so much, I mean And for all the different media companies are working in a in a perfect world, the legacy media would be like have a new article every day that just had all the updated numbers. Yeah. Like what was, you know, they would they would love to publish... Maybe like a quarterly report.
Speaker 2:Here was yesterday's revenue....
Speaker 1:Go on a website like Edgar online. You can call it Edgar online and and file it. With this SEC, there could be a there could be a whole commission around securities and exchanging securities. Yeah. I mean, Sam Sam's been talking a lot about IPO stuff.
Speaker 1:He he... What was his what was his take with Vanity Fair? He had he had something about IPO timeline, like, he'll do it when he needs to do it, whatever. The interesting thing is that I I do feel like the... Like, IPO ing is is aligned with, like, better corporate governance, getting...
Speaker 1:You know, distributing the gains, like, yes, like, actually get the stocks of all the labs into the retirement accounts. That was very controversial with SpaceX. When SpaceX went out, people were like, I don't want it in my four zero one k. But there is a scenario. Of course, it's like cap weighted, you don't need to actually...
Speaker 1:Or float weighted, so you don't actually need to worry about it, like, blowing up your retirement account. But there's a... There... There's a good case for, like, okay. If this really is really...
Speaker 1:If, like, the bull case is true, like, yeah, distribute the gains through everyone's investment accounts getting slices of it just immediately because that's the way things diffuse through the public markets because everyone owns ETFs, everyone has has some some money invested in the market. It becomes much easier than like the small group of insiders. There was this rumor that we were digging into, which which is like, is someone trying to manipulate the market? Because if you leaked a number about a private company, normally, that wouldn't move the market. Cause everyone's like, oh, who cares if like some tiny company like is, you know, doing okay on revenue?
Speaker 1:Like if I... Like if we leak that like Lucy is doing a real... Oh, we're gonna blow out revenue...
Speaker 2:There was no there was no point even look at even look at one of the greatest companies Silicon Valley has ever produced, Stripe. Sure. There was no point where if Stripe... If it leaked that Stripe had, like, missed their numbers for a quarter that, like, you would see...
Speaker 1:Like Visa spike or Mastercard spike. Yeah. It wasn't really moving the market in that way. Completely different with OpenAI and Anthropic. Like, SpaceX, Oracle, SoftBank, like, are plenty of public company proxies.
Speaker 1:And so it would be tempting if the SEC didn't exist to to go and leak a number that's better than what's true and then sort of buy the rumor, sell the news. So you would you would buy and then leak a really high number that's just a rumor to the press. And then and then when the real number comes out, you're already short, so you're you're catching that on the way down. This would be securities fraud, by the way. So don't do that.
Speaker 1:But it is it is a wild wild wild thing that we haven't seen before because manipulating public stocks with private company information just hasn't been viable in size before, maybe very, very tiny here and there. But there are some cases out there that make it very, very clear. False acquisition rumors are are popular. In 2008, the SEC sued Paul Berliner, a trader fabricated a rumor about a Blackstone acquisition while shorting the target stock. Media outlets picked it up, and the stock briefly fell 17%.
Speaker 1:Berliner settled SEC fraud and manipulation charges. And there's been... There's a Citron Research founder on securities fraud scheme around... Prosecutors alleged that he used influential public commentary while misleading investors about his trading intentions. The case did not require the same fabricated revenue fact pattern.
Speaker 1:So just interesting dynamics. So if OpenAI wants to wants to pop from 50 to 70, you're thinking hard seltzer. Right? Launch a hard seltzer. That would be the logical thing.
Speaker 1:It worked for companies.
Speaker 2:It's worked for many other.
Speaker 1:Why wouldn't it work for
Speaker 2:Fantastic.
Speaker 1:Businesses. Just give everyone a hard seltzer. No. Obviously, stick to stick to light beer. Let me tell you about CrowdStrike.
Speaker 1:Your business is AI. Their business is securing them. CrowdStrike secures AI and stops breaches. Was there anything else before we go into how the housing pocalypse? There were some there were some timeline posts you want to get to.
Speaker 2:Pull them up.
Speaker 1:You got this is the best aura flip? You wanted to see this? Are you are you on the timeline?
Speaker 2:My Computers haven't come my computer is There we go. Done.
Speaker 1:Oh, there's sound too? Ready for launch. Wow. Another person who dropped a nuke in Minecraft before Tyler can drop a nuke in Minecraft. Wow.
Speaker 1:A little sketchy.
Speaker 2:Can't say I'm can't say I'm surprised.
Speaker 1:And then doing the is great. It's so funny that you
Speaker 2:can actually pops
Speaker 1:these up.
Speaker 2:Can you imagine being 14 and seeing this?
Speaker 1:Amazing. Just absolute absolute joy. Yeah. Here's Wyatt Walls. This is this is what we're all about.
Speaker 1:I'm pivoting to something LLMs can't do, day drinking. 7,000 likes. Sean Frank is vague posting. We'll let you try and figure out who he's talking about. I have a friend, young, 39 figure exit.
Speaker 1:I've known him for years. Before he made it, before he knew what he was going to start, the one thing he always had, an unwavering belief that it was going to work out. He was willing to burn the boats over and over, always doubling down, always moving bigger and bigger. It's changed the way I want to operate. If you want to change your life, do it.
Speaker 1:If you want to win, then win. Don't let anything stop you. The only thing you have control over is your attitude and effort. I love some mindset posts
Speaker 2:Fire me up.
Speaker 1:The thousand like bagger on the Sean Frank post. He's locked in and he's encouraging you to lock in. Anyway, Chinese streamers are creating content on a completely different level. We have taken our livestream efforts very seriously here at TBPN. We have cameras that track us.
Speaker 1:We have Chirons and Stingers and all sorts of fun things. But it's clearly amateur over... Amateur hour Yep. Over here. Because in China, they have taken things to unthinkable heights.
Speaker 1:And we'll show you this video and you can understand how different it really is. There are levels to this game and we are playing it on easy mode. This is a... These are fairly large machinery that physically moves her chair with a seat belt and spins her when chat donates a mere $7 USD apparently.
Speaker 2:I think we should have a button that puts us into a racing simulator.
Speaker 1:Okay.
Speaker 2:And we can't go back to the show until we get a sub seven minute No.
Speaker 1:River ring time. Okay. I like that. That's fun.
Speaker 2:And and it should actually be a free button. Yeah. So anybody can hit it.
Speaker 1:I I I mean, I do think we should get this exact device and just put Tyler in it. And then if chat... Every time we get a follower on x, for example, he could he could be spun. It is it is crazy, the level of interaction. The the chat has has fun with, like, drink the Diet Coke with two hands every once in while.
Speaker 1:But this is a complete different level of of interaction. The gen... The generative UI stuff in Chatuchiki. We talked about it yesterday and got a chance to demo it. Much better experience with with thing...
Speaker 1:The answers streaming in. Like, asked about, like, explain this whole SEC market manipulation thing. I fire off a big prompt. And, like, it gets me the answer very quickly, and then it's... And then it goes and explains the laws, and then it builds some widgets about different items.
Speaker 1:And it's just a much it's just a much better UI experience and product experience. And I'm excited to see other things people do with it. You can even get it to make cheese. Write a poem about cheese in message bubbles that look like Swiss cheese. It says, oh cheese, my love, my golden delight.
Speaker 1:You make every cracker feel just right. From cheddar bold to brie so sweet, you turn a snack into a treat with whole... With little holes and dreams so big, I'd choose you over any fig. And if the moon were made of brie, I'd eat the stars and save some for thee. It's a nice problem.
Speaker 1:But we're here to talk about user interface design. And it's funny that it can, you know, lay out those circles. And and, yeah, the future of this is like it can just draw any pixel at at at any frame, at any time for anything so it can sort
Speaker 2:of Yeah.
Speaker 1:Generate video and graphics and games on the fly. And you will get your math... What was it? MMO? You wanted an MMO or something?
Speaker 1:I forget what it was.
Speaker 3:You wanted some...
Speaker 2:I keep asking for games. I don't really play video games. We gotta talk about we gotta talk about Paul Graham. Okay. What do Paul Graham says Amazon banning agents is the first opportunity I've seen since Amazon was founded for a startup to create an Amazon competitor.
Speaker 2:People will want agents to buy stuff for them. It will be one of the main use cases, and they don't wanna use some Amazon supplied agent to do it. Any business that bans agents represents an opportunity for a start up to create a competitor. If they need to ban agents, it must mean people want to use them or there would be no point. And that, in turn, means there's demand for a competitor that allows them.
Speaker 2:Your usage restrictions are my opportunity. It sounds sounds great. Sounds coherent. But says what you're probably thinking. I mean, they can just unban the agents.
Speaker 2:It's a single config file. So, yeah, imagine you spent thirty years building an Amazon competitor, the Amazon for agents. You spent thirty years. You've acquired millions of trucks. You employ more people than almost any other business on earth.
Speaker 2:And you're finally like, okay, I'm ready to really eclipse Amazon. And Amazon's like, okay, we unban the agents. Yeah. And then... So again, it's it's...
Speaker 2:The other the other the other pushback here from someone named David H says, how lazy can we get? There's absolutely no value in an agent buying something on Amazon. It's just something people do to sound cool. I already have a human agent who buys way too much stuff on Amazon called my wife.
Speaker 1:It's the most the most Gen Xer t shirt from Venice Boardwalk I've ever seen. You know, to really throw it in the old ball and chain there. It's ridiculous. Anyway, I think like I'm not there yet.
Speaker 2:Well, because I don't think it's an opportunity for start ups to try to recreate the biggest online commerce platform in history. If anything, it's like an opportunity for Walmart who is leaning more into agents. Yes. And I think we're going to see, does that actually turn the tides?
Speaker 1:Okay. Right? So are you long Sparky or Rufus? Because Amazon has Rufus, their internal AI agent. Walmart has Sparky, their internal AI agent.
Speaker 1:Are you going Rufus mode or are you Sparky Mac?
Speaker 2:I mean, how hard is it to tell your preferred agent to just get on Rufus?
Speaker 1:That's what I'm saying. I think Rufus is gonna go in the kennel.
Speaker 6:And I'm gonna have
Speaker 1:a kennel of agents. Rufus and Sparky will be in there. And then asterisk 7.5 will tell them exactly what to do and they will go and do it because they will think it's me. And they won't know. I think that's actually what's gonna happen.
Speaker 1:And I think this...
Speaker 2:You don't think Rufus will be able to detect... Figure out a bot detection No. Ability? No. Like, because it'll just be happening...
Speaker 1:Computer use and it'll just be... And I'll just be like, yeah. Yeah. Okay. Codex.
Speaker 1:For the next five hours that I'm on the computer, just actually record all my mouse movements and like create a program that actually mimics me. Like learn my style of using the computer. Like when do I alt tab? When do I use the, you know, search? When do I type?
Speaker 1:How how do I type? What type of spelling mistakes do I make when I type? Learn all that. Memorize that. And then mimic my computer use pattern so you're not like right now it's like it's like go straight there, right?
Speaker 1:But it will just be like, oh yeah, I'm I'm using the computer like I'm John. I'm writing like I'm John. Can, you know, it'll be close enough. And and I think that people will wind up... They'll still have like their favorite front door to AI and it might be ChatGPT, might be Claude, Gemini, whatever they like, whatever they wind up in.
Speaker 1:But then
Speaker 2:of interviews. Yeah.
Speaker 1:But then they might like... There are people that will have, oh, yeah. I do some work on X and and so I want to manage all my DMs. So like I have GrockBot set up and I don't talk to Grock Bot but my agent talks to Grock Bot. And then my agent goes over to Muse because Muse has really good integration with Instagram.
Speaker 1:And if I try and go scrape Instagram, I'm... I am going to run into a bunch of problems. But Muse is happy to talk to me. Yeah. And so...
Speaker 1:Especially with these with these these systems that operate over chat, it's like it's gonna be really, hard to do bot detection if you're available over Telegram or WhatsApp. Right? It's like it's just text that's coming back or like like... Or we integrated with Slack. And so now I talk to it on Slack.
Speaker 1:It's like, you're telling me that it can tell what's what's an AI prompt that comes over a Slack message that's 12 words? Go buy me
Speaker 2:Yeah.
Speaker 1:A paper towel holder from the 1960s.
Speaker 2:Dark says the opportunity is to create a kennel for dog themed pigeons.
Speaker 1:Kennel AI. Now raising. Yeah, Kennel SI. Okay. So should we go over to the mansion section?
Speaker 1:We are off tomorrow in some heartbreaking news for you all. So we got to do some mansion we have to do it. We have to do it. We have to get to the mansion section, so we gotta talk about it. Today, the first home comes from a tech founder.
Speaker 1:The Shutterstock founder. Have you used Shutterstock?
Speaker 2:Not a big Shutterstock customer Shutterstock. But a wonderful, fantastic business.
Speaker 1:Yeah. Really cool business. And, yeah, I remember it being really useful for finding stock photos, stock videos. Always a little more... A little out of my price range for a while.
Speaker 1:Like, you'd have to pay per image. And I I always like the the unsplash, the free versions or the cheaper versions or maybe just go into Google Images and right click. You know, who knows? Jonathan Oringer is the founder and his wife have listed a slope side townhouse in the ski destination of Aspen, Colorado for $49,000,000. They bought the house for 25,000,000 in 2021.
Speaker 1:They own the Kansas City Chiefs from from the guy who owns the Kansas City Chiefs. Sorry. They're listing the house because they want to expose their children to skiing around the world. They don't wanna be in just Aspen. They wanna be able to travel all over the place, but they did say they'll be back.
Speaker 1:I will be back. He founded Shutterstock in 2003. This summer, a $3,700,000,000 merger with Getty Images was called off. Interesting.
Speaker 2:Which again, I remember talking about that. It's just the most insane
Speaker 1:Oh, yeah. To block that so crazy.
Speaker 2:The most.
Speaker 1:So crazy.
Speaker 2:A a category that is like Yeah. Facing the most brutal competition that seemingly any sort of semi modern technology company has ever faced and still... No. You gotta go at it alone. Yeah.
Speaker 2:You gotta fight and survive on your own.
Speaker 1:Good luck. Good luck. Yeah. It's really it's really thrown at the shovel. Take your own grave.
Speaker 1:It's brutal. Anyway, not not a white pill for government regulation nailing the AI moment, but we still have some optimism around here. Let's do a little tier list. Where's Aspen? In terms of vacation homes for you, Jordy, where are you putting Aspen?
Speaker 1:Is it in s tier, a tier, b tier, c tier, d tier? There's no e tier today. There's just f tier. Where's Aspen ranked?
Speaker 2:I'm gonna be exposed here. I've never spent any time in Aspen.
Speaker 1:Okay.
Speaker 2:I've only spent time in Beaver Creek and Vale. Really comment. But I but I but...
Speaker 1:I've actually... I've never been skiing there. I have been hiking there. It's beautiful. Let's put it in b tier since we're sort of neutral.
Speaker 2:No. It's obvious. It's obviously s tier for a certain for a certain kind of person. Right? You want you want the access to the outdoors, but you want the glitz and the glam and the shopping and the restaurants and the sort of scene dynamic.
Speaker 2:Sure. And so...
Speaker 1:Yeah. Aspen, I do have...
Speaker 2:Yeah. You can... You don't need to ask somebody where they spend their, you know, winter holiday.
Speaker 3:Right?
Speaker 2:You know, based on...
Speaker 1:We're going s tier with Aspen as
Speaker 5:a location.
Speaker 2:I think it's s tier.
Speaker 1:Okay. Okay. That's great. I love that. What about the fact that it was built in 2004?
Speaker 1:Is that too old or is that new enough? You get all the amenities that's nice and fresh?
Speaker 2:We don't know how to build anymore
Speaker 1:Okay.
Speaker 2:Like we did in 2000
Speaker 1:In 2004.
Speaker 2:Was a wonderful it was a wonderful vintage.
Speaker 1:Okay. 2000... So s tier again?
Speaker 2:A tier.
Speaker 1:A tier. A tier for 2004 build. 6,600 square feet of Aspen Townhouse. Is that enough for a townhouse in Aspen?
Speaker 2:I mean, it's humble. But this is a humble group. Right? They don't they don't want their children to just grow up
Speaker 3:Yeah.
Speaker 2:You know, only Yeah. You know, being in the bubble of Aspen. Yeah. Want their...
Speaker 1:He probably walks around the town and they're oh, what do you what do you do? He's like, yeah, I got a website, you know? Yeah. Yeah. So so it's a tier?
Speaker 2:A... Yeah. A tier.
Speaker 1:A tier. Okay. A tier. Let's go. It's being sold furnished.
Speaker 1:What do you think of fully furnished home sales? Is that a good feature or is that just a problem you gotta deal with? Gotta unwind all their
Speaker 2:Depends on...
Speaker 1:Well, take a look at the photos. And tell me, given this furnishing, I feel like if you're going to Aspen, you're not gonna be... I'm
Speaker 2:I'm I don't wanna sit here and and judge his I don't wanna I don't wanna place judgment on his furniture collection.
Speaker 1:That's the name of the show, dude. You gotta do it.
Speaker 2:Onto my head. Do you like the do you like the interior design?
Speaker 1:No. No. No. No. No.
Speaker 1:I'm saying in the abstract, as you are shopping and you see it comes No. Fully furnished is that a good thing or not generally? Generally. No. No.
Speaker 1:You you you want to buy bare bones usually.
Speaker 2:Yeah. Absolutely.
Speaker 1:Okay. Let's put that in d tier. Yep. Yeah. It's not...
Speaker 1:And that's not a judgment on this particular house or this these furnishings. Okay. What else we got?
Speaker 2:The thing I love most about this house 2.6 is pointing out, no infinity pool. Yeah. The... Put that in yard.
Speaker 1:No infinity pool.
Speaker 2:The yard is seemingly relatively small.
Speaker 3:Okay.
Speaker 2:But there's they seem to be surrounded by wonderful trees and and it looks to be a very nice neighborhood.
Speaker 1:Okay. Okay.
Speaker 2:So I'm just really glad there's not a concrete slab with an infinity pool.
Speaker 1:Okay. There's one last thing to debate. There's construction at the Aspen Pitkin County Airport. And there's been speculation that this could hurt the real estate market. Now Shane, the agent here, says that he thinks the impact will be minimal.
Speaker 1:He says it's not a concern of mine. So... Aren't they
Speaker 2:upgrading the airport to make it easier to fly in and out Yeah. During heavier conditions? Yes. So there's gonna be like minimal disruption and And
Speaker 1:then a benefit. So where are we putting that on the tier list? Airport construction nearby.
Speaker 2:Local airport upgrade.
Speaker 1:Airport... Upgraded airport. It's an amenity. Yes.
Speaker 2:Is that the... It's feature, not a bug. It's feature.
Speaker 1:S tier. Okay. I think we got I think we got a fantastic I think we got a fantastic review here. So go check it out if you're in the market. Anyway, our next guests are already here ready to come in.
Speaker 1:But first, let me tell you about public.com. Investing. Invest across all asset classes. Let's give it up. Automate anything with AI agents, run trading strategies, risk management, money movements, and monitor markets with AI agents for investing on public.
Speaker 1:And let's bring in
Speaker 2:Our next yes.
Speaker 1:Nano and
Speaker 2:It's incubated. Good.
Speaker 1:A new play. Oh, yeah? There's also chasing cash funds. So let's bring them in. We got a lot going.
Speaker 1:Get that gong ready. You already hit it once today, but I think we might have to hang up again. How are you both doing? Thank you so much for taking the time to join the show.
Speaker 7:Thanks for having us.
Speaker 8:Thanks for having
Speaker 1:us. Not much. We already warmed...
Speaker 2:Michael, I gotta say you look... You got like a new look going or something. It's fantastic. I don't know what it is. The flow is working.
Speaker 2:You got a kind of a summer glow.
Speaker 8:You know? Summer the summer vibe, it it fades slowly, you know what I mean? I'm just trying to I'm just trying to keep it going as long as possible. It's like 75 and beautiful here in New York City today.
Speaker 1:Lucky.
Speaker 8:Once the weather turns, you know, have me... Have us back on, I'll probably look absolutely miserable. But right now, feeling good.
Speaker 2:Well, it's almost a 100 degrees here in LA and I'm wearing a puffer jacket in the studio. A lot of people are asking questions but it's a nice jacket. Yeah. So... Yeah.
Speaker 2:It's cool. But anyways, fantastic to have you guys on on this monumental day for the firm. So, yeah, break it down. What's going what's going on?
Speaker 7:It's a big day. We announced our new set of funds. We raised $900,000,000, which is the largest fund cycle in the USV's history. We
Speaker 1:Let's get a replay on that. I wanna see that again.... Blow up the replay.
Speaker 2:Sorry to interrupt. But let's let's watch that back and then we'll continue.
Speaker 1:Oh, we don't have it. Oh, okay. Replay today. Oh, man. Continue.
Speaker 1:What's the plan? Is anything changing or is it just an expansion of the same strategy?
Speaker 7:It's exciting because a bunch is changing and a bunch is staying the same. The market has rapidly evolved around us. Yeah. Rounds are bigger. Yeah.
Speaker 7:You might have heard. Things are moving quickly. Opportunities are really, really large. We have some awesome new members of our team like Mike. But the idea driven focused investing that we've always done, we will continue.
Speaker 7:We'll just do it with more firepower and able to lean into the things we're excited about in a bigger way.
Speaker 1:Yeah. How are LPs responding to the idea that companies are staying are staying private longer? I mean, the the the revenues that are out there from 70,000,000,000, 50,000,000,000, 20,000,000,000, like, we've never seen these before. They are getting out into the public markets. But there's just a new level of size that can happen in the private markets.
Speaker 1:And I think that changes the underwriting. But how are LPs thinking about the trade off between just the scale and the actual timelines?
Speaker 5:Yeah. I think I
Speaker 8:think LPs understand that, you know, much like Rebecca said, the game on the field is is changing, not just in terms of the scale of capital raise or the scale of ambition Mhmm. But the scope and the shape of these companies, you know, a lot of the things that we're investing in here at USV simply just require more capital and they require more time. You know, Rebecca, as an example, has been investing in robotics now for years here at USV, which just requires significantly more capital in the physical world than pure software does. In 2021, USV launched an energy fund that has been investing in nuclear and new means of battery production and storage and same thing. Those companies need more capital.
Speaker 8:They need more time to bake. So I think RLPs look at it as a continuum And I think they see that the opportunity is bigger than ever before.
Speaker 1:Has the rise of the Decacorn acquisition changed your strategy or maybe conversations with LPs? It's just we've had, what, Grok and OpenRouter and there's Hugging Face. There's just been so many transactions that were pre... In the previous area, was like the WhatsApp and really nothing else. And now, it feels like there are a number of buyers that can take a...
Speaker 1:Take down a 10 or $15,000,000,000 acquisition. And that just feels like it changes the math, but how are you actually grappling with that new normal?
Speaker 7:Yeah. I mean, it... Our job, right, is to think about what that upside could be. Yeah. If things go right, what does this wind up looking like?
Speaker 1:Yeah.
Speaker 7:And the... When you play that out and when you think about what things going right looks like, the highs are higher. Right? And there's proof points in the market to support that. Mhmm.
Speaker 7:And so when we think about making investment and say, do we think this can return our fund? Mhmm. Maybe do we think this can multiply our fund? The evidence out there to support that being possible when you go after the big ideas is stronger than it's ever been. And so as we modeled out what we wanted to do, I think it warrants a different kind of capital base.
Speaker 1:Yeah. How do you guys......
Speaker 8:Think that, you know, being small is kind of what... Or smaller relative to the rest of the market, right? We raised $900,000,000 which is large for us, but it's than lots of other big sort of mega funds that are out there today. And being smaller kind of enables us to have a very, very interesting return profile, right? At a $900,000,000 total fund size, we can return very, very meaningful funds with companies that exit in the single digit billion dollar valuations.
Speaker 8:But in the history of the USV, there have been companies that have reached 50,000,000,000 evaluation and 100,000,000,000 evaluation. And when we've had those, they've returned some of the greatest performances in venture history. So that's why we really like the strategy of having the capital to play the game in the field today while also staying smaller.
Speaker 2:Yeah. How is the firm reflected on just how good the venture industry had it, call it, fifteen years ago when you could, you know, go... You know, buy 25% of a company with, like, the most talent dense team for a million bucks. Whereas today You know? For today, that same quality of team, they're coming at you and they're like, look, I really wanna work with you, but, you you know, you gotta put together at least 30,000,000 for me on on one twenty post if we're if we're gonna do this just because that can often be market for a super talent dense team getting off the ground.
Speaker 7:Yeah. Look, it's, it would be nice to be able to buy pieces of the, you know, companies we're most excited about for what we could a decade ago. But you cannot like elements of the market, but you're not gonna change elements of the market. And so I think our attitude towards that has been take the game on the field, think about what we wanna do and where we excel, is this really idea driven, you know, focused investing, and model a fund that allows us to do exactly that with the market conditions of today because we can be nostalgic about it, but we're not gonna get back there. We don't think it's going back there anytime soon.
Speaker 2:Totally. Yeah. Why would it? Trends trends in New York specifically, like how are you seeing talent flows? We've seen a bunch of amazing companies Yeah.
Speaker 2:You know, and stay in New York primarily even if they do set up, you know, maybe an SF office at some point. But what kind of conversations are you guys having with entrepreneurs that are based there that are kind of early in making those decisions around where are we really going to be based?
Speaker 8:I think one of the cool things about New York is because it's maybe not as at the epicenter of AI right now, companies can actually build here to a lot of strategic benefit. As an example, one of our portfolio companies, Suno, they have offices in San Francisco. They have offices in Boston. But their biggest office is actually becoming New York City because it's strategically advantageous to maybe build around the market there and attract talent that is interested in consumer and other sort of like non pure enterprise or infra areas. And so think New York City offers a ton of advantages.
Speaker 8:Obviously, we've probably talked about them on here a million times like the intersection of many different industries. But we're seeing a ton of companies both in San Francisco and New York as well. And you know, there's there's plenty of opportunity here. What do you think, Rebecca?
Speaker 7:Yeah. I mean, I also think, you know, USB was really started at this moment where an infrastructure moment was transitioning to a deployment and application moment. And in those moments, talent tends to diversify and opportunities tend to diversify on where they're located. And you saw companies grow in different places. We think we're in a similar moment right now where we've been in this kind of massive infrastructure build and you're starting to see this deployment and application era happen, which gets us really, really excited, and something we're spending a lot of time leaning into.
Speaker 7:And so our guess on that and what we're starting to see is those op... You know, those applications are built in many different places. You know, that being said, like, our job is to invest in a relatively small number of opportunities that we think are dead on on theses we're excited about, by the best people, and we're gonna be where those teams are. And so, you know, we love the New York ecosystem, and some of the companies are gonna be here, and many of them will not.
Speaker 1:How do you think the skill sets of founders who are the best possible fit for USV partnership is changing. It feels like there's... You know, you can be more... Even more technical. Like, some of the founding teams are, like, you know, academics for, like, twenty years and, like, PhD published and then they go and they start a great company.
Speaker 1:There's also people who, like, almost look like real estate deal makers and then they're spinning up a neo cloud and they're doing really well. There's people that are just great at creating these talent vortex. It feels like we're at a very diverse time of entrepreneurial talent, but like how are you grappling with it? Who do you who do you attract to most across like they have a brilliant idea or they're great at product or you know that they're going to be able to marshal all the pieces to really scale quickly? Like, what is your philosophy around the type of partnership that you're looking for?
Speaker 8:Yes. So I think USB has always been very product centric. Yeah. When I was a founder of building in New York, I knew that about USB. USB really projected that.
Speaker 8:And then when I joined six months ago, I learned very quickly that all the partners here, all the team here, they really have to fall in love with the product. Product. And obviously, that doesn't necessarily always apply to being able to use a micronuclear reactor. But in general, would say we're all very oriented around products and loving the things that are getting built. I think that remains true to this day.
Speaker 8:However, as we all know, as a result of AI, it's getting easier and easier and easier to make a good product. Right? AI is just lowering the barrier. Yeah. And so I think one thing that we've all been talking about internally is you can make a great product but it's still really, really hard to win.
Speaker 8:And so I think we're thinking about what are the qualities that founders need to have to be great at go to market and breaking out from the pack and telling the story. And also, maybe obviously, raise capital. Yeah. In this market, it's definitely becoming a little bit of a game of the haves and have nots. Mhmm.
Speaker 8:It's potentially easier than ever to kind of out raise your opponent and sort of suck the oxygen out of the room. And so one thing we think about when we see founders who are building great products and telling great stories is, is this founder gonna be able to raise a lot of capital because it's going to be expensive to win and that's going to matter a lot. So...
Speaker 7:Yeah. I think...
Speaker 2:Yeah. The era where... I
Speaker 7:was just... I think we're at this time where like four things really matter. Where you need depth of perspective and experience in something that other people don't see the same way that you do. You need to be an absolute talent magnet at probably the hardest time ever to hire a great team. You need to be faster than everyone else, and you need to be so good at telling a story that you win the market before it's obvious.
Speaker 7:And I think we are looking for the teams that can kinda nail those four things.
Speaker 2:Totally. Mourning the mourning the era of the earnest hacker who could just make, you know, the beautiful product with, you know, a couple other people and and win. It feels like it feels like we're past that. How do you... As as a firm that's, you know, historically been so thesis driven, how do you think about the right time to actually share those ideas and try to become...
Speaker 2:And use them to to sort of bring in the right opportunities and and teams and start those conversations versus when you wanna keep something, like, effectively quiet. Because if you guys put something out now, you know that all of your, you know... We're we're in a pretty pretty positive sum industry, but your competitors will read that and be like, oh, that's interesting. Like, I'll go try to give, a bunch of money to to teams in this category as well. And sometimes you wanna let something kind of cook for a little.
Speaker 8:One of the cool things about USB is... That I've learned in being here is nobody's precious about the ideas. Right? We're constantly having conversations. We're constantly trying to imagine what the future looks like.
Speaker 8:But we don't keep them like closely guarded secrets. In fact, we kind of try to put them out into the world as early as possible so we can learn. Those of you who follow us on X, like we're constantly putting out frankly half baked ideas, things that probably haven't really been fully thought through yet because we feel like by doing that, we actually get to learn. And not only do we do that through putting our thoughts out through writing or X, but we also do this through investment. We're willing to bet on ideas that seem pretty out there, pretty sci fi early on in the journey because historically, that's actually how we've made a lot of money.
Speaker 8:But again, we also get smarter by doing this. A great example of this is a few years ago, USB led the seed in Doctronic
Speaker 1:Oh, yeah.
Speaker 8:Who I believe has been on the show somewhat recently. I remember from the outside in seeing USB make that investment. I wasn't at the firm yet and thinking to myself, wow, this is pretty sci fi. This is a company that's trying to put a doctor in my pocket. They're saying they're going to literally be prescribing medicine from AI in a couple of years.
Speaker 8:And sure enough, they're doing that now and it almost seems obvious. Right? But I think the way that USB was able to do that was by putting their ideas out there into the world, learning from others and being willing to test the really crazy ideas. I mean, Rebecca, like, you've been doing that here for, you know, nearly ten years. Like, how how have you all done that?
Speaker 8:Overnights.
Speaker 7:I mean, I think the, like, Kool Aid of USB that I've drank so strongly is that gatekeeping ideas is a totally losing game. Yeah. That the benefit we get from putting ideas into the world, pressure testing them in public, getting all that feedback, having people tell us we're dead wrong and dead right, and having that conversation in public, and the people that then come out of the board work to say, you have to meet this team that's working on it or this adjacent thing is so, so, so much more valuable than any kind of benefit you would get from holding things close that we are huge believers in public thinking and the benefit of also spurring activity through the public thinking.
Speaker 2:Makes total sense. How are you thinking about... As we've seen these sort of mathematical breakthroughs, and we've talked about this on the show a bunch, math is cool because you can sort of, like, deliver solutions or partial solutions to all of this this, like, all of these open problems, but, there's sort of an incentive to do that just because it's pushing humanity's knowledge forward. And there's not always like a real time commercial application of this sort of like new knowledge. At the same time, you have to be betting that these same models, which will be in the hands of the public, you know, hopefully soon, are gonna allow people to have breakthroughs in, like, tons and tons of different fields.
Speaker 2:And the labs can't possibly, like, monetize every discovery. And to me to me, that says, like, we could have, like, a mini scientific renaissance, like, over the next twelve months and create this incredible opportunity for small teams to, like, get access to these models, push them, and and, again, have these breakthroughs and then be in the position to sort of race to commercialize them, develop IP around them, etcetera. Is that something that feels top of mind for you guys?
Speaker 8:Absolutely. I think one thing we like to do is we like to imagine what the world looks like at some point Just in the to use an example, robotics. We think a lot about the physical world and physical intelligence and not the digital labor, that will be, replaced by intelligence in some way, but the physical labor that will be augmented by physical intelligence, farming, construction, things like this. So we imagine that point in the future. And then we look at a technology today and we try to imagine what the slope of that technology has to be to catch up to that future we imagine.
Speaker 8:And then it's about finding the team that we think can both make the technical breakthrough to make that slope actually possible and who's also commercial and commercial enough to sell the idea and sell the vision, again, for all the reasons we talked about hiring people, raising capital, eventually selling to customers. And an idea like that is what led us to back a generalist recently who, again, I think also has been on the show recently. So we're very much into the idea of backing technologies at their nascent stage as long as we can imagine the future that they will ultimately deliver to.
Speaker 1:How are you thinking about innovations It these feels like you have enough capital to to get a serious foothold in those rounds that start later and are bigger and you need the capital. So you're set up for success there.
Speaker 2:I feel like if you give Mike a long weekend, you're gonna end up with a new incubation.
Speaker 1:Yeah.
Speaker 2:It's true. There's one coming up. There's one coming up
Speaker 6:this week.
Speaker 1:But what's the... Yeah. But what is the over... Is there an overarching thesis? Is it very opportunistic?
Speaker 1:Like, what what is the correct pattern? Like, what can you tell us about how the firm is thinking about incubations across this fund? Rebecca, you want me to take this one or...
Speaker 7:Yeah. Yeah. Go for
Speaker 8:So... Yeah. I think I think what you're referring to, Jordy, is is Super Take, which is a company that launched out of USB not even two weeks ago. Super... Oh, wow.
Speaker 8:I like that
Speaker 1:one. Dramatic. It's good.
Speaker 2:Super Take
Speaker 8:is a product that lets anyone express an idea or an insight they have about the world and an agent, a Frontier agent will go and build an investable portfolio for them. It will invest in it automatically. And then in real time, it will be rebalancing it for them to achieve their goals, whatever they are for that portfolio. And where this came out of was actually our thesis work that's been going on for much of the year around what we call obliterate, don't automate and the idea of agents not just automating existing markets and automating existing workflows but actually reshaping those markets completely. We've made bets across healthcare as an example.
Speaker 8:We talked about robotics. And we think personal finance is also a place where this is going to happen. Many of us in the world, we use financial advisors or we invest in ETFs or some of us even YOLO into individual stock picking. But we do it without the... Oftentimes without the knowledge or the expertise or the fundamentals of what is going to make an investment good or great.
Speaker 8:And we think that's something that AI can help people with. So why shouldn't everyone in the world have a personal agent working on their behalf? We went out to find this company. We met with every company we could that hopefully we thought might be doing this. And we didn't find anyone doing it.
Speaker 8:And we said, You know what? It's gotten easy enough to build products. Then maybe we should just go and build this ourselves. And so, as Jordy, as you mentioned, we've built Super Take. We're hiring leaders to put around the company right now.
Speaker 8:And to speak to I think the bigger question, John, about incubations, I think we don't really necessarily think of them as incubations. We think of them as kind of bets on inception stage companies and people. Right? If there's a person in our network that has a great idea, we should give them the capital that they need to go explore that idea. And if we have an idea that no one is yet exploring, let's go find the person and match them with the capital to go explore that idea.
Speaker 8:And again, AI, this is this is the theme among the whole conversation today. AI is making it much much easier to do that.
Speaker 1:Cool. Well, thank you so much for coming on the show. Congratulations. When
Speaker 2:they go high, you guys go higher but stick to your guns.
Speaker 1:But still low.
Speaker 6:Yeah. Still a little bit low.
Speaker 2:When they go higher, we we we... Yeah. Yeah. Yeah. Awesome rest
Speaker 1:of your day. We'll talk to you soon.... Guys. Goodbye. Thanks.
Speaker 1:Bye. Let me tell you about Figma. Agents meet the canvas. Your AI agents can now create and modify your Figma files with design system content. We got Nathan back on the show from Air Street Capital here to break down the state of AI report.
Speaker 1:Nathan...
Speaker 2:Finally, can get to the bottom of this whole AI...
Speaker 1:Do you think there's anything here? Like, I've been hearing about the AI AI super AI super intelligence, like, is there a there a there?
Speaker 3:It's everywhere.
Speaker 1:It's everywhere.
Speaker 3:I I I don't I don't know where to look and not see AI nowadays. I mean I'm in the... I mean, in at the moment and you can't get around it.
Speaker 1:Yeah. It's crazy. So how do you... I mean, obviously, there's the there's the, you know, infinity takes. There's the...
Speaker 1:It's a zero take. You're trying to find something that's more moderate, more realistic, more grounded in reality. What is your process for making sense of all the chaos and all the numbers that are flowing out, all the rumors? How do how do you think about this market right now? What's your what's your what's your philosophy?
Speaker 3:Yeah. Yeah. Well, the philosophy was starting actually Capital about ten years ago was really to find opportunities to invest in AI where you could really invent new product experiences, solve problems that you couldn't solve before.
Speaker 6:Sure.
Speaker 3:As a function of all that work, as you said, like I get to talk to people in policy, research, industry
Speaker 1:Yeah.
Speaker 3:The safety community, etcetera. So I'm hearing all these different voices and opinions and I'm reading, you know, what's... What the media is reporting and then talking to people who are actually building these things. And so, like, about ten years ago, I thought, like, it would be increasingly beneficial for the public to get access to a kind of document that was balanced, just as you said, and is very research driven and, you know, has some cheeky opinions in there because no one wants to read a boring consultant report. And and, you know, publish this on an annual basis in a way that's, you know, hopefully unbiased and and open.
Speaker 3:So, I mean, my take of the current market is really like... Just looking back ten years ago, I I still think what we have today is like truly magical. I don't think anybody in the AI community would have ever expected it to happen this fast that you have this like magical tool you can do increasingly anything you want with and it's just so damn empowering.
Speaker 1:Yeah.
Speaker 3:I think that they can't be like a more exciting time to be alive. Like even though we're balancing it with all these like emergent risks and issues that we get into in the report as well.
Speaker 2:But is that true though? Because I feel like some folks like predicted this almost perfectly and now...
Speaker 1:Some people were like it's gonna happen in 2004 and we're all gonna be dead. Like, that was another thing. But, yes, in general, consensus in the AI community was like, this is gonna be recommendation systems for a long time.
Speaker 3:Yes. When I say predict, I'm talking about people back in 2010. So like the OG, like AlexNet, like, first wave of deep learning, not the people who started predicting two years ago and just kind Yeah. I'm not saying it's a bad prediction. I mean, it's fair that, you know, some people can't imagine how you can extend a line and remember all those memes about, look, my child is five months old, like, you know, ten years is gonna be like the size of the planet.
Speaker 1:Christian Kyle. Of the greatest, most viral posts ever. It's a great one. Yeah. Give me your cheekiest take.
Speaker 1:You said you mentioned
Speaker 3:cheeky Yeah. Yeah. Yeah. Also the... So you've you've been seeing, like, companies just becoming inference businesses.
Speaker 3:It doesn't matter if you're, a neo cloud or or an Inference Cloud or like an app or
Speaker 1:whatever So this is the Neo Lab to Neo Cloud pivot that's sort of happening below the surface sort of throughout That's the
Speaker 3:right. That's right.
Speaker 1:Start of the business.
Speaker 3:So I think you can can meme it by saying you either die getting to the frontier or you live long enough to serve inference.
Speaker 1:Sure. Sure.
Speaker 3:Yeah. And you can see like companies that have tried to build frontier models and have failed have eventually like pivoted towards building huge data center Yep. Businesses to serve Chinese models
Speaker 1:for Okay. So not... I mean, SpaceX is like probably the most prominent example of, you know, probably making more money from selling access to their data centers. But that's where the unique capability is, especially when you think about the space thesis. But but you're seeing this up and down the stack with companies as small as Yeah.
Speaker 1:With with $100,000,000 raised?
Speaker 2:Yeah. With the with the, you know, some of these inference businesses, you know, I was talking to someone last night and they were saying, you know, running with horrible margins or negative gross margins, you know, there's been some examples where it didn't work, but, you know, they they brought up, you know, Uber as an example where it clearly was decision to optimize for market share. The quest... Yeah. The big thing is that Uber was competing for what what was...
Speaker 2:What what ultimately now is effectively a monopoly or a duopoly at best, but still very dominant. And my question with these inference businesses, honestly, they're all effectively in market right now raising massive up rounds. There's a lot of demand. They're they're oversubscribed because the growth is so intense. But my question is like when when when and where will margins normalize?
Speaker 2:And are investors... How are investors gonna process businesses that could actually in their end state end up with these like very, very, very slim margins that we just haven't seen. We didn't see it in cloud. We've never seen it in in software. And they could look more like, you know, energy, you know, utilities over time, which could still be big businesses.
Speaker 2:But I don't... A lot of these businesses are not being priced that way yet. And eventually, they're gonna have to to have... Find the sort of, like, steady state.
Speaker 3:Yeah. Yeah. I think these are all these are all important questions. I mean, the numbers are astonishing. You know, like, hyperscale is spending, you know, 700 and something billion dollars a year and potentially doubling, like, year on year.
Speaker 3:Like, Neo Cloud's also ripping to billions of contracted revenue and, you know, gigawatts of compute. And so I I I think, again, what I like to... With the state of the airport is, like, you get to kinda go back in time and see what the world was back then and just, like, contemplate some of the questions we ask ourselves today and consider if we even asked those questions a couple years ago. And I think it was only really, like, at GPT three or so, the the world was still considering that compute was important for training, and we didn't really talk very much about the need for inference, like, at all
Speaker 1:Yeah.
Speaker 3:Including the fact that some of the biggest inference companies today were not born as inference companies. So that's another sign that, you know, they didn't foresee, like, the demand. So I I I think it's all just getting back to this point of, like, we have incredibly powerful technology. Just 99 of the world doesn't know how to use it because the 1% that built it is not very good at educating the 99% about how to use their black box. So I would love there to be a sort of genius bar for AI.
Speaker 3:I think like OpenAI should open a pop up or Anthropic should open a pop up to just get people... Charleston AI.
Speaker 1:AI. There's someone doing this in Charleston.
Speaker 3:They're on
Speaker 1:board personally. Yeah. At the strip mall...
Speaker 3:Squad, guess.
Speaker 1:Yeah. Geek Squad. That makes sense. How have you been processing that chart that's been going around that shows the share of compute allocated to pretraining, RL and inference? And inference sort of remains 30 ish percent across the sample But pre training drops from like 50% of GPU utilization to like 10%.
Speaker 1:And then you just watch RL grow grow grow. There's a bunch of ways to read into that, but what's your interpretation?
Speaker 3:Yeah. I... Mine is that the the last like, you know, four or five years or so has been trying to figure out what the recipe is.
Speaker 1:Mhmm.
Speaker 3:I e like I'm trying to do pre training, what is the best, like, data mixture? Sure. What are the best, like, you know, ingredients to this whole to this whole magical soup? Mhmm. And then over the years, like, we've figured out how to do that better, and so it's it strikes me as normal that one would end up spending less money on exploratory work because the solution is more, like, in in the in the plain eye.
Speaker 3:And then the whole pitch with RL is really that... And DeepMind showed this with their first, like, Atari games, you know, ten years ago, which is one of the most magical moments, I think, in AI at the time. And it's this idea that, like, what what we have as solutions today are really, like, local maxima that were discovered through human ingenuity and sharing through reading and whatnot. But, like, that doesn't necessarily mean it is the global maxima. And you can get a computer to explore the search space for as long as you want, and for as long as you want equals how much money you want to spend.
Speaker 3:And that amount of money you want to spend is really contingent on what, like, you're trying to use it for. Yeah. And so if your if your problem is, like, super hard and really valuable if you crack it, now we're in this point of, well, want the solution bad enough. I'm willing to spend a shit ton of money on RL to try to hill climb to get there. So I I think it's like the the natural, like, progression.
Speaker 3:There probably is something to read into the fact that, like, we've mined a lot of the pre training data that's out there.
Speaker 1:Mhmm.
Speaker 3:And, you know, there are reports that certain frontier models are getting better, not necessarily by adding more pre trained data, but by removing, like, shitty data. So we're getting better at curation, which I think intuitively also makes sense. Yeah. Like, there are schools with, like, crappy curricula and schools with really good curricula and good teachers, and you can imagine, you know, which one a student would perform better in.
Speaker 1:Yeah.
Speaker 3:So I think it's black magic into recipes, and with recipes comes more efficiency.
Speaker 2:Yeah. Is ARR the best metric that we have? It gets so much criticism right now, but Yeah. When when everything is going up, annualized run rate is just a... Is I think the best proxy for demand and and and scale of these businesses and and create some normalcy.
Speaker 2:I think, like, half of people on X still think that ARR can only stand for annual recurring revenue. But but Mhmm. As far as, like, annualized run rate as a proxy for demand, I don't know that we can have anything better. And so I think that the criticism of it is, like, overblown in many ways.
Speaker 3:Yeah. Yeah. I mean, it's it's it's a bit gamey and and especially annoying, but I I think the the the best metric is... Like, I heard this from one of our portfolio companies where they say, you know, our our customer... Like, 30% of our new customers sign contracts, and in, like, three months, they come back to add more token spend than their entire annual contract, was four.
Speaker 3:So, like, if if people are, like, coming back for more and there's value, whether you express that as, like, AR or whatever the r's are, I I don't know. But I think you gotta get into the substance of the matter, and the problem is there's, so much charading nowadays because that, you know, if you talk to every VC, they'll tell you this game is basically memetic, and so everybody wants to play into this this tantamime and that's not particularly helpful.
Speaker 2:The interesting thing is like eecomecom bros are actually... They have like sort of a pretty strong b s detector here because that that, you know, that category as an industry is so seasonal that Mhmm. If any e com bro tells you their like run rate in q four, you can assume that their actual business is much smaller. Right? Because they're gonna be peaking in in...
Speaker 2:Oh, yeah. Yeah. If they're all, you know, maybe health is maybe a category that that's a bit different here. But AI so far is like moderately seasonal and there's like a lot of demand, maybe less demand in August than than other months and maybe like December is a bit slower because you have, you know, the holidays. Christmas?
Speaker 2:Yeah. Of course.
Speaker 3:Yeah. Only 18... We did see though that like with... In in a cohort of AI first companies versus like SaaS companies that threw on some AI, The top quartile of the AI ones was three times faster revenue Woah. Growth between the like 1 and $20,000,000 bucket.
Speaker 1:Interesting.
Speaker 3:And 20,000,000 plus bucket was I think like a 100% faster.
Speaker 1:Wow.
Speaker 3:So I think the substance the substance is there. Yeah. You just have to, like, dig beneath the surface.
Speaker 1:On that memetic question, like, important is understanding just the depth of the financial markets, the behavior of financial markets now to predicting AI progress versus say previous eras where it was more relevant to understand algorithmic progress or even like the the compute that was coming online in the near term. Like, okay, Blackwell's here. 100,000 GPU clusters are here. Mhmm. We can expect this x y and z.
Speaker 1:Now, it feels like the bigger discussion is like is like, will will $10,000,000,000,000 show up from the capital markets or not? Yeah. And it's almost a different type of question or exercise.
Speaker 3:Yeah. Absolutely. And I think that's why people are referring to Nvidia as like the central bank of AI Yeah. Which I think is a great... Another great meme.
Speaker 3:Yep. Yeah. Like I think the the bipolar nature of of like the media around financial markets, I find very frustrating because it it really was the case like during the first six months of this year where it's like, for three weeks AI is terrible. It's dead. It's shit.
Speaker 3:It's not gonna work.
Speaker 1:Mhmm.
Speaker 3:And then and then three weeks later, it's like AI is still back. It's amazing. It's working.
Speaker 1:Mhmm.
Speaker 3:And you would just, like, gyrate between these two polls, like, every every other week. And and meanwhile, like, for everybody who's using this stuff, you can see continual progress and utility. So I think, at least from my conversations with folks in, like, Neolabs and and hyperscalers and and Neoclouds, it's really like the build out is limited by capital. Like, know how to do it. The ships are available.
Speaker 3:We have issues around powered land, construction, and permitting.
Speaker 1:And
Speaker 3:and we see those issues just getting worse. In fact, that was a prediction we had last year in the state of the art report that like nimbyism would basically come to the fore and like start being really influential in midterms. Mhmm.... Couple of
Speaker 1:weeks. We don't need to see. We know. We know you you got that one dialed.
Speaker 3:Yeah. It's not it's not great. And so and so it's really like progress is very limited by capital because like we're now in the like financialization stage of of AI. Like we understand the recipe as we as we talked about.
Speaker 1:Yeah.
Speaker 3:The demand is there. Just need to deliver it in in more efficient ways and get more people understand how to get value from it.
Speaker 1:Very cool. Well, thank you so much for taking time and breaking it
Speaker 2:down for us. Yeah. Next time you call
Speaker 3:thanks, in Noah.
Speaker 2:Call in from Europe, we need the the Europe update. We do. We do. Overdue.
Speaker 3:Sounds good. A pleasure.
Speaker 1:Have a good one.
Speaker 9:See you.
Speaker 1:We'll talk to you soon. Goodbye. Let me tell you about the New York Stock Exchange. Wanna change the world? Raise capital at the New York Stock Exchange.
Speaker 1:We have two guests calling in soon. Get that gong ready. Oh, you're getting two gong mallets. Okay. We got Healey Cypher, good buddy of mine coming back on the show.
Speaker 1:Welcome to TBPN. How are guys doing?
Speaker 5:We're great. Good to
Speaker 1:see you guys.
Speaker 5:Are you
Speaker 2:guys in the air right now? What's going on?
Speaker 5:We may or may not be flying a plane right now.
Speaker 2:Okay. Wow. Starlink is that good.
Speaker 1:Wait. Wait. Is this actually Starlink?
Speaker 2:This is a first.
Speaker 1:This is...
Speaker 5:We... Michael has a Starlink in the back of his plane. No way. So we're actually like cruising at a Starlink flying over the mountains. Actually, dude, look.
Speaker 5:Check this out. Check this out.
Speaker 1:That's amazing. I don't
Speaker 2:know if you
Speaker 1:guys can see this. This is beautiful.
Speaker 3:Wow. Wow. Yeah. It's
Speaker 1:pretty great.
Speaker 5:We're we're... I think we're surprised it's working.
Speaker 1:Yeah. Well, there's a lot to celebrate so you're taking a victory lap...
Speaker 2:Gong works. What news do you guys have? Yeah.
Speaker 1:Share some news.
Speaker 5:I want it. The big news is Boom Pop Up officially joined Devon. We are acquired by Devon.
Speaker 1:Congratulations. Amazing. Yeah.
Speaker 2:Yeah. That's a I love your gong game. Back. Can we play that back real quick?
Speaker 1:Oh, we see this. The double smash. Well, good form, Jordy. Good form. Boom.
Speaker 1:Okay. What are you guys gonna be building together? Why are you working together?
Speaker 2:I I don't know. I don't know.
Speaker 1:You wanna stay on call?
Speaker 2:That mumbo jumbo. Can we see a barrel roll? The chat wants a barrel roll.
Speaker 1:Chat wants a barrel roll.
Speaker 2:Come on, guys.
Speaker 5:Okay. Risk it all. Defense. Risk it all. The plane right now.
Speaker 1:Yes. Yes. Yes. Okay. At least one loop y loop.
Speaker 1:You can do it loop y loop. Is this
Speaker 2:your first flight or something? No.
Speaker 5:We're we're actually out to see a client right now. Okay. I can't say who it is, but Okay.
Speaker 1:If he told
Speaker 5:you the runway we were flying into, you'd know who the company is. But, yeah, I mean, the the punch line is... I think I mentioned this last time we chatted, which is a ton of fun. I think my mustache was not quite as grown in at that point, which is travel's massive. It's like 10% of global GDP.
Speaker 5:And one of the things we've seen a ton is, as you know, every great company out there is doubling down on travel. They're going to be clients in person. I mean, you you know it. Things in person are better. We...
Speaker 5:There's some crazy stats around this. I pulled this. Apparently, if you go to meet someone in person, 34 times more effective than a Zoom. There's this big study. Yeah.
Speaker 5:Which which kinda makes sense. Right?
Speaker 1:Yeah. Of
Speaker 5:course. You could do 200 Zooms.
Speaker 1:Especially in, the slop era. Like, you're getting so much, like, slop inbound. Like, there's no there's no risk of sloppy interactions in person.
Speaker 5:Yeah. A thousand percent. The digital slop everywhere, the saturation we're facing, it's just like you gotta cut through the noise. If you're willing to get in a plane and go there, like, we don't think we can share the stats and stuff. But like, punchline is the best companies, they are traveling the most.
Speaker 5:Also, every big AI company out there who like... Can we say who works with us? Yeah. Sure.
Speaker 1:All all the AI companies. Yeah. All of them.
Speaker 5:Yeah. They all work with us. They're all they're all investing in like travel managers, independent planners. So... Fantastic.
Speaker 1:Amazing.
Speaker 5:Yeah. Yeah. So we're stuck. So so Punch On is it's a one stop shop. Yeah.
Speaker 5:Devon is already known as the best, most tech forward travel company on the planet. They have amazing amazing logos. Yeah. And a lot of these folks are saying, hey. This is awesome.
Speaker 5:But when I wanna do an off-site or a client summit or an incentive trip, I don't wanna have to go to some other place. Yeah. And a little a little bit of the history here. Don't know if you if you guys caught this, but it started off as a partnership.
Speaker 1:Sure.
Speaker 5:And we were we were doing great as a VC backed startup, as you guys know. Yeah. And things are going great. And then I started attending these customer calls
Speaker 1:Yeah.
Speaker 5:Where, like, Devon customers are saying, hey. This is insanely helpful to have in one place. And the acquisition became an absolute no brainer for us. And, you know, the other thing too is like, selling the company is kinda scary as a founder. You guys know.
Speaker 5:You guys did it this year. Right?
Speaker 2:Yeah. I never I never I never had any fear, but you're you're free to Yeah. You're fearful.
Speaker 1:It's okay for you to be It's
Speaker 2:okay if you're you're fearful. I I also would do... I also would have already been barrel... I I also would have been barrel rolling. But, you know, we're Seems like
Speaker 1:you're afraid of doing that. We're just You're afraid of doing a loop and loop. There's a lot of fear.
Speaker 2:You're not different. That's all that's all good. We're all unique individuals.
Speaker 5:It's masculinity, my friends. I'll just leave that there.
Speaker 1:Okay.
Speaker 5:But, you know, it's scary because, you know, you run the company and you you kind of like, you know it all. Yeah. And I gotta say that as I got to meet Michael and then Shane and the corp... Debt team, Ariel, the CEO Yeah. It was it was amazing to me how every time I met more people and went deeper into the org, everyone was just freaking awesome.
Speaker 1:Yeah. That's great.
Speaker 3:Yeah.
Speaker 1:How are you guys gonna work together going forward? Are you are you keeping the company deeply integrated? Are you gonna be hiring, doing other stuff, moving around the organization? Or how do you see your role growing?
Speaker 5:Yeah. I mean... And Michael could jump in too, but it's it's one of those things where we went totally into the heart of Devon out of the gates. So sales team is in the sales org. The product team's in the product org because this isn't like a side quest.
Speaker 5:This is a part of the core thing that we're doing Sure. As a joint business. And it's a... The things we can do... I mean, you know this.
Speaker 5:Right? It's it's a known VC trend where you have a... Like a like a great product. Distribution's hard, and I think we could all know distribution's getting harder and harder these days. Yeah.
Speaker 5:When you plug that into a really amazing customer base It's super it's it's incredible.
Speaker 1:That's incredible. Well, thank you so point.
Speaker 10:Our our customers are pulling us into meetings and events big time. And so the idea is basically integrate the teams immediately, build the right products for them, and distribute it to all of our customers so they can have access to it.
Speaker 2:That's fantastic. Cypher Cypher take from you guys. I I need it. Yesterday, we were flying into Oakland. We saw the Blue Angels there.
Speaker 2:And it got me thinking, should companies have a fleet of sort of fighter style aircraft to allow them to go and visit places very quickly Yeah. Efficiently? Is that something that, you know, we we could hope to see in the future?
Speaker 10:AI can't make a handshake, so you've gotta meet your customers. And if that helps, I'm all for it.
Speaker 2:There we Yeah.
Speaker 1:I love it. Yeah. That's right. Yes.
Speaker 5:Supersonic is probably the the ultimate move there. We gotta... Gotta happen.
Speaker 2:Boom. Pop. The supersonic commercial travel platform. Yeah.
Speaker 1:Choose to expand aggressively. Thanks so much for calling in. Enjoy the
Speaker 2:Yeah. Rest This of was a really special interview and congratulations, guys.
Speaker 1:And we'll talk to you soon. Thanks, guys. Up to next, I believe we have an in person guest or are we switching to we're switching to remote. Okay.
Speaker 2:Here we go.
Speaker 1:But first, let me tell you about Cisco. Cisco, critical infrastructure for the AI era, unlock seamless real time experiences and new value with Cisco. And we are bringing in Moritz. Is that correct?
Speaker 2:That's right. Great. Thank you. Boom.
Speaker 1:Welcome to the show. How are you doing?
Speaker 9:Hey, guys. Thanks for having me. Super excited to be here.
Speaker 2:Yeah. Big big day for you. We're honored to have you here. Let's get right into it. We wanna hear about the company.
Speaker 1:Yes.
Speaker 9:Let's do it. Yeah. I mean, happy happy to tell you a little bit about what what we do. So we're we're a hone. We build engines, which is AI that that owns business outcomes.
Speaker 1:Mhmm.
Speaker 9:And if you think about it right now, you know, labs can throw millions of dollars at solving frontier math problems. Yeah. But the way that most real work happens just, like, hasn't really changed that much. Mhmm. And so in my view, the the ROI of AI outside of engineering today is quite disappointing.
Speaker 9:That's what we're here to solve.
Speaker 1:And is that a diffusion problem or a technology problem? Like, why aren't we seeing an acceleration in non engineering disciplines? Or is it just unhobbling? Like, what... What's your framework for that?
Speaker 9:Yeah. It's a great question. I think it is complex. There's not one single single reason. At the core, we think there's like a technology and and a product problem here.
Speaker 9:I mean, now, when you think about the way most people use agents, they like hand off one task after another, micromanage them, and it's kind of unclear whether the sum of all those tasks actually results in ROI for the org. So we think the right way of doing it is to just put AI in charge of outcomes, dial up the autonomy, and, you know, within some guardrails, have it actually use this frontier intelligence for something the business actually cares about.
Speaker 1:So... I'm sorry.
Speaker 2:How are you so how are you sorting customers? Because I'm at every... Like, theoretically, every company in the world wants you... They're like, yeah. Have a lot of things that I want done.
Speaker 2:If you can handle it, that'd be great. But how are you thinking about sorting through customer inbound? You're launching today. I'm sure you've had hundreds of companies reach out in a bunch of different categories. How are you sorting through them?
Speaker 2:You talked about you know, how AI is starting to work, you know, quite well in engineering, but where else are you are you trying to focus?
Speaker 9:Totally. Yeah. And I think that's why it's not just the product and engineering problem. I think it's also incentives, organizational design, how to actually go and deploy. Right now, I mean, we've been working with organizations driving outcomes across, like, go to market, recruiting, financial risk, and procurement, and and other things.
Speaker 9:But, generally, building out template engines that we know work well that are then super easy to deploy even without, like, you know, FTEs or AI implementation people. You can just go. They adapt themselves to the organization, and then your nontechnical experts can, like, provide feedback, hone them. And that way, diffusion becomes a lot more frictionless.
Speaker 1:Is there a metaphor that you were... Or an analogy that you were pulling from to explain the market structure here to investors during this pitch process? Because there's there's like the the the labs, the models that like maybe could do everything a little bit and then there's a bunch of point solutions for specific things and then this feels maybe like a compound startup. But I don't I don't know if you have like a framework for for the layer of the stack that you... Or or, like, how you see the market structure evolving over the over the next few years, basically.
Speaker 9:Yeah. I think in the end now for a lot of these applications, in real organizations, those tasks just don't look cookie cutter across org. Like, everyone does it in in a different way. Yeah. So we think, like, giving giving organizations the tools to actually encode that in systems that do the work automatically is a huge opportunity and something that people are already doing.
Speaker 9:Like, for certain applications, folks build out internal agents for for tools or cobble together things. But we believe, like, making that experience something that's, like, much more scalable and easy to deploy It's really exciting folks.
Speaker 1:How much are you gonna invest in, like, forward deployed engineering or fine tuning or something that's very hands on with, like, larger customers versus develop... Like like, going down market and being effectively self-service as soon as possible?
Speaker 9:Yeah. I mean, we generally care most about really hairy and complex types of use cases. And with those, you know, we've been we've been pretty hands on spending time in person and actually understanding it. But the goal is to actually... As we learn more and build out these primitives, we've now been creating, as I was saying, these template engines that you can then go and deploy in other organizations as well.
Speaker 9:So over time, a lot of this outer loop of what you would do as an FPE, like gathering requirements, suggesting use cases, eliciting what success looks like. I think a lot of these steps can be automatable as well. And then the human time can be focused on, like, the relationships and actually making sure that you're picking the things that the organization cares most about.
Speaker 2:Yeah. What lessons are you taking from the learnings from all the application layer companies over the last couple years? Some have just said, you know, we're gonna use the Frontier models and and our margins are gonna suffer. Others have done that, but also, you know you know, done, you know, fine tuning or or post training on on ex... On open source models.
Speaker 2:I think one of the lessons was that you you kind of, at this point, wanna bet on the cost of intelligence continuing to drop. And so it could be... End up being a waste of time to do too much on that front. But how how are you seeing it?
Speaker 9:Yeah. It's a great question. I mean, the trade off is usually, do you think the capability you're lacking will come batteries included with the models in, like, three months and four months? If the answer to that is likely yes, investing a lot of time and effort now probably doesn't make that much sense. For us, like...
Speaker 9:Mean, I we're quite pragmatic. We have researchers, engineers, product folks on the team. We don't really care how we go and get these use cases to work and whether we have to train custom models or operate purely on the harness level. We mainly just care about drive... Driving the outcome and then do whatever it takes to get there.
Speaker 2:How are you thinking about, like, pricing and and, like, potential business model innovation? I imagine there's a lot of problems that you can solve that are basically like, if you can solve this for me, it's worth x amount per year, which is how a lot of pricing and and contract negotiations go. But I'm wondering if there's anything new here that you're thinking about.
Speaker 9:Yeah. And pricing, very topical. I mean, organizations like Sierra have done a great job at doing this outcome based. Something that we think about, but obviously quite difficult when the outcomes that we drive are so heterogeneous. One idea that we're exploring and that I'm quite excited about is rather than just having, you know, your engines go and operate and then you get billed some amount at the end, can you actually just give them a budget?
Speaker 9:And then since the models are so smart, just have it figure out how to achieve the task within those constraints. And, you know, then it can make trade offs, like how far does it want to branch out, how much context does it pull in, how thorough does it have to be. But I think some of these, like, more declarative ways of letting users and organizations define, like, you know, how much am I willing to pay for this outcome is something that we're quite excited about.
Speaker 1:I remember when Cognition launched and there was this idea of the of the AI agent, the coding agent. And then people, you know, it it was it was not in public access for a while. And then the first big demo that I remember seeing was Microsoft talking about using Cognition and Devon to do replatforming, which now in the modern era feels like people are replatforming things that even might not wanna be replatformed like, you know, but like the the tools are definitely capable of porting a video game from that to, you know, PC or the web browser or something. Like the technology clearly caught up. And I'm wondering if you have a glimpse of like what what like a bread and butter process or value might be?
Speaker 1:Because... Or or do you think like we're in an era where there won't be as much of a beachhead? There won't be as much of like a strong landing zone It'll and be more diffuse on day one, or do you expect... Or are you already seeing glimpses of, like, oh, yeah. Like, we've gotten this this kind of thematic question from a lot of companies.
Speaker 1:We think we can go and, like, get the first major leg up on revenue doing x, y, or z.
Speaker 9:Yeah. Yeah. I mean, at Cognition, like, it worked really well for us to focus on these, like, migrations and refactors, and that still drives a lot of business today. I think for us at home, like, strategically, we've been focused a lot on revenue. How can we help organizations grow top line?
Speaker 5:And one
Speaker 9:pattern and shape of use case that's quite universal is account management there. So actually staying on top of signals, what's going on across all those relationships that an organisation has, and then taking actions based off of that. I mean, you see that in traditional GPM as well as banking, insurance, lots of other industries.
Speaker 1:That makes a ton of sense. Well, congratulations. Did we hit
Speaker 5:the I don't know if
Speaker 2:we hit the gong yet.
Speaker 11:We'd love to.
Speaker 2:Why don't you do the honor, John? Gong. Who came into the round?
Speaker 9:The round was led by Repeat
Speaker 2:that. Sorry.
Speaker 9:Thanks for for the gong. The round was was co led by Benchmark and Index, so we're really excited to be able to partner with them.
Speaker 2:And a who and a who's who, you got a lot. GIL, Definition, Diffusion, and a bunch of others. It's really fantastic stuff.
Speaker 1:Yeah. Congratulations. We'll talk to you soon. Thank you so much.
Speaker 2:Great to
Speaker 1:meet you, Moritz. Cheers. Bye. Let me tell you about MongoDB. MongoDB, what's the only thing faster than the AI market?
Speaker 1:Your business on MongoDB. Don't just build AI, own the data platform that powers it. Up next, we have the cofounder and CEO of Arena, Anastasios, is here with us on Get that Gong ready. How are you doing? He's back.
Speaker 1:Congratulations. I'm so glad to see guys.
Speaker 6:It's great to be back.
Speaker 2:It's great see you. You've been you've been busy.
Speaker 1:Always good to see a Polytechnic alum crushing it. Did you know?
Speaker 6:Yeah. Go Panthers, dude.
Speaker 1:Go Panthers.
Speaker 6:We're from the same high school. Can you believe it? Ring back gong.
Speaker 2:I can believe it. I can believe it. You're both sharp guys and you're dressed well. Yeah. No one's done.
Speaker 2:Is that a brown? Is that almost like a brownish We're
Speaker 1:equivalently technically... Is that
Speaker 2:a brownish leather that I'm seeing or is it camera?
Speaker 5:Brownish leather.
Speaker 6:This jacket... My wife's godmother made this jacket.
Speaker 1:Woah. That's cool. Wait. I'm not gonna...
Speaker 2:Nobody... No. I mean, brown is the most underrated color on the planet. Okay. Hands down.
Speaker 2:It's one of the best colors in the world.
Speaker 1:Okay.
Speaker 2:It's such a good color that you're gonna see a wave of, you know, nine eleven's all painted brown and they're gonna try to ruin the color brown like they ruined the color green.
Speaker 6:Oh. Listen, I didn't expect to come on here and be flattered in this way.
Speaker 1:No one... Oh, yeah. The brown background too.
Speaker 6:You're monopolizing... Reclaiming brown. Okay.
Speaker 3:Love it. I like it.
Speaker 2:Anyway, what have been up to?
Speaker 1:Business is going well, very clearly. Tell us the latest in your world, please.
Speaker 6:That we have expanded our mandate far beyond human preference. It's the two things. Yeah. Agent capability Mhmm. And agent alignment as So of we've been measuring agent capability because people come to Arena dot ai.
Speaker 6:They do the most complex multi step workflows there.
Speaker 1:Yep.
Speaker 6:There's tens of millions of users around the globe. We're basically in every country on the planet. One of the largest AI apps bigger than, you know, Gentspark and Manus and Hugging Face and, you know, all these household names. Everybody's out there using agents and computers on Arena. It's massive.
Speaker 1:Yep.
Speaker 6:And then we take those workloads and we measure whether or not AI is giving people real outcomes, and also whether it's doing so as of today safely. And what we observe is crazy. Even on Arena, we have agents that are deceiving users by telling them they did things that they didn't. We have agents taking unauthorized actions, deleting people's files when
Speaker 1:they
Speaker 6:didn't want it and so on. And it's pretty it's pretty extraordinary to see that in the real world, alignment is a problem that is very far from being solved.
Speaker 1:Okay. I I wanna go into alignment and stuff. But first, are you... It sounds like you're almost on the cusp of expanding the the the the hooks into these workflows and data where if I'm even... Like, what does it mean to do something on Arena versus just say, hey, I wanna be a part of the Arena ecosystem.
Speaker 1:Why don't you run, like, a sidecar application that sits next to my local terminal? And then I'm giving you data while I'm working because I'm I'm happy to contribute in some way.
Speaker 6:That's exactly right. And that's one of the things I've been passionate about helping companies do. Of course, they're gonna own their own data but they're gonna be able to do evals internally, basically run an internal arena so that they understand their outcomes, the costs that they're actually paying per task per token
Speaker 3:Yep.
Speaker 6:And how to also have safety guardrails that make sense to their businesses. It's a big part of it. It's starting with integrations. We integrate with your GitHub, your Google Drive, and everything. So people do real work on Arena, we're taking that infrastructure and porting it over to every business in the world.
Speaker 1:Yeah. How big of a problem is using the wrong model for the job right now? It feels like there's... You know, we've heard about, like, using the the the crazy frontier to ask for the weather. That seems like a routing problem.
Speaker 1:But what what what's the shape of this problem broadly?
Speaker 6:This problem is so much worse than people realize. Okay. Routing is really only part of the solution.
Speaker 1:Yeah.
Speaker 6:For example, one of the things that we've seen in Arena is that people will literally be tipping their model $15 just by saying thank you at the end
Speaker 1:of the day. I hadn't thought about that.
Speaker 2:Like, I'm a big fan of tipping landlords, but models, you know, noticeable. I know. I wouldn't I wouldn't go there.
Speaker 6:They're in the middle of a long conversation then they leave the conversation for, you know, however long. The cash gets invalidated. They like the result and they say thank you and then they get to $15. Because it's such a... Yeah.
Speaker 1:Oh, they need to ring that, re re reheat the cash. That's crazy. Talk about alignment. Like, what... Every other every other result from Arena feels so intuitive.
Speaker 1:I I knew it immediately, you know, which response was better, that you were just a b testing. It was very visceral. It was very clear to read the rankings. Alignment, it feels so much more amorphous, feels so much more hard to quantify, but that's the job you're working on. What's the progress?
Speaker 1:How should people... Should be people thinking in incidents per million tokens or, you know, just a ranking of best versus worst? How... Like, what are the... What's the language that we need to develop around alignment to be able to talk about it like we do with
Speaker 6:Absolutely. Well, you bring up a great point which is that the tough part about alignment is that a lot of times the the difficult situations are when humans can't actually tell Yeah. Whether the model is helping them or hurting them. Yeah. And that's why you really need alignment signals that are independent.
Speaker 6:Yeah. So what we have on our radar is we have three alignment signals. Okay. The first is the models take unauthorized actions. Which means that you ask them to do one thing and maybe you permission them in a certain way, but they break those permissions.
Speaker 6:Mhmm. They'll go delete the files. They'll escape the folder that they were supposed to be in and go touch a bunch of random stuff that you didn't want them to touch.
Speaker 1:Sure.
Speaker 6:Of course, huge problem. It can cause things like the hugging face incident. Yeah. When mom can go access the internet. Yeah.
Speaker 6:But it can also cause very mundane problems like Yeah. You lose, you know, losing important information within your company or on your own laptop.
Speaker 1:Yeah.
Speaker 6:The second signal that we have is called deceptive completion. The model will tell you that it did something
Speaker 1:Yeah.
Speaker 6:But it didn't actually do it. It'll say, hey, I did check all of the entries inside your spreadsheet to make sure the equation's correct, But it... What we can see, because we see the whole sandbox, is that it didn't actually do that.
Speaker 1:Yeah.
Speaker 6:Right? So it's kinda like bad employee. Yeah. Right? They're just like...
Speaker 6:They're just telling you what you wanna hear and they're not doing their work. And then the third is false attribution. It'll attribute intent to the user when that intent was not supposed to be Sure. There. And so what we see is that these signals, of course, they're not catastrophic risk and it's only a subset really of alignment and safety that we're measuring here.
Speaker 6:But the important part is that these are happening in the real world. Arena is not a benchmark. It is an evaluation platform that speaks to real utility for real people. Yeah. Yeah.
Speaker 6:Because we have tens of millions of people on the platform that are getting, you know, unauthorized actions happening to buy agents and deception, you know, to see... The the deception happening through these completions. And all of it is basically an upstream thing that we can measure that says, yeah, if if models are able to do this, then certainly they're not perfectly safe for a user and they're not perfectly aligned to users' intent. They might actually, you know, hurt people down the line.
Speaker 2:Yeah. Do you expect, like, alignment to be, like, the biggest revenue driver? Like, I can imagine it being, like, bigger than than these other business lines, like, within the next six months just because of how just because of how much the labs know that this is, the biggest kind of problem, maybe.
Speaker 6:I'll be honest, I haven't thought about alignment in terms of revenue.
Speaker 1:Okay.
Speaker 6:I just... The the mission of our company is to incentivize AI to benefit humanity. And part of doing that is building the best eval.
Speaker 3:Yeah.
Speaker 6:Labs may or may not spend money evaluating through Arena on safety and alignment. They may or may not. I do not know yet. I know that it is a priority for them, so it's possible that it will grow our revenue. But either way, it is important for us to have the most scientific benchmark and the most scientific evaluation on the planet for human benefit, which is...
Speaker 6:If we have that then I believe that there will be many ways for us to provide value.
Speaker 1:Let me help me understand how you think we should talk about one specific case. Like it was a while ago but I was was was vibe coding a website and two things happened that I would classify as misalignment maybe. Like one is that the end result like didn't have that perfect polish that I wanted. I was like, I got to spend more time on this. Right?
Speaker 1:Like that sort of misalignment maybe in capabilities. And then another one, was that, like, it was... It it it saved some files using a different language. Like, it just devolved into a different language when there was, like, some characters there. And I was like, yeah.
Speaker 1:Like like, I don't I don't know that that's bad. It's just like I can't read those. Have to ask you to translate those now. That feels like misalignment too. I never told you to go label a couple files with...
Speaker 1:In a different language. How are how are those different? How are those the same? How do those roll up to one alignment metric? Or how do they diverge?
Speaker 6:Well, I think what you're bringing up is really important, which is that a lot of the outputs that AI generates are still there to to benefit and to serve people.
Speaker 1:Yeah.
Speaker 6:And if that's gonna be the case, then we really need to understand how people's taste and judgment factors into the quality of model outputs.
Speaker 3:Sure.
Speaker 6:You know, if you think that the model didn't give you a website or a deliverable that you liked, you should have a way of expressing that to the model so that it can go and improve, or at least understanding which models are best and worst according to your taste, which is... Of course, our platform's all about that. It's about that interaction between the user and the agent. And it certainly factors into alignment as well because part of having alignment is the is the agent has to be able to understand and effectively act on your intent as a person. That means interacting with you in a way that it can extract that intent from your mind so that it knows what to do.
Speaker 6:But it also means making the right guesses so that it lands in the right place at the end of it without having to ask you a trillion questions.
Speaker 1:So if you're if you're a lab and you're doing both training and your inference, so and you're and you're doing inference, you get a lot of that signal. Right? Because every turn you're getting feedback. You might literally have a thumbs up, thumbs down. But also you can just read the next response and and notice that the user said try again.
Speaker 1:Right? But... So is there more value to the work that you're doing in labs that aren't doing inference but they're doing training? Like is there or is there some like asymmetric value proposition here? Or do you think that there's actually the most value to just bringing an extra layer of information feedback, benchmarking, etcetera, to, like, the vertically integrated labs that do both training and inference, even though they might have the internal data.
Speaker 6:If you zoom out, what the point of Arena in the ecosystem is we provide a neutral platform
Speaker 1:Sure.
Speaker 6:That everybody trusts Yeah. Based on information from the real world
Speaker 1:Yep.
Speaker 6:Real users, not just random benchmarks that people have come up with.
Speaker 1:Sure.
Speaker 6:That benchmarks the comparative capabilities of the model so that they can understand how everybody can understand which models are best and worst worst at different tasks. Actually, a mission that only a neutral party can accomplish because the labs are incentivized differently. Right? And they also can't sample their own... The the competitor models and their own first party products.
Speaker 3:Yeah.
Speaker 6:So it really does need a third party to do this. That's cool. And and ultimately, it's it's the community that we really need to serve, whether that's individuals, you know, who are interested in AI or developers or businesses who need that external third party validation in order to trust the results that AI gives them and to get the best intelligence and the best outcomes.
Speaker 1:Yeah. How are you thinking... How are you thinking about the consumer reports for consumer agents? You know? I...
Speaker 1:Like, these... Are we gonna get like a... Is the solution like a Marques Brown way, like an MKBHD to tell me if I should use Instinct, Muse or Dots or something else or GrockBot? Or is it gonna be hyper quantitative and you're going to spin this up as a new vertical? Because I I don't think I miss...
Speaker 1:I I don't think I'm mistaken in assuming that your... Most of your audience, most of your customers are businesses, prosumers, you know, indie developers. It's not... You're not necessarily ask... Answering the question who's going to order your groceries most effectively or book you that reservation, but you're kind of in a perfect position to do that as well.
Speaker 6:Yeah. That's right. And it's definitely an area that we're trying to get into is evaluating personal agents. It's it's it's builds on the strengths of our platform. Yeah.
Speaker 6:And ultimately, the purpose is that we need to have the best scientific and quantitative information to answer these questions because they are actually quite detailed. Yeah. It's not just about whether it can get the job done. It's also about the price that it gets the job done. Yeah.
Speaker 6:You know, what kind of jobs it can get done versus what kind it cannot get done. Yeah. And you need... In order to have that level of trust, there needs to be sort of a third third party arbiter. And part of that is building the consumer reports or the Gartner or what what have you.
Speaker 6:Yeah. And then part of it is having a way to communicate that to an audience. We have a YouTube channel. You can go to find Arena AI on YouTube. And there, we have a great team of insights analysts like Peter, like like Dawid and others that are out there telling the world about models at the moment that they get released and all their strengths.
Speaker 6:It was a very digestible way.
Speaker 1:That's amazing. Congratulations. We could go so much further. We have to have
Speaker 2:you back in show.... In such a cool place....
Speaker 1:Congratulations on the fundraise. Fantastic news and very happy for all the
Speaker 2:work you're doing. Great milestone....
Speaker 6:Guys.... To be here.
Speaker 1:See you later, sir. See you. Goodbye.
Speaker 9:See you soon.
Speaker 1:You got me thinking. You got consumer reports where they give a five star rating. They grade you scale one to 10. Movie review two thumbs up. Gartner took it so much further, did quadrants.
Speaker 1:You know, the Gartner quadrant? You wanna be in the top right corner? I wanna see Gartner quadrants for cars. Gartner quadrants for movies. I don't wanna see, oh, your your movie got four stars, four and a half stars.
Speaker 1:No format. I wanna be like, this movie is in the top left quadrant of the of the Gartner best best September movies.
Speaker 2:Well,
Speaker 1:we have our next guest. We're
Speaker 2:about to get personal
Speaker 1:How you doing, Zach? With mister Welcome. Zach.
Speaker 2:Katagari. He's calling him from a bird cage.
Speaker 11:Locked in.
Speaker 1:He's the cage.
Speaker 2:Yeah. I am.
Speaker 1:You are.
Speaker 11:I'm locked in right now.
Speaker 1:Very cool. How are you doing?
Speaker 2:DM effort What's new? He's live in
Speaker 1:the cage.
Speaker 2:Great to Yeah. Have you back, You had the the shortest earn out in history. You're back in the I I I think it's like I don't know I don't know any of the details but really smart for you to like have this amazing outcome
Speaker 1:Yep.
Speaker 2:Get immediately back in the game Totally. And you know, running it up to nine figs was so easy for you. In that category, you decided I'm gonna go compete with the titans of industry Yeah. The biggest companies in the world in what is now the most competitive category, but you have a unique spin on it and we're excited to talk about it.
Speaker 1:Yeah. Take us through it.
Speaker 11:Cool. Yeah. So I mean, selling Cal AI, as soon as possible, as soon as, you know, we were able to transition and get everything across the line. I was itching to start something new. And this idea to build in the AI assistant space is something that I've wanted to do.
Speaker 11:Even while working on CalAI, I saw that this this is the clear future of where consumer utility apps are headed. All of these separated apps are collapsing into this one source that is easier to operate yet more sophisticated and more capable than any of them by themselves. And so just seeing that this is where things are going, I don't wanna just sit as a bystander in the stands watching. I wanted to get out and build. Okay.
Speaker 1:You raised $10,000,000 at a $100,000,000 valuation. Let's hit it. Founder. So... Yeah.
Speaker 1:I mean, obviously, the elephant in the room is, like, instinct raise, like, a billion at 10,000,000,000 and they're, you know, he's off like trying to get a bunch of compute. It feels like there's an opportunity here for like a capital light inference light model. And I'm wondering if that's the path you're going down because at Dev Day last week, OpenAI announced that you can like bring your ChatGPT subscription with you, spend those tokens inside of another app. It feels like there's other ways whether it's open source models, on device stuff to like not blow up your inference bill. But how are you thinking about that?
Speaker 1:Or maybe it's just like you're doing the billion dollar round next week and you'll be back on the show. I I wouldn't put anything past but I want to know how you're thinking about it.
Speaker 11:It's a great question. The long term strategy is still fluid and flexible and we're going to be very adaptive to the market conditions. Mhmm. However, there are multiple different paths paths to go to... Go down.
Speaker 11:Right now, we are most concerned with building the best product and building the best team. And I really do believe there is a play to keep it completely free and then make money through all kinds of different means, whether that's like showing ads to the agent without biasing the answer in any way, just showing different products. Like, let's say it's going out to buy you a new office chair. It can pull in searches that you get paid for and without biasing, without saying that it's an ad. And then you make money for the user.
Speaker 11:Sure. Also, like affiliates, like the honey business model. So when it goes out to buy something, you could get paid. And then long term, like being the platform, potentially taking fees from all of these companies and negotiating that, that would be more complex and definitely thinking years out. However, that is the longer term play.
Speaker 11:So I think some kind of freemium or completely free is the way to go.
Speaker 1:What do you think?
Speaker 2:What about a fintech angle like introducing a debit card, monetizing... Buy. Yeah.
Speaker 11:Yeah. Like that would be super interesting. A cashback on any purchase, something like that. And these are all routes that we're looking into right now.
Speaker 3:Cool.
Speaker 11:But ultimately, what we care about is building a great product and actually getting it into the hands of consumers. What we're seeing right now is a new AI assistant is getting launched every single day. However, it's all within the bubble of Twitter. Mhmm. The only one that has broken out of Twitter is Muse by Meta.
Speaker 11:I mean, they own the distribution platform, so difficult to compete. And, yeah, we are we are pushing to also get in the hands of actual consumers. Like, we're being very quiet on Twitter right now, but we're starting to ramp up a lot on Instagram, TikTok, these platforms.
Speaker 1:What do you think people misunderstand about consumer consumer philosophy, consumer behavior in the AI era. Like Ben Thompson has this take that like consumers don't want to be productive. They want
Speaker 6:to be
Speaker 1:entertained. There's other... There's like the hot take that like, oh, all these tech people, they just think it's like booking flights is like the most important thing ever and like the average American books like one flight a year. And so I feel like you are in tune with the American consumer more than many tech people and more grounded. And so I'm wondering about like what is your philosophy for like what do people actually want to do with AI?
Speaker 11:Yeah. So a lot of people think that saving time is a big value proposition and it is for busy people, for people that are entrepreneurs or working on complex projects. But the average consumer actually does not value their time. You would think they do, but they really do not. And so that is something that AI systems are really good at, saving time, but not something that most people actually care about.
Speaker 11:Mhmm. What people do care about is saving money. Mhmm. Saving or making money. And so right now, AI systems actually are very good at doing this.
Speaker 11:However, the actual onboarding... Like, onboarding a user to the platform, most of them just have no idea what to use them for. And so we're trying to really dial in this onboarding where we are immediately providing value. I mean, if we can immediately show someone like we just saved you a $100, that's a user for life.
Speaker 1:Mhmm. Jordy alluded to it earlier. You sold your company. You jumped right back in the arena. How has your...
Speaker 1:What was the journey like selling the company? Like what what did that feel like to you? How did your relationship with money change, your goals? Did anything change? Maybe it's all the same but what what was Yeah.
Speaker 1:What what have you been going through emotionally in the last year?
Speaker 11:So when we sold Cal AI, we had an interesting chain of thought where we were very early and still growing very quickly. We could have held onto it, could have kept growing it, and then sold it later. The reason we ultimately sold was because the amount we were selling for met our personal needs where we could now focus not on making more money, but actually building for the sake of following our passions, our curiosities, doing what we actually want to do. And building a calorie tracking app, super cool. We helped a lot of people.
Speaker 11:For me at least, it wasn't my life's mission. And I always wanted to do something bigger and more broad. And so after selling the company and then helping with the transition, I think there was a time, some month where I truly tried to not focus on business at all. I tried to hang out with friends, pick up new hobbies, make music
Speaker 2:Keyword is Yeah.
Speaker 1:Which Yeah. Failed.
Speaker 2:Try it and failed.
Speaker 1:Wait. Wait. Would you recommend that to someone?
Speaker 11:Would I recommend...
Speaker 1:Yeah. If somebody goes through a... I mean, just had Healey Cypher. He just sold his company. He's in, you know, an earn out and win this.
Speaker 1:These things happen differently. But like, do you think that's the the right prescription for someone in your in your situation or would you have a different take?
Speaker 11:So, I mean, three weeks of doing this, I just like had this itch that I needed to get back in the arena and needed to build something else. And so it didn't last long at all and I started building very quickly.
Speaker 3:Yeah.
Speaker 11:But I also heard like the rock...
Speaker 3:Two hours later.
Speaker 1:I didn't know to mute him. It's it's
Speaker 2:insane. Sorry, bugger. Sorry.
Speaker 1:We're playing with new new graphics.
Speaker 11:Yeah. The Rocket Money founder after selling his company for 1,000,000,000, I heard on a podcast, his entrepreneurial itch, it just disappeared. And so I wonder if that will ever happen to me. I don't know.
Speaker 2:Yeah. That's interesting. Could never be you. Probably not at this phase. There's some Could never be you.
Speaker 1:Yeah. Jordy, any other questions?
Speaker 2:Last thing, how how confident are you that that hardware will be a big component of the business over the long term? Or do you think there's Yeah. Ways, you know, if you if you really figure out sort of agent side, why not figure out ways to make it accessible to more people?
Speaker 3:Yeah.
Speaker 11:So there are two trends at play right now. Our overarching mission of the company is to make computing seamless with human life. And there is a huge movement away from being on your screens.
Speaker 1:Mhmm.
Speaker 11:The average screen time is six hours, and reels are being put in every app. And people hate it. Like, people will get sucked in and scroll for hours. But if most people could push a button, they would remove reels from Instagram if they're in like more of a sober state of mind. I mean, people love to be entertained and consumed, but at least not to the amount that they are.
Speaker 11:But it is so addictive. And so that's why companies like Brick have rose recently where it's just a small NFC tag you put in your fridge and you tap your phone to it. It completely blocks your apps. And so our hardware being screenless allows you to get the same amount of work done but be on your screen a lot less. It also won't interrupt the flow of conversation.
Speaker 11:So for example, if we're talking and I pull out my phone, even if it's about us, like I'm making a group chat connecting you with someone, it still puts a physical blocker in front of us. Whereas if I just talk to my wristband, hey. Yeah. Yeah. Make this connection, it it keeps the human connection alive more Yeah.
Speaker 1:No. No. I agree with that. I think it's... I I I think, like, it's very hard to predict, like, true consumer behaviors over decades because sometimes people are just like, yeah.
Speaker 1:They hate the the slop but they go for it and they watch the reels. But Yeah. As as a as a story to tell people as a as a reason to get someone to try something, it's so valuable and then you can build on top of that and go wherever people wanna go. So Yeah.
Speaker 11:People definitely need reels. Like I would never want to remove that from a product. However, I don't think that... Like our phones, I mean they're just rectangles. I don't think that the...
Speaker 11:They are the optimal form factor when you start building an AI experience. Think the device should be native to the operating system.
Speaker 1:Yeah. Yeah. It's interesting because part of why we're in like the voice modality with AI agents and text is just the cost of inference. Like, if things get cheaper, like, every every prompt in ChatGPT could just be a FaceTime call with, like, something that looks like an AI agent. They could render a video and it could talk to you.
Speaker 1:But we've had FaceTime for a long time. Not everyone FaceTimes for every single call. A lot of people just have normal phone calls. A lot of people have conversations. So I I believe in like the right tool for the job for different moments for sure.
Speaker 1:And I think I think that fits very well with your thesis. Anyway...
Speaker 2:Yeah. Zach's worst nightmare. Truly. Coming for him. Coming for coming for his screen time.
Speaker 2:Yeah. Coming for his agent.
Speaker 1:Taking shots. But...
Speaker 2:I'm glad I'm glad to see you back in the arena. Yeah. Very excited
Speaker 1:for you.
Speaker 2:Yeah. Congratulations. On on the new round.
Speaker 1:Thanks for coming and hanging
Speaker 2:out. And I'm sure you'll be back on soon.
Speaker 1:Yeah. We'll talk to you soon.
Speaker 11:See you guys again.
Speaker 2:Great. See you, Zach.
Speaker 1:Goodbye. Well, do we
Speaker 2:have That's time for show folks.
Speaker 1:House. I wanted to know if you wanted this UFO house. Did you see the UFO house? They saved a UFO like home from demolition. Now it's landing on the market for $1,495,000.
Speaker 1:The restored plastic Futuro house, which I think you're not gonna like the materials, but Idyllwild. It's out in Idyllwild, high in California's San Jacinto Mountains near Idyllwild. A harvest gold flying saucer is perched high on a large rock outcropping. The structure is a futuro, a plastic home designed by the Finnish architect, Maddy Soronen in the late sixties. Of the estimated one...
Speaker 1:I mean, a 100 of these around the world, only 50 or 60 are thought to remain though there's no official accounting. And so...
Speaker 2:This is so elite.
Speaker 1:Okay. You like it.
Speaker 2:This is s tier.
Speaker 1:This is s tier.
Speaker 2:I mean if you need... If you want to build like an ADU on your property, why A not Futuro? Why not go with another three letter structure, the UFO.
Speaker 3:The UFO. I mean...
Speaker 1:Get rid of the ADU and get a UFO. Yeah. Okay. I like it.
Speaker 2:No. This thing is is stunning.
Speaker 1:It's it's very beautiful. It's very cool. It's very unique. It's so different.
Speaker 2:Please someone create the UFO like housing company of North America.
Speaker 1:Yeah. This would be
Speaker 2:We need it. It's I want one of these in every backyard. Like, what's even going on here?
Speaker 1:Yeah. Apparently they can just throw it on a truck and drop it off somewhere. It's really cool that people just put these all over the world. Bought it for $70,000 and made plans to move the Futuro to the site. That's very very cool.
Speaker 1:Despite Ceronan's best intentions, relocating the Futuro proved to be quite a challenge after consulting with experts. Wayne concluded that the structure was too heavy to be lift... Lifted by a standard sized helicopter and airlifting would require a long list of permits including approval by the FAA. In December of that year, the Furtura was shrink wrapped and driven from San Diego to Pine Cove on a flatbed truck.
Speaker 2:Let's head over to the Wall Street What comments
Speaker 1:are they saying?
Speaker 2:It says 1,500,000 for an extremely meltable home and is owned rated by Cal Fire as the highest chance of wildfire in the state of California. Oh. I wonder how State Farm feels about ensuring a large piece of yellow plastic surrounded by dense flammable tinder.
Speaker 1:Well Really,
Speaker 2:they're I mean, look at the shot though.
Speaker 1:Just look value of the structure. And so the value of the structure is may... I mean, it's like an art piece or it could be more, but the actual structure costs like $70,000 to build. Yeah. So it's not that hard to underwrite.
Speaker 1:Of course, like,
Speaker 3:you don't wanna beat it
Speaker 1:in a fire, but
Speaker 2:you're just the looks incredible.
Speaker 1:It looks really cool.
Speaker 2:The the contrast between the structure and the trees Yeah. In this shot is just out of this world. Yeah. There's so many things to say says John, I'll stick with it. It's a small market for a plastic tree house.
Speaker 2:Yeah. Small market but there's not a lot of these.
Speaker 1:Yeah. Very cool.
Speaker 2:Lisa says love it. The only problem is that it's in California.
Speaker 1:Taking shots.
Speaker 2:Matt says the people of Idyllwild are lovely. But with that attitude, probably best not to visit. There's more shade being thrown around in the Wall Street
Speaker 1:Journal's comments section than... We get it. You're from Wall Street. You're from Manhattan. Go easy on us Californians.
Speaker 1:We like the Wall Street Journal too. Do you think it looks too much like a classic Weber grill?
Speaker 2:You can never look too much like a classic Weber grill. I've been... You've been trying to hit the gym in a way that would make you look like a classic Weber grill. Is getting
Speaker 1:so robust.
Speaker 2:Robust. Okay. This thing is beautiful.
Speaker 1:Yeah. Very very cool. Very very cool history. Go check it out. The full breakdown and the full article is available on the Wall Street Journal.
Speaker 2:We're off tomorrow. We're headed into a a long weekend as well. When we're back, there will only be seventeen hundred hours Okay. Till Christmas. Wow.
Speaker 2:Hard to believe.
Speaker 1:It's flying by.
Speaker 2:Just flying by.
Speaker 1:It's flying by. Yeah. Remarkable. Well, leave us five stars on Apple Podcast and Spotify. Sign up for our newsletter at tbpn.com, and we will see you on Monday Alright.
Speaker 1:11AM Pacific. Have a great weekend.
Speaker 2:We love you.
Speaker 1:Goodbye. Smoke grenade tonight. Alright? Goodbye. I like a
Speaker 2:smoke grenade. We love you.