Odd Lots - The AI Industry Is Becoming Like Professional Sports

Episode Date: August 4, 2025

When it comes to tech startups, you often hear about VCs making a ton of money, or founders experiencing life-changing exits. But something is changing in the world of AI. Now it's the engineers themselves getting pay packages that can be in the 9-figure range. Why is this? Why is it happening? How is it changing the culture of Silicon Valley and business more generally? On this episode, we speak with John Coogan and Jordi Hays, the co-hosts of TBPN, a daily show about technology, which covers the industry in a sports-like manner. We talk about the economics of these transactions, why they make sense, and who are the industry's top superstars. Read more:Meta Seizes Its Moment to Spend Aggressively in the AI RaceApple Rebound Looks Elusive as AI Woes Draw Investor Scrutiny Only http://Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at  bloomberg.com/subscriptions/oddlotsSee omnystudio.com/listener for privacy information.

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slash audio. That's vanguard.com slash audio. All investing is subject to risk Vanguard Marketing Corporation distributor. Thinking about buying the right home, but not sure when? What if you had the right design, the right lot and finishes at the right price? Not someday, but right now. Register at Democrathomes.com and get everything you want right now. Bloomberg Audio Studios. Podcasts Radio News. Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Wisenthall. And I'm Tracy Allo White. Tracy, I love doing the podcast, but I'm kind of thinking about becoming a PhD level AI researcher. If I do a career pivot, it seems something I'm considering. That's the place to be. Have you seen, have you seen like the salaries

supposedly, like, the salaries and comp packages that supposedly meta and a few others are paying for top AI talent? I have seen some of the headlines. I have also learned a bunch of new words, like exploding offer and aqua hire. Oh yeah. I was thought, exploding offer sounds dangerous. What you want is an explosive offer that you can't turn down. But I take it to exploding hire is like one that lasts like five minutes. Yeah. So you have to make a decision right away. And the company that you're currently working for can't counter offer.

So I read these headlines about some engineer or something getting paid $100 million or $250 million. I actually literally don't believe them. Like I actually literally think that's fake news. Really? No, but I kind of don't. I just think it's probably made up of like all this weird equity structure. Yeah, but I'm sure it's not all up front. Anyway, we sort of seem to be in a moment.

And it's actually not just AI, I would say, journalism, finance, et cetera, where it's like the sportsification of a lot of different industries, individual talent, the demand for individual superstars on anything doing very well. I have a bunch of questions when it comes specifically to AI, such as what makes an AI talent? Because I assume if you're Mark Zuckerberg and you're trying to assemble your AI dream team or something. Okay, sure, you have like a level of technical insight and maybe you hear people talking about, oh, this one guy is fantastic. But I assume it's not like you can look at their individual levels of code and you can't see what they're doing on like a daily basis. I don't know.

I don't know either. I don't know anything about this stuff. But I'm really excited to say we do have the perfect guests, a couple of guys who are right in the middle of all of this and also who have sort of been ahead of the curve in terms of this. Like I said, the sportsification of the industry. We're going to be speaking with John Coogan and Jordy Hayes. They are the co-hosts of TBPN. It's a live show podcast. It's become one of my favorite new media properties it exists. And I'm really excited. We have them both in the studio here with us today. So John and Jordy, thank you so much for coming on Outlaws. Thanks for having us. We're so excited to be here. I love, by the way, you guys make these like baseball cards every time. So it's so clever.

For those who are not paying attention, what do you sort of give the high level overview of like what you see happening in this like crazy talent war. For people who don't know about this, what is actually going on, this talent war for people who know how to do AI or train a model or whatever it is. Yeah, the league or the Mag 7, the serious teams are all worth over a trillion dollars. Tesla's in a different kind of boat some days, but pretty much there's multiple, there's seven trillion plus dollar companies now. So there is an inordinate amount of money to flow around. And if you think about investing 0.1% of your market cap to make your business potentially 5% better, that's a trade you take all day. The number is staggering.

But now you're seeing companies pay hundreds of millions of dollars. I think you mentioned that, you know, there's a lot of debate over whether that was fake news or not. They seem like they are very real offers and they are indeed happening. And there's a whole bunch of different ways that you can kind of underwrite that. But we could start with going through. just a little bit of the history here of how we got into this place or what these AI researchers are actually doing. I'm happy to kind of answer any question. Okay. Well, why don't we start with the baseball analogy then? If I was collecting AI engineer cards, who's the most valuable? Like, who do I actually want to have? And then secondly, what are the stats that are printed?

So taking a little bit of a step back, talking about kind of the sportsification of tech and business, which has been a huge catalyst for our show. I think we realized early on people would call TBPN the Sports Center for Tech or some analogy like that. We were a little bit, it sounded cool, we didn't really know what that meant. John and I don't watch sports at all,

but from, for basically, for basically like two decades now, I'm in my late 20s, John, mid-30s, and we've followed tech and business the way that our college friends follow sports. So in, You have players, personalities, coaches, managers, leagues, teams, and people obsessed over all the details. I never fully understood that. I mean, I understood kind of the draw, but it just was never for

me, whereas John and I would pick up the newspaper as teenagers, and we were tracking the, the talent, the CEOs, the companies, the markets, the industries. And it's just something that we obsessed over. And so this year has been amazing as these AI researchers have been getting these sort of superstar max contracts, which is what we would call them. We would start joking on the show and be like, look, this guy just went over to meta. It's probably, you know, four-year contracts, you know, one-year cliff. And it just got more and more and more real as the kind of demand for world-class AI researchers completely outstrip the supply. And we actually put out something a couple of days ago called the METIS list. And John can go a little bit more into the name behind that. It was kind of

our take on the Midas list, which was a lot of the Mitis list, which was a couple of the MEDA's list, we built a list of top roughly 100 AI researchers and rank them on a bunch of different factors. And so when you talk about what goes into, what makes a great AI researcher, there's a bunch of different factors. One that you can get right into the numbers with is like citations. So if somebody's a researcher, they've been publishing, you know,

studies, papers, et cetera, for quite a while at this point. And you can just see quantitatively what their contributions have been to the industry. And so that's like a good starting point to understand. And historically, Elon even said earlier this week, honestly, it was like an hour after we posted the METIS list, probably unrelated, but who knows, he's basically saying that AI researchers and engineers, we're just going to call them engineers now. And that makes a lot of sense for someone like Elon to do as somebody who's always been, you know, very engineering focused, less focused on, you know, entirely net new innovation, more so how do we make the best possible versions of, products that exists. And it's not to say that he hasn't innovated in a bunch of different ways, but it is a really wild moment in time. And it's been a fun moment because historically you hear about this Midasless investor, you know, making $2 billion of carry on some deal or this founder, you know, IPOing today we have, you know, the Figma IPO. And, you know, there's a bunch of people

that are going to make billions of dollars there. But you don't hear about the 100th engineer that was hired making a $100 million signing bonus. And all of this makes a ton of sense in the context of what John said because you look, meta's up. I checked this morning $195 billion new market cap and think about how many $100 million signing bonuses you can make against that kind of market cap. Should we answer your question about who the who's the best? Who's the white whale? Who is the who is the Michael Jordan? Did you say white whale? Don't. Don't. Don't say anything about whales or whale products or hunting large mammals. Tired to be hearing about movie.

Perhaps he's the white whale. Perhaps he's the Michael Jordan of AI researcher. But Ilya Sutskiver is really, he's at the top of our list for a variety of reasons. And he, if you're not familiar with Ilya Sutskiver, he is an AI researcher who was at Open AI for a long time. And there's a few different ways to characterize him. He is, both coming up with new ways to implement AI algorithms, the way you train the model, but he's also very good at, for a long time, identifying which the shortest path in the tech tree. So there are branches of choices that you need to make as you develop the new AI models. And he was very early at, while he was at Open AI, he identified that the transformer paper from Google. He didn't invent that. It was at Google. But the transomers. transformer technology was extremely important and that it had the ability to do remarkable things when scaled up massively.

And so he was the driving force between kind of identifying the transformer as the correct path. Now we look back on attention as all you need, which is the name of the paper that defines the architecture that is used in these modern large language models that you use when you're in chat chit. There were 25 other potential paths that we could have gone down. he, you know, the story goes is that he really identified that and said, let's go really, really hard on that.

So he can sort of identify the innovation, what's new, and then also identify the most efficient path to actually execute on it. Yes. And if you're familiar with what the more modern. So the first arc of LLMs and these chat bots scaling up were really, pick the transformer paper, understand that that's the correct architecture, and then scale it up really, really big. So you need to be able to not just write the code to implement that that particular algorithm, you need to marshal the capital to say, we're going really, really big, and we're going to

build the big data center. We're going to spend a lot of money, but it's going to be worth it because we understand the tradeoffs here. Then the second kind of innovation or correct call he had was during the Sam Altman ouster and return, he was working on a project that was codenamed QSTAR. And there was a lot of speculation about what QSTAR was. was it the secret super intelligence thing? What did I see? Yes.

Yeah, because he was going back and forth in support of Sam Altman and then leaving and then and then he was back and forth and there was a lot of drama there. Always keep them guessing. Always keep them guessing for sure. And so there was there was a lot of rumors, but what it wound up being was just reasoning, which is now what is available in if you use any of the O3 models in ChatGPT or you use Deepseek R1 and it goes by a number of different names. the project was rebranded strawberry and then the O Series.

And you can think about this as the test time inferences, the other buzzword. But basically, the LLM, you're using the same foundation model, but you're running it a ton more to come up with a bunch of potential answers to questions and then narrowing that down. And essentially applying what's called reinforcement learning to the transformer architecture and the pre-training that's happening. And so he was very critical in leading that project. And so that's allowed him to eventually once he left Open AI go start a new company called Safe Super Intelligence, SSI. And he's gotten that company to, what, a $30 billion valuation, $32 billion valuation. Which ties into the talent war because I think it's, I don't know that it's been officially announced yet, but people are expecting Daniel Gross to join meta. Who was the CEO and co-founder of that company, SSI.

And so back to your dad back to the white whale question. Think about the dynamic here. Yeah, yeah. If you guys get together, they start a company. Within a year, it's worth $32 billion. Yeah. And one of the people on that team decides to actually leave and go join Meta. It sounds insane, probably leaving billions of dollars on the table.

But you can kind of trace this talent war actually back to the OpenAI founding team when you think about all the different players, right? You have Mira Muradi, who's now with Thinking Machine. She was a CTO of OpenAI. You have Ilya Sutskiver. with SSI, you have Elon with XAI, and not to mention you have these sort of hyperscalers who are also competing for the same talent. So yeah, that's really kind of the origin story in the Genesis, and it's been amazing to see Open AI's progress despite, you know, the recent

reporting is around, you know, losing a bunch of top researchers, which is real, but it wasn't that long ago that they lost like two very, very, very key senior execs in Ilya and MIRA. Today's show is brought to you by Vanguard. To all the financial advisors listening, let's talk bonds for a minute. Capturing value and fixed income is not easy. Bond markets are massive, murky, and let's be real. Lots of firms throw a couple flashy funds your way and call it a day. But not Vanguard. At Vanguard, institutional quality isn't a tagline. It's a commitment to your clients. We're talking top grade products across the board of over 80 bond funds, actively managed by a 200-person and global squad of sector specialists, analysts, and traders.

These folks live and breathe fixed income. So if you're looking to give your clients consistent results year in and year out, go see the record for yourself at vanguard.com slash audio. That's vanguard.com slash audio. All investing in subject to risk vanguard marketing corporation distributor. They say abs are made in the kitchen. Cool, but who has time for three hours of meal prep and a fridge full of Tupperware? That's why I started using Factor.

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We're recording this, by the way, July 31st. META came out with earnings last night. The stock is up like 10%. It's up like $150 billion more. The other thing is, and I think this ties into the logic behind the comp, So they're going to spend like something like another 70 billion on KAPX and stuff like that. So we know these are incredibly computationally intensive things. They're electricity intensive things.

You mentioned paper citations, but also actually having the experience of one of these runs. And it does seem very highly intuitive to me that if you've done it before, and if you could even cut down the cost of a training run or set up a big computer rack for five. yourself very quickly. You 5% less, right? This is exactly what just happened in Meta. Then you instantly pay for your salary. I could say because, and this is different from the B-to-B SaaS scenario, right? Yeah.

Because you can, there's these huge KPEX costs. If you could just marginally improve the efficiency of that, that pays that 100 million seller salary. Yeah. And so this literally just happened right before Mark Zuckerberg went on the talent acquisition spree that he's been on for the last few months. Meta's main AI model is called. Lama, and they've released a series of versions of that model, and Lama 4 was the latest and

greatest, and it didn't go very well. And the rumors about why it kind of failed to deliver on expectations was that they kind of went down the wrong path in the tech tree, and they focused a little bit too much on pre-training and scale insanely costly. So they've released a part of Lama 4, but they have yet to release Malamah 4 behemoth, which is their biggest model, the most aggressive, and they had just little details in the implementation, little choices of how you chunk the attention in the transformer model. We're operating in this level of abstraction up here talking about, you know, it's a prediction model. And then we talk

about transformers a little bit. There are sub-alorithms within these systems that you make the wrong decision and you could get a vastly different outcome. Your electricity bill goes up by 100 million. Exactly. So all of that money that was spent on that electricity and that, and build out, of course, meta can absorb that. But when you think about the cost of getting incorrect. One thing is a lot of these projects will be energy constrained too. So efficiency is going to matter a lot. It hasn't been the core focus today.

If you talk to anybody at the big labs, they are focused on maxing out intelligence at the cost of efficiency because they know you want to be on the bleeding edge. You want to have the smartest model. You want to be, you know, there's debates around which benchmarks actually matter. how important AI benchmarks really are, but energy is going to, you know, a lot of people are saying, Zuck won't even be able to spend as much as he wants to spend on data center development purely because of the energy constraint. Can you talk a little bit about what happened with windsurf?

Because this is the one that seems to have, like, captured everyone's attention, in part because there was the drama of, it almost seemed like it came out of like a Silicon Valley, the show script or something, where all the employees were. gathering, expecting to hear that they were going to be bought by Open AI. And then they find out that actually their CTO has just been bought by someone else completely different and they're sort of left in the large. CEO. Oh, was it the CEO? And top 50 engineers. Well, how about I get some prehistory on acquires and you can take us through that actual weekend. So there has been a trend of sort of these zombie aqua hires. Everyone has different names for this. But effectively, when a very large

company, usually a hyperscaler wants to go and acquire a company that has AI talent. They used to just buy the whole company. And this was part of the Silicon Valley social contract that even if I am in operations or sales or finance or HR, if I join a hot startup and it gets acquired, I'm coming along for the ride. You get the exit. And I get the job at Google, at least for a little bit. And then, yeah, if I underperform Google or meta or Amazon, they might lay me off. But even if I'm somewhat redundant. I'm coming along for the ride and I'm cashing out my shares and the reason that you go and take the risk and take the little bit of the rougher ride that is a startup. You don't get as many amenities, but you get the lottery tickets in the form of stock options that hopefully pay out.

But there's a big question about how much of this is the acquirer just not wanting to deal with the deduplication of the back office. There's also the question of the FTC. A lot of the FTC rulings have made it much harder to get these big acquisitions across the finish line. And even if the FTC does approve, they can often hold it up for six months. And we're in a race where if you deliver the best model today, you're going to make more money, you're going to be the hot company, you're going to acquire. It's all this big snowball. So companies, this started with, there were a few, but character AI was the big one with Noam Shazir, who was. And very, very interesting situation where, so Noam wanted to go back to Google, which made sense. Google wanted him there as well. He's one of the greatest AI

researchers of all. Yes. And so having him go back there made a lot of sense. I think he had built this platform. He built like the first at scale AI companionship platform. If you look at character AI's site traffic today, I mean, it's one of the largest sites on the internet still, which is wild. But I think he realized I want to work on AI research. I don't want to work on AI girlfriends, boyfriends, that kind of thing. And as part of that deal, Character AI effectively became entirely employee owned. And they had a really strong balance sheet. They had a crazy amount of users. They weren't monetizing maybe that well yet. But I think a lot of the employees in that situation were like, this is pretty cool. We're basically running a co-op where we all own a lot of this

company. We don't have investors. We don't have the same kind of pressure to perform. And that space is competitive, right? Even Elon is competing there now. Chat GPT. gets used as a companion, but not competitive like co-gen. And so that was the dynamic here that was insane because Google clearly cares about code generation. They see Anthropic adding they went from $1 to $4 billion in run rate this year. They're pacing to be somewhere around 10 at the end of the year. And so that's a Google-sized market, right?

Google is going to ultimately care about co-gen. So it made sense for them to say, hey, let's get, you know, 50 more hyper-talented engineers This is Winsturf. This is Winters for talking about. The issue, though, is if you didn't get brought over to the Google ship, you were on what I was calling a ghost ship. Well, John and I. Everyone who was pro, the strategy, they would call it the Remain Co. I was calling it a ghost ship, which I think is hilarious.

But imagine you're at a company, and the dynamic with WinSurf was fascinating because the company, if you had joined in August of last year, WinSurf, you could have, the product hadn't launched yet. So you could have worked and worked up to the product launch, launched it, seen this, meteoric growth, the last reported number they had with something like 80 million of ARR, you have a term sheet to get acquired from OpenAI for $3 billion. And that falls through. And then all these employees are looking around and they're like, wait, I didn't even hit my, I haven't even hit my one year cliff yet. I don't actually have a right to, I mean, they might technically own shares or options,

depending on how it was structured. but they, as I'm sure everyone here knows, you oftentimes, like sometimes founders would, try to accelerate their employees, get them compensated as part of a transaction like that, but there's not necessarily a contractual right to do that. And it's part of a negotiation. And so in this case, you have this team that's effectively split up. And we were covering the whole thing live because we were hearing that, you know, employees that Friday were, like, crying.

And there was a ton of confusion and chaos. and everybody was learning facts kind of over that 48-hour period. Luckily, our friend Scott Wu at Cognition, the company that ultimately bought the Remain Co, flew to meet the Winsurf team, the new CEO, WinServe, and basically spent the weekend doing this insane deal and it ended up being a great outcome, I think, for everybody involved. Didn't like WinSurf have their like marketing department in the room or something when they made the announcement, the initial announcement, and everyone was like, because they were expecting to be bought by open. AI, right? And so they're like, we're going to film this for posterity. And then it turns out to be

something completely different. Yeah, I believe the timeline is that, you know, windsurf launches a year ago is in a knockout, dragout fight with cursor. Cursors doing very well. They're also growing to 80 million or so open AI. Cursors in the hundreds of millions of revenue. Yeah. WinSurf was very clearly the number two player in AI IDE market. Yeah. And so Cursor is staying independent, but Open AI wants to continue. you to get a foothold in this space. So they make an offer. That falls through. The rumor was because Microsoft would have had IP look through exposure via the more complex open AI structure, which is ongoing. Which is continuing to be ongoing. And then when Google came through, they said that they wanted just to buy the team, leave the Remain Co. Because it's potentially cleaner from an FTC perspective.

But there's a whole bunch of different reasons and no one really comments on exactly what. what happens. But when these deals happen, this just happened with scale AI and meta, the CEO, even though there's some sort of amorphous FTC risk, the CEO, if they're going through one of these zombie acquisitions, does have the ability to kind of set the team up with certain expectations and say, hey, we're going to take care of you. Trust me. You're going to get a check that you would expect based on your ownership. So if you own 0.01% of the company, you would expect that you get X of the headline number, and yes, it's coming to you. The weird thing about the windsurf deal was that that was not messaged. And so there was like this, the zombie ship was more zombified.

But so like, well, yeah, and imagine, imagine you're in a, you're in a hyper competitive market where you're competing with Google and Anthropic and cursor and all these different players. And then you lose your CEO and your top 50 engineers. And they're like, and you guys are going to do. Don't worry. You guys have a strong balance sheet. You guys are going to do great. And we're also going to be, we're also going to be competing with you at, at Google, but you guys, good luck, guys. And so, and great, thanks, thanks for the help. Yeah. But this break, but like, okay, cognition came in. It sounds like all the employees will get something. It's not the end of the world. Yes, because there was cash on the balance sheet that got dividend out. But it didn't have to be that

outcome. And so there's two things here that strike me. One is, again, this does not seem like the B2B SaaS era where it's like, if you created the hottest billing product for. dentist offices. And that was gathering traction. No one would like just buy the talent that built that. Totally. So that's really different. Right? Because the, so the ability to take value out via the talent channel rather than buying the product itself. But then also, and I'm just, you know, the knock on effects. Okay, fine. The windsurf employees did fine. But going forward, there's no guarantee that they could have. And so I'm wondering from either a VC perspective or a future employee's perspective, how this is going to change the sort of calculation that anyone makes

when participating in a new AI startup. The fact that the enterprise may not be, or the value actually is. So the first question is like this kind of is the example you gave of like buying the team that built the dentist B2B SaaS because Winsurf does not train foundation models. And Google is exceptional in training foundation models with Gemini and DeepMind. The DeepMind team is extremely well staffed on AI researchers and continually seems to push the frontier, both the qualitative and quantitative metrics. And if they were weak anywhere, it was maybe product.

Exactly. And so this is the, like, I don't want to be like, they're just product people. Winsurf has some amazing AI researchers, some amazing AI engineers. But what Winsurf really did, they did not train a frontier model that was about to disrupt Gemini, Deep Mines Foundation model. They were going after, you know, would you use a Google product? No. You would use Winsurf on top of anthropic or another foundation model. And ultimately, I don't think it's a systemic risk to the social contract of our industry. And the reason for that is that when you see a deal like the Google WinSurf deal get done, it was very concerning. A lot of people were extremely angry. It ended up being a good outcome. A lot of employees were looking around, probably concerned about, you know,

is the million dollars of stock that I've been working for for years worth anything at all? I think that was a good question to ask. But I think there's two things. One is this is AI is a category. And of course, it's touching kind of every category of venture in the private markets. But we're not seeing, you're not seeing a defense tech founding team or engineering group get. There's no real aqua hire value there. The aqua hires that are getting done in hard tech are, hey, you, you clearly have good engineering capabilities. We're happy to have you join the team. But there's no like real premium being placed on that kind of talent might be able to get a great comp packages, but they're certainly not getting these sort of multi-hundred million dollar premiums. The other factor here is like we have a set,

like right now, if you are one of the top 100 AI researchers, you could probably get a hundred million dollar comp package like within a week if you really wanted it, right? And there's even people that are at thinking machine, you know, the reporting from this week was that, and it was kind of hotly debated, but that people at thinking machines, Miramarotti's company, former CTO of OpenAI, We're turning down these sort of $100 million, multi-hundred million dollar. There was a rumor that somebody had turned down a billion dollar five-year contract. And I don't believe that those deals will be getting done in three years. Now, I might be wrong.

And there's going to be, like, probably some exceptions. But the idea that the 30th AI researcher on your team is going to make more than a mag seven CEO, like, that doesn't feel hyper sustainable. Either Tim Cook has to make more money. I researches to make less. Tim Cook is looking dramatic. There have been existences on Wall Street where like a top trade or top dealmakers consistently make more than, or will often make more than the CEO.

Yes, Citadel. Oh, absolutely, because they're the ones that get the money in the door. Yeah. They say abs are made in the kitchen. Cool, but who has time for three hours of meal prep and a fridge full of Tupperware? That's why I started using Factor. Factor delivers fresh, never frozen,

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I've interviewed everyone from Heads of State to fashion icons about the news of the moment. But I've always been curious, who are these people as leaders? I don't think there's one right way to be a leader. Make decisions. A poor decision is always better than no decision. Listen to new episodes every other Monday. Follow leaders with Francine Lacroa wherever you get your podcasts. What separates good leaders from transformational ones? I'm Jessica Chen, and in season two of Leading By Example,

we'll sit down with executives like Grace Chen of Bertie Gray to find out. It's important to understand where you spike, but also really acknowledge where you don't and find people who can fill those gaps. Listen to Leading by Example, executives making an impact on the IHeart Radio app, Apple Podcast, or wherever you. you get your podcasts. Can you talk a little bit more about, I guess, the fungibility of the IP here in both the practical and legal sense? So if I'm a VC or some sort of investor, I invest in an AI company because I get to own that technology, that intellectual property. But then,

let's say the main guy walks out the door, gets hired by Google or whoever. Like how much of what he learned at his original firm or developed is immediately going to be replicated. at Google. Almost all of it. Yeah, I guess this is like, this is a long-winded- And that's exactly why they're paying that much money. This is a long-wended way of saying how many lawsuits are we going to get after all these aqua-hires. So I think one way you can look at some of Zuck's deals are like unauthorized aquarers. It's like the other CEO is not even participating in the deal, but, you know, Zuck is able to come in and be like, would I pay, if these 10 people were working on a company independently, would I pay a billion dollars to

bring them on to my team? 100%. Okay. Let's do this. the deal. It doesn't matter that you're kind of piecing it all up from a, I think going back to like the kind of Wall Street example of like a top trader who maybe is like putting up consistently incredible returns with some sort of like differentiated approach. That person can go and as long as they have capital at the new company, they're going to be able, I imagine to continue to just follow that same type of strategy. I do think that AI and large language models are somewhat different in that you have to look at what is the AI product that these companies are going to sell. much of an impact is that individual engineer or researcher are going to have. And, you know, it's fairly

obvious with Open AI. They're a consumer tech company, right? They sell subscriptions to chat CBT. They have some other use cases. It's obvious that companies like Anthropic, where they're in the cogeneration business. They don't really have a consumer business. And at meta, it's a little bit less clear right now because the sort of ongoing product strategy is not entirely clear yet. The thing that is obvious is that if you can make a 20 billion dollar training run more efficient than you pay for yourself pretty quickly. We should just point out the Wall Street analogy is not perfect, right? Because if you're a high flyer at a Wall Street firm, either client facing or if you're trading, if you join someone else, you usually have a non-compete agreement in one form or another. So you can't take all your

existing clients with you. And then B, you're often on an enforced gardening leave for a really long time. Oh, yeah. I imagine in the massive race that is AI at the moment, there's no gardening. Also, the nature of the intellectual property, I believe, is a little bit different still. I think of this, I think it's a 2012 example from Citadel in Chicago where a high frequency trader stole some code. And they sent, and he had it on his hard drive. You know the story? Yes, it was really famous when it happened.

He like sent it to himself or something. Yeah, yeah. So he exfiltrated some code, some very definitive code of how to make money in the market and get an edge. and he figured out that they were on his trail and he threw his hard drive into the river and they sent scuba divers into the river and got it back. I didn't know that part.

The other high profile SV example was the guy going from Google self-driving to Uber. Yes. Yeah. And so taking specific code, that's not happening. Okay. There's probably going to be like one example of this that pops up. But mostly it's just you get someone who says, I understand that at my previous job,

we scaled up the transformer. a little too far and we didn't focus on reasoning enough. And so we need to shift this spend to test time. And for instance, it's almost like you're leaving, you're leaving a company and you're, and you're just, you're leaving with like a mindset of like the right balance of capx to OPEX or something like that. What was the recent Jane Street example that came out in that lawsuit around it? Was it the, the training strategy?

Yeah. It only, we only really all learned what was happening because there was this lawsuit over, over basically the team bringing over some type of IP or methodology. Yeah. Can we talk about like the broader sort of economic world right now? Because there's a bunch of other sort of things going on and things that you talk about, touch on them. Last thing because I think it's pretty interesting.

So we put out the METIS list. I think it was Monday. And we immediately had a ton of inbound from a lot of these researchers, basically like critiquing like the ranking, right? And the ranking was like, we got, we got. people that work in AI to kind of like rank them we this is the beauty of ranks you do you get so much source it's amazing I understand right every yeah yeah this is the beauty of rank and there's one there was one example that was interesting where as somebody that was ranked fairly high that I know

has gotten one of these nine figure comp packages and somebody that work with him basically said this person was kicked off of every team they worked on and just like effectively like over multi-year period like consistently demoted over and over and over. And they just got the back. Now, now, like, got, you know, this incredible. Please, can you factor in soft skills into your rankings? Well, yeah, and it was,

it was more of, like, a technical ability thing. It was not even, because I think soft skills, he was a jerk. Soft skills are getting a little bit, you know, they don't, they don't count for the bills anymore. You don't care that LeBron James, like, might get a little angry at somebody if they underperform, right?

Yeah, sorry. No, no, no, no, no, there's great. So this phenomenon of, like, superstars, Like, it's not just, it's not just in tech. And like we see it, I mentioned in journalism where, you know, the sort of like median reporter job in a newsroom, a lot of that's hollowing out. But you have some people who've become insanely well compensated either because they're really smart and they write a great newsletter or they're really good looking and they can do

front-facing video, et cetera, or, you know, like the four of us, you know, like can do something on video or something like that. You see it in a range of areas. And then like the amount of betting that's going on, which of course adds to this. And so the fact that all of these events, there's some market out there that you can bet on that. And I'm curious, like from your coast, you know, you're here in New York, but from your coast, what does the world look like in terms of just like the state of this economy? Yeah.

There's something interesting going on in tech where the idea of a company going from a hundred billion to a trillion seemed unfathomable. Yeah. And there's this take that in fact, that was the easiest 10x of all where the heart. Artist 10x was going from zero to one, from going to zero to a million dollars and getting these systems inventing page rank. And then once you had Google humming at a hundred billion dollar market cap getting to a trillion, not to, you know, knock of the work that they did to get that. Sure.

But these, the numbers have just kept growing and growing at internet scale, at internet speed. And so the leverage that you're getting from an AI researcher is ever more increasing. And it just adds 10x every couple years. And so you're seeing the comp packages increase that way. We have a couple things like the internet is the greatest distribution engine for information and digital products and apps and services ever. And so at least in venture, that just means that everything is faster now. Even in media too.

If you're, if you are somebody's working at a legacy media company and they set up a substack, they can get a million dollars of ARR on the first day that they launch. If somebody is working at a media company and really good at a. certain type of YouTube video, they can launch and immediately the YouTube algorithm will serve that video to all of their fans within the first week. And what that does is it just changes, you know, the power dynamic between media companies and individual people. You see this leverage come in through financial markets. If you can lever up or just marshal more capital, one idea can generate billions of dollars in value. Same thing in technology. But we aren't seeing it everywhere.

I don't think we're seeing it in hard skills, woodworking, unless you get into true, like, art territory. But there is this interesting question about, like, what's up next? And we were kind of noodling on, like, there might be a law firm in the future that's extremely high leverage in the same sense that you might have a lawyer who's so good at what they do. And they are so good at resolving these and the connections and everything that they're doing. They're making billions, but they have a very, very lean team. And so you see a much, a much steeper power law in law. And there's a number of other professional services that are going through like a transformation with technology bringing increased leverage to the profession. And that could drive more of that power law outcome in terms of earnings.

Yeah, the thing, you know, in the private markets on the West Coast, which is the dynamic right now that's fascinating is like I feel like a lot of people have like a little bit of PTSD from the 2020 to 2022 era where. many of the things that in hindsight were incredible top signals are like all popping up again right now. And like we like track, we track them just for fun. We are not in the business of like calling, calling the top or anything like that. And in many ways, it feels like, you know, there's so many positive indicators. But the interesting dynamic in venture is like as an angel investor, I've invested in probably 65 or so different companies at the pre-Cid to Series A stage. and there was a bunch of deals that I did in 2021, 2022 that ultimately, like, I paid too much or just like the team wasn't good enough to execute against the vision they had. But there was also

just like a handful of deals that I did that have been, you know, performed so well that it doesn't matter that I did a bunch of silly deals. And so venture right now is this interesting kind of dynamic where everybody knows that it's crazy, right? You have hundreds of billions of dollars of value created in the private markets that there's a lot of real real. revenue growth, but there's also hundreds of billions of dollars of value tied to zero revenue, right? And I think that there's something that we've been tracking is like this underlying kind of feeling from people that are maybe under 30 of there's like this meme that's become very prevalent. And I think it explains a lot of economic activity today, which is young people feel that they

have two years to accumulate capital to escape the permanent underclass. And so I think that drives a lot of investing activity today in that, you know, you'll see young people on the timeline saying that, like, you'd be stupid not to use leverage, you know, and they're just like, you know, they're not a professional investor in any capacity. Don't do it. And, you know, any, any time you have people in venture making public market stock predictions, we get a little concerned. Are we going to get AI talent agents? Not AI agents. We have those already. But AI talent. talent agents because like we do so we do they're called venture capitalists oh well but yes I this was going to be my next sentence so if the money is no longer in you know I invest in a startup and eventually

they get the exit they get acquired or they list or whatever if the money is in eventually the guy that started the startup gets bought by someone like meta or whoever wouldn't I invest my money in that person versus in the startup indenture service yes yeah you can't quite do that but there are ton of roles that are indexed to these high performance packages. And yes, there are people right now in Silicon Valley who are effectively talent agents who get a cut of those big packages and they help negotiate. What do they call just talent agents? Called venture capitalists. No, no, no. So you can, you can, if you find a great researcher and you think that, okay, maybe they will build a business, but I'm sure that they are going to be worth hundreds of millions of dollars a year and you can

just invest in their company, you will almost certainly see a good return on that investment, even if the product never gets to scale, because when they get an aqua hire, you will get a payout. Other scouts going to a lot of these like Carnegie Mellon, the IMO gold medal. And like going there and like finding, you know, the soft lores who are like. Oh, absolutely. It's now, it's now so normalized to invest in college dropouts that you actually see people like investing in high school. I'm not kidding about this. So the IMO gold medal is the math, And there are venture capitalists who will give calls to every single student that performs well on the math olympiad. And these are high school students. So do they what are these investments exactly? Like are you're funding their tuition or whatever?

No, no, no. It's it's it's I am taking 10 or 20 percent of a company of a Delaware C-Corp most likely that that you will build something in. And who knows where it goes. Maybe it turns into a great business. Maybe it turns into aquire. But the downside is extremely limited because. There's always this, at least right now, there's this aqua hire on the table where it used to be. But to be clear, that it's only an AI. It's only for the people that you would consider to be the top 500 in their industry. It's not. To go back to the cursor windsurf thing, like Instagram was the power law winner, got the billion dollar acquisition from meta. Hipsomatic was the second largest photo filtering app, did not get a billion dollar aquire from Google. right? But now we're in the market where if there's a leading product with a bunch of AI researchers

over here and they get a multi-billion dollar acquisition for the product and the product's working and it's growing and it is a great business and you buy it for the value of the business, then the second best team might get acquired just for talent, which is a completely different downside protection. Yeah, and there's a lot of talent acquisitions, traditional acquires where it's only the talent that benefit, right, in the sense that they get basically a job offer at the new company. and VCs get some capital back or in some cases a small amount of money. Can I ask a TBPN question? This should be like the ultimate compliment, which is that I got a DM from some random

person. I had never seen them the other day. And he said, I can build TBPNs for X. So like that stack, that sort of live thing, which means it's sort of like it's become like Kleenex or one of these things where it's like a category. And where are you going with it? What are your plans for? It's so funny that people are calling it TBPN for X because our show, while it's unique

in a variety of ways, looks very much like traditional television. Yeah, it does. Which is interesting. And so we can't take credit for an inventing business television. We invented TV. We invented media. We invented ads. But there has also been live streaming before.

Of course. Normally it doesn't really, I don't know. It's like, why would I watch cable news on online? I think the thing that's exciting is that in the private markets and venture and tech, If you had a podcast, there was one format, which was a once a week interview show. And it was a great strategy to do that 10 years ago around the time that you guys. No, and it still works. You're going to stick around forever.

You're locked in. But if we tried to clone this, everyone would be like, why is that a knockoff? There's nothing new about this. Nothing fresh about this. Like, why do I want to go on your knockoff? I'd just go on the real thing. Yeah. And so our edge in launching the show,

at the beginning of the year is that media is not zero-sum. Content is not zero-sum. We have a friend that jokes that he's so competitive. He wishes media with zero-sum. It's truly zero-sum. But our edge early was that we just took it 10 times more seriously than anyone else. So everybody that was creating content for the private markets was doing it as a part-time gig, and we were happy to compete with a bunch of people that were part-time.

Yeah. You know what actually I was wondering? in 2021 or 2022, that craziness. Someone once reached out to me, and they're like, you know what, you should like... Bitcoin Treasury vehicle. You should go direct. It was probably the page. No, this should, but this was the interesting thing.

He said, you should go independent and what you should do is attach a VC arm to it. And that because of the quality of the guests that you could get, you would get really good access to deal flow. But it seems like basically, it's very uncomfortable to me because I'm not going to like, highlight startup founders and say, oh, you're the perfect guest. And it's like because, like, I have like 5% equity in them. But I'm curious about because you mentioned doing Angel, the sort of link, TBPN at some point. Yeah. So, yeah. So Angel investing is, angel investing I look at as a hobby. Okay. Not a great financial activity, right? Locking up capital. I've heard locking out. I mean, like, you can generate great returns, but it's not the most

logical way to invest your time, but it's really fun. Like, we just enjoy supporting founders early when it's an idea and a team, and it's just, it is genuinely addicting. I would joke about people in San Francisco with Angel, you know, people across the country, sports betting addictions. San Francisco, you know, Angel investing addictions are, I think, real. But for us, for us, that was, you know, as we started having some success, a lot of people, they would probably get a message today, when's the fun coming? When's the fun coming? And that's been a way to monetize an audience within tech. If you have an audience, go raise a hundred million dollar fund. You get the fee stream. Some stackers do that. You get a bunch of upside. And we joke because we have the shows every single day.

We go live at 11. We have to eat. We have to work out. We have to prep the show. We have to talk with partners. We have to manage our team. There's all these different things. And so, somebody might immediately think, okay, these guys talk to six founders and investors a day. They have this, like, media property that, like, I think everybody in venture now is going to see our content, like, once a week in some form or another if they're online. But I joke at them. I'm like, so we're live for three hours every single day during the middle of the day. And so if you think that we with a show would effectively compete at VC is about winning allocation. right you can be cool and and connected and have an audience that might get you a hundred k allocation but if you have a hundred million dollar fund you need to be putting size into deals and so i know that if john and jordy have a VC fund and we're competing with these other guys that have a live show that they're live for three hours a day and they have a VC fund we'd smoke them because we're like okay while they're live i'm going to fly to the founder i'm going to meet with

them and we're going to be like yeah why don't why don't you let them put in like 200k so the thing to watch out for when the VC fund is coming is when you start reducing your live hours. That'll be the sign. I think we didn't get into this to start a fund. And I think there's a lot of people that get into content because they see it as a way to do that. And we just love talking about tech. It actually was the key insight. Was that not enough people. Yeah, it's very fun. But not, but very few people in tech were actually taking media seriously. Yeah. It was everyone had a fund. Everyone. That was the high status thing. And doing. media was like lower status or people didn't think we could have as much of a power law outcome as it very clearly can. And so this idea of just what if you actually took it completely seriously and just made it the main thing. And we've had a bunch of people copy our format, you know, major legacy media companies all the way through friends of ours. It doesn't really bother.

I mean, it bothers me more than John. But at the end of the day, if you want to spend. You're the Tracy, by the way. Yeah, it bothers me too. I totally get it. And so it's like, we basically know. So John and I basically hang out for 12 hours a day. And the entire time, the entire time we're thinking about the show, we're just talking about the show. We're just talking about the show doesn't look very different than when we're hanging out offline.

And so I just joke. I'm like, okay, if you want to compete with us and you're willing to put it 100 hours a weekend, by all means. Yeah. Go for it. Like this is probably your life's work. But if it's not, good luck. It doesn't matter. John Coogan, Jordy Hayes, thank you so much for coming on Oddlots.

Thanks for having us. And congrats and good luck. And looking forward to continue watching TBPN. We'll be live from NICSI later. Oh, nice. This is it for the Figma IPO. FI. FI.P.O. and odd lots in the same day.

What a day. And in the wake of meta. Finch me. Yeah. Tinch me. All right. Thank you for having us. Thank you so much for having us.

Tracy, that was a lot of fun. It was. A little bit media navel gazing, but that's fun. A little bit media navel gazing. I mean, there is this thing that's happening. It's been happening in media for a while. And of course it happens in Wall Street. And now happening in AI where you just have a lot of talented people.

And they wonder about the degree to which they need their existing platform. They're, you know, like a star banker can take a book of business or a star lawyer can take a book of business. And in the case of AI, it's not taking a book of business, right? Because they don't have like their individual clients. Right. But it's this knowledge and transport it. And it's instantly worth a lot of money for someone somewhere else. The thing I thought was really interesting about that discussion was the emphasis on how capital intensive all of AI is.

And so that kind of changes the economics of why you're paying such massive money for someone who's able to like eke out even a slight efficiency. This is huge. This is huge. This was like, it suddenly is what made it all made sense to me, right? Because we know about like how costly one training run is, right? And we know just the insane numbers for data center set up, et cetera. And I'm sure there's progress being made on the literal design of like how you string together in video GPUs, et cetera.

And so if you could get some margin, if you know, have that know how to get some marginal improvement out of it. And this is fundamentally what was. One trillion dollars for you. Maybe. Not a trillion. No, not a trillion net. But like, this was not the case when tech was not so capital intensive in the 2010s where, yeah, I'm sure talented people always made a lot of money and there's always improvements.

But where it's so that link between some sort of efficiency gain and instant cost savings is so linear and so straightforward. Yeah. And I take their point that, okay, VCs exist and they're already investing in some ways in specific talent. But I do kind of wonder if you're going to get some sort of like specialized headhunters at the very least who are going to like seek out these big AI talents and try to like graph themselves onto them. Yeah, or just people whose expertise is in reading through undersighted, undercited AI research papers. Yeah. Well, we can't give you Sam Altman, but what if we could replace Sam Altman in the aggregate? But this guy has 50 citations in the following papers.

Should we leave it there? Let's leave it there. This has been another episode of the Odd Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Allaway. And I'm Jill Wisenthall. You can follow me at The Stalwart. Follow our guest, Jordy Hayes.

He's at Jordy Hayes and John Coogan at John Coogan. And check out TBPN at TBPN. Follow our producers, Carmen Rodriguez, at Carmen Armid, Dashill Bennett at Dashol Bennett at Dashbot and Kale Brooks. And for more Odd Lots content, go to Bloomberg.com slash OddLots. We have a daily newsletter and all of our episodes. And you can chat about all of these topics 24. in our Discord, discord.g.g. slash oddlots. And if you enjoy Oddlots, if you like it when we talk about the sportification of AI

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