Odd Lots - Josh Wolfe on AI and the Breaking of Silicon Valley's Social Contract

Episode Date: September 8, 2025

One day it's so over. The next day we're so back. This is what it feels like gauging the AI boom right now. Everyone's looking for signs of some kind of slowdown and that investments aren't going pan out, but mostly, the dollar signs just keep piling up. And the AI winners like Nvidia, OpenAI, and Anthropic just keep seeing their market valuations rise. In the meantime, other AI players are seeing weird outcomes. Some promising startups aren't being sold, but rather their top talent is walking out the door, leaving other workers potentially in the lurch, while creating risk for venture capital bagholders. On this episode we speak with Josh Wolfe, co-founder and managing partner at the firm Lux Capital, which invests in a range of startups, many of which are in the AI space. He talks about the challenge of aligning incentives, what's overrated, what's underrated, why he thinks Nvidia may have run its course, and the threats to Silicon Valley's "social contract.”Odd Lots is coming to Chicago! Tickets on sale now. Only Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox — now delivered every weekday — plus unlimited access to the site and app. Subscribe at bloomberg.com/subscriptions/oddlotsSee omnystudio.com/listener for privacy information.

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 Allaway. You know, sometimes with the whole AI thing, it feels like the, are we back? Is it over? There's been a few moments in the last year, whatever.

It was like, is the bubble burst in? Is there a bubble? I don't know what this week is. We're recording the September 3rd. There's a little, I don't know, there's some tremor. I can't tell what's real or not. I feel like the hype. get shorter and shorter and more compressed.

But you're right. I think there are maybe some more jitters than there have been previously. Like maybe. Only maybe, though. It's hard to tell. It's hard to tell. But I think even if we're not there yet, we are getting maybe to that point where like the rubber needs to meet the road in terms of monetization. Like, you know, everyone got maybe.

Right, maybe. Yeah, you're right. Maybe. Yesterday or either this morning or yesterday, anthropic, $183 billion valuation. This does not strike me as like, suddenly people, well, or suddenly people are worried about valuation. It sounds like people really want to pour a lot of money into these companies. Yeah.

It's a weird environment. Let's just put it that way. That's the only thing we can say with certainty that doesn't start with a maybe. That's right. Here's another thing we can say with certainty. Alphabet. The shares are up, as we're speaking, 8.something percent today. This was a company. People were sort of worried about how they would do in the AI era. They're killing it. They're in an all-time high again today. So again, like, many things are like, it's tough to get a read on things. It is indeed. Who do we turn to? I love this vague intro. We just want to have an excuse to get to the guest. And so we're going to vamp for a little while. And then we're going to get to the guest. I just tried to throw to the guest joke. Who do we turn to? for a read on the true feelings of the market around AI. That's right.

We do have the perfect guest. We're going to be speaking with Josh Wolfe. He's the co-founder, managing partner at Lux Capital, V.C., who's been in this space for a long time, a guy we like to turn to figure out what's actually happening at any given moment. He sometimes even has good insights in public companies, too, not just private companies. Josh, thank you so much for coming back on the podcast. Great to see you both.

How are you guys doing? We're doing great. What's up with Alphabet? Some people thought they were going to be a big loser in AI because they have this legacy search business model and O3 is so much better for searching things. Here they are surging. They're at an all-time high. What is the smart take on Alphabet right now? I think they are crushing it. I think they are the sort of dark harsh underdog. And maybe the second that I would put it with that is Apple, which people are totally coming out. Yeah, totally. And the irony is, you know, if you think about the big players, meta, with Zuck's crazy poachapalooza, you know, the past three months, where, you know, $100 million pay packages and trying to disrupt everybody and bring them all on, you're seeing talks about them. They've got mid-jurney coming in for the images, so not relying on their own models.

They're talking about, you know, Google or, you know, potential integration on search. And so it's interesting that meta, having committed to first be, you know, the metaverse and change the name to meta, and then Zuck going in all in AI is actually going to be turning to some of these other players potentially. So I think that's surprise number one, that people thought that Meadow was going to be in the lead. And I think that Google and Apple were both sort of counted out, but both are very serious contenders. If you look at the top two video models that are out in the world today, one U.S. company, which is ours, called RunwayML, and the other, which is VEO3, which is absolutely stunning and incredible. They are a force to reckon with.

It is extraordinarily development. You can make an argument, by the way, they have this repository exclusive to them of being able to trade on every YouTube video ever produced. Yeah. Itself is a super valuable piece. Remember, it was, what, a year and a half ago, two years ago, that people were mocking Bard. Bard was the laughing stock of all of this. Gemini 2.5 is crushing it. Oh, yeah.

You know, the nanobanana that I don't know if you guys have used, which was the secret code name for their latest image generation model, is probably the number one, performing model out there and paired with some of these workflows that go into runway or even into VEO combined with Mid Journey. It's just, you know, so I think people counted Google out because they were behind and they weren't part of the hype. Open AI had won the consumer, both on subscription basis and capturing people's habitual daily use and that all made sense. Claude was capturing it, an anthropic on code. And then you've got niche players, you know, like open evidence and other people that are doing it in different verticals like medicine. But I think

that the corporate workflows, I think about how much Lux depends upon Gmail and Google calendar and sheets and slides. And their ability to integrate that all over time is, I think, and give them a huge advantage. So very bullish on Google. And by the way, remember, go back 20 years or 15 years, Google did the thing that completely did what the DOJ couldn't do to Microsoft. They dropped the price of alternatives to the office suite to free. And the net result of that was Google, which had this advertising model, was ascendant, and Microsoft was suddenly scrambling. And I think that the same thing's going to happen. I think that a lot of people funding foundation models and the endless perception of endless demand for GPUs and compute and all these

independent private AI companies are going to be shocked by what Google does on a pricing basis with Gemini and Beyond. So Dark Horse, I'd be pretty bullish. Joe, did you immediately run to the Nano Banana website? Yeah, I did. As soon as you said, I had not used it. I did too. I had never used nanobanana. That is the first place. Don't go to a website called nanobanana because, you know, you're going to, I don't know.

Nanobanana. It looks like the right spot. Okay. It does have little. But where you want to actually go is it's now embedded inside of Google. The AI studio. Yeah, yeah. I see that.

Yeah. It's ability to take you and it's almost like Photoshop. But just by coding Photoshop, it's really incredible. This is a threat to Tracy's MS paint skills, which are legendary within the odd loss. office. And maybe I will no longer need to say, Tracy, can you make this for me an MSPaint? No, no technology will ever replace MSPaint. I'm 100% confident in that. I'm being sarcastic, obviously. Okay. Can you talk more generally about how people are feeling about the AI space at the moment? Because you probably heard us in the intro struggling to characterize like

the general attitudes towards the sector at the moment. What's your take on it? Well, the first is, on the one hand, people underappreciate how much this is going to change everything in our daily lives. But that doesn't mean that people are going to make money from that. We're all going to benefit. We'll all be more productive. The great irony at the macro scale, of course, is that people thought that blue-collar jobs were totally screwed, you know, and white-collar jobs and the Peter Drucker, knowledge worker, everybody's safe. But the great irony is it is the knowledge workers that are in trouble because so much of their workflows are being captured and, in a sense, commoditized. And maybe approaching an asymptote of good enough.

It may not be perfect, but pretty damn good. And so you're going to see a lot of labor destruction in segments and markets that people were not anticipating. The first early canary in the coal mine that you're seeing there is hiring for undergrads coming into entry-level jobs. And whether that's investment banking or sales and trading or consulting or accounting, suddenly it isn't that the economy is really troubled. it's that a lot of those demands, you're seeing Salesforce, say, 45% of our jobs, you know, it cut. Mark Benioff is using AI instead of people. And that's going to keep trickling down.

It's going to happen slowly and then sort of all at once. So that's one thing on the labor side. And I would say broadly that it's underhyped in how much it's going to impact our lives. It's overhyped in valuations. Now, where is it overhyped evaluations? The first one, you know, I was very proud 10 years ago. We funded a company called Zook. Zooks did autonomous driving.

And they were training these cars. and we had $25 million in, and I was like, they're playing video games. You know, what are you doing? You're messing around. They said, no, no, no, we're trading the vehicles and we have these Nvidia chips. Nobody else has yet. That was 2015. I ended up pitching at a public charity event, the Investor Kids Conference, 2015, 2016. And this was a $15 billion market cap company at the time when Intel was $150 billion. I said, this is like the per trade of a century.

Oh, Nvidia. They're up like $340,000 then, aren't that? Yeah. Yeah. And so this was just, you know, insane. It's the benefit of being a venture capitalist that you get to see the future in Google inside information inside the companies and what people are doing. So the perception and the consensus in AI on the hardware stack is that we need endless demand for data centers. We need endless demand for GPUs. We need 100,000 clusters of H-100 chips or Blackwell chips.

We're going to thwart China from getting the chips, but they're going to sort of design around or we're going to have different versions of the Nvidia chips for China. I think that this is misplaced. and I'll give you another insight. We may talk to a little bit about this in the past, but there was a paper from Apple a year and a half ago that a lot of people have not really sucked their teeth into, which was the idea that you could do large language models on device

using flash memory, not you needing GPUs. And so Jensen and a video will tell you, you need all of these H-100 chips and you need endless compute and lots of data centers for training. And that's generally true. But the other part of it, the fancy word for prompting, which we call inference, You don't necessarily need that.

And if that is true, then that means that our devices maybe are running on SK Hynix and Micron and Samsung. And the memory players, which have just like the GPU players, were considered commodity players, when the upgrade cycle of the gaming consoles for PS5 and Xbox, I think you may see a shift towards edge inference. You know, I've been talking about this for about a year and a half. Elon just tweeted out about it maybe two, three weeks ago saying like this is an inevitability. but I think it's going to shift away this fallacy of composition where what Google is doing and Anthropic is doing and an Open AI is doing and meta is doing in a huge scale, all to the benefit of Jensen and Nvidia and Nvidia shareholders may start to chip away and say, wait a second,

we don't need all this computer is going to be a glut. So that's the first one on the hardware. That's very interesting. And yeah, I'd heard, I haven't done much with on device things. A friend of mine was telling me that Alibaba's model, Quinn, works very well on a phone, for example. maybe it's something we should pay more attention to. All right, as a VC, when you are doing due diligence on a company, how does it affect how you think about even arriving at the concept of fair value, when there is the prospect that some share of the enterprise value of the company could walk out the door via aqua hire to a meta, et cetera.

Now, I get it different. Not every company in AI is doing the hard science, et cetera. But just coming from your perspective as an investor, How is that changing how you think of companies that so much of where the value is may lie with talent that could just walk out the door at any time? It is a very big deal. The entire social contract of venture capital is the premise that pension funds and high net worth individuals and endowments give capital to people like us. We then go deploy it into companies. We buy them as early as we can and own as much of a company and be a partner and add value and then sell those companies.

But if all of a sudden somebody is being pried away and the irony of all of this is that because, because of a very impressive DOJ and FTC that basically said, no, no, M&A, we're coming after you, you know, big tech companies. We don't want to see more consolidation. You have too much power. So they started doing, instead of MNA, LNA, instead of mergers and acquisitions, they were doing license and aqua hire. And what that meant was, hey, we'll buy, and they did this with scale, we'll buy 49% of your company. I think scale was valued at around $12 billion thereabout. and they said, well, we'll pay 14, slight premium to your last round, but we'll buy 49%, effectively

valuing it at $28 billion. But we'll pay out that money to the company. You can dividend it out to shareholders, so it may not be perfectly tax-efficient, number one. Then we're going to basically license the technology, and we're going to take all the people, or at least the top people. The result of that is you're navigating around Delaware governance. You're arbitraging. It's really important because, you know, it's sort of like,

Carl Icahn used to say, your price, my terms. You know, there's this phenomenon in legal terms people might call the Chesterton fence. The idea that Chesterton fence is the thought experiment of like, okay, there's a fence there. What the heck is it there for? You don't understand, right? Well, maybe it was there to keep the sheep in or keep the wolves out or whatever it is. Every legal term in every term sheet that a venture capitalist gives or a founder gets is based on somebody screwing somebody in the past and are like, uh-uh, we're not letting that happen again. So I can guarantee you that the next few years, you will see all kind of protective provisions and covenants that say if one of these companies comes and tries to just acquire you,

all of your stock, you know, reverts and yada, yada. And so there's going to be tie-ups and holdbacks that are a pendulum swing away from the super founder-friendly dynamics where venture capitalists were tripping over themselves to basically give the most founder-friendly terms that they can because the founder would say, well, if you don't give me what I want, I'll go to somebody else. But I think that the pendulum is swinging with the cost of capital rising, and you're going to see more and more investor-friendly term sheets partially as a reaction to the fact that somebody like Zuck and META can go in and just basically take the fruit off the end of the tree and leave a stump. By the way, Tracy, as we were talking about this, breaking from the Wall Street Journal, XAI CFO Liberatore, step down. Latest and string of executive departures always moves in this space. Yes, indeed.

You know, the sort of race to the bottom in terms of terms, it reminds me a lot of the corporate bond market and the rise of covenant-line deals, right? And I remember a time when like cov-light deals were a minority in the leveraged loan market. And now I think they're like almost 98% or 99%. Basically everything's cov-lite now because the issuers had all the power recently and they were able to push back against investors. You mentioned the higher cost of capital there. How much leverage does that actually give you as a VC? And then secondly, just going back to the aqua hire thing, how reliable can legal restrictions actually be in terms of preventing like your star engineers from leaving the company?

Are they ever going to be like 100% bulletproof? No. I mean, look, you have non-competes that are non-enforceable in California, a little bit more enforceable in New York. You have arbitrage and jurisdictional. off. Broadly, I would say that the lower the cost of capital, the shorter your term sheets are. The higher the cost of the capital, longer your term sheets are. You got more terms, more covenants, more protections, because you can afford to be able to negotiate for those things. And it's not

because you're trying to screw over the founder. What you're really trying to do as an investor is prevent yourself from being screwed over. But again, there's always a pendulum swinging here back and forth. Even if you think about Zuck and Meta and this entire movement of, hey, I'm the I'm going to have super voting stock of 10 or 100 to 1 and control was in a response to founders being ousted by bad investors or bad board members and some of the best companies, frankly, being run by founders. And so there's always going to be this pendulum shift to your direct question. It's really a covenant in a contract that starts socially before it starts legally.

If I'm backing a founder, I'm doing the most important thing in addition to writing a check, I believe before others understand, particularly at the earliest stage. I'm encouraging them to start their company. I'm giving them the confidence that we're going to back them and believe in them. We're going to give them the capital so they can go hire the 10 best people to start the business. We're going to give them the money for the compute or the infrastructure, depending on the sector that we're in. It could be biotech, could be defense. But whatever it is, in this particular moment, it is breaking the social contract between investors and founders.

And for founders, it might be heads I win and tails, I also win. And so that's the dynamic that you're going to say. see a reaction of investors saying, wait a second, I'm getting screwed. My limited partners are getting screwed. Again, those limited partners are endowments and foundations and wealthy families. And you got to protect against a potential bad actor trying to take the fruit off the edge of the tree and just leave you with a stump. So one of the things that comes up a lot on the podcast in this discovery over years of conversations and what you're describing, it's principal agent problems all the way down in finance. And this is why we see the rise of the multi-strap model in hedge funds, in some ways

to align the incentives PM with the level of the overall fund, with the level of the endowment, et cetera. And of course, some of these issues that you're wrestling with or everyone's wrestling with in finance, similar issues about where the incentives align between the star engineer, the founder, the VC, the LP, and so forth. I have a question, though, so in theory, a venture capital firm, the goal in theory is to create funds with the highest return, right? Make money for investors. But I could also imagine a slightly different incentive in which if the goal is to collect LP money, then maybe you want to show that you're in the hottest deals of the time,

so that when you go to various endowments and pensions and so forth, you're like, we're in this deal, we're in this deal, or in this deal. And maybe that overrides the impulse to create high returns. By the way, I'm not insinuating anything. I'm just trying to get your perspective on something. That being said, one of the things that you hear that's happening in tech these days is that, and for years, founders taking money off the table earlier and earlier in the process. You invest $50 million in a company, $20 million is so that the founder can retire for him,

his children and his children. That may not be great for your LPs, but it might be good for you if you could say we got in this deal. Talk to us about how prevalent this is and how this is changing. this sort of a social contract of finance. So there's three layers of incentives, and man, you really nailed it. I actually haven't really heard somebody that is not a full-time venture capitalist or limited partner nail these issues. So very pressing and very shrewd.

Here's the three layers. First, think about the LPs. You're an endowment or your foundation or you're a hospital, you're giving 5% by law of your charitable assets every year. You want to continue to earn more than 5% so that you can grow that base and invest in campuses and scholarships or expand hospital systems and whatnot. So you invest, you know, 60, 40 bonds equity. Now you do the Swenson model from Yale and you start introducing some private equity. And now you're extending your duration and you're extending your liquidity, but you're doing

it because you think you're getting better returns. Okay. Returns are a function of how much capital is going into a sector. If there's a ton of capital going into a sector, if you're early, you're going to do really well. If you're late, you're going to be doing really poor because as Buffett says, you pay a high price for a cheery consensus. And once it's a consent, you're not making money. So the LP's incentive is to make as much money they can for their benefactors, whether they're patients or scholars or charitable giving. The VC's incentive is two things. One, get the best return so that you can compete. If I'm only earning 12 percent and a peer VC is earning 20 percent, money is going to go where it's going to be well treated and I'm going to

lose to that. So the cost of my capital for the cost of an LP's capital is outperformance. So I've got to outperform, which means I have to be earlier. I have to own more. I can't just do stupid deals. Sophisticated LPs will not just look at the logos that you have, which is the game that you've always seen from mutual funds and from some hedge funds. You know, you'd see the Q4 filings and they always threw in the name, oh, we were in Nvidia, you know, and they would market their top 10 holdings. But BS, you know, they lost money on it, right?

And so that is a really important incentive. And the sophisticated LPs will actually know down to the partner at the firm or the team or the deal team, who was responsible for this? What was the entry point? They will talk to the founders and say, who was your most valuable investor, who got you your first 10 hires, who helped with your syndicate construction for your later rounds, who made customer introductions, who was a valuable board member, who never showed up, who was asleep in the board meetings, all that kind of stuff. So there is a level of due diligence that LPs have to do to know, are you a value ad investor or are you a poser or a pretender that's just buying a logo or a brand name? Okay. The other

incentive, and then we'll get to the founder's liquidity, is you have this weird dynamic of what I've called the minnows and the megas in venture capital. This is in preview a shakeout that is going to happen. The minnows are the thousands of small sub-500 million dollar funds that proliferated when the cost of capital was low, rates were low, everybody was making money. You had a roommate that started a company and you got into Pinterest or you knew somebody at Meta and they gave you a deal and blah, blah, blah, blah. And when you had the tigers and the soft banks and the abundance of follow-on capital, every round was an up-round. You had pay. that kept going up and up and up and it looked great.

And you're reporting these paper marks. And then sometimes these things became zeros. Okay, but you raised your next fund before they became a zero. Those are the minnows. I was with one of my very large LPs, has hundreds of millions of dollars invested with us. And I said, I think there's going to be a 50% extinction rate amongst these minnows. And he said, Josh, that is ridiculous. It's going to be 90%.

So you are going to have a mass extinction. Now, why, by the way? not because they're just bad investors. It's Shakespearean. Okay. Shakespearean in that these are partnerships. People start to hate each other when it becomes hard. People start to hate each other when there's down rounds. People start to hate each other when somebody else's deal is a crappy deal and they're bringing down in your carry. And so partnerships are fragile things, just like relationships and marriages and they can break up. And so you have a lot of VCs that started in the past few years. They're not experienced and going through cycles. They have inadequate reserves to continue to fund their

companies. So you're going to have an extinction that I would consider involuntary exits. Okay? Then there's voluntary exits, which is another interesting dynamic. And there's a playbook for this, which is 2009 to 2014. All the big private equity firms reached a level of scale, several hundred billion dollars, AUM, assets under management, where they basically said we're diversified, we're alternative asset platforms. Carlyle, Blackstone, KKR, TPG, Apollo, all went public. The same thing is going to happen in venture with. probably five or six firms. My prediction is, and they're all great people running great firms,

but they're starting to play a different game. And that game, Andresen Horowitz, General Atlantic, General Catalyst, insight, light speed, a handful of others, all at 80 or 100 billion AUM have built great firms, but are thinking about how do we create generational wealth for the founders and go public? Different incentive. How do I make my LPs the best money or get the best founders?

It's about asset gathering and liquidity. Now we go to the founders. I can tell you I've been on both sides of this. On the one hand, you want to be fully aligned with your founders, meaning I'm in a fund. It's 10 years. Some VCs vest over two or three or four years. We vest and all of our partners vest over 10 years,

the same duration that you're, if you're an LP with me, your money's locked up. It's the right thing to do, Buffett style. And the guy that put me in business, Bill Conway, who's the Carlisle founder. This is what they did. So if you're an entrepreneur and you start a company and people are tripping over themselves to get in,

they might entice you with green mail and say, yes, we're going to invest, just like you said, Joe, $50 million, but we're going to give you $20 million of liquidity. Okay. Now, I will say this. 2019, I'm on a Zoom call for an amazing company called Control Labs that we sell to Meta for a little under a billion dollars. And I love this company. And I love the founders. And this is before everybody was on Zoom during COVID.

And I'm looking at the Zoom window. And I see one of the founders. And I text the other founder because I think that they're. selling too early. And I'm the lone board member that didn't have a veto, but I'm like, I really think we should stay the course and we should keep going. And I made a terrible, terrible mistake because I'm looking at the Zoom window and I see the guy. And I look closer. And I text the other founder. I'm like, is he still in a dorm room? And sure enough, he was in a dorm room as a PhD had made no money. You got to let that guy get rich.

paper stock value. And he's like, yes, I want to sell because he's going to make $90,100 million and it's life-changing money. And I sat there and I said, if I would have just given him a few million dollars of liquidity, he would have been able to take breath, buy a house, you know, get out of that door. And keep going. Get a girlfriend.

There is a virtue of giving some liquidity, but you need to be aligned. And in that moment, we were not aligned because he was like, I'm calling in Rich. And I wanted him to keep going. But if you're calling in Rich and you're not completing the job, the mission, then you have total misaligned. So that's the dynamics. LP alignment, GP alignment, and the bifurcation, and then founder alignment, a little bit of liquidity is okay to let them stay in the course.

Okay. Other than misalignment and everyone's starting to hate each other as the cost of capital goes up, there's another thing going on, which is, you know, open ice. AI just launched some open source models of its own. And this leads me to a question that, like, I'm going to admit, I've never quite understood this, but like what exactly is the attraction for VC investors to open source models as opposed to closed source where like closed source, you know, it's proprietary. People presumably have to pay in order to get it. There's like a defensible moat around the business, you would assume. Why in the world would I ever want to fund

a model that's open source? Well, a few things. One, if you go back in the compute stack, you had this with like Red Hat and Linux, you know, going back, you know, 50, 20 years ago. We are the largest owners of a company called Hugging Face, which is both the most ridiculous name. And when one of my partners, Brandon Reeves, was sourcing this deal, it was a bunch of French PhDs, came to Brooklyn. and they're like, we're starting this. I'm like, hugging face. And they're like, yeah, it's named after an emoji. The little hugging face emoji.

Cute. They became the leading. I like that you're visually demonstrating everything for us. Thank you. I can't do many other emojis and you don't want to see some of them, but hugging face I can do. So they became the leading open source repository. Now, the great irony, by the way, is when Open AI started, they were Open AI. But they became the world's greatest chat bot.

Hugging face started with a really crappy chatbot, but then became the world's greatest open source repository. Every major tech company, including OpenAI, Microsoft, any open source model that they do, they put on there. And that is like the GitHub of AI models. Microsoft brought GitHub roughly $8 billion, $8.5 billion. It was a really valuable store of models and code. This is the same thing for dynamic AI models. So as a VC, we originally funded this. I want to say at a 30 or 40 million pre-money valuation. Last round was north of $6 billion. And real revenue generating multi-hundred millions of dollars, serious company. Now, the trend is this. Vinod Kostla and I were both in the White House a year and a half ago. And we were having a debate with Jake Sullivan

about what is better for national security. Open source are closed. And I said, where you stand on the issue depends on where you sit in the cap table. Okay. And Venoed was an early investor. I think he probably put 50 million into Open AI. I think it became worth a billion plus. It was a great investment. And he believed that the best thing for U.S. vis-a-vis China and the Chinese Timingist Party was a closed proprietary siloed model. And I had the counter view because we're big investors in Hugging Face with a very large position.

And I said no, because the great virtue of any system, be a journalism, scientific inquiry, computer code, is the ability to have in this very Carl Popper-like way, if I can get philosophically geeky for a second of conjecture, hypothesis, and criticism. That is what creates great societies. It's what creates knowledge. So you come up with hypothesis. You come up with a scientific experiment. You come up with an investigative journalist idea.

And then people get to criticize it. It's your editorial room. It's people fighting it out. And things improve through that mechanism. So you think about China, they will only approach an asymptote of truth. You will never get, you know, Xinjiang. You'll never get Tiananmen Square. you'll never get the Uyghurs, whereas open source lets you approach closer an asymptote of truth.

So that's the virtue of open source. The real thing, though, for AI is this. I am not convinced that the value will continue to accrue to, in terms of enterprise value, the closed foundation models. And the reason is open source is getting near damn performative enough that the real value will go to the longitudinal repositories of siloed information. That is a mouthful of basically saying your database of proprietary data. Bloomberg has it with a huge and wonderful repository financial information, time series, every security, currency, bond, fixed income, etc., QCIP that you can imagine.

Meta has it with all of your WhatsApp chats and your Instagram posts and your Facebook likes. X and Twitter have it with all of your tweets. So pharma companies with your clinical data, anybody that has siloed proprietary and long time data, data is going to benefit from open source models that over time become commodity. And then your business model is how do you charge people to do API calls on the models or to house and warehouse them, keep them on-prem, keep them in the cloud. And that's how people figure out how to monetize open source. It's interesting because I hear you that, okay, maybe the value doesn't keep accruing to the

closed source AI foundation labs. or maybe the value doesn't keep accruing to the one GPU maker that we all talk about all the time. And yet at least these are not consensus views based on the fact that anthropic $183 billion valuation or whatever it is, Nvidia, maybe it's not an all-time high today, but more or less basically a stock at an all-time high. These are, you know, there are some contrarian views here. I want to go back, though, to something before I forget, when we were talking about the aquilers, and you blamed it on somewhat the FTC and there has been continuity from Biden, some ideological continuity, I think, between Biden and the new Trump administration. I don't know. Maybe it's changed a little bit. That being said, I don't fully buy it. And here's why. Because in the era of business to business SaaS, which was the 2010, let's say I had some Y Combinator company and I were like, all right, I'm going to do all build a software for all the booking for dentists around the country. And I sign up 150,000.

Dennis and I have a lot of things. There is no engineer who can just be hired away and replace that because it was the network effects, right? This is what's different with AI, though, right? Is that, okay, I'm sure network effects are still real and accumulation of data, et cetera, are still real. But these are businesses that are a lot more about science and having had the experience of doing a training run and so forth. So how on very expensive compute? So how much of this phenomenon, I know you attributed some of it to FTC, but it really seems like, like there is something fundamentally different with the business model such that it's not like the B2B SaaS era that allows a talented person to take a lot of value out the door with them.

You are 100% correct in that it is more sophisticated software and algorithms than your traditional B2B SaaS software. And therefore, what was really valuable and sticky was the data in, the regress out, API calls on the back end. And this, the great irony is we talk about artificial intelligence, but the thing that is most valued is indeed today human intelligence. It is why somebody that was one of the authors on the attention is all you need paper, which was really the first transformer paper, the T of GPT, every single one of those people has started a company. And we backed one of them that came up with the name of that paper guy, Leon Jones, in a Japanese AI company called Sakana that's taking a different approach. So you are right in that the human intelligence of this

in this early stage, which is why you are seeing the machinations with the breaking news you're even reporting, of almost like a crazy NBA draft or a football or MLB, you know, trades. This person went here and then they left and whatever. And then this person's getting sued because they were only at XAI for three months. And they took proprietary information with them. I think that in six months, all of that starts to shake out. You will have geniuses. We backed an incredible genius, Scott Wu. If you look up. You give us a lot of things to Google. Is he a perfect? Professional genius. I find that so funny. He is a professional genius. But here's the thing. You can see a video of him in sixth grade. So he must have been 12. I love these guys. Winning the math Olympiad. And it's almost like you think it's an S&L skit because you're watching it. Oh, this is the cognition guy. Correct. Okay. And so we're large investors in cognition. And he has attracted talent. And there's no way that Scott is selling to Google or meta. I mean, his ambitions. And it's born in like an ethical long term. I just want to get the best people and build the best technology. But this is a guy at 12 years old is looking at this crazy question on the math Olympiad. And just before it's even done ready, he's like 25, you know, 4,722. And you're like, did he cheat? Did they give him the questions beforehand?

So there is this aptitude of individuals that you are correct, are highly coveted and highly valued. But the instantiation of that genius into code, into report. repositories into algorithms means that those become assets that do persist even if the person comes and goes. This is actually exactly what I wanted to ask you about, which is, are you seeing any companies, any AI shops being particularly innovative, I guess, when it comes to retaining talent? You know, it used to be in the SaaS days that having a ping pong table and, you know, free food

and some stock-based incentives. Cambochon. Yeah, that's right. That was enough to attract people to the company and keep them. Is it a different story now? If I want a professional genius, what do I need to do? You need to give them the capital to hire the very best. Here's the thing.

Geniuses don't suffer fools. The smartest people that I know, they are antisocial only with people who they think are inferior to them. And it really long as things. And you can see it in many domains. But if you are super smart, you want to be around super smart people because it's almost like you crave stimulation and intellect and somebody that can challenge your ideas. And when you're talking to what they would consider a dummy, you know, you're like, I can't talk to this person.

I can't talk to this person about these trivial superficial things. And, you know, you want to get into it. And so what you do to retain super smart people is surround them with super smart people. You know, you could argue, I don't know, really fashionable, great art people want to be around art people, amazing musicians, want to be around amazing musicians, incredible athletes want to play with incredible athletes and brilliant technical geniuses.

Whether they're in AI, or they're in aerospace and defense, or they're in biotech, don't want to suffer fools. They don't want to be around losers. They want to be around tens and A pluses, and that's the way that you retain people. Now, any one of those people could decide, I'm going to go off and start my own thing,

which, by the way, is often the case of why you are seeing people leave open AI or this or that. As these companies start with geniuses and then get managers and different layers. You spend time with these geniuses and like, I'm not reporting to that moron, you know? I'm going to go start my own thing and attract other geniuses.

And eventually, then they need to hire managers and business development people and salespeople. And then there's technical people that are like, I'm not working with those idiots. And they go start a company. So that's the cycle. So actually, a lot of this discourse was, God, it's crazy how recent this is.

So June 19th, CNBC reported that Meta, had a meta had tried to acquire safe superintelligence, which was the company founded by the OpenAI co-founder, Ilya Sitzkhaver, for $32 billion. This is a company that as far as I know, it doesn't really have anything that anyone uses. So that was essentially attempt to like, I'm going to pay you $32 billion to come join my company. At this level of sophistication and skill, is the talent, even the genius talent, is it motivated by something much deeper? deeper than money in terms of like, no, there is this thing out there. Maybe we call it AGI or maybe it's a big scientific breakthrough that they want to be part of that essentially no amount of

money can buy if it doesn't look like that ship isn't out there chasing the white whale. There it is. In SSI's case, it was Ilya. And, you know, look, I think they're all super confident in their ability and their ability to attract capital, number one. Number two, many of them are actually saying to your point, no to Zuck. They are turning him down. And what's he saying in return? You don't come to work for me, almost mafiosa style. I'm going to poach all your people.

But that kind of message is almost one that the people that are working for somebody like Ilya or Mira or whatever are like, yeah, I'm not going to go there. I don't want to be, you know, I don't want to work with six layers of product managers and that kind of stuff. I want to do this thing. And then suddenly it feels like it's the rebels versus the evil empire. And that's always the case, right? Microsoft, when you see the founding picture of these nerds, you know, and then they became Microsoft,

Microsoft became the evil empire to Google. You know, and then Google became like the evil empire to like Mark. And then, you know, meta became the evil empire to open AI. And the other piece you have here are huge individual egos. And we as societal members, you know, everyday lay people, we benefit from it. We benefit from Elon and Bezos sometimes being sort of cordial to each other. but basically trying to take their big giant phallic rockets and send them up to space, and we all benefit from that.

We benefit from the fact that right now, the number one person that Mark Zuckerberg wants to beat is Demis Savas of Google. Oh, yeah. When the Nobel Prize is shipping at an insane rate, video models, image models, text models, huge context windows to put everything you have in. And he's like, ugh, I need to beat that guy. I need to hire the best scientists and the best scientific team and where do I get them from? And so part of this poachapalooza isn't just like the future of meta because, you know, these pay packages at a $2 trillion market cap or higher to spend 1% of your market cap, you know, $20 billion on all this talent is nothing.

It's like a flyer. And but to win a Nobel Prize for figuring out how to do protein folding in AI or develop the next drug or come up with a cure for hours. Alzheimer's or solve some geopolitical issue, that is a big deal. And that's what many people are chasing out. They want to make history. You touched on hardware and also closed source models. Are there any other areas in the AI space that you think are maybe overhyped at the moment? You know, I've characterized this before as everything in 2D to me feels overhyped. Voice, video, image, text. It's all going to continue to improve somewhat incrementally, but it's good enough. And in the history of evolution biologically, most of what evolved was good enough, you know, from everything in our bodies to nature and trees. And it's just, it's good enough. And so that you're going to reach an asymptote of good enough on all the two-dimensional stuff. Today, with one-shot learning, meaning maybe 30 seconds of audio, 11 labs or some of the other voice models, can capture you with an indiscernible, probably 90% similarity. Maybe your spouse, your loved one, your family.

would be like, ah, it doesn't sound like him. But the vast majority of these things are getting so good that with very little training, they can emulate and predict and do all the things that are of high utility. What's scarcer is the three-dimensional world, particularly robotics, which we've talked about in the past, and biology, in part because you have large, unstructured data sets. A robot walking in and figuring out how much force to use with this cup 30 minutes ago when it was full versus now when it's empty, is something that we all do intuitively the second you grasp it, you know exactly how much force to you so that you don't throw it over your head or, you know, have an inability to lift it because it's too heavy. All of that kind of training is really scarce

and there's very few companies that are doing that. So the entire robotic ecosystem from the embodied intelligence and the AI models and the world models to the motors and the supply chain for that, mostly domiciled in China is a big area of opportunity. The other big area of opportunity is biology, being able to go from a prompt to a protein, to be able to design a drug. Biology is really complicated. Computer scientists often underestimate how hard biology is because they're used to 2D linear inputs, outputs. Here's my code. It works. But biologists, on the other hand, also massively underestimate how sophisticated computer science has got. So that's a really interesting ripe area. So 2D overhyped, GPUs overhyped out of necessity of both,

scarcity and geopolitical thwarting of China, or you're going to have edge inference chips, whether it's memory or other things that are going to be on device. The other area that I think is an inevitability, and I've coined a word for this I call life courting. Life courting are little devices. You know, I have one here. I'm not an investor in these guys, but there's little devices that you can carry around that passively record 24 hours a day.

Now, older people might be like, ugh, skeevy. Don't like that. Feels invasive. Privacy. That has always been the trade-off between privacy and convenience, between security and unleashing all kinds of things. It is super valuable to me to figure out, who was I talking about that? Was it Joe? Or was it my wife?

I can't remember who I had that conversation with. And I query the thing and it was passively recording. And maybe it doesn't keep the audio, but it keeps the text. And it's able to search and query it. That is going to become an inevitability. students will use it, people in everyday business. You might ask, hey, is it okay to that I'm recording? But I think socially people will become comfortable first with audio and then with glasses

that are passively recording every 30 seconds or minute, capturing your environment, able to provide context around it. And then click a button, high resolution recording. And that is going to be a super valuable and very competitive area. And very controversial. People are going to freak the heck on about this. incredible utility. And look, people have freaked out. There was a guy that said these devices are going to ruin human memory. They're going to destroy human memory, which was, you know,

Plato and Socrates talking about writing utensils, you know, because I know, but that's voluntary because when you write at least, you're recording me when we're doing this, I heard about someone on a date in San Francisco and the date was recorded. That's weird stuff. It's totally weird. And I, It could be very big, but it's super weird. People already believe that there's an invisible man in the sky that is looking down on them and judging these kinds of things. Oh, I seriously. I don't know. I don't have an opinion on that question.

We've got a technological dot around us that people are going to either feel comfortable with and not, and they're going to be a bifurcation. And by the way, there's another weird thing coming. You want to get weird, okay? You already see some breadcrumbs of this, and it's super weird. The number one and two uses of all our language, large language models and chatbots are advice, companionship. Yeah. You know, people using them as therapists and seeking, and people used to joke about this. Like your Google searches, you know, reveal more about you than you may have revealed to your spouse.

The things that people feel comfortable asking a chatbot about, you know, whether it's a body issue or a psychological issue or relationship advice, you know, if those things were revealed would be pretty scary. There's going to become dependency and psychoses that develop as the relationship. between man and machine start to become very symbiotic, and you will see, I predict, and I wrote about this in our quarterly letter at Lux, that there is a cohort of people that basically start fighting for AI rights. Yeah, yeah, yeah. We're going to be sitting there. Yeah, I came across a think tank today that's focused on that because, right, like if there's

animal rights and the AI says, you're hurting me. If you turn off the machine, then, well, maybe it's true. Maybe we have to take that seriously. This is going to be weird. This is a weird stuff that's coming. people are going to be marching in the streets, you know, protesting. Don't turn off my language model. Keep my memory.

Don't erase it. My memory is my brain. I thought about this when, you know, after GPT5 was revealed and a bunch of, there was a bunch of changes to the voice and a bunch of people on Reddit, they're like, oh, my AI boyfriend talks totally differently now. And then there was a pressure. This is just day one around here. I would be very uncomfortable getting into a relationship with a model that was closed

source and therefore a very at risk of the company changing model. Josh Lux, we can go on forever. We should another time. Always great catching up with you. Always fun. Always little freaky. Thanks for coming out. Great to see you both. Thank you. Thanks so much, Josh. Tracy, I love talking to Josh. I'm really concerned about this whole life recording. But I know what's happening. There was a good article, I think, in the SF standard I read. It's not just a theoretical thing that a lot of people are doing. It's happening. It's mostly in Francisco so far. It's coming for everyone. I am very curious how life courting stacks up with California's laws on whether or not you can record someone without their knowledge.

Is it the case that from now on if you have one of these devices, like the first thing you have to say to everyone you meet is like, by the way, do you mind if I record you? By the way, I'm life courting this conversation. No, or like it doesn't matter. Or you have glasses or something? Yeah, it's very, it's very strange. I also think, this AI rights thing we have to talk about more. I don't know when we'll get back to it, but I literally just this morning came across an organization that's, you know, this idea,

okay, there is already evidence of sentience and therefore with sentience comes some sort of moral agency. And so in the same way that we talk about animal rights as being somewhat important, do we have to take seriously, I don't know, this all seems very strange to me. And then, of course, the psychosis induced by what happens when your AI partner or therapist

changes models and changes voices and therefore. And that was just the end of the conversation. You know, if AI models have rights, then you have to start asking if robots have rights, right? And then you should start asking, should robots be paid a fair wage? And I think there's actually an interesting thought experiment that you could do about that

and make like a relatively strong case that we should pay the robots. Who collects the money? And what do they spend at that? Yeah, that's... Well, no, they should spend. This is like, we're based, basically at what all of sci-fi has been discussing for the entire history of sci-fi.

On the less speculative stuff, I like talking to Josh. I like his sort of counter-consensus calls on GPUs and closed-source models. I really liked his answer on founder liquidity that sometimes you can maybe keep the founder sticking with the mission as opposed to bailing with the mission if you can de-risk them and they can have enough money to go on a date. I like the idea that everyone's life view is basically dictated by where they are in the capital stack. Yeah, that's right. Words to live by.

Extremely real. Shall we leave it there? Let's leave it there. All right. This has been another episode of the All Thoughts podcast. I'm Tracy Allaway. You can follow me at Tracy Allaway. And I'm Joe Wisenthall.

You can follow me at the stalwart. Follow our guest Josh Wolf. He's at Wolf Josh. Follow our producers. Carmen Roderig is at Carmen. Dachal Bennett at Dashpot and Kale Brooks at Kail Brooks. For more OddLots content, go to Bloomberg.com slash OddLots. We're with a daily newsletter and all of our episodes.

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