Speaker 1open source is a bit of a fake because China's paying for all the training. The market is set. The game is clear. And this is the classic thing about bull markets is that you can be intellectually right, but the narrative can keep going a long time. I think the big story of 2027 in AI and
Speaker 2the enterprise and all the margins in the enterprise, right, is show me the ROI next year.
Speaker 1So you're really talking about seven, eight percent of the labor force being replaced by tokens for the math to work. I literally was doing a pitch this week and the founder was going
Speaker 2on about their moats and I immediately didn't want to invest. Like I just, enough. Your moat
Speaker 1can be LLM lifted away. There's only one thing worse than a seat-based model, Jason, and that's
Speaker 2a model that's based on bodies. You don't get to make 10 million for working 18 hours a week. You get a watch. You get an Omega. You want an Omega or you want to be rich? Make your choice, boys.
Speaker 1The whole reason the open AI and entropic models work is because other people are going to be rich. And that's why we're going to be rich. Other idiots have spent the $300 billion on their behalf.
Speaker 3This is 20VC with me, Harry Stabings, and it's my favorite show of the week. Jason Lamkin, Rory O'Driscoll, discussing everything you need to know that's gone on in the last seven days. So what do we discuss today? Google loses two generational scientists in 48 hours. That's a tough time. Deep Sea closes $7.4 billion Series A. Is the Series A at a $50 billion price? But only China gets voting rights. Interesting. And then finally, the $700 billion series A $125 billion question. Wall Street is finally asking, who's actually going to pay for AI? But before we dive into the show today, are you a founder working nonstop to raise your next round? Are you an investor doing all you can for your portfolio companies to help them stand out? Funding and scaling a vision is challenging. Banking should not be. HSBC Innovation Banking caters to tech and healthcare founders all over the world who need a really great banking partner that matches their pace, offering fast, onboarding, product packages designed for your business, and capital solutions built for high growth startups and the VCs investing in them. With HSBC Innovation Banking's rapid onboarding, you can get access to your new accounts and facilities quickly, so your team can stay focused on building and scaling what's next. You'll be paired with your own dedicated team of venture ecosystem veterans who have the network and experience to guide companies in your specific sector at your specific stage. And behind that support is this real strength, HSBC's $3 trillion balance sheet and global network that provides this stability in international reach needed to grow your operation with confidence. To see how HSBC Innovation Banking can support you, whether you're on day one or day a thousand, visit innovationbanking.hsbc to learn more and connect with an innovation banking specialist. That's innovationbanking.hsbc. While HSBC manages your corporate banking needs, Deal helps you build the global team behind it. Founders scale startups faster on Deal. Grow without borders. Deal handles the hard parts of global hiring, so you can stay focused on growth. Set up payroll for any country in minutes. Hire anyone, anywhere, and get visas handled fast. Deal takes care of onboarding, HR, IT, EOR, benefits, and compliance. Everything your startup needs to scale quickly. All done fast in one place. It's why more than 40,000 fast-growing companies like Airwallex, Eleven Labs, and Intercom trust Deal to move fast and get back to building. Visit deal.com slash 20VC. That's deal.com slash 20VC. Deal handles the global team, and Framer handles the front door. A website should help your business grow, not slow it down. If updates to your .com feel harder than they should, Framer is the shortcut you've been looking for. Framer is an enterprise-grade, no-code website builder that works like your team's favorite design tool, and it's used by companies like Perplexity, Miro, Mixpanel to move faster. Designers and marketers can fully own the site with real-time collaboration, a robust CMS built for SEO, and advanced analytics that include integrated A-B testing, so you're not just shipping pages, but you're maximizing what works. And when you're ready to ship, changes go live in one click. Publish. Without relying on engineering. Plus, Framer is built for scale, with premium hosting, great security, and 99.99% uptime SLAs. Whether you want to launch a new site, test a few landing pages, or migrate your full .com, Framer has programs for startups, scale-ups, and large enterprises to make going from idea to live site fast. Learn how you can get more out of your .com from a Framer specialist, or get started building for free today at framer.com slash 20VC for 30% off. 30% off a Framer Pro annual plan. That's Framer.com slash 20VC. Framer.com slash 20VC for 30% off. Framer.com slash 20VC. Rules and restrictions may apply. You have now arrived at your destination. Boys, it is so good to be back. Jason, you are back. It is so good to have you back from China. We're going to start with the news that I put at the top of the list, which was DeepMind loses two generational scientists in 48 hours. Namely, we have first Norm Shazier, who was character AI, and then we have John Jumper, Nobel Prize winner, co-creator of AlphaFold, also leaving to join Anthropic. How significant are these moves? What should we read from this?
Speaker 2Look, listen, it's easy to pick a turnover in any organization, right? There's so much turnover in any organization. On the other hand, you know, when you talk to some of the smartest engineers and developers in AI, they're really looking to be in a very specific environment, right? Where they get to pursue exactly the same thing. They're looking to be in a very specific environment, what they want to do, especially on the research side. They want to work on what they want to work on, on the best of the best. And, you know, I was thinking back in the day, I went to pitch Google for my last startup and Vint Cerf was there, like one of the creators of the internet popped into the meeting. Okay. And I didn't get it at the time, but Google back in the day, right? Pre-AI days had created this environment where the best researchers in the world wanted to be there. And I think that's how they lured the DeepMind guys in, right? Was the story, right? This persistent to create this environment. Look, you're going to get to stay in London. You guys, you're going to get to build your own thing. And I think this is probably just a sign of the cracks of the realities of having to try to be number one in AI and forcing an environmental change potentially that your competitors can welcome, right? Anthropic and OpenAI can say, like, just come over here and work on whatever you want to work on for $500 million, $2 billion. And it's, I just, when I talk to folks at the
Speaker 1bleeding edge of AI, that's just so appealing. It's funny because that feels like a one-dimensional thing, right? And I think that's why it's so important. And that's why it's so important. And I think that's why it's so important. And I think that's why it's so important. And I think that's why it's so important. And I think that's why it's so important. And I think that's why it's so important. Because in one sense, yeah, you know, you have that whole, you know, researchers just want to go do what they want. On the other hand, you listen to a lot of people who left Google, and it's a little bit of the frustration of not being able to ship. I mean, there's a lot of frustration that they had a chat GP alternative, and then the bureaucracy just kind of smothered the product when, you know, OpenAI just jammed it out the door and as a result, took a lead on them. When you have a historic, you know, an existing business, you're kind of damned if you do, damned if you don't. Sometimes people want to just do research. Other times they want to actually get shit done and ship and you're getting in the way of that. My sense is, first of all, these two people in terms of their research pursuits are somewhat different. And then, you know, Noam obviously was at Google, did the original attention paper, left, did character, got bought back to Google, in large part to be, you know, it was a very clever acquisition, the brains behind their restarting their, you know, after OpenAI stole a march on them. And I think it was a couple of billion dollars. So a good slug of that was going for him. So assuming four-year vesting, he's probably left half of whatever he's offered on the table. That's a lot. I can imagine there it's a combination of some version of, hey, as you say, more research, more ability to do things, plus a more certain ability to ship and make shit happen. Because I think even though we all did the last year, we did the yay, Google is amazing because unlike the other three, unlike the other hyperscalers, they have their shit together. They have an AI story. And the stock has reflected that in the last year. It's one of only two of the Mag7 that's up on the last 18 months. But at the same time, when you look at things like having a viable coding model, having really kind of that next level up from just going from just shipping a model to shipping interesting products, the truth is Google hasn't done an amazing job. And OpenAI and Entropic have. So if you're in the into product shipping, which I think might well be, then I can see why going to one of those two makes sense. Totally different, you know, on the jumper. I mean, this is someone who's pure research science around undergraduate, postgraduate degree, I think University of Chicago, all focused on protein folding. Remember, got a Nobel Prize. DeepMind has been the only company he's ever worked at since his, I don't know if the graduation is the right word after you get your postdoc, but whatever, since academia. So basically, it's been a whole bunch of academia, a whole bunch of time at DeepMind, pick up a Nobel Prize. You got to go into Entropic because there's some story there about being able to do more research, which is almost kind of high. Yeah, and science, which again, they've had Entropic announce an issue on that. So kind of somewhat different. But I think what it speaks to stepping back is, you know, when you're top of the heap, you can promise everyone everything to everyone in a way that you're not. And the truth is top of the heap right now means you're the new model, you're the new companies, you're not unconstrained by history, you're unconstrained by the install base, like that old, you know, proverbial joke about, you know, hell is the install base. And you got a stock and a currency that is huge, and no one's giving you shit about stock-based comp. So if you're Entropic and OpenAI, you know, you can buy whatever you want, including people, and you can let them do whatever they want, including whatever it is they've been promised to do in a way that you've much less constraints than the incumbents.
Speaker 3You know me, I would never deal in rumours. I don't do rumours. But the rumour mill that I heard was... Harry, you're a rumour slut. But come on, bring it on, baby. Is that candidly, Anthropic have clearly had a breakthrough that a small number of people know about. And that's why John Jumper went there. The reason I'm sceptical of that, Harry, is the
Speaker 1lag time between I've had amazing invention and revenue in the kind of core LLM space is a year or two years or three years. The lag time if you have a medical invention, an idea, and turning it into real meaningful economic value is 10 or 15 years. The amazing thing is the protein folding advance has collected its Nobel Prize and as yet has not had meaningful commercial success or a drug in production yet. So I doubt it's like, oh, my God, we've discovered something new. And this is magic. And if you don't join in the next three months, you won't be part of this thing. My guess is the initiative, whatever initiative AdTropic has around next generation science is a multi-year thing. And it's just a question of where do you want to spend the next five years of your life doing research?
Speaker 2It is a vibe for what it's worth. And listen, I really hate to be one of those like VC dad tell stories about their kids or what happens in their spoiled kid's school, you know, as your thesis for investing. But my son is very good at a certain type of math. I don't understand Jensen Feinstein theory and all this stuff. But as a college student, he's one of those like top 10 in the country. So my point of the story is the labs find him. He doesn't have to apply per se. He got an internship offer without looking from AdTropic for amount of money that when I was in college or grad school would, you know, and the opportunity is incalculable when you graduate even today. And so he instantly turned it down, right? With no job. But he's just like, I can't do enough research. I want to do my type of research for my type of math and my type of AI. And so, you know, everything that we do on this show or the other stuff, he kind of makes fun of me because he's so far ahead of how inference works, how open source or I mean, really, you should replace me with him. But my point of the personal, the dad VC stories, which I hate is he instantly just said he's not worried about money. He's not worried about any of this. If they can't create the right learning environment for him, he just won't do the job. Right. And so I don't mean to map my family to two of the top researchers in the universe, but I feel the same. Like in today's world, we're on a bull run like we've never seen in our entire lifetimes. There's nothing like this where a so-called startup can pay billions of dollars to acquihire you and then you leave with 50% of your billions invested. We've never been like this. And it just creates an environment for the best of the best where you just will only do what you want to do. You just won't do the job that isn't 100% what you want to do. And it's really hard for if you're not anthropic, how do you retain? Now, it's not deep seek, but how do you retain this talent? How do you let people work on what they want to work on when you need the chat bug fix so you can compete with Sierra? I mean, how do you contain them?
Speaker 1And I think it's very hard. First of all, you're exactly right. I mean, these are two of the most talented people on the planet. One of them was one part of the attention paper and has made plus or minus a billion dollars, and the other has a Nobel Prize for medicine and is still relatively young. I mean, these are not top one. They're not top point one. They're top point zero, zero, zero, one percent people. And the funny thing is. The funny thing for Google that the so hard thing is they're struggling to keep people like that while at the same time they're not getting the tactical shit done and just making the stuff happen to kind of continue to make progress. And the amazing thing about someone like anthropic is they're able to do both. They're able to create these wonderful research environments for the leave me alone, let me do research people while at the same time they're obviously executing tactically brilliantly in terms of product, making stuff happen. That's its momentum. I mean, that's my aha here. It's like everything in life. When you start winning, everything starts going your way. People start going your way. The brakes start going away, you know, winners win and they compound until something happens to break that chain. Right. And this is just it and magnified. Listen, Google overall, I think, actually has been on fire.
Speaker 2But I think as I continue to learn and I'm only so smart about the role of open source versus closed source, the most vulnerable is going to be number three for talent, for people, for revenue, for and for your ability to do things that maybe aren't core. If you're number three as the closed source, LLM, that's where you're going to hit the most pressure from open source. And it's just sometimes when you're in that, and I'm not saying this is correct, but sometimes when you are in that place, you feel like you don't have the luxury of letting folks do what they want to do because you're under such intense pressure. Maybe Anthropic feels like as competitive it is, it has a luxury that its competitors don't have.
Speaker 1I think that's true. And just I want to come back on the Google is executing amazing or is it not comment here. Objectively, over the last 18 months since 2025, only two of the Mag7 have outperformed the S&P. Nokia and Google, which is another way of saying people like Facebook, Amazon, and Microsoft are doing shit. They're not relevant here. Google has done an amazing job of being relevant here. And that's true, statement one. But statement two that's equally true is you don't wake up every morning and say, let's try the new Google model. Let's try the new Google harness. Let's try the new Google coding tool. You do try cloud code. You do try co-work. You do try open AI. So the fact is they are relevant and in the frame, but they are definitely number three in terms of innovation. Which is a whole lot better than being Microsoft are meta and having to say, we don't have something yet, but we spent 70 billion. We might get something next year.
Speaker 3Jason, just so I understand, why is number three the worst position and like the powerless one in a way that it's not for cloud?
Speaker 2I think the reason is there are two threads happening at the same time. On the one hand, clearly the price of tokens, token maxing, the budget issues are real. And so the amount of folks routing models, running multi-model is exploding. Right? So at a small level, the open routers and your own sources, that stuff's all a big deal. And everyone is realizing they just have to get smarter and smarter, much more quickly on routing workflows to different models. Okay. That is clearly very true today. And it may be 90 days on the pod, but it wasn't clear how big a deal that was. It is across all everything, except the smallest of startups are routing. So what do you route to? Well, if you have three vendors and Rory is the professor, especially with his background, number two was often simpler. Number three was usually cheaper, right? Some variants of that, right? Frankly, GCP, old Google cloud blew up because it was cheapest. It was cheapest. You know, a generation ago, Google cloud struggled in the beginning and then it was just cheapest and simple. And so people would move certain workloads a generation ago to Google cloud. Now you're trying to do the same thing with your massive, massive AI spend. And the question is open source is really complicated. Open source in inference and training is not free, unlike Linux. It's not free. There are substantial costs, but it is materially cheaper. Which we could talk about, forget about the fact in theory with open router and others, you could route to 10,000 models in practice is the number three thing. You're going to figure out what's the best open source model I can use for my application. Or is it the number three closed source? There's so much going on in open source, right? It's fueling these crazy base 10 and fireworks and all this. There's so much innovation in open source that number three just might get swamped by all the subsidies of the Chinese government, everything else subsidizing open source because open source is a bit of a fake. China is paying for all the training. It's not open source like a generation ago. The reason this is because there's so much innovation and cost savings. So your number three, there just might not be enough energy for the number three closed source model. Even if Google has the billions to fund it, they do have all the money it takes to fund it. But developers may lose interest other than it being a setting in open router inference. It's a big deal. And literally this morning, I got an email from Anthropic. This is there. I've shot across the bow for open source saying your prompt cash hit rate is low. Your prompt cash hit rate is out of the blue. I don't know if you guys got this email this morning, but what Anthropic is doing here aggressively is fighting back at open sources and trying to get you to cash your prompts, which are very expensive. And they offer such a massive discount on cash prompts if they work for you, that it actually can be cheaper than open source, right? This didn't say, you know, fighting Gemini. This is an email they sent to their, maybe their entire base saying, cash your prompts, so that it's cheaper than open source. That's why I think it's just hard. Number three, you just, you can't be cheaper and you can't keep the researchers and the projects are less interesting.
Speaker 1The short answer is I agree. You're right, Jason, is that look, you don't end up with perfectly competitive tech markets in the economic sense of millions of players. You end up with small numbers in a tight oligopoly where there's a leader, a number two, and then depending on the size of the market and the competitive dynamic, maybe there's a three and a four. And the vast majority of the revenue goes to one and two, right? It's just. It's the structure in the cloud market where AWS was first, Microsoft second, and Google Cloud third. The interesting thing about Google being third, right, well, there's a couple of things. One is unlike the typical third, if they were a standalone company and didn't have the Google balance sheet behind them, I think it would be incredibly tough. And I think implicit in that statement is it's almost impossible for a number four, a same business model, closed source number four to emerge and catch up at this point. I mean, Elad Gil had a nice piece on that six, 12 months ago. The market is set. The game is clear. And the only reason Google can keep on punching is because they have a whole balance sheet behind them. Because I agree with you, Jason. The other part of it is if there is a compelling alternative to this entire set of competitors that's, you know, 5x cheaper, which is what open source is, then it's going to grind everyone down. And when an industry gets ground, what tends to happen is the number one guy makes a little less money, the number two guy makes quite a lot less money, and the number three guy goes bust. In this case, obviously not bust because they got Google behind, but you're right, it's real powerful downward pressure on the profitability across all these. And that is, I think, the big story now kind of from open source with lots of caveats.
Speaker 3Roy, how do you think about sovereignty then? and sovereign models amidst all of the world, if there's no room for number four?
Speaker 1First of all, there's two ways to answer that. We'll answer sovereignty first, and then the impact of China. On sovereignty, look, Europe is effectively saying, if someone says we need a sovereign model, rephrasing that, what you're saying is we no longer are part of the market over there in America where our product is third. We are, in fact, first, instead of being fourth in the worldwide market for closed source, state-of-the-art foundation models, we are first in the European market for state-of-the-art foundation models. So you're effectively saying I couldn't compete as the fourth player worldwide, but I can compete, profitable for me, but probably less efficient for the system as a whole, as the dominant player in Europe. And look, a million years of economic theory explains why that's a dumb idea from an efficiency perspective. By definition, everyone in Europe is getting the less good model at a higher price. But someone's deciding that there are political or national, security reasons to pay that tax. And that's a political decision that's beyond the economic analysis. It's just something that government may choose to do or not. But the other part of it, and I'm going to segue to Jason's trip there, is the other part I thought the sovereignty question is, the fascinating thing is all the competitive models to the foundation models, all the open source models, bar not quite all, but most of, are Chinese-based. And, you know, Jason, you're just back from China. In the context of sovereignty and security, it is amazing that the entire open source initiative is running on, you know, four or five models, all of which are built in China. So what was your takeaway from there?
Speaker 2Well, it was, you know, it was interesting. I mean, folks probably already know that, you know, when you go to China, and interestingly, even Hong Kong, where Hong Kong has its own sovereignty, it can do whatever it wants. Anthropic and OpenAI don't serve there, like, intentionally for security. Like, they don't allow you to access Anthropic or OpenAI. You can't access it. You can do it over a VPN, but it's harder. You can hack it on your phone. But it's tough. They intentionally try to block whatever IP address blocking or whatever. It's not China. It's them blocking it, right? But Gemini doesn't. So when I'm in China for two weeks, it's DeepSeek and Gemini. Those are my friends. It's a parallel universe. And DeepSeek is intentionally crippled in China. It's not as good as it is here. It can't search the web, and it is trained on different data, as near as I can tell. But you get it. I don't think I got the sovereignty argument until I was in China, right? This is China basically saying, "We do not want to be subservient to the United States." Whether this was the original goal of DeepSeek and others, and Alibaba and others, I'm not sure. I don't have my professorial background. But it clearly is where it is today, right? Massive government subsidies, right? DeepSeek's raising this round at $80 billion. The government's the only one getting voting shares. It says all you need to know. And it is an existential sovereignty thing. The Chinese does not want to be reliant on Anthropic and OpenAI to run the next generation economy. It's very smart. And whatever investment the government has quietly made to subsidize all of these providers, it's a drop in the bucket, isn't it, at the sovereign level? $10 billion, $20 billion, even $50 billion. I mean, this is nothing compared— What does an aircraft carrier cost? A lot. It's easier just to subsidize an open-source model and pretend the training costs are $10 million. What did DeepSeek claim their training model was back when it launched? $15 million to train an OpenAI competitor?
Speaker 3Like, of course it wasn't true, right? The round itself is priceless. It's pretty wild in a couple of different ways, in that $7.4 billion at a $50 billion give-or-take price. The thing that's crazy is the founder is committing $20 billion himself, which is like 40% of the round. Yeah. I mean, it's like $3 billion the founder is committing himself. Wait for it. And there's fewer than 10 investors, including JD.com. I saw this, and I was like, "What?" And then none of them are getting any— The only people getting rights is the Chinese state, which retains governance control.
Speaker 2It's wild. I mean, it's a different world, right? These are not companies that, you know, it goes back to the old hallway drama. They're not independent of the government in any way, shape, or form, right? And so you either do it the right way, or you end up thrown out of your own company, or, you know, having to recycle and give back your $2 billion that Meta took from you. I mean, you've got to play by their rules, right? Yeah. And so this is the rule for DeepSeek.com. It's the rule to not be taken from you.
Speaker 1You know, a couple of comments here. First of all, your comment on, "Oh, only the Chinese government's getting voting rights," I felt like saying, "So the only people getting voting rights are the only people who don't need them." Because the Chinese government doesn't need voting rights, because they have sovereignty, right? They have an army. As we've seen by Manus, they can make you discharge back your money after you've got it just by leaning heavily on you. So it's not like—I mean, in one sense, the run's not surprising at all. And zooming out two levels, two other things on that, one is, yeah, it's a $50 billion. The leading two American companies, closed-source models, which by definition yield more, have more economic upside, are trading plus or minus a trillion. So $50 billion for the open-source competitor feels roughly right, you know, $120 to the price. And, you know, there is a couple of these—I think Zipu, Z.ai, which is the Americanized version of it, is actually public now in China, and it's trading at $100 billion or something like that, 1,000 times revenues. So again, plus or minus, if you think of these as the sovereign alternative and the open-source— open-source alternative, they're trading at numbers that aren't crazy at all compared to the U.S. And then the other thing is, just to say, we're doing a little bit of the, oh, you know, you have—and you do, you have a lot of state interference in China, and, you know, you're seeing it over and over again in your property rights a week. We saw it with Alibaba back in the day, with Jack Ma, we're seeing it here and now. But, you know, at some point, we'll have to talk about the fact that you're seeing more of that in the U.S. as well. We're seeing, you know, Anthropic unable to ship their most recent model until they satisfy the consumer. That's one of the concerns of the U.S. government. And I got to give Leo and his 2024 situational awareness credit here. It's like, he called it that about now, national governments are getting involved, and we can argue with that a little bit overwrought, but in both, you know, we can't give China grief for this kind of thing when we're doing the same thing ourselves. Both governments are feeling this technology is pretty existential, pretty impactful, and trying to figure out, do you regulate it, do you take over it, do you stop it from being used by other people? I mean, we're seeing a bunch of policy questions going well beyond economics that are being raised here, and China's solving it their way, and we're solving it ours, and Europe is doing theirs.
Speaker 3Rory, you mentioned Xipu, I hope I pronounced it right, Xipu AI's GLM 5.2 beats GPT 5.5 on coding benchmarks. How significant is this? This was deemed one of the most consequential open source AI releases.
Speaker 1I mean, look, to me, all it says is these guys are cranking. There's probably six total open source Chinese models, three of them top at or close to U.S. performance, three more just behind it. The a-ha for me is, to Jason's point, it's a compelling competitive alternative. You know, many U.S. companies are starting to build their own model based on the open source alternative, so it's providing a ceiling on profitability for some of the U.S. closed source vendors. It's really economically significant, and I think the most compelling fact is that there's six of these companies pounding it out there, and there's just a lot of competition on the market. I mean, we can talk later about distillation and how much of what they do is a function of, you know, the ability to learn from what the frontier models are doing, but they're there, they're relevant, and they're providing a competitive drag, what Anthropic and OpenAI can charge.
Speaker 3Behind all of these, there is actually the infrastructure, and a core part of that infrastructure is seeing increases in price, memory being one of the biggest. Price of memory has gone up four to five hours. I mean, in certain cases. Tim Cook told the Wall Street Journal that Apple faces a 100-year flood in memory costs driven by AI infrastructure demand. DRAM contract prices rose 90% to 95% in Q1 alone. How significant are these cost increases? Who feels them? How will we see this? What should we take from this?
Speaker 1I think, other than a profound regret on not buying SanDisk and Micron technology a year ago and making a 20X, I mean, look, I think it's funny. I was thinking about this and going back to the kind of discussion of the researchers at the start. Very different topics, but actually they're all about the same thing. The investment in AI is commanding resources, right? And then via the price mechanism, everyone else is getting impacted by that. And the impact of this, it's going to manifest itself, as you say, I mean, I can take four, DRAM pricing, right? It's going to manifest itself in the price of your iPhone. It's going to manifest itself in the price of your electricity. It's going to manifest itself in the price of your house in San Francisco. And it's going to manifest itself in terms of 20% of you losing your jobs if you're one of the companies that wants to kind of go all in on CapEx for AI like Oracle, right? So what you're seeing is the positive side. And again, this is not a, oh, AI is bad as much as economics just sends its signal. It doesn't have morality. It just says, oh, you want to devote more resources and more DRAM to a data center in Tennessee or Mississippi. That means you're going to have to have less DRAM for resources. That means you're going to have to have more resources. So you're going to have to have more resources. And the only way to make that happen is to raise the price of the iPhone, right? And that's just what's going to happen. I mean, this is price filter. And the amazing thing about the AI CapEx explosion is it's so huge that it's literally impacting everything. And this is just one of the examples. It's just sucking in the money. It's sucking in the resources. And specifically, what it's going to mean is Apple has clearly decided wisely not to swallow the loss and lower their margins. They're just going to raise prices. And they'll sell a few less iPhones because there's some price elasticity. will just have to pay more for their iPhone.
Speaker 3Goldman Sachs projected their be 7.6 trillion in cumulative AI capex from 2026 to 2031. But it was fascinating. And it just brought up now the $725 billion question. I remember David Kahn from Sequoia, the 50 billion,
Speaker 1I think. 600 billion. I remember it well. It was a good piece. Are we going to have a trillion
Speaker 3dollar question next year that we're going to be discussing? What Goldman Sachs is saying is
Speaker 1the hyperscalers are now spending $700 billion in capex a year, which times five or six years is three, $5 trillion, some astronomical sum. And you only do that if you expect revenue to be greater than expenses, because that's how American capitalism is meant to work. So that implies that at some point, someone has to spend $700 billion in revenue for you to make a buck. And right now, we're nowhere near that. We're probably well under $100 billion. So AI as a whole is getting $100 billion in revenue and spending $700 billion a year. That's not a great business. What you're basically saying is how does that end? And with the increase in capex, does that not just increase the revenue requirements? Yeah, absolutely. People are going the other way. And that's the funny thing to say. I really thought that David Kahn piece a year ago was really good. And it made me think. And I was wrestling with the same things. And then you step back and you say, since then, all that's happened is people have doubled their conviction and doubled their willingness to spend. At that point, it was, oh, my God, capex is like 60% of free cash flow for the Mag7. 120% of free cash flow for the Mag7, and they're borrowing to fund it. So even though a year ago, and this is the classic thing about bull markets, is that you can be intellectually right, but the narrative can keep going a long time. It's very hard to know when it ends.
Speaker 2I just think in my lifetime in tech, being aware of things, I can't think of another time where this level of demand was infinite. And so how we deal with it, how we deploy capital, but if you're sitting on the other side of infinite demand, you can choose not to embrace it, right? That's like shorting everything, but where does it get you, right? I mean, the demand for AI is an order of magnitude more than it is today, if it could be served cost-effectively, right? We would all be consuming tokens 24-7 if we could. So the demand is so much higher than is delivered today. And do you play the game, or are you a Debbie Downer, right? I don't know what the right answer is, but certainly in venture, you don't make money not deploying your fund. I mean, fees are nice and all, but you've got to deploy into demand cycles, and just hope to God you get the money. You get liquidity before it crashes down on you. Diving one level deeper, because you're right,
Speaker 1Jason, you can't just say, oh, I think this is irrational and go home. First of all, that would clearly have been the wrong decision for the last two years. B, it's not the business we're all in. We're in business investing the future, but you do have to think about it. And the comment you said was interesting, which is you've never seen this level of infinite demand, and then you caveat it in a very important way, because you said price adjusted. And I think that's the difference between this and, you know, kind of prior generation. I think that's the difference. I think that's the difference. I think that's the difference. If it's $1,000 a year a seat, but if it was, you know, only $500 a seat, I'd buy 2,000 seats, right? I didn't buy more. I bought seats for my people if I needed them, and then I didn't. Whereas intelligence is this thing, as you say, where the real question is not, do you want it? The real question is, how do you have, you now have to have this new skill that didn't have to exist in kind of the prior world. If you're a CIO, how do you decide how much to spend and where the real question is? Do you think that shows up in actual slower revenue growth for Entropic and Open AI? Because that's the rubber hits the road question. If they're sending you an email, Jason, trying to effectively cut your bill by saying, if you're more efficient, you won't be spending as much with us. Do you think that reduces the growth from 10X to 5X? How does all that shape
Speaker 2out in terms of revenue growth? You know, it's interesting. I got that email. You guys got that email. But also I'm on the, I'm on the max plan where for $200, I get $10,000 of inference a month if I can use it properly, right? So, so for me, they may have more of an incentive to send me that email than someone on an enterprise plan where for $10,000, I spend $10,000. A month. Entropic and Open AI are running an AB test where they have a free segment of their base, which, which they are subsidizing, right? For sure. It's just bigger at Open AI. They have an enterprise base, which is, it has, you know, between 40 and 70% in gross margin, just on inference. And then there's this prosumer one, the max, the max guys, right? That some of us, they're making a profit on, but some of them is massively subsidized. The kid vibe coding $10,000 of tokens a month, paying a hundred dollars, $200 for a max plan is massively subsidized. And that that's not a joke, right? That's the whole open claw drama. It's a real issue. And so we have, we have this weird spectrum of free massively subsidized. And actually, if you just look at inference and not training fairly, fairly profitable enterprise customers, right? Fairly profitable. Well, I don't know what the term they use is, right? It's not, it's an unblended gross margin, but the inference margins are attractive. And that's what open source is attacking that enterprise customer, the lucrative customer. Yes. Cause it's, it's
Speaker 1tender, right? And the question is, is it profitable for the customer? In other words, have people said, assuming you're not on some kind of cap plan, and I think those are increasingly harder to get for the enterprise, assuming you're on the API, can you really afford to give everyone free untapped intelligence all the time? And does the math of that work? And as I say, the question is what's happening. I mean, I'd love to know what's happening in real time now on usage and revenue across those companies. Cause that's, that's the be all and end all question.
Speaker 2Token maxing isn't really the story. I think to what Roy said, I think the big story of 2027 in AI and the enterprise and all the margins in the enterprise, right? Is show me the ROI next year. Right now it's token maxing because 2025 into early 2026 is guys just go do it. I don't want to be behind, right? We can make fun of token maxing, but it was the best way to get teams AI fluent. Just go build it guys. Here's, here's a hundred million, 50 million, 10, 5 million of whatever guys just go build it. We've even seen in our portfolio companies. Then the reaction was guys, it's actually not a joke. You spent too much, right? You blew through the, the it budget is bounded. It turns out right. Then you're already starting to see it, but I think going into 2027 CIOs and others are gonna be like, okay, just show me the F and ROI. We're not just going to ration tokens based on who we like the most in the company and, and who makes the best PowerPoint pitch. Show me the ROI. And if you have, if you have ROI, if you were able to lay off 20% of your department, or you have the highest growing division in our company, we will give you more tokens. And this group that can't ship, or can't get anything done, or is in decline, we're just not going to give you the tokens. And so for the first time, ROI is really going to have to connect to a lot of this spend.
Speaker 1I think you're exactly right, Jason. If you're running a big company and you said, okay, I wrote off the first half of 2026, we spend way more than we thought, but at least my people now know how this shit works. Write it off as a one-time thing. You're exact. And the question now is ROI. And then bringing it back to the $750 billion question, the Goldman CapEx question. I mean, I, I did this math a while back on the fly, on the podcast. I'll, kind of revisiting again. Let's round up to a trillion, because if you're spending $750 on CapEx, you got to pay for electricity too. So you need a trillion dollars of revenue. And if companies are going to give you a trillion dollars, they got to be getting more value than that from that spend. So trillion and a half plus or minus, you know, right? The total spend on labor in the US across everything is GDP, 30 trillion, you know, it's sub 20 trillion. So you're really talking about 7, 8% of the labor force being, replaced by tokens for the math to work. I mean, when you look at that, you go, it's a dauntingly high bar, right? There's a little part of me that says, I think this is amazing, but I don't know if that last dollar in CapEx is going to earn a return. And if it does, it's going to be because there's a huge amount of labor force disruption and product. Let's call it labor force disruption is the negative spin. The positive spin is of course, productivity improvements. If the trillion dollars is going to earn a buck, you're going to have to have huge productivity improvements from it. AI and huge product improvements, you know, result in labor displacement and eventually new jobs for that labor that's displaced. But if one's going to happen, the other's got to happen.
Speaker 2The problem with productivity calculations is there's also a parody tax, right? You know, if you're the only one using a tool, you can achieve certain types of productivity, even if it works, even a lot of software productivity was BS, but let's assume it even works. But then all my competitors deploy Salesforce and Anthropic, and all of a sudden it doesn't show up because we have parity. So you can't go back, but we may reach a situation in 2027 where we just cannot prove any of these productivity gains. And so going to your math, everyone may just need to lay off 10% of their company. In addition to all other layoffs, right? Maybe the Oracle layoffs and others are just early views of where it's going and Robinhood. And everyone's just going to have to say, listen, we have to fund this. We have no choice, right? We can talk about ROI and productivity, but if we don't do it, we're going to lose to our competition. So our team has to be 15% leaner next year, guys. It's just this simple. It has to be 15% leaner.
Speaker 1And now I'm going to do the professor thing just for a second. That is productivity. You're right. You can have an improvement in productivity in an a massive improvement in productivity in industry by virtue of the advent of an enabling technology like AI, and no improvement in profitability for that industry if everyone adopts the technology. If every bank adopted ATMs at roughly the same time, everyone could, in theory, save on tellers. In fact, they ended up taking more tellers because the business expanded, but we'll skip that for a second. But the profitability didn't change, and the same could happen here. Everyone adopts AI, and everyone's cost function reduces by the same amount, but the people who don't adopt it are dead. That's your point, right? Yeah, which is why you have to lean in. And I think that's probably likely in a bunch of these industries. If consulting or any of these companies, any of these white-collar jobs are 20% more efficient with AI, then everyone should adopt it, and then the cost of those white-collar jobs, like audit, should reduce by 20%, all other things being equal.
Speaker 3My question is, do we see the same acceleration in model progression capability that we saw in coding in legal, in accounting? Because if we do, where Andrej Kapathy says, I move from 20% to 80%, in a six-month period, if you're able to see that same advancement in legal, whoa.
Speaker 2Listen, obviously coding is uniquely well-suited, but we just built an AI VP of finance while we were in China, and it's already better than any of the humans on our team. It does. It creates the quote. It builds the contract. It ships the contract. It gets it signed. It updates the opportunity in Salesforce. It logs into bill.com, where we're always an investor. It sends the invoice. It follows up on the invoice. It gets it paid. It interacts with Brex, and it closes out the entire transaction. Then it logs into QuickBooks and makes sure for the first time in 10 years, our books are accurate. They've never been accurate before, and the revenue's properly characterized. The agent does all that. This is what the model's today, which are not tuned for this workflow. It's so good. We had a human that for Sastr AI Annual this year, Aurora was at, forgot to invoice $80,000 of revenue. We had to write off, now listen, I can afford it, but it was so frustrating that this person just didn't do it. It had no, didn't even explain any reason why. Just didn't do it. So Amelia's like, I'm going to fucking build an agent to do this. In China, when we're there for Sastr, and then boom, like it's just better than the humans.
Speaker 1Did you have a full human being doing all that? Did you contract labor as a result of this? Or is that person doing different stuff now?
Speaker 2No, I mean, we use contractors, and for us, it's more that we will use less hours of the contractor on accident. But this is an issue too, right? Variable cost human labor, which does exist, right? You may even use less of it incidentally due to agents than intentionally. This is not replacing for humans on the team. This is, for us, this is the agentic story. We replace things that humans are unwilling to do on our team. They're unwilling to follow up. They're unwilling to get the, update the sales floor job. They're unwilling to send a proper invoice. They're unwilling to do these. And rather than fight the fact that as humans, we really only want to do like 5% or 10% of our jobs, including investing. I only want to do 5% or 10% of that job too. We just have the agents do the other parts, right? And if it's a variable price, you could see what you pay to these humans fall by 50% to 60% without even intentionally trying to save money.
Speaker 3Brandon from record posted, uh, nine hours ago, uh, which I, I sent it to the whole team here. Training agents will be the largest job category in five years time. Largest job category. Training agents will be the largest job category in five years.
Speaker 2Well, look, I'm sure he's right. He's much smarter than me, but didn't we say this about prompt engineers when the show started? Yeah. How many prompt engineers have you hired on the 20 VC team? I think the point I would sort of agree with, but the fact that we were able to build a director, we're going to call him a VP, a director of finance remotely in China that is better than any human on the team in a single digit number of hours is kind of this point, right? And then Rory asked, why isn't everybody doing it? And my response is they can, they just don't have the mindset to do it today. They just don't have enough of, they just haven't spent a year vibe coding. So they don't know what's possible and they haven't scaled a certain amount of human learning to have the comfort. You know, it's like doing your first venture investment. It's a little scary, right? Um, you know, but when you've been doing it for a while, the next one doesn't seem so complicated, even if you know how to put the pieces together. So I, it's kind of like that. I do think it's true. I think this is the number one skill is being a master of agents. What, and, but, but I think what that means is going to get redefined each year. Again, when we started the show, people were still hiring prompt engineers because prompts were complicated to craft. When we started the show, whatever, 60 weeks ago, if you didn't craft the right prompt, the software that came out was, was unusable. Today, I can vibe code something. I can say, build me an AI VP of finance, connect it to bill, QuickBooks, Salesforce, and our other app reuse. What we have and just automate all of our billing process. That can be my prompt. I think most people can write that prompt, can't they? So you don't need a prompt engineer, but you do need a master of agents to understand what you're going to get out of that, where the limitations are, where it's going to break, what it's not going to see, where it's going to get lazy. The, the, our AI VP of finance last night admitted it didn't fully read a contract. We asked it why it's like, I don't have a good answer for you. Wow. Okay. So being a master of agent is not throwing your monitor out the window when you hear that or calling it, it's like, okay, I get what happened. Okay. This is, this is running on Sonnet. Sonnet rapidly goal seeks. It tries not to finish complicated behaviors. I need to, to work with the agent, to change how we do it and make clear all contracts must be read from beginning to end. And then, and then, and then I have to be comfortable with it will still not do it sometimes. Right. So I know I'm rambling, but in a year, maybe we won't like the models and the harnesses will get so good. You won't have to do that anymore. Right. So what that means is gonna, is gonna change even this whole idea of folks talking about loops and agents looping is like an early view of where everything's going to go, because if your agent is constantly looping and improving itself in the background, which is already happening, it just fundamentally changes the way we build agents. They're not static, they're looping, they're, they're constantly improving themselves in the background. And again, when we started this show, you need to be a fucking prompt it. When they would joke, this was the highest paying job out of college was prompt engineers were making $150,000 at a college because they knew how to write a prompt that skills worthless today. So I think Brandon is right for sure. It'll be really interesting. What skills it takes to be an agentic expert in three or four years. It's just the rate of change here. It's just so crazy that with a very mediocre prompt, we could build an AI VP director of finance from China.
Speaker 3So we have a lot of early stage founders that listen. I saw this brilliant tweet that I did actually want to discuss with you guys. And it was from Nicholas at Y Combinator, formerly founder of Algolia as well. And he said a most common reason I see good companies fail to raise their series AB right now, isn't growth. It's margin. I keep meeting founders doing real revenue and growing fast, but once you remove delivery costs, there's almost nothing left. Investors don't fund revenue. They fund the margin on it. If that's you fix the unit economics first. Fast growth on revenue you don't keep is a trap. That was the tweet.
Speaker 1I like this. We've looked at Algolia back there. I think it's awesome. I disagree with him on this. I look, I'll go further than that. The objective reality is that's not what was happening. In other words, companies with. Tough gross margin profiles and hyper growth have been getting funded and frankly have been able to improve their gross margins and pull it off. That describes the foundation models. It describes the inference providers and it describes the coding agents. So historically, what he's saying is not correct. Now he may be picking on something in real time, which is that ability to build a company with a tough gross margin profile and then fix it over time. Probably makes the most sense in that kind of big bang stage of AI, which I think was the last three years where, you know, went from nothing to something and you had 10 X growth and it just paid you to grab the ground like it paid cursor to grab the ground. It may be that investors now are saying, hmm, as a first generation coding environment, you know, you can be cursor, you can have negative gross margins, you can still be worth 60 billion because you just grabbed the space. It may be that next generation they're seeing because they see a lot, you know, obviously, given the volume of wise, they're seeing a little more focus on gross margins now, which is plausible. But there's no doubt that to date the bet that my gross margins are shit, but my growth will cover it. And I will figure it out, even though it sounds stupid when you say it has in fact been a hundred percent true.
Speaker 2I think he was synthesizing all the learnings across the Y combinator portfolio and this change, right? Is it okay for startups to have negative gross margins or no clear path to positive margins? Cause they'll figure it out. That's Rory's point. And I think you could argue both sides to Rory's point, but maybe that age is ending partially, right? Maybe it should, should end, right. You know, we're sitting here and, and Menlo just raised a $3 billion fund after 50 years on the back of a crazy bed, an anthropic that went very well, right when the margins were crazy, but maybe that era doesn't last forever. And I'm sure all three of us are sitting on a portfolio company investment we made in the last 12 to 15 months where inference was the marketing strategy and the gross margins were negative. And we're sitting here today and we're like, company's doing okay, but I'm not sure we're going to get right side up on that investment. We're all sitting with a couple of investments like that. And then we see a few others that are wildly efficient. Like they took advantage of AI in other ways. That's why wildly efficient. And we're wondering in that two by two, do we really want to be in the, in the bottom, is it the bottom left highly inefficient and not a better growth, not top 0.1% of growth, I don't think we want to be in that, that two by two, do we?
Speaker 1Everyone will have some deals where you go. We thought we'd earn our way out of the gross margin problem. And for whatever reason, we couldn't. Either because we didn't grow quickly enough to get to scale or because the foundation model company started grinding us, our competition bundled it in. And now you're just in a shitty gross margin profile business, which will go bankrupt because when things slow down, no one says, Hey, let's do an acquisition of an adjacent gross margin, negative thing. And it's going to be fun.
Speaker 2Yeah. That's all these startups will just fail, right? If they don't achieve massive growth, they will all fail. Good simplification. Right. And so for an investor, that outcome is not that fun.
Speaker 3- Jason, you mentioned Menlo. Obviously, one of the biggest winners from Anthropic, raised $3 billion. I'm sure we've all got friends at Menlo. Awesome, good people, happy for them. They're also in Legora. They're in Lovable. They've been some of the best AI ambassadors of this wave, I think, categorically. The interesting thing, honestly, for me, and you're going to kill me for this guy's child or the boom, whatever, whatever, why is it not more? They legitimately are one of the best. They'll deliver billions and billions of dollars back in the wake of Thrive and GC and Lightspeed. This $3 billion is pretty
Speaker 2conservative. Any thoughts? I thought that. I actually don't have an answer. The only answer I could come up with, look, not only is Menlo wildly successful, but they're omnivorous. They'll invest at every stage. They will take lead positions, but they will also do smaller checks in small positions. If they lose the round, they'll do 2% into the round, or maybe they'll invest more later. Maybe they won't. If it's a great one, they'll just do it, kind of like how Felicis got going, but it's any size, like 20%, 1%. At first, my answer is that that's all they could raise. Poor guys could only raise $3 billion. Okay, this was my first read. Rory's shaking his head, the professor. Listen, I don't know. I shouldn't have gone first with the answer, but I think my secondary read is, look, they'll just raise another $27 million in other vehicles like they did for Anthropic. This is just one or two funds, and they'll raise another $10 or $20 billion in SPVs or sidecars or other funds. The headline number of the fund size is not always correlated to the amount they'll end up deploying over the life cycle of those investments, right? I think they were smart.
Speaker 1Yeah, to raise that amount, because given their amazing performance, they'll always have access to more. By not starting the clock on a much larger growth fund with the fees and the drag that that entails, you just set up for success. Remember, all other things being equal, provided you've got a big enough check size to play in any round, as a GP, you actually, quote, unquote, have higher risk-adjusted return if you have smaller funds, because then you have less cross-deal aggregation. Two separate $1 billion funds versus a single $2 billion fund, all other things being equal, you have more probability of winning on at least one of the two funds than on the single $2 billion fund. So that's not the reason, by the way, they did it, but just to point that out. The zoom-out question is this. $2 billion is a lot of money. How many deals are there going to be like Anthropic or OpenAI or Amdor, where you can put $1 or $2 billion to work? And the question is, do you create your main vehicle for that, or do you accept that there are anomalies, and you know you have access to them? I think you have access to that capital via SPV. So you run your business on what you think you can deploy across normal cycles on normal deals, including wildly successful $10 billion, $20 billion outcomes, and then you accept that two or three times a decade, there's going to be a trillion-dollar outcome, and you want to have access to that capital, but you can get that via an SPV. I think it's a reasonably rational structure. I mean, your fund size dictates your strategy. Once you raise $10 billion in a fund, there's relatively few places you can put it. You are signing up to put, you know, $1 billion in a fund, but you're not going to be able to put $1 billion in a fund. You're really signing up to put a whole bunch of money in those deals,
Speaker 3and that's the only place you can put it. I kind of like it in the way that you're like, hey, we're conservatively sized, and we can just take advantage of deal-by-deal carry on SPVs if anything does pop, and we'll have great fund returns if not. Yes.
Speaker 2As long as you can spin up the SPVs on demand, it's a great, it's the better model.
Speaker 3Oh, dude, they'll be able to spin them up like never before.
Speaker 2No, I know. Listen, as long as you can, like when I started in investing, I had two hedge funds at LPs, and they said, listen, we'll each give you a blank check SPV in your winners. And I'm like, okay, that sounds like a great LP. I took it. I called them up for a winner. Both of them said, well, we need to meet the founders. We need to do some diligence. I'm like, this is the worst deal I ever got. For the SPV work, it needs to be one WhatsApp message, and I get the $200 million to invest. Then it's the best model of venture there is, right? I just need $100 million. I know you haven't heard it. It's a good one. Did you read my last investor update? I need it by five. I think the
Speaker 1impressive fact that I haven't internalized about that announcement, because obviously I've known folks at Menlo since the mid-90s, is that they're 50 years old as a firm. Which makes them 30 years younger than you.
Speaker 2Is Menlo still with the firm? Is Mr. Menlo still with the firm? Do we know?
Speaker 1Menlo's not with the firm.
Speaker 2Does he still come into the office a couple of times a week? Does he have an office in the back
Speaker 1or in the front of the office? Stop, guys. Keep it sane here. My point is this. They're pursuing a strategy that I think is designed to survive and be successful across the cycles, rather than... My worry would be if you were reaching and doing a $10 billion fund, you've got to be really sure you can put that somewhere and without changing your strategy dramatically. It's not impossible. I think Founders Fund have done it very successfully. But again, to repeat, the number of companies that can ingest billion and $2 billion checks is pretty limited, even on a decade-by-decade basis. So I give them huge credit. I think they've done an amazing job with Entropic, and they've done an amazing job lasting 50 years.
Speaker 3Speaking of people crushing it, Kalshi passes $2 billion run rate, starts prep for IPO rumored to be. How did we think about this news?
Speaker 1Americans like to gamble, and we weren't allowed to gamble for years and years and years and years. And then the Supreme Court said, screw that. So Americans started to gamble, but the states regulated it. And that's why FanDuel and DraftKings did well, but there was always regulations. And Kalshi found a way to pretend that it's a prediction market, found a way to get U.S. jurisdiction from the CFTC. And have convinced everyone that it's predictions, which is different than betting. 90% of what they do is sports betting. They're on a roll. They found a regulatory arbitrage to a wildly popular pursuit, says the person who's betting as we speak on the World Cup, right? So it's 80 to 90% sports betting. Sports betting is really popular in America. It was illegal because of the Puritans for the longest time, and these guys are riding a wave. End of complex analysis.
Speaker 2I do think if Meta really can copy it, right, for real, like if they're comfortable going as far as Kalshi has gone in Polymarket, I do think there's a chance they'll clean up. It would be so convenient to go into Facebook, right, in my feed or whatever, and just instantly bet on anything my friends are betting on. Like the social aspect for Facebook could be so powerful. I just don't know if they'll cut the same corners or not. I'm ignorant, but I actually think this is one where Meta could win big. Because like, I'm a little bit slow to Kalshi, but if I logged into Facebook and I saw that Harry was doing something for five bucks or 10 bucks, I might just do it with him on Facebook, right? Grandma and grandpa might. Like, there's a whole... Kalshi's scary. What does it mean? Is it safe? Right? Like, Facebook's still doing pretty good, right? I think it's a great place to bet.
Speaker 1Coming back, just again, to remind folks, Harry started by asking the question, why is Kalshi doing well? And Jason, when you jumped in, you covered something that we hadn't explicitly said, but just to say it, Kalshi's doing amazing, and it's talking about going public, it's doing two billion in revenues. Facebook, as I met it, just said they might offer a competing product, which right now doesn't exist, but you're speculating on, will that take the business from Kalshi? If that's Kalshi's only problem, I think they'll be just fine.
Speaker 2Yeah, no, no, I'm not saying it'll hurt Kalshi. I'm just saying I could imagine it being wild, instantly wildly successful. If they're comfortable legalizing betting on their own platform and making it as elegant and as fun and as social as these platforms are, I might do that rather
Speaker 1than mess around with these llamas. I don't know. I think the demographic for sports betting is young and male, and I don't think that's the Facebook demographic anymore in a million years, right, to be honest. I think it's actually smart of Facebook to think about it because, yeah, I think that the core Facebook demographic is aging fast, and this is where, as I say, especially young males are playing. I don't know if it'll turn out to be good business for them, but I don't think they launch it on Meta and they get a whole bunch of traction. I mean, my son's an avid bettor. I worry about that sometimes. And let me tell you, he ain't, I don't know, he's been on Facebook for five years.
Speaker 3Kalshi, what price does it go out at when it does go out?
Speaker 1Hell knows. I mean, you know, they're talking about 10 times.
Speaker 3Well, it's the prediction marketplace, Rory, so let's predict.
Speaker 1That's actually a very fair response, Harry. You win on that. And that would be good because it would be one of the 10% bets that isn't about sports betting. Look, the growth is so amazing that, you know, if they're talking about 10 times, if it's at 2 billion today, by the time I could go public, it could be, that could only be seven or eight times. So it could be a really big win. I looked at it before I came on, but I can't remember. Fan duels and DraftKings. So one obvious question is, where do they get revenue from fan duels or DraftKings, which are 100% betting? Those guys have some kind of geographic issues. They have to have state-by-state licensing. Kalshi, because it's regulated as a prediction market, has been able to avoid all that. So they might have an edge on that. Obviously, it's not quite the same thing because, just to be clear, the structure of the business is not quite the same thing as fan duels and DraftKings are a classic betting house. In the case of Kalshi, they're just a clearinghouse and they match buyers and sellers. So it's not quite the same thing. But to a rounding error, the experience is much of a muchness. So right now, it feels like a really good play. Sorry, is there a chance that in a
Speaker 3non-Trump administration, the party stops? Yes, is a quick answer. You are vulnerable
Speaker 1to regulation. There are currently some states suing Kalshi, saying you really are doing betting, so we should regulate you. Right now, the administration's perspective has been to swat that down because they want uniform federal jurisdiction. And it was the Supreme Court that basically legalized sports betting. So yes, there is regulatory risk consistently in these businesses, but that's just part of the bet.
Speaker 3The next question is, in two years' time, will prediction marketplaces be an ongoing activity within Meta? Will it be a part of that product that you can do in two years' time?
Speaker 2I just think that Facebook is one of these things that we underestimate, the scale and the reach of it. It's pretty powerful and it keeps growing. It's the one we underestimate. And I think once it's of natively overlapped and became part of Instagram, it got much better. And I just think it's super powerful.
Speaker 3Are there any that we've missed that you want to touch on before I do a rage bait, but real? Well, we didn't do it. I don't know. Is Accenture worth the effort? We can discuss it. I don't understand it personally.
Speaker 2I just think, I don't understand all of it. I just think it's interesting that, you know, 30 days ago it was an AI beneficiary. Now it's not, right?
Speaker 3The context is Accenture plummets 19%. That's on already being down, I think about 20%. So it's down about 40% for the year. And to Jason's exact point there, I thought these were meant to be services that benefited from AI. Why is it down 40% year on year?
Speaker 1Yeah. And I think I can chime in on that because I think two separate things are happening at the same time for Accenture, right? One is the business of helping companies adopt Gen AI is probably exploding because all the companies need help. So if, you know, that was a zero part of their business a year ago, two years ago, three years ago, it's probably exploding in the real world. I see every company in America needs some help on Gen AI. Separate comment, the core business therein that, you know, 99%, 90% of their revenue is other consulting. And there are few markets more primed for disruption by AI than consulting in general, because it's wide collar work. It's already been outsourced. That's why Accenture has it. And we're seeing a whole series of companies kind of the mental model for us is AI for SI, right? So the whole system... Systems integrator market is prone to disruption. And so if you look at some of the core business that Accenture used to do, you know, SI consulting for an SAP deployment, there are companies like Tessera and Conduct that are doing that. AI systems integration for Salesforce integration, there are companies like Swantide and others doing that. In all these markets, Accenture was probably billing, you know, maybe $20, $40 million for an SAP implementation. And today, they might only get $20 million for that, because $20 million of that can be done by using LLMs. So I think what happened is their core business came under pressure. So even though the new business of, you know, kind of helping other companies adopt AI is exploding and doing nicely, ironically, the core business defending yourself against AI is in a pretty tough place. It's not the end of the world, but it totally makes sense, right? Because I actually think one of the investment themes we'd love to find an interesting bet on and have talked to some is around this AI for SI space. Because all those consulting dollars, they're massively vulnerable to, you know, compression from AI. Because if you look at the kind of tasks they're doing, it's gathering requirements, it's building statements of operating procedure, and then it's writing, you know, fairly simplistic code to say, how am I going to deploy Salesforce? How am I going to deploy SAP? Those are precisely the kind of roles that AI will replace.
Speaker 2Yeah, when I was a VP at Adobe, there was an entire floor of Accenture for five years deploying Salesforce, an entire floor of people. And I don't know what they made off that deal, but it was $26 million a year for Salesforce. So I'm guessing they charged at least $26 million a year for five years to get Salesforce up and running, right? That business has to be partially disrupted. And also, seat compression hurts them. You know, the one thing I would add to the list we get, I didn't know Devon was on such a role. We talked about it right before we started. Devon, up 30% this year, one of the big winners. You know, we talked about it. The IPO crashed. It's up 3x from the bottom. And when I kind of did the other day, I was just trying to do my own little to oversimplify the public markets. Everyone selling seats for the most part is getting crushed. Everyone selling variably one way or the other is winning. Not all of them, but pretty damn close. Variable might be because it's directly attached to AI spend, right? But it might just be because it's attached to the economy that's doing well, right? And so a lot of the Salesforce Accenture thing is tied to seats, man. So that's going to get crushed by definition, right? I think you're right, Jason. The seat
Speaker 1element is part of it. The long-term trend isn't great, but I think the short-term crushing is less because the number of Salesforce seats at Adobe went from 1,000 to 900. That sucks. But to your point, the real point is you had literally a floor consultant sitting there getting $20 million a year for three years. And when you look objectively at the work they're doing, it's probably some of the easiest work for LLMs to do. It's automated work.
Speaker 2Yeah. It was migrating from Siebel and your own databases to Salesforce. It took five years to lift data. And now Databricks, which is on fire, we won't hit it this week, Databricks claims they can do that lift in 30 days for their customers, an entire lift to Databricks for anybody using LLM. It is actually under-discussed story that Databricks promised, what are they growing, 80% at 6 billion or something like that? Accelerating. I didn't really get this until one of the founders came to Sastr and then I researched it, but they tell you in 30 days, we will do an LLM lift of all of your data, all of your data, whatever, how much it is. I'm sure there's exceptions and answers, but compare that to a five-year lift with Accenture to go to Salesforce. And we just did one, like we've been trying to get it off Marketo for five years. It's our worst software. And then believe it or not, Salesforce used their LLM thing and moved us to their product in a couple of weeks. They just lifted it with no humans. Big deal, this lift, like it's under-discussed and it's a moat destroyer. It is a moat destroyer. It's a moat destroyer when LLMs will lift you from one vendor to the other. And I literally was doing a pitch this week and the founder was going on and on about their moats. And I immediately didn't want to invest. Like I just, enough. Your moat can be LLM lifted away. And the interesting thing, staying
Speaker 1with the kind of Accenture problem is that, look, I mean, one of the things we had done about two years ago is just say to yourself, when you think about what work AI will replace, you know, white collar work, you can say BPO is a good proxy for that because anything a company is willing to outsource to India, they're probably willing to outsource to AI. So in fact, you can literally just take the BPO spend and look at the BPO and start saying, okay, there's a whole bunch of places where AI will win. And you know, when you do that, Accenture is top of the heap. They have all that business. What's challenging for those companies, you would think that they would want to adopt this technology. But the problem is their business model, there's only one thing worse than a seat-based model, Jason, and that's a model that's based on bodies. If your business as Accenture or any of these SI companies is, I bill out a hundred people, I pay them 200 grand a year, I bill them out at 500 grand, and that's how I run my world. If I don't need a hundred people, if I only need 40 people and some AI, that makes my head hurt. I got to get rid of these 60 people. I'll find another project for them. I got to figure out how to build that. My whole margin structure collapses and my take, being on top of the heap, goes down because what we're seeing is some of the biggest consulting companies, they're adopting AI technology, but they're really struggling to pass on huge price increases, which leaves the room open for newer companies to come in and say, look, we are AI first. We are using these tools. You got a bid for 80 million from Accenture. We'll do it for 15. And that kind of thing gets the attention of the CIO. And we think that's a super interesting place to invest. So yeah, Accenture has a lot of structural questions. I mean, this is going to be a constant theme for the next couple of years, the pressure on those kind of businesses. Welcome law, selling hours. It's a little like the law common, but even worse, I would argue, because I think law at some level, yeah, you want the work done, but you also want the wise guidance. If you're doing SAP migration, you just want the damn thing migrated to the new version of SAP and you don't want to talk to these people ever again. We're going to do a rage bait,
Speaker 3a little clip that went slightly, slightly rage bait-y this week. Five and a half million views. Ryan Peterson at Flexport said, kind of jokingly, obviously, work from home is white collar fraud. I have two kids when they come home at three from school. Of course it interrupts my work. Yes, I have a home office, but of course it does. It's not the same as in person. Rage bait or real?
Speaker 2I saw it. I thought it's just dated was my read for what it's worth. What I mean is, listen, I get in a little trouble on this show. I agree with him. I think a lot of work from home, the era was working 15 to 20 hours a week. I saw it on my own team and it involved a lot of distractions from home, right? On the other hand, plenty of folks do make it work, right? The reason I say it's dated is companies aren't hiring people, the companies we want to invest in, and this is a very narrow set of the universe. They're not hiring folks that want to work 20 hours a week from home. Our portfolio companies, at least our newer ones, our older ones still are, but our newer ones just aren't. The whole way you build a startup to your first 100 or 200 employees, I think is radically changed and is under-discussed. When we started this podcast 60 weeks ago, it was toxic to be running cognition and telling folks they had to run seven days a week and laying off half of windsurf when you acquired them because they weren't willing to work hard enough. That was utterly a toxic thing for the founder to say. Today, it is how you build a winner. You can't win in your marketplace if people are working 20 hours a week. You can't win. The only thing I thought Ryan's running, I thought Ryan's running 20 hours a week. I thought Ryan's running 20 hours a week. I thought Ryan's running 20 hours a week. I thought Ryan's running 20 hours a week. I thought Ryan's running 20 hours a week. I thought Ryan's running 20 hours a week. I thought Ryan's running 20 hours a week. I thought Ryan's running 20 hours a week. I thought Ryan's running 20 hours a week. It's old. Flexport is old. I think he's struggling with trying to modernize his team and being competitive with the way startups are today. He's struggling with the fact you can't change out your entire team. What do you do with the folks that are unwilling to change at these big companies? What do you do with our 10-year-old portfolio companies where 90% of the folks are unwilling to change? That's what he's really railing against is the folks that really just want to work 20 hours a week. I think it's dated. I hope that no new investment I make is structured the way. I feel like the majority of my older ones are. I want small, high-paid teams that work in the office over six days a week. I'm not interested in investing in anything else. I'm just not interested. It's not because I don't have empathy. It's because they're going to fail.
Speaker 1Yeah. Jason, empath is the word I think about when I think of you.
Speaker 2I am an empath.
Speaker 1It's okay. Let me just ask a question here. Let's do a simple... Pretend I'm a consultant, poor guys. A two-by-two. Is your dimension of objection is... There's two dimensions. It's do you want to work hard or not? In other words, words, you know, hardworking, not hardworking, and then effective, not effective, right? Do you think the problem with work from home is that people don't want to work or do you think it's
Speaker 2not effective? Yes, it's both. We need to hire teams that are half the size they used to be that are paid top of market. They get double the equity because the teams are smaller. And we, we come into the office frigging the Corgi guy, whatever his name, what's the insurance guy you had on the show that people make fun of with his cafe. I want to invest in startups with a cafe that runs 24 seven, because you can't win when Corgi is running 24 seven, you're not going to win. Here's the problem. And I'm struggling with this intellectually. It is not a sprint. It is a marathon, but it actually is today. It's a series of endless sprints. This is so hard because it's a sprint. You get about five minutes to relax. And then today open AI released the jalapeno chip. We didn't even know it was coming out today. Now they have their own inference chip. Maybe we don't need Cerebus anymore. Maybe the whole market's going to be disrupted in 60 days because open AI is going to run its own inference. Do we get to breathe? Guys, you don't get to breathe anymore. Sorry, guys. We no longer get to breathe. If you want to make any money, do you want to make money from your equity or do you want to make $180,000 a year? Like this is going to be your choice in tech. Do you want to make money from your equity or do you want to watch? Honestly, this is a choice that you want to watch or you want to make 10 million or a hundred million. There's nothing in the middle. You don't get to make 10 million for working 18 hours a week. You get a watch, you get an Omega, you want an Omega, or you want to be rich. Make your choice, boys. Pick your path. I say to people, pick your path, go work for that software company growing 8% a year. Go work there and make your 180 or 220 and wear a really nice, they all have nice watches at these companies now, or go work in the office six and a half days a week at the Corgi Cafe with a chance to make eight figures. That's the choice today. This is a different world and I don't want to invest in anyone in the middle and I can't afford to invest in the folks that want watches. Enjoy your $12,000 Rolex or your $8,000 Omega. I hope
Speaker 3it's meaningful to you. Okay. I'm letting it go. Boys, do you want to have one final addition in the five minutes? And it's OpenAI have announced that they are doing a custom chip. If Google has TPUs, Amazon have Tranium. Well, OpenAI now has Jalapeno, co-developed with Broadcom. OpenAI says this chip beats current state-of-the-art GPUs on performance per watt. Broadcom CEO on record saying it cuts costs by 50% of a typical GPU. Zoom out. Inference is 50, 60% of revenue,
Speaker 1plus or minus. And we know... Oh, you're peering through CapEx. When you look at CapEx, GPUs are well over half of that, so that's 30%. So maybe if you could replace all of them and NVIDIA makes 70% margins, you can make some kind of intellectual case for you can save some money with this. But honestly, my real response to it is you've got plenty to be doing elsewhere, OpenAI, winning on the top line side. I hope this isn't distracting you from the things that matter. So yeah, I'm trying to give a shit.
Speaker 2I'll throw out one. I'll throw out a little thought, and maybe we could talk about it next week for what it's worth, because one topic I thought we were going to do more this week, which we didn't, is talk about open source even more. I do think that open source, as token maxing becomes a bigger deal, I think open source is more and more important. And I think how do you win for certain workloads if you're OpenAI or Anthropic? Well, it's you cut your inference costs cheaper, actually cheaper, than commodity open source providers can provide it, because they still have to buy the GPUs. Open source is not... Again, it's not free. Right? Listen, if I can run my own inference farm, one way or another, buying somewhat expensive NVIDIA chips, which is how I have to do it, right? But the advantage is I get to keep the margin, right? Or I get to keep most of the margin. Or OpenAI can provide me with a cost-competitive product because its inference costs are half of what it costs me to do it on open source. This is not just about cutting costs. This could be about shoring up yourself from open source, which has the potential to kind of destroy the... The middle of their market. People are always going to use the best models for frontier applications. And actually, at the bottom of the market, these guys are pretty competitive, but the middle is open to massive disruption. But if you cut your inference costs in half, the truth is open source is probably only twice as cheap for a lot of use cases. And if you cut your inference in half, your model still makes sense. That's why I think in theory, it's a big deal. It's not just about capacity and driving down costs. It's about this flabby middle. The flabby middle in open... In AI is at risk. Okay. Next week, you're going to agree with me.
Speaker 1No, no, no. I'll disagree right now. I understand the bet. OpenAI and Anthropic have discovered the single best tech market in terms of consumer demand in the last 20 years. And they should put all their effort into meeting that demand. Vertically integrating backwards, down the stack, two levels down, not just vertically integrating to own a data center, but vertically integrating to own a chip that goes into a data center doesn't strike me as the highest and best use case. I don't think it's a good idea. I don't think it's a good idea. of resources. Why do I say that? You've got three or four cloud providers who are dying to do business with you. You've got Oracle, you've got Google, you've got Microsoft, you've got Corweave. So you've got a whole bunch of vendors one level down for you. Some of those vendors themselves have chips. Google has a chip. Amazon now has a chip. There's a whole ecosystem of people breaking their picks to provide you with cheap compute and taking on all the capital risk of that. God bless their poor little selves, right? Oracle will rue that day sometime, as will Microsoft. And you're sitting in here and you should be putting all your effort right now into grabbing those end customers in enterprise. If you've got additional time and resources and you can also take on building a chip, have a go and knock yourself out. And I get it intellectually. You're right, Jason. At some point, to the extent that's become a cost business, you know, optimizing the whole vertically integrated stack may make sense. But if the whole point, I mean, if you look at it, you said it, but right now, if OpenAI and Anthropic had to do vertical integration today, they'd be fucked. Because the whole reason they worked is because they have outsourced $300 billion in capex to other poor fools. And the definition of vertical integration is taking in-house things that were done outside, right? The whole reason the OpenAI and Anthropic models work is because other idiots have spent the $300 billion on their behalf. I mean, at some point, maybe you have to vertically integrate, but it just, it doesn't
Speaker 2strike me as right. Well, look, it's not that I argue with you. And first of all, the fact they launched this as we're recording this is obviously a decision that was made in a different era, right? It was even before the TBPN era, right? It was an era of abundance. And so if this decision was made today, it would be a more, it would be an interest, very different conversation as it makes sense today. So all that caveat aside, we're actually looking at a, you know, some sort of early 2025 decision when the world is radically different. Okay. Having said that, I just think that OpenAI and Anthropic have massive existential risks that didn't exist at the start of the year. Which is we did, we glossed over this GLM 5.2 open source, whatever, what it's not just performance. It's the fact that as we come under cost pressures, the middle of their market is under massive threat. And what you would do if you had a high margin product like software is you would just have a cheaper offering. You would just subsidize the middle of the market. They really only have the high end and the low end today. They have Sonnet, Opus, and Friends, which is great. And then they have Haiku and Mini, which are these super small models. They don't have this middle product. And the problem with the middle product is it's too expensive to provide. And so if you can cut your inference costs to below open source inference costs, then you can have this middle market where every single workflow you can serve rather than have your middle, your flabby middle hollowed out for you. I think the flabby middle is high risk for these just when they're all ready to go IPO, they have a brand new existential risk, the flabby
Speaker 1middle, just when things are getting good. Agreed on the cost side. And I totally agree that in order to minimize your cost, you have to vertically integrate backward two steps. I not only own the data center, I query whether you really need to vertically integrate it back through the hyperscaler and also integrate into the chip level. It just feels like a lot of backwards vertical integration that we're in a competitive market. And there are three or four chip providers and there are three or five or six hyperscaler providers. Surely you can just beat the crap out of them on cost because you are the largest buyer on the planet of this shit. Let's check if the market cares. Why do I say that? Well, we should actually see how much the stock, what happened to Cerberus today. If you remember, Cerberus went public, had a decent quarter going really well. And their big traction is a $20 billion chip order from OpenAI. Did the stock go down a lot? Because presumably that is now, I mean, you know, yeah, it did. It went down 16%. So there you are. The market said, poor old Cerberus. OpenAI is going to build that chip instead. Now, the question is, would they have done just as well by saying to Cerberus, you know, we're playing you off versus Itanium, TPUs and 20% discount. Did they need to build their own chip to do that? I don't know. It doesn't matter
Speaker 2because it's so many generations ago. The decision was made as irrelevant today. The world's changed
Speaker 1so much, right? That's totally fair, which, by the way, gets to how these cycles end. I really believe what you're saying is if the next stage is when someone comes in and says, what we need to do is build a foundry and make our own DRAM. At that point, you'll know the cycle is about to go really badly down. If you vertically integrate back into memory, then you know it's over. Just you and the Koreans.
Speaker 2Look, I'll just say one thing, maybe we can find out next week and as the months go on. I firmly believe, especially for, we just want to tie this to B2B software and stuff for some of the heart of the show. I firmly believe we need these middle level models from the closed source providers for the business to work, okay? Not everyone can afford to run every workflow on Opus and Haiku and Mini are too small. And this is where open source is going to disrupt the market. And so this is more important than it looks if it can work because you got to head off this middle disruption.
Speaker 1middle layer. Jason, first of all, I totally believe it. I think the number one question is, software companies, how will they access kind of mid-priced, high-quality intelligence that's not got frontier pricing? Because we're seeing people start to gag on pricing. I totally agree with you on that. In my comment, I don't think it's a question of optimizing the stack that gets you there. It's what's the business model for these guys to provide it? Maybe with only two frontier providers, there's not enough competition. If I was someone like Google, I'd be looking at, that's why I go back to, come on, guys, wake up. I'd be looking at this market and saying, how do I put a lot of pricing pressure on Opus and on GPT 5.5 by providing a just almost just as good US-based competitively priced product? I think they have. Yeah, they just haven't made
Speaker 3it happen. Boys, it's so good to have you back from China, Jason. We missed you. I thought the
Speaker 2guy from Benchmark was pretty good. I don't mind being replaced. But before we leave you today,
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