AI Profitability: 1999 Dot-Com Bubble, or Sustainable?

Masayoshi-san from SoftBank, he basically said, look, we're looking at four trillion dollars in infrastructure spend for AI over the next, you know, few years. Four trillion dollars? You then have to ask, what's the return on investment? Exactly. What are you getting? And how much these companies would have to make in terms of profit to pay for this over what period of time? Exactly. Welcome back to Silicon Valley Front Row. I'm David Lam. This co-founder and general partner at Turing Capital. So today we're going to talk about many topics, but first the AI, supposedly the AI bubble. So Steve, why are we talking about this today? Well, we talk about this because it probably affects a lot of people. A lot of people are interested in what potentially can happen. We lived through at least two that I remember, the real estate was 2008 and the dot-com bubble in 2000 or so. It's just a huge amount of money that's being invested in AI in one way or another. And I was looking at some numbers and I'm going to quote here. This is a story that came out today in Axios. So we're talking about AI looking to spend 1.4 trillion dollars in the next few years on infrastructure and projects. Tell us about how crazy that is. When you first hear the number, and it's not just 1.4 trillion, if you go further back, I think it was late last year, Masayoshi-san from SoftBank at I forget which conference or forum, he basically said, look, we're looking at 4 trillion dollars in infrastructure spent for AI over the next few years. When you first hear that number, it's incredulous. Four trillion dollars? Now, obviously, 1.4 trillion is part of that larger number. And again, the headline number sounds fantastic, all hard to believe. But obviously, it's not all happening at once. This is over many years. This is across not just one company. This is many companies that are going to be spending a good amount of capital standing up new infrastructure. So in taking an aggregate, it's still a very large number. But you can start to make at least some sense of it. It still feels like, wow, I'm a little bit skeptical of, you know, and not skeptical of the spend. What you then have to ask is, what's the return on investment? Exactly. What are you getting? And how much these companies would have to make in terms of profit to pay for this over what period of time? Exactly. And we're talking about, you know, I think I've seen numbers where even if you were to think about like a 10 percent rate of return, you're talking about having to generate, you know, profits in the hundreds of billions of dollars per year from this infrastructure spent. Now, you contrast that with where we are today or where we were, let's say, until very recently, where if you set aside the infrastructure, you know, data center, chips, power, networking, and you look at, you know, the actual applications or use cases people had built and how they would be monetized, that total was about 12 to 13 billion dollars in one year. And further, if you break that down, how much of that is tech GPT subscriptions, anthropic API licenses versus true new applications? It's minuscule. So if there's any reason to feel concern is to say, wow, this is really lopsided. This is way more lopsided than the early days of cloud, early days of mobile. I would argue even the early days of the Internet. People like to draw analogies. Well, this is just like, you know, this is like what happened with Cisco and the fiber build out that happened. But that's not a it's not a perfect analogy. If you look at what's going on right now, there's just not enough GPUs and the GPUs that are there are completely maxed out. They're 100 percent utilized. If you listen to what Google has said recently, they're having to plan for doubling their infrastructure capacity every few months. That's how much demand there is. So, but again, the demand doesn't necessarily bring back the revenue. I think that there's a nuance there, right? If you if you look at certain players who are losing a lot of money, even if they have top level demand. So they're not profitable and they're far from profitability. On the other hand, you have companies like Google that are profitable, that are generating both top line as well as profits and have an insane amount of demand that they have to keep running hard and fast to meet. So you have it depends on the player that you're looking at. So and then the question is the sustainability of that demand. What is driving that demand today? There's still a lot of experimentation going on. People are trying lots of different new things. You probably have a chat GPT subscription. Maybe you have Gemini. Maybe you have Anthropic. Maybe you're paying for more than one. At steady state, how many of these things are you going to keep paying for on a monthly basis? There's probably going to be some amount of consolidation. And you're not going to say I'm spending hundreds of dollars every month for my AI chatbot. Right now, it's new. It's novel. You're trying things. So people are doing that. So the question is, can you extrapolate from that sustainable revenue in the long run? So that's one question. And there's reasons to believe, well, this will get optimized. And when things get optimized, then costs will be squeezed out up and down the chains. Costs will be squeezed out for the consumer. Costs will be squeezed out for the model providers. Costs will have to be squeezed out somewhat for the infrastructure providers. The answer can't be, OK, the people building the infrastructure are making healthy margins and healthy profits. But the people in the middle are getting squeezed and consumers are just emptying out their pocketbooks, keep paying for these subscriptions. So I think the big question is, at steady state, what does this look like when everything is optimized? And at that point, are you generating enough top line and profitability to justify this huge infrastructure spend? I think we've already seen from all of the big tech leaders, they've acknowledged that this is an arms race and they can't afford to slow down at this point. And they have acknowledged that there will be some fallout. There probably will be some amount of overbuilding of capacity. But at this point, they can't afford to slow down because they're in an arms race. And I think they've acknowledged that. I think other folks have also said some very rational things like Demis Hassabis from DeepMind and Google, which is when you see, you know, 30 billion dollar seed rounds, you know, raising two or three billion dollars at the seed stage there's no product. There's just a team and an idea. How can that be sustainable? We have enough evidence that if at that stage you're already worth that much, even if you say you're going to go public at some point, what are the public markets? What can they tolerate? What did they ingest? Right. The multiples they're willing to pay a 30 billion dollar seed company. What is it in an IPO? Is it a trillion dollar company? You know, like, are we going to have, you know, I call these triceratops, you know, we have unicorns and decacorns. So triceratops are these trillion dollar, you know, startups with a trillion dollar valuation that are like these mythical creatures. Is that sustainable? Like, are we talking about trillion dollar companies? If that manifests and plays out, then, you know, this infrastructure spend is not hard to justify. And how many of those could be successful? Because there's only so much people that could buy the subscriptions. That's exactly right. That's right. And so when you have the leading provider, which is OpenAI at, you know, close to a billion, 850 million, you know, plus monthly or weekly active users, you can say, fine, there's still more people. There's 8 billion people on this planet. But how many of them are going to pay $20 a month? If you have close to a billion now, is it 2 billion? Is it? It's certainly not 8 billion. It's certainly not 6 billion. At what point are you going to saturate? And also, it's not just one player. There's others, right? There's Anthropic, there's OpenAI, there's certainly open source models, which many times have a much better, you know, cost profile than continuing to pay the big frontier model companies. So I think these are the reasons to have a good amount of concern about the sustainability of what's happening right now. But I think we also need to understand that this is not an exact repeat of what happened in 1999, what happened with the fiber build out, what happened with networking for the internet, and, you know, oversupply, and then there was, you know, the demand didn't catch up. In the long run, obviously, all of the investments in fiber did pay off, like we were able to use it, it just took a lot longer. It wasn't like the ROI happened in the time frame that was imagined when they spent all this money to do it. It's a possibility here. But right now, the signal that we're seeing is that demand for compute is not going down. If anything, it's getting even more pronounced, it's increasing. So from that angle, you could say, look, you see justification for standing up more and more data centers with more and more compute. The things about this that are perhaps not abnormal, but are distinct, is you have one player who owns more than 90% of the market, has a very rich margin profile, and others are still trying to figure out how to make money. NVIDIA, right? Right. And so in some ways, it's not surprising that if NVIDIA is so healthily profitable and has owned so much of the market, they're a market maker, it's in their interest to ensure there's a healthy ecosystem. And if that means in the early days, you support the ecosystem by investing in them, by doing some of these deals, that's not surprising. But the question is, if you play this out, the key other players in this ecosystem will have to stand on their own legs versus, hey, we've got these interesting, call it subsidy, call it revenue round-tripping type of deals, that okay, fine, in the early days to get the ecosystem to a certain place, you needed to do this. But over time, they need to stand on their own legs in terms of they're generating their own revenue that justifies their own infrastructure usage. And it doesn't require these interesting models that we're seeing today. So of course, we see here the main players involved in financially with the technology. Now, you mentioned it's almost like an arms race. What would you say is the driving force for these companies? I mean, obviously, arms race, they get to a point and then they disarm, right? So what would you say is the driving force? Is it just they don't want to be left behind clearly? But is there rationality in your mind about going all out in this way? I think there's some variation here, depending on which of these players you look at and who's being more rational versus irrational. So my view, I think Google, probably the most rational. The demand is real. The usage of their products is real. And as I said, they're having to double capacity or plan for doubling capacity every few months. They are in a interesting position where they're not just relying on NVIDIA. They have their own silicon called TPUs. They're doing both. But they are not doing this where they're so deeply in the red that there's hopes and dreams at some point, you know, it'll be cash flow positive and then it'll start to, you know, the ROI is going to start to be there. Now, you can't say that about all the other players. I think the other players are doing things, betting on a certain future playing out that would then justify these investments at this stage. And there is a lot of risk that it could not, it may not play out that way. I don't want to get into specific names, but yeah, I think that it depends on who we're talking about. But Google's Gemini, what percentage would you say people are using Gemini versus chat GPT? It's very small, right? I mean, it's actually not. So it's caught up quite a bit. I mean, Gemini was, Google was seen as, hey, they're, you know, they missed the boat. They didn't execute. And, you know, open AI is the natural leader. If you look at where we are with Gemini, and if you say Google is at 850 million users, Gemini is now at 650 and growing faster. Which in a way makes sense, because you use Google, you know, why would you go to a different tool? If it's good enough, and it's giving you, where you don't have to go to another chatbot, then you're absolutely right. Like the systemic advantages that Google has with search, with all of the other assets, then there's good reasons for you to stay in that ecosystem. So I think, yeah, so I think it just goes back to who we're looking at. Like, you know, it's a different answer if you talk about AMD versus Nvidia versus, you know, other silicon companies. It's a different answer if you look at Google versus Microsoft versus Meta. You know, I would argue, I think some of this is instigated by, you know, some, like the FOMO point, we can't afford to slow down. I mean, Meta is the perfect example, right? And what is happening around recruiting and paying these multi-million dollar salaries to, you know, researchers chasing artificial superintelligence. You know, it's not just Meta, then that impacts everybody because now you're like, well, that's the new benchmark for what we have to pay for elite AI researchers. So that means we have to spend this much money. Those people are going to need compute. They're going to need to build new classes of frontier models. They're going to need insane amounts of compute. If we don't have that, we're not going to be able to attract them. And that's on the research side. On the commercial side, you know, for many of them, they are seeing the demand, right? Like Google is seeing the demand. I would argue, you know, some of the others are seeing demand that would justify that build up where they can't keep up with the demand. So there's lots of different factors at play here. Let's talk a bit about that, David, with superintelligence. Is that related to autonomous AI? Is it the same thing? So I think you can achieve autonomous AI.