We have a whole new technology. That's the most important technology ever. And you need a whole new infrastructure.
Normally when we talk about the infrastructure world, we're talking about the servers, the storage, and the network. Here it goes all the way down to the copper mines. That's how widespread this thing is going to be.
It used to be when you built something, it was an engineering problem. And here it feels like it really is a resource limitation. So whether it's tokens or not, we're pouring a ton of money into systems, and then those systems are producing a result. And right now we're bottlenecked on the system's ability to actually match the resources we're pouring into them.
The leading memory vendor said the demand they have today will take them three years of capacity to supply.
If this fund does what we think it will do, how do we see the world in five to ten years?
America wins in the infrastructure, and that would be awesome.
Ben, Martin, Raghu, welcome.
All right. Thank you.
I want to start with a Marc quote to introduce this new fund. This is the biggest technological revolution of my lifetime. This is clearly bigger than the internet. The comps on this are the microprocessor, the steam engine, and electricity, or maybe the wheel. Guys, the Machine Age Fund. Please introduce it. Ben, start us off.
Well, basically what's happened is we have a whole new technology that's the most important technology ever. And what happens every time there's a dramatic new way of using all of the things that we love — infrastructure — you need a whole new infrastructure, and never has it been more high impact as it is on this one. So not only do we need new chips, new system software, we need new ways of doing power, we need to replace copper. I mean, like, it's absolutely everything. So it's a very exciting time. So, you know, particularly for the kind of hardware aspects of this new era, we needed a new approach.
Yeah, I would agree. I mean, normally when we — at least in computing — when we talk about the infrastructure world, we're talking about the servers and the storage and the network. Here it goes all the way down to the copper mines. That's how widespread this thing is going to be. That's number one. And number two, I think what we have seen over the last three years is the steady increase of the capabilities of the models, where the model is no longer the bottleneck. And in fact, using AI, these models are getting better faster and faster and faster. Now the bottleneck is all what I call south of the model, and so that's why we need to work on that.
You know, the only thing I'd add very quickly is like we tend to follow founders, and we've been watching over the last couple of years is the number of very strong teams going after complex hardware problems has increased. I don't know the actual numbers, but I was trying to estimate it over the weekend. So I think we'd get you maybe 5% of the deals from top founders would come in would be hardware before. Now I would say north of 20% or 30% right now. So like the founder community, which tends to be much smarter than the VC community, has identified this as a very active area for innovation, and they're responding.
I think 5% is probably generous.
Yeah, it's very low. It's very low. Yeah. 3%. Yeah.
And explain some of the macro conditions that have led to this change in terms of the surplus of founders pursuing these ideas. Like, what are they seeing that's enabled?
Well, I mean, the obvious is like, you know, the demand for AI is basically infinite, and as a result of that, every part of the supply chain is under duress. I mean, everything, including like materials used to make things like memory. It's also very interesting whether there's something unique about AI, which because the demand is infinite and growth is infinite, what you tend to worry about is the margin of companies, which is how efficient it is. Like normally you worry about growth, like can I just, you know, can I just get people to buy this stuff. You don't have to worry about that here. The question is, can you do this in a way that's profitable? And a lot of the efficiencies are actually strictly a physical limitation of hardware. And so even the business model of the AI wave is really putting a lot of stress on the existing systems because they weren't built for AI. They weren't built for those workloads. And I think there's just this, you know, this global observation that we actually need to change the core components to get that efficiency to help drive the growth and to drive the value of the businesses.
Yeah. And how do we know that demand is actually outpacing supply here rather than this being, you know, another hype cycle?
Well, I mean, there are any number of signals today. Firstly, it is that some of the smartest judges of demand are cutting huge purchase orders. I mean, if you look at the hyperscalers, right?
Their capex spend has been exploding. Next year supposedly it's going to reach a trillion dollars collectively across the big hyperscalers. This year it's about 700 billion dollars, right? And if you think about the hyperscalers' position in the industry, they see demand from everywhere, right? They see obviously the frontier labs wanting their compute, they see the AI-native companies, they see the enterprise, they see the US geography, the international geography. So if anybody has visibility, it is them, and they've been jacking up their capex like it's never been seen before, right? So that's a clear, clear sign. And secondly, if you look at the companies that we see on a day-to-day basis, they are all ripping. All the application companies, the growth is insane. The frontier labs, the growth is insane. It's been documented. So I would say on the demand side, the signals have never been clearer that this is not a hype. And to top it all, prices are going up.
Like we've never seen prices go up on chips.
You keep your prices went down. They always go down. Yeah. It always goes down. If you look at the price curve, it went like this and then went back this.
And we know only like 5, 10% of the addressable market is served today.
I mean, the supply, if you look at the supply across the board, it's basically all booked out to 2028.
I mean, it's so bad we've actually seen multi-day auctions for a few thousand GPUs. You know, the other side of that, of course, is demand. And as Raghu said, we've seen the fastest growing companies we've seen in the history of the industry.
But also the unit of work that AI can do, the value of that unit of work keeps increasing. But underneath the covers, the number of tokens that are consumed is going by orders of magnitude, right? If it's one token for — I mean, 100 tokens for chat or an agent, it's thousands of tokens, right? So you got expansion of both sides of demand. One is the unit of work is becoming more and more consumptive of tokens, and then secondly the number of people therefore that are going to benefit — it's not just the developers, it's going to be all knowledge workers and then all of beyond that. So that's what we see.
You said the key components in supply are sold out to 2027, maybe in 2028. What does it mean for an entire industry to be sold out that far?
I don't know this has ever happened before. Do you guys recall? I mean, remember in the internet days when we were doing massive buildout, the majority that was actually being put in the ground was speculative and was dark. Remember the dark fiber? And here basically every GPU that's being created is already pre-sold.
So yeah, we weren't quite there. I mean, there was a lack of bandwidth like in the '98-'99 timeframe, but there wasn't that much real demand for it because there just weren't that many people on the internet. So like it was a two-sided thing, and the companies were all rushing there and needed more bandwidth theoretically, but there weren't the users on the other side to consume it necessarily. And then to really consume a lot of bandwidth, you have to do high bandwidth things like video, which weren't really viable for, you know, a number of reasons that had nothing to do with how much bandwidth was in the data center. So it smelled similar, but it wasn't this. This is like we're flat out and people are reselling GPUs for four times what they bought them for and this kind of thing. Like it's just not. And then, you know, we're also out of power and cooling. And then on top of that, it's really hard to build because there's these incredible political headwinds going into it. So it's really unprecedented in my career that we've had anything like this.
No, I want to give you a quick anecdote. So I was talking to a CFO of a large company, large public company who had historically been very resistant about going into the cloud. So they had a lot of servers and they were doing an inventory check and they realized that the memory in their servers had increased so much it could fund the entire migration to the cloud. So I just feel like we're in a very unusual situation.
Yeah, that's right. We're out of many things: power, cooling, memory, GPUs — like you name it, we're out of it.
Yeah. So the flagship conference for the industry, the one called Hot Chips, is going on at Stanford, and the leading memory vendor said the demand they have today will take them three years of capacity to supply it. It's just today, it's not even future demand.
So in terms of being about everything simultaneously, is it because people just underestimated how good the models would be, how useful they would be, they just couldn't have foreseen the demand?
Well, I don't even think it's that. I mean, this stuff came out of nowhere, right? We're only four years into this. So even if we had a perfect oracle, once it started working, I don't think we could have built the capacity.
We could have built the capacity. There's no way. And we're talking about like chip cycles, which tend to be three to four years. We're talking about breaking ground and building data centers, which is, you know, four to five years. We're talking about connecting, breaking down and building them and having a power source. So like you either have to build your own power or like usually both — you've got to build your own power and have a power source, which is not easy.
Yeah. The ML industry that's used — if it's growing at 20, 30%, it's a great growth rate, right? And it's being connected to an AI software industry that's like triple digits as the base, you know. So you can see the disconnect, right? So it's just wide open.
And so why didn't this fund exist, you know, five years ago or seven years ago? Or why was it not a great category to invest in in the same way Brad?
Well, I would say we're probably — I'd like to think we're just in time, but you know, we probably would have been well suited to have it at least a couple years ago.
I will say you could actually point on basically every epoch to an independent company that came up, right? Clearly the move from mainframe to client-server, we saw a bunch of companies come up. The move to the internet, we got Cisco and Juniper. Even in the mega data centers, which by the way was largely driven by the incumbent cloud providers verticalizing, you saw the arising of Arista. So there has been the ability to invest in, you know, silicon and hardware, but it's been relatively minor because the change has been relatively minor — like one chip company, one switch company — where here everything is. And so I agree with Ben, we're probably, you know, we probably could have started a little bit earlier, but the amount of change is so high now that it's just an obvious thing.
And the other thing is the demand for intelligence is so vertical with really no end in sight. I mean, because every company that's adopted it is growing very fast in its usage, and then most companies haven't adopted it to a high degree, and then consumers are just getting started. And so it's going to probably — the demand for tokens is probably going to grow close to 1,000% a year, which you cannot grow supply that fast. Like we're not — like the amount of just work we're going to have to do across the board to get to the point where we can grow infra at that kind of rate is pretty vast. So I think there's a lot of investing opportunity on the way. And by the way, the other thing is like all the architectures of the hardware systems were built for a whole different era of computing. And so more than just we need more capacity, we need capacity to build — there's lots of opportunities to build different kinds of infrastructure.
Yeah, they're all reaching their physics limits for what they were designed, right? Like what Ben was talking about, copper and so on and so forth. And you could go across every one of these categories and you could find, okay, this is the limit of this type of technology. So now you got to get some technical breakthroughs to get to the next one.
Yeah. I want to dive deeper on the demand side for a second. As we've moved from chatbots to reasoning to agents to multi-agents, each step has multiplied the number of tokens a single task takes up by orders of increasing orders.
Yeah, nobody likes to use AI more than AI.
So why does that keep happening instead of a leveling off? Do you just see that happening, you know, indefinitely, just continuing to?
Well, so there's a couple. The first one is for sure right now if you look at the way we're achieving scaling, the way we're doing it is through a lot of inference, so through a lot of tokens, right? If you think about what RL is, you know, it's a lot of inference. If you think about chain of thought, it's a lot of inference. You think long-running agents, of course, it's a lot of inference. And so that's just basically been one of the approaches that we've been using to scaling. I think if you want to step back and say kind of what is the macro trend here, it used to be when you built something, it was an engineering problem and you throw a bunch of engineers at it, and that doesn't scale, and that would have a natural law of engineering physics, which is where the mythical man-month came from. And here it feels like it really is a resource limitation. So whether it's tokens or not, we're pouring a ton of money into systems, and then those systems are producing a result. And right now we're bottlenecked on those systems' ability to actually match the resources we're pouring into them. And so I think like tokens right now is probably where we are on the scaling curve, but we don't have a natural regulator like engineering like we did before. So I think we should expect this to continue, and we have to build a supply to support it.
Yeah. Like the simple way to think about it is any problem that you have can be solved with enough infrastructure — basically GPUs and power and money. And so until we run out of problems, we're not going to run out of demand. And that's the challenge.
I mean, AI's answer to getting better and better is to use more AI, right? Inference is one basic building block that it keeps using over and over and over again, and so that's why these tokens multiply at each step.
Yeah, even the autocatalytic effect — so even the idea of using AI to create more AI, like creating a GPU kernel, of course, is just using more AI as part of the process. So again, one way that we think about it is in the past money would come in, you have an engineering problem, we know that it takes two years, normally fails, you know, it's a national governor, and then you get the product on the other end. This — there's nothing between the money going in and then the hardware, you know, creating intelligence. And so now we're just limited by our ability to create supply. It's a very, very different — be more GPUs and our sol.
Yeah, that's right. So that's the cycle. As long as you have the money, the GPUs, and the data, you know, for the foreseeable future, you'll be able to scale these things.
And it's fascinating because, you know, over the last decade, it feels like there were so many, you know, people and the pervasive sentiment was there's too much money going to startups. We're overfunding these startups. There's too much money in venture capital. Say more, Ben, about what that means because there used to be this sort of skepticism that the more money you put into the industry, that, you know, there would be bigger outcomes. And now, you know, we were saying at the offsite that there's to some degree the market is as big as we collectively contribute to it.
Yeah. So this is — look, the one thing we all knew in the startup world is that if I have a two-year lead on you and you try and catch me by hiring a thousand engineers, you're going to wreck your company. Like, that never works. It's a mythical man-month. Nine women can't have a baby in a month. That's it. Like, that never works. Okay, now that works. But it's not hiring a hundred thousand engineers. It's taking $3 billion and like lighting up a magnificent cluster, and then all of a sudden, you know, whatever — Grok can come out of nowhere and like, oh, all of a sudden it's real, or Kimi, or what have you. It's just like these leads — you can throw money at the problem, and you can throw money at almost any problem, and that works. And so that is just completely different than anything we've ever lived through. So we're, by the way, we're all psychologically adjusting to this.
The ChatGPT app has a billion weekly actives. There's about 30 million developers who are using, you know, a relatively big portion of compute demands. How do we think about compute demands needs now and in the future in light of what people are actually doing with AI?
That's the progression, right? So ChatGPT was a casual app, and it's coding for professionals right now. Using coding, you have now built amazing tools for knowledge workers, so that's the next frontier. And now there are over a billion knowledge workers in the line, right? And with that, it's a long ways to go for that demand. And by the way, the work that they do — all this workflow and automation and so on — and then you get to the back office, which is all the agents. So progressively, each of these things unlocks, I would say, order of magnitude more demand. I mean, just at the start of this — well, and now you have Grokbot, which is kind of, you know, what happened with coding is kind of happening with all use of computer via it. And so we're in a whole another wave of demand, and most certainly there's going to be more to come. So it does seem quite unlimited at the moment, and we haven't even gotten into embodied AI or robots, which are going to be another source of demand.
Martin is the expert, but my understanding is that part uses computer use, which is just like a human being sitting inside the computer typing away.
I literally used it over the weekend to update my credit card with a bunch of services that I'd been like lazy to do and cancel a bunch of subscriptions. I mean, this is not coding or whatever. This is true computer use.
All of a sudden, you're creating like half a billion knowledge workers, except they're all sitting inside of the computer doing what?
I do think that Marc Andreessen is right. It's like the right analog here is like the steam engine or electricity in the following way. Like we've introduced this new thing that you can turn to work, and there are some very obvious applications now, but there's probably 30, 40 years of throwing computer at problems — anything with a clear reward signal. And we're just starting — like we've got language and code, that's it, and just starting computer use. But like what else are we looking at? We're looking at in terms of science, materials, biology. I mean, of course, creativity is a massive use. And so listen, we're at the very, very early part of a very long journey, and we've removed this key bottleneck, which is, you know, traditional software engineering. Now, of course, you know, bottlenecks will move, and there'll be kind of more complexity elsewhere. But I think we're at a very early in a very long run of throwing computer at problems. So let's expect, you know, this compute need to persist for decades.
But because we mentioned it, Marty, talk about Grokbot. Because we were talking at the offsite about how, you know, what struck you about it. Obviously we're involved in every possible way you could be involved, but what did you find so interesting about it?
