The a16z Show - The Infrastructure Behind the Machine Age

Episode Date: August 28, 2026

Ben Horowitz, Martin Casado, Raghu Raghuram, and Erik Torenberg discuss the launch of a16z's new Machine Age Fund and the infrastructure buildout behind AI, from chips, memory, and networking to power, cooling, and data centers. Why a dedicated fund now? The group argues that the bottleneck in AI is increasingly shifting from the models themselves to everything beneath them. Hyperscaler CapEx is surging, critical components are booked years in advance, and each new generation of reasoning and agents requires dramatically more compute. They unpack why this cycle looks different from previous infrastructure booms and how AI is turning problems once constrained by engineering into problems that can increasingly be attacked with capital and compute. They also explore where the next generation of infrastructure companies could emerge, why founders are returning to hard technical problems across hardware and systems, and what it will take to rebuild the computing stack for the Machine Age.   Resources: Read more about the Machine Age Fund : https://www.a16z.news/p/the-machine-age-fund Follow Ben Horowitz on X: https://x.com/bhorowitz Follow Raghu Raghuram on X: https://x.com/RaghuRaghuram Follow Martin Casado on X: https://x.com/martin_casado Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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, we're talking about the servers and the storage and the network. Here it goes all the way down to the mines of copper mine. That's how wide spread 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 bottle-inite, the system's ability to actually match the resource for pouring into them. The leading memory realm said the demand they have today will take them three years of capacity in supply. If this fund does what we think it will go. How do we see the world in five to ten years? America wins in the infrastructure day.

And that would be awesome. Today, A16Z is announcing the Machine Age Fund, a new fund dedicated to the infrastructure powering the next era of AI. I'm joined by Ben Horowitz, Ragul-Ragel-Ragelam, and Martine Casado to explain why we're launching it now, and why we believe the next major bottleneck in AI isn't necessarily the model. It's everything underneath it. Chips, memory, networking, power, cooling, and data centers

are all being pushed beyond what they were originally designed to handle. At the same time, AI is changing an old rule of technology. Throwing money at an engineering problem didn't necessarily make it move faster. Increasingly, capital conditions, be converted directly into compute and compute into more capable intelligence. We unpack what that shift means, where new infrastructure companies can break through, and why a new generation of founders is returning to some of the hardest problems in computing. Ben, Martine, Ragu, welcome.

Thank you. All right, thank you. I want to start with a mark 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. 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. 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 particularly for the kind of hardware aspects of this new era, we needed a new approach. Yeah, I would agree. Normally when we, at least in the computing, when we talk about the infrastructure,

we're talking about the servers and the storage of the network. Here it goes all the way down to the mine's copper mine. That's how widespread this thing is going to be. And that's number one. On number two, I think what we have seen over the last three years has the steady increase of the capabilities of the models, where the model is no longer at the bottom line. 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. The only thing I'd add very quickly is 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 harder 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 maybe 5% of the deals from top founders would come in, would be harder before.

Now it's the north of 20% or 30% right now. So it's like the founder community, which tends to be much smarter than the BC community, has identified this as a very active area for innovation, and they're responding. I think 5% is probably generous. Yeah, it was very low. It was very low.

Yeah, 3%, yeah. And it explains some of the macro conditions that have led to this change in terms of the surplus of fibers pursuing these ideas. Like, what are they seeing? That's an idea. Well, I mean, the obvious is 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. 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. Can I just get people to buy this stuff? You don't have to worry about that here. The question, 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 AIAW 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 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. And how did we know that demand is actually outpacing supply here rather than this being

another hype cycle? Well, I mean, yeah, there are any number of cities. Firstly, it is that some of the smallest judges of demand are carrying huge purchasers. I mean, if you look at the hyperscale, actually, right? Yeah. Their cap-expand 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, right? And if you think about the hyperscalellular's position in the industry, they see demand from everywhere, right? They see, obviously, the frontier labs, warning their compute. They see the AI-dative companies. They see the enterprise. They see the U.S. geography, the international geography, So if anybody has visibility, it is down.

And they're being 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 clear, that this is not a hypertz.

And prices are going up. Like, we've never seen prices go like up on chips. The GPU prices went down. They always go down. It always goes down. If you look at the price curve, it went like this and then we back at this. And we know only like 5, 10% of that is the market to stop today. I mean, 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. The other side of that, of course, is demand, and as regular as we've seen, the fastest-growing companies you'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 colors, the number of tokens that are consumed is going by orders of magnitude, right? If it's 100 tokens for chat or agent, it's thousands of tokens, right? So you've got expansion on both sides of demand. One is the unit of work is becoming more and more consumptive of Tompkins. And then secondly, the number of people 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 20208. 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 Mass. build out, the majority that was actually being put in the ground was speculative and was dark.

Remember the dark fiber in here. Basically, every GPU that's being created is already pre-sold. 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 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 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 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. I want to give me a quick anecdote. So I was talking to a CFO of a large company, a 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. That's right.

We're out of many things. Power, cooling, memory, GPUs, like, you name it. We're out of it. So the flagship conference for the industry is the one called hot chips is going on in Stanford. And the leading memory wrestler said the demand they have today will take them three years of capacity

to supply. 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 models would be, how useful they would be, they just couldn't have foreseen the demand?

Well, I don't even think it's dead. 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've got to build the capacity. We couldn't build 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 four to five years. And connecting, breaking down and building them and having power source. So, like, either have to build your own power or, like, usually both. You've got to build your own power and have power source, which is not easy.

Yeah, you have a industry that'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 typical just as the base. So you can see the disconnect, right? So it's just widening. 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 the same way, bra? Well, I would say we're probably I'd like to think for it just in time, but you know, we probably would have been well-suited to have at least a couple years ago. I mean, I will say you could actually point

on basically every epoch to an independent company that came up, clearly the move from the mainframe to the client server. We saw a bunch of companies come up. They moved to the internet. This one, we've 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 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 to do.

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 a thousand percent a year. which you cannot grow supply that fat.

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, like, infrared 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 are reaching the 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 can find, okay, this is the limit of this type of technology. So now you've got to get some technical breakthroughs to get to the next one. Yeah. I want to dive deep around the advanced 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 increasing orders of...

Yeah, nobody likes to use AI more than AI. What does that keep happening instead of a leveling off? And do you just see that happening indefinitely, just continuing to? Well, so there's a couple of answers that. The first one is, for sure right now, if you look at like the way we're achieving scaling, the way we're doing it is through a lot of inference. So through a lot of token, 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 about 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. You'd throw a bunch of engineers out of that doesn't scale, and that would have a natural law of engineering physics,

which is what 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 resource for pouring into them. And so I think tokens right now is probably where we're 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 similar way to think about it is any problem that you have can be solved with enough infrastructure 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. AI's answer to getting better and better is to use more of the eye, 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. Yeah, even the auto-catalytic. 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. There's nothing between the money going in and then the hardware creating intelligence. And so now we're just limited by our ability. to create supply. It's a very, very different dynamic. Yeah, it's giving more GPUs and the solid is gone.

Yeah, that's right. As long as you have the money, the DPUs 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's so many, you know, people in 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 it more 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 startup world is that if I have a two-year lead on you and you try and cast 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. Like, that's it. Like, that never works. Okay, now that works. But it's not hiring 100,000 engineers.

It's taking $3 billion and, like, lighting up a magnificent cluster. And then all the sun, you know, whatever, Grock can come out of nowhere and like, oh, all of a sun is real or a 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 by the way, we're all psychologically adjusting to this. The chat GPT app has a billion weekly active. There's about 30 million developers who are using relatively a big portion of compute demands. How do we think about compute demand needs now and in the future in light of what people are actually doing with air? That's the progression, right? So, Chadrick, we use a casual act, and it's coding for professionals, right?

Now, using coding, you're not built amazing tools for knowledge workers. So that's the next frontier. And now there are over a billion knowledge workers in Hawaii, right? And with that, it's a lot of ways to go for that demand. And by the way, the work that they do, all this work grow in 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, an order of magnitude, more to that. And we just have the start of this. Well, and now you have Grockbot, which is kind of, you know, what happened with coding is kind of happening with all use of computer via Grockbot. and so we're in a whole other 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 a Glockbuckh uses computer use, which is just like, you know, being sitting inside the computer type of way. 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 can't have, like, also believe knowledge work cases, except they're all sitting inside of the computer. Doing what. I do think that Mark Andreessen is right. It's like the right analog here is like the steam engineer 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 problems. I didn't think with the 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 on 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 very early in a very long run of throwing computer problems. So let's expect, you know, this compute need to persist for decades. But because we mentioned it, Marty, talk about Grogbot because we're 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?

So I think we've, like, I think we've as an industry gone through kind of multiple realizations for how AI enters our lives, right? And very early on, we're like, okay, well, you add AI to a product and it's like a whatever, it's like a search bar. And then you kind of, you know, you do chat with it and the chats back. Because that's kind of the traditional way to do it. And then OpenClock kind of showed up, and that was earlier in the year. And with OpenCla, I say, okay, well, maybe like it just being like Google but better,

maybe that's not the full embodiment of it. How about we'll have it be a standalone thing, but it'll be an extension of you, and it'll share your keys and it'll know your password. and it'll just kind of do stuff that you would do, right? So it's kind of an extension of you, but it's more like a human, an extension of you. And then when I think Grogbach got really right is, no, how about it is just actually an employee?

So now you have this thing that's an entity. And it doesn't have like special access to your keys or whatever. It has its own computer and it has its own browser. And because these are the smartest models in the world, it can do whatever an employee can do. And it's kind of interesting because now, actually, if I want something done, my first thing I think is like, well, can Grog Grog do it for it?

me. And often the answer is yes, even if it's something you wouldn't expect it. So the obvious ones are like, whatever. It'll like manage my calendar, it'll like book a meeting. But there's also non-obvious ones as well. Like so, for example, I'll have it read through my email and do triage. And I don't tell it how to do that, but it will know to check with me before actually doing the triage. So like these things are sophisticated enough that you can give it relatively high-level task and it'll do kind of, you know, like sophisticated things as a result. Ben, I know you're thinking a lot, a lot about this and how this works in the organization. You think a lot about culture, of course.

What are your thoughts, you? Well, I mean, I think if you just look at us, you know, it's like having a new kind of employee and there's going to be a lot of them. And we have to just like, you know, with our, we spent many, many, many years figuring out how to work with our kind of regular human employees. And now we've got these other kinds of employees. And, you know, there is a learning curve with them. So they can burn a lot of tokens and spend a lot of money and get nothing productive done.

They can forget stuff. They can make stuff up. You know, they can have good behavior. They can have bad behavior. Like humans. They can create security problems. So, like, there's all those aspects to it, but, like, they can also be, like, super-duper productive.

And so I think figuring out how to integrate them in, have them work nicely with the people that they're working with, the actual humans, is all something that we're learning how to do. I mean, I don't want to sit up here and say, I've cracked the code. We've got this marvelous loop, and the whole firm is just completely automated now, and I'm going to slowly get rid of all the humans

because I can. Like, that's not at all where we are. We're much more going like, okay, how do we make all our human superhuman without, like, wrecking the place because the bots get out of control. Yeah, and it's interesting because we've run, we've tried a couple of different ways.

It's how best to get agents into the system, if you will. And eventually, it was Martin's insight, just treat them as people and get it done. And that's what we're doing. That turned out to be the most durable way of getting this thing going inside of an organization. I want to go back to the supply side and go deeper into the bottlenecks. We were talking about how, you know, in terms of the data centers, the chip architecture, system software, facilities themselves, that none of them were designed with AI in mind.

What would it look like for them to be designed with AI? Like what is sort of the mental model for thinking about what that could mean? Yeah. And so, I mean, if you start with the statement that you just say, hey, original model of infrastructure on any of these models has to change. You can go category by calling and see varied breaks, right? And then you start unlocking the modern and sending it's any tone of these things. So eventually you have to get to a session where if you look at what an inference engine does, right? It takes up a lot of memory.

It generates new tokens along with the compute. And so you can just think about how do I optimize all this. What does the memory need to be? What does the computer need to be? How do they need to talk to each other? How much power does each of them need? And if they need all of these power, how do you call each of these, right? And then how do you put the collections of these things together?

That is the exercise that's our domain in the industry right now with a lot of the founders. So they're breaking down the problem into its fundamental compotes and saying, what is the exact nature of the compute that's getting done? Okay, how's going to be matrix multiplications?

How do I optimize when the computer and that kind of a scenario? And they all need memory progressively to generate these tokens. What is the best way of hierarchically arranging

this memory, right? And then how does the power consume then you've got to connect it together. What are the ways of connecting it on the same chip but it caused chips and it caused data centers? How much power?

How does each of this data transmission take? So you have to progressively break it all down and rebuild it from these fundamental building blocks. And that's what we see in the way and what's where we see that generally. Let me need an interesting mental model to think about how the landscapes change. So today to build a frontier model costs, let's say,

three to five billion dollars, right? So, and that's to train it. And so the inference has to pay back at least that, of course, right? you know, in order for any of this stuff to be viable, let's say two times that. So let's say that now infrared has to make $10 billion. So if you can save 20% of efficiency on that, that's $2 billion.

And you can easily build an ASIC for $2 billion, right? So we've actually gotten to this interesting point in the industry where it actually makes sense to build an ASIC per model just because the amount of capital investment in that model. And then unlike traditional software, traditional software has a lot of state and a lot of, you know, it's very dynamic. These models are fixed.

The model weights are fixed. And so we don't know if the world goes to per model A6, but it gives you a great mental model of how you would evolve the architecture to be far more bespoke for these massive capital investments we're doing. I don't think in the history of the industry, we've ever created a digital artifact with something like $5 billion that went directly

into that artifact. And so, you know, like this, I think, is going to put the greatest demands on hardware that we've ever seen. Well, to that end, rack power requirements are moving from roughly 5 to 10 kilowatts to 100 to 50 kilowatts. Compute density is climbing something like 70X. Cooling is moving from air to liquid as a requirement. What are the investment opportunities as a result of this? Well, first of all, when you get to that level of power per rack, AC power doesn't work anymore. So like that's a pretty wild thing.

So now you're into DC power, which, by the way, also requires its own cooling. And is like, by the way, super fucking dangerous, which is kind of ironic because this was Edison promoted DC power by claiming how dangerous AC power wasn't demonstrating it by like electrocuting animals. things. The horse, yeah. The horse, yeah. So, but he was right, but around his own kind of power, which is extremely powerful. It's a good news. So, you know, just starting with power, yeah, that's going to be, like, very, very different.

I think with cooling, so this gets into, so, yes, we're going air cooling to liquid cooling. I think we're already at liquid cooling for any state-of-the-art data center. like that's already kind of a done thing. But it gets into, okay, you know, given the political environment and so forth, like liquid cooling isn't enough. It's got to be eco-friendly liquid cooling. And, you know, kind of D.C. power is not enough. It's got to be power that contributes to the power of society, not takes away from it.

And so you have data centers who have been behaving, badly, small percentage actually, probably 10%, wasting a lot of water. You know, not as much as pistachios or almonds and so forth as people demonstrate on the internet, but like they could be a lot more efficient with that. And then there are ones that, you know, kind of are parasites of power and don't contribute power back. I think all that's going to end. It's going to have to end just because like that, we've kind of gone through a one-way door on that. So that requires, like, a level of engineering that, you know, many haven't invested in yet, so that's coming.

And then, you know, like, if racks are that dense, there are other things that, like the way the floors are designed, have to support that kind of weight, you know, that kind of thing, is actually for real. And then, you know, I think that there's, you know, you just need a lot of everything. And so there's going to be, you know, also the things are really loud.

So you have to build the data center with thicker walls or you're going to disturb the piece in the neighborhood, which is not going to be acceptable. Like I don't think any state's going to allow that. And so a lot of the ways people have architected and designed the buildings themselves are already completely obsolete. Like once we get to Feynman,

a much smaller percentage of the data centers that we have today work. Everybody talks about memory prices, but one of the fastest areas that prices is increasing is reinforced concrete for their sense. The other thing that happens

when these data centers are sending 800 worlds to the WAC is it's become so dangerous, number one. But secondly, we don't have enough electrical contractors that have the expertise to deal with 800 volts inside the data center. Because this is high voltage. Only 2% of electrical electricians in the U.S. have been certified on DC power. So like that gives you an idea.

Now, META's got a whole program to train people up and so forth, which is great. It's like a new job court where they train people for free to do this job. But, you know, it's funny. AI is taking all the jobs. AI is going to create a lot of new electricians. Yeah, I think we're just doing something in space, too. The guys that own the big cloud data centers, they all are furiously experimenting with robots, right,

to do the work of assembling or putting servers into the data center, etc. And so you will see that increasing as a result of the evolution. an AI. By the way, to be clear on the actual fund that we're raising, our focus is on computer science infrastructure. So anything a model runs on, that's computer science, right? So think, you know, chips network, interconnect, storage, all the way down probably to the electricity. Yeah. And say more about the robotics arm in terms of what we'll be doing first, maybe American dynamism or how to think about that. Yeah, for sure. So, you know, again, we think that, platform that AI will run on.

Like, what are the great breakthroughs that AI does is it allows computers to interact with the physical world, right? It can see, it can hear, it can talk, right? And this means new platforms, right? And the simplest way, people say edge device, but that doesn't really mean anything, right? I mean, it could be a mobile device, it could be a CDN, it could be a laptop, but it also could be an embodied, you know, device that goes around. And so, again, we, we, as, you know, as infrastructure.

structure-focused investors don't do heavy regulated industries or more verticalized industries, but any sort of computer science platform that's going to push AI further out, we're quite interested in. Yeah. Going back to the data centers, by 2028, new data centers are going to need something like 44 gigawatts of additional power against maybe 25 gigawatts of expected grid additions. Hold on. Hold on.

We use that word gigawatt. No. It's like, it's like, oh, we'll have a hundred gigawatt. Martin, what's a gigawad? I mean, how big is it? It's multiple football fields. I mean, it's massive. It's 50,000, 50,000 people.

What do you mean? What is it power? Like, the equivalent is like 50,000 houses? City. 50,000, 50,000 homes. 50,000 homes. It's like, I grew up, I grew up in Flagstaff, Arizona, which is a town of 40 to 60,000 people,

depending on the universities. We have less than a gigawatt of power consumption. I mean, this is true. Basically, light up and air condition your entire town for a gigawatt. Yeah, I mean, this is... He's just throwing them around. No, but, by the way, everybody talks about the gigawatt. There's very few gigawatt data centers that are actually up.

I mean, we've got a long way to go. To pretend, why can't utilities and hypers just build faster? Oh, there's so many things. Well, there's... First of all, right now, you need humans to build them, so there's just like the regular construction. But much more than that, you need... permits, you need access to power that you can plug in. So you're doing a combination of you've got to get access to power,

which is a massive kind of regulatory bidding struggle. There's very limited kind of amounts and things you can tap into in terms of natural gas, power grids, what have you. But then you also have to build your own power. and guess what we've got shortages of transformers and turbines and everything that goes into that. So it's just, you know, like you've got to get all that stuff. It's not this is not a software problem. It's not just like a bunch of engineers, can't you like work weekends and that type of stuff.

Like this is not that that works anyway, but there are real bottlenecks in this. And these lead times are not that easy to. to compress. And look, we have the best minds in the world trying to figure out how to compress them. But it's not easy. It's not easy. And the demand is not slowing down. So we're already behind. The demand is growing, you know, 10x a year right now. And the supply just can't grow that fast. By the way, it is so bad that right now, if we have new companies going from GPUs, is often in Mexico or Australia or another country just because it is so difficult in the United States.

Yeah, we're creating huge jobs, both job and long-term economic opportunity in other countries by banning data centers here. I think, look, the right answer would be to set a standard where a data center contributes back to the community. Like that power gets better, there's no noise, there's no water, issue and it's adding jobs. Like, that ought to be the standard. And then everybody ought to be

just held to that center. And by the way, like, there are data centers that do that now. Like, that's not a, you know, like a futuristic dream or something. Rates, energy rates have gone down, like every year they're there. And the reason is they provide their own power. They give power to the state during the day. And then at night, they borrow power. from the state when the state doesn't need it because the way power plants work is you're always generating peak capacity and since the data center has steady capacity

during day and night and the city goes way up in the day and way down at night, that's a symbiotic relationship. Zooming out, why do we think the, you know, we were batting around the name for a little bit. Why do we think machine age is a compelling term for what we're doing here? Well, listen, let me take a cry. So the first one is, I think Ben's absolutely right.

Artificial intelligence was the wrong word. Like, we shouldn't have called it. It's machine intelligence. You'll say more about that. Why is that? Because it's not how humans think necessarily, right? I mean, it is a cache of how humans thought is a collection of humans' thoughts. But, like, today we don't know how to take an AI with no knowledge and put it out in the world and have it reconstruct language, right?

Like, that's not what we've done, right? We've built something that can learn off of everything we've already learned and then use that in a productive way. And, listen, AI is a general term that goes back 70 years in computer science formally that applies to many different things. And, of course, it's got a lot of baggage either from science fiction or from, you know, Nick Bosstrom, who wrote about it or whatever. And so the first one is just an acknowledgement like this really is machine intelligence.

And then you want to emphasize the machine part of it. I mean, there's kind of this deep irony. And this is from the software's eating the world people that you've really come to a place where you pour money into something and then you're limited by the actual machines below it. And so I think it is kind of a nod to like the hardware component is so significant in this wave

and we want to acknowledge that. Yeah, I think that's what's going to create the next breakthroughs. In analogies is the qualitative machines are not need. So that's basically a reason to be named. It's also a cool name. It sounds good. It's futuristic. Yeah.

Given how much has been spent on AI infrastructure to date and how CAPEX intensive these businesses can be, are we past the point where new companies can break in at sort of material levels? You know, why not incumbents like Nvidia, Corrie, et cetera, just take the lion's share of these markets? Well, there's no question about it, right? But to our discussion earlier, when you get, You need fundamentally new innovations to keep the growth continuing or the pace of improving continuing, whether it's tokens per second per dollar or tokens per watt, tokens per rack, right, or power.

You take any metric. If you want to have a 10x on those metrics, you got to have new innovation. A new innovation traditionally comes from brilliant founders thinking about solving the problem from first principles and a different. different way, right? And that's what's needed here for the next jump in innovation. I mean, this is the law of markets, right? I mean, let's assume that the existing silicon incumbents are multi-trillion dollars in market cap, which is absolutely the case. Even 5% of that is a massive private company, massive private company, right? We're talking annual. And you could say, well, but Invidio could do that. They could. But why would they

if they're focused on things that are in the 90%, which is also driving the same amount of growth. And you always ask these questions. We ask these questions during the cloud days, right? Like, well, we wouldn't Amazon do this? You would have asked these questions during the Microsoft days. Why wouldn't Microsoft do this? There's a very natural law of markets.

Once you get to a certain scale, there's tremendous opportunity for innovation at the margins. Yeah, there's a funny quote from our partner, Alex Rampel. He had this startup called Trial Pay. He was trying to sell it or sell that. It's his services to meta, then Facebook. And Dan Rose, who was head of Corp Dev at the time, said, Alex, that's great.

It sounds like you can collect a lot of silver brooks, but I'm like, I have so many gold bricks. I can't even pick them all up. So the last thing I'm doing is looking at a silver brick, and I think Nvidia's in that position. 100%. Yeah. We were talking about as a basis of model providers

that if you're in the sweet spot of what Open AI Aerithropic can do, you know, one of their main sort of interest areas, that might be a tough place to be, but anything outside of those, maybe, you know, three to five areas might, you know. As markets expand, they fragment, right? And it happens all the time. And remember, in the early days of Ford,

there was a 1913, there was the Rouge River plant. Literally, this, you know, this was like, made cars, like in went like water, coal, and rubber trees, and out came cars. By the way, he bought a whole rubber tree plantation. plantation in the Amazon jungle. And there's a great book called Fordlandia. Because he wanted to own like the complete vertical thing

where he created this city called Fordlandia in the Amazon jungle, which had like, it was all Americanized bandstands and ice cream and all this kind of stuff. And it actually worked for a while until he made people like show up to things on time. And then they were like, screw this, get the fuck out of it. So now, if you look at the car industry, of course, there's multiple levels of supplier,

and there's a bunch of companies. And this always happens. So, you know, as markets expand, they fragment. And then once that growth slows down, they tend to consolidate. The consolidation can either be acquisition, or it can be like new challengers rise up. And that is the, you know, everlasting cycle of private markets.

Yeah, because the use cases are multiplying. And there's no way, like if you're the biggest company, you can get to the biggest use cases, but there's so many use cases. And all, as Martin was saying, very valuable use cases, it's just very hard to get to in a great way. Yeah, you would influence history

on simple architecture, right? No longer, it's just like, it's so complex now. It's inevitable that you can optimize things in a different way. By the way, it's a very interesting thing. People don't, often don't understand that,

like, margins kind of fell out of the standard way of doing the technology with software. Right? Like it wasn't really a technology problem. Like once you got the business working, you tended to have pretty good margins because that's just kind of how software, certainly when you shipped it, but even as a service. And that's not necessarily the case with AI. So we may actually be entering an era where the optimization in the hardware is absolutely meaningful to the upside of the business in a way that we haven't seen in the past. So there's a lot of opportunity here. Let's get deeper in talking about the types of companies we'll be investing in. Maybe we could start by either illustrating the subsectors or if we can talk about a few or a couple investments that we've made. I know there's some that haven't been announced yet,

but Robbie, do you want to take us down? Yeah, I mean, the subsectors, as we've been talking, how long is every one of these categories, right? The obvious ones are compute chips, but these days, it's not enough to build a chip. You need to build a full system. And then therefore, what goes into the system? There's potentially memory innovation. There is potentially networking innovation. There is potentially power tools.

And so on and so forth. So each of these categories are categories where you can see public company-style companies emerging. And those are all the things that we are looking into. And then once you put it all together, there's a load of software around it to automate all of these things, to manage these fleets, and so on and so forth. So that is a other important area. So these things keep building on each other, but every one of these categories is important. Talk about what's different about these kinds of companies

from the usual company. I mean, one thing you can tell from the companies we announced is their first rounds have been massive, you know, hundreds of millions. Is it a different kind of founder? What else is different as we think about just the practice of, you know, building and investing these kinds of businesses

relative to our traditional software. Well, I think the big thing is, you hit on one of the big things, which says a lot of money goes in, before they get to a product. And that's just kind of the nature of it. Now, that's true on big models too, but I would say that's a little more of a known path,

whereas this has got a little more risk and a little more money than some of the other things that we've done. And, you know, look, a lot of the chip founders are here from the past. You know, like the guys who know how to make memory, they're not young. So it's, you know, that part is different too. But it's kind of exciting, you know. Yeah, the other thing about these founders, they all got to be systems founders.

So what I mean by that is it can't just be a researcher or a great computer scientist, right? You got to be able to architect and design the chip or the system, whatever it is. is that you got to think about how is this thing when you actually had manufactured, right? Who's going to be supplying this? And a whole bunch of these downstream things, which normally if you're building software, you don't have to think about all of these things.

So to really the best founders, of course, Jensen is the Michael Jordan with this, right? They think the entire ecosystem right from the get-go before they start designing the chain, right? because of the nature of the bottlenecks and all these things that have to come together. So that's a big characteristic but is different.

There's two environmental factors that are important to you. The first one is the labs are so desperate that they will engage with startups. And so, like, we actually have quite a bit of signal early on because the labs are inking deals with companies before they actually have hardware available. And that's a big, big shift than, you know, five years ago, right? Like, you just didn't go and, you know,

sell your kind of janky hardware thing to Google or whatever. So that's a shift. The second one is the capital availability has loosened up a lot. I think there's general consensus that it is the time to reshape this stuff. And so follow on rounds, there's a lot of capital available, which, you know, of course, you want to be investing in two areas where there's capital available. And so the atmospherics are also just different. Patrick Coulson, you know, remarked a few years ago, said, hey, it feels like there's

less younger founders today in the way that it was suck, you know, in college, building next Facebook or Gates, you know, in the same way with Microsoft. And, of course, you know, the Michael Trulls of the world. There's still, you know, some young founders building iconic companies, but it does seem, you know, to your point, that there's more older founders building these companies or less 20-year-olds. I'm curious if you resonates and why. Well, I think it's Raghu's point that if you're building something that has like a very

complicated supply chain, has to manufacture things, and is technically complicated, that, you know, some experience helps. And, you know, if you look at Elon or Travis Kalanick, there are companies when they were young were software companies. It wasn't until they got like a lot, even those guys, the best guys, needed some experience in building a company's, building technology, and so forth to kind of graduate to the much more kind of complicated or I would say elaborate domains. There's just much more,

there are many more moving parts in these things. And so, look, when you're learning how to build a company, it's hard enough if you completely understand the product. If you don't completely understand the product and have to learn it while you build a company, that's just such a steep learning curve for a brand new entrepreneur. So I think that what we're saying is you see Michael, on the one hand, who is a very young guy, brilliant, but what he built was kind of a pure software AI thing.

And then on the other end, you have like an Elon or a Travis who's got enough experience. I think Michael could probably do that, you know, 10 years from now. But today that would have been hard. And it's important to remember, like, it's been defocused by the entire industry in academia for the last 20 years, right? It just hasn't been the same opportunity. Like, it's been there, but, like, it's never been a growth area. The growth areas have been, you know, software, networking, things like that. And so I also think we just have a positive of people coming out of the universities or having experience at large companies that have done this.

I mean, they're just not that many. Like, you don't go intern and, like, build a chip. But a lot of that's changing now. like, listen, we're going to create a whole generation of founders that come from these new companies that will know how to do this, and they'll be hired in much more junior. I'd say actually one of the greatest legacies of Elon towards this is, of course, he's created these great companies, but the amount of entrepreneurs that have come out of SpaceX that are changing the entire industrial complex, maybe even greater legacy than the companies themselves,

and I think we're going to see the same thing for computer science and hardware. As a matter of fact, one of our investments isched was started by two founders in their committees, but if you go and walk from their offices, you see the experience people as well. So it's ideal combination here. Yeah, yeah, it doesn't necessarily have to be the founder with experience, but that founder better be able to tap into that experience in a real way. Yeah, yeah, yeah, well, and then be able to work with them and they have to be good and all these kinds of things. It's complicated.

Speaking of experience, this is a big new fund we're launching and there's no new GPs. We're sort of collecting. It's because you guys have a lot of experience in the rest of the group, you know, in this field that has been kind of latent and dormant.

Yeah. Well, it's kind of funny. I think we almost had to be warned against it almost just because like our backgrounds are from. It's hard. And I think the reason that we needed a reminder is because all of this has been so much in our careers, systems and hard were kind of drawn to that.

And so listen, we've been clearly. invested in Harbor other years, right? We're in SpaceX. We're in Addera. These are very early checks. We're in astronauts. We're in Waymo.

You know, so even early on, we did a number of those investments. But, like, you know, this is because it's so much in our DNA. And so I don't think this is necessary to increase the deeper competency is just for us. If this fund does what we think it will do, how do we see the world changing or look like in five to ten years?

Well, you know, hopefully America wins in the infrastructure day. And we have lots of like super eco-friendly, efficient data centers out there and lots and lots of an abundance of chips and abundance of memory and abundance of power. And, you know, that would be awesome. And I think it goes back to like we really think America is a special place. And we're reporting not only to everybody here, but anybody in the world who wants to kind of make a contribution and do something bigger than themselves. It's kind of the best place to come with nothing and do something profound. So we'd like to keep that going.

And I think that doesn't continue to go if we lose our lead in technology. I think we'll be in another era and there'll be another country. And maybe they have a different set of values. around now. Thanks for seeing me. It was fun. Martine, Ben, Bargou. Thank you. Thank you. Thank you.

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