"The Cognitive Revolution"
00:43:09 · Prakash Narayanan, Nathan Labenz
[00:43:09] S1: among, [00:43:09] S0: you know, people who care about it or, you know, who are, just conservative enough that they wanna make sure they're absolutely on the right side of the law. And at the same time, that sounded chaotic enough that, if you told me that, like, I don't know, you know, 20% of the GPUs, like, somehow don't get their regular NVIDIA six month check-in, and of those, you know, some percentage found their way to wherever, that wouldn't shock me at all, you know, based on on what he said. It sounds like there's, you know, eighty twenty is obviously a simplistic way to describe it, but it sounds like there's a lot of sincere effort going in and then also just enough noise and and chaos that probably... The the opportunity for things to slip through is also pretty substantial, I would bet. Then Prakash turned to China and the pressure on American companies over their Chinese customers.
00:44:10 · Prakash Narayanan
[00:44:10] S1: Like, for example, Oracle selling capacity to Tencent, $7,000,000,000 of capacity, that was canceled. Right? So the national security infrastructure is pressuring these companies to cancel that. It's an American company doing business, it's outside China, and not sending chips into China. And at the same time, there is no pushback on the political infrastructure that is allowing this. Why don't you just make laws? Why pressure the companies? I just don't like it. If you wanna make laws, just make laws. But if you don't have the political willpower to make those laws, and you try to slide this under the gate, like why? You have a mandate now, right? You have a mandate in Congress, like do something, right? We say compute is the new oil, right? And so there is also this impact on compute. Let's say you decide not to sell compute to China, like zero, zero Chinese content, right? Do you also mean that YouTube cannot show AI generated video to Chinese participants, to Chinese viewers, to Chinese customers? Because that is an AI product. And Google needs to generate that AI product. Google has to use compute. And that compute is gonna be used on behalf of a Chinese customer. You can restrict it at the data center level. You can restrict it at the raw compute level. You can restrict it at the product of compute level. But at some level, you're going to end up selling to the Chinese because they are global consumers. Right?
00:45:48 · Nathan Labenz
[00:45:48] S0: I had said we get carried away with these restrictions. Here's why. I really do think we're gonna need cooperation with the Chinese. You know? That... That's... In terms of why all this matters to me, it's one, because, you know, I just generally don't wanna see tensions ratcheted up between The US and China in general. But specifically on AI, I really think we are going to need cooperation between The US and China on AI governance. I I really don't see how, I'd be extremely pleasantly surprised if we can get through all of this without the two countries coming together in some meaningful way. When you have, as we recently talked about, a super senior person at a frontier company telling me at the curve that there's a level of intelligence we probably just shouldn't go beyond, if that's the view that has now taken hold at the top levels of frontier model companies in The US, the only way to achieve that is gonna be through some sort of agreement. And we're really not putting ourselves in a great position to make that agreement. So a lot of this stuff seems ultimately quite self defeating, and it makes me sad. Part five, what investors are superhuman at. Back to Friday and Bryan Bischof of Theory Ventures, an early stage venture firm. First, the compute. I asked how much slack there really is in the market when we keep hearing both that you can't get GPUs and that you can get them on demand if you'll pay a multiple.
00:47:27 · Bryan Bischof
[00:47:27] S4: Totally. I think there's a couple things at work here. One, if you go to a Neo Cloud and you say you got compute, they're like, nope. It's all locked up. It's all contracted out for the next two years. And they're not lying. Like, they're not telling you an untruth, But there's also been quite a lot of discussion and even, you know, Satya talked about how there's a lot of compute that is locked up as you hear. This is essentially underutilized compute due to either, like, the geolocation of it or the available, like, energy. And so these are both true at the same time, where there is locked up compute that actually isn't super useful. There's also the reality of sort of utilization, utilization of GPU. Yesterday, I was chatting with some folks working at the infrastructure level around, like, GPU utilization, and one of the big problems is you actually can't run at maximum capacity all the time because it's unsafe from a scheduling perspective. You actually wanna build in Slop. And so there... There's actually so many more dimensions than simply do you have contracts on the available compute? And this is why you see many different routes to big success and, like, embarrassingly large numbers of ARR in the compute market. This isn't just because of FOMO. This is because there's actually so many profiles that you need to kind of, like, maximize the value of whatever compute is available to you. And so we run, you know, thousands of jobs here at Theory Ventures that are powered by AI agents. If you ask me, what's the distribution of immediacy for those jobs? Well, I'll tell you. Okay. Maybe five to 10% is right now. And maybe 40 to 60% is I'd like to see it before my next meeting. And the rest is, I don't know, maybe, like, this week would be neat. Like, you know, and that even just in, like, this one sort of niche of the kind of work that we're doing, we already have quite a broad variance of inference needs, and that's true of, I would say, almost every AI application layer company that I've ever spoken to since 2023. And so from that alone, you can see that if someone tells you there's simply no GPUs to be had, well, that's not really, like, accurate, but also they're speaking from a particular, like, bias. And the bias they're speaking from is probably for the workloads that is under demand right now for me.
00:50:12 · Nathan Labenz
[00:50:12] S0: Then how an AI native venture firm actually runs, how they think about token spend, run experiments, and build research agents.
00:50:22 · Bryan Bischof
[00:50:22] S4: Everyone is technical, and that's all the way from sort of our talent partner to our GTM partner through the investors. Everyone is using AI. Everyone's been using AI since the day that I joined and more and more. The way that we think about token spend is, you know, you should be thinking very, very clearly at all times if there is a way to accelerate by using AI tools. You should be thinking about experiments that you wanna run, and we're a really experimental bunch. To give you a sense, every quarter, we do our little, like, off-site where we talk about just everything that happened over the quarter. One of the things that we talk about every single quarter is what experiment every individual did that quarter with AI. That's not an exaggeration. This is not like, you know, oh, I asked Claude to do something new this quarter. No. These are... You preregister what kind of experiments you wanna do related to your job that use AI, and then you check if they worked. This happens every quarter because we're so insistent on rethinking what does it look like to work in a venture firm in, you know, this year, next year, and the year after. How are we adapting to those things? As the head of AI, my engineering team is obviously still operating at another level, which is how can we build tools that use AI to transform other people's workflows? Our goal is not to replace an investor with an AI system. Our goal is to make that investor 10 to a 100 times more effective and efficient by using AI tooling and building the AI tooling that will make that possible. Let me give you a quick example. Everyone talks about, like, company brain no matter what kind of business you're working in. I've used a couple company brain products and candidly found them all to be useless. The main reason is because they are trying to build sort of what we used to think of as this internal knowledge system. You can think of Notion, for example. Notion is like a stack of files, and maybe you can build a good search on top of it. Is that really your ambition? My ambition's a lot higher. And so my ambition for a company brain is agents who are consistently working to connect the disparate nodes in the graph of understanding from your business. And so that's why I can easily say, yes.