The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:22:54 · Harry Stebbings, Naveen Rao
[00:22:54] S1: Do you worry that you bite off more than you can chew and then deprecate product quality? In that transition from energy to intelligence, the more that you can eat in the... In between, the more money you're gonna make. But I had the founder of Modal on the show yesterday, Eric. And, I mean, I I love him, but the dude just wants to do everything. I mean, everything. And I was like, at what point does product breadth damage depth? Do you think about that? [00:23:22] S0: I do think the world is changing now with AI. We can take on more breath. Our surface area that we can explore is greater than it was before. [00:23:31] S1: Just because of speed of dev is much faster, why is that now every company is a compound startup, so to speak?
00:23:37 · Naveen Rao
[00:23:37] S0: Yeah, I I can give you almost a pretty concrete example. In our first chip, we built a set of coupled oscillators. Okay, they have a coupling matrix, have a bunch of oscillators that sort of influence each other. And so when we're doing this, I would ask in the old world, say before AI, I might have a question like, okay, well, how does noise propagate if we don't do all to all connectivity? So meaning I have like a daisy chain of oscillators and I have some noise characteristic at the beginning, I wanna see how that degrades information over, say, four or six hops. In the old world, I'd have to ask my research team, hey. Can you go model this? Put some sort of noise model in, and I just want a zero with order understanding here, right, just to kinda guide my own thoughts. That might take a week or so because they're like, ah, I'm working on this other stuff. I'll I'll come back to it next week. Maybe it takes a week to write the code and verify it and give me an output. So I got a couple of weeks to get an answer. I can literally ask this question to a model. It spits out the code. I can introspect the code. I can go back and forth with it with it a few times. I get an answer in an hour. So something that would have taken me two weeks to think through has now taken me an hour. So I can think through more things per unit time because of AI. Now I I think we still have a problem. I actually just posted on this yesterday that we have an AI slot problem. We have too much crap coming out of these models, and it's very hard to verify it. Some of it's good, some of it's not, and we need to figure out how to to do that at scale. But I can go faster. I can cover more surface area than I could before.
00:24:58 · Harry Stebbings, Naveen Rao
[00:24:58] S1: What makes you say we have too much crap? Is it just like looking on the axe and seeing, like, the the dumb takes on, you know, the thing that is just covering my timeline right now? No disrespect to The US, but it's your politicians who are rather interestingly structured faces would be an English way of saying it. That's all of mine, but there's, like, different AI generations of them. There's aliens and coming out of conquer... I mean, it's nuts. Is that what you mean by AI slob? [00:25:24] S0: It's not. [00:25:24] S1: I entertainment. [00:25:26] S0: Yeah. It's more entertainment. So I will say, by the way, the English have cornered the market on self deprecating humor. It is so much better than we can ever do in The US. [00:25:33] S1: So Honestly, by the way, we're all looking at this from The UK being like, wow. We've been moaning about our own politicians. Fuck. These guys.
00:25:33 · Harry Stebbings, Naveen Rao
[00:25:33] S1: So Honestly, by the way, we're all looking at this from The UK being like, wow. We've been moaning about our own politicians. Fuck. These guys. [00:25:43] S0: Yeah. I know. I know. We... We've taken it to new levels. I told you we're innovators. Alright? And so, actually, this this is a perfect example of you see an output and you say, okay. Does that... Does it meet your gut check first? And you're like, maybe not. So what's really interesting is older people today see images, and they don't have that gut check. They're like, wow. That's real. I mean, I've had my parents say, like, show me these AI generated videos of Trump playing the guitar, and they're like, wow. He plays the guitar. I'm like, mom, he's not playing that guitar. It is... That's an AI generated thing. She's like, oh, okay. So for me, it's quite obvious that it's AI generated. So that verification problem we see in companies. I see it in our company. We can build all kinds of artifacts. We can build code. We can build models. We can do all kinds of things. But you have to close the loop and make sure each one of those things is correct to actually trust the results. And doing that actually starts to become a big bottleneck because you're producing so many artifacts. I think we've moved the problem from producing to verification. [00:26:40] S1: And the value of verification is if you own the verification layer, then you own the automation layer because you have, like, verifiability that you can then progress there on. Is that it?
00:26:40 · Harry Stebbings, Naveen Rao
[00:26:40] S1: And the value of verification is if you own the verification layer, then you own the automation layer because you have, like, verifiability that you can then progress there on. Is that it? [00:26:50] S0: Yeah. I suppose. And I think it's a necessary part of using AI to develop things. But if you own the verification layer, do you own the production layer? I... I'm not entirely sure. I think you have to own the spec as well. Producing a useful output, building a car or a bridge or something, you have to be able to spec it, figure out, am I solving a problem that's useful? Then each one of these artifacts that's produced has to be verified to be correct, and then marching towards that spec. And eventually, we have to say, yes, I did indeed implement the spec as intended. So it's an end to end process, not just verification. So verification is a piece of it. I don't know how one would value that. I think I value it as part of the whole, like getting to the end result. [00:27:30] S1: We have Mike on the show from Atlassian, awesome guy, and he was talking about kind of the importance of chain of thought and understanding, you know, agent led decision making versus human led decision making across different functions in in companies. And part of me was like, I totally get that, and that makes absolute sense. And part of me was like, that feels kind of old to think of it like that. And, like, what does that mean? We don't trust agent decisions as much as we trust humans. That feels kind of weird too.
00:27:58 · Naveen Rao
[00:27:58] S0: We don't. I mean, look, I I agree with you. I don't think we should trust Asian decisions as much as humans in a lot of cases just yet. I mean, it's like, we we train for certain things for many years to build judgment. I mean, becoming a neurosurgeon, right, it's a seven year training after med school. There's a reason for that. It's not that learning how to throw a stitch is that hard. I've done that in grad school. It's not the physical mechanical part of being a surgeon that's the problem. It's having the judgment on which direction to go. You have to have seen enough cases, seeing the output, seeing how people think through these problems to then build that judgment up. And the way we're defining intelligence right now is we take a model that's it's stochastic, right? We can prompt it several times and we'll get different sorts of outputs. And what you're doing is you're exploring the distribution of possible solutions. And then we need to pick which of those solutions is good. Know, you'll get a lot of different outputs and most of them are not good. And you could argue that a human does this too, right? We say, okay, How do I make a decision? Well, I might think through all the outputs, and I have some kind of heuristic of deciding what's good. We do this internally, maybe it's internal monologue or some sort of internal process. But a model spits out a chain of thought, and then we have many chains of thought, and we decide what's good, and we take that path. That deciding element is actually not well understood for every task. And so I don't think we should trust these things. I think the longer and longer decision upon decision upon decision chain you build, the more off the rails it can go. And you can get things that look very robust, but they're completely wrong. So that's where we are today. That will probably change in the future. I think we need a lot... A few more breakthroughs to get there, but that's where we are today.