The Exchanges

Q Yeah. You mentioned the tick stack, Peter. Uh, so I just wanted to give you some reign to just go into it. I'm interested in where by nutrition, uh, starts and ends in, in, in, in some sense, what won't you do? What do you do that's common among all the verticals that you cover?

A There's a few buckets of, of work that we do and, and we've been at this for almost 10 years now, so the technology's pretty broad, but, uh, we got started with a thousand engineers, like you could work on lots. There's lots of stuff you have, especially with AI tools now, but yeah. So we had our start in, in simulation and simulation tooling and infrastructure. And so generally, if you're trying to build a very complex software system that involves moving machines, you need to test that. And the best way to test it is it's a combination of virtual developments, a simulation, and then also obviously real world testing. And then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation is a very deep topic. We have a whole, whole suite of products in that, and we can talk for many, many hours about that specifically. Um, but that, that is one part of what we do as a company. Reinforcement learning as a sub part of that is also super critical. I think a lot of the, a lot of the best advancements happening in, in a lot of these AI systems right now in some way relate to reinforcement learning and With now we have lots of compute, and you can do tons of interesting things in reinforcement learning. The second bucket of work that we do is operating systems techn…

AI assessment note: “There's a few buckets of, of work that we do”

Q I'm curious about the, um, coding agent adoption, just like since you're mentioning more esoteric languages, like what's the adoption internally? What have you learned?

A Yeah, we, we use everything. Um, I mean, so cursor was, I think the, the hottest tool in the company for a good while. Now, Claude Code, I think, has, has taken the, the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoption, uh, with, within the company. And, uh, yeah, they're phenomenally useful. I mean, it's, uh, honestly, we, we take inspiration from, from some of those tools also, and how we're adapting some of that mindset of thinking to the physical realm. Like, if it's so easy to, to build an app for this or that thing that lives just on a screen, We can, we were taking out a lot of the same ideas and, and applying that to, okay, well, if you wanted a physical machine to do something, how easy can we make that, uh, using our own tooling and platform as well?

AI assessment note: “cursor was, I think the, the hottest tool... Now, Claude Code”

Q The other thing I was thinking about is just in terms of like AI, uh, adoption, does that change your hiring at least a little bit? Or how do you, how do you sort of manage engineers, um, differently?

A Yeah, absolutely. It does. Um, we, I think like every company in the Valley right now are evolving our, our hiring practices, um, because the, the skills required to be effective are changing so fast, right? I mean, you, you used to really select for just rote implementation ability and, and now it, it is more the AI engineer skillset, right? Where it's like, yeah, you know how to implement, but actually Just banging out code is, is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the, the interviews that we give now, I think are way harder than they've ever been, but, but we also allow, right, selective use of AI tools to solve the problems. And I think in that you, you start to see more of a bimodal distribution of engineers, right? You, you start to see like, wow, there's, there's this subset of, of people that they, they really get it. Like they're, They're all in and they've, they've clearly invested the, the hours needed to learn these tools and, and how to be effective. And then there's sort of the, the group of people that haven't done that and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really, really into this.

AI assessment note: “Yeah, absolutely. It does. Um, we... are evolving our, our hiring practices”

Q I guess the question is like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like temperature is like a great example, right? Well, why did you make my robot worse when it runs in like a freezer? So it actually shouldn't worry about that. You know, it's like, yeah, how do you design these simulations?

A This is honestly the, the, this is what makes simulation so hard, right? Uh, it's because you, simulation is, is fundamentally about You're trying to optimize the development of a system, right? How can I build the system faster and better and cheaper? And, and what are all the levers that I have to actually accomplish that? And, and because it's simulations, just a software program, you can, you can change it a lot more easily than you can hardware systems. And then what's particularly awesome about the, let's say world models and using that as a part of simulation is now the simulation doesn't just scale with, let's say adding new math equations in, but we can actually scale the simulation environment now with, uh, with Additional real world data. And, and that, that also unlocks a whole new field of robotics.

AI assessment note: “we can actually scale the simulation environment now with, uh, with Additional real world data.”

Q In one of your blog posts, you mentioned research on large, large scale transformers that are similar to those doing modern generative AI. What are like the big differences? Other than you're absolutely right. I should steer the car. So I don't, you probably want to remove that.

A We have a diversified bet strategy internally. And, and the reason we, we've done that is because we operate in now a bunch of industries, a bunch of geographies, and, and each of the approaches has honestly a different risk to them. And so, uh, like we're not going to put all of our eggs in, in a single, in a single basket for a, a single approach because that approach may not work out. Uh, and so that's, that's one of the bets that we have, and it has certain advantages in, in certain scenarios. And then, but the way that these things play out in practice is It has certain benefits, it also has certain drawbacks. And, and then, and then the research team tries to then work on the, the situations where that's actually worse than, than these other approaches and to ultimately arrive at, at a really great solution for all of these things.

AI assessment note: “We have a diversified bet strategy internally.”

Q all be there. In your case, you're like in production on real streets with like a lot of customers. What, what are like the things people are underestimating? The same way the Waymo demos seven years ago, Were great. And then took seven years to actually get them on the street. Can you share about maybe like the last one percent that was really hard to, to get done technically?

A Yeah. So certainly productionizing stuff is really challenging no matter what. Um, so I, I maybe would, I would split the answer maybe into research and then also into production first on the production side. There's just so many problems that you find when you actually get the stuff to, to go in the real world. And so, I mean, the classic problem in, in humanoids right now is these systems are actually pretty brittle. Uh, and so, um, I'm not talking about any one company, but just as an industry, these systems are pretty brittle. I mean, interestingly, I saw this thing, uh, the other day that, uh, I think China is doing a, a marathon with humanoids. Yeah. So in, in government and not China specifically, but in any government, there is a, um, There's a concept called, uh, prize policy, which is so that there's, there's different ways of, of influencing an industry to go a certain direction. Like you can, you can regulate it, right? You can do mandates, or you can actually just do these competitions. So the US version of this was the DARPA Grand Challenge.

AI assessment note: “There's a concept called, uh, prize policy, which is so that there's, there's different ways”