00:06:02 · Ulrik Hansen
[00:06:02] S0: Yeah. No. I think the one thing that, like, Tesla really has going for them is they just have a huge and compounding data advantage over, basically, like, every single other player in the market. They are collecting live human feedback data. They have, like, the equivalent of, like, a thumbs up and then some... A thumbs down button that you're used to enchantability, right, embedded in the cars because they have people driving the cars. And so, like, this data advantage is, like, compounds massively over time, whereas, like, Waymo, they have to rely on, like, collecting their own data. Right? They don't have, like, the distribution advantage that that the Tesla has. So, inevitably, like, I think that's a much slower rollout and a much slower build. Like, that being said, of course, like, they've sort of decided to add additional sensors on top of, as you pointed out, like, Tesla's relying on RGB, additional sensors to help inform the planning and trajectories of, like, how the car, like, drives and operates, which I think, quote, unquote, like, should be a safer option, but, of course, it's way more expensive and, like, takes longer to build. I am, like, very bullish on on Tesla, actually, like, getting ahead on on this. And now they're starting to, of course, like, roll out their own, like, robotaxi fleet, which they can probably, like, do very quickly. Now the question is they, of course, like, probably don't have the same advantage or, like, the same lead with working with the regulators that that Waymo has. So I think that'll be an interesting dynamic to to observe over the next, like, three to five years. It's like these systems actually, like, go live in, like, the different cities around The US.
00:07:20 · Ksenia Se
[00:07:20] S1: Do robotics have the same bottlenecks as, autonomous systems like self driving or it's different?
00:07:25 · Ulrik Hansen
[00:07:25] S0: Yeah. No. I think, a lot of the... They have a lot of the same constraints. So I think, ultimately, the difficulty in deploying, like, these, like, robotic systems is, like, you have the additional constraints of you have to deploy the model on, like, specific, like, hardware devices on edge. Right? So you have GPU constrained environments that you don't have your... If you're leveraging a foundation model like a chat interface. You have to, first and foremost, like, make sure that, like, your systems can actually work on these, like, embodied AI devices, which is, I think, no small feat of of engineering. And then, like, creating the iterative feedback loop between the actual product usage and the model itself is also much, much harder because you might have, like, a robot folding laundry in your house, and it might fail. So now, look at, okay. How do you relay that back into, like, the system and, like, how it's trained? It becomes, like... There's, of course, like, latency for, like, uploading the data. You might, like, be in, like, an area without, like, Wi Fi, so you don't necessarily have Internet connectivity. Whereas, like, where we have seen the fastest progress in AI has been, like, in the pure digital workflows. Right? So for language models, like, ChatGPT and and otherwise, for coding, where, like, the feedback loop between, like, the actual user and the model itself is instant. That's another luxury that you have in physical and and embodied AI. So the companies that are building those types of domains, like, they have to really think carefully about, like, how they collect that feedback. And we see it, like, being solved in a number of different ways. Like, some companies are adding 10 operations to, like, help steer the robot if they kind of go off the rails. But, ultimately, like, if you want them to work on a fully autonomous basis, right, you don't necessarily, like, will be able to rely on that. So being clever about, like, how you build those, like, feedback loops is gonna be what determines a lot of the future compounding advantage.
00:08:59 · Ksenia Se
[00:08:59] S1: Feedback loops, we will see more and more of that in AI agentic systems. Right? How does infrastructure evolve to address that?
00:09:08 · Ulrik Hansen
[00:09:08] S0: Yeah. That's a great question. It's something we've thought a lot about because we we are, like, building a a key part of, like, that infrastructure that makes, like, the human feedback possible. I think what we've seen, like, generally over the last couple years is, like, the companies have evolved from building, like, out the kind of core foundations of, like, the models that they've been building and, like, as they go live in production, then, like, building the human feedback infrastructure, like, becomes much more important. And per use case, right, like, you have to have a lot of, like, custom tooling. So if you're building, like, a model that can work, like, LIDAR or three d data or, like, video data, you need, like, good tools and, like, the whole, like, AI tool chain, like, it's just very much, like, in development. Right? Like, a lot of this stuff, like, hasn't really been built yet. So, like, we will see that happening over the next, like, I think, five to ten years. If you think about, like, how the Internet economy has been built over the last thirty years, the development life cycle has, like, gotten to to where we are now. I think AI is likely to be much more... It will happen on a much more compressed timeline. And, yeah, of course, like, we're kind of, like, one of the companies that are building that space, us alongside, like, other ones. What that tool chain would look like exactly, I think it will probably look like a lot of the functionality that we have in in software development. So, like, version control systems, like, we have, like, good networking protocols and all these things. We will have to build out, like, similar things for for AI specifically. But the interesting quirk about AI systems is that becomes a little bit more difficult to do because they are, like, dynamic. Right? And they're sort of, like, working in a real environment. So, like, not constrained to to the same environments, like, software are working in, which are, like, inevitably, like, much more programmatic.
00:10:36 · Ksenia Se, Ulrik Hansen
[00:10:36] S1: Yeah. A lot of uncertainty to deal with. [00:10:38] S0: 100%. [00:10:39] S1: You were talking about involving more PhD students for annotation, working with data. So if expert feedback is the new goal, what is the role of humans in this human? Is it, like, coexisting task with agents? Are humans just global functions in this in this system? Who are humans here?