The Exchanges

Q And I think, you know, it seems like the one other thing or transition that's happened is basically a move from a lot of, um, sort of, uh, edge case designed heuristics associated with it versus end to end deep learning. And that's what other shift that's happened recently. Do you want to talk a little bit about that and sort of what that?

A Yeah, I think that was always like the plan from the start, I would say at Tesla, as I was talking about how the neural net can like eat through the stack, because when I joined, there was a ton of C++ code, and now there's much, much less C++ code in the test time package that runs in the car, because, uh, there's still a ton of stuff in the, in the backend, uh, that we're not talking about. The neural net kind of like takes, uh, takes through the system. So first it just does like a detection on the image level. Then it does multiple images. It gives you prediction. Then multiple images over time give you a prediction, and you're discarding C++ code, and eventually you're just giving steering commands. And so I think Tesla is kind of eating through the stack. My understanding is that current Waymos are actually, like, not that, but that they've tried, but they ended up, like, not doing that is my current understanding, but I'm not sure because they don't talk about it. But I do fundamentally believe in this approach, um, and I think, um, that's the last piece to fall, if you want to think about it that way. And I do suspect that The end-to-end systems for Tesla in, like, say, 10 years, it is just a neural net. I mean, the videos stream into a neural net and commands come out. You have to sort of build up to it incrementally and do it piece by piece. And even all the intermedi…

AI assessment note: “how the neural net can like eat through the stack, because when I joined”

Q Right. Um, because I, I would argue like, well, you have the hardware, but you've now thrown away the software or the UX layer of it. Um, do you think that's what people want?

A Yeah, I think there's this, like, there's this sense that these apps that are in the app store for using these smart home devices, et cetera, uh, these shouldn't even exist kind of in a certain sense. Like, shouldn't it just be APIs and shouldn't agents be just using it directly? And, um, wouldn't it, like, I can do all kinds of home automation stuff that, uh, in any individual app will not be able to do, right? Um, and an LLM can actually drive the tools and call all the right tools and do, uh, do pretty complicated things. Um, and so, In a certain sense, it does point to this, like maybe there's like an overproduction of lots of custom bespoke apps that shouldn't exist because agents kind of like crumble them up and everything should be a lot more just like exposed API endpoints and agents are the glue of the intelligence that actually like tool calls all the, all the parts. Um, another example is like my treadmill. Uh, there's an app for my treadmill and I wanted to like keep track of how often I do my cardio. Uh, but like, I don't want to like log into a web UI and go through a flow and et cetera. Like, All this should just be like, make APIs available. And this is kind of, you know, going towards the agentic, um, sort of web or like agent first, uh, tools and all this kind of stuff. So I think the industry just has to reconfigure in so many ways that it's like the customer…

AI assessment note: “Yeah, I think there's this, like, there's this sense that these apps... shouldn't even exist”

Q Usually when people talk about one-on-one learning, they talk about the adaptive aspect of it, where you're challenging person at the level that they're at. Do you think you can do that with AI today? Or is that something for the future? And it's more today, it's about reach and Multiple languages.

A I think the low-hanging fruit is things like, for example, different languages. Super low-hanging fruit. I think the current models are actually really good at translation, basically, and can target the material and translate it like at the spot. So I think a lot of things are low-hanging fruit. This adaptability to a person's background, I think, is like not at the low-hanging fruit, but I don't think it's like too high up or too much away. But that is something you definitely want because not everyone is coming in with the, with, um, with the same background. And also what's really helpful is like if you're familiar with some other Disciplines in the past, then it's really useful to make analogies to the things you know, and that's extremely powerful in education, so that's definitely a dimension you want to take advantage of, but I think that starts to get to the point where it's not obvious and needs some work. I think, like, the easy version of it is not too far, where you can imagine just prompting model. It's like, oh, hey, I know physics, or I know this, and you'd probably get something, but I guess what I'm talking about is something that actually works, not something that, like, you can demo and work sometimes, so I just mean, like, it actually really works in the way a person would.

AI assessment note: “This adaptability to a person's background, I think, is like not at the low-hanging fruit”

Q Is it okay if I ask you Noam's question? You know, you could be doing that, right? Auto-researching with a lot of compute scale and a bunch of colleagues at one of the frontier labs. Like, why not?

A Well, I was there for a while, right? Like, and I did re-enter. So to some extent, I agree, and I think that there are many ways to slice this question. It's a very loaded question a little bit. Um, I will say that I feel very good about like what people can contribute and their impact, uh, outside of the frontier labs, obviously, not in the industry, but also in like more like ecosystem level roles. Um, so your role, for example, is more like ecosystem level. My role currently is also kind of more on ecosystem level. And I feel very good about like impact that people can have in those kinds of, uh, roles. I think conversely, there's, there are definite problems in my mind for, um, uh, for basically aligning yourself way too much with the frontier labs too. So fundamentally, I mean, you're, you have a huge financial incentive To, uh, with these frontier labs. And by your own admission, the, uh, the AIs are going to, like, really change humanity and society in very dramatic ways. And here you are basically, like, building the technology and benefiting from it, like, and being, like, very allied to it through financial means. Like, this was a conundrum that was in, um, at the heart of, you know, how OpenAI was started in the beginning. Like, this was the conundrum that we were trying to solve. Um, and so, you know, that, so it's kind of, um, it's still not resolved. The conundrum…

AI assessment note: “there are definite problems in my mind for, um, uh, for basically aligning yourself”

Q You feel like we are not near the limit of that unlock, right? Because I think there is a discussion of, of course, like the data wall and how expensive another generation of scale would be. Like, how do you think about that?

A That's where we start to get into, like, I don't think that the neural network architecture is like holding us back fundamentally anymore. It's like not the bottleneck, whereas I think in the previous, before Transformer, it was a bottleneck, but now it's not the bottleneck. So now we're talking a lot more about what is the loss function? What is the data set? We're talking a lot more about those, and those have become the bottlenecks almost. Um, it's not the general piece of tissue that reconfigures based on whatever you want it to be. And so that's where I think a lot of the activity has moved, and that's why a lot of the companies and so on who are applying these technologies, like, they're not thinking about the Transformer much. They're not thinking about the architecture. You know, the llama release, uh, like the, the transformer hasn't changed that much. Uh, you know, we've added the rope positional and the rope relative positional encodings. Um, that's like the major change. Everything else doesn't really matter too much. It's like plus three percent on a small few things. Uh, but really it's like rope is the only thing that's slotted in. And that's the transformer as it has changed since the last five years or something. So there hasn't been that much innovation on that. Everyone just takes it for granted. Let's train it, et cetera. And then everyone's just innovating …

AI assessment note: “now we're talking a lot more about what is the loss function? What is the data set?”

Q The thought experiment is like, are you willing to give up ownership and control to rent a better brain? Because I am. Yeah.

A So I think that's the trade off. I think we'll see how that works, but maybe it's possible to like, by default use the closed versions because they're amazing, but you have a fallback in various scenarios. And I think that's kind of like the way things are shaping up today even. Right? Like, um, when APIs go down on some of the closed source providers, people start to implement fallbacks to, like, the open ecosystems, for example, that they fully control and they're, and they feel empowered by that, right? So, so maybe that's just the extension of what it will look like for the brain, is you fall back on the open source stuff, um, should anything happen, but most of the time you actually.

AI assessment note: “maybe it's possible to like, by default use the closed versions because they're amazing”