The Exchanges

Q You clearly know a bunch of people working at sort of large, uh, language model research labs. Well, when you looked at what they were doing, how did the focus come to be on agents in particular? And how's that different from a general language model?

A Yeah, I, I think we've always been interested in agents in not just, you know, recommender systems or classifiers or things like that, but in systems that are going to go do real work for us, right, that are going to actually be useful in the real world. Right now, you can ask some kind of chatbot something and it'll give you back a response, but the burden is sort of on you to go do something with that to verify whether it's correct or not. I think the real promise of AI is if we can get systems that can actually act on our behalf and kind of accomplish goals and kind of do these larger things and sort of free us up to, uh, To focus on the things we're interested in.

AI assessment note: “chatbot something and it'll give you back a response, but... act on our behalf”

Q Is that one of the reasons you all decided to focus on code as one of the first types of agents that you have started with? Or could you explain more about the logic behind that?

A Yeah, I think for us, code is useful when we're thinking about reasoning. One way that we are, one way that we're sort of making, you know, collectively reasoning agents today is founders are just hard coding the reasoning process of like, okay, if there's a customer support complaint about this thing, then I do this. If it's like this, then I do that. And so you have this like very special case version of the thing, right? And there's a spectrum between code and language or more kind of general reasoning abilities, but it's a spectrum. It's not a binary thing, I think. And so you can have code now that we have these language models that kind of mixes the language models and the code layer, right? Where it's like, sometimes you're using the language model to decide what to do. Sometimes you're using an if statement. And so it's more about like a fusing or like melding of these two different things and being able to like be in the right place on that spectrum. And so code is actually like a really important part of this. And as you do things that you want to do more robustly and you want to do in a more repeatable way, then you want to move it more towards code, right? And so To the extent that you've never seen this task before, maybe you should be doing it in this more kind of nebulous intuitive sense, and then over time, get better at it, critique it, and turn it more into co…

AI assessment note: “code is useful when we're thinking about reasoning.”

Q How do you guys, um, do evaluation for both these, like, let's say categories of agents that you're working with today from the, I assume, more, uh, closer to production grade to do to broader coding agents?

A Yeah, the evaluations, I think are actually one of the most important parts and one of the places where we spend the most time and think about it kind of the most. There's, there's a lot of work in specifying exactly what you want from the to do agent, for example, right? Like, how do you know, like, it gives you back some code. Okay, is that good? There's sort of a spectrum. But like, if it's faster, it's better. If it gives you less code, that's better. But if there's bugs, that's not good. So you really need to take it and like break down what did I really want to happen here? And I think when you start to break this down, you start to say, okay, there's some things that are kind of qualitative, like, do I trust it? Did it come back with tests? Can I run this code immediately? Like the kind of feel of it. There's other things that are just for the code itself. There are different attributes. Is it in the same style? Does it have good variable names? Like, is it a minimal change? Or did it change all sorts of stuff that it didn't really need to change? Each of those things are actually something you can measure a little bit more easily than the overall task. So you can make another kind of metric that's like, okay, how good are the variable names? All right. How similar are they? You can break that down. You can kind of keep breaking it down until you get to a point where it'…

AI assessment note: “Each of those things are actually something you can measure a little bit more easily”

Q One major milestone that you had recently was you announced a two hundred million dollar fundraise from, uh, Astera, NVIDIA and a variety of other folks. How do you think about, um, what proportion of that will go to things like compute versus team and how in general should AI companies think about, uh, the capital they raise and how to deploy it relative to different potential objectives and outcomes?

A I mean, I think actually a significant fraction of that is going to go to compute. I think I can't speak to other companies how they should deploy it. But I think for us, given that our goal is to make agents, what we really want actually as a company is not to become a huge company. We don't want tens of thousands of people. We want to make our product actually work so that we can make agents so we can have some huge impact and have a relatively small, close knit team where the communication is much easier. It's really hard to communicate with 10,000 people. It's much easier to get a hundred people in a room and know, you know, what the heck you want to do and agree on things. And so I think We're trying to ideally leverage ourselves, and we're already starting to do that today, and what that looks like is by spending a bunch on compute. Today, you know, we don't have AI agents that are running off and doing all sorts of things on their own, but we do have the beginnings of those. You know, we do have our internal, you know, hyperparameter optimizer, for example, which saves us a ton of time. Instead of our researchers manually deciding like, oh, this learning rate, I should do this experiment. We just go, we come back after the night, and it's like, oh, great. Everything is optimized. This is really nice, right? But that used a lot of compute, like we're using a huge amount o…

AI assessment note: “a significant fraction of that is going to go to compute.”

Q Can you tell us a little bit more about why you decided to focus on coding and what are the types of systems you're really focused on building?

A Yeah. So there's a, there's a bunch of different reasons for focusing on coding. One of them is that the evaluate we talked about before the evaluations are much easier to do and subjective. Another one is that coding is part of reasoning. Another one is that coding really helps us accelerate both our own work and the agents that we end up building. So as we're making the tools for ourselves, we already are starting to see like this kind of leverage from the systems that we've built where like, We can run this agent. Now, you know, I think probably within the next year, we'll probably, you know, not be hiring as many recruiting coordinators because, oh, we're going to do some of the scheduling with the agent that we've built, right? But we also can do the same thing on the software engineering side. We're writing unit tests literally right now automatically. Okay. And that's just helping accelerate us, helping, you know, remove the bugs. It's additive. It's incremental. It's like, okay, we get a five percent gain, a 10% gain here. But as we make more and more tools, those things compound. And I think over time, it's going to be possible to make much more robust systems much more quickly. And we're building, you know, we're using these coding agents to write the coding agents. And I think this is kind of the like, you know, sort of recursive self-improvement thing that people ha…

AI assessment note: “there's a bunch of different reasons for focusing on coding.”

Q Maybe if we just project forward a little bit, like what are you guys most excited about? You want to be a, um, tools at different levels of the stack company. Uh, what are you imagining people build or what are you already seeing people build that you think is going to be, let's say useful a year from now and useful five years from now?

A Yeah, I think a year from now we're going to start to see some of these use cases actually work that today you could you can write these like we have the capabilities you can make some kind of agent to triage your email or to do scheduling or many of these workflows like we really should like why don't we have that today that definitely can be done right like there's nothing stopping us and I think five years from now we're going to have something where it's not just you know, okay we have a scheduling bot we have this other thing but we really have these more general more robust systems where Each of us can individually say, like, I want a thing that does this. I want to do this particular weird research workflow, and I want it to work like this and blah, blah, blah, and just specify in language. I think one thing that personalized agents. Yeah, one thing that our recruiter mentioned yesterday that I thought was kind of funny is he's been describing to candidates like, we're actually sort of a software dev tooling company. But the idea is that in the future, everyone is going like, As we make these things easier and easier to program, really everyone's going to be at like sort of software engineer in that sense. Like we'll be able to make our own agents, right? Well, just by sort of working in natural language and like describing what we want to do and how we want it to be don…

AI assessment note: “a year from now we're going to start to see some of these use cases”