Nathan Labenz: belief

Transcript context

…nt agents. There's a lot of team enablement features you care a lot about. That's where we really shine. A lot of the work to make that work well is actually fundamental to the way the agent is built. I talked about our context manager. The reason our context manager is built the way it is is because you want to have triggers as sort of regular messages into the agent, which means these agents, if you have an agent that's running a trigger every time you get an e-mail, that agent might fire 10,000 times this year. And so you need an agent that can fire 10,000 times and still remember things at the beginning of the chat and still behave in a reasonable way. And that's a pretty different thing to optimize for than a coding session. The way Claude codes or resets context. Makes total sense in a coding environment. Doesn't really make sense in a world where it's like processing all your emails. So that's how I see us differentiating. The other note, a couple notes I want to make here on differentiation is one, the market is just freaking huge. So if you look at coding agents and you might say like, ah, clearly Claude Code and Codex have one, but like, Cursor is going to sell for like $60 billion. And even the fourth and fifth and sixth, Cognition's doing just fine. Factory's doing just fine. Even Windsurf that had to sell, it was a pretty good exit. I'd love to be the number one here, but if we end up being number four or five or six, they could still be a very significant exit. And then the last thing I want to note is, and I think this is probably the most important point, When we go and pitch a business, what we're trying to help them do is deploy AI for real inside their company to automate stuff. And those typical companies, they don't want to have to spend all their time researching AI models and placing bets on which lab is going to win. They want to choose a platform that's going to serve them well, and they want to benefit from everybody's improvements over time. We can go in there and say, Hey, a bet on us is not a bet on Anthropic or OpenAI or anyone else. It's a bet on us and a bet on everybody. We're going to give you Anthropic models and OpenAI models and Google and all the open source models, and then we will be a neutral arbiter of what you use. To the extent that we can build features to help you choose the right model for the job and optimize your costs across the different things, you can trust us because none of these are ours. We're getting the same margins on everything. a neutral party versus if you go back to Anthropic, right now, it's just Anthropic products. Even if they decided to provide other models to their products, which they could, although I don't think they're going to, but they could, I don't know if you'd really trust them to do that in a neutral way. I think that's a pretty compelling part of our sales pitch. Yeah. I think you've maybe navigated this about as well as anyone could in the sense that betting on Anthropic and kind of going all in on whatever the best model is, which has been clawed to make it work as well as possible while the capabilities curve was getting to critical thresholds. And then kind of pivoting to being a more neutral abstraction above the model layer now that there are multiple options that seem like they're, you know, able to deliver the kind of performance that people want. It wasn't obvious. I don't know if it was obvious to you that that's how it was always going to play out, but I wouldn't say it was obvious to me. I think I would have said about, you know, your kind of position six months ago, like, yikes, it is pretty tenuous to be all in on Claude, but I think you kind of tied timed it pretty well on a couple different levels. So how much would you say that's foresight and genius and how much is good luck? Yeah, I'm glad you think so. This was very much the plan and I do think it's worked out really well. So, yeah, I'm happy. I'm happy with how it's turned out.…

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