Andrew Lee: evaluation

Transcript context

…At a high level, I kind of think that everyone is building the same thing. Like you have all of these different agent companies and basically over time, as the models get smarter and the agents built in more sort of general purpose tools, you know, computer use and file systems and whatnot, you can do very similar things in many things. So you can go into Claude code and you can do all kinds of non-coding things in Claude code. You can end, you know, Codex and Claude Code and many other startup products are all able to do coding and non-coding things pretty well. I think where you start to differentiate, and I do think you can differentiate within this space to some extent, is really around what you're optimizing for and what the ergonomics are. In our case, take Tasklet. You can totally write code with Tasklet. You can hook it up to your GitHub, and you can have it generate PRs, and it does it just fine. We knew this for marketing, for example, if we put on a new blog post or whatever. I write the content in Tasklet, and then I just have it generate its own PRs, and it works fine. But it's not going to be as smart and definitely not as cost-effective for heavy-duty coding as going and using an actual coding harness. And it's definitely not going to be as nice to use because the actual coding harness is going to be in conductor or something that's designed for a coding workflow, and our product is not set up that way. So I see a future where you can pick up any AI agent and do anything. But different ones are going to have sort of different like cost and performance trade-offs and different ones are going to have like just different ergonomics for like the different types of work. Where we really shine is 24/7 automation of knowledge work for companies, especially knowledge work for companies that is not like your personal work, but like something the company owns. So if you have, you know, simple example, you have some like complex invoicing process, say you're a company. You don't want to be running that in your local cowork. If you close your laptop and the company can't invoice people anymore, that's bad. You don't want to put that in OpenClaw and put it on your Mac Mini in the corner. Because again, if something trips over the power cord, well, you can't run your invoicing. What you really want is something that's running in the cloud and is manageable by many people, and you have a lot of infrastructure around it to manage and provide oversight, and have audit logs, and you have guardrails around the thing, and you can control costs and your different 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. 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?…

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