Dan Balsam: prediction

Transcript context

…thing with with Quad. And I think that's huge. Like, at the end of the day, I think even in the world of AI, specialization wins. Like, that sort of craft that goes into imbuing the right types of taste and the right types of decision making capabilities into any agent goes a super long way. Actually, I said three things, but maybe there's four things. Another thing is that I think, like, the the UX, it's, built for research. Like, research is all about understanding and provenance and being able to drill down at different layers of abstraction. We want our users to feel like, like, maybe a PI managing a army of a 100 grad students who can go out and run experiments for them and answer questions, who have, like, reasonable taste and judgment, but the human being fills this more like orchestrator role. And a big part of that is communicating the information really effectively and in a trustworthy way. So making sure that when a user is seeing a result, that result is a, correct. B, they the human being can verify in a bunch of ways. They can see the code. They can drill into it. C, presented in a beautiful and intuitive way that helps them grasp concepts, helps them learn quickly about domains that maybe they're less familiar with. All those things are super important. And then the the final thing I'd say is, like, long horizon. Like, research is fundamentally horizon task. We're not currently making significant money at this price, but we do think it's really important that users have enough sort of credits to be able to do long running autonomous experiments because the value of Silica reveals itself when you do long running autonomous experiments. And in order to do that, you do have to burn a certain amount of tokens to be able to do that effectively. So I think one thing that we're really focused on is both, like, coherence over long horizon objectives, but then also finding clever ways to reduce cost for long horizons. And my hope is we're starting out with a thousand dollar a month subscription. We'll be able to bring that down over time because we're able to come up with more and more clever ways that we could have agents, like, remit coherent, really strong at these research objectives that just fundamentally by their nature are long horizon, but do it with fewer tokens than we than we do today. How do you think about the of all the know how? Because you guys have previously monetized that by, like, doing 7 figure deals with huge companies that have, like, very high value questions. Right? So there's a sort of in your where you're a startup, you got venture capital, and, you can maybe afford to disrupt yourselves more than incumbent companies can. But there's definitely some, like, interesting trade off there, right, where you're like, companies have proven that they're willing to spend a lot of money to come hire us to do this work. Now we're going to try to allow them to do it. We're gonna try to productize our know how. Does that maybe you just have so much demand, you're not really worried about it, but how do you think about kind of what will be the primary driver? And and do you do you have some sort of defense against the diffusion of the hard won knowledge?…

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