Nathan Labenz: observation

Transcript context

…Last. You know, month, a couple of months. Yeah, man, it's going to be an interesting conversation today because the pace of events is not slowing down at all. And Zvi, who's coming up in a little while, just expressed his exhaustion yesterday at seeing 5.5 drop. His queue seems to be getting longer, not shorter. So I appreciate that he's going to take 1/2 hour out and come to talk with us and think your thesis, you know, for why we should be doing this is, is looking better and better all the time. You know it's yeah, I live sense making is kind of demanded in this world. You can't put this stuff on the shelf and come back to it in a week. Yeah, yeah. The, the, the entire point that, you know, why I wanted to start doing live was because the pace of developments is starting is going to start to be hard to keep up. I, I, I feel especially because I think Noam Brown and some of the other people from open AI Rune, etcetera said that they are actually using these models in research. So we had at least Aiden, Aiden McLaughlin Rune, Noam Brown have all said that they're using them in, in research. And so that that is going to be interesting to see if the pace of developments, you know, we are handing off extremely powerful research helpers to the best AI researchers in the world. And if they are able to make something of them, we should we should see it fairly soon, right? It seems like yeah, this year is not unreasonable at this point to to really see an acceleration. I was just looking back yesterday at my IT. It turns out this is kind of tough to score, but the AI forecast 2026 challenge where last year I was proud to have landed in the top 5% on the 2025 prediction challenge. And this year it seems like for a variety of reasons, it might end up being kind of hard to score some of these things because it's not clear that all the benchmarks are even getting updated in a timely fashion. Like how many of the uplift studies is meter going to be able to do etcetera, etcetera. It might be tricky to really figure out exactly where we land. But on the main meter chart, one of the things that we were asked to predict is what will the doubling time be of task length. And it seems like everybody has kind of estimated a higher number than the trend so far suggests, which is like kind of a little under four months doubling time for task length, which means it will be greater than 8X maybe somewhere in the like 11:50 X over the course of just a year. And that is pretty wild. And it certainly doesn't leave too much headroom left before they're going to be making a very meaningful impact to real frontier R&D. Indeed. It's it's a very interesting time and not just in the foundation model world, I think in the rest of AI as well. Our first guest today is Anna Patterson with Ceramic AI. Anna is one of the most experienced people that ever in search. and not just in the foundation model world, I think in the rest of AI as well. Our first guest today is Anna Patterson with Ceramic AI. Anna is one of the most experienced people that ever in search. I guess she's you know, while while reading through the dossier, I was like, she has an article written in like 2005, which is, you know, recommended as the, you know, base basis article for what search is. Anna runs Ceramic AI and ceramic currently is advertising I think $0.05 per 1001 thousand search queries. So they're they're doing industrial, industrial volumes of search queries. I think they're in a space. I think we've seen Exa in a similar space. Parallels the the company formed by the former Twitter CEOI think there are a couple of other people there as well. And, you know, she is the most qualified. I think she's a she, she was on the search team. She was AVP in Google on the search team. She was at Gradient Ventures. And so it's going to be interesting to see what she has to say. I'm going to pull her up right now. And hi, Anna.…

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