SemiAnalysis Weekly
00:24:50 · Max Kan, Andrew Megalaa
[00:24:50] S1: Yeah. Like like, I think I think most of them, the majority of them are like more or less solved. I think heading into '27, you're not gonna need frontier models for most of these. And we... Like, not only do we think there are sort of poor tasks to use to measure frontier model capabilities, we also think it's probably gonna sort of not be the majority of revenue for OpenAI and Anthropic and the other frontier model companies. And sort of because the thing that we're most interested in tracking with this benchmark is sort of a, where frontier model companies... Sorry, where frontier model capabilities are going, and then b, can you sort of, like, do any like TAM or actually revenue analysis based off of that? We sort of very explicitly focus on just the frontier domains. [00:25:46] S2: Yeah. And like when we say like day to day tasks, like that actually doesn't even exclude like some of the domains people want to use like these models for. Like it was surprising to see how bad the models are in like finance and legal for how much people talk about like, they will automate finance and legal. Because, like, when we looked at some of the tasks that we got from, like, living context and chakra, like, we went through those rollouts ourselves because we were confused. I I actually thought the models were better at [00:26:11] S1: these Yeah. Andrew Andrew is, like, a tech bro supremacist, and he was like, dude, the model's definitely saturated finance already. That was his prior coming into this.
00:26:11 · Max Kan, Andrew Megalaa
[00:26:11] S1: these Yeah. Andrew Andrew is, like, a tech bro supremacist, and he was like, dude, the model's definitely saturated finance already. That was his prior coming into this. [00:26:19] S2: Yeah. And we actually had like some of our finance friends look at some of these tasks. And like when we were like choosing partners for these, you know, we had different rollouts and tasks. And, like, some of them were like, yeah, these tasks are, like, pretty bad. [00:26:34] S1: Yeah. Like, there there are some tasks from some potential finance partners that we were like, woah. This is, like, not realistic of sort of actual finance work. Like, and the only reason why it's sort of sufficiently difficult for, you know, Opus and Astra is because it's, like, lacks realism and it's also unfair in these ways. And then after you correct with the unfairness, like, oh, look. Opus is, like, saturated. [00:26:56] S2: And so that would make you think that these tasks are solved. But then we got, like, the really high quality tasks. Yeah. [00:27:01] S1: Yeah. [00:27:02] S2: Yeah. And then, like, we had, like, our finance friends look at them, and they're like, yeah. These are just totally realistic of, like, a real thing I would have done in, like, banking and PE. And the model is just, like, failed us.
00:27:13 · Max Kan
[00:27:13] S1: Yeah. And, actually, I I wanna talk more about the finance task in particular because I think it's probably the most understandable kind of domain to the average listener. And I think this will be useful in giving people a sense of, like, why GDPx is such a high quality benchmark. Sort of how people have historically made finance tasks in the past, if you look at something like GDPval from OpenAI, is they would go to, say, a company like Mercor, which has this vast, like, expert network of finance bros and be like, you finance bros, sit down for two hours and try to, like, come up with a task that's, like, representative of something that you would actually do in your job. And then also give me sort of the golden solution for that task. And then, you know, that's gonna be the task that I'm gonna give you about. And, obviously, you sort of have a ton of sort of biases when you're telling someone like, hey. You know, make a task for my for my benchmark and just please try your best to make it realistic, man. What, like, our finance partner did is they took a real private equity transaction where, like, some private equity firm bought a, like, family owned snack company. I think they they paid, like, a little over 800,000,000 for, like, 60 percent of the company. They... Again, they they sort of had this actual transaction, like, all 4,000 files sort of corresponding to it separated into the deal rooms for the private equity firm that was involved, the investment bank that was involved, the accounting firm that was involved, and then also sort of the snack companies sort of own VDR because they know they were selling themselves.
00:28:52 · Andrew Megalaa, Max Kan, Jordan Nanos
[00:28:52] S2: And it was organized or disorganized exactly how they would Yeah. [00:28:55] S1: It was like it was like the real the real, like, files for this real I [00:29:00] S0: was looking at this. And anybody who has worked in a VDR, like a a data room, for any m and a transaction has seen exactly this. And [00:29:11] S1: So they had they had the real files. And then, obviously, they sort of, like, deanonymized it. But along with the files, it actually has sort of all of the real tasks that, you know, analysts and associates and other people, like, working on this deal had to do. And they essentially converted this, like, you know, actual deal room into an environment they can evaluate the models in. And then they, like, found the emails that, you know, some accounting partner told their associate to do as part of this task, and then they had the model do it. I think this is this is obviously sort of just like a categorically different in terms of realism and interestingness and quality versus like just taking some random, you know, banker off the street and being like, come up with a task
00:29:11 · Max Kan, Jordan Nanos
[00:29:11] S1: So they had they had the real files. And then, obviously, they sort of, like, deanonymized it. But along with the files, it actually has sort of all of the real tasks that, you know, analysts and associates and other people, like, working on this deal had to do. And they essentially converted this, like, you know, actual deal room into an environment they can evaluate the models in. And then they, like, found the emails that, you know, some accounting partner told their associate to do as part of this task, and then they had the model do it. I think this is this is obviously sort of just like a categorically different in terms of realism and interestingness and quality versus like just taking some random, you know, banker off the street and being like, come up with a task [00:29:59] S0: for Okay. One more thing about this, Max, is the life cycle. I thought this was fascinating, which is they could effectively replay what the data room looked like when that task was signed. Right? So, you're gonna add in these thousand files over time, but not every task is assigned at that same time in the data room. I thought that was like, yeah, obviously super fascinating that this is the depth at which people can can go to to try to make this task realistic. And some of
00:29:59 · Jordan Nanos, Andrew Megalaa, Max Kan
[00:29:59] S0: for Okay. One more thing about this, Max, is the life cycle. I thought this was fascinating, which is they could effectively replay what the data room looked like when that task was signed. Right? So, you're gonna add in these thousand files over time, but not every task is assigned at that same time in the data room. I thought that was like, yeah, obviously super fascinating that this is the depth at which people can can go to to try to make this task realistic. And some of [00:30:28] S2: the failure modes are so esoteric when I looked at them. But then I had to ask my finance friends and they told me, yeah, that you would totally be expected to just have done this. It'd be like some email from somebody who was like, no. Actually, adjust these numbers to, like, whatever q four or something. It's it's just one email where it mentioned this. And if the model didn't find that email, it would fail because, you know, it had it had to find that email. Yeah. To, like, actually evaluate all the context relevant to the task. And the models didn't look hard enough sometimes. And so they just miss it and then have the wrong numbers go out. [00:31:00] S1: And if the human associate failed, would have gotten yelled at. So it's [00:31:02] S2: the Yeah. Apparently, that's actually That's exactly how it would have worked. Like, if you were an analyst and you did not read it all [00:31:08] S1: the same. That one email,