Invest with AI
00:42:29 · Brett Caughran, Robert Yang
[00:42:29] S1: Um certainly the tolerance for air is uh incredibly low. Yeah, exactly. One error and the one lazy error in the wrong cell of a financial model can be really destructive. [00:42:40] S0: Yeah, exactly, right? You can get fired, right? If you just put give some slop to your uh PM. Uh so so these so these people they really felt the difference. And uh and uh um and I think in retrospect, uh this is a really great match between our company gene and and our customers, right? Our company gene has always been like very research and tech driven, right? That's why I think when a lot of people just are kind of just like a lot of our competitors, maybe not Anthropic, but you know, like there are other competitors, they don't really have benchmarks for a long time, right? It's just kind of vibed, right? Just feel the vibe of it. And we had benchmark which allow our agents to be better, and we can compare it against Claude for Excel and know for a fact it's better, right? Like we can compare it and say, oh, like the token efficiency is higher, the performance is higher, the success rate is higher, right? So that's I think that focus on accuracy, because we have been doing agents, we have been benchmarking agents since 2023, um, just resonated with uh like our investor customers who are also very much about accuracy. And precision and performance.
00:42:40 · Robert Yang, Brett Caughran
[00:42:40] S0: Yeah, exactly, right? You can get fired, right? If you just put give some slop to your uh PM. Uh so so these so these people they really felt the difference. And uh and uh um and I think in retrospect, uh this is a really great match between our company gene and and our customers, right? Our company gene has always been like very research and tech driven, right? That's why I think when a lot of people just are kind of just like a lot of our competitors, maybe not Anthropic, but you know, like there are other competitors, they don't really have benchmarks for a long time, right? It's just kind of vibed, right? Just feel the vibe of it. And we had benchmark which allow our agents to be better, and we can compare it against Claude for Excel and know for a fact it's better, right? Like we can compare it and say, oh, like the token efficiency is higher, the performance is higher, the success rate is higher, right? So that's I think that focus on accuracy, because we have been doing agents, we have been benchmarking agents since 2023, um, just resonated with uh like our investor customers who are also very much about accuracy. And precision and performance. [00:44:02] S1: Yeah, that makes sense. Um and uh you know, building product in um in the world of AI sort of feels like a constant building of sand castles and the better lesson sort of evolving, like as has clock directly has gotten better. How do you think about you know Excel agent as a as a platform?
00:44:23 · Robert Yang
[00:44:23] S0: Yeah, absolutely. So this is something we have been grappling a lot in the company in the last right six months, uh six even twelve uh six months. So first we had like first we were worried about okay, like can this product work at all, right? Let's find uh product market fit, right? So once we found product market fit, um at least I was pretty concerned about the long-term prospect, right? It's you like there were one point early on where like, oh, like maybe this is everyone who uses Excel would use this, right? But at some point it's becoming clear this is not gonna happen, right? The big labs, like most people are gonna use this like widely distributed product, right? So if you're going with uh like um like a customer profile like hedge fund, then you can't you then it's it's like if you're trying to serve them at some point, there's not that much you can do with Excel agent alone that's keep delivering them value, right? Um, so I was thinking a lot about this, and uh and at some point I came to the conclusion that shortcuts or like the generally the product that FRL build have to uh kind of go from Excel agent and go like from this foundation and go beyond it, right? Um and if we look at if we look at analysts, a lot of what they do is is uh well, you build your financial model, you update them, um, but then you have to do research, right? Uh you have to come up with like some thesis, some hypothesis, right? So so there's a lot more than building the financial models. And also these things are are not independent, right? As in the financial model you build can inform your thinking and then inform the research you do, right? Vice versa. So naturally uh we want to expand on this, right? But it's it's not some like arbitrary expansion, right? Because you can say, well, I I have an Excel agent. Now let me have a Word agent and a PowerPoint agent, right? Just like expand along the office suite, right? So we didn't want to just like expand like across more softwares, right? We want to expand, we want to expand along like to go from doing work to really creating value, right? Um, or at least we want to be able to get to the point where uh our users can create value uh better and more easily, right, by using our product.
00:46:59 · Robert Yang, Brett Caughran
So so a hypothetical experiment is like, okay, if you can build an agent or like a team of agents that operate like a hedge, like a hedge fund, or even just like a team within a like a multi-manager hedge fund, right? Like a portfolio manager and analyst, right? If you just have uh agent teams like that, and you show that they can actually do paper trading and uh and make good outcome, right? The sharp ratio is high, etc. etc. Now you provide that foundation to real professionals, and they can build on that and they can customize that and they can make it really, really good, right? So that's that's what we're thinking now, right? The core of it is still like the workhorse is an Excel agent that's really good at like understanding models, but beyond that, you're gonna have agents that is able to do research, right? That's that's able to like keep monitoring news, right? And um, and uh all these agents need to be orchestrated, for example, by some portfolio manager. So this is this is what we're building next. [00:48:10] S1: Yeah, yeah, yeah. That makes sense. And what what has been your reaction to the rise of the personal agents? So sort of instinct was the first to hit the national sort of awareness, then Muse far behind, and now we have Hark Labs and Rockbox getting a lot of attention recently. Like what was what was your take on what what was and is your take on those personal agents?
00:48:35 · Robert Yang
[00:48:35] S0: Yeah, yes. So the first shock was OpenClaw, right? So OpenClaw, I was I think earlier this year, right? It feels crazy because it feels like a long time ago, but I think it was just earlier this year. Um, and uh when that first came out, we did a journal club like every week on Open Claw for three weeks in a rope because we felt like this is so important. And uh, in many ways, it's very similar to what we did back in Minecraft, right? These like long-running agents that had their soul, that had their memory that keep getting updated. Um, they're autonomous, they're proactive. So I think we felt a sense of um maybe loss, like, oh, like we missed out on this opportunity. Um, but now there's an opportunity to for us to combine, I guess, personal agent with what we have been doing. Um so I definitely think the but overall, I think the the rise of personal agent reflects a really deep desire to have a better interface with agent, right? Because the traditional interface we have had as people who, for example, use codecs, uh, cloud code, and etc., is like, okay, you have these threads, right? Everything you want to do is a thread, right? Um, and at some point it becomes very like annoying to manage, right? You have all these threads, and and it was okay when you're just working on like three threads or like two threads at a time, but as the agents become more and more long-term autonomous, right? So this is almost mathematical. As the agents become more and more long-term autonomous, right, they require less intervention from you. And as a result, you can run more threads concurrently, right? So if you have to, if an agent can be independent for five minutes and require one minute, and then you have to intervene and it takes one minute for you to intervene, then you can run five agents at a time. But if they can run 50 minutes and it takes one minute to intervene, then you can run 50 of them at a time, right? If you can afford it, etc., etc. But when you can run 50 agents at a time, then having 50 threads that are just there and you have like jumping back and forth is crazy. And I know this because I've done it, right? I've had time like having like literally 50 agents running, 50 threads running concurrently. At some point, I had three monitor, I had thread desk, I had threet desk, seven monitors, right?
00:51:13 · Robert Yang, Brett Caughran
And each monitor had like six terminal opens, terminal windows open, each with was a like a cloud code or codex red. So I would literally have have like 40 terminals open just like all around me, and I was like managing them. I would like turn my chair and manage them, and that's a very bad user experience, right? So yeah, that's a very bad user experience. Whereas in reality, right, you can talk to someone who manages a large team without having to worry about micromanaging every single person, right? Um, so I think that's fundamentally what the personal agents are doing. Most users don't really care how the work is done, right? They just want it done. They don't want to be like individually managing all the 50, 50 sub-agents, 50 agents that's that's working. Um, and I think we see that definitely in our users as well, right? Our some of our power users are really like going very like um very hard, very hard on like multi-agent, right? And we want to provide them also a better interface so that they can just talk to like one agent who will essentially manage all their agents for them, right? And they can talk to them just like you talk to a human instead of having to type out some prompt. [00:52:35] S1: How close do you think we are to working with a digital hedge fund analyst? And as a sort of existing analyst, we just interface with that with that being as we would a new junior analyst on the team.