Invest with AI
01:02:18 · Robert Yang
And then human like the labs essentially post-train them to behave in a certain way, right? So it turns out you can actually have their early evidence for that, um, not just from ourselves, but from the community, that you can like further post-train them to unlearn a lot of these behaviors, right? Um, then you can actually get them to not be so like spineless, right? Um then they can actually have some like stronger opinion, right? So I think that's that would be the second step is like okay, post-training, um like post-training an open weight models to for it to write character-wise, right, like uh personality-wise to to be able to hold their opinion. Uh uh at the same time, knowledge-wise, they can really understand like a specific sector much, much better, right? Um, you can have like an agent that really dig into like like 20 companies, right? 50 companies, they understand everything that's out there about these companies, right? They understand the management, they understand the the personal history of the management, the the relationship between the management, all the related sectors, the historical like performance. They understand all these things and they keep reasoning about these things. And so, so then I think we get closer to uh these agents being really, really useful for investors to the point that they can probably suggest kind of long, short, you know, decisions that turn out to be pretty good, right? Like a human would probably still like take this and make discretionary like decisions, but they would increasingly realize, oh, um, it turns out the agents' suggestions were just like almost just right, right? There's not like I I need to fact check it, I need to think about it, and but but it turned out they're very good. Uh and I think the final stage, right? So there's a skills, there's a post-training. I think the final stages we'll get to a point where we might question the question the entire kind of like the the paradigm, right? The architecture, the way we do training. A great capability of human um is we're able to learn from very, very small amount of data, right? And this is this is opposite from the paradigm of like current AI, right? Current AI is train on and human, like it's just like incomprehensible large amount of data, right?
01:04:52 · Brett Caughran
[01:04:52] S1: You can say, right, like read the whole internet, and yeah.
01:04:55 · Robert Yang
[01:04:55] S0: Yeah, it's like hundred trillion tokens, right? So it's it's uh it's and uh and that's um that's just a very different paradigm. Whereas a human, like kind of from a baby to right now, don't really process that much data, right? Now some people are like, oh, but but how about the human civilization, right? We have processed data that went into our gene, et cetera, et cetera. But your gene doesn't really encode that much information, or at least it doesn't encode the specific information, right? Like your gene is not encoding like the stock price, right? So um so human, despite not having all this information, is able to do great reasoning about like the market, understanding about like the world. And that's a remarkable capability, right? If we have agents that can do that, right, we can imagine every single day the new information that comes in, they'll be able to absorb it and like think about how that should change their investment decisions. I think that would be very, very powerful, essentially having this like truly continual. Learning system. You can build a continual learning system today, right? But but have a truly continual learning system where it's able to see some one piece of information that's like really significant and change its entire like worldview almost, right? It's like, oh, like today is election day, right? I still remember 2016 election day. Essentially, like all the predictions were like Clinton is gonna win and then turn out Trump won't, right? So it's I think I think a good model would be able to take that one bit of information, right? It's literally a single bit, right? It's like a single bit of information, a binary decision. That one bit of information and really like, okay, like let me like reorganize all the knowledge I have processed in the past and change my worldview so that I can really understand what has happened here and really better understand how I should do investment in the future, how do I do decision making in the future? Right. So I think getting to that point is probably longer. It's like maybe five, five years, five to ten years.
01:07:11 · Brett Caughran
[01:07:11] S1: Fascinating. Fascinating. Uh so what's what's what's a plan? What are what are we what are we doing here?
01:07:17 · Robert Yang
[01:07:17] S0: Yeah, yes. So I think I think for us, um building, kind of starting with the Excel agent that we already have, that a lot of like um top investors already use, right? Just keep going from there to okay, can it do company research? Can it do stock, like suggest stock picks, right? But not just but in a way that's really productive to the human, right? Because if you're just out there just like suggesting stocks, but the human is not able to really understand the rationale, uh, it's not able to kind of improve the system with their own like uh opinion, then it's useless, right? Like you you can't do that with a with a systematic like approach anything, right? So the important thing is to build this product that is increasingly capable, but really from a perspective of how do you enable human and really allow them to express themselves, right? So I think that's that's a critical thing. And if we can do that like step by step, um I think it's it's I I'd be very happy. And I think we'll get to a point in two years where humans would really trust these agents a lot about making decisions, and they would they would kind of step back and think more about oh, like how do I build this system, right? If you think about like uh someone who runs a uh like an entire hedge fund, right? Like the owner of a hedge fund, they're not out there picking stocks every single day, right? Um, at least that's not most of what they do, but doesn't mean they don't have work, right? They're kind of thinking, okay, how do I build the culture? How do I build the system, right? How do I build the organization, right? And I think humans would transition, a lot of humans would transition to be more like that in the next two years, right? Analysts would be more like PMs, PMs wouldn't be more like CIOs.
01:09:14 · Brett Caughran, Robert Yang
[01:09:14] S1: Yeah, and it and I sort of view my role as like a continuation of what I've been doing the last four years. I've been training human fundamental analysts and you know, sort of sort of joining FRL is a lot about training this digital analyst, which is uh sort of you know interesting opportunity to um uh starting with the skills pack and going into the model weights and yeah, we'll work through how to back like create clean back tests for some of these qualitative signals, uh which is sort of a fascinating exercise. [01:09:46] S0: So yeah. So something I'm really excited about is I think I think it's one thing to be a great investor, right? Like there are lots of people who are who uh who like okay, like really understand their sector, and um, but it's an it's another thing, I would say related, but uh but not the same skill set to be able to teach people, right? Now so I was a teacher myself before, right? So to teach people, you need to not only like know the craft, you need to also have the humility to understand the craft is a little different in every firm, right? And it's not about okay, like this is how you should do it. It's about like I'm gonna provide you the basics so that now you're enabled and you can go on and like do even better things, right? So I think exactly that's aligned with what we're trying to do here, right? It's through our product. And so I'm just like super excited for you to come in and like teach our agents so our agents can then empower the our our clients.