Invest with AI
00:02:37 · Doug Clinton
[00:02:37] S0: Absolutely, Brett. Good to be with you. Good to be with UK. And Intelligent Alpha is a company that I started, two years ago now officially. And it was born out of an experiment that started three years ago where once kinda hit the market late twenty twenty two, I was curious with a simple question, which is can ChatGPT beat the S and P 500, which a lot of human managers can't do? And so we started running some tests to see if that was possible, and the short answer was things looked very promising very early on. Maybe it's a little beginner's luck, but I was excited enough about what I was kind of seeing from very early LLMs and betting on the trajectory of these LLMs continue to get smarter and smarter over time that we wanted to launch an investment business that was really built around the idea of harnessing the intelligence from frontier models, allowing them to do investment analysis, and then ultimately, portfolio management. The one of the small piece I'd add about my background and kinda why we even played in this space in the first place is almost a decade ago, I started a venture firm called Deepwater. I'm still a partner there. I I run kind of both sides of the coin being a human allocator and also relying on AI to allocate at Intelligent Alpha. But we invest in early stage technology startups. We invest now in late stage tech startups and public equity at Deepwater. And so it was kind of this fun, natural testing ground where we've been in this world, we've been investing in AI companies, and Intelligent Alpha was the next logical progression for us from that business at Deepwater.
00:04:11 · Brett Caughran
[00:04:11] S2: Super, super interesting, and I have many, many questions about that. One of the first is how you thought about just the fundamentals of large language models, operate on lexical intensity, right? LLMs know that Paris is the capital of France because it's seen it 14,000,000 times in the training corpus. Know, when I've sort of in the past gone and asked ChatGPT for a portfolio of 10 stocks, it seems to operate on that same sense of lexical intensity that the stocks like Nvidia that are talked about a lot on the on the open web get cited as individual ideas in the portfolio. That could be a signal or that could not be often sort of over the cycle of markets is probably a negative signal. And retail driven markets, probably positive signal. How have you thought about that concept of lexical intensity prevalence in the training corpus and building a market beating portfolio?
00:05:05 · Doug Clinton
[00:05:05] S0: Yeah, I'd say very early on, one of the things we sort of realized was in '23, the paradigm for using LLMs effectively to invest was prompt engineering. I mean, that was all the rage then and really understanding what what is the context that you're providing to the models in terms of the task you want it to output. And so like you just said, Brett, if you just say, give me a stock of 10 portfolios, you're probably gonna get, I would say, eight of the 10 largest companies in the S and P 500 would be my my bet, which is fine. That might be an okay portfolio. But I think when we think about really using AI to make intelligent investments, that's not what we want. And so giving it a different framework where, you know, maybe you narrow the field to small caps, and maybe you define that within certain parameters for market cap. Maybe you pull in some external data from sources where maybe it's fundamental data, maybe it's your own research about a handful of companies. I think that's where it starts to get much more interesting using these language models and seeing how they sort of interpret what is all the data that's out there that humans are are looking at. Right? And and language models can process way more qualitative data and quantitative data than any human analyst can. So trying to figure out how can you get that right data and the right selection set into the models is kind of step number one. And then I think as I'm sure you guys have seen too, right, the frontier has evolved very quickly where it was all about prompt engineering a couple years ago. I think now it's about agentic workflows, you know, orchestration. And I think even beyond where we're at right now, the thing that we're getting really into is how do you sort of manage organizational context? You know, instead of just narrow task context, you know, you're trying to pick a large cap portfolio. That's one specific vertical that you might be working on. How do you look at the entire corpus of every portfolio you've ever generated? Or maybe if you have multiple team members generating different portfolios with their agents, how do you sort of understand the entire broad, knowledge base that's in your company and make it useful to the models when you're doing investment work.
00:07:15 · Khe Hy
[00:07:15] S1: Doug, on that point, did you mention kind of the evolution from prompt and it seems like we went from prompt engineering to context engineering to agentic workflows. And now with kind of the Fable like models kind of intent engineering or, you know, output based prompting, I'm curious how that last bit... I mean, we're still early in the Fable five days, but, has that last bit changed your approach?
00:07:46 · Doug Clinton
[00:07:46] S0: I'd say our approach is always changing slightly, and I think it has to, actually. And and I think about these models in some ways as a reflection of of how alpha changes in markets. You know, what what might generate alpha today probably won't generate alpha tomorrow. And whatever you're doing to generate alpha, and and that's a very broad word that probably needs a real definition, but let's just say outperformance relative to some benchmark right now. Whatever you're doing to generate that outperformance today with the models probably won't work tomorrow because there are other people that are experimenting with these models or finding out creative ways, I think, to make investment decisions. It might be much more of a human oriented process than what we do at Intelligent Alpha where we rely very much on the models to kinda work end to end. But, you know, I think we're always trying to change our approach and see what has sort of maybe stopped working based on what we were doing in the past and all the data that we have over the past three years. And then our intuition, and I think this is where the human still comes in, sort of our intuition of where the market's going, what might work in the future. And in some ways, that might be a little bit like how some quants operate, where a lot of times thinking of the next factor or thinking of the next sort of dataset that you might believe has alpha is much more of kind of an intuition than than just math.
00:09:07 · Brett Caughran
[00:09:07] S2: Yeah. This is this is a huge structural difference versus building code, for example. One of the stats I've been... I sort of pointed to all my students is only 17% of stocks outperform the S and P 500 over a rolling ten year period. So probabilistically, that stock you pick at random is going to be, a stock that underperforms the S and P 500. Sort of broadly discusses 70 of long only managers underperform the S and P 500. And so public markets alpha is truly a power law game. How do you how do you think about capturing? How do you think about capturing that power loss or right tail alpha essence in the agentic structure? Because if we're sort of like taking our investment process and turning that into an agent structure, if I'm a median investor, I'm actually augmenting a median process, which structurally does not have the essence of that of that alpha capture ability.