Business Breakdowns
· David Plon
[00:00:00] S1: There's two main ways I'm seeing investors use AI for this type of use case. One is understanding companies that are exposed to a given trend or a given development. So for instance, when tariffs were first announced – I guess April of last year – there were a lot of investors, especially on Portrait, trying to figure out which companies are going to be most exposed. And maybe the second-order thinking is even trickier: which companies, for instance, have a predominantly US-based supply chain and their competitors have an international supply chain? Like, that type of analysis. And AI is really useful for that, even out of the box. There's just a lot of world knowledge built into a modern state-of-the-art model about first- and second-order effects and who might be affected and so forth. The more nuanced, and I'd say difficult case, and where we spend a lot of time, has been working with firms to find ideas that fit like a nuanced definition of a mental model. So for instance, in my past life, I had this mental model of companies that were previously high-performing and there were really attractive elements of the business model that you could believe in and underwrite. And there was some sort of hiccup. It could be something macro-related or really bad product cycle, some sort of execution mess-up, and the market is now revaluing the franchise value of that business. And a lot of times, I could isolate the research to that one specific headwind and figure out if it's temporary or not. And if it was temporary, then you could buy it. That's a pretty nuanced thing to find. It's really qualitative in nature because the headline numbers are going to look bad whether it's temporary or not in the near term. And so, finding ideas that fit the mold of that is challenging for two reasons. One is that just requires a lot of qualitative reasoning along with the figures to do that. But two, I'd say even enunciating that clearly is hard. When I ask a lot of investors, "What are the mental models that you think about when finding a really attractive idea?" Some of them have been very explicit about writing down, "I'm looking for attribute A, B, and C," but a lot of times it's more of a gestalt feeling, right?
· Matt Reustle, David Plon
[00:00:00] S0: You know when you see it [00:00:00] S1: And actually figuring out how do you translate that into a query, for lack of a better term, is challenging. And that's one area where we spend a lot of time at Portrait as well, is helping folks look at their history and figuring out like, "Okay, what does a 10 out of 10 idea look like for you given your historical trading context and firm and all of that?" So I think the combination of those two things is difficult, but when it works, it's incredible. There are few feelings as exciting in this business as getting served up a pitch where you're reading it, you're like, "Oh my God, yeah, this is really exciting. Clear my calendar, I'm going to spend the next week just figuring this out." That's a really magical moment and something AI has been really helpful with.
· Matt Reustle
[00:00:00] S0: Yeah, I can tell you from doing the show, I get to experience all these different businesses and I'm trying to draw the patterns and match them up, and some of them come more simply than others. The mission-critical part of the supply chain that's a small percentage of the overall cost, but they're a dominant player, they have all the market share, pricing power should be there. That's one that's emerged and I think gained a lot of appreciation over time. And then there are the more subtle ones like what you described before, which really, articulating them can be more challenging. But that is an interesting approach, and actually something that I've seen a few investors who have a differentiated framework try to lean into – finding ways to take that proprietary knowledge or pattern recognition that they developed over time, and get this tool that can get the most out of it in terms of uncovering new opportunities. I think one of the things that you've made clear to me in other appearances that you've made and as we've talked, is there are skill sets to get the most out of AI, and I don't think they're always that obvious, or at least they weren't obvious to me. But I was hoping we could just talk through some of the things that seem to make a massive difference in terms of whether investors are getting the Google search better quality output, versus really building on what they're doing, becoming massively more efficient. And the first is just the prompt writing. So how would you articulate how important prompt writing is? Some of the basic things that you think investors benefit from when thinking about prompts, and just feel free to wax poetic on it.
· David Plon
[00:00:00] S1: It's an interesting question, that the answer changes every three months in terms of what makes an effective prompt, because the model capabilities change, as well as the tools available to the models are constantly changing. In the investment research context, and I think this is true for other contexts as well but particularly research-based workflows with large language models. The mental model that I think still works really well is, imagine you are writing an email to somebody maybe overseas who's going to work overnight and is going to be doing a task for you. And assume they're smart but maybe lack a lot of context on you. What information would you want them to have to be able to do a good job? Whatever you end up writing is probably a pretty good starting point for an effective prompt. And to dig a little bit deeper on some of the subcomponents, when I write prompts today, I usually outline a specific task and why the task is happening, in the same way where if you give an analyst, "Build this cost curve," they say, "Okay." If you say, "Hey, build this cost curve because I think it might be shifting and that could imply something about future changes in the pricing," that's helpful. So I usually provide some background context. I provide a task. Depending on the nature of what I'm looking for, I might specify an output. I might say, "Hey, I want your output to look like this: have an intro paragraph, then outline these data points, show me a table that has X, Y, and Z." That sometimes is useful; sometimes that can be hurtful in that you're constraining the model's output the same way you would a human analyst. You might want to give them some slack on the rope to be able to be creative in how they want to synthesize the information. And then I usually have a section of my prompts where I have specific task guidelines, and then also just general domain knowledge. So for instance, in terms of guidelines, these are things that I would just think about. Maybe I'm doing that guidance prompt. Make sure you capture soft guidance that doesn't have a specific figure on it, because otherwise if you say just capture all guidance, it's just going to capture a specific quantitative guidance.
· David Plon
And then in terms of transferring domain knowledge, I have like a set of bullet points that I try to encapsulate – hre's how I think about being an analyst and some of the things you might learn through your experience. One of those is I always remind the model, like, "Look, you're going to largely be reading commentary from management teams. They are always biased positively. It's important you take a skeptical eye to anything they're saying." And even something that simple really helps because the models have been trained to want to be helpful, and that usually influences them to be more positive than they otherwise should be. So just little things like that, where you could imagine taking an eager college student who's excited and believes in the good, and trying to impart a little wisdom – "Hey, actually management teams tend to inflate things a bit." Just to wrap that up, defining the task, the context behind the task, some specific outputs if I want it, some guidelines, and some kind of domain context, I think is a really good starting point. And if you can send that to a human and they read it and understand the task, you're probably going to do pretty well with the model as well.
· Matt Reustle
[00:00:00] S0: Yeah, the level of instruction, if you almost humanize it, that was one of the initial things that I think you mentioned months and months ago that made a material change for me. And then on the idea of context and where --- and we'll use an LLM, assume off-the-shelf LLM for this question – but when you're thinking about when to just give them massive freedom versus when to load in documents. How does that change the scale and the output quality in your experience?