Business Breakdowns
· David Plon
[00:00:00] S1: It's interesting because I was someone who – and still do – loves doing the pre-buy work. My wife likes to joke that my favorite Saturday morning activity is reading a 10-K. And the thing that's interesting, what I was mentioning before, where to me that's an important part of the conviction building process is building up that context and feeling like you've read all the material and you can start piecing together the various data points. Where I think AI can be really helpful in that process is two things. One is getting you enough information to know whether an idea should be killed. I used to do this accounting at the end of every year when I was an investor: how many ideas did I really look at? A remarkable low number that made it through to the deep research stage. And a lot of times I could have killed an idea much quicker once I'd surfaced enough information to understand, "There's maybe an existential risk here I'm never going to be able to get past, or the management compensation is such in a way where I'm just never going to feel comfortable that their incentives are aligned." And so I think where AI can be really helpful in that process is giving you enough baseline context to be able to triage the idea of, "Oh yeah, this is passing the initial sniff test. I want to spend more time on it," and maybe spend 12 hours reading through all the historical content. And I'd say the other thing that has been really helpful is just expanding what gets moved up earlier in the pipeline in terms of types of analyses. So for instance, I mentioned earlier CEO compensation. One of the things that I would do if I was really getting into a name is go through the last five proxies and try to map out what are the metrics that the CEO is being comped on, and how have those changed and the weighting of those changed. A lot of times that's a useful signal into what the board is thinking about. That's a bit of a pain, and now on Portrait it's a click of a button. So things that historically would be more in like the deeper dive category, now I can move up. And that to me is one of the most powerful use cases of AI.
· David Plon, Matt Reustle
So as an analyst, you're able to turn over far more rocks in a given period of time than you would otherwise, and spend more of your deep research creative time on ideas that you know already passed your initial sniff test and are worth that time. [00:00:00] S0: When thinking about really tapping into that, would that be having a list of non-negotiables, in terms of whether it's compensation elements or something else that you can screen through quickly? Or is it that you don't necessarily know what you're going to hit that might break it, but you're just going to get there faster through the various iterations of getting up to speed? I know it's a little bit technical, but I'm curious mostly if there is a way that you think that you could templatize that versus not.
· David Plon
[00:00:00] S1: Oh yeah. There's two categories. There's definitely things that you can templatize. So for instance, I mentioned CEO compensation. There's certain accounting things I would look for, with respect to like aggressive revenue recognition or changes in certain assumptions that would inflate figures. And then I'd say there were patterns that, if I saw, would turn me off. So one analysis that now is very trivial but took a lot of time historically was, I would go back through the last three, four years and lay out every piece of guidance that the management team had given, both obviously the hard specific guidance, but also anything qualitative or soft, like "Oh, we expect revenue to accelerate sometime in the second half of the year." And I think building up a picture of management's guidance style and credibility is really important. And that's the type of context that someone who's followed a name for a long time intuitively has, that being fresh to a name, I wouldn't have. That's a situation where, if my thesis is based on some sort of turnaround execution plan that the management team is undertaking, I can do the work and say, "Oh, actually over the last three years every year they've said they expect margins to expand and it never happens," that would be enough to maybe kill the idea depending on other circumstances. So I think that's another cool example of how it helps – I think where AI can be really useful surfacing patterns that historically were pretty painstaking to put together and now it can happen far more easily.
· Matt Reustle, David Plon
[00:00:00] S0: Yeah, 100%. It's a very valid point too on the credibility. I think there's a trust level, and once investors show they don't really trust a management team or a stock, it could be a free fall, and you see that quite frequently. [00:00:00] S1: Yeah, and it can be really subtle too. There are times where if you just look on the Bloomberg screen, it says management beats guidance every quarter. And then you actually dig into it, you realize when they give full-year guidance in the Q1 call, they end up revising it down quarter after quarter after quarter. So by the time they get to Q4, sure they're going to beat their numbers, but if you're building your model looking out a year, you probably should shade whatever management's saying at that point, subtly.
· David Plon, Matt Reustle
[00:00:00] S1: Yeah, and it can be really subtle too. There are times where if you just look on the Bloomberg screen, it says management beats guidance every quarter. And then you actually dig into it, you realize when they give full-year guidance in the Q1 call, they end up revising it down quarter after quarter after quarter. So by the time they get to Q4, sure they're going to beat their numbers, but if you're building your model looking out a year, you probably should shade whatever management's saying at that point, subtly. [00:00:00] S0: Yeah, there's effectiveness in just kitchen-sinking and taking all the pain in one quarter, and that can be better than just consistently revising down. So there's all types of things that, to your point, feel they're definitely quantitative in nature, but the ability to extract that information was not always easy. And I think that's an entire category where AI is showing up. The last section is in that sourcing of new ideas, and maybe something you alluded to before, where you have certain frameworks or characteristics of businesses that you really like, and it's not always easy to find all the companies out there that fit that or to know if you're looking at a company that might be fitting that. So where in the idea sourcing, idea generation are you seeing AI be most effective? And I will just mention, to me personally, it feels like there's always a step before you get to sourcing – you're usually introduced to a name for other reasons – but I'm curious where you're seeing this coming into play.
· 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?