Business Breakdowns
· Matt Reustle
[00:00:00] S0: Yeah, I can definitely speak to that as a transport analyst trying to monitor truckers and knowing that there was valuable information coming out of CPG companies talking about freight costs. I spent many late Thursday nights, while the rest of the world was enjoying Manhattan happy hour, Ctrl+F'ing through these various transcripts and earnings releases. So I think that is a major one that has evolved and just the ability to gather all of that information in a much more efficient way and not Ctrl+F'ing the heck out of things. When you next move into the building up research on a name or the pre-buy research process, getting up to speed, this is another one where I think it's the widest net of possibilities. But when you think about maybe through the lens of how you used to approach it versus how you're seeing other investors approach it, what are some of the use cases that you're seeing people implement to really get the most out of it and maybe build better conviction or gather more information, whatever it might be?
· 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 I want to spend more time on it," and maybe spend 12 hours reading through all the historical content.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.