Business Breakdowns
· David Plon, Matt Reustle
There was more than one earnings season when I got smacked because I was the last person to realize that end-market demand was weakening or suppliers were pushing prices through, and those types of things. So ultimately, I would say those are the types of workflows – high-level information processing to help triage new data points – broadly speaking, are the types of things where AI can be really useful. And we can get into some specific applications of that, but that's how I would think about where AI can fit in a research process without replacing the parts of the process that are important for building conviction. [00:00:00] S0: Yeah, I think that really resonates and seems to align with the people that I've seen getting the most out of it. A lot of it is productivity and efficiency, which on the surface level sometimes people can poo-poo, but when it comes to making decisions and having clarity of thought and being able to take in the most valuable information, I think there's a lot more to it. So I like the idea of getting into some of the use cases, and I actually wanted to start backwards in terms of the buying process, and reference what you just brought up there in terms of position monitoring. What are some of the tangible use cases of AI today that you think investors are using or you would recommend they use, to just monitor what's in your portfolio and stay on top of it as effectively and efficiently as possible?
· David Plon
[00:00:00] S1: I think there are many good solutions today for staying on top of what is happening with an individual name. There's plenty of ways you can add it to a watch list on any number of services and staying on top of all the new filings and transcripts and news related to that specific company. What I think historically has been hard but now is much simpler, is being able to cast a wider net of pulling in relevant data points in a much sparser set of data. So for instance, let's say you own Expedia. One of the key investment factors for Expedia is always going to be what's happening in the hotel ecosystem and how volume is trending, how pricing is trending, or what OTAs are doing with their distribution strategy and how it relates to OTAs. The challenge historically is if you're an Internet analyst or you're a generalist following Expedia, you probably aren't going to want to subscribe to every piece of news of what Marriott is saying. You don't care what their profitability necessarily looks like, or if they're adding new units to take share from Hilton. What you really care about is any specific data point which would add to the mosaic around what is happening in the hotel landscape with respect to consumer demand for travel, or if they're seeing trends in market share shift with respect to OTAs. But historically, it was impossible to pick up all those data points unless you really consumed every piece of news. There was no smart filter on top of all that data. I think with AI today – and obviously the company I run, Portrait, we have solutions that target this – but even outside of Portrait, there are plenty of ways today to now much more efficiently pick up those data points across that surface area, such that where today, a certain level of understanding was unique to someone who just covered travel, you're now able to pick that up much more efficiently as it relates to your investment thesis.
· 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.