Business Breakdowns
· Matt Reustle
[00:00:00] S0: When you think back to some of those stints, whether it's Baupost, Slate Path, even potentially on the trading floor, can you think of any scenarios where you were spending a significant amount of time that were pain points that are maybe easily solved by AI today? We'll get into some of the very specific use cases, but I'm just curious, when you look back at that time, some of the workflows that you had where you could see the biggest lift today versus where it was 10 years ago.
· David Plon
[00:00:00] S1: It's very different today. It's interesting because the output of an investment research process is ultimately a decision, and hopefully a high-quality decision. It's very unlike other fields where there is actually a widget or a service being provided. When I thought about the investment research process, there were aspects of it that had friction, but that friction was helpful because it helped me build conviction personally. I was one of these guys that could never really outsource model building. I always had to build a model myself in order to feel conviction in recommending a position based on that company. But there were certainly aspects that I'd say limited me in terms of how productive I could be in finding and researching the best ideas. Essentially, I would bucket it all into the category of there's just a ton of information out there that I could potentially consume. Consuming it in a way that was efficient and additive to the research process was challenging, just given hours in a day. So, I would put it into three main categories. There's certainly idea generation, so finding the ideas that best fit my mental models. I knew there were probably dozens out there that were totally in my sweet spot, but at any given time it was very hard to know if I was working on one of those. There's certainly the initial context building that goes into an idea, especially as a generalist. Not even developing a differentiated insight, but just getting to table stakes on how a company and an industry works, and the narrative. And Business Breakdowns has always been a very useful resource whenever I'm working on something where there's an episode, that was always a huge help. And then I'd say lastly, just monitoring an investment idea. As a generalist, it was often challenging to stay on top of not just the companies, but the surrounding ecosystems – the customers, suppliers, competitors. And if someone lived in a given sector, they usually were able to put together a real-time mosaic based on all those data points that they're absorbing, about what was happening in the industry and so forth. And I always found that really challenging as a generalist.
· 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.