Business Breakdowns
· Matt Reustle
[00:00:00] S0: This is Matt Reustle, and today we have a special episode breaking down how investors are using AI. This is a question I get from many of you, and while there is no shortage of content on the implications of AI, I know there's an appetite to learn more about tangible use cases. How to make sure you're getting the most out of these tools, how to think about advancements in the technology, and ensuring that you're keeping pace with the innovation curve. So my guest today is David Plon, founder of Portrait Analytics. Now, David and Portrait have been a partner of Business Breakdowns since last year, but I specifically asked David to do this episode because, one, he is really front and center to how investors are using and applying AI, but two, and maybe more importantly, him and his team come with a background in investing. So while the conversation doesn't really focus on Portrait – you'll hear references to what him and his team are building, and how they've shaped it for investors – you'll very much understand when you hear David talk that he is someone who understands the pain points of an investor. I think everyone will find something in this episode that will benefit them in their day-to-day, and I would love to hear the feedback. Now, onto the episode. David, I am excited to have you here to finally do this recording. I think over the holiday break, many investors were spending time trying to figure out how they can get more out of AI tools. It's constantly evolving, but at least for a snapshot in time, I think you'll be able to give us some really good perspective on use cases, some of the skill sets that you can use to leverage the most out of the technology. But I actually just wanted to start out with your background, because I think this is really unique and can speak to why I find you such a valuable resource when it comes to thinking about this through the investor mindset. So can you bring us back in time prior to Portrait, what you were doing, and how that led up to starting this business?
· David Plon
[00:00:00] S1: Yeah, absolutely. Real pleasure to be on here. So I spent my career as an investor in a few different seats before starting Portrait. I started at Barclays on the trading floor in the Special Situations Group, was then at a long-short hedge fund called Slate Path Capital where I was a generalist, and most recently was at Baupost, which is a large hedge fund based in Boston, where I was a generalist on the public markets team doing both public equities and distressed credit. I think I probably first got bit with the idea of using AI within the investment research process when I was at business school at Stanford. And this was like 2015 and 2017, so deep learning was very much in the zeitgeist. We were still pre-transformer right up until I graduated, but it just seemed inevitable to me at some point that this technology would be really impactful for a lot of the research workflows that I went through. And after spending a lot of time studying and exploring and prototyping, I got the conviction to throw myself full-time into this project. So that's my background leading into starting Portrait.
· 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.