Proof of Work: AI Value Creation
00:14:00 · Joe Zein
[00:14:00] S2: Yeah, I think I think to me to just you know add on top of everything Yann saying, which I completely agree with, again, it really depends on the industry, right? If you're looking at a B2B company, then you you wouldn't be at a B2B company who's selling SaaS licenses, then you probably it doesn't matter that it's on the younger stage, then it probably doesn't matter to have a daily report of how many contracts they signed, because the reality is in most days it's zero contracts, right? But if you're looking at a B2C company that is, I don't know, selling dog toys, you want to see the daily transactions. And those are usually you know indicators of how Ibita is gonna trend over the month. And you know, this conversation was very hot a few years back in the arts space when people were trying to come up with all sorts of alternative data sources to come up with signals that they could link to Ibita, which is a delayed metric. But again, at the end of the day, I think it's very company dependent. You have obviously macro signals that come in all the time that are applicable to all companies, and this is usually what sparks questions coming in from the LPs or the deal teams. Something happens in the world, there's a war going on, there's some tariff that's been imposed. Now all of a sudden, everyone wants to look at the health of the portfolio and see, okay, how is this going to impact our portfolio and our holdings? And so these are, you know, you have the macro signals that are applicable to every company, but then you also have micro signals which depend deeply on every single company, the data model industry, the size of it, the check, the check size you've put in. And you can, you know, at the end of the day, at some point, you know, unless you're ingesting all of the raw data of that company, which is an impossible task, right? You're gonna have signals, but it will, again, it will depend on on what's what's the level of granularity you want to pick and decide on.
00:15:50 · Stuart Willson
[00:15:50] S0: Yeah. I'd love to dig into signals a little bit more on two sort of different directions. One is that, and this is a conversation I've been having over the past six months, there's been an explosion in new data sources. Software is creating exhaust, and companies are emerging that integrate and clean and organize this data. What are some of the signals, or the sorry, the data points rather that you've seen emerging over the last year that can be early warning signals as to a change in EBITA or margin compression or covenant drift and things like that? What are some of these new data points and understanding that every industry is different and private credit is different for private equity? What are some of those new data points that are increasingly relevant to investors?
00:16:38 · Joe Zein
[00:16:38] S2: Yeah. Again, right, it it's the more depth, the more, the more deep you go into a company, right, the more you're able to understand what constitutes a certain aggregate metric, which is or or down-the-line metric, which is EBITA, something like EBITA, right? In the credit space, I mean, it's kind of like a an interesting space because lenders put up covenants when they they write, they underwrite a loan. And so those covenants, which are essentially rules to make sure that, you know, you know, are are tied to those aggregate metrics. But then we've been seeing more and more rules that are more granular than just saying debt to Ibita should never go above that specific threshold, right? Because there's a lot of things that constitute debt, and there's a lot of things that constitute Ibita. And it's really about decomposing that metric, that metric that is either the sum run uh long sum of a lot of subline items. And and I don't want to say like there's been like specific signals, everyone is tracking covenants, but then everyone is tracking what's making up those covenants. It depends a lot about like how deep you want to go in a company, right? A big deployer like Blackstone will not, you know, and this is you know, every everything that's being talked about about in the credit world that, you know, the credit boom, people have been funds have been, you know, under have a lot of drag powder that needs to be deployed. Uh, and private credit is a very interesting and an appealing asset class. So prices have gone up, the covenants have become looser, and what this ends up happening is like people are are running away, are are having less signals than they had because the pressure is to deploy, right? But then if you if you go in and and and dig deep onto every company, then this is usually what the smaller funds do. It's like I go in, I dig very deep, I set up very narrow uh covenants to make sure that you know uh this company's not gonna go under.
00:18:25 · Stuart Willson
[00:18:25] S0: Again, it's it's it's it's a very company by company based uh one thing that I'm seeing, you see it on LinkedIn, you see it on X, is that someone on the deal team or the operating team builds a monitoring tool? Maybe they do it in Claude Code or Claude Cowork or OpenClaw or whatever is hot that weekend and it works and then it doesn't. What goes wrong when um individuals build monitoring tools like that? Maybe Joe, you can start.
00:19:01 · Joe Zein
[00:19:01] S2: Yeah, I'm gonna start very simply. Uh and I wanna let Yann hear what Yann has to say about this. One of the hardest pieces of technologies to build is portfolio monitoring. Because of how high impact it is, this is something that is touching that you're sending to LPs. This is these are numbers that are getting reported to the SEC and to other governing bodies. So this is not the kind of thing you want to botch. And anecdotally, a lot of things that we've been hearing is from associates, as you said, it's amazing that we have all this that you know this level of curiosity, and people are trying, and people are trying to do new things, but people don't understand what they're building. The answer is always like DAI did it for me. I have no idea how this is working. We haven't been hearing a lot of, we've been hearing people oversimplifying it and saying, yeah, at the end of the day, this is only serving us as a database. But then you dig a little bit deeper and you realize that there's actually a lot more than just storing data points. There's a lot of calculations, there's a lot like on the 73 string side, there's valuation. And most people are, I want to say, smart enough to know that this is like territory that we shouldn't go into. But the apps that we've been seeing being vibe coded over the weekend are generally not portfolio. We've heard people say portfolio monitoring, you know, we're not using using it to its full extent. So maybe we want to hard code, we want to vibe code something that is, you know, a lighter version, which I hear. But if you are using a portfolio monitoring tool as you should be using it to its full extent, this is not something that that you can vibe code. Um I think the 73 string has been doing it for half a decade. The high-level guys have been doing it for decades. Like a lot of people, like and and there's a lot of complexity that goes into that, right? Like IRR calculation is not a simple summation. Um, anyways, Yann, I think.
00:20:53 · Yann Magnan
[00:20:53] S1: Yeah, no, uh, and to to uh to maybe um stress what you just said, uh Joe, at the end of the day, this uh the private capital industry is an industry that uh needs to get to 100% accuracy. There's just no no other way. You can't get to 99.5. If you run an algo for to do whatever once, uh and you and then you run it again one hour later, you have to get to the exact same conclusion, not a slight difference. And and the reality is that at this point in time, LLMs can do a pretty good job, 100%. They can do a pretty good job in surfacing insights and intelligence. But if you if you don't get the basics, the basics right in the in the first place, which is a very sound, robust, governable, auditable, as Joe was saying, data model. If you're not developing something that will you know make sure that your regulators, LPs, you know, trust what you what you provide, at the end of the day, it's garbage in, garbage out. As simple as that. Well, if you do that, if you have that first layer where you have a very robust data model where you can overlay that with Some solutions that are common. If you can create that data model when it makes sense so that it's consistent consistent across across a portfolio, then that's when you can create massive um uh intelligence insights and value. And and and and people like a Soal Labs can basically do that work to overlay uh this type of intelligence on top, on, on top of that new structured data model. But like I said, the starting point is you have to get the data right in the first place. If you don't, if you're not getting that right, I mean garbage in, garbage out, and recipe for a disaster at some point in time.