Investology: Investment Management Intelligence
00:18:09 · Andrea Carnelli Dompé, Andrea Carnelli Dompé, George Aliferis
[00:18:09] S1: And that is a massive component into the productivity gain because I think a system [00:18:13] S0: that where only 99% is accurate, but not easily auditable, wouldn't save time at all, because somebody wouldn't trust it, wouldn't have an easy way to audit it, and would have to kind of do the job from scratch. So having that auditability, massively important. [00:18:29] S2: That answer brings in two reflections. First of all, how different it is [00:18:34] S1: to use data or to have a streamlined use of data in public versus private market. [00:18:40] S2: In public market, this has to some extent been solved. [00:18:44] S1: I mean, there's so much more to be done in terms of data and improving it. [00:18:48] S2: But accessing the data is already very advanced, whereas in private markets, [00:18:53] S1: there's so much to be done there and therefore so much differentiating factors for [00:18:59] S2: those that play this game. The second one is, I guess, [00:19:03] S1: more about building startups in general in this age where AI is everywhere. And I can totally get why being LLM agnostic is a good way to play this game because there's so many resources that are being built and are available pretty [00:19:23] S2: freely. [00:19:24] S0: Absolutely. [00:19:24] S1: And the way we approached it was by basically building a massive backtesting framework, in fact. So we have this beautiful proprietary data set of documents coming from different [00:19:34] S0: GPs, different asset classes, different type of data contained.
00:19:34 · Andrea Carnelli Dompé, Andrea Carnelli Dompé, George Aliferis
[00:19:34] S0: GPs, different asset classes, different type of data contained. [00:19:38] S1: And we have the ground truth that we have been able to establish by working with our clients. [00:19:43] S0: Every time a new model comes out, [00:19:45] S1: we can very quickly check if using this model alongside the entire infrastructure [00:19:50] S0: that we have set up, there's an element of interaction with our systems, but tell me, [00:19:58] S1: very quickly if this new model can improve the performance of our systems and tell [00:20:03] S0: me exactly where it improves it, where it does not improve it. And based on that, [00:20:07] S1: we can integrate that new model or totally discard it where it makes more sense in [00:20:12] S0: our architecture. And that's super powerful. Otherwise, every time a new announcement came out, [00:20:17] S1: it could be absolutely in the dark whether that is going to be helpful or not. And that makes me reflect on something that I've seen from the CEO of Bright Data, [00:20:27] S2: and I will not quote him exactly, [00:20:29] S1: saying that the calculation power is always going to increase exponentially, [00:20:35] S2: but you can build data modes. [00:20:39] S1: And I guess by that he means accessing and processing the data is where you can [00:20:45] S2: really make a difference and a sustainable advantage for yourself. [00:20:51] S0: Yeah, well, yes. I think, I mean, at least from a pure technology perspective, [00:20:55] S1: precisely because these systems have to be highly trained domain to be really
00:20:55 · Andrea Carnelli Dompé, Andrea Carnelli Dompé, George Aliferis
[00:20:55] S1: precisely because these systems have to be highly trained domain to be really [00:21:01] S0: driving big efficiency gains. They will do okay if they're not trained. [00:21:06] S1: There's so much to be gained if you train them specifically on the domain that having access to the information in that domain is a moat for you because you'll be able to get a better mousetrap out of that versus somebody who doesn't have access [00:21:19] S0: to that data. [00:21:20] S1: And as you accumulate more data, you're going to have a quicker edge, for sure. [00:21:25] S0: I think eventually that kind of advantage might level out. [00:21:29] S1: But as of today, it's all about gaining that data and optimizing the LLMs on that data. [00:21:36] S2: And I guess it's also a technical perspective. [00:21:38] S1: It reminds me of the time when Uber was pretty much offering free rides and WeWork [00:21:45] S2: were incentivizing people to come in their offices with a lot of perks. I can see as well the evolution. Gmail, not so long ago, was trying to make me pay for the use of Gemini, and now they're trying to force it into my email account. It's a good opportunity that may not last forever. [00:22:03] S1: Every single penny lost in P&L by one of the LLN providers is one percent gained by [00:22:09] S0: the consumers of these LLNs.
00:22:09 · Andrea Carnelli Dompé, George Aliferis, Andrea Carnelli Dompé
[00:22:09] S0: the consumers of these LLNs. [00:22:11] S2: what you're trying to reach and how much accuracy you're trying to reach, are you aiming for 100%? Then it's probably going to be exhausting. Perhaps in what you do, if you're aiming for just 90%, then this 10% means that there's absolutely no efficiency. [00:22:26] S1: Whereas if you reach, let's say 99%, then that's something that's really effective. [00:22:33] S0: Absolutely. [00:22:33] S1: So I think the best way to look at it is in terms of time saved, the person overseeing process. It's a good proxy to think in terms of extraction accuracy, if you like, but it's just a proxy. And there comes a point where 1% of additional improvement doesn't really help the [00:22:52] S0: person because he still needs to check it. So we try to think in terms of time saved. Ultimately, that's what we want to optimize for. [00:23:00] S2: Now I realize, Andrea, we also have to reflect on this democratization of alternatives, which is such a big topic, and investing in private assets. So you're talking to sophisticated people and make it easier for them. But it's also part of this democratization or enlarging the access. Now that you don't need that many resources, that's when you may be able to start in this very complicated world. [00:23:29] S0: I think democratization could be great if done properly. So in principle, if you give more options, more investment options to a wider audience, more people will be able to choose what's best for them. That's in theory. What's happening often, though,
00:23:29 · Andrea Carnelli Dompé, Andrea Carnelli Dompé, George Aliferis
[00:23:29] S0: I think democratization could be great if done properly. So in principle, if you give more options, more investment options to a wider audience, more people will be able to choose what's best for them. That's in theory. What's happening often, though, [00:23:46] S1: when it comes to democratizing corporate markets is that products are being pushed [00:23:51] S0: onto people who shouldn't really have it in their portfolios or into, say, liquidity and maturity constraints. And B, [00:24:00] S1: sometimes these products are mainly a way for some intermediaries to drive more fee [00:24:08] S0: generation. So if we reduce these opportunities for bad acting, I think democratization could be great, to be honest. [00:24:17] S1: But if instead these trends continue and people see it as a way to make more fees [00:24:23] S0: and push the wrong products to the wrong people, I think it's going to be a boomer in the industry. So I'm kind of worried about it. [00:24:31] S2: Yeah, [00:24:32] S1: democratization could be that you no longer need 20 people to really be an NLP, [00:24:38] S2: a player in the game. [00:24:39] S1: That brings me to my final question, which I call the fantasy fintech question. How do you see private markets evolve in the best possible way in the near future [00:24:51] S2: if there's no constraints? [00:24:53] S0: Well, I'll tell you how I would love it to evolve, [00:24:55] S1: as I think it should evolve in this way to be really value additive to the [00:24:58] S0: community for investors.
00:24:58 · Andrea Carnelli Dompé, Andrea Carnelli Dompé, George Aliferis
[00:24:58] S0: community for investors. [00:25:00] S1: I think, first of all, information should flow as it goes in public markets. [00:25:06] S0: It shouldn't be something hard to access and extract and understand. It should flow as easily as in public markets. So that's number one. Number two, [00:25:14] S1: we should make advances in having a unifying language to compare publics with [00:25:20] S0: privates. Because as we insert these investments in broader portfolios, we need to understand interactions. We need to understand what makes sense to add private versus not. And we still lack a unifying language. There's massive debate simply of why IRRs are misleading. So that's point number two. Point number three, [00:25:38] S1: I see more secondaries as a day-to-day tool to exit positions you don't want [00:25:44] S0: anymore, just like in the stock market, just like in the fixed income market. Lower fees, I think private market generally generate value, but I think oftentimes there's lots of fee capture, value capture from GPs. So I hope that the fees and carry will go down. And finally, back to your point before, responsible democratization. I think with all these things happening, private markets will be a great force of good for portfolios. [00:26:10] S2: Okay, we probably need to do one episode for each of those four points. So four more. But that's a great conclusion. Thank you so much, Andrea. It's been a wonderful conversation. [00:26:21] S1: Perhaps you want to tell us as well what's the best way to find more information [00:26:24] S2: and contact you.