01:01:56 · Alistair Smallwood
But ultimately, we think that an increasing number of funds, which we've seen of people building at the frontier, will want full sovereignty and ownership over their equity research system, because it really will be a system. It won't just be another tab that we're also used to adding every three or six months, oh, there's another tool out, we'll trial it, we'll have it in a new tab, and we'll forget to use it. Ultimately, these systems are going to be absolutely central to how you do that work. And so we've been super fortunate over the last few months to be working with some of the very large funds on licensing the tools that sit underneath Primer to form the kind of basis of the infrastructure or their day to day research system, which gives them the full flexibility to build and customise on top without having to worry about any of the plumbing that sits underneath it. Because ultimately the plumbing is, as we have found, difficult to do and to do it well, and you want that plumbing to improve consistently as models improve, harnesses improve. The thing that you as a PM or analyst should be focusing on is how can I use this system to discover new methods of activity research, test them, try them and fundamentally do a lot of the stuff that I was already doing faster and on autonomous a basis? And I think that the job will shift from trying to translate what I do now into a system. I think that will happen fairly quickly over the next kind of eighteen months. But ultimately, the job thereafter will be look significantly more like an AI researcher, where I'm trying to push the bounds of what every new model release can do within the system that I've built, to try and find interesting signals that I can then read, understand and potentially act on going forward.
01:03:35 · Brett Caughran
[01:03:35] S1: Yeah, that makes a lot of sense to me. I mean, you can start to get ingestion with internal RMS and model architecture, meeting notes, etc. When you sort of deploy locally, right within the within the sort of fund firewall, and that seems to have solved a lot of compliance and infosec concerns, right?
01:03:58 · Alistair Smallwood
[01:03:58] S0: Yeah, exactly. And everyone's at a different stage in terms of their infosec issues, and some people are still really keen for zero day retention when you pass anything externally to a foundational language model. Other people are very happy with the setup, and ultimately there is this big trade off of red tape versus competitiveness. And trying to toe that line is I'm not envious of it at all, but it's probably quite difficult of really trying to protect your internal IP, but at the same time of not being left behind because you're going take the next eighteen months to build your own system and frankly build it well, which is an interesting thing that we're having conversations with. We've just been through the process with a very large UK based hedge fund, where we were the first tool for them to license that had MCP connection and also had in built off-site large language model capability. And so we essentially had to work as their guinea pig to run through this, and have spent a huge amount of time with legal compliance and InfoSec. Obviously, giving that, I suppose, comfort on how we process information and why it doesn't leave us and row level security and all of those things. But definitely not something that I thought when moving into this role would be a decent chunk of what I do in terms of helping people go through that process and be comfortable with the way that we... That businesses like us process information, because it ultimately is the new wave of what will be coming. And I suppose it's always that naivety of when you move to the new job, you only think about the thing that you're super excited about, which is trying to push AI to its to its absolute limits and equity research. I don't think about the contracting and the compliance processes and frankly having to deal with the systems that those compliance systems sit on is the worst thing in the world. I mean, they were built in 2001 and impossible to interact with. They're so bad that browser use of large language models can't use them.
01:06:10 · Brett Caughran, Alistair Smallwood
[01:06:10] S1: Yeah. Yeah, so folks, the application layer does not scale as fast in finance as it does in coding for this very fundamental, sort of very fundamental reason. The good news is once you're wired in, today if I am using Instinct, I can switch to Muse or whatever the next thing is, like the sort of mode around many of these businesses. The coding agent mode is almost zero, but it feels like the vertical layer here will be as large or larger, but really sort of a co developed collective system, which should make for some really high quality businesses that can serve asset managers to help them cross this transition. [01:06:55] S0: Yeah, totally agree, Brett.
01:06:57 · Brett Caughran
[01:06:57] S1: Well, I feel like we, I got to like a third of my list. I wanted to get to modeling and I wanted to get to so many other things, but we're over an hour now. So I think we're going to have to do a part two at some point in a few months. This was really fun to discuss and debate these things with you. And I would encourage people to follow Alastair on LinkedIn because he's putting just some interesting thought pieces. Really like just sort of the... If people ask like, What's the future investing? I don't know. Why are you asking me on Twitter? It's sort of an unknowable, right? But it is what we all as an industry make it. Best thing we can do is sort of study priors and pay close attention to the technology and try to get a sense of where frontier firms are at, frontier hedge funds and long onlys are at. So I think we agree most funds are less far along than most would think. The technology is just coming together. We didn't even get to the sort of the rewiring of the investment process. You have sort of the infosec. Then you have the issue of you're a tech analyst covering 60 stocks, getting that person off the desk away from earnings, away from model building. Sort of be like, Hey, you have to completely revamp how you do everything with these new tools. It's sort of like, leave me alone, nerd. Like I'm trying to basically, have a print in three days. Yeah, exactly. So changing the sort of engine in flight analogy. So it's going to take a it'll it'll take a few years to go through this full transition, in my opinion.
01:08:32 · Alistair Smallwood
[01:08:32] S0: I agree. I agree. And I think that a huge part of it is around educational, the educational element of people really understanding how to use these tools effectively. The massive issue, as you full well know, is that if you're in a risk taking job, you don't have any time. And the people that we have seen really make leaps over the last twelve months are those guys that for whatever reason have been in the garden between roles. And we've even seen users within the platform where I've enabled them in month one of their gardening leaves. And the quality of usage that I've seen from month one through month nine has been absolutely phenomenal in terms of real core understanding of how to use these systems properly. And ultimately, AI is a skill like anything else. It's a ten thousand hours job, and you do have to spend the time to understand and stay up to date with what's moving.