00:00:00 · Alistair Smallwood, Brett Caughran
[00:00:00] S0: Large language models can broadly natively process charts and tables, although they're still nowhere near where they need to be in terms of doing that consistently and reliably. [00:00:08] S1: The metagame of all of these systems is is where the actual alpha alpha sits. [00:00:14] S0: Pragmatic thinking and testing of anything that you're doing with AI is still super, super important because the accountability still has to exist within human. [00:00:27] S1: Great. Welcome to the next episode of Invest with AI, where we explore the intersection of fundamental investing and artificial intelligence. I'm really excited to have Alastair Smallwood here today, who's Head of Applied AI at Primer, and like me was a former investor. Al, sir, thanks so much for being here. [00:00:46] S0: Delighted to be here, Brett. Thanks for having me. [00:00:48] S1: Maybe just start by walking through your background, sort of leading up to your conversion moment. I feel like we're all sort of vampires in a way that we got bitten by something at some time and now it's sort of hard to unsee what you've seen. So just sort of walk me through your career as an investor and what got you walking down the AI path?
00:01:11 · Alistair Smallwood
[00:01:11] S0: Yeah, absolutely. It was partially by accident. I worked on the sell side for eight years and was approached by a fund which was launching in London, running an originally pan European long short market neutral strategy, and the core thing for that PM was for all joining analysts to be code proficient on starting, because the modus operandi of that fund was to be state of the art from a technology and AI perspective. The PM was a big believer in potential advantages from both productivity, but also ultimately alpha generation that it could bring. His key thing was that he wanted there to be a very short space between deep domain expertise within fundamental investing and how that could translate into the building of AI tools, rather than farming it out to either an internal team or somebody that sits separately within the PM and analyst team. So I was sitting in my seat at UBS and I was approached to join this fund, but a prerequisite of joining this fund was understanding how AI works, the coding piece, this is obviously pre coding agents, this was kind of late twenty twenty two. So I kind of set about understanding how coding works, building a few tools in the background, looking back on them, they were worse than average, but ultimately there was a huge learning process that came with that. Then continued to work in the fund for two years, building and maintaining a decent amount of technology infrastructure within that, which spanned from scraping alternative data processing and machine reading of research. The And PM of that fund is the largest shareholder in Primer. It became a separate business very quickly off its founding. And the excitement that I had in terms of ultimately where AI and more broadly compute can take equity research was particularly exciting for me. And so I made the switch from the fund into Primer about twelve months ago.
00:03:08 · Brett Caughran
[00:03:08] S1: Great, great. I think you had the benefit of getting your hands dirty very early in sort of the applied AI moment around GPT 3.5. Can you sort of like, I think in the rapidity of new technology, sort of it's now like a three week cycle, it's easy to lose context of how far we've come. Can you sort of like take a step back and what are your reactions to the curve of improvement in the tools for this craft over the last four years?
00:03:39 · Alistair Smallwood
[00:03:39] S0: Yeah, absolutely. I think the best way to communicate it is really to try and give context of something that we do now versus how we were doing it back in 2023. And a great example of this would be the core of any AI and equity research system is your ability to process and machine read primary filings from companies, so 10 Ks, 10 Qs, annual reports, quarterlys, trading updates. When we first started building this at Primer, we were dealing with large language models with context windows of 4,000 tokens. They couldn't process PDFs, they couldn't process charts, they couldn't process tables. And as a result of that, we had to build our own fully custom pipeline to do that in the most equity research specific way. What I mean by that is that instead of just passing a PDF and an agent hopefully understanding the things that were important, we had to build a totally custom pipeline to make sure that some of these annual reports that were a million tokens plus were being broken down into the right sections and then being pieced together, and ultimately the output format was as agent readable as it possibly could be. Because as we know, doing this job, if you miss a number or a footnote in the accounts, you could totally break down the whole working process. So where we've come to since then is we now have million token context windows, people are no longer chunking bits of information for code to process them, and large language models can broadly natively process charts and tables, although they're still nowhere near where they need to be in terms of doing that consistently and reliably, so our custom pipeline is still running. But there is a huge amount of work that you can now do from that perspective. I think one of the key things in terms of our own internal technology development that we have seen change enormously is in previous worlds when you build software, you will have alerting systems that suggest that there's a problem here or there's an error here, and then it requires you as the individual to go in and check that and debug it and try to understand and then rebuild and fix it.
00:05:43 · Alistair Smallwood
We've now got to a stage where coding agents are so good from our perspective that every time that there's an error logged, we have internal agents that go and look at those errors, fix those errors, and then propose the fixes to us, which take significantly less time to check, which means that general reliability and uptime of platforms is so much better. But it also means that from a productivity perspective of us, the engineering team, almost all of the work that is done is go forward work rather than maintenance and tech debt that has to happen on an ongoing basis, which I think from our perspective and something that we've already experienced means that product development and velocity is just unbelievably fast. And I think that I don't think it will be news to anyone listening to this that, yes, coding agents are really good and you can build stuff really, really quickly. But now sitting within an engineering team, it's now very obvious to us that all of your time can be spent doing that rather than bug fixing and historical stuff and making sure that tech debt is fixed on an ongoing basis. And that is a huge step change for us in terms of potential velocity of the business.
00:06:54 · Brett Caughran
[00:06:54] S1: And one of the things I hear as a theme from people building AI tools for investors is just this almost sort of endless obsolescence, sort of building sandcastles that get washed away by the next model iteration. Can you sort of walk us through that dynamic and how you navigate? You build something, the next model comes out, it's obsolete, you're decommissioning, you're almost sort of recreating the spine. Maybe that's a sort of oversimplification, recreating the spine of the technology every few months.