00:18:14 · Alistair Smallwood
[00:18:14] S0: Yeah, I think it's a great point. I think the first point that I would make is that I wouldn't view this as a single end to end idea engine, because ultimately, there's a huge amount of stuff that you layer on top, which is all of those softer points that you talk about could be technical, could be crowding based, ultimately could be your fundamental relationship with the management team. But ultimately, really what we're trying to do is generate as many possible signals as possible in order to drive further investigation that then sits on the top, rather than replacing, oh, I've now got a seven quarter previous testable signal that I think you should trade on. Ultimately, I think that any fundamental investor that's been doing this job for any time won't feel that that's anywhere near enough to build the level of conviction that they would require. Ultimately, what it's trying to do is to add to lines of inquiry and fundamentally add to the potential conviction that they could have over an existing idea. In the same way that I've been to a conference and I've met a management team, they've mentioned something, I've then been at the same conference and a peer of theirs has then mentioned something else. Ultimately those two things together has forced me down a particular line of inquiry that might then lead to an idea in a few days time once I've done the work. Ultimately systematising that with public information is the same process, and because these things run autonomously, it then allows the investor themselves to have more time to layer on their pieces over the top to drive that holistic view of the business and potential investment idea.
00:19:50 · Brett Caughran
[00:19:50] S1: Yeah, yeah, that makes sense. And one other question I had, it's, we talked about the bitter lesson and prompt engineers and certainly trainers of prompt engineering were fairly rapidly bitter lessened, your chain of thought prompting and all these other duct tape fixes got very quickly abstracted into the skill. I've sort of been trying to understand, I've found actually increasing alpha in being really thoughtful and detailed about skill creation, but I'm curious your thoughts. Do you think that is durable or does this skill file become better lesson into the harness or into self learning or into post train models, sort of eval loop? What's your thought?
00:20:39 · Alistair Smallwood
[00:20:39] S0: Yeah, it's a great question. I think that the self learning piece is the most interesting angle here. I think ultimately, we start to run an increasing number of systems in a fully evaluated loop, whether that's human as the last stage of the loop or whether it's agent as last stage of the loop, I think the skills will be continuously improved autonomously as a result within a system that they work within. In the same way that if you as an analyst were doing consistent amounts of work on a given company, your understanding and ultimate thesis on that company improves over time. And I think that that's what we'll see from particularly loop driven agentic systems, where they are essentially self improving as they gather more context. What is particularly interesting about markets is they are essentially their own evaluation system. Ultimately, if you're not trying to call share prices and you're trying to call expected earnings, the learning loop that an agent can have as a result of that is really, really powerful. So let's say I ran my Mosaic theory at scale system for three days, found four really interesting relationships which would suggest that there is a beat coming in the next earnings for those three companies. If my loop continues to run, it will then see the eventual earnings that were then reported, try to understand why it worked or didn't work, and improve the skill accordingly. It might have missed something or misunderstood a transmission mechanism between the two companies and you just get this slightly self fulfilling process that happens as a result. In terms of the kind of bitter lesson build around skill writing, we still think the most effective way of skill writing is initial draft with whatever AI system you're using, but the handwork around them is still producing much, much better results. I would expect that advantage to slim over time. And ultimately, will come down to how well you can explain yourself to the AI in the first place to write the first draft.
00:22:37 · Brett Caughran
[00:22:37] S1: Yeah, what advice would you give to funds who are walking down that skill writing path right now? Has worked effectively for you?
00:22:48 · Alistair Smallwood
[00:22:48] S0: I think it's a great question that ultimately doesn't necessarily have a silver bullet answer to it at the moment. I think that learning that skill writing process, and being able to AB test and evaluate output properly is an unbelievably valuable skill that irrespective of whether the skill writing is something that you continue to have to do, being able to evaluate and build systems to evaluate outputs that isn't just human in the loop of you reading three different documents and deciding which one you like better is still probably the most valuable skill within finance. I mean, The reason that I say that is because ultimately I think that over the next five to ten years, could be three years depending on how AI polled you are, I think that a huge number of fundamental teams will look more like quantitative teams, where they spend the majority of their time improving and maintaining an equity research system that they are running. The biggest skill within that is being able to evaluate output properly, and being able to do that in a systematic fashion. And so from a skill writing perspective, I think that understanding what type of format works with skills to get a better output is a hugely fundamentally important skill. Ultimately, whether the agent writes that or you write that doesn't necessarily matter, providing that you are learning how to evaluate the output of it properly. And being able to do that, frankly, at scale.
00:24:09 · Brett Caughran
[00:24:09] S1: Yeah, yeah. One of the like the of the fundamental Yeah, I totally agree with you sort of fundamental quant conversion happening. If think you about the fundamental alpha pools, to vastly oversimplify it, quant sort of bet on recurring patterns. Hey, we've seen this story before. December tax loss selling bounced back in January. I can back test that 80 bps of alpha net of slippage, press the button, go, right? Whereas fundamental investors, it's a lot around regime shift and of one scenarios, unique situations. And sort of one example I give is, semiconductors used to be traded on sort of a normalized earnings, of a cycle, of the sort of predictable cycle for twenty years, right? We saw generative AI and that entire lens of analysis just went out the window. It's actually hugely destructive. Like you were short Nvidia the entire way up if you were valuing it on normalized earnings. And so a system sort of trained on history is more likely to sort of over learn based on a sort of regime of analysis, right? Versus the human investors like, Hey, something's changing. It's almost more of like a more VC style visionary aspect, certainly in tech investing. I know it's sort of an unknowable question, but a fun one to discuss. Like how do you think about, that's what humans are great at doing, or the best humans are great at doing. How do you think about systematizing that? Because that's ultimately where a lot of alpha comes from.