00:18:11 · Brett Caughran, Ying Hua
[00:18:11] S1: And to do that by hand for a fundamental investor, enough there's probably of an ROI to get five bps more accurate, but combining many of those signals together into a forecasting mechanism sort of could scale that precision and forecasting in a... [00:18:30] S0: Yes. Now it's a... Think about every part of that process a lot easier. Like, it took us a few months to build it ourselves, but now scraping is a lot easier, doing analysis is a lot easier. I can schedule run it a lot easier. I don't even have to go like before there's also technical problems of like we run a python program, it spits out a number. How do you pull that number into my excel spreadsheet? My model. Like even that was a hurdle right because I basically had to write a script to open my excel sheet, put in a number in there, refresh it, save it. Like a lot of things are a lot easier now. So I think my hunch is more and more these like alpha edge, things that will be nice to do before. Now you actually have time to do it and it's a lot easier to do it. I think it's going to become increasingly more important.
00:18:30 · Ying Hua, Khe Hy
[00:18:30] S0: Yes. Now it's a... Think about every part of that process a lot easier. Like, it took us a few months to build it ourselves, but now scraping is a lot easier, doing analysis is a lot easier. I can schedule run it a lot easier. I don't even have to go like before there's also technical problems of like we run a python program, it spits out a number. How do you pull that number into my excel spreadsheet? My model. Like even that was a hurdle right because I basically had to write a script to open my excel sheet, put in a number in there, refresh it, save it. Like a lot of things are a lot easier now. So I think my hunch is more and more these like alpha edge, things that will be nice to do before. Now you actually have time to do it and it's a lot easier to do it. I think it's going to become increasingly more important. [00:19:19] S2: Yeah. Can I just ask on that data scraping question? Is that an AI specific skill in the sense that AI is going out to do it? Because that's what strikes me as, like you said, a Python deterministic process that, you know, you go find the site, you know, you clean the data and you put it in a different place. And so can you clarify, like, the AI role in that? Is it that it's just easier to code that into existence? Or is there an actual, like, native AI activity in what you just described?
00:19:52 · Ying Hua, Khe Hy
[00:19:52] S0: Yeah. So I'll split it two parts. I think one, it's easier on the data collection side. So get the raw data. We're probably one of the earliest ones to have an agent that does scraping. It does most of the scraping, human in the loop, we clean it. There is a second part is, it's very similar to how everyone reads 10 ks a little bit differently. Everyone should process data slightly differently. And from that point it's no longer deterministic. So gathering data is just deterministic, cleaning data is deterministic, there is a right answer. But how each team use that data, how each team... What insight are they trying to get from that data is non deterministic. But that part is also I think accelerated by AI. And this is especially important for... We have a lot of even teams inside of large firms even though they have a lot of resources technically but there are still a lot of things you're like it will take me too long to explain it to the data team or by the time I explain to the data team the catalyst would have gone. Now they can just do It takes a few minutes. They can take a first crack and does it worth more time invested? Then if it does, then let me do a deep dive and none I can pass. So I think that part is the AI native part. [00:21:08] S2: And just on that, do you think that Brett and I have had this conversation, like the prevalence of Claude code for non coders? [00:21:15] S0: Yeah.
00:21:15 · Ying Hua, Khe Hy
[00:21:15] S0: Yeah. [00:21:16] S2: Do you, you being closer to it and us being kind of the Claude, the non coder coder types, is realistic that a non coding analyst can go in and be like, Okay, Cloud Code, help me generate this scraper that pulls this deterministic data and so on? Or is that a bit of a pipe dream that is gonna collide really harshly with reality?
00:21:39 · Ying Hua
[00:21:39] S0: I would probably say that I definitely think we're biased, right? If everyone can use Cloud Code to build every process, then I think we don't really need to exist. So I'll probably say I do think my answer is biased. Don't think it fully works and I'll tell you why from a few angles. Number one is data pipeline. So you need to schedule build it right. Something is easy you run it every day every whatever. Sometimes it's not. Some data comes out of random interval. How do you figure out when to scrape? All right that's number one. Number two is you need to store that somewhere. You're not going to run everything at like all at once right. Like you need to store that data somewhere. Number three, if anyone has done scraping manually, you will know that website changes, whatever deterministic thing is going break, pretty much for certain. In the span of a year, every single scrape you build probably has some changes. How do you adapt to that, Right, and then lastly I always say I'll tell you this and this is I think, I don't think it's fully solved but when we first started out implied, we actually wanted to do quantitative stuff meaning like natural language quantitative stuff and we quickly basically decided that language model wasn't good at it. And even though language model has improved, I don't think some of the problems have been solved. I'll tell you why. AI generates Python code. How do you verify something? How do you verify the logic you're building there is correct for a non technical person who doesn't cannot read Python code? It's like we think AI can help. I don't think it solves completely. And the reason why that's important is I remember very interestingly when I was at Citadel, we had, data scientists, so they put data scientists onto our team. And we would describe something and they'll come back with this is relevant, this is not relevant. And every time I was like I need to see your code. And I picked up Python when I was at Citadel but I need to read the code to understand what you wrote. And then I can tell you oh you made a logic flaw here. It could be just like one sign difference. It can be very small. And it's just really hard for non technical people to truly evaluate that. I think some of ourselves, like the way we approach it, is you might not know the code but you should know what the right answer is.
00:23:57 · Ying Hua, Brett Caughran
So you can evaluate if the answer is good or bad based on certain points like kind of sanity check. It's kind of like if your intern hands you excel it's not you can check every cell you're going sanity check a few slots. No way to do that. But I think, [00:24:12] S1: a [00:24:13] S0: lot of especially the more complex things I think is really tough. So those two things I think is really really tough. It's like I think clock co building something is very good for one off things. It's harder for a continuous production level self improving, like that part is difficult. And I think going back to just from an industry perspective, I don't think like this is actually true for people's process today. Think a lot of the work that individual team has done is duplicative work. Meaning think about company release earnings. How many Amazon release earnings? How many teams right now are updating their model? There should be one single source of truth. It should not be everyone doing their own thing. Same thing with data pipelines. Like I don't think it makes sense for everyone to try to, you know, I cover 50 stocks, have 50 data sources to manage all those 50 data sources with a pipeline through. Would I rather just use a company that can take care of all that? And how I analyze it is up to me, but how the data is gathered should be just one company. But that's another way to think about it.