00:13:51 · Matt Stockton
[00:13:51] S1: Yeah, no, that's a really good question. Like, how do you, if this is not the thing, has a job, and it's not to learn how to use AI, right? They have an actual job. And so how you stay in tune to what's happening? My view, and this is just, it's kind of silly to say it, is like, you just kind of have to be in it. Like, you have to use the tools. You have to spend time experimenting. There's no replacement for just going and doing the thing, right? Like going to try something. Even for me who's in the weeds, these things change, right, over time, and so you just have to be in it. So I think it's more, it's almost more like a process or a team operations concern. It's like you kind of have to both have people at the ground level that are curious and willing to experiment and have the time to experiment. And I think also you kind of have to have the view as a firm of like, Hey, we're going to spend time using this, and we're going to try to integrate it into our processes. In terms of where to look to learn, I mean, Anthropic, OpenAI, all of their resources, all of their training is really good. Their documentation is great. So you can just kind of go there and do their tutorials, read their docs, and get up to speed relatively quickly. But I think the most important thing is actually spending the time doing it, which it's hard, right? Doing something else and it's not learning AI.
00:15:31 · Khe Hy
[00:15:31] S2: Brett, I'm curious in your world, you see folks that, CIOs and PMs that if they're talking to you, they have the vision that this stuff is important, but they probably are super time crunched that they're not firing up a Cloud Code instance on a Saturday night to test the Rag implementation. How are you finding that? And how are you seeing that kind of in the folks that you talk to kind of balance the, you know, I've got to, like, I need to be the CIO that uses the tool. At the same time, I'm the CIO. I've got 9,000 responsibilities and playing with Cloud Code is probably not one of them that takes priority.
00:16:11 · Brett Caughran
[00:16:11] S0: I think we've probably all seen the same thing of usually at a firm of 20, you have two or three who had some sort of moment and they get a little bit of AI psychosis. I say that kindly because I think we probably all three have some degree of AI psychosis. And I think what stimulates that, at least for me, is like trying something that you think is impossible for AI to do and it actually does it. And you're like, Holy moly. Like, I can't believe that these tools can do these things. That takes to your point, Matt, like a bunch of experimentation, but that is such a seductive, addictive feeling. And all of a sudden you're doing, you're sort of trying other things late nights and weekends. So I'd say pretty much every firm has like one or two of those people that are trying to bang the drum for the other individuals to sort of bring them along. I think the single player versus multi player game like really, really resonates. I think that's the tricky challenge of how do you diffuse that information to the broader firm. Because most of those people, like you ask them how many hours they've spent experimenting to get to that level of fluency with the tools, and it's usually like hundreds of hours. So that's a trick. And then even me, almost every week, the interface on Codex changes or Cloud Cowork changes. You're like, Wait, what's this button? What is Codex projects? What's perplexity computer workflows? Just staying up with the architectural changes of the UI is hard enough to do. But...
00:17:58 · Matt Stockton
[00:17:58] S1: Yeah, I really love your example of trying something that you don't think is possible for these models to do. I agree that when you kind of frame it for people to do that and then they go do it, that's kind of generally when they hit their moment because, you know, lots of people are still using these tools more like a Google search for what it's worth. They don't really truly understand how capable these tools are. I mean, recent example for me is I've been really into trying to take written content that I've used the models to build using upstream data sources, instead of just having that written content, basically turn that written content into an interactive website. So you think about you you built a company deep dive, or you're doing some research on some topic, right? And, you know, you get this 10 page document. It's like, okay, do you really wanna share that? Is someone gonna read it? Turn it into a website, have... Send them a self contained HTML, JavaScript CSS, where they can just click around. And, like, that just kinda works now. Like, if you if you if you tell the model to turn this stuff into a website, you can get an HTML, send it to someone. Like, I don't know. If you would've asked me if that was possible, like, six months ago, I'd be like, no way. Can't. It can't just one shot a file that you can share that someone can just click around in.
00:19:22 · Brett Caughran, Khe Hy, Matt Stockton
[00:19:22] S0: Yeah. You guys probably saw the Tim Ferriss tweet where he talked about his books having declined 80% over the last five years, and he sort of attributed the recent accelerating drop to AI. Makes sense to me, man. I used to read 20, 30 books a year, and I sort of look back at myself like maybe I've read one or two this year. Because exactly that, to go get all this information on a topic and learn it in a distilled way is a superpower of these tools. It's pretty crazy, actually. [00:19:55] S2: Did you see, yesterday they announced Claude Code can create artifacts that are then shareable within your team. So you don't even need to fire up, like, a hosting environment. So that same example, Matt, you described, you could create this, like, beautiful, I don't know, an initiating coverage report via HTML on Snowflake and then just push a button and automatically deploy it to your entire firm. [00:20:22] S1: Yeah, yeah, I saw that announcement. I haven't dug in. I have an ever growing backlog of things to read about. I think companies ship even faster and faster. [00:20:31] S0: So we talked about the data cleaning problem. When a client comes to you, Matt, and says, We want to make these things work. How would you bucket that exercise? What are the big buckets of workflows that you think firms need to focus on to really go from interested in concept? Do we actually want this productized and enterprise grade to make decisions with agents?
00:20:59 · Matt Stockton
[00:20:59] S1: Yeah, yeah. I mean, I like to take like a job to be done type approach. It's like, okay, like, what are your what are you doing now? Like, what are the workflows that your analysts or your team are doing? What documentation are they producing? How they sharing that? What are the upstream things that they're gathering? Right? And it's kind of a discovery process where you can talk to the people and say, What are you doing? Where are you getting your data from? What are you looking at? How do you store your notes? And so just understanding the workflows, right? Acknowledging that part of what these tools might help you do is optimize that workflow. Maybe it will help you gather the upstream information faster, distill it in the ways you want it to faster. But it might be something new, like some new process that's kind of like goes across your existing flows. So I think it's like understanding the key data sources, right? And, you know, in this space, like there's a handful of them that seem pretty important, right? Like transcripts, SEC filings, obviously your own personal notes, your RMS. And so it's figuring out how you can get those in a way that can be accessible to the models, right? You know, when you look at things like SEC data or transcripts, like there's providers now where you can essentially pay money, get access to that information and have that accessible to the models. You have to wire it up. You know, I think the more interesting pieces are what are you doing with this? Like, what are you planning to do with the data? How do you want to structure it? How do you want to distill it? And there's a lot of utility in helping teams write that down, like talk through that. Because I think a lot of this stuff is kind of just, you know, it's in your head, right? It's this institutional knowledge that's just in people's brains, right? So I think a big benefit is extracting that kind of that specialized intent or knowledge in writing it down. Because it turns out if you can really specify what you want these models to do, and you have access to the data that has that source information, they're really good. I think a lot of the work goes into just kind of writing down what you want to do. And I always like to tell people when they're learning AI is like, hey, are you a good communicator? Like, can you write things down? Okay.