00:04:43 · Khe Hy
[00:04:43] S1: It was. Yeah, it was. It was funny because like you, I was pretty skeptical throughout '24. I kind of had to force myself to use it because, you know, I was creating content writing and so on. So it was definitely helpful for like lightweight marketing and writing and so on, but I didn't really have the moment. And then, in fact, I interviewed Dan Shipper from Every, who's gonna be a future guest of ours. And my interview with him in the middle of 'twenty four was, Is AI overhyped? That was the theme of the episode. And so, Dan, if you're listening, I've been converted. I'd say for me, the magic moment was in January '25. And I had been playing around again, mostly for writing and proposals and legal stuff. It was fine. And I took Nat Alaisen's course called Build Your Own Apps. And I guess it was kind of right when Andre Karpathy, who's kind of a prominent AI researcher, released... Coined the term vibe coding. Now, I'm a failed computer science major. I got all Cs in 1997 to 2001, and I never coded professionally ever. I don't know how to code. And I took this course, and I was just kind of mucking around. And then all of a sudden, I migrated a 400 site, 400 page website from WordPress to Next. Js hosted on Vercel. Like, again, this might sound like foreign language to many people. It was to me at the time as well. And I had budgeted like 50 ks for that 40 ks and I did it in three weeks and it's still up and running. I was like, oh my god, this is this is no joke. So that was probably the first one. And then I would say the one that kind of tipped it with my clients was the release of, the reasoning model. So one pro kind of in, maybe February '25 and then 'three. I mean, 'three was kind of when I fell in love with AI. I was like, This thing is amazing. And I had started an email newsletter or Substack that was basically trying to scratch my own itch, which was How can AI make knowledge work better? So probably a little bit more general than the approach you've taken, Brett. And all my most of my examples were finance adjacent, just given my own background. And immediately after I sent kind of like the first three issues, a bunch of my old friends from the hedge fund industry emailed me. They're like, Come into our offices and teach us everything you know. And that's how I accidentally started an AI training business.
00:07:28 · Khe Hy
I wanna ask you, Brett. You had mentioned kind of the shift from chat to agents. And I feel like in 2020 the dates are so confusing now. In 2025, agents was the big word, right, last year. But I was always calling BS on agents. Like, no, the only agent people are using is deep research, where you kind of give it a task and you kind of walk away. And then, you know, Claude Code kind of came around in June ish of last year. But I'm curious on your end, when did that kind of shift from this is a Q and H question and answer thing to, oh, this gets real work done. How did that when did that happen for you?
00:08:10 · Brett Caughran
[00:08:10] S0: Yeah, that's a great question. I think we probably have the same personal Sherpa in the learning journey of Andre Karpathy. And I think he's done such a service to both of us in terms of what he shared publicly on YouTube with many of his videos. And I just find him to sort of be the rare Venn diagram of technical fluency on these tools and also not having the right incentives, not trying to hype his company to raise valuation. And the way he explains things is very good. I think Kay and I both recommend his videos on YouTube as a good zero to one in learning what LLMs are. And I had a similar insight to you on 'one, 'three with the deep research functionality. That was the first time where I had something in my research stack that I found to be institutional grade, particularly if you pointed it towards open web information, things where there was a lot of government data or something where the raw data was in the open arena. Deep research reports were wonderful at distillation. And for really at the same time, the spring of twenty five, I found myself starting research processes with deep research reports, which I found to be often better than sell side reports and customize the specific thing. There were certain use cases, I come from the quant world where natural language processing is sort of a key fundamental alpha. And from a hedge fund world where we're constantly trying to understand the body language of management, that word selection really matters. There's information laden in word selection. So I think that was an initial use case that I found quite helpful out of chatbots. Is the company using more bullish or more bearish language? But these were small pieces, right? With my Sherpa Andre, I think it was in October, he went on Dwarcache and he called agent Slop and said, It's going to be a ten year journey. And so I'm like, All right, well, Andre says it, it's good enough for me. Everyone talking about agents, I never really understood the agentic thing in '25 either. OpenAI came out with their agent functionality and it's just what is this thing? It just didn't work at all. So I wrote it off as hype and followed Andre over Christmas as he sort of went through the coding agent thing. And he came out in January. He's like, listen, like what I said in October is completely different. It's completely changed.
00:10:40 · Brett Caughran, Khe Hy
So I started going down sort of the understanding of agents. And I have a full set of like evals and different things I want to try to do with these tools and chatbots failed the majority of them. I think when did Claude sort of come out with Claude Code and Claude CoWork? But that was really like the big agentic shift of the Claude. Exactly. Of the Claude CoWork. [00:11:03] S1: Yeah, Claude Code was I think Q2 of twenty five and co work was January '20 [00:11:10] S0: Yeah, so the Claude code, like the December, January moment was like a big inflection in the agentic revolution. And even people talk about Claude Code and an IDE, but even the things you can do in Cloud Chat are now agentic. I can go build Excel, you know, build Excel things just directly in Cloud Chat. So Cloud really, I think, took this big step ahead from OpenAI, and you've seen more agentic tools. It's really just the last ninety days, there was like, call it 5% of my workflows, five to 10% of my workflows as an investor was the TAM for AI in '25. Now I feel like it's pushing up to 30, 40, 50, 60%. And the imagination you can apply with these tools now is well beyond what you could do with chatbots. Now there's still some issues that we'll explore this chatbots. Issues, many jagged edges, context window limitations haven't gone away. Multi document retrieval accuracy hasn't completely been solved. But the pace of evolution just in six months is, I think, well ahead of the bull case I envisioned, you know, summer twenty five.
00:11:10 · Brett Caughran, Khe Hy
[00:11:10] S0: Yeah, so the Claude code, like the December, January moment was like a big inflection in the agentic revolution. And even people talk about Claude Code and an IDE, but even the things you can do in Cloud Chat are now agentic. I can go build Excel, you know, build Excel things just directly in Cloud Chat. So Cloud really, I think, took this big step ahead from OpenAI, and you've seen more agentic tools. It's really just the last ninety days, there was like, call it 5% of my workflows, five to 10% of my workflows as an investor was the TAM for AI in '25. Now I feel like it's pushing up to 30, 40, 50, 60%. And the imagination you can apply with these tools now is well beyond what you could do with chatbots. Now there's still some issues that we'll explore this chatbots. Issues, many jagged edges, context window limitations haven't gone away. Multi document retrieval accuracy hasn't completely been solved. But the pace of evolution just in six months is, I think, well ahead of the bull case I envisioned, you know, summer twenty five. [00:12:34] S1: Can I ask you because I feel like the the word agentic gets used a lot? And what is how do you personally define the word? And then I'm happy to share my definition.
00:12:45 · Brett Caughran
[00:12:45] S0: Yeah. No, I'm curious yours. I, I think about the investor toolbox as like workflows, kind of end to end workflows. Hey, I want to go do an earnings preview process or this company has a new CEO, I want to go and explore the background and the potential of this CEO. And within that, there can be 20 steps along the process. I want to go understand or her stock grant, past performance, do a background analysis on if this person has nefarious historical affiliations, etc. In the past, I had to create each of those as individual prompts because as you know, a prompt past five or six pages started to lead to a garbled output with chatbots. So I couldn't build a workflow to do all of these things. And many of the tool providers tried to do that, chaining together prompts and building all of this complex engineering around it. Now I can one shot that an agent, So with a skills it really just makes the tools much more user friendly. It gives me an end to end workflow that still isn't perfect, but is shocking in terms of the quality and caliber. So my three zero six prompts have gone down to 30, really like 18 orchestrated pipelines and then individual skills that I can pull off the shelf to dig in more deeply. So you're seeing this abstraction in the complexity of working with these tools. And there's been innovations around MCP and MCP and data as well, too, that sort of the one example you'd see that if you went into ChatGPT last year and asked a question, often the quantitative metric will be pulled from a blog somewhere, right? So if I went in and I said, what is the ten year history of Nvidia's earnings per share? These were exercises that a chatbot couldn't answer accurately. So it's like, if I can't answer a basic question like that, like get out of my face with this AGI thing. But now I can plug Claude Cowork into an MCP from Dilipa or Faxit or FinShat, and that data is piped in through the MCP process with, in my experimentation, much better accuracy. So it's not a solved problem, but there's been huge innovation. Do you think about agentic workflows and what agents unlock?