The Exchanges

Q There's certain things that were missing initially that are now in place in terms of Everything from certain forms of inference time compute on through to forms of memory and other things that allow you to maintain some sort of state against what you're doing. What do you view are the things that are still missing, or need to get built, or what sort of foment progress on that end?

A I think the technology component level, there's stuff that I hope will improve. For example, computer use, you know, kind of works, often doesn't work. Um, I think, so the god rails, evals is a huge problem. How do you quickly evaluate these things and drive evals? So I think the, the component is this room for improvement. But what I see is the single biggest Barrier to getting more, uh, agentic AI workflows implemented is, is actually talent. Uh, so when I look at the way many teams build agents, the single biggest differentiator that I see in the market is, does the team know how to drive a systematic error analysis process with evals? So you're building the agents by analyzing at any moment in time, what's working, what's not working, what do you improve? As opposed to, uh, less experienced teams kind of try things in a more random way, then it just takes a long time. And we're looking for a huge range of businesses, small and large. It feels like there's so much work that can be automated through agentic workflows, but, you know, the talent, the skills, and maybe the software tooling, I don't know, just isn't there to drive that disciplined engineering process to get this stuff built.

AI assessment note: “the single biggest Barrier to getting more, uh, agentic AI workflows implemented is, is actually talent.”

Q What is the, um, if you just look at the spectrum of agentic AI, what's the strongest example of agency you've seen?

A I feel like Bleeding edge of agentic AI. I've been really impressed by some of the AI coding agents. Um, so I think in terms of economic value, I feel like there are two very clear, very apparent buckets. One is answering people's questions, uh, probably, you know, open AI, chat, GPT seems to mark the leader of that, with real takeoff, lift off velocity. The second massive bucket of economic value is, uh, coding agents, where coding agents, like my, my, my personal favorite, Claude Deva Tu, Right now it's cloud code. Maybe it'll change at some point, but I, I, I just use it. Love it. Uh, highly autonomous in terms of planning out, you know, what to do to build a software, building a checklist, going through it one at a time. So this ability to plan a multi-step thing, execute the multiple steps of a plan, uh, is one of the most highly autonomous agents out there being used that, that actually works. Uh, there's other stuff that I think doesn't work, like, Some of the computer use stuff, like, you know, go shop for something for me and browse online. Some of those things are really nice demos, but, but not yet production.

AI assessment note: “I've been really impressed by some of the AI coding agents.”

Q What do you think investing firms Or incubation studios like yours will not do two years from now? Like, not do manually. Sorry.

A I think there's a lot could be automated, but the question is, what are the tasks we should be automating? So, for example, you know, we don't make follow-on decisions that often, right, because of portfolio of some dozens of companies, so do we need to fully automate that? Probably not, because we're very, very hard to automate. Um, I feel like doing Deep research on individual companies and competitive research, that seems right for automation. Uh, so I, I might, I don't know, I, I personally use whatever Open Eyes Deep Researcher and other Deep Researcher types of tools a lot, uh, to just do at least a cursory market research things. Um, LP reporting, that is a massive amount of paperwork that maybe you could simplify.

AI assessment note: “doing Deep research on individual companies and competitive research, that seems right for automation.”

Q do you think are, are common? I mean, I know, um, people have been talking about, for example, uh, it almost felt like there was an era where being hardworking was Kind of poo-pooed, or do you think founders have to work hard? Do you think people who succeed? I'm just sort of curious, like, aggression, uh, hours work, like, what else may correlate or not correlate in your mind?

A You know, I work very hard. There are periods in my life where, you know, I, I encourage others that want to have a great career, like, work hard, but even now I feel like a little bit of nervousness saying that because in some parts of society it's considered not politically correct. To say, well, working hard probably correlates to your personal success. Um, I think it's just a reality. I know that not everyone, at every point in their life, is in a time where they work hard. You know, when, when my kids were first born, that week I did not work very hard. It was fine, right? So acknowledging that not everyone is in circumstances where they work hard, just the factual reality is, people that work hard accomplish a lot more. Um, but of course you need to respect people that aren't in the face where they-

AI assessment note: “the factual reality is, people that work hard accomplish a lot more.”

Q been, um, some versions of things where people, for example, are trying to generate market research by having a series of bots kind of react in real time, and that almost forms your market or your user base as a simulated environment of users. Have you seen any tool like that work or take off, or do you think that's coming, or do you think that's too hard to do?

A Yeah, so there's a bunch of tools to try to speed up product management. Um, I feel like, uh, well, the, the recent Figma IPO is one, you know, great example of design. AI, high DNA, you know, Dylan did a great job. Um, then there are these tools that, uh, are trying to use AI to help interview prospective users, and as you say, we looked at some of the scientific papers on using a flock of AI agents to simulate, you know, a group of users, and how to calibrate that, It all feels promising and early and hopefully wildly exciting in the future. I don't think those tools are accelerating product managers nearly as much as coding tools are accelerating software engineers. So this does treat more of the bottleneck on the product management side.

AI assessment note: “It all feels promising and early and hopefully wildly exciting in the future.”