"The Cognitive Revolution"
00:50:22 · Bryan Bischof
[00:50:22] S4: Everyone is technical, and that's all the way from sort of our talent partner to our GTM partner through the investors. Everyone is using AI. Everyone's been using AI since the day that I joined and more and more. The way that we think about token spend is, you know, you should be thinking very, very clearly at all times if there is a way to accelerate by using AI tools. You should be thinking about experiments that you wanna run, and we're a really experimental bunch. To give you a sense, every quarter, we do our little, like, off-site where we talk about just everything that happened over the quarter. One of the things that we talk about every single quarter is what experiment every individual did that quarter with AI. That's not an exaggeration. This is not like, you know, oh, I asked Claude to do something new this quarter. No. These are... You preregister what kind of experiments you wanna do related to your job that use AI, and then you check if they worked. This happens every quarter because we're so insistent on rethinking what does it look like to work in a venture firm in, you know, this year, next year, and the year after. How are we adapting to those things? As the head of AI, my engineering team is obviously still operating at another level, which is how can we build tools that use AI to transform other people's workflows? Our goal is not to replace an investor with an AI system. Our goal is to make that investor 10 to a 100 times more effective and efficient by using AI tooling and building the AI tooling that will make that possible. Let me give you a quick example. Everyone talks about, like, company brain no matter what kind of business you're working in. I've used a couple company brain products and candidly found them all to be useless. The main reason is because they are trying to build sort of what we used to think of as this internal knowledge system. You can think of Notion, for example. Notion is like a stack of files, and maybe you can build a good search on top of it. Is that really your ambition? My ambition's a lot higher. And so my ambition for a company brain is agents who are consistently working to connect the disparate nodes in the graph of understanding from your business. And so that's why I can easily say, yes.
00:52:57 · Bryan Bischof
We don't have, you know, an agent for every human at Theory, and we don't have 10 agents for every human at Theory. We have thousands. And there's a reason for that. It's because there's a lot of work to get done. My ambition is that every piece of work that an investor does during the investment process, the research process... Because remember, we're very research focused. Every piece of work that they do during the research process is automatically linked, enriched, and sort of expanded. This is why when people say, what are you building? I say, I'm building research agents.
00:53:37 · Nathan Labenz
[00:53:37] S0: How do you evaluate success? I mean, obviously, one of the huge challenges in venture historically, and this hasn't changed yet, is it's a long time to find out if you made the right bets or not. Right? So Totally. Sometimes I do wonder if... Including for myself, you know, I've got... I've... I I periodically go do, like, a cron job cleanup where I'm like, there were ideas that I asked about taking, you know, every bit of data exhaust and, like, processing them in some other way and finding connections and being, like, super galaxy brained about it or, you know, scouting this or reporting that or whatever. And I'm like, probably two thirds of those if I'm real about it. Like, I never actually look at the outputs of. And so it's easy to sort of have these ideas, spend a bunch of tokens, and sort of just end up logging a bunch of text text somewhere without really making an impact. How do you know what is actually working and and what is, you know, potentially subject to leaving on the cutting room floor?
00:54:43 · Bryan Bischof
[00:54:43] S4: Let me tell you a story about Blue Bottle. When I was at Blue Bottle, we wanted to build inventory forecasting. And, specifically, we wanted to build a machine learning system that could help us predict how many pastries we were gonna sell on a given day and other things like coffee and milk and so on. But pastries was the first focus. And what was our key, like, north star, our guiding light? Well, it was actually... If this is successful, it's not that we sell a lot more pastries or that we waste a lot fewer pastries. Okay? Those were, you know, two metrics that we tracked. But what would we have believed with success? What we would have believed as success is if the cafe manager who's already got about 70 jobs, if that cafe manager essentially goes into the system and when it comes time to order, they just click okay. Okay. Okay. Okay. Okay. Okay. Now we don't want them to just not think and just autopilot. We actually don't want that at all. And there's a reason. And this will surprise you, but the variance in sort of what a cafe lead understands with respect to their cafe demand is shocking. It is... It would it would blow your hair back how much they understand about the demand in their cafe that is not just in the numbers. We have these weirdly deep, like, you know, tacit understandings. So what was our north star? Our north star was that a cafe manager has more opportunity to spend that time with customers. Because a cafe manager is not superhuman at looking at data and saying, like, oh, I'm gonna need, like, 25 cookies. No. A cafe manager is superhuman at understanding hospitality. And that may sound weird because because you can't manage that on the dashboard. You can't look at hospitality on the dashboard. But what you can do is you can ask them periodically, how much more time do you have available to spend with customers, to spend on the floor and notice that there's, you know, a coffee spill, to understand that your baristas are slammed right now and actually, like, staffing was off. Also my fault. But, and so these are the things that we always cared about. Why do I bring this up? Well, yeah, you could say, like, okay. It's, like, seven to fourteen years to find out if this, like, you know, diligence was good. Sure. But it's a pipeline. So first of all, you wanna ask yourself, how fast can you get through the research pipeline?
00:57:25 · Bryan Bischof
How fast can you go from identification of a potential opportunity or market to a clear understanding to be able to make a decision? But more importantly, hospitality. What does hospitality mean in venture? It means that the investor has time to talk to human beings, to talk to potential or existing companies, to talk to founders and competitors and employees, to try to understand the human element of this market and what's going on. Investors are superhuman at a few things, but I don't personally think investors are superhuman at looking at websites. But they are superhuman at sort of understanding potential, understanding upside, understanding sort of the relationship between, like, you know, customer need, willingness to buy, and sort of framing what the vision in the future can look like. That's what they're superhuman for. And so when you ask me, like, how do I measure success? Well, I measure success when an investor tells me, yeah. Actually, I get to spend 30% less time staring at, you know, the website and trying to figure out, like, the important details I look for every single time. Or I get to spend 20% less time filling out, like, you know, these specific answers to questions which are important for diligence, and instead picking up the phone five more times and talking to five more people who have credible opinions on this deal. That's success. And you don't need to wait seven years to find out.
00:59:03 · Prakash Narayanan
[00:59:03] S1: Do you find there is kind of a spectrum of, like, let's do more research to... We need to cut off the research and we need to make a decision now? Because that research process doesn't really have a defined endpoint. You could always dig deeper, correct? Totally. How do you make those decisions? Are the agents the ones making the decisions? Is there a stepping off point where they have to come back And you make that... A human is making the decision. How does this loop work?