Bryan Bischof
I lead ai at Theory Ventures.
“A cafe manager is not superhuman at looking at data and saying, like, oh, I'm going to need like 25 cookies. No, a cafe manager is superhuman at understanding hospitality. … Investors are superhuman at a few things, but I don't personally think investors are superhuman at looking at websites. They are superhuman at understanding potential, understanding upside.”
The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis · AI Research & Frontier Labs · November 2026
Context
Asked how his venture firm judges whether its AI tools are working, Bischof answers with a story from an earlier job at a coffee company. His test for an AI tool is whether it frees people for the work only humans are good at.
Transcript
…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.
…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 tech somewhere without really making an impact. How do you know what is actually working and what is, you know, potentially subject to leaving on the cutting room floor?
…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? 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 going to need like 25 cookies. No, a cafe manager is superhuman at understanding hospitality. And that may sound weird 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…
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 it is that research process doesn't really have a defined endpoint. You could always dig deeper, correct? Totally. How do you, how do you make…
Totally. I want to reference something that Nathan said earlier. You know, he's got this list of things and he's like, some of this should have been left on the cutting room floor. We would call that taste and sort of like knowing what should be cut and what shouldn't be cut is taste. And yet…
How goes your teaching of data science at, I believe, Rutgers?
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