It’s Tuesday and your tech stack is obsolete (again). Now what? | Theory Venture’s Bryan Bischof

**Andrew** (0:05)
Today, I'm joined by Bryan Bischof, the head of AI at Theory Ventures.
Bryan has a PhD in pure mathematics. He teaches AI and data science at Rutgers, and he's built teams and led projects at places like Hex, Weights and Biases, Blue Bottle and Stitch Fix. And Bryan, I've really been looking forward to having you on our show, because I got to see your work in action just last year at your America's Next Top Modeler Hackathon, which is an amazing name. We're gonna dive a little bit more into the things behind it as well. It was an amazing time and I learned a lot that's really influenced my own personal journey and the things that we've talked about on this show. So it's really great to have a mind in the mix behind all of that that kicked it off. And we're gonna get into some of your work at Theory Ventures as well.
But first, of course, we have to talk about what's happening right now at the AI Council 2026 Conference where you and I are both on site. And Bryan, I'm thrilled to have you here. Congrats again on being named a track chair as well at the conference.

**Bryan Bischof** (1:10)
Thank you. Thank you. Yeah. I'm really excited about AI Council this year or FKA Data Council.
Some of us still think of it as Data Council. Let's just be real.

**Andrew** (1:19)
Totally.

**Bryan Bischof** (1:20)
This reminds me of an interesting debate that I was just clued into. Is it AI and data or data and AI?
I think it's a really interesting question.

**Andrew** (1:30)
I think you got to say AI and data that kind of rolls off the tongue a little better, doesn't it?

**Bryan Bischof** (1:35)
I think that's probably right. AI and data, data and I, I, I. I can't even say.

**Andrew** (1:40)
You can't even say. You can't even say.

**Bryan Bischof** (1:42)
Literally the answer is AI and data with I can't speak the other one out loud. If the utterance is unutterable, then the decision has been made. Okay. So this amazing conference of data and no AI and data.
Yeah, I'm really excited. Pete likes to throw me a challenge. And this year, his challenge was inference systems. Last year, it was foundation models. But his question to me was, I'd like to put together something about inference systems.
Have fun. And I like the challenge, but I also every time he throws down one of these gauntlets, I find myself thinking like, I don't know anything about this. And so, that begins the journey. And so, I research it sort of like how I would research an article or how I would research like a book topic. And I basically just like dig in to start trying to learn things. In this case, the way that I thought about it is, not so much like I want to learn all the things about inference systems during this research, but what are the things that I would try to learn if I were to try to learn this topic anew. And then what I do is I basically jot down like, okay, these are the major holes in my understanding. And then I just ask some of my friends who are very smart and very knowledgeable how much they know. And I try to figure out like, am I the only dumb one in this particular area? And for many of these, the answer is yes. Everyone else seems to know except for me. And in other ones, it's kind of like, no, I don't really know much about that.
I like to understand how that works but not like a deep way. And so one of the benefits of this approach is because I have all these smart friends that I can ask, I kind of naturally get this exposure to what is known, what is known by basically everybody. So I know it. And then what is known by the really strong people, but not experts in those areas.
And then I find the things that are missing from that. And I say, okay, now I need to go and hunt the people for that. And so that's basically how I engineered both of the tracks the last few years. And so this year it was very much like a lot of people had questions of what does it really mean to optimize at the inference layer? What does it really mean to like think about cost in a more sort of like holistic way?
What exactly are we doing when we are sort of choosing an optimizer for training? Things like that. These are questions that a lot of people felt like they had like a little bit of knowledge but not as deep as they wanted. And so then I went and I hunted down the best experts I could find.