The Exchanges

Q I'm on the roster. Thank you so much. Um, what, why, so why spend time being a VC? Uh, and, and organizing all these events. You're also a very busy CEO and, you know, why, why spend time with that? Why is that an important part of your life?

A Yeah, for me personally, I really like helping founders. So, um, Ali, my, uh, investing partner, is fortunately amazing, and she does everything for the fund. Um, so she, like, hosts the Thursday night events, and she finds, uh, folks who we could invest in, and she does basically everything. Josh and I, um, are her co-partners. So Ali was our former chief of staff at Sorceress, and we just thought she was amazing. Um, and she wanted to be an investor, and Josh and I also Like care about helping founders and kind of like giving back to the community. What we didn't realize at the time when we started the fund is that it would actually be incredibly helpful for imbue. So, uh, talking to AI founders who are building agents and working on, you know, similar things is really helpful. They could potentially be our customers and they're trying out all sorts of interesting things. And I think being an investor, looking at the space from the other side of the table, it's just a different hat That I routinely put on and it's helpful to see the space from the investor lens as opposed to from the founder lens. Um, so I find that kind of like hat switching valuable. It maybe would lead us to do slightly different things.

AI assessment note: “for me personally, I really like helping founders... helpful to see the space from the investor lens”

Q What do you think about the other, the different approaches people have kind of like text first, browser first, like multi-on, um, what do you think the, the best interface will be, or like, what is your, you know, thinking today?

A Uh, I think chat is very limited as an interface. It is sequential, um, where these agents don't have to be sequential. So with a chat interface, If the agent does something wrong, I have to like, figure out how to like, how do I get it to go back and start from the place I wanted it to start from? So in a lot of ways, like chat as an interface, I think Linus, Linus Lee, you had on, on this. I really like how he put it. Chat as an interface is skeuomorphic. So in the early days, when we made word processors on our computers, they had notepad lines because that's what we understood, uh, you know, these like objects to be chat. Like texting someone is something we understand. So texting our AI is something that we understand. But today's word documents don't have notepad lines. Um, and similarly, the way we want to interact with agents, like chat is a very primitive way of interacting with agents. Uh, what we want is to be able to inspect their state and to be able to modify them and fork them and all of these other things. And we internally have kind of, like, think about what are the right representations for that. Like architecturally, uh, like what are the right representations? What kind of abstractions do we need to build? And how do we build abstractions that are not leaky? Because if the abstractions are leaky, which they are today, like, you know, this stochastic generat…

AI assessment note: “I think chat is very limited as an interface. It is sequential”

Q Yeah. Let's jump into GI now and Boo. Um, when did you decide recruiting was done for you and you were ready for the next challenge? And how did you pick the agent space? I feel like in 2021, it wasn't as mainstream.

A Yeah. So the LinkedIn says that it started in 2021, but actually we started thinking very seriously about it in early 2020, late 20 19, early 20 20. Um, Not exactly this idea, but, uh, in late 2019, so I mentioned our housemates Tom Brown and Ben Mann, they're the first two authors on GPT-III. So what we were seeing is that scale is, scale is starting to work, um, and language models probably will actually get to a point where, like, with hacks, they're actually going to be quite powerful, and it was hard to see that at the time, actually, because, uh, like, GPT-III, the early versions of it, You know, there are all sorts of issues. We're like, ah, it's not that useful, but we could kind of see like, okay, you keep improving it in all of these different ways, um, and it'll get better. And so what Josh and I were really interested in is how can we get computers that help us do bigger things? Like, you know, there is this kind of future where I think a lot about, uh, you know, if I were born in 1900 as a woman, like my life would not be that fun. Uh, I'd spend most of my time, like, carrying water and literally, like, getting wood to put in the stove to cook food and, like, cleaning and scrubbing the dishes and, you know, getting food every day because there's no refrigerator. Like, all of these things, very physical labor. And what's happened over the last 150 years since the In…

AI assessment note: “we started thinking very seriously about it in early 2020, late 20 19”

Q What's the culture like to make that happen? To have both, like, kind of, like, Product oriented, research oriented, and as you think about building the team, I mean, you just raised two hundred million. I'm sure you want to hire more people. Uh, what, what are like the, the right archetypes of people that work at Imbil?

A Hmm. Yeah. I would say we have a very unique culture in a lot of ways. Um, I think a lot about social process design. So how do you design social processes that enable people to be, you know, effective? Um, I like to think about team members as creative agents. So, because most companies, they think of their people as assets, and they're very proud of this. And I think about like, okay, what is an asset? It's something you own. Uh, that provides you value that you can discard at any time. This is a very low bar for people. This is not what people are. Um, and so we try to enable everyone to be a creative agent and to really unlock their superpowers. So a lot of the work I do, you know, I was mentioning earlier, I'm like obsessed with agency. A lot of the work I do with, with team members is try to figure out like, you know, what are you really good at? What really gives you energy? And where can we put you such that? Um, and how can I help you unlock that? And grow that. Um, so much of our work, you know, in terms of team structure, like much of our work actually comes from people. CARBS, our hyperparameter optimizer, came from Abe trying to automate his own research process, uh, doing hyperparameter optimization, and he actually pulled some ideas from plasma physics, he's a plasma physicist, to make the local search work. A lot of our work on evaluations comes from a couple me…

AI assessment note: “we try to enable everyone to be a creative agent and to really unlock their superpowers”

Q Um, so like, uh, what's, what's, what's an example of human agency that you try to promote?

A Yeah. Like, uh, with all of my friends, I have a lot of conversations with them. That's like helping figure out what's blocking them. Um, and I guess I do this with a team kind of automatically too. And I think about it for myself often, like, building systems. I have a lot of systems to, like, help myself be more effective. At Dropbox, I used to give this onboarding talk called How to Be Effective, which people liked. I think, like, a thousand people heard this onboarding talk, and I think maybe Dropbox was more effective. And I think I just really believe that, like, as humans, we can be a lot more than we are, and it's what drives everything. I guess completely outside of work, I do dance. I do partner dance.

AI assessment note: “At Dropbox, I used to give this onboarding talk called How to Be Effective”

Q Well, so this goes back into what MB was going to offer as a product or a platform. How would, are you going to actually help people deploy those agents?

A Yeah. So our current hypothesis, I don't know if this is actually going to end up being the case. Um, we've built a lot of tools for ourselves internally around like debugging around like Abstractions or techniques after the model generation happens, like after the language model generates, uh, the text, uh, like interfaces for the user, uh, and the underlying model itself, uh, like models talking to each other. Maybe some set of those things, kind of like an operating system, some set of those things will be helpful for other people. Um, and we'll figure out what set of those things is helpful for us to make our agents Like what we want to do is get to a point where we can like start making an agent, deploy it, it's reliable, like very quickly. And there's a similar analog to software engineering, like in the early days, in the seventies and the sixties, like to program a computer. Like you have to go all the way down to the registers, but, um, and write things in assembly. Eventually we had assembly. That was like an improvement. Then we wrote programming languages with these higher levels of abstraction, and that allowed a lot more people to do this and much faster. And the software created is much less expensive. And I think it's basically a similar route here where we're like in the like bare metal phase of agent building, and we will eventually get to something with much …

AI assessment note: “we've built a lot of tools for ourselves internally around like debugging around like Abstractions”