AI Agents That Reason and Code with Imbue Co-Founders Kanjun Qiu and Josh Albrecht

**Sarah Guo** (0:05)
Imbue is a company developing AI agents that can reason and code. Today, Elad and I sit down with Kanjun Qiu and Josh Albrecht, co-founders of Imbue, to discuss training large foundation models for high-level reasoning. Why agents require architectures different from large language models or language token prediction models, and how current computers are getting in the way of their users.
Kanjun, Josh, welcome to No Priors.

**Kanjun Qiu** (0:30)
Thank you.

**Sarah Guo** (0:32)
Perhaps you can start by just telling us the story of how you guys know each other and where the idea for Imbue came from.

**Kanjun Qiu** (0:37)
Josh and I met at a conference and then started a big house together. It was a big house, 20 percent house, and also started the first company around the same time. I've always been really interested in agency, and how do we enable humans to have more agency.
Josh has always been really interested in AI, and so it made sense. We, at that time, talked about, someday we're going to be able to have AI systems that give humans a lot more agency. Fast forward to 2018 or so, we were running an AI recruiting company called Sourceress. That was actually the first AI agent that we built.
It was not transformer models, more old-school NLP, but it was a system that recruiters used and automatically got candidates in their inbox. We learned a lot about, oh, if you have an autonomous system like this, what do you actually need to make it work? Around that time, some of our housemates were building GPT-3, and we were seeing, oh, scaling works. If we just keep scaling, actually you're going to get pretty far with a lot of these language models.
Our question at that time was, how far can we get with language models? Does this kind of self-supervised learning, which is working so well on language, work in other modalities as well? In early 2020, that's when we first started seeing self-supervised learning working across video, and images, and language. We were like, there's something really interesting here where maybe machines are learning the same kinds of representations or similar representations what humans are learning. Maybe they can get to a point where they can actually do the types of things that humans are able to do. And that's when we first started Imbue, or started talking about Imbue.

**Sarah Guo** (2:25)
You clearly know a bunch of people working at sort of large language model research labs well. When you looked at what they were doing, how did the focus come to be on agents in particular, and how is that different from a general language model?

**Josh Albrecht** (2:39)
Yeah, I think we've always been interested in agents, in not just recommender systems, or classifiers, or things like that, but in systems that are going to go do real work for us, right? That are going to actually be useful in the real world.
Right now, you can ask some kind of chat bot something, and it'll give you back a response, but the burden is sort of on you to go do something with that to verify whether it's correct or not. I think the real promise of AI is if we can get systems that can actually act on our behalf and can accomplish goals and kind of do these larger things and sort of free us up to focus on the things we're interested in.

**Kanjun Qiu** (3:10)
Yeah, one thing that I think we often forget because we're in it every day is our computers are actually like, they need to be micromanaged. The reason we're in front of our computers every day is because nothing really happens. Like they can't make decisions on their own. Nothing really happens unless I'm in front of it.
And I'm like in front of it doing all this really detailed stuff, kind of like operating a factory machine with all of these like little knobs that are really specific.
And there is a future where computers don't need to be micromanaged that I can like give a computer an instruction, whether it's natural language or something, some other kind of instruction, like it can go off and understand what I'm trying to do and help me do it. The diff between this, where we are today, and that is kind of like the diff between the first calculator and where computers are today. Like the first computer, very first digital computer was a room size calculator. All it did was calculate Fourier transforms and things like that. And I think that's kind of the potential for where AI can be given the current, like where the technology is going, it's very possible.