Adept CEO David Luan and Stanford's Percy Liang | Words into Action

**Heather Mack** (0:04)
Hi, everyone, welcome to Greymatter, the podcast from Greylock where we share stories from company builders and business leaders. I'm Heather Mack, head of editorial at Greylock. Today we're rebroadcasting our episode featuring Greylock general partner Saam Motamedi's conversation with David Luan and Percy Liang. David is the co-founder and CEO of AI startup Adept, and Percy is a computer science and statistics professor at Stanford. While text and image generating AI tools like ChatGPT and Dali are all the rage right now, Adept is developing tools that take things a step further by actually executing actions based on text commands. The company just raised 350 million in series B funding to further its development of a tool that can be thought of as an AI teammate, trained to use every software tool and API for every knowledge worker. Greylock contributed to the latest funding round, and the firm has been partnering with Adept since collating the company's series A in 2022
In this interview, David, Percy, and Saam discuss how advancements in large language models are paving the way for the next wave of AI. This interview took place during Greylock's Intelligent Future event in August 2022 The summit featured experts and entrepreneurs from some of today's leading artificial intelligence organizations. You can read a transcript of this interview on our website, and you can also watch the video from this interview on our YouTube channel. Both are linked in the show notes. And if you aren't already a subscriber to Greymatter, you can sign up wherever you get your podcasts.

**Saam Motamedi** (1:20)
Okay, David, Percy, I'm excited about this. There's no doubt that large scale models are topical for all of us here. And I'm really excited to have the two of you to discuss them with. For those of you in the audience who aren't familiar with these two gentlemen, Percy is the Associate Professor of Computer Science and Statistics at Stanford, where among other things, he's the Director for the Center for Research on Foundation Models. And David is one of the co-founders and CEO of Adept, an ML research and product lab building general intelligence by enabling humans and computers to work together. And before Adept, David was at Google leading a lot of large models effort, and before that at OpenAI. And we're fortunate to get to partner with David and the team at Adept here at Greylock. Percy, David, thank you guys for being here and for doing this. So I want to start high level and just start with the state of the play. There's a lot of talk about large models, and it's easy to forget that a lot of the recent breakthroughs and models that we're all familiar with, like Dali and GPT-3, are actually fairly recent. And so we're still in the early innings of these models running in production and delivering real concrete customer and user value. Maybe just give us the state of play, David, starting with you. Like, where are we with large scale models, and what's the state of deployment of these models today?

**David Luan** (2:34)
Yeah, I think the stuff is just incredibly powerful, but I think we're still underestimating how much there is left to run on this stuff. Like, it's still so incredibly early. Like, just take a look at a couple of different axes, right? Like, when we were training these models at Google, it became incredibly clear upfront that, like, you could basically take a lot of these hand-engineered machine learning models that people had been spending a lot of their time building, like, rip it out with this giant model, give it some fine-tuning data, and turn it into a smaller model again and serve it, and that would just end up outperforming all of these things that people had done in the past. And so, like, the fact that, like, they're able to sort of improve existing things that companies are already using machine learning for, but, like, also just, like, how great it has been as a way to be able to create brand-new AI products that couldn't exist before. Like, it's fascinating to me to watch things like GitHub Copilot and, like, Jasper and stuff like that, like, just, like, hit a nerve so fast and go from zero to hero in terms of adoption. I think we're just in the very early innings of seeing a lot more of that. So I think, like, that's Axis 1 I think Axis 2, too, is just that, like, primarily what we're talking about so far has been language models, right? But, like, there's so many other, like, modalities, sources of human knowledge, all of this stuff. Like, what happens when, like, it's not just, like, predicting the next token of text, right? It becomes about predicting all of those other different things. And, like, we're going to end up in a world where a lot of humanities knowledge is going to get encoded in, like, various different, like, foundation models for many different things. And that's going to be really powerful as well.