AI could turn the firm into a human-agent workforce

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 10:36

   maybe even specialist employees? Yeah. I mean, I think that that's right. So the way to think about this, you know, there are two ways I come at this, right? Which is, let's take one analogy and I'll come back to it, right? If somebody in the early 80s had come to us and said, you know what, there are going to be 4 billion typists who are going to wake up every morning and start typing. And we were said, what for? It makes no sense, right? We have a typist pool, we have a slight pool, and we're, you know, fine, you know, with that. But except we invented this complete new thing called knowledge work, where everybody was typing and creating artifacts and so on. So what I think we have not yet conceptually gotten right, a good understanding, and a shared understanding is what is this future of work going to look like when you have,

2. 11:24

   let's call it, you know, let's take Microsoft. We have 200,000 employees and we have, let's say, 2 million agents or 20 million agents all in a loop. What is happening? What is the tacit knowledge that gets created? What is, you know, the artifacts that are going between agents, between agents and humans and so on, all that's now going to be played out in the next whatever, even multiple years. You can see early forms of this in coding, right?

3. 11:57

   It's a great place to observe, in fact, even this sort of social change, right? I mean, think about it. We started in a good old IDE and said, hey, AI is in there and it's doing code completion. That itself was useful, easy to understand. We've always had spelling correction and IntelliSense was there in BS code, you know, from 15 years ago and it just got better. Then we said, oh, you know, instead of going out of band and going to a browser and, you know, going to Stack Overflow and searching.

4. 12:30

   you can now bring all the coding knowledge to a chat session. That was also easy to understand. And you still were in the IDE, you had sort of the chat. And then we said, okay, you now have reasoning models and some preliminary agent loops and so on. And so you can assign tasks. So we had agent mode, right? So you not only had chat, but you can give it small tasks and you could see it complete. And then you could accept what it did and then you could insert it and what have you.

5. 13:00

   And then came the big breakthrough of total autonomy and agentic loop working for long periods of time where you could literally fire forget, right? Where you could assign a high level intent and you go off and do the full PR and you would accept the PR. So that transition, right, is what I think is going to happen across all work. And now we're seeing it with, even in Copilot, we now have chat and we have co-work.

6. 13:28

   And now, in fact, at Build, we announce something called Scout and Autopilot. So what is happening in coding will happen even in knowledge work. Interestingly enough, one of the things we launched even at Build in GitHub was a new feature called Canvas. Because what has happened is, as we've all gotten so good at using these coding agents, In fact, you know, the biggest challenge we now have is I have 100 CLI sessions open where I'm trying to operate these 100 agents.

7. 14:00

   And now the cognitive load on me managing this is so high, right? I mean, first of all, I don't, I mean, think about it, right? It's a linear chat session in a command line, and I have 100 of them. So guess what? We now are back to an IDE, right? So I have a new IDE. We give it a new fancy name. It's called an ADE. It's an agentic development environment. That's what the new GitHub app is. The GitHub app looks like an inbox, except it's an inbox of agents that are working across all the repos in allowing me to do the micro steering of the macro delegation I gave them.

8. 14:37

   But it's a complete UI for the agents to deal with me and for me to deal with agents. And so I feel, so we introduced this new feature called Canvas in GitHub where it can, like, for example, you know, sometimes it's easy to have a Kanban board visualization as a way to run down your PRs. Very useful, both for agents and me to interact versus. some chat session. So I think that that type of innovation is how work will change. In fact, one of the other fascinating things for me is a line of even AI research would be the models will also get much more tuned to learning how to stay the course and understand human preferences in steerability, right? Because that's what I want. In fact, I want models that not only do instruction following, but are also really steerable. And when you have that, that's when

9. 15:34

   you have confidence in it. So I think the work of the future is about tacit knowledge that gets created by this interplay, just like knowledge work got created because of digital artifacts and human capital. The new one would be this AI capital and the human capital working together, creating, in some sense, the digital artifacts.