Clear specs matter more when agents write the code

Transcript

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

1. 31:23

   could be a good problem to work on. Now let's assume you found a problem to work on. We're going to talk a bit about how do you become an AI native founder? How do you really become good at it? You in the past said that managing a fleet of agents, let's say 50 or 100 agents, is all about writing really good, crisp design docs or specs. And how do people get good at that? What do those look like? Yeah, I mean, I think you'll have a lot more success when working with your virtual agents.

2. 32:00

   if you can clearly specify what it is you want, and the clearer you are on what it is you want, the more the agent will have sort of guidelines and sort of rules of, you know, an outline of what it is trying to accomplish, whereas if you don't specify very much stuff, the agent has to sort of infer what it is you meant, and in many cases it might infer things that are different than what you imagined. So we've always told computer scientists from the very beginning that it's really important to specify what the software that you're writing is trying to accomplish before then going and writing it. And so now we actually have agent-based systems that can do the writing, but the importance of specifying what it is you want has actually gone up. Because before, you'd be handing it off to a very intelligent human

3. 32:48

   who maybe has context or can ask you follow-up questions. And agents can sometimes do that, but I think clear specifications is a really good idea. And to give you an example of a use of a coding agent that works extremely well is you can ask today's models to translate software from one computer language to another very effectively because in that case, you actually have an incredibly detailed specification.

4. 33:18

   You have the whole software that says what the system is supposed to do. And so if you have a Python implementation of something and you want a Go implementation of it, you know, that is something that the models seem incredibly capable of doing these days because you can, it can sort of take all the tests that are in Python, make sure they pass in the Go version, translate the test to Go, you know, compare behavioral differences between the implementations until there aren't any and be, you know, highly effective because that spec is so clear.

5. 33:50

   Hmm. Now, let's assume now every founder gets good at running hundreds of agents at the same time and all the code is written for them by the agents. What becomes the scarce skill? yeah i mean i think it's really having incredibly good taste in what you ask your agents to work on right that is the the crux of you know from my background uh a research problem you know a researcher can have all the tools and all the techniques but often most of the battle is what problem are you gonna spend your time on and if you pick the problem well and you succeed in in solving it, that's way better than if you delightfully execute a research investigation into a rather boring problem.

6. 34:41

   And so that high-level wisdom of what to work on, I think, is incredibly important. I think models are not necessarily going to be that good at it. So you're going to have people steering a lot of AI-assisted computation in order to accomplish great things and more quickly. But that essence of what it is you want your models to do is the key thing you should focus on. So let's talk a bit more about taste because it gets talked