Building Developers Tools, From Docker to Diffusion Models

**Ben Firshman** (0:00)
I think Docker built this incredible bottoms up developer motion, but they jumped too fast to trying to sell to enterprise. So they, almost from day one, they built this enterprise product that they sold top-down to very large companies. And the people inside those companies just didn't know what Docker was. You know, the people who knew what Docker was and was getting the value from it were these people on the ground who were using it from their day-to-day work. So I think the lesson there is if you're building a bottoms up developer business, build it bottoms up step by step. You know, make something for developers, sell the developers, then maybe sell something that's useful for their team and then work your way up. And then maybe in five years, you can sell something to the CTO of Walmart or whatever, but you're not going to be able to do it from day one.

**Derrick Harris** (0:42)
You're listening to the a16z AI podcast. I'm Derek Harris. I'm joined on this episode by a16z partner, Matt Bornstein and Replicate co-founder and CEO, Ben Firshman, to discuss the nexus of developer ecosystems and generative AI. Ben previously led open source product development at Docker and created Docker Compose. So he has a well-honed sense of what developers want and how to build tools that deliver on those needs. Throughout this discussion, Ben explores some of that history while digging deep into how developers are using generative models today and how a community approach like Replicate allows more people than ever to access, deploy, build upon, and even release their own fine-tuned models. Matt also shares some of the things he's seen and lessons he's learned after a solid two years of being neck-deep in AI startups. What works, what doesn't, and how to drive the wave of popularity and a subsequent trough that comes with each new model release. If you're working on developer tools in the age of generative AI, you should come away from this discussion with more than a few pointers. So without further ado, here's my talk with Ben and Matt.
As a reminder, please note that the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a16z fund. For more details, please see a16z.com/disclosures. So Ben, after traveling the world on a bike and in a van for a few years, you started Replicate in 2019 What did you see a few years before generative AI really took off and led you to the conclusion that Replicate was going to be your next company and this was a field you wanted to chase?

**Ben Firshman** (2:28)
It actually started with science, with academic infrastructure. So I got really interested in academic infrastructure just as a field. It was just sort of a field that operated like it still did 100 years ago, just happened to be on the internet. I came from the open source world and looked open source, and just like all of this sort of, you know, collaboration, incredibly fast moving world that is open source, and looked at science, and just like, why doesn't that work more like that? That's what got me collaborating with my now co-founder Andreas, because he was a machine learning researcher, who was still called machine learning back then. This was all pre-AI hype. And a lot of his job was implementing papers, because back then, machine learning was primarily published as PDF academic papers on the archive, which is this repository of academic papers. And a lot of his job was taking these academic papers and trying to turn them into running pieces of software. You know, the tragedy of this is at some point, somebody in this research lab who produces this paper produced a running piece of software. They compressed it down to prose and diagrams of math in a PDF. And Andreas' job was trying to uncompress it back into running software. And it often was just impossible. And this is what kind of then got me on the track of like, oh, actually, machine learning is this really interesting like subsets of science that is really fast moving. It's software, so it connects back to my software world. And that's what got us building tools for machine learning researchers.

**Derrick Harris** (3:53)
How did your earlier software experiences actually help inform that decision? Because you built Docker Compose, which obviously was no small feat in the world of software engineering. Did the other stuff play into it in the sense of, you know, is this general interest in terms of, okay, I have this skill set that I probably could apply to this?