Best of the Year: Building AI Companies

**Derrick Harris** (0:10)
Thanks for listening to the a16z AI podcast. I'm Derek Harris. And today we're doing a best of episode featuring advice and anecdotes about building AI companies. We've had some great founders on the podcast this year with a wide range of experiences, from product leadership at Docker during its heyday to building state-of-the-art models inside Google. And during those discussions, they've shared some of their personal thoughts, journeys, and insights on building products, spotting opportunities, and so much more. And we've compiled them for you in this episode. You'll hear from the folks behind the Flux family of image models, ideogram, replicate, and more after these disclosures.
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. One of the major shifts in the AI landscape over the past year or so has been the market's maturation from experimentation with and education about gender models to a solid understanding of how they work, where they excel, and how to build around them. Along with a plethora of widely used tools and products, we've also seen an influx of systems and developer tooling experts, as opposed to AI experts and researchers, move into the space. One of those people is replicate co-founder and CEO Ben Ferschman, who appeared with a16z partner Matt Bornstein on a November 15th episode titled Building Developer Tools from Docker to Diffusion Models. Ben has years of lived experience in the world of developer tooling, including as the creator of Docker Compose, and a key member of the Docker team during its hypergrowth phase about a decade ago. Here's what Ben had to say about being realistic early on when building any products for developers.

**Ben Firshman** (2:15)
There's lots of hard lessons to learn from Docker as well. I think the core thing we should really take into heart is, 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. 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. Make something for developers, sell to 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** (3:00)
Later in this episode, Replicate's Ben Ferschman discussed how, from a product perspective, AI is not too dissimilar from other types of software. The key is to get building and be prepared to use a lot of duct tape in the early going.

**Ben Firshman** (3:12)
I think one of the most exciting things, I think, about being a developer building on AI right now, is that there's just so much unexplored green space. The way to build great AI features, the way to build great AI products right now, is not to copy what somebody else does, it's for you to just tinker about and see if you can find something new that applies to your product. Please don't build another chat bot, there's plenty of them. There's some new interesting thing that applies to your product or your problem space that you can make possible with AI now. So just tinker with it, experiment, and just don't get too attached to things, try 50 different things and see what works and what doesn't. I think that's the way to find the really interesting things right now, because 90% of what is possible just hasn't been discovered yet. And then I think what I would... This is something we kind of touched on as well, is that it's really easy to build prototypes. It's very difficult to build real products with these AI systems because they're so unpredictable compared to normal computer systems. Just be prepared that once you've tried these 50 different prototypes and found this one neat thing that works really well, be prepared that you've only done 10% of the work by that point. There's the 90% of duct tape and heuristics and prompt engineering to get it to behave well with the mess that is the real world. But once you've got through that gauntlet, then you'll have something really interesting on your hands. I think for building developer products particularly, I think something that we say a lot at Replicate is that AI is just software. Like it's an incredibly extraordinary piece of software that is doing things that we didn't think were possible with computers before and frankly, superhuman. But it really is just a form of software, and at its heart, this machine learning model is just, we like to say it's an inference on a machine learning model that you pass params to or whatever, but it's really just a function call with some arguments that has a return value. Just happens to be this model running on a GPU inside. A lot of the same problems that apply to software also apply to machine learning. And this is certainly something that we've been just pattern matching with like, okay, what tools have been built for normal software that we can apply to machine learning? I think Replicate is just like we kind of smushed GitHub and Heroku together, and that's really, and Docker, and that's really where a lot of Replicate came from. And you can apply, just look at everything else that's happened in normal software and be like, hmm, does this thing need to exist in machine learning? There's some new problems in machine learning, and that like, you can't review the code in machine learning. So the only way to understand the behavior of the system is to pass data through it and just see how it behaves in the real world. And that's like a new thing about machine learning, and you need new tools there. But like, so many of the tools are just we can just map for normal software as well.