If DNA is Code, Can AI Help Write It? Scaling Cell Programming and Synthetic Biology, with Ginkgo Bioworks Co-founder and CEO Jason Kelly

**Elad Gil** (0:06)
Biology is undergoing a digital revolution as we build developer tools and production infrastructure for synthetic biology.
How will it change industries? How does it intersect with AI? And how do we rethink biosecurity? This week, Sarah and I are joined by Jason Kelly, co-founder and CEO of Ginkgo Bioworks, to discuss their goal of making cells as easy to work with as computers, their data strategy, and the tech keeping the next pandemic at bay, and in general, what cell programming will do for the future of food, medicine, and agriculture. Jason, thanks so much for joining us today.

**Jason Kelly** (0:34)
Yeah, thanks for having me on.

**Elad Gil** (0:36)
So I think there's a lot of talk about synthetic biology and how biology and DNA and proteins are effectively just code and you can manipulate them in different ways now and things like that. I'd love to just get your view of both what Ginkgo does as well as what does synthetic biology actually mean.

**Jason Kelly** (0:50)
So I think the founding idea of synthetic biology is that DNA is code, right? And inside of cells are ATCs and Gs, essentially on like a tape. And it is very surprisingly analogous to zeros and ones inside memory in a computer.
That's roughly where the similarities end. Okay, once you get to the next step of what the cell does with that code, we are in a totally different world.
It is not virtual is the first thing, right? It is a physical thing. The code itself is literally physical, right? It is a polymer and it is going to use that to make proteins, which are basically little pieces of nanotechnology. And they're all going to be bumping into each other. And it's all crazy. It's not physically isolated, like you would imagine with a semiconductor chip. It's not built by humans. So you have this really interesting thing where the hook is there for people in tech to engage with biology. But then once they get in, they're like, what the fuck?
And so I'm happy to talk about those pieces, but I think you're right. The core idea of Symbio is that it runs on code. And then what can we bring over from programming into this world that actually sticks? And so I think what Symbiobiology has been really since it got going. I met the founders of Ginkgo back when we met at MIT in 2002 That was like early days of Symbio. It's about 20 years now.
It's basically engineers asking the question of what can they bring over into biology that's actually going to work. And some stuff has been left by the wayside and some things do work. And the latest technology that's being tried now is AI.

**Elad Gil** (2:18)
Can you walk us through what you actually think does transfer over and then where there are one or two unique challenges and then how does AI help to solve for some of those things?

**Jason Kelly** (2:25)
I'll tell you like a funny story, right? So one of the fellows I started the company with is this guy, Tom Knight, right? And Tom Knight started on the faculty at MIT in 1972, okay, right? Like mainframe computers, punch card computers. He was a computer architect for a very famous mini computer, which was like the size of a refrigerator called the Lisp Machine, okay? Like Symbolics, that company is one of the founders of like old school classic, Stephen Levy hackers in the book kind of guy, right?
Mid 90s, he realizes this thing about DNA as code. And basically, it's like forget computers, I'm moving into programming DNA. He's still Tom, right? He's been like teaching the semiconductor course for 20 years at MIT at this point.
Opens a wet lab in the MIT Computer Science Building, starts growing bacteria, freaking everybody out, right? And he puts up this flag and he's like, hey, computer scientists, like DNA is code. If you're interested in this thing, like come over and try it out, right? Some of us came over and we're like, all right, cool, we got there, got our hands wet and we're okay with it. A lot of computer scientists, they get there. Tom's like, okay, here's the lab bench. Remember, this code is physical. So if you want to compile it, I'm going to have to teach you how to do molecular cloning. And here is a pipette. And you're going to sit at this bench and you're going to do these steps, okay? And the person would do them. And they'd get a result the next day. They're like, wow, that's really interesting. And then they'd do the same thing again the next day. And they would get a different result.