93. Jason Kelly, CEO and co-founder of Ginkgo Bioworks, on the future of autonomous laboratories

The reason it works so well in computers, the reason computers have never ended the way that, like, planes ended, okay, is it is a coded discipline. So the range of what you can do with computer code is like wild, man.

And cells are like that. Like biology is the only working nanotechnology. It's four billion years of evolutionary effort. It's totally programmable, and we suck at programming it. If we got good at programming it and then it was accessible to everybody, it is like computers but in the physical world. It is unending applications.

Sure. I grew up in Florida. I went to MIT for undergrad, did chemical engineering and biology. I always wanted to be a genetic engineer, like Jurassic Park, played at a young age, and then there was no bioengineering major at MIT, by the way, back then. I was like, "Okay, I do biology and then I need to pick an engineering field because it's like genetic engineering." And so I picked chemical engineering because cells are made out of chemicals, right? And about two years in, I realized that chemical engineering is designing oil and gas plants primarily. The, you know, the post is a good engineering background. But then I did my PhD in bioengineering with Drew Endy and kind of like Tom Knight. We had like mutual lab meetings, and then after grad school, myself and four other folks I went to school with started Ginkgo along with Tom Knight. That was back in 2008. We were the first YC biotech, and we took the company public in 2021.

So Sam Altman, who's famous now, had just taken over YC, and he put this blog post up that was like, he was like, you know, they do this like call for startups at YC. Have you seen that? Yeah. And so it was like, I want to see nuclear energy companies, material science, biotech. I think the Silicon Valley model could work in these like hard tech areas. And Ginkgo was like five years old at that point. We had been like bootstrapped on government grants. We were like 15 people in a lab here in South Boston area. And I emailed him and I was like, we don't make sense for YC. I got like a lab in Boston, but thanks for writing this blog post because it's like an oasis in the desert, you know, like if you weren't making a therapeutic and you weren't making software, there were no venture capitalists for you. And so basically, I was like, you know, thanks. And he's like, oh, you got to come meet me. So I flew out to San Francisco and met him and was like, you know, love at first sight. It was awesome. Like YC is the best. And so we had just this amazing experience doing it. And by the way, everyone in Boston was like, why are you doing YC? They don't know anything about biotech.

Dumb argument. I was like, I wasn't getting, you know, these guys weren't investing in me anyway. So like, yeah, like who cares on a terrible...

Yeah, so basically, a couple of things come together. By then we had started to get commercial deals. So like we started on government grants, but then in like 2013, you know, 2013, about a year before we did YC, we started to get our first like commercial customers with our like solutions business, which is kind of like if you're in the pharma space, it's like an Amgen-style business model. Like we do a research partnership, we get some money, and then we get royalties and milestones on the back end. And except our model was like, we'll do that for any genetic engineering project. And back in 2013, we got those first projects in engineering microbes for like flavors and fragrances and nutritional ingredients, like weird biotech. And so we did YC and we had like commercial momentum, which really helped us because then we went to generalist tech angels, but they were able to look and see, oh, okay, well, you're getting business. I understand the gist of what you're talking about. And they invested, and so then we basically got treated like a tech growth company.

Yeah, love to. Yeah. And maybe the last one I'd make on just the general startup story because I know you have a lot of folks listening in who are, you know, grad school or students and things like that. So I mean, we started the company in 2008, which was like a little bit like the vibes of biotech today, like horrible times, you know, right? That like it was, you know, financial crisis, all that. My co-founder Barrett and I were basically buying up equipment from shuttering startup biotech companies around Cambridge and getting all the equipment like 10 cents on the dollar in auctions and stuff. Like for the first year, I was basically like moving equipment. I actually think it's a good time. It's a really good time to start a startup.

Yeah. Yeah. You know, and some things have gotten better. Like, you know, we didn't have any rent-a-bench stuff. Like, you know, these things like QB3 had, whatever it's called now, LabCentral, all these different things. Like, you know, we... it wasn't existing. So, you know, like we built our own lab in like an office building because it was like the only way to make it cheap enough. Like, you know, like it was just like nonsense. And so some things have gotten better, but like still, like the venture community is not really on the hunt for young, inexperienced founders in biotech.

Right. So my point is the good fact is you're going to have a hard time anyway. At least if you want to do something like apply for grants or do it in some like oblique way in a down environment, you have like more time. Like you're not like racing anybody for your idea as much. So I actually think it isn't a terrible time for like people who want to kind of bootstrap it and go for it. Half the battle is getting started. And applying for grants, that was a real boon for us early on, just as another thing that people never do or get told not to do, it takes too long or whatever. Like just do it. Like, you know, it's non-diluted money. It's great. You should do it. You know, that like first four million into Ginkgo or so was SBIRs and first R01s. Like, you know, I don't know, probably 30 grants.

Still gobble stuff, but like it was like basically our initial translational venture money was USG back then, you know. And so that's partially... The other point of my background, I chaired a national security commission on emerging biotech for like the first two years of the commission. And like, you know, part is just like I feel like we should give back. Like we really do exist thanks to that kind of money that is there to translate tech out of the lab into the private sector on the US side. I would just still pitch to people to go for it right now especially. So, but yeah, I can talk about automation now. But anyway, so you were asking like how do we get here and maybe a little bit on like autonomy and Genesis Mission. So I'll try to break it up. So first off, like how did we get here, right? So Ginkgo's focus was always on a lesson I learned from Tom Knight, and I don't know if you know Tom's background.

Yeah. Okay. So Tom's an interesting guy, right? So he's in his 70s now. He started on the MIT faculty in 1972 in electrical engineering and computer science.

Working with punch cards. Yeah. Absolutely. Punch cards. Yeah. And so he grew up on that and he watched the transition of that era of computing. And he was a computer architect. So he designed like a pretty famous minicomputer called the Lisp machine back in the day. And he watched the transition into planar semiconductor manufacturing and he became a chip architect and did that whole thing for like 25 years. And then in the 90s he switched his focus from ECS to biology, basically I think because he was getting bored. And that wasn't exactly why, but you know, because as the other like programmatic discipline...

Right, like DNA is the other like coded engineering field basically along with computers. And so you get the cell, got computers, they both run on code, that's true. All right, a lot of other differences, but that part is true. And so Tom, to his great credit, because he's like a hands-on guy, opens up a wet lab in the MIT computer science building, sets up benches, starts freaking out his colleagues by growing bacteria and like gets his hands dirty. And like back then we tried like a million different things from electrical engineering and computer science to like bring them into bio, like logic circuits and all this symbio stuff that like ended up being stupid. And like I'm overstating, like a little bit of logic is really valuable, right? Like is my therapy next to the tumor?

Yeah. Right. You see like really cool stuff, companies like Strand, there's like cool RNA stuff, cool blah blah blah. There's like a lot of really cool like simple amounts of logic.

All right. But Tom brought an idea that I think is like foundational to autonomy and automation and to Ginkgo that he brought from electrical engineering and computer science that I think like everybody who's a biotechnologist should internalize. All right. And this is the idea of abstraction.

All right. And the reason Tom understands this deeply is he uniquely lived this transition in electrical engineering and computer science. So when Tom started, in order to be a computer scientist, you had to be an electrical engineer. Okay. Because like how could you program a computer if you didn't know how it worked? I mean, like obviously there's no way in the world you're going to be able to program that thing if you... Okay. You got wires, you got all this stuff. You know all that stuff. Like of course, right? Okay. Right. But now somehow, right, my 10-year-old can like drag boxes around on an iPad or something, they program a computer today. Okay. What happened?

Right. And you know it started at the low level. You had assembly language, and then you had operating systems, and then you had programming languages, and then you had graphical programming languages. And now you have ChatGPT and you could talk to it and it'll write code for you. And the consequence of that, like why did that matter? Okay. Right. And the reason it really, really matters is it let computer science and electrical engineering become two different fields, two different disciplines that could talk to each other through the abstraction layers and benefit from each other's innovations. Okay. You made a higher processing chip and now suddenly you could do a new type of software. Oh, you created new software applications that create demand. Now we're buying more high processing. Oh, you invented 3D graphics. Guess what? We need GPUs. Okay. Right. So you had this like tic-tac between the computer science people and the electrical engineers that benefited each other because everyone's innovation could get passed back and forth. All right, that's point number one. Point number two...

Let me tell you, the culture and community of computer science and electrical engineering are super different. Okay, so computer science, okay, this is like a theory of the mind, right? It's about math and algorithms and okay, right? Electrical engineering is this is like physics. It's not math. Okay. So it's about automation. It's about lithography. It's about miniaturization. It's about atoms. Very different people do that than do this.

Okay. So why do I tell the story? So Tom gets into biology and he's like, "All right, let's program some DNA, baby." Right? Like that's why I'm here. Like I want to write some code in this new substrate. And okay, I want to program it. And the first thing I got to learn how to do if I want to program DNA is pick up a pipette, right? [laughter] Okay. Because how do you have the right to design a gene in E. coli if you don't know what PCR is? If you don't know what a restriction enzyme is, if you don't know how to do a miniprep. You don't get the right to design DNA if you don't know how to work at the lab bench. Nick, that feels right to me as a scientist at least. [laughter] That has meant that the only people who get to design organisms are the people who can tolerate like the minutia of like working at the lab bench.

And it is very much like if you think about the number of computer scientists that could also handle electrical engineering at the same time. Small.

Interesting. So you know Tom brought this concept that we got to abstract the design of the organism, the design of the genome, which is fundamentally what bioengineering is about, is designing genomes from the lab work. And my argument is it's very analogous to computer science and electrical engineering just in a slightly different way. If you're designing the genome, what you really are is a biologist. If you're a person who understands the biology, you understand how the cell works and this, that, blah, all this. My god, it's like turtles all the way down in complexity. It's crazy how, you know, right? Like how much there is to understand about how a cell works, you got to have all that in your head to be good at designing it. Okay. But over here, if you want to drop the cost of a miniprep by a factor of 100 or blah blah, like that is automation and miniaturization. It's being a hardware person and a software person, you don't have to know anything about the biology. Okay. You could do some real damage there. And so those communities need to be split and talk to each other with abstraction. Does that make sense?

Yeah, so maybe I'll take like a little bit of a drive down autonomy lane, automation lane, and then we'll come back to like how will people use it. So the point I want to make about the Ginkgo journey is that whole concept of abstracting away the lab from the design of the DNA was in the founding of the company. So we have been working on that problem for 17 years now. Yeah. Yeah. It's not an easy problem. I'll tell you how we attacked the problem first. The big thing we tried was what we called a foundry. All right. And the idea was the foundry was like the electrical engineering half of the house. Okay. It was centralized, automated. It was filled with people that understood lab automation, processes, operations stuff. And then we had a separate team, our codebase, our organism engineers, the people that were like the biology experts. And we did separate them. The idea was these people would order from the foundry. We built custom software to manage their requests. So you would request the work you wanted and it would get handed to the foundry to do your lab work. Okay. So we did structurally do that. And I'm like, no one does this. Like if you walk into a research lab at Merck or Takeda or something, it's like everyone's at a bench. They're thinking about designs and they're doing work at the bench. It's like all like it's, you know, it's exactly what it was when I was in grad school. Yeah. So we tried to do it this way and I think importantly we tried to do it this way across whatever kind of research we were asked to do.

That's where we stole the name. Yeah. Yeah. It's not exactly a perfect analogy because it's not really manufacturing. Yeah. The core idea behind it was separate the people doing the lab work from the people ordering the lab work.

All right. And then what I did in the foundry was I operated it with what I'll call like a services-based architecture. Right. So we had a team that built DNA. We had a team that purified proteins. We had a team that ran genomics. We had a team that did metabolomics. And we had like, you know, 40 different foundry groups. And inside the groups were sub-teams and like you had like specialists and each team did... And the level of automation we had was a mix of walk-up. So walk up to a Hamilton, walk up to a Tecan, whatever, and also work cell.

Yeah. You should read my...

Okay. Yeah. What we call like narrow lab automation. So the lab automation was to do a certain operation at high... Like I'm the DNA build team. I have a thing that stitches two pieces of DNA together. I have a different operation that stitches four pieces of DNA together or whatever, right? But if you wanted just some random experiment, I can't do that. So I have like a suite of services and then I pass among those services to get your complete job done. And that was how the foundry worked. And what we found was you saw efficiency gains through the centralization, but then you had efficiency losses through the handoffs among the services that were run by these different groups of people. And like I'd say we were net more efficient, but we weren't like a thousand times more efficient. We didn't see like crazy like planar semiconductor gains on that type of setup. It was getting better every year, but like not at the end of the day, it didn't get good enough. All right, I guess it's maybe the short way to say it. And so okay, fine. And no one had tried this before, you know, and so what was our learning? So our learning was the split, I think, is still right. Like Tom is fundamentally right about the abstraction. It makes no sense to have the same people do these things. And this comes back to your taste thing. So what would be a new thing to try? A new thing to try is the same kind of like I want to order, but there is no independent groups doing the services. There's a giant level two general lab automation. And when I say level two, just so you know what Nick was talking about, we wrote a blog post that was like talking about levels of lab automation, L-O-L-A, just like there are levels of autonomous driving. And so there's like zero to five on like autonomy in a Tesla. And so we have zero to five for autonomy in the lab. And the first level is you're working at the bench by hand. That's level zero. Level one, so it's all manual. Level one is like walk-up and work cell. So I've got a system, but I walk up and I tell it to do one thing and it runs it. And then level...

Two, is general lab automation where I can submit lots of different requests into one big system. And that's what we have now at Ginkgo. And that's what the Genesis mission just bought for PNNL, Pacific Northwest National Lab, is a general purpose system. And so we sell our, like, unit of sale as these rack carts. So it's like a reconfigurable automation cell. And it's basically a robotic arm, a piece of Magnemotion track, and an instrument inside. And so the videos of, watch videos of seeing these things run are so fun.

Like, we now have 36 of them in Boston.

It's going to be 50 soon. Like, it's really neat setup to see.

You should come by SLAS. You'll like it. Anybody who's on SLAS is welcome to come visit. Like, SLAS is like the automation meeting in February in Boston. And anybody that's listening that just wants to come check it out, we're very happy to have people come see it. But in any case, what we've done is we basically can Lego block these carts where you connect as many pieces of lab equipment into one integrated setup and you can send a sample to any piece of equipment by just like passing it along on this magnetic rail and it gets delivered to an arm. The arm picks it up and puts it on the equipment and we have like an arm for every piece of equipment. It's like overdo it on the arms. Like the arms are reliable and not a bottleneck. The transport system is reliable and not a bottleneck. And so you're kind of taking away a lot of the limitations from traditional work cells. Let me give like one more analogy on what I think is different about traditional lab automation work cell, like you would get from like a high-throughput autonomous lab we're building. So that still has to take transport. Again, transportation. A subway is automated transport. You're not driving it, right? Like you get in the subway and it takes you somewhere. It is automated transport. We've had it for 100 years, right?

Okay. A Waymo is autonomous transport.

And the difference is that automation has basically been applied like since the industrial revolution to things that we are just repeating the same thing over and over again.

Driving your car was not like that.

It was variable.

So you want to go different places every day. You don't want to go to the same place every day. Different things are going to pop up in front. There's a car, there's a kid, there's a stop sign, whatever, right? Like it's raining. There's all this variability and so autonomy is the Waymo that can use smarts to manage that variability. Okay. And so why do I give this analogy? Because the work cells, all the automation we have built so far for biotechnology outside of what I have here in Boston and we're selling to other people is subways. Run my high-throughput screen 30,000 times a week.

Right. It's the same thing over and over again. So I want TECAN, right? Bang, bang, bang, bang. Right. Like the origin of the work cells was the Human Genome Project. That was like when we first had integrated automation in labs because they were like sequencing same thing, same thing, same thing. But the problem is it's limited to only going to the stops on the subway.

And so what has been the flexible and dynamic tool for genetic engineering over the last 30 years? The lab bench. So the lab bench still remains the only thing that is flexible enough to like, it's like driving the car. It does get you there and you can't automate it and so we're stuck with it.

And most of the places you want to go are on the lab bench. So 95% of what people spend money on in biotech R&D is the lab bench.

You should eventually be able to talk to it roughly like you would talk to a technician. Explain the protocol you want done, go back and forth, tighten some stuff up, specify, give them a protocol for a protocol and like they get the work done and you get to talk about results later. That's what it should feel like.

There's a few different, there's a bunch of, there's like a million problems to work on here. Okay. So the first one is can I just connect like all the equipment you need in a lab to go wherever you need to go. Because if you walk around a lab at Merck or an academic lab or wherever, there's 150 different pieces of benchtop lab equipment around.

So one of the key things is the system at a hardware level needs to be able to connect a lot of things all at once.

We think we solved that problem with our automation carts, racks. Next, it now needs to handle, this is level two automation, getting many orders submitted to it all at once.