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

Ginkgo Bioworks is using DNA as code to digitize the cell programming revolution. Ginkgo is using AI and synthetic biology to keep the next pandemic at bay, and accelerate our production capabilities for medicine, food, and agriculture. Ginkgo’s co-founder and CEO Jason Kelly joins hosts Sarah Guo and Elad Gil to discuss bioengineering protein as a foundational model, specialized data learning from an evolutionary perspective, what we need to prepare for a future pandemic, and more. Jason has served as a member of our board of directors since Ginkgo’s founding in 2008. He has also served as a director of CM Life Sciences II Inc. (Nasdaq: CMII), a special purpose acquisition company with a focus on the life sciences sector, since its initial public offering in February 2021. Jason holds a Ph. D. in Biological Engineering and a B. S. in Chemical Engineering and Biology from the Massachusetts Institute of Technology. Show Links: Jason Kelly - Co-founder & CEO of Ginkgo Bioworks | LinkedIn Ginkgo Bioworks The Plausibility of Life: Resolving Darwin's Dilemma Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @jrkelly Show Notes: (0:00:00) - The Difference Between Software Engineering and Biological Engineering (0:06:51) - Abstractions and Infrastructure in Synthetic Bio (0:09:23) - The Role of AI, Foundation Models that Speak Biology (0:13:17) - AWS for Cell Engineering (0:17:52) - Where are the AI-discovered Drugs? And Data at Gingko (0:19:12) - Pandemic Response and Biosecurity in the Age of AI (0:22:47) - The Likelihood of Existential AI Risk from Lone Actors Harnessing Viruses, and The Need for Defense-in-Depth (0:31:47) - Will Progress in AI Be Biologically Inspired? And Evolution

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:06Speaker 00

  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 Genco 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.

• 0:34Speaker 01

  Yeah, thanks for having me on.

• 0:36Speaker 00

  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 Genco does as well as what does synthetic biology actually mean?

• 0:50Speaker 01

  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 like surprisingly analogous to zeros and ones and, you know, inside memory in a computer.

• 1:07Speaker 01

  That's roughly where the similarities end. Okay, like, 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 nano-technology, 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, and so like, 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.

• 1:50Speaker 01

  And then, what can we bring over from programming into this world that actually sticks? And so I think what synthetic biology has been, you know, really since it got going, I met, you know, the founders of Genco 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.

• 2:18Speaker 00

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

• 2:26Speaker 01

  I'll tell you like a funny story, right? So one of the fellows I started the company with, 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 List Machine. Okay, like symbolics, that company is one of the founders of, like old school classic, Steven Levy hackers in the book kind of guy, right?

• 2:51Speaker 01

  Mid 90s, he realizes this thing about DNA is 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, they, 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

• 3:35Speaker 01

  the person would do them. And they'd get a result the next day. They're like, wow, that's really interesting. And then, and then they'd do the same thing again the next day. And they would get a different result. And they'd be like, Tom, I just did the exact same thing and I got two different results. Like what's going on? And he was like, you're never going to know. And, and, and it would just break their brains, right? Like they'd be like, fuck this, I'm out of here. You're leaving a world in computer science of like pure logic, right? At the end of the day, if there's a bug, you can always run it to ground. And, and that's because A, these are systems that run like clocks, B, we design them. We design them, right? And so like you, at the end of the day, can go in and figure it out. And in biology, it's like, sometimes you can, right? And sometimes it's just a part of

• 4:19Speaker 01

  the biology that we just frankly don't understand well enough, that's broken and like tough luck. And you, and you gotta like stomach that. And one of the things, my favorite things about AI is like, how does that neural network?

• 4:31Speaker 01

  Nobody knows.

• 4:32Speaker 00

  Yeah.

• 4:32Speaker 01

  Like you're about to experience like, like, like the analysis of these neural nets is going to look like systems biology, right? It's going to be like, go in and like, try to back, figure out a thing that you didn't design, my friends. And so like that, that'll be your first taste of really feeling like biological engineer, right? But like, why bother? Like, why work with these neural nets that like, God, you actually can't easily debug and understand why it's hallucinating and all this stuff? And the answer is because they're powerful. It's worth it. And that's the same reason you want to do biological engineering. Like even though it's unpredictable, even though you're going to, it's going to be so frustrating, it's not going to do what you want and blah, blah, blah. People are going to like, it's because the substrate is incredible, right? It self replicates, it self assembles. We have nothing else like it in the physical world.

• 5:17Speaker 01

  So you want to work with it, even though it's hard, that, you know, that, that's, that's what, that's what ultimately gets people passionate about this stuff.

• 5:23Speaker 00

  Yeah, it totally makes sense. And I think one can argue that neural networks are actually heading even more in that direction because as people build systems that can code themselves, we're going to end up with evolutionary systems that are completely non-designed.

• 5:33Speaker 01

  Yeah.

• 5:33Speaker 00

  And I think then we're truly in the world of biology where you have evolution kicking in and you know, to your point, evolution is really messy, right? It's always optimizing for the utility of something versus the form of it. It reuses parts aggressively. It creates enormous redundancies in weird ways that you don't know that there's a perturbation here and it propagates across like in weird ways. And so I think people are really underestimating what happens once we have self, self-evolving neural nets, which I think is coming quite soon.

• 6:01Speaker 01

  Be still, my heart. It's, it's going to be great. It's so cool. I mean, this is so cool. It's so worth it. Yeah. I mean, it's neat because there's a magic to it, right? Like, again, people, people like different stuff and like, I get that. I think this would be like one of the big cultural divides. There's a certain kind of mind that really likes things to be predictable. There's some people who just like the magic, right? Like some of what's cool about biology is the, is how hard it is to understand.

• 6:22Speaker 00

  So how does, how does Ginkgo go about harvesting all this, you know, this shift in biology in terms of the ability to manipulate these systems using molecular biology and molecular cloning techniques and then software tooling and other things? So can you tell us a bit more about the company and where you focused and what you've done with it all to date?

• 6:37Speaker 01

  So one of the ideas that we tried to bring over from computer science was abstraction, right? And what is abstraction? Well, in Tom's era of computing, in order to be a computer scientist, you had to be an electrical engineer. Because how do you program a computer if you don't know how a computer works? Now, obviously, today, like, eight-year-old is able to program a thing on their iPad by drawing boxes around. It's like, what happened, right? Like, well, assembly language, operating system, programming language, graphical programming language. We build all these abstraction layers to split the disciplines of electrical engineering and computer science into their own paths, both of which had very long roads, right? And so, one of the big things we did at Ginkgo, when we started the company, you know, 15 years ago, unimaginably a long time here, was to do that split from the get-go.

• 7:20Speaker 01

  So, we had part of our infrastructure, we call it a foundry, taking a page from semis, that is basically a group whose whole job is automate and scale the lab work. Okay? And move away from a system where that lab work is being done by hand by a scientist. And then, the DNA programmers, who are really typically scientifically trained and PhD biology types, they order from that system to get their work done. That is actually very difficult to pull off. It's culturally difficult, because like, a scientist does not want somebody else to do their experiments, and like, they're good scientists, you know, like, there's all da-da-da-da-da. There's a whole laundry list of why it's hard. Not to mention that when you first try to build the infrastructure, it sucks. Okay? And so, the foundry team has been able to drive enormous scale economics in doing the lab work,

• 8:05Speaker 01

  which gives us the data of lots of different genetic designs that we've tested, which is exactly going to be useful for the AI stuff, but is also just generally useful, right? Because you gotta try a lot of designs to get the cell to do the thing you want it to do. And so, that's been probably like, one of the biggest activities the last decade at Ginkgo, has been like, driving that scale.

• 8:24Sarah Guo?

  Jason, what's the right way to think about the abstraction between like, your customers? And then, your DNA programmers? Like, what's the spec that gets passed over? Or how should we understand the science they do versus you?

• 8:34Speaker 01

  So, today, the way that it works is basically, a customer of Ginkgo's would be like, you know, like, a recent customer is Merck, okay? Like, Merck, Novo Nordisk, Biogen on the pharma side, Bayer, Sygenta, Corteva, the biggest ad companies in the world, all customers, then a lot of startups, right? And the way they interface with us is that we basically are agreeing on a spec. We're like, okay, here's what I would like the cell to do. They tell us and we agree on like a timeline to develop it. And we're kind of like a prop software development shop. Like we're gonna like make it for the customer and then license it to them and they'll take and they'll own it to go develop their product. And in exchange for that, we'll get a royalty and we'll also get some payment along the way. All right. That's the business model. Today, their scientists don't use our infrastructure. My scientists, yeah, our scientists here at

• 9:19Speaker 01

  Ginkgo, they do the, they use the infrastructure and they have this interface with the customer about hitting goals. That's mostly a technical limitation. I think it would be very cool ultimately to have scientists at all these companies accessing our infrastructure directly. It's just too early. That's the problem.

• 9:35Speaker 00

  And then how does AI come into the picture? When and how did you start using it? And has this current wave of AI impacted you? Or how much do diffusion models, LLMs, et cetera, matter relative to what you're doing?

• 9:46Speaker 01

  So the short answer is like, we have, we do a lot of protein engineering. So you know, you want to program a bacteria, right? Okay, so bacteria has a 3 million letter genome. And like a customer has asked you to express a protein. And remember, a protein are basically like the little pieces of nanotechnology inside the cell that bump into each other and like do all the things. Like you're sitting there, you're like a big giant bag of proteins, right? And so, and so like, they want to make a lot of this protein because it's going to go into cold water laundry detergent. Okay. So people don't realize this, but like the reason cold water laundry detergent doesn't need hot water is because there's enzymes in there. Okay, proteins. And so they want to make a lot of this protein. And by the

• 10:26Speaker 01

  way, if they could make it more active, like break up dirt faster, like whatever reaction it is catalyzing. So if you remember chemistry class, like a catalyst makes a certain chemical reaction happen faster than if you don't have the catalyst. Okay, so this enzyme is a catalyst and I want to make it also just better. Like, so I want to improve the quality of the enzyme and I want to make a ton of it. All right. That's the spec. And so how do we do that today? Well, we would have, for example, a host strain that's really good at producing a lot of protein to begin with. Okay, so so think of that more like an existing software library. So that's one form of leverage from existing data assets. It's literally like a hard, physical asset, an actual microbe with a genome that I engineered in a project previously that is useful for your project.

• 11:11Speaker 01

  So now now we're starting from a good place. We're already starting to make a lot of protein. But you wanted to make it more active. You want to make more of the cat catalysis. Okay, how do you do that? Well, remember that protein is encoded in DNA and the sequence of DNA determines the effectively everything about that protein. But in this case, what you care about is how good of a catalyst is it? And so you go in there and you have certain tools to try to model the protein, this, that, and the other thing, and you make some choices and along with the software tools and you say, I want to try these 1,000 designs of the of the DNA in the lab and see how they do. All right, and you then get that data back on how they perform. You use that to update these design tools you have and you do it again.

• 11:56Speaker 01

  And that's what we've been doing at Ginkgo for a long time, including with neural nets and all this stuff, the latest and greatest, you know, all that. But just on that, like, data asset. I think the new idea is is on the back, and this is we just announced a deal with Google a couple of weeks ago. The new idea is, can I make a foundation model that will be additive to what I've previously been doing just with the data I was getting on enzymes? And so now I have a foundation model that really is is not specific to catalysis or anything else. It like speaks protein, you know, right? Like, just like GPT-4 speaks English, right? That's what we're going to try out with Google. That's what we think is like a really new new idea. And people, you know, there's people obviously work on it, and obviously Google themselves with AlphaFold was like one of the first generations of this.

• 12:41Speaker 01

  But we did, we see a lot of ways to make it better and make it bigger and all the things. And so we'll see. And we'll see how it goes. Let me just also give you like one other thing why I think bio is particularly interesting for folks that are interested in AI in general. So this whole idea of like a foundation model plus fine tuning with specialized data, right? Like, we all like all the people that pay attention to AI like, understand this idea, right? So let's let's just take it in in one of the categories of English, like legal. Right? Like LexisNexis, they have all this data. We're gonna fine tune GPT also. Okay. That that thing has to compete with like a lawyer at Ropes and Gray. Right? And a lawyer at Ropes and Gray is trained for 15 years, uh, you know, being taught by other humans how to do law.

• 13:21Speaker 01

  They are writing contracts that were designed to be understood by human brains. They work the way we think. Uh, they're writing that contract in English, a language that have co-evolved with our brains. You know, just language in general, co-evolved with our brains, uh, to also, uh, give us leverage from how our brains work. And we're asking this computer brain, a neural net, to compete with us on our turf. But it's a pretty high bar that it's got to compete with. Now let's go over into biology. I remind you, it runs on code, sequential letters, feels a lot like language. It ain't our language. Right? We did not invent it. We did not understand it. We do not speak it. We do not read it, write it.

• 14:03Speaker 01

  And so I feel like these computer brains are gonna kick our ass a lot faster in this domain than they do in English. Right? Like you, you know, like I think the applications in AI are all gonna be like, replace the intern. Uh, not, not the partner at Ropes and Gray at best for a while. Whereas over in bio, it should be, it could quickly become the best. If you're looking to understand like where AI is really gonna split the script and not be like kind of a low level Clay Christensen style disruption, which I think is sort of what's happening in English, but rather be a like split in the atom, it's bio.

• 14:42Speaker 00

  When people talk about protein folding related models, you know, and to your point, there's things like AlphaFold. And there's a few new companies that have been set up to basically focus on protein folding models because of the breakthroughs in AI. They kind of divided into a few markets, right? There's sort of the pharma market, which is better designed for pharmaceuticals and biologics that are used as drugs. And then there's more of the industrial, the catalyst, the ag, those sorts of things. I'm sort of curious, like, how do you think about those relative markets, just in terms of sheer market size? Because if you look at the cost of developing a drug, it's like 1.5 billion per drug, but very little of it actually goes into the underlying molecule that's being used relative, most of its clinical trials. But for ag or for catalyst or for other things, a lot of it could actually go into the molecule. So I'm just sort of curious how you view those as relative markets for this kind of stuff.

• 15:27Speaker 01

  So Ginkgo has like an AWS style business model, right? Like you need compute. I don't really care if it's, you know, if you're AWS, whether it's medicine or they're a startup or you're video streaming like rock and roll, right? So, so we have an attitude that like we're supporting cell engineering wherever it is. It is definitely different by market, like pretty substantially, right? Both the assets you need, the enterprise sales, everything is different. The biotech, you know, it's not, it's not a small industry, but it's, it depends like which side of the house you're looking at. If it's fees, it's probably a little more the, the more valuable markets that you could get more research fees. But royalty is a different game, right? Like certain things go to market a lot faster. That's how I see it. But the real problem is like there aren't any platform services.

• 16:09Speaker 01

  So like the other thing that's just wild to people in the tech industry is like, where's the fast platforms? Like where is all the horizontal stuff? Where's the operating systems? And it's like nowhere, like, like vertical integration, vertical integration, vertical integration, Merck, Pfizer, Bayer, Shingenta, like every one of these companies is like its own tech stack top to bottom, you know, and the closest thing they do to anything vertical is buy equipment from the same people. But like it's really fascinating, like totally different industry structure. And so that surprises people, right?

• 16:37Sarah Guo?

  Jason, what's the most rational way to understand that? Cause you, you look at that as a non-bio person and you're just, yeah, it doesn't make any sense, right?

• 16:46Speaker 01

  So the rational way is the work being done cross-product is too dissimilar to support common platforms. I don't agree with this. Obviously my entire business model is predicated on that statement being false, but that, that is the reason. You're like, oh, well, because the, the platform you build for customer A will never read on customer B. And so now, now you're just bad, right? Like you're, you're having to build a new platform for every customer. You're getting, you know, leverage, you're getting none of the reasons that like operating systems and, and data set, like why does data centers work? Cause compute is really generic, right? And like you can use software to make it different. But the hardware underneath is all common. And now we're seeing a little edge case difference here, right? Like, actually, the CPUs aren't that useful for the AI. Now everyone's freaking out about the GPUs.

• 17:31Speaker 01

  So we're having like an instance of like hardware variability. But the argument would be that that type of hardware variability you're seeing is sort of like per company. Okay, right, like, you know, or at least per modality in pharma, which modality is like a fancy word for type of drug, right? Like gene therapy is gonna have very different stuff then, you know, whatever. And there's truth to that, right? Like, like, it's not, that's not a false statement. It's just a question of degree. And we happen to believe that when it comes to the engineering of organisms, that that is at parts common. But plenty of people think we're wrong.

• 18:01Sarah Guo?

  Yeah, it seems like if you just go back to this analogy of like, human design computation, where you're building systems from the ground up that you can understand, but you're all discovering the same set of systems with common building blocks and the need for data analysis, it would, it would shock me if that would not eventually be true. And it's a, like a temporal cultural figment of these companies. Okay, maybe one more general question about how to think about AI in, in pharma writ large. Why do you think we haven't seen AI discover drugs yet? Because people have been talking about it for a long time. Will we and will we see it soon?

• 18:36Speaker 01

  Well, and, and so first off, I would say like, the people have been talking about for a long time is sort of like saying, three years ago, like, why haven't we seen good natural language processing in AI? People have been talking about it forever. Right? So I think there is an element of like, you need the breakthrough, right? How's the neural nets been big enough? The big limitation in bio is the availability of, of data to train these things, right? And so you have this tough situation where like, everyone is doing these models and training on the same data, right? And, and so one of our advantages is because we just have a ton of data. That's a real gap that I think is, I think partially it's like, have people gone big enough for it to have happened yet? And I think, I think now people are trying. Like, we're gonna try recursion is another great example. And like, yeah, it might still not be big enough, or more likely, it's not enough data, right?

• 19:21Speaker 01

  And like, there's nothing stopping you from making a giant neural net at this point, you know, like, the tech industry is gonna commoditize that, that infrastructure. But like, you might not have enough data to give it. To solve the problem you're asking, Sarah, right?

• 19:33Sarah Guo?

  Where does Ginkgo's data come from? Is it like your own experimental data?

• 19:37Speaker 01

  Yeah, we have a 300,000 square foot robotic lab that they built in the last 10 years. And so we generate that and we do it in service of our customer projects. We can do our own data generation, but yeah, that's where it comes from.

• 19:47Sarah Guo?

  I want to talk a little bit about one area that Ginkgo has been an expert in, which is infectious disease, right? Can you talk a little bit about the work you guys do here? And I think a question everybody cares about is like, are we prepared for another global pandemic? What has changed since COVID-19?

• 20:04Speaker 01

  Yeah, I think like the reality is infectious disease is really scary and bad, right? But like the big, the big lesson of COVID is modern healthcare systems and our current infrastructure does not render us immune, even in the developed world, from pandemic scale infectious disease. Period. We don't just allow ourselves to not have defenses against things that are like society killers that are known to exist, you know, right? Like, this is not like, like, you know, a fanciful idea. Like, it fricking happened two years ago, right? Like, you know, and so the, so what should we build, right? And, and the answer is like, a lot of different things, right? Like, we should build rapid vaccine response, which is really good through Operation Warp Speed and kind of what we figured out with mRNA vaccines.

• 20:49Speaker 01

  And just like, I got a target, I got vaccines for the entire country in three months. Not every version of this thing is vaccinatable. The other one, when we've been big believers in, is like monitoring, like radar. I grew up in Florida, right? Like, we have radar systems that warn us for hurricanes, okay? My co-founder, Tom Knight, obviously a lot older than me, was explaining to me that when he was a kid, they would get three hours' warning for a hurricane. Currently, we find out about Hurricane COVID after it has landed in New York City a week ago. Okay, like unacceptable, right? So, so one of the things we're doing is like with the CDC, we run programs, we collect wastewater from inbound airplanes and we sequence the DNA and we look for pathogens, we look, we

• 21:33Speaker 01

  monitor variants and all this, both for flu and COVID and I can add other things to that list. We have a similar program actually in Doha Airport in Qatar, we've got a program in Ukraine and think of these like bio radar stations and that's get you baseline because you also want to, you will look for anomalies, right? So, so like that whole thing has been missing and so we think that's like where you start and then you want rapid response, we're going to like basically patch. Think like cybersecurity, like it should feel like that's your answer, right? Like, like cybersecurity, I think is a bit the mental model for like what the future of infectious disease response looks like, persistent monitoring, rapid response, kill it. And, and the beautiful thing is, and it's scary and beautiful, remember these things replicate, so if you can snuff it out at the beginning, you win. Like, speed will matter, right?

• 22:18Speaker 01

  I'm chair of a national security commission down in DC, I'm emerging biotech and like the DOD just put out their biosecurity, it's like basically bio defense posture review. And like, the DOD maintains like millisecond preparedness in this country, right? Like, I know that seems crazy, but like, that's kind of like how you ought to treat these things.

• 22:35Speaker 00

  Yeah, you actually saw that with SARS, right? Because SARS, they both snuffed out with the original form, but then it leaked four times in the first two years after it was cultured in a lab. It kept leaking from what eventually became the Wuhan Institute of Biology when they moved it from Beijing to Wuhan. And it was really rapid response in terms of shutting down SARS outbreaks that really helped prevent it from spreading. And so I think to your point, there's good precedent in terms of trying to prevent spread and having it be effective, assuming that the coefficients on the disease spread are reasonable.

• 23:02Speaker 01

  Yeah, exactly. Certain ones are going to be harder than others, right? But like, it's not going to be precedent, you just need logic. If you could get it early, you win, right? Now, there's a question of how hard is that? And like, COVID was a lot harder than MERS, right? Because for a variety of reasons. But there's probably a level of tooling that could even have stopped, that could even like snuff a COVID since we had nothing when that happened.

• 23:24Speaker 00

  Sure. I guess one of the things that people in the AI safety community bring up quite a bit is that one of the big risks that are associated with the use of AI and LLMs and these foundation models for biology is that there's some risk of a lone actor somewhere deciding to, to build a virus that is infectious and deadly and can sort of run through the population rapidly. How much of a risk do you think that really is?

• 23:46Speaker 01

  The idea that like, we know how to like, exactly like, design for that sort of thing is low. You could try something, but it's not like, oh, I know for sure, it's just someone's waiting to do it. Remember, you need data. It's hard to accumulate that. Yeah, it's easy for me to accumulate data on enzymatic catalysis. It is a little bit hard to accumulate data on case fatality rate.

• 24:04Speaker 00

  And it's very hard to do because the argument I always hear from the safety community is, oh, the lone actor will of course be somebody who isn't that well versed in biology anyhow. Because the people who are well versed in biology are unlikely to do these types of attacks. So it's kind of this really weird needle that's threaded in the community to try and make arguments that, to your point, seem to not really hold up relative to the reality of what's needed to actually pull something like that off. At least today.

• 24:28Speaker 01

  I would basically agree with that today. The only thing I would say, though, is like,

• 24:33Speaker 01

  we are unacceptably exposed to these things. Like, we would not tolerate, like, on our computers, our human defenses against viruses. Like, we would not allow our computers to be as exposed to viruses as we allow ourselves to be. Okay, talking about like, technological solution, things in the background, right? And there's an entire edifice handling that, right? Including like, detection, letting other nodes know all around the world, all this stuff, like, but all happening in the background. That's what this should feel like if it's done properly. So whether you're worried about lone act or not, you don't even have to worry about that. Nature's going to toss it out at us again. We should, we should have it ready for that.

• 25:09Speaker 00

  Yeah, I think you laid out like, a really rational program around pandemic response. And I think most of the people lay out things that are, I feel, in some cases, actually subtractive. So I think your points on global monitoring makes a ton of sense. Your points on having rapid response vaccine generation makes a ton of sense. So I think those are like, really smart, grounded approaches. It's kind of interesting because one of my big lessons from COVID, when I looked at the biosafety levels that were actually enacted at some of these labs, and you're collecting large masses of bat viruses, right? And I remember when I used to work in a lab at MIT, I'd be working with different viruses and different agents and things like that for gene therapy purposes. And you look at the biosafety level and there'd be somebody in a hood and they'd kind of rub their shirt and they'd walk out the door. And, you know, one of the things that I almost get comfort in is I'm like, wow, there's been so few actual lab leaks

• 25:54Speaker 00

  over time relative to the poor behavior in labs themselves, that it's really hard to actually have something jump into humans.

• 26:01Speaker 01

  Well, I mean, look, look, Eli, I mean, to some degree, we are the evolutionary product of Being able to defend against that stuff, right? Like, you know, like if it was easy, we like, we wouldn't have made it. Right? Like, you know, like there's other species that like, didn't get our, I mean, didn't get the immune system we got. And what you have in every organism on the planet is the integration of four billion years of incident solar radiation on the entire planet.

• 26:24Speaker 00

  They've been trained over evolutionary time.

• 26:26Speaker 01

  Yeah, a time that we have a really hard time comprehending how much energy that is because of the time scale.

• 26:31Speaker 00

  Yeah. Well, one of the things that I think is really unique about Ginkgo is some of the decisions you've made as you've built a startup. So for example, I believe you have super voting shares for all the employees as like a public company. Could you tell us a little bit more about ideas like that that you've enacted and how you thought about them? It's, it's really cool stuff.

• 26:46Speaker 01

  Yeah, we're kind of a weird bug, right? Like, we started out of grad school in 2008, right? So like, straight out of school, totally common in tech, not at all common in biotech. Okay, right. So we were like unfundable, couldn't raise money, it was 2008 and we weren't developing a drug. And so like biotech people don't back that. We were developing like a platform for programming cells. And the tech people wouldn't back it because like, what are you kidding me? A wet lab? And so we were like, not on it. Well, we, five years of government grants, DARPA, ARPA-E, NSF, SBIR. So we were the first biotech to do YC because Sam had just taken over from Paul and he wanted to do nuclear biotech, all these things, right? I do think like the entrepreneur energy is right, is common. So even if you're a hard tech company, I don't think there's like a different, you don't need a different entrepreneurial training than what like YC has perfected.

• 27:31Speaker 01

  You know, right? Like that, that's a good thing. So I think there you're okay. But I think we were like finding out too late that things like social networks and large scale tech platforms also have enormous real world consequences. But they were sort of getting a pass because they were in the world of bits. And people are like, yeah, bits, you know, right? Like, whatever. It doesn't feel that dangerous, right? But once you're talking about a drug, you know, you're putting a, a thing in a kid's body, you know, like, like, like, it's like a medicine. You know, like, there's just stuff that's like, you know, you can't fuck around with it, right? And so we're building a powerful platform at Ginkgo. So one of the questions was, who should control it? Like, how do you make these decisions about like, who can use it, like platform ethic stuff? All this stuff that now being talked about in AI because AI is finally making people be like, a little bit like, oh, bits, you know, right?

• 28:12Speaker 01

  Like, maybe it is scary. And so like, it's just a little more at the front for those of us that have already been hanging out in Adam's world, right? And this is why people like, I think there's like a hard time between pharma and tech in terms of cultural, like not overlap. Because like the tech people feel like the therapeutics people are like losers and slow because they're lame and not ambitious. But they're all, but these guys. Have clinical trials where people die, right? And so like it's being at the coalface of actually building things that really inflect on on people and the world in a way that's not second order. Like information technology creates a different kind of culture. Okay. And so who should control that a platform that if we're right and is successful will powerfully read on people's lives, right? One answer is like the founders, right? That's Facebook, right?

• 28:57Speaker 01

  Like Mark's got super voting and Finn's kids do or some insanity. The other option is capital markets, right? You know, like BlackRock, you know, right? Like that's just every normal corporation that isn't founder controlled in Silicon Valley where like the the voting shares are a majority held by arm's length capital and they if the CEO is not doing a thing they want, they bring in a different board and fire the person. But like it it comes down to the control is is who has the share votes when this is in a public company ultimately hires and fires the CEO ultimately then sets the control of the platform. Okay. And so at Ginkgo, we took the Silicon Valley idea of founder super voting shares and extended it to the entire employee base. So it's not just us.

• 29:39Speaker 01

  Anyone who's at the company and it goes away if you leave gets 10x voting for their B shares versus the A shares which have 1x voting. And so the way the math works is that the employees own more than 9.1% collectively, multiply that by 10 and it outvotes the remainder. And so the theory was who should control the platform? Humans. Okay. Not not divorced capital because that's not their priority. They're kind of like, my job is to get a high return. At the end of the day, it's your job, company leadership, to like decide how to do it. But in reality, what that means is company leadership primacy is the return. Okay. And so that seems eh. And then what we've decided is a persistent thing is the employees.

• 30:20Speaker 01

  Okay, the workers, because they are humans and they actually work there and they go home to their families on Thanksgiving and have to explain like why they work at this company and are proud of it. And that that may be long term. This is a theory. It is a is a good group to give governance to.

• 30:35Speaker 00

  Yeah, it's a it's a really cool approach. And I think a lot of the early sort of super voting share stuff was pioneered in the media world, right? So the New York Times, the same family still controls it, you know, 100 years later because of super voting shares in the family.

• 30:48Speaker 01

  And that's why it's the New York Times.

• 30:50Speaker 00

  Sure.

• 30:51Speaker 01

  Only reason that plays is the New York Times is because like like humans have control. Not capital.

• 30:58Speaker 00

  So I guess one potential question about the model, because I think it's a really smart and unique model, is sometimes CEOs have to do really unpopular things. And if you don't have the founder authority and you come in and you do something really unpopular, maybe you do a big riff and the people who are left are really upset about it, but you really have to do it for the business to survive, or you make tough choices that may be at odds with the employee base. How does that impact governance later, where to some extent, you could argue the motivation, if you're not really answering to your board, you're answering to your employees purely, you may fall into more dynamics around popularity contests, or trying to appease people around tough decisions to make. And as a founder, you have the moral authority to do those. As a hired gun CEO, I think it's much harder.

• 31:41Speaker 01

  Agree completely. Yep, I don't dispute any of that. So, and the answer is share voting. Okay, so it's not like one person, one vote.

• 31:50Speaker 00

  Sure.

• 31:51Speaker 01

  So how do you accumulate more shares at a company? Work there longer. Okay, to build the value, like work there when it was cheaper and grow the value. So there will be a waiting, again, it's all theory. I don't know, but like, what I'm imagining is you'll have, you know, employees who own a lot and are like, yeah, that's a hard decision, but it's right for the organization. Right? And like, I don't think that that's out of school. I think you can see that happen, but we'll see. I don't know, we're trying it.

• 32:20Speaker 00

  Yeah, that's a very exciting experiment. That's cool.

• 32:22Speaker 01

  But it originates from the platform governance. That's like why we're actually doing it. I think some of these other reasons I think are interesting, and I like them because I'm like generally, I lean worker ownership. But I think the real point is like, at the end of the day, someone has to have platform governance.

• 32:34Sarah Guo?

  Jason, you've expressed like this awe around the result of humans, you know, in four, four billion years of evolution, we're incredibly energy efficient versus neural networks, everybody knows that. You now have the very largest labs, talking about spending literally a trillion dollars in compute over the next decade. If you think of that as maybe half energy, and then you have to make assumptions about energy prices, but now you're talking trillions of kilowatt hours, and maybe you're off by magnitude, but like, where's that money going to come from becomes a big question. These things have to get more efficient. If you compare that to like humans, like maybe we spend 5,000 kilowatt hours before we learn to read. Right? Do you think we get more biological inspiration in AI? Or do you have any point of view on the intersection of this from an architecture perspective?

• 33:19Speaker 01

  I don't have a great intuition, other than I think, you know, the other option is we just do giant brain in a vat. And we throw that up against GPT-4, you know, like, why are, why are we just limiting ourselves to a brain that fits in our head? Why don't we grow a room-sized brain and just go straight biological? Yeah. Have you thought about that, Elon? I mean, I think that what's cool about neural nets is that like, brains basically allowed computer scientists to like, escape their world of like, logic, like back to the beginning of our conversation. It was an excuse for them to basically build a piece of software that was gonna like, they weren't gonna understand how it worked. There's probably a lot more things like that, because the community that builds software wants to understand it, because

• 34:03Speaker 01

  that's the kind of people that historically have been good, been good at building software, right? So like, open your minds, right? Like, like, I'm sure the neural net is not the best architecture, right? But like, you know, and I know people are working on it, but like, really go in different directions, right? Like, do something crazier.

• 34:15Speaker 00

  You're raising a really key point, which I think was back to part of the conversation around evolution as the driver for all sorts of optimizations that you don't expect. And if you are a rational person, and you look at a biological system, right, there's the gene that can be coded, that you can produce RNA in either direction, and it produces two different proteins, and why would you ever do that? And one of them is actually duplication of this thing that got repositioned for catalysis for this other thing. And so it's really messy, weird systems that evolved. And I think the second you have self-replicating systems, where you have code writing its own code, and you start going down that evolutionary path, you should have hyper-optimization for energetics and for all sorts of other things, because it's just gonna be part of the utility function that gets selected for

• 34:57Speaker 00

  by that system. And I think that's when you get out of the realm of, you know, handpicked design and logic and laying it down and just like an explosion of stuff, right? It's kind of, the Cambrian explosion happened for a reason, and that reason was evolution and resources, right?

• 35:10Speaker 01

  Yeah. There's a very good book, if you're, if you like to nerd out on this particular line of stuff, called The Plausibility of Life. It's awesome. And I personally think instead of learning from brains, I agree with you, you want to learn from evolution. Like, because evolution itself evolved things to become more evolvable, right? Like, the example they give in the book is really cool, I'll just say. It's like a skeletal system. So your skeletal system, like, you can have a person, you've seen like, that can have like a sixth finger, like, that happens sometimes, right? Have you noticed it's not just like a bone jutting out of their hand? It's like, wrapped in skin and nerves. Well, that's because your skin and nerves are adaptive to bones.

• 35:50Speaker 01

  And that, as you can imagine, for exploring body plans is a much more efficient way to explore the space of body plan. So if you jut out a bone, and maybe it's going to be better, but it's definitely not going to be better if it's not wrapped in skin. Okay. And so like, however it happened, there was like this layering in evolution where like, we created, the system created skin and nerves to be adaptive and the exploratory part of it is the bones. And again, I'll emphasize four billion years. It's a long time, a lot of energy that has been spent. I know it was a random walk, but like, evolution figured a lot of cool stuff out. And I think that is like totally untapped.

• 36:23Sarah Guo?

  For any of the scientists exploring alternative architectures out there, if you're like, gonna do any sort of crazy mixture of exports routing, like wrap the skin around the bones, is the advice we have from Jason. Thank you so much for doing this. This is really fun.

• 36:37Speaker 01

  Yeah, super fun. Thanks, Sarah. Thanks, Elon.

• 36:40Sarah Guo?

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