How Autonomous Labs Will Transform Scientific Research: Ginkgo Bioworks’ Jason Kelly

Jason Kelly founded Ginkgo Bioworks in 2008 with a simple but radical idea: DNA is code, and cells are programmable. Sixteen years later, AI is finally making that vision real in ways that could reshape science itself. Jason describes a landmark collaboration with OpenAI in which a reasoning model with access to a robotic lab beat the state of the art in biochemistry by 40% - not by being smarter than scientists, but by running experiments 24 hours a day and sharing data across a hundred parallel hypotheses simultaneously. He argues that the biggest inefficiency in science isn't intelligence, it's manual labor. Once AI helps scale research, the cost of discovery collapses and breakthroughs follow, with profound implications for biopharma, national competitiveness, and human health. Hosted by Sonya Huang and Pat Grady, Sequoia Capital

• 0:00Speaker 00

  All of the previous revolutions in tech, internet, right, like social media, like whatever, have been totally meaningless to biotechnology and biopharma. Like, yeah, it's nice. We communicate slightly better or whatever. It's just some like back office IT crap, right? Like, not this. Like, this is actually going to change the fundamentals of how we do science and our big science industries like biopharma are going to get disrupted. I really believe that. And that's not been true for the last 30 years of tech.

• 0:46Speaker 01

  We are thrilled to have Jason Kelly, founder and CEO of Ginkgo Bioworks with us today. Thank you for joining us.

• 0:50Speaker 00

  Yeah, thanks, Anya.

• 0:51Speaker 01

  So you started Ginkgo Bioworks in 2008 with the goal of making biology programmable. And programmable has taken on completely different meaning in the era of AI. So I'm very excited for the conversation today. Maybe tell us about the journey so far.

• 1:05Speaker 00

  I mean, I'll do the Ginkgo journey in short, right? Yeah, we started in 2008, but we didn't actually raise any capital until 2014. So we bootstrapped for four or five years, which like, if you're not a bioperson, this doesn't make sense. But in biotech VC, they really don't like, like, young people, for example. So we had started the company like straight out of grad school. It was 2008. We weren't trying to make a drug. So we were like totally uninvestable.

• 1:27Speaker 02

  Were you full time focused on the company for those for six years?

• 1:30Speaker 00

  Oh, yeah.

• 1:31Speaker 02

  Okay.

• 1:31Speaker 00

  Oh, yeah, yeah. We were basically like applying. We did government grants and service business. It was like pretty brutal start. And then summer 14, Sam Altman, now Mr. Famous, writes this blog post because he just took over YC. And he's like, hey, I think the Silicon Valley model can work for like deep tech, you know, nuclear fission, biotech, material science. And so I wrote him an email. I was like, oh, man, like, thank you for, I mean, we're like five years old. I got 15 people in a lab in Boston. We don't make any sense for YC, but this is like an oasis in the desert, you know? Like, nobody will invest in weird companies like this. And he's like, no, you got to meet me. So I flew out to San Francisco, met him. And he's like, you should do YC. I was like, I should do YC. So then we did YC. So we kind of, that was sort of when, you know, if you really want to mark Ginkgo from like having capital, it was sort of in 2014.

• 2:13Speaker 01

  And how has the product changed since 2014?

• 2:15Speaker 00

  Oh, well, so the mission hasn't changed, but the product has gone all over the place, different, different roads. So we've always wanted to make biology easier to engineer. That was the idea. And so if you're, you know, this is very much.

• 2:25Speaker 02

  Hang on, I remember make biology programmable. Has, have the words always been make it easier to engineer? Cause I feel like that's a slight.

• 2:32Speaker 00

  I was always, when I was talking to Sequoia, it was always like this, the, the computer science wrapper on make biology easier to engineer. But yeah, that was always our mission.

• 2:40Speaker 02

  Okay, got it.

• 2:40Speaker 00

  But the analogy is solid, right? So, you know, DNA is code, right? It's ATCs and Gs, not zeros and ones. It's really our only other like coded product other than computers is really biotechnology. And so the core idea of Ginkgo was, well, if you could design DNA code, you can program cells to do things. And cells are, you know, they're programmable like computers, right? But unlike computers, which just move information around, cells move atom around. So if you can build whatever you want, that's, we think ultimately going to be a huge market, a huge opportunity. But the challenge is our ability to program cells today is really bad. And so how could you fix that? That was the core idea behind Ginkgo.

• 3:21Speaker 01

  And how's the product itself changed over time?

• 3:24Speaker 00

  Yeah. So the way we went to market at first was we're going to try to build what we called foundries, which was sort of a centralized laboratory that would kind of automate the lab work associated with doing biotech. And, and the reason, again, if you're a computer scientist, the way to think about this is if you wanted to compile and debug DNA code, that's a physical process.

• 3:42Speaker 02

  Yep.

• 3:43Speaker 00

  Right? So you're like ATCGGG. Like we have to do phosphorammonite chemistry, you got to build the piece of DNA you want, and then put it into a cell, grow the cell and test the cell. And that's your kind of compile debug cycle. Does that make sense? And so one half of what we worked on technologically was how do we make that cheaper? Right? Because if you want to get better at doing this, you got to do more of it faster and for less expense. And then the second thing we worked on was how do you get better at your programming? In other words, like that design you choose to test in the lab, how do you improve the odds that it does what you want? So sort of like get better at designing the biology and make it cheaper to try things. We're like basically for the last 15 years, the twin activities. And in an era of AI, I see you see opportunities

• 4:26Speaker 00

  on both sides for that today. And that's we've shifted a little bit over the years in terms of how we do it. But does that make sense, the two kind of halves?

• 4:33Speaker 02

  And roughly speaking, the design piece sounds like it's a bit more software. The testing piece sounds like it's a bit more hardware controlled by software.

• 4:41Speaker 00

  Very much. Yeah, that's exactly right. And today, the folks leading on the design side, you might see companies like Chai Bio, for example, like has like these protein models, bolts. The folks at Arc Institute are just came out today with a paper, we'll call it about Evo 2, which is like a genomic model. There's a whole community of people now trying to solve the problem of designing biology with AI. The big change at Ginkgo over the last two years is I've kind of stopped working on that problem. I'm like, we had our own approach to solving it. It's hard, right? Like designing cells is tricky. We're going to try to solve this half of the problem, which is how do you make it cheaper and faster to try things in the lab? And how can you, we could talk a little bit more, we did a project with OpenAI. How could you have AI models help you do that?

• 5:25Speaker 02

  Is anybody else focused on the backend, so to speak? I kind of think about design as the front end of the process and testing is the backend. And when I say anybody else, obviously, people do this. Is anybody else with a similar approach focused on that part of the market?

• 5:38Speaker 00

  There's some new companies, right? So there's companies like Medra out here is one that's doing it with like robotic arms, trying to accelerate it. You have like the life science tools industry, but I would say it does not have a Silicon Valley attitude about things in the sense that they're not really trying to change the fundamentals of how you do it. They're sort of like just providing the next tool to people doing it the way they've always done it. And so we've always been this kind of unique force trying to say, hey, is there a new platform, right? Is there something like the jump to planer semiconductor manufacturing in electronics at the beginning of Intel? You know, like is there some way we should just do all this stuff differently that could make it way better in the future? And that's always been the Ginkgo, like what I think is unique about what we've been doing the last 10 years.

• 6:23Speaker 02

  What catalyzed the focus on this part of the business?

• 6:27Speaker 00

  On this half of the house?

• 6:28Speaker 02

  Yeah.

• 6:30Speaker 00

  I think this is an engineering problem and I think this is a science problem.

• 6:33Speaker 02

  Okay.

• 6:34Speaker 00

  So, and I went after both initially and I kind of took my licks for that. And I think the, the good thing about an engineering problem is you can ultimately like render it

• 6:42Speaker 02

  Yeah.

• 6:42Speaker 00

  to dust, right? A science problem, I think, is great if you hit it, but it's much more unpredictable. And so in this era of Ginkgo, I've got like the resources marshaled at this point to go after this and kind of see it through. And so that's why you see me pointing in that direction.

• 6:56Speaker 02

  And with the efforts that we see with the Ark Institute and others of that ilk, what inning are we in, so to speak? Like, is the ecosystem around the science problem?

• 7:05Speaker 00

  Good question.

• 7:05Speaker 02

  Going to start producing meaningful results soon, other than papers?

• 7:11Speaker 00

  It's a good question. I, I think the, the hard part about designing biology, so biology is amazing, by the way, right? Like just as a substrate, again, right? Like if you think about what's happening inside of a cell, it is producing, you know, Intel or now Nvidia TSMC level caliber atomic placement, basically for free.

• 7:29Speaker 02

  Yeah.

• 7:29Speaker 00

  Right? And so it's able to do molecular assembly, it's self repairs, it's self replicate. It's like as a, as a physical substrate, it's insane. It is the product of four billion years of evolution. So the complexity embedded in a cell is actually a lot bigger, I think, than people like give it credit for. And so there's a march there. Now that said, you know, all, you know, more than half of your drugs today are produced by biotechnology. We cure cancer, we do this. We have huge value coming out of even with the tool, the limited tools we have today. So you don't have to solve the whole problem.

• 7:58Speaker 02

  Yeah.

• 7:58Speaker 00

  Over here to create a lot of value. You just have to be better than how we do it today. Does that make sense? And so I think they have a really good opportunities already in the near term with all the protein models. You're seeing that, right? Like Chai just did a big deal with Lilly, like there, there's real opportunities there, I think, right in the near term.

• 8:14Speaker 01

  Yeah.

• 8:15Speaker 00

  Does that make sense?

• 8:15Speaker 02

  Hmm.

• 8:16Speaker 01

  So speaking of OpenAI and Mr. Altman.

• 8:19Speaker 00

  Yeah.

• 8:20Speaker 01

  You recently announced a partnership research result with them. Can you say more about that?

• 8:24Speaker 00

  Yeah, okay. So this is, this is pretty exciting, I think, for the, that folks that are following AI, it's pretty neat. So basically what we did was we took our, we call it an autonomous lab, right? And so. I can talk more about this, but the short answer is if you really want to drive efficiency on the lab side, you need to get the human beings off of the lab bench. Right? So the way we do, and this is true in biotechnology, it's kind of true for science broadly. The way we do science today, 95% of science, the stuff that's not theoretical, so not math, you know, as everybody's working on math, like, let's work on Terrence Tao, let's get a Terrence Tao in a box, or whatever, right? Like, you know, like, the reason is, you could just simulate all that stuff on a computer, let's play chess, you know, right? Like, no kidding, right? But if you look at the majority of like, what we spend money on in the United States,

• 9:08Speaker 00

  and just generally across the world in science, it's largely on experimental work. Yep. And the reason is, if you want to learn something new about the world, which is what science is fundamentally, you have to go out usually and like poke it. Yeah. You have an opinion of a hypothesis, but you got to go test it to actually figure it out. So it's experimental science that moves the needle, in my view. And so the question was, could a reasoning model do the work of experimental science if you gave it a robotic lab? That was the question. And the answer was, yeah, it's actually pretty damn good. So we did, basically the way the project worked was, we had, there's a biochemistry problem called cell-free synthesis. Okay. So you take a piece of DNA, ATCGGG, right?

• 9:50Speaker 00

  If you were to put it in your cell right now, remember like, Central Dogma in high school, right? Like, it's like, DNA makes RNA, makes a protein, right? And so, you put that DNA into a cell, and it'll make a protein. Well, you can do a thing called cell-free, where you pop a cell open, take the guts, put it in a test tube, and then add the DNA to that. And because the guts are still there, it makes the protein. Mm-hmm. So this is kind of like, it's like the world's smallest 3D printer, or something, right? Okay? And so, scientists use this, they try to optimize, it's very expensive usually. And so there was a paper that came out of Stanford from Mike Jewitt's lab in August, that set the benchmark for like, how cheap people had been able to do cell-free protein synthesis. And so we said, alright, let's try to optimize that with the model.

• 10:33Speaker 00

  And so we gave the model, we did, each round, we would do 100 384 well plates. Okay, so each well in a plate is like, a little kind of cup of liquid, and you can do an experiment in there. And so we gave it, you know, 30,000 experiments to run, and after it would run those experiments, gets the data back, and designs another set. So after four rounds of that, we beat state of the art. And after six rounds, we beat it by 40%.

• 10:56Speaker 01

  Wow.

• 10:57Speaker 00

  And so that was a, I, I, I think it's the most interesting sort of model doing experimental work result that's been shown to date by a lot.

• 11:06Speaker 02

  And the 40% was a function of what? Just faster cycle time or more intelligent experiment design?

• 11:11Speaker 00

  Yeah, like how did it beat the state of the art? Uh, so this is, this is my point. This is, I think, my, my larger point about science, because I think we're gonna do science differently in the future, in my view, based, based on what I'm starting to see here. So what does a scientist do when they're doing experimental work? They're coming up with an idea and then they're trying to design an experiment to ask a question about that idea, and then they're gonna run the experiment, take the data back, interpret it, and then poke again based on what they learned. And they're gonna go through that process a few times to like resolve something. Oh, this is how, you know, whatever, this cancer works. This is how this piece of materials, you know, works. This is this, this is that. And, and so that cycling is just logic.

• 11:54Speaker 02

  Yeah.

• 11:54Speaker 00

  Right? And so it doesn't require you to model biology or simulate anything. It's not that half of the house. It just requires you to be almost like a programmer, like you need to be logical, run through a set of things, do data analysis and draw conclusions. And so that, like, that's all it has to do. Does that make sense?

• 12:12Speaker 02

  Yeah.

• 12:13Speaker 00

  And so we didn't do anything other than that.

• 12:15Speaker 02

  Okay.

• 12:16Speaker 00

  Right? What really let it break through wasn't that it was so smart, it was that it could run experiments. And the question was just, could it design them like a scientist could?

• 12:24Speaker 02

  Mm-hmm.

• 12:25Speaker 00

  And the answer was, yeah, hell yeah, it could. And so now I think that opens a real interesting question about like how we do science in the United States.

• 12:33Speaker 02

  Well, and it's easy to imagine a version of the future in which the, you know, the scientific method, the design and the hypothesis testing and all that is done by reasoning models of some sort. And the actual testing is done by ECO, you know, autonomous labs.

• 12:47Speaker 00

  Yeah.

• 12:47Speaker 02

  What's wrong with that vision of the future? And if, and if that is the right vision of the future, how far out is it?

• 12:54Speaker 00

  I mean, I think this is how it's gonna happen. I really do. Like, I mean, I'm probably more, substantially more aggro on this than like the average scientist today. But like, it's, so I'll just explain like why, where I think if you had a heads up competition, which I want to do this, right?

• 13:10Speaker 02

  Yeah. And then tell me how the average scientist would push back and say like, no, no, no, it's not gonna happen that way because.

• 13:15Speaker 00

  Right. So I think what the scientists will push back on is like, this thing can just be as creative as me or something, right? Which I actually, I, I'm sympathetic to that.

• 13:28Speaker 02

  Yeah.

• 13:29Speaker 00

  I'm not saying it's gonna be more creative. I'm saying.

• 13:33Speaker 02

  It's gonna be way more creative.

• 13:34Speaker 00

  I'm saying you, yeah, no, not that Silicon Valley, I live in Boston.

• 13:40Speaker 00

  I'm saying it,

• 13:44Speaker 00

  it can run a lab 24 hours a day.

• 13:47Speaker 02

  Yep.

• 13:47Speaker 00

  It, I'll give you another example. Like, the way science works today is you would have a lab, you'd have a lab, I'd have a lab. We're all working on the same area. Let's say we're working on like Alzheimer's.

• 13:54Speaker 02

  Yep.

• 13:55Speaker 00

  You have hypothesis, you have hypothesis, I have hypothesis. We each kind of pursue it. We're collecting data over the course of a year or two. And then based on what I see at the end, I write a paper. And when it comes out in the published literature, you get to read it. And you get to read it. And you're all doing the same thing. So we're kind of like exchanging information every year or two. And I'm not getting to see every experiment you did, by the way. I'm getting like the distilled output of what you think you saw over two years. Does that make sense?

• 14:19Speaker 02

  Yeah.

• 14:19Speaker 00

  All right. So let's contrast that to like what I think should start to happen now, based on what I saw with this OpenAI project. What I think should happen now is you should have a robotic lab that has every piece of equipment that we all have in our labs. So it can run any experiment you want. We can talk in a minute. That's actually a pretty technically difficult problem, but let's just wave that away. Okay, solved. All right, great. So then I'm gonna put 100 AI scientists on top of this thing.

• 14:40Speaker 02

  Yep.

• 14:40Speaker 00

  Each one is gonna pursue a different hypothesis for Alzheimer's. All right, great. And they're gonna run their experiments, just like you would in your lab that day. But at the end of the day, they're gonna pass the data on those experiments, like what experiment they ran and the raw data that came off it, to the other 100 AIs.

• 14:56Speaker 02

  Yep.

• 14:57Speaker 00

  Daily, every fucking day. Okay? And so they're gonna learn from each other. Like, even though your hypothesis is different, we're working in the same area.

• 15:04Speaker 02

  Yeah.

• 15:04Speaker 00

  So your failed result. My, like, for example, say your experiment went the wrong way from your hypothesis. That data might be relevant to my hypothesis, and I would never see that normally. Does that make sense? Yep. And so that's all just chugging along. And every week it dumps, you know, a lab notebook entry or like a mini paper, like a conclusion about what the hundred of them have figured out that week that we can all read and see and use that we can direct, we can say this, hey, cut that line of research or whatever. And so like, that's number one. I think the information sharing and like the ability to handle like really broad context across a lot of projects for the AIs is just better than it's just socially different even than how we do it today. Does that make sense? That's unfair advantage, number one. Yep. Okay, unfair advantage, number two.

• 15:47Speaker 00

  If you look at how we spend money in science, remember all this stuff like the NIH was like, what's up with the indirect rates at the academic universities and all this hullabaloo, right? Well, what's an indirect rate? Well, it's basically paying for manual laboratories. That's what it pays for. Okay, you've got these labs and they're there 24/7, but they're used five days a week. Yep. Okay, they have equipment, but every lab, all three of our labs, we have same copies of the same equipment. Yep. Because we all got to do the same work. We don't share each other's, no, no, no. I have like a door in my lab, only my lab gets to use it, your lab uses yours. So we have all low utilization rate of our equipment. It's just how it works. Okay, right? And so you have a very inefficient, like if you look at the spending on research, and this is true, the 60 to 80 billion dollars a year that biopharma spends

• 16:32Speaker 00

  or the 40 billion that NIH spends, less than 5% is on the reagents. Hmm. Everything is on overhead, is basically overhead, the people, the regulatory, the lab space. Mm hmm. If we were running it efficiently, you would budget a research program at the NIH, not on indirect and heads and everything else, but just on the reagents. Because that's like the usage based pricing of science. Yeah. Because to actually do experimental work, I have to consume some chemicals. I have to consume a piece of plastic plateware, like whatever the hell it is. Like I'm actually doing atoms in the physical world. I got to burn some stuff up. That should be the dominant cost. It's the opposite right now. Yes. It's like less than 5%.

• 17:13Speaker 00

  So the other advantage those AIs will have is if they're able to run robotic labs. Now they're running where 90% of the cost of a research project goes to the reagents.

• 17:23Speaker 02

  Yes.

• 17:23Speaker 00

  Oh, my God, right? So that's like a 10x increase in the amount of data per dollar that you're getting compared to how we do it today. So I think you combine those two things and but without the AIs even being smarter, right? They can even be dumber than the scientists. I think they win. I really think they win. And so I think we got to reevaluate like how we fund, what we fund with the NIH. I think every biopharma head of R and D needs to care about this. And like, and I think there's a blind spot. By the way, like we did YC. I know all the tech people, you know, CEOs. I've always been adjacent to this stuff, right? All of the previous revolutions in tech, internet, right? Like social media, like whatever have been totally meaningless to biotechnology and biopharma. Like, yeah, it's nice. We communicate slightly better or whatever. It's just some like back office IT crap, right? Like,

• 18:07Speaker 00

  not this. Like, like this, this is is actually going to change the fundamentals of how we do science and and our big science industries like biopharma are going to get disrupted. I really believe that. And that's not been true for the last 30 years of tech.

• 18:20Speaker 02

  Our, our partner, Konstantin, has a good framework for that. He talks about how there are revolutions in computation and revolutions in communication.

• 18:27Speaker 00

  Yes.

• 18:27Speaker 02

  Communication is about the distribution of information. Computation is about the processing of information.

• 18:32Speaker 00

  Yes.

• 18:33Speaker 02

  And what you're talking about here is just a different way to process the information like. And so the last several revolutions have been about the distribution side of the equation, which doesn't get to the core of what it is you're doing.

• 18:43Speaker 00

  Completely agree. That and that's, I think, fundamentally true. And so again, I think the leaders of biopharma companies and the and also the leaders of research universities and these people that are in the business of doing science to produce either products or for the government.

• 18:56Speaker 02

  Yeah.

• 18:58Speaker 00

  Cannot ignore this. They cannot ignore AI. It is. It is just different. And I am, I'm telling you, I'm a person who has been adjacent to this crap for 15, 20 years now. And this is the first time I've like, uh, the tech guys finally did something cool.

• 19:10Speaker 01

  And just so you know, like, so you think AI is the catalyzing force behind, you know, cloud labs should be a thing. Nobody really ever moved to them, but like AI will be the reason they move.

• 19:18Speaker 00

  Yeah, well, we can talk about cloud labs. Yeah. So let's talk about autonomous labs and then I'll explain the cloud. All right. So why has it been hard? Right? Like, like? The average tech person's look at how science is done, where you have literally PhD-trained people. These are brilliant people. Yeah. Paid decent amount of money. Standing, I did a PhD at MIT in bioengineering. It's five years of moving liquids around the lab bench by hand. I swear to God, like, it is like, like, like my friends from undergrad just, like, they would never. Right? Like, you know, right? Like, it's ridiculous. You would do like manual labor, right? Like absurdity, right? In our group, the, but like, that's what you have to, you have to do that. If you want to play at the edge of science, you got to do physical work. And so that's what you learn to do. Okay. And so it's like, well, everyone in Silicon Valley is like, well, just automate it, bro. Like, you know, like, like, why don't we just do that?

• 20:03Speaker 00

  Okay. So why is it, why is it hard? All right. And the reason it's hard is it's like, the technical, like, automation term is like high mix, low volume work. Hmm. Yeah. Okay. And this is true at like places like Hadrian today, for example, that are working on this on the manufacturing side industrial. High mix, low volume. Yep. Is hard to automate. Yep. Historically. All right. And my like transportation analogy that I've been giving to people in bio is like, okay. So imagine on the Y axis, you have like level of automation. Yep. And then on the X axis, okay, you have like flexibility, like that variability, the mix in what you're being, you're asking it to do. So in transportation, low mix, high automation, that's like a subway.

• 20:45Speaker 00

  Sit down, takes you away, right? Like, you know, maybe you're like, don't have to do anything, but you got to want to go to one of the stops on that subway line. Yep. Low automation, high variability, that's a car. Yep. Right? Hands on the wheel, foot on the pedal, take you right to your house or the grocery store. And that's what the transportation system looked like for the last hundred years. Until, thank you very much, you know, Google, we got Waymo up here in the corner where you get the flexibility, you know, the automation of a subway, but the flexibility of a car. Okay. And, and it's so surprising that we don't even call it automation anymore. We make up a new word. We call it autonomous, autonomous car. Because the way I look at it is since the industrial revolution, we've basically been automating everything that is low mix. That's like low variable from the loom on.

• 21:30Speaker 00

  Yep. Okay. Right? And we just hit a wall. And AI, we hit a wall on flexibility of what you can do and AI pushes us past it. Yep. That is like every part of the physical, like our physical infrastructure post-industrial revolution, everything has to get looked at again with that lens as we move up the variability. Does that make sense?

• 21:48Speaker 02

  Yeah.

• 21:49Speaker 00

  And so that's what we're sold, so like that's the tricky bit in like lab land. We actually have automation, but it's like subways.

• 21:56Speaker 02

  Yes.

• 21:56Speaker 00

  It's just repeat the same experiment, you know, at a diagnostic company like Quest, they would have automation. If you're a high throughput screening in pharma, there's automation, but it can't do the variability. So 99% of the work, just like 99% of miles traveled is in cars. 99% of the lab work is still at the frigging bench.

• 22:13Speaker 02

  Yeah.

• 22:14Speaker 00

  And that's what you gotta fix.

• 22:15Speaker 02

  And what's, and so like the Waymo analogy is an interesting one because it's now such a magical thing that so many people have gotten to experience.

• 22:22Speaker 00

  Yes.

• 22:23Speaker 02

  And in that case, you know, you kind of had your sensor suite, you know, you have your radar and your lidar and your cameras.

• 22:28Speaker 00

  Yeah.

• 22:29Speaker 02

  You know, then you had your software suite with the perception and the planning and the actuation, which then had to tie back into, you know, whatever vehicle manufacturer you're working with. And then there are gazillion corner cases that you have to simulate because you can't get enough of them in the real world. Like what would that set of words be for your world?

• 22:45Speaker 00

  Yeah.

• 22:46Speaker 02

  Like what are, what are all of these specific things that are hard to get right?

• 22:49Speaker 00

  Hundred percent. And I think if you're like trying to generalize the problem of bringing automation to autonomy.

• 22:54Speaker 02

  Yeah.

• 22:54Speaker 00

  It's going to be different in every domain.

• 22:56Speaker 02

  Yeah.

• 22:56Speaker 00

  So for cars, the hard part is the physical world is changing.

• 22:59Speaker 02

  Yeah.

• 23:00Speaker 00

  Every mile you drive, the world, oh my God, I'm in a new place, right? Like this one has a cone, it's raining, like whatever, right? Like that's not the problem in the lab at all.

• 23:07Speaker 02

  Yeah.

• 23:07Speaker 00

  Lab, I can make it, it's my lab every day. It's the same fucking room that the robots are in. Nothing is changing. Okay, the physical environment is not changing at all. The, so like, like it is not at all the same stack that brought you autonomous cars that will bring you autonomous labs. That's not my problem. Okay, so, so in my world, it's the variability in like what the scientist is asking for that makes it hard, right? So they're like, I want to use this piece of equipment, I want to use that piece of equipment. This is my combination of things, right? And so one of your big problems is getting a thousand long tail pieces of third-party

• 23:43Speaker 00

  Like you can't believe the software on these things. Benchtop lab equipment.

• 23:46Speaker 02

  Okay.

• 23:47Speaker 00

  Integrated into one big system.

• 23:48Speaker 02

  Okay.

• 23:49Speaker 00

  So that they all can be controlled by your software. That's like problem number one. It's like integration of benchtop equipment. Problem number two, what are we doing with our hands when we do science? Largely, in bio anyway, it's liquid handling. So you pick up a pipette, which is like, if you didn't do this in high school, it's like the world's fanciest straw. And you like suck up a little bit of liquid and you squirt it out in the right spot. And like, but the thing is, if the liquid is viscous, like it's like syrup. Then you naturally with your thumb like adjust the pressure of the straw. Cause you can see with your eye if it's working or not. So it's actually liquid handling turns out to be a trickier like problem than you think. And you are doing some work as the human to like manage that.

• 24:28Speaker 02

  Yep.

• 24:28Speaker 00

  So you have two big buckets. One, solve liquid handling. Two, uh, send samples to a thousand different pieces of equipment.

• 24:36Speaker 02

  Okay.

• 24:36Speaker 00

  Does that make sense?

• 24:36Speaker 02

  Yep.

• 24:37Speaker 00

  If you nail those two, you're done.

• 24:39Speaker 02

  Well, that sounds, that sounds tractable.

• 24:41Speaker 00

  It is fucking tractable. Yeah, I agree. Yeah, it's totally tractable. And so it's just a lot of work.

• 24:47Speaker 02

  Where, where are you guys in like working through that?

• 24:50Speaker 00

  I think we basically have it working. Yeah, that's the honest truth. I mean, we, we basically.

• 24:53Speaker 02

  What's the last like major hurdle?

• 24:55Speaker 00

  One reason it's hard for people to do this technically is they want to build the hardware.

• 24:58Speaker 02

  Yeah.

• 24:59Speaker 00

  But they don't do research. So they kind of have to go to a customer and be like, hey, you want to use my robot? That's where it's like, no. Right? Like they're at the bench, they're like, no, I don't like to try. You're like, so there's like an adoption issue that I think has made it really hard. And, and so we have this advantage that because we have a research, we saw like our original business, which was research partnerships, which we still do. Means I have a bunch of scientists employed at Ginkgo. These scientists are basically like, remember the Google engineers that like sit with their hands like this next to the wheel? Five, seven years ago in Palo Alto, like grabbing it if it like the Waymo drove into a mailbox or something, right? Like, yeah, that's my scientist today.

• 25:36Speaker 02

  Yeah.

• 25:36Speaker 00

  So they are, they're dog fooding on our, we have like 50 robots. We're going to a hundred in our big lab in Boston. And so they're the ones like trying out and breaking it, things that have broken. Running a bunch of work in parallel across that system is a scheduling challenge. So you have to be able to manage all that. So like just handling the scheduling with tight timing on experiments is like algorithmically tricky and we've had to figure out a bunch of stuff. Getting the equipment to work all like when it works all day long, making that reliable compared to it's being barely used at the lab bench, that's tricky. Right. So it's just these like things that we have to keep knocking down, but they're they're engineering at this point.

• 26:12Speaker 01

  And if you solve like pipetting and liquid handling?

• 26:14Speaker 00

  Yeah. The good thing is there's, there is a whole industry that's worked on that problem, like liquid handling robotics. It's just a matter of having like all the different liquid handlers. And if you have them all, you can kinda, and you know, your, your liquid class you're dealing with, you can manage it. Oh, one other one, big one. Scientists don't code. Yep. Okay. So, oh, cool. Use my robots. This is what everybody's done. They've made like visual programming languages. Like if you're a scientist, there's a thing called LabView. It's like complete trash, but like it's, you know, make a flow chart, right? Cause you can't write Python or whatever. Horrible. Okay. Like even that they hate. Okay. Right. And so no one will program Shet. And, and so we ran into this issue where we now have all these scientists using the automation directly.

• 26:55Speaker 00

  So like, I don't know, three weeks ago or something, we had two, the two instances where we sent a plate. So like, again, this is like kind of a bunch of little wells with liquids in them and it, and we seal the plate when we put it in storage. So it doesn't evaporate. So they sent a sealed plate to the pipetting robot and pipette comes down and it gets stuck in the seal. You're like, and people in our Slack are like, hey, what the hell? You know, like de-seal the plate before you send it to the liquid handler, dum dum, right? Like, you know, and it's like, and this is horrible for a scientist who has basically an expert at liquid handling at the bench. And now they're like making like, you know, basic mistakes here and they feel horrible. That's a bad UI. Okay. And, and I was just like, this is nonsense. We're from now on only what the way we're going to interact with writing the code is through Claude code or codex. Yep.

• 27:38Speaker 00

  Like you will now submit a written protocol, what you want, and the model will figure it out. And if the model sends a plate sealed, we will update the skills file and it will never do it again and we will get through this.

• 27:50Speaker 01

  Okay.

• 27:50Speaker 00

  And so that is a big win for usability. Yes. Of robotics for scientists is what's happening with, with Claude code and codex. Does that make sense? That does make sense. So like, thank you. You know, right? Like, so these are the things that, but that's like, that had to get knocked down too. And so all this is in flight, but like right now in Boston is like a very unique experience, experiment happening where we have like. Fifty scientists submitting jobs into one big robotic setup that exists nowhere else on the planet right now. And so it's pretty neat to watch it.

• 28:18Speaker 01

  Do you see a future for humanoids?

• 28:20Speaker 00

  No.

• 28:21Speaker 01

  Really?

• 28:21Speaker 00

  In the lab?

• 28:21Speaker 01

  Because the best argument I've heard for humanoids is like the world, the physical world is designed for them. And like, I would think that the existing labs are made for like humans walking around, pipetting things, walking between different machines.

• 28:32Speaker 00

  Yeah.

• 28:33Speaker 01

  And so you could, you could try to create a robotic arm that's able to like, you know, orchestrate all this, or you just have a humanoid do what a human lab scientist does.

• 28:41Speaker 00

  Yeah. So again, like, the primary thing that the human is doing is moving samples around the, the environment.

• 28:49Speaker 01

  Yeah.

• 28:51Speaker 00

  There are much better ways to do that than like walk them bipedally among things. You just put them on a track. Like our system has like a nice little track and the, the plates move with extremely high liability. They get delivered with micron specificity to where they are. The arm picks them up. It's like, you know, that problem just disappears. Okay. And then the other reason is in the long run, the humans are the limitation.

• 29:11Speaker 02

  Yeah.

• 29:11Speaker 00

  Right. It's not like, oh, is, are, are humanoid robots going to disrupt TSMC? Are they going to go in there and etch the, the fucking chips? Like obviously not, you know, right? Like, like, like, no, it's a microscopic discipline. Like biology is a microscopic discipline. Like these things are, no, they, it makes no sense.

• 29:29Speaker 02

  Does, how does it change the unit of scale for a lab? Like I'm, I'm imagining that labs in the future are going to be these enormous things, the way the data centers have become these enormous things.

• 29:39Speaker 00

  So it's actually going to make them smaller.

• 29:41Speaker 02

  Really?

• 29:42Speaker 00

  At first, yeah. Because again, you've probably not seen this, but like if you were to walk through Merck's campus, you'll see a million square feet of laboratory benches.

• 29:50Speaker 02

  Yeah.

• 29:50Speaker 00

  Across a bunch of different buildings everywhere, whatever, right? And they're, they're set up for basically humans to be able to walk in and find a piece of equipment. Again, underutilized, but basically available whenever they need it to run whatever experiment they've come up with by thinking over the last two weeks and not even working in the lab. And so like that kind of like cycling is, is kind of how it operates. And you need it local. Like if you have a team now in this new place, because you bought this company, they need a lab. You replicate another lab, right? The labs have to go wherever your scientists are. Well, let's now instead imagine the scientists are ordering all their experimental work. Through computers.

• 30:26Speaker 01

  Mm hmm.

• 30:27Speaker 00

  And it's going to some centralized autonomous lab. You can think of that like a local cloud if you want. Okay. Well, now you don't need a lab where the scientists are. So you get rid of all the just duplication that you have because of physical people. You also get wildly better utilization of the benchtop equipment. Like, we're talking going from like sub 20% utilization at the bench to like 70%. So now you need less equipment.

• 30:49Speaker 01

  Mm hmm.

• 30:49Speaker 00

  And then, assuming you didn't decide to have humanoids, like you can just jam it all in around a track system, so it's actually a lot tighter.

• 30:56Speaker 01

  Yep.

• 30:57Speaker 00

  Yeah, so we have like a major space reduction at the moment. That's one of the big savings. Like, when we talked to, like, we just sold 97 robots to the Department of Energy for this, like, Genesis mission. This is like the AI for science thing that Trump's doing. And like, that is basically going to be ultimately much more dense than the equivalent set of labs would have been that would have like housed that otherwise. And that's part of the sales pitch. It's like you could have less spending. Remember, I told you earlier, like, the spending is not on the reagents. It's on basically like roof space.

• 31:24Speaker 01

  Yeah.

• 31:24Speaker 00

  Like laboratory space and people.

• 31:26Speaker 01

  What is the unit of work? You said you sold 97 robots. Is that 97 boxes?

• 31:30Speaker 00

  Yeah, so our particular device, we call it a rack. It's like a reconfigurable automation cart. It's basically like a box that has a piece of benchtop equipment in it, a six-axis robotic arm. This is like, nothing special for labs. This is like traditional, this is like coming out of manufacturing tech. And then a piece of MAG Lev track. And what you do is you Lego block the carts. So we have like 50 of them all together in our lab in Boston. And then a sample can move on the track. And in front of every piece of equipment is an arm. And the arm picks it up and puts it on the equipment.

• 31:58Speaker 01

  Got it.

• 31:59Speaker 00

  Okay, does that make sense?

• 31:59Speaker 01

  Yeah.

• 32:00Speaker 00

  Yeah, and so our unit is we sell the box. We sell like a subscription, basically, like service fee plus software subscription for, per box.

• 32:08Speaker 01

  Yep.

• 32:08Speaker 00

  And then eventually, what I want to sell is like automation-friendly reagents.

• 32:12Speaker 01

  Yep.

• 32:12Speaker 00

  Because that's kind of like the usage pricing. So that would be my, that's like one half of my business now is like, I'll build you an autonomous lab, you know, Pacific Northwest National Lab, DOE, or Merck, or whoever. Does that make sense? And then the other half is what you said. I'll run my lab in Boston as a cloud, and you could just order from it.

• 32:29Speaker 01

  Yeah, totally. Can we talk about training? Like, a lot of what we've been talking about, to me, seems like inference. Like, it's a use case of the reasoning. It seems to me that you have, you are generating an enormously helpful dataset here that should be used to like backprop into the weights themselves.

• 32:42Speaker 00

  Yeah.

• 32:43Speaker 01

  How's that going to play out?

• 32:44Speaker 00

  Uh, it's a good question.

• 32:47Speaker 00

  I don't know yet. I think I think there'll be one training. So there's two, there are different levels of challenges. One was the thing I mentioned earlier, like you're submitting a piece of physical work. And again, I think this applies to labs. This will also apply in like light manufacturing, right? Like you want to do a slight, oh, you're like a prototyping shop or whatever, any place where there's like variability. Okay. And so I see a lot of variable requests from scientists. I see all the edge cases of how it breaks the physical equipment. Yeah. That you can't like compile out. Yep. Oh, make a digital twin. No, like, you know, right? Like, like you have to like actually do this stuff. Like, I do not think, like, I like, I don't buy it, right? Like, like a lot of its edge casey, you know, liquid classes, you can't really pick it up on a camera. There's just like all this stuff. Yep.

• 33:32Speaker 00

  That are like the edge cases that Waymo had to see driving around are edge cases we see by doing a lot of variable work on the same system. That is one type of training. I don't know that it's fully like model training as much as it probably is just like a giant file or something, but like, that's one. Does that make sense? Yep. The bigger one is sort of the model's ability to take your intent and turn it into an experimental plan. And that's interesting. That's back to like, could these things just like blow science out of the water? And, and, and that I think you could have a really cool loop that has more to do with the results of every experiment. Like this OpenAI project. Like, as we saw what experiments worked and didn't, you could theoretically then teach the si- the model to be a better scientist. It's like Newton, Einstein, like, you know, like, these are the people that moved the species forward at the end of the day, right?

• 34:17Speaker 00

  Like, everything else is kind of noise. Like, we're just running around in circles. You know, like, the Romans did it, you know, right? Like, we're just doing the same stuff, right? The, the Greeks. But, but except for science, right? Does that make sense? And so I do think this is like, if you crack that nut, like, if you can, if models plus, again, I think you've got to have the experimental work. If that really 10x's or 100x's the speed we do scientific discovery, like, That 10x or 100x is the progress of the species, in my opinion.

• 34:45Speaker 01

  Yeah, it just seems to me that the results of what you're generating in the lab needs to feed back into updating the model weights somehow, otherwise we're not going to get to that point.

• 34:53Speaker 00

  I agree, but I think that's totally doable. Like, I think that loop, and I think this is something that the frontier models are starting to care about. Like, getting better at that, I do think is, again, in my view, some of the most important part of human intelligence is our ability to push the frontier of knowledge. I mean, spreadsheets are cool too, you know, right? Like, doing back office for a dentist, also fine, you know, right? Like, you can make money with that, but like, if we really want them to be smart,

• 35:21Speaker 01

  Yeah.

• 35:21Speaker 00

  this is where it matters the most.

• 35:23Speaker 01

  I agree.

• 35:24Speaker 02

  You mentioned Project Genesis earlier.

• 35:26Speaker 00

  Yeah.

• 35:27Speaker 02

  What is it, why does it matter?

• 35:29Speaker 00

  Yeah, so this came out of White House and OSTP, so the Office of Science and Technology Policy, like, my crassio is there. And so, it's run by the Department of Energy. And that's in part because, like, the Department of Energy is, if you're a science nerd, that's where we do, like, our big science projects.

• 35:45Speaker 02

  Okay.

• 35:45Speaker 00

  So, like, starting with the Manhattan Project, but also, like, the Human Genome Project, actually, was like a Department of Energy project.

• 35:50Speaker 02

  Hmm, okay.

• 35:51Speaker 00

  So, when it's, like, project-based, it kind of tends to live in Department of Energy and, like, more open-ended science is, like, the National Science Foundation. Okay, so DOE is running it, so it's a project. And so, they're like, "All right, great, we're going to have a list", and they actually put out a list of, like, "These are areas where we would like to see breakthroughs that are relevant for the American public."

• 36:09Speaker 02

  Yep.

• 36:09Speaker 00

  Okay, and one of them's, you know, a couple of them are bio-related, but there's other stuff too, material science, new energy, all these different things, right? And then what we want to do is bring AI models into the national labs, which is where we do a lot of our big science in the country, and accelerate them. And their target is to double the acceleration of science in the next few years, all right? So, that's the idea. And so, one way you're going to do it is they want to take the existing data That is at the national labs and like basically feed it into models and see if you can find new stuff from data we've already collected.

• 36:39Speaker 02

  Mm hmm.

• 36:40Speaker 00

  But then the other way they want to do it is to have autonomous labs that can generate new data in the direction of models.

• 36:46Speaker 02

  Hmm.

• 36:46Speaker 00

  And that was, and so when we, we did that deal with Department of Energy Secretary, the Department of Energy Secretary Wright and I like ribbon cut the first 18 robots up in Washington and like signed it. It was really cool. And so like, I think that's a, to me, again, as a science nerd, like that's a good push for us. Um, and I think you want to see some good results soon because ultimately they got to, you know, Congress has to get excited about this. You, you would need like a bigger bolus of money in the future.

• 37:10Speaker 02

  Yeah.

• 37:10Speaker 00

  Right. To really make it a big deal, but I like what they're doing. Like I think it's the right thing for the country.

• 37:14Speaker 02

  And in a dream scenario, what does that do for America?

• 37:16Speaker 00

  So I, I chaired this, um, National Security Commission on Emerging Biotech in DC for like two years and Senator Young in Indiana chairs it now. And there was a similar one on AI that Eric Schmidt chaired like seven years ago. So it was super fascinating to see like, like learn more about DC, number one, Congress and, but also like, the point is how does the US stay competitive in technology areas that are strategic?

• 37:39Speaker 02

  Yep.

• 37:39Speaker 00

  And so there's one for cyber like 15 years ago or something. Then there was AI and then this was on, on bioengineering, biotech. We have had an unfair advantage, like since the Soviet Union fell, basically where like we were automatically at the lead in science.

• 37:54Speaker 02

  Hmm.

• 37:56Speaker 00

  Like there was no one else that had the money to spend on science, basically. And because it's science is like, it's not just spending on the science. What would you, you also need scientists. So you have to have research universities to train these people. Like it is esoteric. So that was true. That was true. And now with China's rise, it's not true anymore. Right? Like they're actually, like, if you look at the number of like scientific papers published, it's more from China now. If you look at in my world, like biotech drugs, not manufacturing drugs, like making new drugs.

• 38:26Speaker 02

  Mm hmm.

• 38:26Speaker 00

  The way it works in this, in our industry is like startup discovers a drug and then they try to sell it to Merck or Pfizer and they're, they're the kind of go to market channel and Merck or Pfizer buys it for two billion or five billion or 10 billion, depending on where it is in like clinical trials.

• 38:39Speaker 02

  Yep.

• 38:40Speaker 00

  Great. Three years ago, less than 5% from China. Last quarter, 40% plus.

• 38:46Speaker 02

  Wow.

• 38:47Speaker 00

  Okay. Yeah. So we, and that's innovation. That's like discovery, right? So we're, and why is it, why are, why are there, why is that growing so fast in China? They have just as many scientists as us. They're just as smart as our scientists. They get paid less. And remember, it's like a hands in the lab. You gotta do, science is driven by experimental work. So if you now have more experimentalists in China and you get more research per dollar, I don't see why they don't win in research. Yeah. And so, so from my standpoint, we need to, we need to make this change, both in how we do experimental work and bringing in like the AIs to just increase our amount of like intellectual horsepower. If we're gonna keep up in science and you don't want to be surprised, right? Like if you know like DARPA, like the, you know, like Yeah, yeah.

• 39:32Speaker 00

  Thanks for the internet DARPA, right? Like the founding point of DARPA was like after Sputnik, when the Russians like were the first ones to put a satellite up and it was created to say like, we will not be technologically surprised again. It's behind the scenes thing, but it is very important, right? It's really scary if you get technologically surprised. And so I think that, that is why it's, I think, important from a national security standpoint, important for the country, important for the species is to, is the rate at which we get scientific discovery. Does that make sense? Yep. Other thing I'm curious, I want to ask you guys about. I mean, God bless Sam and this friggin' OpenAI thing, but like, you know, when they started that, it was like a pie in the sky research project. Yeah, yeah.

• 40:12Speaker 00

  Right? This is like, almost like Bell Labs-y kinda, you know, like whatever, like go for it. Everyone's like, it's bullshit. Oh, what is this? Nonprofit? All the, okay. But here we go. It's now worth what? What was the last round done at? Half a trillion or something? Eight hundred and thirty billion. Okay, great. Eight hundred and thirty billion. Alright, so, so I, I, to me, it signals, um, in fundamental research in industry can be valuable. Yep. And I think that's also a thing that we've kinda forgot about over the last thirty, forty years, cause so much of like where the money was, was just like engineering, engineering, engineering, and it wasn't really about trying something that was just like,

• 40:51Speaker 00

  this is probably not gonna work, but we should give it a try. Pharma's been like that, but lots of the rest of the economy has not. Does that make sense? Yep. And I wonder if that's gonna change. Like, I'm curious if you think like, I don't know, every big industry, like, you know, the chemical industry, like, should everyone just get back, be like, well, based on these models and like some acceleration in science? Like, actually, the most valuable thing Dow Chemical would do would be some fucking crazy breakthrough.

• 41:17Speaker 02

  Mm-hmm.

• 41:17Speaker 00

  Not, uh, you know, let's do another chemical plant or run the numbers on like putting something in Louisiana. But like, you know, like, but like, actually, Nick, no, no, we're gonna like, go for it. You see that at all? Like, you know, like, like, do you, like, like, do you, you know, I don't know. But, but that would be like one of my naive hopes is the industrial side of the house wakes back up.

• 41:37Speaker 02

  Yeah.

• 41:37Speaker 00

  On doing research.

• 41:38Speaker 02

  Yeah. Sonya, I know you have a point of view on this.

• 41:41Speaker 01

  I would say we've seen a few of these. I mean, you mentioned Chai earlier.

• 41:44Speaker 00

  Yeah.

• 41:44Speaker 01

  I think it's likely to come from researchers on the research side of the house that are, you know, fundamentally rethinking, for example, the protein design, uh, process. Uh, I think it's, my, my gut instinct is more likely to come from folks like that who are just taking really big swings. Um, and the tricky thing is just the, the song and dance of how to get funded, especially through, we're still in biotech winter.

• 42:04Speaker 00

  Sure.

• 42:05Speaker 01

  Right? And like, how do they even prove out to the world that like, hey, we have better discovery engines? We have, we have better candidates, cause I think if there's like a real problem in, in the biotech world, there's asymmetric information. And you're just like, you can't tell.

• 42:16Speaker 00

  Yeah.

• 42:17Speaker 01

  Um, I feel Google with isomorphic feels like the closest, cause it's actually got deep pockets behind it.

• 42:22Speaker 00

  Yeah.

• 42:22Speaker 01

  Um, to actually prove that story out. But yeah, my bet would be a vertically integrated research team, um, taking big swings.

• 42:29Speaker 00

  One of his other.

• 42:30Speaker 01

  Yeah, yeah. The challenge is like funding these companies all the way through.

• 42:32Speaker 02

  Well, and I think, um, to the point on, should there be more breakthroughs, more fundamental research? I think the answer is unequivocally yes. Like, that is one of the wonderful dividends, so to speak, that's gonna come out of this whole AI wave.

• 42:47Speaker 00

  Yeah.

• 42:47Speaker 02

  And I think Phase One is sort of becoming human-level intelligent across a bunch of different categories. And I think that will largely go to just doing the things that we do today, better, faster, cheaper.

• 42:59Speaker 00

  Yep.

• 43:00Speaker 02

  I think Phase Two is gonna be becoming super intelligent across specific categories, one at a time.

• 43:06Speaker 00

  Yeah.

• 43:06Speaker 02

  And that's where all the breakthroughs are going to come from.

• 43:08Speaker 00

  Yeah.

• 43:09Speaker 02

  And I feel like we're kind of in this transition phase where we're getting to the point of human level intelligence across a bunch of different things.

• 43:15Speaker 00

  Yeah.

• 43:15Speaker 02

  We're about to start being super intelligent in a bunch of different things.

• 43:18Speaker 00

  Yeah.

• 43:19Speaker 02

  And we're about to start seeing a bunch of breakthroughs.

• 43:21Speaker 00

  Yeah.

• 43:21Speaker 02

  And so I, I think it's like we're within months or years of it becoming really interesting.

• 43:27Speaker 01

  We give an example, we just backed this team that did the AlphaChip project at Google. So they like, they're using AI systems to actually just design chips that perform better than what human chip designers can do. And so like, I think we're going to see these pockets of super intelligence in different corners of industry. And there's definitely teams going to play.

• 43:45Speaker 02

  What was the AlphaGo move 38 or whatever?

• 43:46Speaker 01

  Move 37?

• 43:47Speaker 02

  37, okay. I always forget the number. I know. I thought it was like 83. Yeah, 37? Great. It's like move 37.

• 43:53Speaker 00

  Right, yes. Like that kind of stuff.

• 43:54Speaker 01

  Yeah.

• 43:56Speaker 00

  Yeah, I mean, if that's true, right, like, and I think there's two ways to get at it, by the way.

• 44:00Speaker 02

  Yeah.

• 44:00Speaker 00

  My secret on this would be, one is the thing you're saying, like, it's going to be super intelligence, right? It'll intuit something that a person wouldn't have. And then, like, I really think if you can solve the problem of greatly accelerating the experimental work, it's almost the same.

• 44:16Speaker 02

  Yeah, the combinatoric approach.

• 44:17Speaker 00

  It's, you know, like, you can't, something, a lot of the, the physical world stuff is not simulatable.

• 44:26Speaker 02

  Yeah.

• 44:26Speaker 00

  Right? And so the, it's just, can you run that machine faster? And then importantly, like you're saying, like, can you feed it back and make it smarter based on what it's actually seeing in the world? And then, then, okay, like, and then, and then I think if you, if you start to believe you can get those breakthroughs, I do think you have to ask, what's the, how do you commercial? Like, what's venture look like in that scenario? Right? Because you're like, oh, crazy, I got this insane break, you know, we got a room temperature semiconductor, or, you know, right? Like, now what? Right? And is it like, you go to, you go to the big guys, you do it yourself, right? Like, there's a whole thing there. I think it's fascinating.

• 44:57Speaker 02

  Yeah, we, we had this conversation the other day on. Will the venture capital industry shrink because the cost of producing everything gets cheaper because it becomes so much more efficient?

• 45:08Speaker 00

  Yeah.

• 45:09Speaker 02

  And if we analogize back to the cloud transition, when all of a sudden you didn't have to build your own data centers, you could just spin up a cloud service.

• 45:17Speaker 00

  Yeah.

• 45:17Speaker 02

  You might have thought the same thing would happen, but actually the opposite happened. There was this explosion in creation.

• 45:23Speaker 00

  Yeah.

• 45:23Speaker 02

  Which made distribution that much more competitive.

• 45:26Speaker 00

  Yeah.

• 45:27Speaker 02

  And so there were a million different companies, but then all of them had to fight so hard to like break through the noise.

• 45:32Speaker 00

  Yeah.

• 45:32Speaker 02

  And I would, I would, I would guess the same thing happens, which is the cost of creation goes down and down and down.

• 45:37Speaker 00

  Yeah.

• 45:38Speaker 02

  The cost of distribution goes up because there are just so many different things out there.

• 45:41Speaker 00

  That's interesting. Yeah, that could be right. Yeah.

• 45:44Speaker 01

  Do you think language-based foundation models are the right substrates? Or do you think somebody's got to train like a, you know, ACTG native model?

• 45:52Speaker 00

  Yeah. So, so that is like the ARX Evo is an ACTG native model.

• 45:57Speaker 01

  Yeah.

• 45:57Speaker 00

  Right. So it's trained on a trillion bases of DNA and, and I, I think that's awesome. Right. I think it's super exciting. I think it will apply. It, it'll be like a tool available to the reasoning model to do its job.

• 46:08Speaker 01

  Yeah.

• 46:08Speaker 00

  That's already the case.

• 46:09Speaker 01

  Yep.

• 46:09Speaker 00

  Right. Like the reasoning model working on, say, our, or even our OpenAI project could go access AlphaFold, design a protein, get it synthesized as, add that as a reagent into the project. Like that's allowable.

• 46:21Speaker 01

  Yeah.

• 46:21Speaker 00

  Right. And so I, I think those will end up being powerful tools. Um, but I still think, I'm, I think the reasoning models are really, they can do the job of an experimental scientist. And so now we have like a thousand experimental scientists in a box.

• 46:34Speaker 01

  Yeah.

• 46:36Speaker 00

  That, that's already true. I don't need like a miracle, right? Like that's, um, and, and yeah.

• 46:41Speaker 01

  So here's a question.

• 46:42Speaker 00

  Yeah.

• 46:43Speaker 01

  AlphaFold, all these, all these papers have come out on how AI is changing drug, drug discovery.

• 46:47Speaker 00

  Yeah.

• 46:47Speaker 01

  Do you think the pace of drug discovery has actually accelerated or not?

• 46:50Speaker 00

  Okay. Yeah. So let's nerd out now on like what the hell you can actually do with bio, which is like a pile of trash all the time. Right? Okay. So, so like bio is right, like, like, I think the down, so God bless. Like I, I basically, you know, Jurassic Park came out when I was 13. All I want to do in life is like make dress park. Engineering is awesome, right? Like, that's why I'm doing this.

• 47:10Speaker 01

  Did you see that one company that brought back the woolly mammoth?

• 47:12Speaker 00

  Yes, yeah, I know them well. Yeah, yeah, yeah, man, over there. Colossal, yeah, it's great. I love that stuff, right? They haven't brought back the woolly mammoth yet, but they brought back a dire wolf.

• 47:18Speaker 01

  Oh, a dire wolf, sorry.

• 47:19Speaker 00

  Mammoth is coming, so I'm sure. The, uh, but yes, okay, right? So, but really what I'm excited about is like the ability for, you know, kids someday to design biology like they program computers.

• 47:32Speaker 02

  Hmm.

• 47:33Speaker 00

  Right? Like, that is what I want. Like, that's the world I want to exist. And so the question is, like, how the hell do you get there? And one of the issues we have in programming biology, design DNA, genetic engineering, whatever you want to call it, is that the only working app ecosystem is therapeutics. There is like 85% of the market for biotechnology is therapeutics, and then there's like 10% is ag, remember months on to boogity boogity? Right? Like, like, that's, that's genetic engineering in plants. And then there's like 5% that's like industrial, like you, when you have cold water laundry detergent, that's a product of biotechnology. There's enzymes in there that break up dirt without you having to make your laundry, um, hot.

• 48:16Speaker 00

  And so that was the, that's 5%.

• 48:19Speaker 01

  Okay.

• 48:20Speaker 00

  That is the totality of apps we've come up with so far for programmable matter compilers, i.e. cells. It's embarrassing. Okay, right? Like, like, the, the fundamentals of this, but again, like, let's imagine computers, the only application for computers was drug discovery. We'd all be like, wow, good, they're so, you know, man, it's such a pain in the ass with these computers, right? Like, you know, and so, so, so there is like a distinction between like what we're really working on at Ginkgo and other places like us, which is like, how do you really make it easier and faster and cheaper to just design biology, make it do new things?

• 48:53Speaker 02

  Yeah.

• 48:54Speaker 00

  And then the fact of the matter being that the only apps that really, like, have ROI are drugs.

• 49:00Speaker 01

  Yep.

• 49:01Speaker 00

  Okay. And drugs have like annoying features. The biggest one being like the time it takes from like inception of the drug to making money. Is a killer and it has to do with regulatory, basically. Like it's like, well, we don't like to stick things in people. Yeah, be careful, like all that stuff is fine and then we can get faster on that. Like China's also eating our lunch. Like you might've seen this, but like they can do a trial in six months. It takes us like two and a half years, like for like phase one, it's crazy. Australia is actually eating our lunch. I think our FDA will just match Australia soon, which is great. So, so we will get faster, but it's still not like launching a phone app. Okay. And so that, does that make sense?

• 49:38Speaker 01

  Yeah.

• 49:38Speaker 00

  So that does remain like, like a problem, I would say. We try, there's been a bunch of other attempts at other things, you know, like Amelfree Me and. Like, like it's never quite been good enough to like disrupt another industry yet.

• 49:51Speaker 01

  Yeah.

• 49:52Speaker 00

  I would love to see that happen. That would be a big accelerant, not just for whatever industry, but ultimately for genetic engineering. And then if you accelerate genetic engineering, you, you try to create that flywheel that we got in computers where it keeps going, going. Now, that said, to pick our app of drugs, it has gotten more expensive to develop drugs, not less, year over year for the last 25 years. So that's not great. That's the opposite of what should be happening, right? And so, and why is that?

• 50:22Speaker 00

  Because we do it manually.

• 50:24Speaker 00

  That's my opinion. We have, we have not, and it's like biomiles cost disease or whatever, like the scientists are getting more expensive. The rent is getting more expensive. That's it. They're actually more productive, like we, we give them new tools, like we are getting like slightly better, but the majority of the cost is manual work.

• 50:41Speaker 02

  Yeah.

• 50:42Speaker 00

  That shit does not get cheaper. And so, so that is what I think is the root of it, actually. And that's why you see me 15 years into this being like retrenching to solve that problem first. Because I don't believe we really get out of the mud until we've got the people out of the lab. Then from that base, we can start to climb out.

• 51:02Speaker 01

  Yeah.

• 51:02Speaker 00

  Right? Now it's fully automated, you can do all kinds of crazy stuff and eventually it looks like chips someday. Right? It's like alien techno, you know, you're doing it in some way that humans never, remember before chips was vacuum tubes. It was like human scale electronics.

• 51:14Speaker 02

  Yeah, yeah.

• 51:15Speaker 00

  And then we, we were like, okay, cool. Like let's get, you know, we, we saw the curve that will happen for lab work and genetic engineering. I promise you, but the first step is like put down the vacuum tubes, right? Like get onto some system that does not need people in the middle. Does that make sense?

• 51:30Speaker 02

  How does the application space change?

• 51:33Speaker 00

  Uh, I don't know. That's very unpredictable. Um, well, I'll tell you some things I think I'm excited about in the near term. Um, you're familiar with like the GLP-1 drugs, right? Lilly's worth close to a trillion dollars. Those are great. Like, that is, in my opinion, like a consumer product. Hmm. I'm on the clips. It's awesome. It is like the best thing since the iPhone. You don't think about food. It's like you get to spend your day thinking about work or kids, whatever else you want to do. You don't have to like think about, oh, I gotta intermittent fast through lunch or I'm gonna be obese. Right? Like, you can like, you can just get your willpower back. It's awesome. Okay, right? Awesome. Um, but that's, to me, a, the reason it's worth so much money is because it's not treating a disease. Right? Like, the biotech industry, the therapeutics industry today is really the disease industry. Yeah.

• 52:16Speaker 00

  Right? And how much of your life, and again, depends on the person, but how much of your life do you have a disease? It's like a small amount of the time. How much of your life do you wanna like weigh 15 pounds less? How much of your life do you wanna sleep better? How much of your life do you wanna have more muscles? How much of your life you wanna feel better? Like, the applications in the consumer space Yeah. for biotech are bananas. Hmm. Oh, it adds two years to your lifespan. What's that worth? Like, what is the value of a biotech product that adds five years to lifespan? This is Sequoia Capital. Throw a number at it. Depends on your customer. 50 trillion? Like, it's infinity. There's no limit on the value of something that would like stack extra years of healthy life onto people's life. Like, it's nuts. Yeah.

• 53:01Speaker 00

  Right? Like, you know, that's effectively what our healthcare system is trying to do. And it's like, they can like the total consumption cost of that. Yeah. Right? So if you could have that in a pill and a shot? So, that, but right now, today, we don't even have a good pathway to get something like that approved. Right? Because all of the regulatory, the FDA and everything, is oriented around treating disease. Yep. And this is actually where like, oh, the people are like, oh, Maha, blah, blah, blah. Like, I think like that line of the Maha thing, which is like, hey, actually, it's about not just about disease, but about being healthy Yeah. when you don't have disease. I think it's really good. Yeah. Like, I think that's a really good thing for the industry. Uh, and so I do think you'll see that, that set of things, um, happen.

• 53:46Speaker 00

  And so that's one half. It's like new drugs there. And then the other one, our first investor out of Y C, do you know who it was? Our angel, Mr. Brian Johnson. Okay, back when he was like, you know.

• 53:55Speaker 00

  No kidding. Oh, yeah. But he was like pudgy VC Brian. Oh, yeah, of course. Yeah, uh huh. Amazing. Not like, you know, longevity, like, you know, like Jesus. Yeah, back when, yeah, when he was like, like a normal person, right? Like, yeah, not like jacked. He's awesome now, right? Like, the, uh, and so, but like, what he's done, what's interesting about what Brian's done is he has normalized the monitoring. Like, cause I asked him, I was like, how are you, you know, like, these are like all these interventions, like you're a, like, well, you know, like, like Brian's got a good life. You know, like, would you be, oh, you're taking some random thing and trying it out? Like, isn't that scary? And he's like, well, I'm monitoring all the time, right? So like every week, he's like taking all these tasks and everything else. He's like most measured person, $2 million a year of diagnostic stuff, right? Like, that is the other area.

• 54:38Speaker 00

  So like, oh, we all love our aura rings and everything. This is pathetic. Okay, right? Like, it's great. Your heart rates, like, it's like telling you nothing. All right? Like, like the real, and I love aura, by the way, I've had it for 10 years, but like, the real meat of what's going on inside your body is molecular. Yeah. So what we really should be doing is like taking a blood sample every week and giving you like a whole readout of a ton of stuff, like longitudinally over time, so that you can try different interventions for you and see how it affects you molecularly. Cause that's what actually matters. Like molecularly, like aging is molecular, right? It's not your frickin', whatever. The, uh, and so, so like that whole world, these stuff like functions getting going, doing quest tests.

• 55:20Speaker 00

  Like, I mean, my God, I did it over Christmas, but it's like 10 vials. It's like the worst experience in the entire world, right? Like, like that.

• 55:26Speaker 01

  There's the at-home stuff now too, right?

• 55:27Speaker 00

  Yeah, but it, but it's, but it's so early, right? That's my point. Like, so I think that line is another place that could be a big, if you're asking about near-in apps, biotech, that one. Yeah. Is the other one, right? And so I think, I think you could see that. I think you could see other things like the Glimps. Um, those are ones I'm excited about. Awesome. And then I'm always hopeful.

• 55:46Speaker 01

  Jurassic Park.

• 55:46Speaker 00

  Something like that. Yes. You know, right? Like, like, uh, that there'll just be some other weird thing. Uh, and we did just launch a cloud lab service at Kinko where you can, um, like, we have experiments as cheap as $39 that you can just run. And we don't send you anything. Like, we won't send you a sample, but we will send you back data. So it's like you do the experiment, we run the experiment for you, you get the data back. Right. And so my, my last point on this one is like, I think if you like, science is thought of as this very like precious genius thing. But really what it is, is like formalized human curiosity. It's like a process by which humans of which all of us are curious about things, like do curiosity right. Like really try to answer our curiosity. Does that make sense?

• 56:30Speaker 02

  Yep.

• 56:31Speaker 00

  But I, and I think everybody's curious. And so I believe that if you drop the cost of like, like, like a lot of what blocks people from science is actually not like the esotericness of it. It's in my view, the lab.

• 56:45Speaker 02

  Yeah.

• 56:46Speaker 00

  It, it is that that is brutal, right? Like you don't have access, A, it is a total gate kept. Like you cannot get access to one. It like almost legally, you can't get access to one, right? Like, and, and so there's just this whole thing. And, and I'm like, well, what if that went away? What if people, everyday people could, could order an experiment? What if the model would help them design the experiment to ask a question that they have about the world? Would they suddenly ask questions and do these experiments? Would they be, would everybody, would millions of people want to be scientists?

• 57:13Speaker 02

  Yeah.

• 57:13Speaker 00

  And I know that sounds like, well, that's nuts, blah, blah, blah. But like, if you rewind the clock, God bless Silicon Valley and the computer industry to the 1960s.

• 57:21Speaker 02

  Yep.

• 57:21Speaker 00

  When it was IBM and it was mainframes and you told people that kids would program computers.

• 57:29Speaker 02

  Yep.

• 57:30Speaker 00

  They would say, you're fucking insane. And so I believe if you do manage to drop the cost of all this stuff, you may have kids and everybody else wanting to just ask original scientific questions and being able to do it. And that would be a cool market, right? Like, and so anyway, that, that, all this stuff I feel is on the other side of, of getting this AI for science stuff working, but I'm excited about it.

• 57:50Speaker 02

  Extremely cool vision for the future.

• 57:52Speaker 01

  Great not to end on. Thank you.

• 57:54Speaker 00

  Yeah.

• 57:54Speaker 01

  Very inspiring.

• 57:55Speaker 00

  Thanks for having me on.