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

[00:00] All of the previous revolutions in tech [00:02] Internet, right, like social media, like whatever, have been totally meaningless to biotechnology and biopharma. [00:08] Like, yeah, it's nice. We communicate slightly better or whatever. It's just some like back office IT crap. Right. Like, not this. But 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. [00:29] *music* [00:45] We are thrilled to have Jason Kelly, founder and CEO of Ginkgo Bioworks with us today. Thank you for joining us. Yeah, thanks, Anya. 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. [01:05] I mean, I'll do the Ginkgo journey in short, right? So 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 bio person, this doesn't make sense. But in biotech VC, they really don't like... [01:20] 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. Were you full time focused on the company for those for six years? Oh, yeah. OK. 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.

[01:47] 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 and met him. 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 for like having capital, it was sort of in 2014. And how's the product changed since 2014? [02:17] changed, but the product has gone all over the place, different roads. So we've always wanted to make biology easier to engineer. That was the idea. And so if you're... [02:25] Hang on. I remember make biology programmable. Have the words always been make it easier to engineer? Because I feel like that's a slight downgrade. When I was talking to Sequoia, it was always like the computer science rapper on make biology easier to engineer. But yeah, that was always our mission. Okay, got it. But the analogy is solid, right? So DNA is code, right? It's ATCs and Gs, not zeros and ones. It's really our only other... [02:47] like coded product. [02:49] 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... [03:01] 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 Geico. And how has the product itself changed over time?

[03:31] that would kind of automate the lab work associated with doing biotech. And the reason, again, if you're a computer scientist, the way to think about this is, [03:37] if you wanted to compile and debug DNA code, [03:41] That's a physical process. Yep. Right. So you're like ATCGGG. Like we have to do phosphoramidite 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 compiled debug cycle. Does that make sense? And so one half of what we worked on technologically was how do we make that cheaper? [03:59] Because if you want to get better at doing this, you've got to do more of it [04:04] 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 were like basically for the last 15 years, the twin activities. And in an era of AI, I see you see opportunities on both sides for that today. And that's [04:28] we shifted a little bit over the years in terms of how we do it. But does that make sense that you kind of have? And roughly speaking, the design piece sounds like it's – [04:36] A bit more software. The testing piece sounds like it's a bit more hardware controlled by software. 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 it has like these protein models, Bolts. The folks at Arc Institute just came out today with a paper 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.

[05:03] The big change of Gingo 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. Yeah. [05:12] 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 talk a little bit more? We did a project with OpenAI. How could you have AI models help you do that? Is anybody else focused on the back end, so to speak? I kind of think about design as the front end of the process and testing as the back end. [05:32] And when I say anybody else, obviously people do this. Is anybody else with a similar approach focused on that part of the market? [05:37] there's some new companies, right? So there's companies like Medra out here as one that's doing it with robotic arms trying to accelerate it. You have like... [05:45] the life science tools industry, but I would say it... [05:49] does not have a Silicon Valley attitude about things in the sense that they're not really trying to change the fundamentals of, [05:56] 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 planar 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

[06:26] this. [06:27] On this half of the house? Yeah. [06:29] I think this is an engineering problem, and I think this is a science problem. Okay. So, and... [06:35] I went after both initially, and I kind of took my licks for that. And I think the good thing about an engineering problem is you can ultimately like render it 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. 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? [07:04] Good question. Going to start producing meaningful results soon, other than papers? [07:10] Uh, it's a good question. I think the hard part about designing 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. [07:21] It is producing, you know, Intel or now NVIDIA TSMC level caliber atomic placement basically for free. Yeah. Right. So it's able to do molecular assembly. It self repairs. It self replicates like as a physical substrate. It's insane. [07:36] It is the product of 4 billion years of evolution. So the complexity embedded in a cell is actually a lot bigger, I think, than people give it credit for. And so there's a march there. Now, that said, 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 limited tools we have today. So you don't have to solve the whole problem over here to create a lot of value. You just have to be better than how we do it today. Does that make sense?

[08:06] term with all the protein models, you're seeing that, right? Like, Chai just did a big deal with Lily. Like, there's real opportunities there, I think, right in the near term. Does that make sense? [08:16] So speaking of OpenAI and Mr. Altman, you recently announced a partnership research result with them. Can you say more about that? Yeah. Okay. So this is pretty exciting. I think for the 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. [08:42] Right. So the way we do and this is true in biotechnology is kind of true for science broadly. The way we do science today. [08:49] 95% of science, the stuff that's not theoretical. So not, you know, everybody's working on math, like, let's work on Terence Tao, let's get a Terence Tao in a box or whatever, right? Like, you know, like, the reason is, you can 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, and just generally across the world in science, it's, [09:11] 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. You have an opinion of 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 science? [09:30] a reasoning model, do the work of experimental science, [09:34] If you gave it, [09:36] A robotic lab.

[09:37] That was the question. And the answer was, yeah, it's actually pretty damn good. So we did basically the way the project work was we had there's a. [09:45] So biochemistry problem called cell-free synthesis. So you take a piece of DNA, ATCGGG, right? 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. [10:01] 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. [10:09] And because the guts are still there, it makes the protein. So this is kind of like it's like the world's smallest 3D printer or something. Right. OK. And so scientists use this. They try to optimize. It's very expensive usually. And so there's a paper that came out of Stanford from Mike Jewett's lab in August that set the benchmark for like how cheap people had been able to do cell free protein synthesis. [10:28] That's what we said. All right. Let's try to optimize. [10:31] that, and [10:32] with the model and so we gave the model we did each round we would do 100 384 well plates [10:37] 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. [10:50] So, [10:51] So after four rounds of that, we beat state of the art. And after six rounds, we beat it by 40 percent. And so that was a I think it's the most interesting sort of model doing experimental work result that's been shown to date. [11:05] by a lot. And the 40% was a function of what? Just faster cycle time or more intelligent experiment design? Yeah, like how did it be? The state of the art? So this is my point

[11:15] This is, I think, my larger point about science, because I think we're going to do... [11:20] science differently in the future, in my view, based on what I'm starting to see here. So, [11:24] What does the sciences 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. Then they're going to run the experiment, take the data back, interpret it. [11:37] and then poke again based on what they learned. And they're going to go through that process a few times to, like, resolve... [11:42] 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 so that cycling is, [11:53] is just logic. Yeah. 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, [12:11] Like, that's all it has to do. Does that make sense? Yeah. And so we didn't do anything other than that. Okay. 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. [12:22] could it design them like a scientist could? And the answer was... [12:26] Yeah, hell yeah, I could. [12:27] And so, [12:28] Now I think that opens a real interesting question about like how we do science in the United States. Well, and it's easy to imagine a version of the future in which 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. [12:45] you go, you know, autonomous labs. Yeah. What's wrong with that vision of the future? And if, and if that is the right vision of the future, uh,

[12:52] How far out is it? [12:54] I mean... [12:55] I think this is how it's going to 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... [13:05] So I'll just explain why I think if you had a heads-up competition, which I want to do this, right? And then tell me how the average scientist would push back and say, like, no, no, no, it's not going to happen that way because – [13:15] Right. So I think what the scientists will push back on is like, [13:20] This thing can... [13:22] just be as creative or as me or something. Right. Which I actually, I, I, [13:27] I'm sympathetic to that. Yeah. [13:29] I'm not saying it's going to be more creative. [13:31] So, [13:31] I'm saying it's. [13:44] It can run a lab 24 hours a day. I'll give you another example. 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 Alzheimer's. You have a hypothesis, you have a hypothesis, I have a 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. [14:04] 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? Yeah. 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...

[14:33] wave that away. Okay. Solved. All right. Great. So then I'm going to put [14:38] 100 AI scientists on top of this thing. Each one is going to pursue a different hypothesis for Alzheimer's. [14:43] 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, [14:49] They're going to pass... [14:51] The data on those experiments, like what experiment they ran and the raw data that came off it to the other hundred AIs. Yep. Daily. [14:58] Every fucking day. Okay. And so they're going to learn from each other. Like you're, even though your hypothesis is different, we're working in the same area. Yeah. So your failed result might 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. [15:17] chugging along and every week it dumps a 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 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 ai's is just better than it just it's [15:41] socially different even than how we do it today. Does that make sense? That's unfair advantage, number one. Okay, unfair advantage, number two, 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 [16:00] manual laboratories. [16:02] That's what it pays for. Okay. You've got these labs and they're there 24 seven, 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. Cause 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.

[16:23] 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 a year that biopharma spends or the $40 billion that NIH spends. [16:35] less than 5% is on the reagents. Everything is on overhead. It's basically over the people, the regulatory and the lab space. If we were running it efficiently, we're, [16:45] You would budget. [16:46] a research program at the NIH, not on Interact and heads and everything else, but just on the reagents. Because that's like the usage-based pricing of science. 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. It's like less [17:16] run robotic labs, but [17:18] Now they're running where 90% of the cost of a research project goes to the reagents. Yes. Yes. [17:23] 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. 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&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, I've always been adjacent to this stuff. Right.

[17:53] in tech, internet, right? Like social media, like whatever have been totally meaningless to biotechnology and biopharma. [18:00] Like, yeah, it's nice. We communicate slightly better or whatever. It's just some like back office IT crap, right? Like, [18:07] Not this. 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. Our partner, Constantine, has a good framework for that. He talks about how there are revolutions in computation and revolutions in communication. Yes. Communication is about the distribution of information. Computation is about the processing of information. Yes. And what you're talking about here is just a different way to process the information. I got it. [18:37] 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. [18:43] Completely agree. And that's, I think, fundamentally true. And so, again, I think the leaders of biopharma companies 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. [18:57] cannot ignore this they cannot ignore ai it is it is just different yeah 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 yeah and just so i'm 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 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

[19:26] The average tech person's look at how science is done, where you have literally PhD trained people. These are brilliant people. Yeah. Paid a decent amount of money. [19:34] 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, like, it is like, like, like my friends from undergrad just like they would never write like, you know, write 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, like, we'll just automate it, bro. Like, you know, like, like, why don't we just do that? [20:04] Why is it? Why is it hard? All right. And the reason it's hard is it's. [20:09] Like the technical like automation term is like high mix, low volume work. Yeah. OK. And this is true at like places like Hadrian today, for example, that are working on this on the manufacturing side, industrial high mix. [20:20] low volume is hard to automate. [20:22] Yep. Historically. All right. And my like transportation analogy that I've been giving to people in bio is like, OK, so imagine on the Y axis, you have like level of automation. Yep. And then on the X axis. OK, you have like flexibility, like that variability, the mix and what you're being you're asking it to do. So in transportation. [20:41] Low mix, high automation. That's like a subway. [20:44] Sit down, takes you away, right? Like, you know, maybe you're like, [20:48] You don't have to do anything, but you've got to want to go to one of the stops on that subway line. Yep. [20:53] Low automation. [20:55] High variability, that's car.

[20:57] 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... [21:02] system look like for the last hundred years until, thank you very much, Google. We got Waymo up here in the corner where you get the automation of a subway, but the flexibility of a car. And it's so surprising that we don't even call it automation anymore. We make up a new word, [21:18] 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. OK, right. And we just hit a wall. [21:33] And AI, we hit a wall on flexibility of what you can do, and AI pushes us past it. Yep. [21:39] 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? And so that's what we're so like, that's the tricky bit in like lab land. We actually have automation. [21:55] but it's like subways. Yes. 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 fricking bench. Yeah. And that's what you got to fix. 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. And in that case, [22:24] You kind of had your sensor suite. You have your radar and your LiDAR and your cameras. Then you had your software suite with the perception and the planning and the actuation, which then had to tie back into whatever vehicle manufacturer you're working with. And then there are a gazillion corner cases that you have to simulate because you can't get enough of them in the real world. What would that set of words be for your world? What are all of these specific things that are hard to get right?

[22:54] Yeah. It's going to be different in every domain. Yeah. So for cars, the hard part is the physical world is changing. Yeah. Every mile you drive the world. I got them in a new place. Right. Like this one has a cone. It's raining like whatever. Right. Like. [23:06] That's not the problem in the lab at all. Yeah. 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's not changing at all. Yeah, so that is not at all the same stack. [23:17] that brought you autonomous cars that will bring you autonomous labs. [23:21] That's not my problem. [23:22] Okay, 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... [23:37] getting a thousand long tail pieces of third party, uh, [23:43] Like you can't believe the software on these things. Benchtop lab equipment. Okay. Integrated into one big system. Okay. 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.

[24:13] up. [24:13] then you naturally with your thumb, like adjust the pressure of the straw because 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 – [24:24] problem than you think, and you are doing some work as the human to manage that. So you have two big buckets. One, solve liquid handling. Two... [24:32] uh, send samples to a thousand different pieces of equipment. Does that make sense? Yep. If you nail those two, [24:39] You're done. Well, that sounds tractable. It is fucking tractable. Yeah, I agree. Yeah, it's totally tractable. And so it's just... [24:46] a lot of work. Where are you guys working through that? [24:50] I think we basically have it working. Yeah, that's the honest truth. I mean, we basically – What's the last, like, major hurdle? [24:55] one reason it's hard for people to do this technically is they want to build the hardware. Yeah. But they don't. [25:00] do research. [25:01] So they kind of have to go to a customer and be like, hey, you want to use my robot? That's what we're 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 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. It means I have a bunch of scientists employed at Ginggo. [25:23] These scientists are basically like remember the Google engineers had 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. Yeah. So they are they're dog fooding. [25:38] On our, we have like 50 robots. We're going to 100 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 know, 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 got 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.

[26:08] that we have to keep knocking down. But they're... [26:10] They're engineering at this point. Have you solved like pipetting and liquid handling? Yeah. The good thing is there is a whole industry that's worked on that problem, like liquid handling robotics. Okay. [26:20] It's just a matter of having like all the different liquid handlers. And if you have them all, you can kind of, and you know, your, your liquid class you're dealing with, you can manage it. Oh, one other one. Big one. [26:31] Scientists don't code. [26:32] Yep. Okay. So, oh, cool. I 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 lab view. It's like complete trash. But like it's, you know, make a flow chart, right? Because you can't write Python or whatever. Horrible. Okay. Like even that they hate. Okay. Right. And so no one will program shit. And so we ran into this issue where we now have all these scientists using the automation directly. [26:55] So like, I don't know, three weeks ago or something, we had two... [26:59] Two instances where we sent a plate. So, like, again, this is, like, kind of... [27:03] a bunch of little wells with liquids in them, and we seal the plate when we put it in storage so it doesn't evaporate. [27:08] 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 on our Slack are like, hey, what the hell? You know, like, do you seal the plate before you send it to the liquid handler? Dumb, dumb. 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. OK. And I was just like, this is nonsense. We're from now on only with the way we're going to interact with writing the code is through cloud code or codex. [27:38] 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,

[27:45] We will update the skills file and it will never do it again. And we will get through this. Okay. And so that is a big win for usability of robotics for scientists is what's happening with cloud 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 had to get knocked down too. And so all this is in flight. But like right now in Boston is like a very unique experiment happening where we have like 50 scientists submitting jobs into one big robotic setup. [28:15] planet right now um so it's pretty neat to watch it do you see a future for humanoids no really in the lab because the best argument i've heard for humanoids is like the world the physical world is designed for them right and like i would think that the existing labs are made for like humans walking around pipetting things walking between different machines yeah and so you could you try to create a robotic arm that's able to like you know orchestrate all this or you just have a [28:45] doing is moving samples around the [28:49] the environment yeah um 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 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 yeah right it's not like oh is our humanoid robots going to disrupt tsmc are they going to go in there and etch the the fucking

[29:19] obviously not, you know, right? Like, no, it's a microscopic discipline. Like biology is a microscopic discipline. Like these things are, no, it makes no sense. How does it change the unit of scale for a lab? Like 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. [29:39] So it's actually going to make them smaller. Really? Okay. 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. Yeah. Across a bunch of different buildings everywhere or whatever. Right. And they're, they're set up for basically humans to be able to walk in and find a piece of equipment. [30:00] 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 kind of how it operates. And you need it local. [30:13] 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. Mm-hmm. [30:21] Well, let's now instead imagine the scientists are ordering all their experimental work through computers and it's going to some centralized autonomous lab. You can think of that like a local cloud if you want. OK, well, now you don't need a lab where the scientists are. [30:35] 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. 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. Yeah. Yeah. So we have like a major space reduction at the moment. That's one of the big savings.

[31:05] 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... [31:18] have less spending. Remember I told you earlier, like the spending is not on the reagents. It's on basically like roof space, like laboratory space and people. What is the unit of work? You sold 97 robots. Is that 97 boxes? 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 coming out of manufacturing tech and then a piece of maglev track. And what you do is you Lego block [31:48] 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 okay does that make sense yeah yeah and so our unit is we sell the box we sell like a subscription basically like service fee plus um software subscription for per box yep uh and then eventually what i want to sell is like automation friendly reagents yep uh because that's kind of like the usage pricing so that that would be my that's like one half of my business now is like [32:18] lab. [32:19] You know, Pacific Northwest Natural 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. [32:27] as a cloud and you could just order from it. 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. Seems to me that you have, you are generating an enormously helpful data set here that should be used to like backprop into the weights themselves. Yeah.

[32:43] How's that going to play out? That's a good question. Um, [32:47] I don't know yet. I think there'll be one training. So there's two different levels of training. [32:51] 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 like, oh, you're like a prototyping shop or whatever, any place where there's like variability. [33:07] OK, and so I see a lot of variable requests from scientists. I see all the edge cases of how it breaks the physical equipment that you can't like compile out. Yeah. Oh, make a digital twin. No. Like, you know, right. Like you have to actually do this stuff. Like I do not think like I don't buy it. Right. Like like a lot of it's edge case. You know, liquid classes. You can't really pick it up on a camera. [33:37] 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? [33:47] The bigger one is sort of... [33:50] the model's ability to take your intent [33:52] 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 that I think you could have a really cool loop that has more to do with the results of every experiment. Like this open AI project. Like as we saw what experiments worked and didn't, you could theoretically then teach 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. Like everything else is kind of noise. Like we're just running around circles, you know, like the Romans did it.

[34:22] up, right? The Greeks. But except for... [34:26] 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 or 100x is the speed we do scientific discovery, like, [34:42] That 10x or 100x is the progress of the species in my head. Yeah. It just seems to me that the results of what you're generating in the lab need to feed back into updating the model weights somehow. Otherwise, we're not going to get to that point. 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. [35:12] 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're really want them to be smart. Yeah, this is this is where it matters the most. [35:23] I agree. [35:24] You mentioned Project Genesis earlier. Yeah. What is it? Why does it matter? [35:29] Yeah, so this came out of White House and OSTP, so the Office of Science and Technology Policy, like Mike Kratzios there. And so it's run by the Department of Energy. [35:39] 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. OK, so like starting with the Manhattan Project, but also like the Human Genome Project actually was like a Department of Energy project. OK, so 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. OK, 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.

[36:09] Yep. 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. [36:18] 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, [36:33] 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. 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. And so when we did that deal with the Department of Energy, Secretary Wright and I like ribbon cut the first 18 robots up in Washington and he like signed it. It was really cool. And so like I think that's a to me again as [37:03] to see some good results soon because ultimately they got a, you know, Congress has to get excited about this. You'll need like a bigger bolus of money in the future, right? To really make it a big deal. But I like what they're doing. Like I think in a dream scenario, what does that do for America? [37:16] So I chaired this National Security Commission on Emerging Biotech in D.C. for like two years. And Senator Young in Indiana chairs it now. And there's a similar one on AI that Eric Schmidt chaired like seven years ago. So it's super fascinating to see like – [37:30] Learn more about D.C., number one, Congress. But also, like, the point is, how does the U.S. stay competitive in technology areas that are strategic? Yep. And so there's one for cyber, like, 15 years ago or something. Then there was AI. And then this was on bioengineering, biotech.

[37:45] Thank you. [37:46] We have had an unfair advantage, like, since the Soviet Union fell, basically, where, like... [37:51] we were automatically... [37:53] at the lead in science. [37:56] Like there was no one else that had the money to spend on science, basically. And because science is like, it's not just spending on the science. You also need scientists. So you have to have research universities to train these people. Like it is esoteric. [38:10] So, [38:11] 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. [38:23] Not manufacturing drugs, like making new drugs. The way it works in our industry is like a startup discovers a drug and then they try to sell it to Merck or Pfizer and they're the kind of go-to-market channel. And Merck or Pfizer buys it for $2 billion or $5 billion or $10 billion, depending on where it is in clinical trials. Yep. [38:40] Right. [38:41] Three years ago, less than 5% from China. [38:44] Last quarter, 40%. [38:46] Plus. OK, 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? [38:56] They have just as many scientists as us. They're just as smart as our scientists. [38:59] They get paid less. [39:01] And remember, it's like a hands in the lab. You've got to do science is driven by experimental work. So if you now have more... [39:07] experimentalists [39:09] In China, and you get more research per dollar, I don't see why they don't win in research. Yeah. And so from my standpoint, we need to make this change both in how we do experimental work and bring in the AIs to just increase our amount of intellectual horsepower.

[39:25] If we're going to keep up in science and you don't want to be surprised. Right. Like if you know, like DARPA, like, you know, like, 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. [39:44] 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. [39:51] why it's, I think, important from a national security standpoint, important for the country, important for the species, is the rate at which we get scientific discovery. Does that make sense? [40:01] Other thing I'm curious, I want to ask you guys about. [40:04] I mean, God bless Sam and this freaking OpenAI thing. But, like, you know, when they started that, it was like a pie-in-the-sky research project. Yeah, yeah. [40:12] Right. This is like almost like Bell Labs kind of, you know, like whatever, like, go for it. Everyone's like, it's bullshit. Oh, what is this? Nonprofit. Okay. But here we go. It's now worth what? [40:21] What was the last round done at? Half a trillion or something? 830 billion. Great. 830 billion. All right. So to me, it signals – [40:31] in fundamental research, [40:33] in industry, [40:34] can be valuable. Yep. [40:36] And I think that's also a thing that we've kind of forgot about. [40:41] Over the last 30, 40 years, because 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, that's probably not going to 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. Yep.

[40:59] And I wonder if that's going to change. [41:01] I'm curious if you think like [41:03] I don't know, every big industry, like, you know, the chemical industry, like, should everyone just get that 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, not, 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, no, no, we're going to like. [41:26] Go for it. [41:28] Do you see that at all? Like, you know, like, do you, you know, I don't know. But that would be like one of my naive hopes is... [41:35] the industrial side of the house wakes back up on doing research. [41:39] Sonny, I know you have a point of view on this. [41:41] I would say we've seen a few of these. I mean, you mentioned Chai earlier. Yeah. [41:44] I think it's likely to come from researchers on the research side of the house that are fundamentally thinking, for example, the protein design process. I think it's – my gut instinct is more likely to come from folks like that who are just taking really big swings. And the tricky thing is just the song and dance of how to get funded, especially if we're still in biotech winter. Sure. Right? And like how do they even prove out to the world that like, hey, we have better discovery engines. We have better candidates.

[42:14] symmetric information and you can't tell. [42:18] Google with isomorphic feels like the closest because it's actually got deep pockets behind it to actually prove that story out. But yeah, my bet would be a vertically integrated research team taking big swings. Yeah, the challenge is funding these companies all the way through. Well, I think... [42:34] to the point on... [42:36] 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 going to come out of this whole AI wave. Yeah. And I think... [42:48] Phase one is sort of becoming... [42:51] human level intelligence 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. I think phase two. [43:01] is going to be becoming super intelligent across specific categories one at a time. Yeah. [43:06] And that's where all the breakthroughs are going to come from. Yeah. 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. Yeah. We're about to start being super intelligent in a bunch of different things. Yeah. And we're about to start seeing a bunch of breakthroughs. Yeah. And so I think it's like we're within months or years of it becoming really interesting. Let me give you an example. We just backed this team that did the alpha chip project at Google.

[43:36] better than what human ship designers can do. And so I think we're going to see these pockets of super intelligence in different corners of industry. What was the AlphaGo Move 38 or whatever? Move 37. 37. I always forget the number. I know. I thought it was like 83. Yeah. 37. Great. Yes. Like Move 37. Right. Yes. Like that kind of stuff. Yeah. [43:55] Yeah, I mean, if that's true, right, like, and I think there's two ways to get at it, by the way. My secret on this would be, one is the thing you're saying, like, it's going to be super intelligence, right? Yeah. [44:05] It'll intuit something that a person wouldn't have. [44:08] And then I'll. [44:09] I really think if you can solve the problem of greatly accelerating the experimental work, it's almost the same. Yeah, the combinatoric approach. You know, like, you can't... A lot of the physical world stuff is not... [44:25] Simulatable. Yeah. Right. And so the it's just can you run that machine faster? And 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 that. OK, like. [44:36] And then I think if you start to believe you can get those breakthroughs, I do think you have to ask, how do you commercialize? Like, what's venture look like in that scenario? Yeah. Because you're like, oh, crazy. I got this insane break. We got a room temperature semiconductor. Or, you know, right? Like, now what? Right? And is it like you go to the big guys, you do it yourself, right? Like, there's a whole thing there. I think it's fascinating. Yeah, we had this conversation the other day on... [45:00] Will the venture capital industry shrink? [45:03] because the cost of producing everything gets cheaper because it becomes so much more efficient. Yeah. 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. Yeah. You might have thought the same thing would happen, but actually the opposite happened. There was this explosion in creation, which made distribution that much more competitive. And so there were a million different companies, but then all of them had to fight so hard to break through the noise. Yeah.

[45:33] I would guess the same thing happens, which is the cost of creation goes down and down and down. The cost of distribution goes up because there are just so many different things out there. That's interesting. Yeah, that could be right. [45:43] Yeah. Do you think language based foundation models are the right substrates or do you think something's got to train like a, you know, ACTG native model? Yes. So that is like the ARX Evo is an ACTG native model. Yeah. Right. So it's trained on a trillion bases of DNA. And I think that's awesome. Right. I think it's super exciting. I think it will apply. It'll be like a tool. [46:05] available to the reasoning model to do its job. That's already the case, right? Like the reasoning model working on, say, or even our open eye project could go access alpha fold, design a protein, get it synthesized, add that as a reagent into the project. Like that's allowable, right? And so I think those will end up being powerful tools. But I still think [46:27] 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. Yeah. [46:35] That that's already true. I don't need like a miracle. Right. Like that's and yeah. [46:41] So here's a question. Yeah. Alpha Fold, all these papers have come out on how AI is changing drug discovery. Yeah. Do you think the pace of drug discovery is actually accelerated or not? 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, like, bio is, like, I think the down, so, God bless. Like, I basically, you know, Jurassic Park came out when I was 13. All I want to do in life is, like, make Jurassic Park. Engineering is awesome, right? Like, that's why I'm doing this.

[47:11] the woolly mammoth? Yes, yeah, I know them well. Yeah, the mammoth over there. Colossal. Yeah, it's great. I love that stuff. They haven't brought back the woolly mammoth yet, but they brought back a dire wolf. Oh, a dire wolf, sorry. Mammoth is coming, I'm sure. But yes, okay, right. So, but really what I'm excited about [47:25] is like the ability for... [47:28] you know, kids someday to design biology like they program computers. [47:33] Right? Like that is what I want. [47:35] Like that's the world I want to exist. And so the question is like, how the hell do you get there? And, [47:40] One of the issues we have in programming, biology, design, DNA, genetic engineering, whatever you want to call it, is that the only working... [47:48] app. [47:49] ecosystem. [47:51] is therapeutics. There is like 85% of the market for biotechnology is therapeutics. And then there's like 10% is ag. Remember Monsanto? Ooh, boogity boogity. Right? Like that's genetic engineering in plants. And then there's like 5%. [48:06] That's like industrial, like you and your 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 hot. And so that was that's five percent. OK. [48:20] That is the totality of apps we've come up with so far for programmable matter compilers. [48:25] IE cells. [48:26] It's embarrassing. Okay, right? Like the fundamentals of this. But again, like let's imagine computers. The only application for computers was drug discovery. [48:37] we'd all be like, "Well, man, it's such a pain in the ass with these computers." Right? So there is a distinction between what we're really working on at Ginkgo and other places like us, which is how do you really make it easier and faster and cheaper to just design biology, make it do new things? And then the fact of the matter being that the only apps that really have ROI are

[49:00] Drugs. Yep. Okay. And drugs have like annoying features. [49:03] So 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. Yeah. Like it's like, well, we don't like to stick things in people. Yeah. Be careful. Like all that stuff is fine. And we can get faster on that. Like China's also eating our lunch. Yeah. Like you might have 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. [49:33] not like launching a phone app. Okay. And so that, does that make sense? Yeah. So that does remain like, like a problem, I would say, uh, [49:42] We try, there's been a bunch of other attempts at other things, you know, like animal-free meat, nutrition, like, like, it's never quite been good enough to, like, disrupt another industry yet. Yeah. Yeah. [49:52] I would love to see that happen. That would be a big accelerant, not just for... [49:57] whatever industry, but ultimately for genetic engineering. And then if you accelerate genetic engineering, you try to create that flywheel that we got in computers where it keeps going and going. Now, that said... [50:07] to pick our app of drugs [50:10] It has gotten more expensive to develop drugs. [50:12] not less year over year for the last 25 years. Yeah. [50:16] So that's not great. That's the opposite of what should be happening, right? And so, and why is that? [50:22] Because we do it manually. [50:23] Hmm. That's my opinion. We have we have not like it's like Baumol's cost disease or whatever. Like the scientists are getting more expensive. The rent is getting more expensive.

[50:32] That's it. [50:33] They're actually more productive. Like we give them new tools. Like we are getting like slightly better, but the majority of the cost is, [50:39] is manual work yeah that shit does not get cheaper [50:43] 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, [50:51] We're trenching 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. [50:59] Then from that base, we can start to climb out. Yeah. 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 technology. You know, you're doing it in some way that humans never remember before chips was vacuum tubes. It was like human scale electronics. Yeah. And then we were like, OK, cool. Like, let's get you know, 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. [51:29] the middle. Does that make sense? How does the application space change? [51:33] I don't know. That's very unpredictable. Well, I'll tell you some things I think I'm excited about in the near term. [51:39] You're familiar with like the GLP-1 drugs. - Yeah. - Right? - It's worth close to a trillion dollars. That's great. Like that is in my opinion, like a consumer product. [51:47] I'm on the cliffs. 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 and kids, whatever else you want to do. You don't have to like think about, oh, I got an intermittent fast through lunch or I'm going to be obese. Right. Like you can like you can just get your willpower back. It's awesome. OK, right. Awesome. 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

[52:17] How much of your life, and again, depends on the person, but how much of your life do you have a disease? [52:21] It's like a small amount of the time. [52:23] How much of your life do you want to weigh 15 pounds less? How much of your life do you want to sleep better? How much of your life do you want to have more muscles? How much of your life do you want to feel better? The applications in the consumer space for biotech, [52:35] are bananas. Hmm. Oh, it adds two years to your lifespan. What's that worth? [52:40] Like what is the value? [52:42] of a biotech product that adds five years to lifespan. [52:47] This is Sequoia Capital. Throw a number at it. [52:50] Depends on your customer. 50 trillion? Like, it's infinity. There's no limit on the value of something that would, like, stack... [52:57] extra years of healthy life on the people's lives. Like, that's nuts. Yeah. Right. Like, you know, like we, that's effectively what our healthcare system is trying to do. And it's like, think of like the total consumption cost of that. Yeah. Right. So if you could have that in a pill and a shot, [53:11] So that but right now, today, we don't even have a good pathway to get something like that approved. [53:17] Because all of the regulatory, the FDA and everything is oriented around... [53:22] treating disease. Yep. [53:23] And this is actually where like all the people like, oh, like I think like that 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. [53:37] I think is really good. Like, I think that's a really good thing for the industry. [53:41] And so I do think you'll see that. [53:43] That set of things happen. And so that's one half. It's like new drugs there. And then the other one, our first investor out of YC, do you know who it was? Our angel.

[53:53] Mr. Brian Johnson. Okay, back when he was like, yeah, yeah, yeah, like, you know, longevity, like, you know, yeah, back when he was like, like, a normal person, right? Like, I got like, jack, he's awesome now. Right? Like, so, but like, what he's done. [54:12] What's interesting about what Brian's done is he has normalized the monitoring. Like, as I asked him, I was like, how are you – you know, like, these are, like, all these interventions. Like, you're a – [54:21] Like, well, you know, like Brian's got a good life, you know, like, would you be like, 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. [54:29] Right? So like every week he's like taking all these tasks and everything else is like most measured person, $2 million a year of diagnostic stuff. Right? Like, [54:36] That is the other area. So like, oh, we all love our aura rings and everything. This is pathetic. Okay, right. That's great. Your heart rate. It's like telling you nothing. All right. Like the real, and I love aura, by the way. I've had this thing for 10 years. But like the real meat of what's going on inside your body is, [54:51] is molecular. Yeah. Yeah. [54:53] 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. [55:00] like longitudinally over time so that you can try different interventions for you. [55:06] and see you how it affects you. [55:08] Molecularly, because that's what actually matters. Like, molecularly, like aging is molecular, right? It's not your freaking, whatever. And so, like, that whole world. [55:17] Stuff like functions getting going and doing quest tests. 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 that. There's the at home stuff now, too. Yeah. 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. Yeah. I was like, that one. Yeah. Is the other one. Right. And so I think I think you could see that. I think you could see.

[55:41] Other things like the glimpse. Those are ones I'm excited about. And then I'm always hopeful. [55:46] Jurassic Park. Something like that. Yes. You know, right. Like that'll just be some other weird thing. And we did just launch a cloud lab service at Ginkgo where you can like we have experiments as cheap as thirty nine dollars 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. [56:04] So it's like you do the experiment, we run the experiment for you, you get the data back, right? And so my last point on this one, [56:10] is like, [56:12] 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. That makes sense. Yep. But I, and I think everybody's curious and, [56:33] So I believe that if you drop the cost of like, like a lot of what blocks people from science is actually not like the esotericness of it. [56:43] It's, in my view... [56:44] the lab. Yeah. 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 so there's just this whole thing. And I'm like, well, what if that went away? [56:59] What if people, everyday people could order an experiment? [57:02] 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 what everybody would millions of people want to be scientists? Yeah. And I know that sounds like, well, that's nuts. But like if you rewind the clock, God bless Silicon Valley, the computer industry to the 1960s. Yep. When it was IBM and it was mainframes and you told people.

[57:26] That kids... [57:27] would program computers, they would say, you're fucking insane. [57:32] And so I believe if you do manage to drop the cost on 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. [57:45] All this stuff, I feel, is on the other side of getting this AI for science stuff working, but I'm excited about it. [57:50] Extremely cool vision for the future. [57:52] Great note to end on. Thank you. Yeah. Very inspiring. Thanks for having me on. [57:56] Music.