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

19:49Jason Kelly:Like absurdity, right. In our group that the, uh, 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, we'll just automate it, bro. Like, you know, like, like, why don't we just do that? 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. 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, low volume.

20:21Jason Kelly:Yeah. It's hard to automate. 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, okay, you have like flexibility, like that variability, the mix in what you're asking it to do. So in transportation, low mix, high automation, that's like a subway. Sit down, takes you away, right? Like, you know, maybe you're like, I 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 car.

20:57Jason Kelly: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, 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. 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.

21:30Jason Kelly: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? Yeah. And so that's what we're so like, that's the tricky bit in like lab land. We actually have automation, but it's like subways. Yes. It's just repeat the same experiment 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.

22:06Jason Kelly:So 99 % of the work, just like 99 % of miles traveled is in cars, 99 % of the lab work is still at the freaking bench. And that's what you got to fix. And so the Waymo analogy is an interesting one because it's now such a magical thing that so many people have gotten to experience. Yes. And in that case, you kind of had your sensor switch. 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.

22:42What would that set of words be for your world? What are all of these specific things that are hard to get right?

22:49Jason Kelly:100%. And I think if you're trying to generalize the problem of bringing automation to autonomy, it's going to be different in every domain. Yeah, exactly. So for cars, the hard part is the physical world is changing. Yeah. 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. 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 like it is not at all the same stack that brought you autonomous cars that will bring you autonomous labs.

23:21Jason Kelly:That's not my problem. 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 getting a thousand long tail pieces of third party. 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.

23:53Jason Kelly: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 if 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 liquid handling turns out to be a trickier problem than you think. And you are doing some work as the human to manage that.

24:28Jason Kelly:So you have two big buckets. One, solve liquid handling. Two, send samples to a thousand different pieces of equipment. Does that make sense? If you nail those two, you're done. Well, that sounds tractable. It is tractable. Yeah, I agree. Yeah, it's totally tractable. And so it's just a lot of work. Where are you guys in working through that? I think we basically have it working. Yeah, that's the honest truth. I mean, we basically benefit - What's the last major hurdle? One reason it's hard for people to do this technically is they want to build the hardware, 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?

25:06Jason Kelly:The customer's like, no. They're at the bench. They're like, no, I don't want to try. You're like, so there's 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 our original business, which was research partnerships, which you still do, means I have a bunch of scientists employed at Gingo. 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?

25:34Jason Kelly:Like that's my scientist today. 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 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 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.

26:06Jason Kelly: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. And 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. 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 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.

26:36Jason Kelly: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? 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. So like, I don't know, three weeks ago or something, we had 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 we seal the plate when we put it in storage so it doesn't evaporate.

27:08Jason Kelly: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 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 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.

27:38Yep.

27:38Jason Kelly:Like you will now submit a written protocol of 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. Okay. And so that is a big win for usability of robotics for scientists is what's happening 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, 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 experiment happening where we have like 50 scientists submitting jobs into one big robotic setup that exists nowhere else on the planet right now.

28:16Jason Kelly:And 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 try to create a robotic arm that's able to, you know, orchestra all this, or you just have a humanoid do what a human lab scientist does. Yeah. So, again, the primary thing that the human is doing is moving samples around the environment.

28:49Yeah.

28:51Jason Kelly: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, are humanoid robots going to disrupt TSMC? Are they going to go in there and etch the fucking chips? Like, obviously not. You know, right?

29:20Jason Kelly:Like, no, it's a microscopic discipline. Like biology is a microscopic discipline. Like these things are, you know, they make 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. 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, whatever.

29:52Jason Kelly: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 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 and it's going to some centralized autonomous lab.

30:29Jason Kelly:You think of that like a local cloud if you want. OK, 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 percent utilization at the bench to like 70 percent. So now you need less equipment. And then, assuming you didn't decide to have humanoids, you can just jam it all in around a track system so it's actually a lot tighter. Yeah. So we have a major space reduction at the moment. That's one of the big savings.

31:00Jason Kelly:We just sold 97 robots to the Department of Energy for this Genesis mission. This is the AI for science thing that Trump's doing. And that is basically going to be ultimately much more dense than the equivalent set of labs would have been that would have 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, 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.

31:33Jason Kelly: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 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 and then eventually what I want to sell is like automation friendly reagents yeah 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 I'll build you an autonomous lab, you know, Pacific Northwest Natural Lab DOE or Merck or whoever.

32:23Jason Kelly: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. 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 data set here that should be used to like backprop into the weights themselves. Yeah. How's that going to play out? That's a good question. I don't know yet. I think there'll be one training. So there's two different levels of challenges. One was the thing I mentioned earlier, like you're submitting a piece of physical work.

32:57Jason Kelly:And again, I think this applies to labs. This will also apply in light manufacturing. Right. Like you want to do a, 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. That you can't compile out. Make a digital twin. No. You have to actually do this stuff. I do not think, I don't buy it. A lot of it's edge casey. Liquid classes, you can't really pick it up on a camera. There's just all this stuff 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.

33:39Jason Kelly: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 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.

34:11Jason Kelly: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 in circles. You know, like the Romans did it, you know, right. Like we're just doing the same stuff. Right. 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 10 X's or 100 X's the speed we do scientific discovery, like that 10 X or 100 X's the progress of the species.

34:45Yeah, 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.

34:53Jason Kelly: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 like getting better at that, I do think is 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're really want them to be smart. Yeah. This is this is where it matters the most. I agree. you mentioned project genesis earlier yeah what is it why does it matter yeah so this is came out of uh white house and ostp so the office of science and technology policy like mike kratzio is there and um and so and it's run by the department of energy and that's in part because like the department of energy is and if you're a science nerd that's where we do like our big science projects okay so like starting with the manhattan project but also like the human genome project actually was like a department of energy project okay so when it's like project based it kind of tends to live in the Department of Energy and like more open ended science is like the National Science Foundation.

35:57Jason Kelly: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. Yep. OK. And one of them, you know, a couple of them are bio related, but there's other stuff to 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.

36:28Jason Kelly: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 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 ribbon cut the first 18 robots up in Washington. He signed it. It was really cool. And so I think that's a, to me, again, as a science nerd, that's a good push for us.

37:03Jason Kelly: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'll need like a bigger bolus of money in the future to really make it a big deal. But I like what they're doing. In a dream scenario, what does that do for America? 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, learn more about D.C., number one, Congress.

37:32But also, like, the point is, how does the U.S. stay competitive in technology areas that are strategic? Yep.

37:39Jason Kelly:And so there's one for cyber like 15 years ago or something. And then there was AI. And then this was on bioengineering, biotech. We have had an unfair advantage since the Soviet Union fell, basically, where we were automatically at the lead in science. 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. 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.

38:19Jason Kelly:If you look at in my world, like biotech drugs, 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 kind of go to market channel. And Merck or Pfizer buys it for two billion or five billion or ten billion, depending on where it is in like clinical trials. Yep. Great. Three years ago, less than five percent from China. Last quarter, 40 percent plus. Wow. 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?

38:56Jason Kelly: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've got to 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 bring in like the AIs to just increase our amount of like intellectual horsepower. If we're going to keep up in science and you don't want to be surprised.

39:29Jason Kelly:Right. Like if you know, like DARPA, like, you know, like, yeah, 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.

39:59Jason Kelly:does that make sense yep other thing i'm curious i want to ask you guys about i mean god bless sam and this freaking open eye thing but like you know when they started that it was like a pie in the sky research project yeah yeah right this is like almost like bell labsy kind of you know like whatever like go for it everyone's like it's bullshit oh what is this non-profit all this okay but here we go it's now worth what what was the last round done at half a trillion 830 billion. Great. 830 billion. All right. So to me, it signals in fundamental research in industry can be valuable. Yep. And I think that's also a thing that we've kind of forgot about over the last 30, 40 years, because so much of like where the money was, was just like engineering, engineering, engineering.

40:47Jason Kelly: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. And I wonder if that's going to 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. 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 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 like 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 yeah on doing research yeah sonia i know you have a point of view on this i would say we've seen a few of these i mean you mentioned chai earlier yeah 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.

41:53I 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 through we're still in biotech winter. 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 because I think if there's like a real problem in the biotech world is asymmetric information and you're just like you can't tell yeah um it feels google with isomorphic feels like the closest because it's actually got deep pockets behind it yeah um to actually prove that story out but yeah my bet would be a vertically integrated research team um taking big swings yeah yeah the challenge is like funding these companies all the way through well i think um to the point on should there be more breakthroughs more fundamental research I think the answer is unequivocally yes.

42:41Like that is one of the wonderful dividends, so to speak, that's going to come out of this whole AI wave. 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. I think phase two is going to be becoming super intelligent across specific categories one at a time. And that's where all the breakthroughs are going to come from. 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.

43:15We're about to start being super intelligent in a bunch of different things. And we're about to start seeing a bunch of breakthroughs. 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. So they're using AI systems to actually just design chips that perform better than what human chip designers can do. And so I think we're going to see these pockets of superintelligence in different corners of industry. What was the AlphaGo Move 38 or whatever? Move 37. 37. I always forget the number.

43:49I know. I thought it was like 83. Yeah. 37. Great.

43:52Jason Kelly:Yes. Like Move 37. Right. Yes. Like that kind of stuff. Yeah. Yeah. I mean, if that's true, right? And I think there's two ways to get at it, by the way. Yeah. 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. Yeah, the combinatoric approach. It's, you know, like that you can't tell me a lot of the physical world stuff is not simulatable. Yeah. Right. And so the it's just can you run that machine faster?

44:30Jason Kelly: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, okay, like, 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? Right? 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.

44:56We 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. 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. You might have thought the same thing would happen, but actually the opposite happened.

45:20Jason Kelly: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. And 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. Yeah. Do you think language-based foundation models are the right substrates, or do you think somebody's got to train like an ACTG native model? Yeah. So that is like the ARX EVO is an ACTG native model.

45:57Jason Kelly: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 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. Yeah. Right. And so I think those will end up being powerful tools. But I still think I think the reasoning models are really they can do the job of an experimental scientist.

46:32Jason Kelly:And so now we have like a thousand experimental scientists in a box. Yeah. That that's already true. I don't need like a miracle. Right. Like that's. And yeah. So here's a question. Yeah. Alpha fold. All these 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 what the hell you can actually do with bio, which is like a pile of trash all the time, right? Okay, so bio is... God bless. I basically... Jurassic Park came out when I was 13. All I want to do in life is make Jurassic Park.

47:07Jason Kelly:Engineering is awesome, right? That's why I'm doing this. Did you see that one company that brought back the woolly mammoth? Yes, yeah, I know them well. Yeah, yeah, man over there. Colossal. Yeah, it's great. I love that stuff, right? They haven't brought back the void 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 is like the ability for, you know, kids someday to design biology like they program computers. Hmm. Right. Like that is what I want. Like that's the world I want to exist.

47:36Jason Kelly: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 Monsanto? Boogity boogity. Right? Like that's genetic engineering in plants. And then there's like 5 % that's like industrial. Like 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 hot.

48:16Jason Kelly:And so that's 5%. 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 the k the the fundamentals of this but but again like let's imagine computers the only application for computers was drug discovery we'd all be like well 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 yeah and then the fact of the matter being that the only apps that really like have ROI are drugs.

49:00Jason Kelly:Yep. 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. 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 then 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 is crazy. Australia is actually eating our lunch. I think our FDA will just match Australia soon, which is great.

49:31Jason Kelly:So we will get faster, but it's still not like launching a phone app. Okay. And so that, does that make sense? Yeah. So that does remain like a problem, I would say. There's been a bunch of other attempts at other things, you know, like animal-free meat. It's never quite been good enough to disrupt another industry yet. Yeah. 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 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.

50:16Jason Kelly:So that's not great. That's the opposite of what should be happening. Right. And so and why is that? Because we do it manually. hmm that's my opinion we have we have not like and it's like baumau's 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 give them new tools like we are getting like slightly better but the majority of the cost is manual work yeah 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.

50:59Jason Kelly: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 put down the vacuum tubes, right? Get onto some system that does not need people in the middle.

51:30Jason Kelly:Does that make sense? How does the application space change? 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. You're familiar with the GLP-1 drugs, right? Lily's worth close to a trillion dollars. That's great. That is, in my opinion, like a consumer product. 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 think about, oh, I got an intermittent fast through lunch or I'm going to be obese, right?

52:00Jason Kelly:You can just get your willpower back. It's awesome, okay? Right? Awesome. But that's, to me, 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, right? 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 want to like 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 you want to feel better like like the applications in the consumer space yeah for biotech are bananas oh it adds two years to your lifespan what's that worth?

52:40Jason Kelly: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. It's infinity. There's no limit on the value of something that would stack extra years of healthy life onto people's lives. It's nuts. That's effectively what our healthcare system is trying to do. Think of 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 is oriented around treating disease.

53:23Jason Kelly:Yep. And this is actually where like all the people like 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 i i think is 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 and so that's one half it's like new drugs there and then the other one what our first investor out of yc do you know who it was our angel mr brian johnson okay back when he was like no yeah but he was like pudgy vc brian oh yeah of course yeah uh-huh not like you know longevity like you know like yeah back yeah when he was like like a normal person right like i got like jack 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 because i asked him i was like how are you you know like these are like all these interventions like you're uh 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 right so like every week he's like taking all these tests and everything else is the most measured person,$2 million a year of diagnostic stuff, right?

54:35Like that is the other area.

54:38Jason Kelly: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 this thing 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 because that's what actually matters like molecularly like aging is molecular right it's not your freaking whatever the uh and so so like that whole world there's 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?

55:25Jason Kelly:Like that. There's the at home stuff now too, right? Yeah, but it's so early, right? That's my point. So I think that line is another place that could be a big, if you're asking about near-end apps for biotech, that one is the other one, right? And so I think you could see that. I think you could see other things like the glipse. Those are ones I'm excited about. Awesome. And then I'm always hopeful. Jurassic Park. Something like that. Yes, you know, right? Like that there'll just be some other weird thing. And we did just launch a cloud lab service that where you can, like we have experiments as cheap as$39 that you can just run and we don't send you anything.

56:01Jason Kelly: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 got the data back. Right. And so my last point on this one is like, I think 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 make sense yep 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 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 and so there's just this whole thing and i'm like 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 what everybody would millions of people want to be scientists yeah and i know that sounds like well that's nuts but like Like if you rewind the clock, God bless Silicon Valley and the computer industry to the 1960s when it was IBM and it was mainframes and you told people that kids would program computers.

57:29Yep.

57:30Jason Kelly:They would say you're fucking insane. And so I believe if you do manage to drop the cost and 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. Right. And so anyway, all this stuff I feel is on the other side of getting this AI for science stuff working, but I'm excited about it. Extremely cool vision for the future. Great note to end on. Thank you. Very inspiring. Thanks for having me on.

58:13Thank you.