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

All of the previous revolutions in tech, internet, right, like social media, like whatever, have been totally meaningless to biotechnology and bioparma. Like,

yeah, it's nice. We communicate slightly better or whatever. It's just some like back office IT crap, right? Like,

not this, like this. This is is actually going to change the fundamentals of how we do science and and our big science industries like bioarma are going to get disrupted. I I really believe that and

disrupted. I I really believe that and that's not been true for the last 30 years of tech.

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

us today. Thank you for joining us.

Yeah, thanks Anya. Uh so you started GKO bioworks in 2008 with the goal of making biology programmable and programmable has taken on completely different meaning uh in the era of AI. So I'm very

excited for the conversation today.

Maybe tell us about the journey so far.

I mean I'll do the Genko journey in short, right? So yeah, we started in

short, right? So yeah, we started in 2008, but we didn't actually uh 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 uh biotech VC, they really don't like like young people for example. So we had started the company like straight out of

grad school. It was 2008. We weren't

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 first six years?

Oh yeah. Okay.

Oh yeah. Yeah. We were basically like applying. We did government grants and

applying. We did government grants and service business. It was like pretty

service business. It was like pretty brutal uh start. And then summer 14, Sam Alman, now Mr. famous uh writes this blog post because he just took over YC and he's like, "Hey, I think the Silicon

Valley model can work for like deep tech, you know, nuclear fision, biotech, material science." And so I wrote him an

material science." And so I wrote him an email. I was like, "Oh man, like thank

email. I was like, "Oh man, like thank you for I mean we're we're like 5 years old. I got 15 people in a lab in Boston.

old. I got 15 people in a lab in Boston.

We don't make any sense for YC, but this is like an oasis in the desert, you know, like nobody will invest in weird companies like this." And he's like, "No, you got to meet me." So I flew out to San Francisco, met him, and he's like, "You should do YC." I was like, "I should do YC." Yeah. So then we did YC.

So we kind of that was sort of when you know if you really want to mark Genko from like having capital it was sort of in 2014 and how has the product changed since 2014.

Oh well so the mission hasn't changed but the product has gone all over the place different different roads. So

we've always wanted to make biology easier to engineer that was the idea and so if you're you know this is very much hang on I remember make biology programmable. Has have the words always

programmable. Has have the words always been make it easier to engineer because I feel like that's a slight I was always when I was talking to Sequoa it was always like this the 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 you know DNA is code right it's ATC's and G's not zeros and ones it's really our only other like coded product other than

computers is really biotechnology and so the core idea of GKO was well if you could design DNA code you could program cells to do things and cells are, you

know, they're programmable like computers, right? Um, but unlike

computers, right? Um, 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 GGO. And how's the product itself

GGO. And how's the product itself changed over time?

Yeah. So the way we went to market first was we're going to try to build what we called foundaries which was sort of a centralized laboratory that would kind of automate the lab work associated with doing biotech and and the reason again

if you're a computer scientist the way to think about this is if you wanted to compile and debug DNA code that's a physical process.

Yep.

Right. So you're like ATCG like we have to do phosphoridite chemistry. You got

to build the piece of DNA you want and then put it into a cell grow the cell and test the cell and that's your kind of compile debug cycle. Does that make sense? And so one half of what we worked

sense? And so one half of what we worked on technologically was how do we make that cheaper, right? Because if you want to get better at at doing this, you got to do more of it faster and for less

expense. And then the second thing we

expense. And then the second thing we worked on was how do you get better at your programming? In other words, like

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 where 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 we've shifted a little bit over the years in terms of how we do it. Um but does that make sense the two

it. Um but does that make sense the two kind of halves?

And roughly speaking the design piece sounds like it's a bit more software the testing piece sounds like it's a bit more hardware controlled by software very much. Yeah that's exactly right.

very much. Yeah that's exactly right.

And today the folks leading on the design side um you might see uh companies like Chai Bio for example like has like these protein models bolts um the folks at Arc Institute are just came

out today with a paper we'll call about EVO 2 which is like a a genomic model.

There's a whole community of people now trying to solve the problem of designing biology. Yeah

biology. Yeah with AI.

The big change at 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

solving it. It's hard, right? Like

designing cells is tricky. We're gonna

try to solve this half of the out of the problem, which is how do you make it cheaper and faster to try things in the lab and how can you We can talk a little bit more. We just did a project with

bit more. We just did a project with OpenAI. How could you have AI models

OpenAI. How could you have AI models help you do that?

Is anybody else focused on the backend, so to speak? I kind of think about design as the front end of the process and testing as the back end.

And when I say anybody else, obviously people do this. Is anybody else with a similar approach focused on that part of the market?

um you there's some new companies, right? So there's companies like Medra

right? So there's companies like Medra out here is one that's doing it with like robotic arms trying to um accelerate it. You have like the life

accelerate it. You have like the life science tools industry, but I would say it does not have uh a Silicon Valley attitude about things in the sense that they're not really trying to change the

fundamentals of how you do it. They're

sort of like just providing the next tool to people doing it the way they've always done it. Um, 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

right? Is there something like the jump to planer semiconductor manufacturing in electronics at the beginning of Intel, you know, like is there some 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 goko like what I think is unique about what we've been doing the last 10 years.

What catalyzed the focus on this part of the business on the on this half of the house? Yeah,

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

Okay.

So, and I went after both initially and I kind of took my licks for that. And I

think the the good thing about an engineering problem is you can ultimately like render it to dust, right? A science problem I think is great if you hit it, but it's much more unpredictable. Um, and so in

this era of Genko, 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 direction. And with the efforts that we see with the Arc Institute and others of that ilk, what inning are we in so to speak? Like is the ecosystem around the science problem?

Good question.

Going to start producing meaningful results soon other than papers.

Uh it's a good question. I think the the hard part about designing bi so biology is amazing by the way, right? Like just

as a substrate again, right? Like if you think about what's happening inside of a cell, it is producing, you know, Intel or now Nvidia TSMC level caliber atomic

placement basically for free. Yeah.

Right. So it's able to do molecular assembly. It self-repairs. It

assembly. It self-repairs. It

self-replicates like as a as a physical substrate. It's insane. It is the

substrate. It's insane. It is the product of four billion years of evolution.

So the complexity embedded in a cell is actually a lot bigger I think than people like give it credit for. And so

there's a march there. Now that said, you know, all of you know, more than half of your drugs today are produced by biotechnology. We cure cancer, we do

biotechnology. We cure cancer, we do this. We have huge value coming out of

this. We have huge value coming out of even with the tool, the limited tools we have today. So you don't have to solve

have today. So you don't have to solve the whole problem. Yeah. Over here to create a lot of value. You just have to be better than how we do it today. Does

that make sense? And so I think they have really good opportunities already in the near term with all the protein models. You're seeing that, right? Like

models. You're seeing that, right? Like

Chai just did a big deal with Lily. Like

there there's real opportunities there, I think, right in the near term. Yeah.

Um does that make sense?

So speaking of OpenAI and Mr. Alman, yeah, uh you recently announced a partnership research result with them.

Can you say more about that?

Yeah. Okay. So this is this is pretty exciting I think for the the folks that are following AI, it's pretty neat. So

basically what we did was we took our we call it autonomous lab, right? And so I can talk more about this, but the short answer is if you really want to drive efficiency on the lab side, you need to

get the human beings off of the lab bench, right? Right. So the way we do

bench, right? Right. So the way we do and this is in true in biotechnology is kind of true for science broadly. The

way we do science today 95% of science the stuff that's not theoretical. So not

m you know everybody's working on math like let's work on terren towel let's get a terren tow 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 but if you look at the majority of like what we spend money on in the United States and and just generally across the world in science it's largely on experimental work yep and the reason is if you want to learn

something new about the world which is what science is fundamentally you have to go out usually and like poke it a you have an opinion of hyp hypothesis, but you got to go test it to actually figure it out. So, it's experimental science

it out. So, it's experimental science that moves the needle in my view. And

so, the question was, could a reasoning model do the work of experimental science if you gave it a robotic lab?

That was the question. And the answer was, yeah, it's actually pretty damn good. Uh, so we did, basically the way

good. Uh, so we did, basically the way the project worked was we had there's a biochemistry problem called self-free synthesis.

Okay?

So, you take a piece of DNA ATC GG, right? If you were to put it in your

right? If you were to put it in your cell right now, remember like uh 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

right? And so you put that DNA into a cell and it'll make a protein. Well, you

can do a thing called cell free where you pop a cell open, take the guts, put it in a test tube, and then add the DNA to that. And because the guts are still

to that. 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? Okay. And so

something, right? Okay. And so

scientists use this. They try to optimize. It's very expensive usually.

optimize. It's very expensive usually.

And so there was a paper that came out of Stanford from Mike Jwitt's lab in August that set the benchmark for like how cheap people had been able to do self-free protein synthesis. And so we

said all right let's try to optimize that with the model. And so we gave the model we did each round we would do 384 well plates. Okay. So each well in a

well plates. Okay. So each well in a plate is like a little kind of cup of liquid and you can do an experiment in there. And so we gave it, you know,

there. And so we gave it, you know, 30,000 experiments to run. And after it would run those experiments, gets the data back and designs another set. So

after four rounds of that, we beat state-of-the-art. And after six rounds,

state-of-the-art. And after six rounds, we beat it by 40%. Wow.

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

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

Yeah. How did it beat the state-of-the-art? Uh, so this is this is

state-of-the-art? Uh, so this is this is my point. This is I think my my larger

my point. This is I think my my larger point about science because I think we're going to do science differently in the future in my view based based on what I'm starting to see here. So

see here. So what does a scientist do when they're doing experimental work? They're coming

up with an idea and then they're trying to design an experiment to ask a question about that idea. Then they're

going to run the experiment, take the data back, interpret it, and then poke again based on what they learned. And they're going to go through that process a few times to like resolve something. Oh, this is

how, you know, whatever this cancer works. This is how this piece of

works. This is how this piece of materials, you know, works. This is

this, this is that. And and so that cycling is just logic.

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 like that's all it has to do. Does

that make sense? Yeah.

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

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 could it design them like a scientist could?

And the answer was yeah, hell yeah it could. And so now I think that opens a

could. And so 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 you know the scientific method, the design and the hypothesis testing and all that is done by reasoning models of some sort and the

actual testing is done by you know autonomous labs. Yeah.

What's wrong with that vision of the future? And if and if that is the right

future? And if and if that is the right vision of the future, how far out is it?

I mean I think this is how it's going to happen. I I really do like like the um I

happen. I I really do like like the um I mean I'm probably more substantially more aggro on this than like the average scientist today. Um but like it's so

scientist today. Um but like it's so I'll just explain like why where I think if you like had a heads up competition which I want to do this right so 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 right so I think what what the scientists will push back on is like this thing can just be as creative or as

as me or something right which I actually I I'm sympathetic to that I'm not saying it's gonna be more creative I'm saying uh it's going to be way more creative.

I'm saying you don't Yeah. No, not that Silicon Valley. I live in Boston. Um I

Silicon Valley. I live in Boston. Um I

mean, uh I'm saying uh it it can run a lab 24 hours a day.

Yeah.

It uh I'll give you another example.

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

same area. Let's say we're working on like Alzheimer's. Yeah. You have

like Alzheimer's. Yeah. You have

hypothesis, you have hypothesis, I have a hypothesis. We each kind of pursue it.

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 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 open AI project. What I think should happen

AI 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

a minute. That's actually a pretty technically difficult problem, but let's just wave that away. Okay, solved. All

right, great. So then I'm going to put a hundred AI scientists on top of this thing. Yeah, each one is going to pursue

thing. Yeah, each one is going to pursue a different hypothesis for Alzheimer's.

All right, great. And they're going to run their experiments just like you would in your lab that day. But at the end of the day, they're going to pass 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. Every [ __ ] day. Okay. And and

so they're going to learn from each other. Like your even though your

other. Like your even though your hypothesis is different, we're working in the same area. 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?

that normally. Does that make sense?

Yep. And so that's all just 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 rack we can say this hey cut that line of research or whatever and so like that's number one I think the information sharing and like the ability to handle like really broad

context across a lot of projects for the AIS is just better than it's just socially different even than how we do it today. Does that make sense? That's

it today. Does that make sense? That's

unfair advantage number one. Yep.

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 at the academic universities and all this halaloo, right?" Well, what's an

indirect rate? Well, it's basically

indirect rate? Well, it's basically paying for manual laboratories.

That's what it pays for. Okay? You've

got these labs and they're there 247, but they're used five days a week. Yep.

Okay. Uh they have equipment, but every lab, all three of our labs, we have same copies of the same equipment. Yep.

Because we all got to do the same work.

We don't share each other's. No, no, no.

I have like a door in my lab. Only my

lab gets to use it. Your lab uses yours.

So, we have all low utilization rate of our equipment. It's just how it works.

our equipment. It's just how it works.

Okay. Right. And so, you have a very inefficient like if you look at the spending on research and this is true the 60 to$80 billion a year that bioarma spends or the $40 billion that NIH

spends less than 5% is on the reagents.

Everything is on overhead is basically the overhead, the people, the regulatory, the lab space. Mhm.

If we were running it efficiently, you would budget a research program at the NIH, not on indirect and heads and everything else, but just on the reagents because that's like the

usagebased pricing of science because to actually do experimental work, I have to consume some chemicals. I have to consume a piece of plastic plate wear.

Like whatever the hell it is, like I'm actually doing atoms in the physical world. I got to burn some stuff up. That

world. I got to burn some stuff up. That

should be the dominant cost. It's the

opposite right now. Yes. It's like less than 5%. So the other advantage those AI

than 5%. So the other advantage those AI h AIs will have is if they're able to run robotic labs now they're running where 90% of the cost of a research project goes to the reagents.

Yes.

Oh my god. Right. So that's like a 10x increase in the amount of data per dollar that you're getting compared to how we do it today. So I think you combine those two things and but without the AIs even being smarter, right? They

can even be dumber than the scientists.

I think they win. I really think they win. And so I think we got to reevaluate

win. And so I think we got to reevaluate like how we fun what we've done with the NIH. I think every biioarma head of R&D

NIH. I think every biioarma 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 and you know cos I've always been

adjacent to this stuff right all of the previous revolutions in tech internet right like social media like whatever have been totally meaningless to biotechnology and biioarma like yeah

it's nice we communicate slightly better whatever it's just some like back office IT crap right like not this like like this this is is actually going to change the fundamentals of how we do science and

and our big science industries like biioarma are going to get disrupted. I I

really believe that and that's not been true for the last 30 years of tech.

Our 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

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 like and so the last several revolutions have been about the distribution side of the equation which doesn't get to the core of what it is you're doing.

Completely agree that and that's I think fundamentally true and and so again I think the leaders of biioarma companies and the and also the leaders of research universities and these people that are are in the business of doing science to

produce either products or for the government cannot ignore this. They cannot ignore AI. It it is it is just different. And I

AI. It it is it is just different. And I

am I'm telling you I'm a person who has been adjacent to this crap for 15 20 years now. And this is the first time

years now. And this is the first time I've like uh the tech guys finally did something cool.

Yeah.

And just so you understand like so you think AI is the calizing 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 the average tech person's look at how science is done where you have literally PhD trained people. These are brilliant people.

people. These are brilliant people.

Yeah.

Paid decent amount of money standing I I did a PhD at MIT in bioeng engineering.

It's five years of moving liquids around a lab bench by hand. I swear to God like like it it is like like like my friends from undergrad just like they would never right like you know right like it's ridiculous you would do like manual

labor right like absurdity right in our crew 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 well 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 automationy term is like high mix low volume work.

Yeah.

Okay. And this is true at like places like Hadrien today for example that are working on this on the manufacturing side industrial high mix low volume.

Yep.

Is hard to automate.

Yep.

Historically. All right. And my like transportation analogy that I've been giving to people in bio is like okay so imagine on the y ais you have like level of automation.

Yep. And then on the x- axis, okay, you have like flexibility, like that variability, the mix in what you're being you're asking it to do. So in

transportation, low mix, high auto 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

a car.

Yep.

Right. Hands on the wheel, foot on the pedal, take it right to your house or the grocery store. And that's what the transportation system looked like for the last hundred years until Thank you very much, you know, Google, we got Whimo up here in the corner where you

get the flexibil, you know, the the automation of a subway, but the flexibility of a car. Okay? And and it's so surprising that we don't even call it automation anymore. We make up a new

automation anymore. We make up a new word. We call it autonomous. It's

word. We call it autonomous. It's

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. Okay. Right. And we just hit a wall

on. 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 postindustrial revolution. Everything

postindustrial 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 sol. 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

like subways. Yes. It's just repeat the same experiment uh you know at at a diagnostic company like Quest they would have automation. If you're a high

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

variability. 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. Yeah. And that's what

freaking bench. Yeah. And that's what you got to fix.

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

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

cameras. You know, then you had your software suite with the perception and the planning and the actuation, which then had to tie back into, you know, whatever vehicle manufacturer you're working with. And then there are

working with. And then there are gazillion corner cases that you have to simulate because you can't get enough of them in the real world.

Like what would that set of words be for your world? Like great question.

your world? Like great question.

What are all of these specific things that are hard to get right?

100%. And and I think if you're like trying to generalize the problem of bringing automation to autonomy, it's going to be different in every domain.

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 [ __ ] room that the

day. It's the same [ __ ] room that the robots are. Nothing is changing. Okay.

robots are. Nothing is changing. Okay.

The physical environment is not changing at all. Yeah. So like like it is not at

at all. Yeah. So like like it is not at all the same stack that brought you autonomous cars that will bring you autonomous labs. That's not my problem.

autonomous labs. That's not my problem.

Okay. So so in my world it's the variability in like what the scientist is asking for that makes it hard, right?

So they're like I want to use this piece of equipment. I want to use that piece

of equipment. I want to use that piece of equipment. This is my combination of

of equipment. This is my combination of things. Right? And so one of your big

things. Right? And so one of your big problems is getting a thousand longtail pieces of third party. like you can't believe the software on these things. Uh

benchtop lab equipment, okay, integrated into one big system, okay? So

they all can be controlled by your software. That's like problem number

software. That's like problem number one. It's like integration of benchtop

one. It's like integration of benchtop equipment. Problem number two, what are

equipment. Problem number two, what are we doing with our hands when we do science? Largely in bio anyway, it's

science? Largely in bio anyway, it's liquid handling.

So you pick up a pipet, 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 it you 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 problem than you think and you are doing some work as the human to like manage

that. So you have two big buckets. One,

that. So you have two big buckets. One,

solve liquid handling. Two, uh, send samples to a thousand different pieces of equipment. Does that make sense? If

of equipment. Does that make sense? If

you nail those two, you're done.

So tractable.

It is [ __ ] tractable. Yeah, I agree.

Yeah, it's totally tractable. And so

it's just uh a lot of work.

Where are you guys in like working through that?

I think we basically have it working.

Yeah, that's the honest truth. I mean,

we we basically What's the last like major hurdle? One

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

Yeah.

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?"

Customer is like, "No." Right. Like

they're at the bench. They're like, "No, you know, I don't want to try." You're

like, so there's like an adoption issue that I think has made it really hard.

And and so we have this advantage that because we have a research, we saw like our original business, which was research partnerships, which we still do, means I have a bunch of scientists employed at Gingo. These scientists are basically like remember the Google

engineers who had to 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 Whimo drove into a mailbox or something right like yeah that's my scientist today so they

are they're dog fooding 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 um 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 the the just a handling the scheduling with tight timing on experiments is like algorithmically tricky and we've had to

figure out a bunch of stuff getting the equipment to work all like when it works all day long making that reliable compared to it's being barely used at the lab bench that's tricky right so it's just these like things that we have

to keep knocking down but they're they're engineering at this point have you solved like pipe heading and liquid handling yeah the good thing is there's there is a whole industry that's worked on that problem like liquid handling robotics

it's just a matter of having like all the different liquid handlers and 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. Scientists don't

code.

Yep.

Okay. So, oh, cool. Use my robots. This

is what everybody's done. They've made

like visual programming languages. Like

if you're a scientist, there's a thing called Lab View. It's like complete trash, but like it's, you know, make a flowchart, right? Because you can't

flowchart, right? Because you can't write Python or whatever. Horrible.

Okay? Like even that they hate, okay?

Right? And so no one will program [ __ ] And 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 two instances where we sent a plate.

So like again this is like kind of bunch of little wells with liquids in them and it and we seal the plate when we put it in storage so it doesn't evaporate. So

they sent a sealed plate to the pipe heading robot and pipe head comes down and it gets stuck in the seal. You're like, and people in our Slack are like, "Hey, what the hell?" You know, like, "Deal the

the hell?" You know, like, "Deal 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 expert at liquid handling at the bench. And now they're like making

the bench. And now they're like making like, you know, basic mistakes here. And

they feel horrible. That's a bad UI.

Okay. And and I was just like, this is nonsense.

We're from now on only what the way we're going to interact with writing the code is through cloud code or codeex.

Yeah, like you will now submit a written protocol what you want and the model will figure it out and if the model sends a plate sealed, we will update the skills file and it will never do it

again and we will get through this.

Okay. And and so that is a big win for usability. Yes.

usability. Yes.

Of robotics for scientists is what's happening with with cloud code and codex. Does that make sense?

codex. Does that make sense?

That does make sense.

So like thank you, you know, right? Like

so these are the things that's like that had to get knocked down too. And so all this is in flight, but like right now in Boston is like a very unique experience experiment happening where we have like 50 scientists submitting jobs into one

big robotic setup that exists nowhere else on the planet right now. Um so it's pretty neat to watch it.

That's cool.

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 was designed for them. And like I would think that the existing labs are made for like humans walking around pipetting things, walking between

different machines. And so you could you

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

Yeah. Um so again like the primary thing that the human is doing is moving samples around the 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

things. You just put them on a track like our system has like a nice little track and the plates move with extremely high reliability. They get delivered

high reliability. They get delivered with micron specificity to where they are. The arm picks them up. It's like,

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 are are are humanoid robots going to disrupt TSMC?

Are they going to go in there and etch the the [ __ ] chips? Like, obviously

not, you know, right? Like like no, it's a microscopic discipline. Like biology

is a microscopic discipline. Like these

things are No, they uh makes no sense.

Yeah. Does how does it change the unit of scale for a lab? Like I'm I'm imagining that labs in the future are going to be these enormous things. The

way the data centers have become these enormous things.

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 Merk's campus, you'll see a million square feet of laboratory benches.

Yeah.

Across a bunch of different buildings everywhere, whatever. Right. And they're

everywhere, whatever. Right. And they're

they're set up for basically humans to be able to walk in and find a piece of equipment.

Again, underutilized, but basically available whenever they need it to run whatever experiment they've come up with by thinking over the last two weeks and not even working in the lab. And so like that kind of like cycling is is kind of how it operates.

And you need it local like if you have a team now in this new place because you bought this company, they need a lab. So

you replicate another lab, right? The

labs have to go wherever your scientists are. Mhm.

are. Mhm.

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

autonomous lab. You can think of that like a local cloud if you want. Okay.

Well, now you don't need a lab where the scientists are. So, you get a rid of all

scientists are. So, you get a 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

benchtop equipment. Like we're talking going from like sub 20% utilization at the bench to like 70%.

So now you need less equipment.

Mhm. And then assuming you didn't decide to have humanoids, uh 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. Like when we talked to like we just sold 97 robots to the Department of Energy for this like Genesis mission. This is like the AI for

Genesis mission. This is like the AI for science thing that uh 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

otherwise. And that's part of the sales pitch. It's like you could have less

pitch. It's like you could have less spending. Remember I told you earlier

spending. Remember I told you earlier like the spending is not on the reagents. It's on basically like roof

reagents. It's on basically like roof space. Yeah. Like laboratory space and

space. Yeah. Like laboratory space and and people.

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

that Yeah. So our our particular device we

Yeah. So our our particular device we call a rack. It's like a reconfigurable automation cart. It's basically like a

automation cart. It's basically like a box that has a piece of benchtop equipment in it. A six-axxis robotic arm. This is like nothing special for

arm. This is like nothing special for labs. This is like tradition. This is

labs. This is like tradition. This is

like coming out of manufacturing tech.

and then a piece of mag lev track. And

what you do is you Lego block the carts.

So we have like 50 of them all together in our lab in Boston. And then a sample can move on the track and in front of every piece of equipment is an arm and the arm picks it up and puts it on the equipment. Go ahead.

equipment. Go ahead.

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. Y

reagents. Y uh because that's kind of like the usage pricing. So that that would be my that's

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 National Lab DOE or Mercur or whoever. Does that make sense? And then the other half is what

sense? And then the other half is what you said. I'll run my lab in Boston

you said. I'll run my lab in Boston as a cloud and you could just order from Yeah, totally. Can we talk about

Yeah, totally. Can we talk about training? Like a lot of what we've been

training? Like a lot of what we've been talking about to me seems like inference like it's a it's a use case of the reasoning. Seems to me that you have you

reasoning. Seems to me that you have you are generating an enormously helpful data set here that should be used to like backdrop into the weights themselves.

Yeah.

How's that gonna play out?

Uh it's a good question. Um

I don't know yet. I I think I think there'll be one training. So there's two there's different levels of challenges.

One was that thing I mentioned earlier like you're submitting uh 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,

right? like you want to do a like, oh, you're like a a prototyping shop or whatever. Any place where there's like

whatever. Any place where there's like variability. Okay. And so I see a lot of

variability. Okay. And so I see a lot of variable requests from scientists. I see

all the edge cases of how it breaks the physical equipment.

Yeah.

That you can't like compile out. Yep.

Oh, make a digital twin. No. Like you

know, right? Like like you have to like actually do this stuff. Like I do not think like like I don't buy it. Right.

Like like a lot of it's edge CC, you know, liquid classes. You can't really pick it up on a camera. There's just

like all this stuff that are like the edge cases that Whimo had to see driving around are edge cases we see by doing a lot of variable work on the same system.

That is one type of training. I don't

know that it's fully like model training as much as it probably is just like a giant file or something, but like that's one. Does that make sense?

one. Does that make sense?

Yep.

The bigger one is sort of the model's ability to take your intent and turn it into an experimental plan.

And that's interesting. That's back to like could these things just like blow science out of the water and and and that I think you could have a really cool loop that has more to do with the results of every experiment like this open AI project like as we saw what

experiments worked and didn't you could theoretically then teach the 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

right? Like everything else is kind of noise. Like we're just going around

noise. Like we're just going around circles, you know, like the Romans did it, you know, right? Like we're just doing the same stuff, right? The the

Greeks, but but except for science.

Yeah.

Right. Does that make sense? And and so I do think this is like if you crack that nut, like if you can if models plus again, I think you got to have the experimental work. If that really 10xs

experimental work. If that really 10xs or 100xes the speed we do scientific discovery, like that 10x or 100xes the progress of the species in my op.

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 I

doable. Like I think that loop and I I think this is something that the frontier models are starting to care about like like getting better at that I I do think is a 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 this is this is where it matters the

most. I agree.

most. I agree.

You mentioned project genesis uh earlier. Yeah.

earlier. Yeah.

What is it? Why does it matter?

Yeah. So this is came out of uh uh white house and OSTP. So the office of science and technology policy like myio there and um and so 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. So when it's like project based, it kind of tends to live in Department of Energy and like more open-ended sciences like the National

Science Foundation. Okay. So DOE is

Science Foundation. Okay. So DOE is running it. So it's a project and so

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."

public." 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

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 our big science in the country and accelerate them. And their target is to

accelerate them. And their target is to double the acceleration of science in the next few years. All right. So that's

the idea. And so one way you're going to do it is they want to take the existing data that is at the national labs and like basically feed it into models and see if you could find new stuff from data we've already collected. Mhm.

already collected. Mhm.

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 that was and so when we we

models. And that was and so when we we did that deal with Department of Energy, uh Secretary the Defense Department of Energy Secretary Wright and I like ribbon cut the first 18 robots up in Washington and like signed it. It was

really cool. And and so like I think that's a to me again as a science nerd like that's a good push for us. Um and I think you want to see some good results soon because ultimately they got to you know Congress has to get excited about

this. you would need like a bigger

this. you would need like a bigger bololis 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?

So I I chaired this um National Security Commission on emerging biotech in DC for like two years and Senator Young in Indiana chairs it now. And there's a similar one on AI that Eric Schmidt chaired like seven years ago. So it's

super fascinating to see like and it like learn more about DC number one Congress and but also like the point is how does the US stay competitive in technology areas that are strategic.

Yep.

And so there's one for cyber like 15 years ago or something there was AI and then this was on on bioengineering biotech.

We have had an unfair advantage like since the Soviet Union fell basically where like we were automatically at the lead in science 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 how you you also need scientists so you have to have research universities to train these

people like it is esoteric so that was true that was true and now with China it rise it's not true anymore right like they're Like if you look at the number of like scientific papers published, it's more from China. Now if

you look at in my world like biotech drugs, not manufacturing drugs, like making new drugs, the way it works in this in our industry is like a startup discovers a drug and

then they try to sell it to Merc or Fiser and they're they're the kind of go to market channel and Merc or Fiser buys it for two billion or five billion or 10 billion depending on where it is in like clinical trials.

Yep.

Great. Three years ago, less than 5% from China. last quarter 40%. Plus, wow.

from China. last quarter 40%. Plus, wow.

Okay. Yeah. So, we And that's innovation. That's like discovery,

innovation. That's like discovery, right? So, we're And why is it why are

right? So, we're And why is it why are why are there why is that growing so fast in China?

They have just as many scientists as us.

They're just as smart as our scientists.

They get paid less. And remember, it's like a hands in the lab. You 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, right? Like if you know like DARPA like

right? Like if you know like DARPA like the you know like thanks for the internet DARPA, right? Like the the founding point of DARPA was like after Sputnik when the Russians like were the first ones to put a satellite up and it

was created to say like we will not be technologically surprised again.

It's behind the scenes thing but it is very important right it's really scary if you get technologically surprised and so I think that that is why it's I think important from a national security standpoint important for the country

important for the species is to is the rate at which we get scientific discovery. Does that make sense?

discovery. Does that make sense?

Yep.

Other thing I'm curious I want to ask you guys about I mean God bless Sam and this freaking open AAI 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 [ __ ] Oh what is this nonprofit all?

Okay but here we go. It's now worth what what was the last round done at half a trillion or something? Great. 830

billion. All right. So, so I I to me it signals um in fundamental research in industry can be valuable.

Yep.

And I think that's also a thing that we've 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 and

it wasn't really about trying something that was just like this probably not going to work but we should give it a try. Pharma's been like that, but lots

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 chem you know the chemical industry like 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 Dowo chemical could do would be some [ __ ] crazy breakthrough. not um you know, let's do

breakthrough. not um 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 like actually no, we're going to like go for it.

Do you see that at all? Like you know, like like do you do 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 on doing research.

Yeah. Sony, 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, you know, fundamentally rethinking, for example, the protein design process. Um, I think it's my my

design process. Um, I think it's my my gut instinct is more likely to come from folks like that who are just like taking really big swings. Um, and the tricky thing is just the the song and dance of how to get funded, especially through

we're still in biotech winter, 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 there's like a real problem in in the biotech world is asymmetric information you just like

you can't tell. Yeah.

Um feels Google with isomorphic feels like the closest because it's actually got deep pockets behind it um to actually prove that story out. But yeah

my bet would be a vertically integrated research team um taking big swings one.

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

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

AI wave. Yeah.

AI wave. Yeah.

And I think phase one is sort of becoming human level intelligent across a bunch of different categories. And I think that

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

cheaper. I think phase two is going to be becoming super intelligent across specific categories one at a time.

Yeah.

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.

Yeah.

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

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

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

Let me give an example. So, we just backed this team that uh did the alpha chip project at Google. So, uh they like they're using AI systems to actually just design chips that perform better than what human chip designers can do.

And so, like I think we're going to see these pop pockets of super intelligence in different corners of industry. And

there's definitely going What was the Alpha Go move 38 or whatever?

37.

37. I always forget the number. I know.

I thought it was like 83. Yeah. 37.

Great.

Like move 37, right? Yes. Like that kind of stuff.

right? Yes. Like that kind of stuff.

Yeah.

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. Yeah, right.

intelligence. Yeah, right.

It'll intuitit something that a person wouldn't have. And then like I really

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

just you don't you know like the you can't something a lot of the the physical world stuff is not simulatable.

Yeah. Right. And so the it's just can you run that machine faster? And then

importantly like you're saying like can can you feed it back and make it smarter based on what it's actually in the world and then okay like and then and then I think if you if you start to believe you can get those breakthroughs I do think you have to ask what's the how do you commercialize like

what's venture look like in that scenario right because you're like oh cra I got this insane break you know we got a room temperature semiconductor or you know right like like now what right and is it like you go to uh 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 we had this conversation the other day on will the venture capital industry shrink because the cost of

producing everything gets cheaper because it becomes so much more efficient.

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.

service. Yeah.

You might have thought the same thing would happen. But actually the opposite

would happen. But actually the opposite happened. There was this explosion in

happened. There was this explosion in creation.

Yeah.

Which made distribution that much more competitive. Yeah. And so there were a

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

would 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.

right. Yeah.

Do you think languagebased foundation models are the right substrates or do you think somebody's going to train like a, you know, actg native model?

Yeah. So, so that is like the ARCs EVO is an actg native model, right? So, it's

trained on a trillion bases of DNA and and I think that's awesome, right? I

think it's super exciting. I think it will apply it. It'll be like a tool available to the reasoning model to do its job. That's already the case. Yep.

its job. That's already the case. Yep.

Right. Like the reasoning model working on say our even our open eye project could go access alphafold, design a protein, get it synthesized as add that as a reagent into the project. Like

that's allowable. Yeah. Right. And so I I think those will end up being powerful tools. Um but I still think I'm I think

tools. Um but I still think I'm I think the reasoning models are really they can do the job of an experimental scientist.

And so now we have like a thousand experimental scientists in a box.

Yeah.

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

So here's a question.

Yeah.

Alphaold all these all these papers have come out on how AI is changing drug drug discovery. Yeah. Do you think the pace

discovery. Yeah. Do you think the pace of drug discovery has 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, so like bio is right like like I think the down

so God bless like I I basically you know Jurassic Park came out when I was 13.

All I want to do in life is like make Jurassic Park genetic engineering is awesome, right? Like like that's why I'm

awesome, right? Like like 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. The

band over there. Colossal. Yeah, it's

great. I I love that stuff. Right. They

haven't brought back the mammoth yet, but they brought back a direwolf.

Oh, direwolf. Sorry.

Mammoth is coming. I'm sure the uh but yes. Okay. Right. So, but really what

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,

right? Like that is what I want. Like

right? Like that is what I want. Like

that's the world I want to exist. And so

the question is like how the hell do you get there? And one of the issues we have

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 a remembertogy, right? like like that's that's genetic

right? like like that's 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 bio biotechnology.

There's enzymes in there that break up dirt without you having to make your laundry um hot. And so that was the that's 5%.

Okay.

That is the totality of apps we've come up with so far for programmable matter compilers i.e. cells. It's embarrassing.

compilers i.e. cells. It's embarrassing.

Okay. Right. like like the ca the the fundamentals of the but but again like let's imagine computers the only application for computers was drug discovery we'd all be like well they're

so you know man such a pain in the ass with these computers right like you know and so so so that there is like a distinction between like what we're really working on at Genko 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.

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 and it has to do with regulatory basically. Like it's

like well we don't like to stick things in people. You got to be careful. Like

in people. You got to be careful. Like

all that stuff is fine. And 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 like for like phase one. It's crazy.

Australia is actually eating our lunch.

I think our FDA will just match Australia soon, which is great. So, so

we will get faster, but it's still not like launching a phone app. Okay. And so

that does that make sense? So, that does remain like like a problem, I would say.

Uh 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.

Um I would love to see that happen. That

would be a big accelerant. Not just for whatever industry but ultimately for genetic engineering and then if you accelerate genetic engineering you you try to create that flywheel that we got in computers where it keeps going going.

Now that said it to pick our app of drugs it has gotten more expensive to develop drugs not less year-over-year for the last 25 years.

Yeah.

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.

That's my opinion. We have we have not like and and it's which like bal caus disease or whatever like these scientists are getting more expensive the rent is getting more expensive that's it they're actually more productive like like we give them new

tools like we are getting like slightly better but the majority of the cost is manual work that [ __ ] 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 in this being like retrenching to solve that problem first because I don't believe we really get out of the mud

until we've got the people out of the lab.

Then from that base we can start to climb out.

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 techn you know

right? It's like alien techn you know you're doing it in some way that humans never remember before chips was vacuum tubes. It was like human scale

tubes. It was like human scale electronics.

Yeah. Yeah.

And then we we were like okay cool like let's get you know we we we saw the curve. That will happen for lab work and

curve. That will happen for lab work and genetic engineering. I promise you. But

genetic engineering. I promise you. But

the first step is like put down the vacuum tubes, right? Like get onto some system that does not need people in the middle. Does that make sense?

middle. Does that make sense?

How does the application space change?

Uh I don't know. That's very

unpredictable. Um well, I'll tell you some things I think I'm excit I'm excited about in the near term.

Um you're familiar with like the GLP1 drugs, right? Lily is worth close to a

drugs, right? Lily is worth close to a trillion dollars. Those are great. Like

trillion dollars. Those are great. Like

that that is in my opinion like a consumer product.

It's I'm on the clips. It's awesome.

Best thing. It is like the best thing since the iPhone. you don't think about food. It's like you can like you get to

food. It's like you can like you get to spend your day thinking about work or kids, whatever else you want to do. You

don't have to like think about, oh, I got to 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. Okay.

willpower back. It's awesome. Okay.

Right. Awesome. Um but that's to me a the reason it's worth so much money is because it's not treating a disease, right? Like the the the biotech

right? Like the the the biotech industry, the therapeutics industry today is really the disease industry.

Yeah.

Right. And how much of your life and again it 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 the applications in the consumer space.

Yeah.

For biotech are bananas.

Oh, it adds two years to your lifespan.

What's that worth?

Like what is the value of a biotech product that adds five years to lifespan?

This is Sequoia Capital. Throw a number at it.

Depends on your customer.

50 trillion. Like like it's it's infinity there. There's no limit on the

infinity there. There's no limit on the value of something that would like stack extra years of healthy life onto people's like that's nuts. Yeah.

Right. Like you know like we that's effectively what our health care 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 in a shot. So 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.

Yep.

And this is actually where like oh the people are like oh maha blah blah blah like like I think like that that line of the maja 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 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 Angel Mr. Brian Johnson. Okay.

Back when he was like Oh, yeah. But he

was like Pudgy VC Brian. Oh, yeah. Of

course. Yeah. Uhhuh. Not Not like, you know, longevity like, you know, Jesus.

Yeah. Back when Yeah. When he was like like a normal person, right? Like not

like jacked. He's awesome now, right?

like the 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 cuz I asked him I was like how are you you know like these are

like all these interventions like you're a like you know like like Brian's got a good life you know like would you be oh you're taking some random thing and trying it out like isn't that scary and he's like wow I'm monitoring all the time right so like every week he's like

taking all these tests and everything else like most measured person $2 million a year of of diagnostic stuff right like that is the another area. So, like, oh, we all love our Aura rings and everything. This is pathetic. Okay.

everything. This is pathetic. Okay.

Right. It's great. Your heart rate 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 the stuff like functions getting going 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

vials. It's like the worst experience in the entire world, right? Like like that there's the at home stuff now too, right?

Yeah. But it but it's but it's so early, right? That's my point. Like so I think

right? That's my point. Like so I think that line is another place that could be a big if you're asking about nearin apps for biotech, that one is the other one, right? And so I think I think you could see that. I think you

could see other things like the glyphs.

Um those are ones I'm excited about.

Awesome.

And then I'm always hopeful Jurassic Park something like that. Yes. you know,

right? Like like uh that there'll just be some other weird thing. Uh and we did just launch a cloud lab service at GKO where you can um like we have experiments as cheap as $39 that you can

just run and we don't send you anything like we won't send you a sample but we will send you back data. So it's like you do the experiment, we run the experiment for you, you get the data back, right? And so my my last point on

back, right? And so my my last point on this one is like I think if you like science is thought of as this very like precious genius thing, but really what

it is is like formalized human curiosity.

It's like a process by which humans of which all of us are curious about things like do curiosity, right?

Like really try to answer our curiosity.

Does that make sense?

Yep. But I and I think everybody's curious. And so I believe that if you

curious. And 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. It's in

my view the lab.

Yeah.

It it is that that is brutal, right?

Like you don't have access a it is a total gatekeep like you cannot get access to one. It like almost legally you can't get access to one, right? Like

and and so there's just this whole thing and and I'm like what if that went away?

What if people everyday people could could order an experiment? What if the model would help them design the experiment to ask a question that they have about the world? Would they

suddenly ask questions and do these experiments? Would they be would

experiments? Would they be would everybody would millions of people want to be scientists? Yeah.

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

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

Yep.

They would say you're [ __ ] insane.

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 all this stuff I feel is on the other side of of getting this AI for science stuff working, but I'm excited about it. Extremely cool vision for the future.

Great not to end. Thank you. Yeah, very

inspiring.

Thanks for having me on.