200. That's One Cell of a Machine: Jason Kelly of Gingko Automates Labs to Accelerate Discovery

Jason [00:00:00] it would be like saying the only use of a computer is, I don't know, computational drug discovery or something, right? Like we obviously understand that it's a machine that could do a million other things, too. A cell is a machine that could do a million other things.

Erum Hey, Carl. How's it going?

Karl Iram, it's going great. Do you know this is our 200th episode?

Erum Yes, of course, because we've done a lot of planning.

Karl And we're gonna throw a big party.

Erum Yes, we are. Wow, I can't believe 200 episodes went by just like that.

Karl Yeah. It's kind of crazy, especially when you go back and look at all the people we've talked to and this network we've created and the topics we've covered.

Karl so many interesting people. We're so blessed to have them, to talk to them and to hear them talk about their technology and how they're using biology to make the world a better place.

Erum Yeah, absolutely. And we've learned so much. What a great education by having this podcast and talking to experts and [00:01:00] people in the, world building and learning and creating, making some mistakes, been making some lessons that we can all learn so others don't make those mistakes, and it's just a great community.

Erum We've built such a great community here, a wonderful ecosystem around this concept of being able to use biology to transform every industry. And I love our little tagline. We say, "Let's grow." And we've grown. We've grown.

Karl We have grown. And I think, like you said, how do you transform every industry?

Karl And I think one of the things I've been saying is that biotech is the, remediation layer on top of all industry or the re-imagination layer on top of all industry. it's still not very mainstream at all. last week we talked about going to Bio-Innovations in Omaha. You did not go to Houston.

Karl I went to Houston after Omaha. and, that was a much smaller event, but it was focused on the investor side of the world, and there was a lot of people there who are very interested in investing in these biomanufacturing [00:02:00] projects because there's so much potential.

Karl So I found that to be very exciting, and I love the people that I was able to talk to there. Shout out to Veronica Breckenridge and the team at Bio-Innovations for organizing that event. I think it went off really well. I heard from everybody that they really enjoyed it, and I really, had a good time leading a panel on business model innovation to commercialize industrial biotech, which is kind of full circle for me because the first thing I ever wrote

Karl about synthetic biology was about business model innovation because there's a lot of room for that really across all industries, but in biotech in particular, it's a very exciting thing to think about how do you change business models.

Erum So what's one key takeaway from that?

Erum What is a business model innovation that is being used now or that you're excited about?

Karl this is something you and I have talked about here on the podcast where people are creating these ecosystems before they go out and raise money. innovator talking to end customer, talking to potential, funder of the infrastructure or having the [00:03:00] infrastructure partner, the person who's gonna have the steel in the ground.

Karl That is an innovation instead of trying to do everything yourself, innovate, build a huge facility, and then go out and sell. Having all those things in place earlier, I think makes things much easier, and that's one model that I think is very exciting. What I'm looking forward to is seeing more subscription models, kind of like what, you see on Amazon.

Karl You save some money if you subscribe or with a lot of the software that we use. But I want to see some biotech subscription models.

Erum Yeah, I think it's gonna be difficult because it's a different type of innovation, right? A software's in a computer and you're constantly using it I actually personally do not like subscription models for physical products.

Erum 'cause anytime you go on a store and oh, subscribe to this toothpaste. I'm like, I don't wanna subscribe to your toothpaste. I don't know if I like it. I actually am not a loyalist to any toothpaste brand, so, if anyone is a toothpaste innovator, call me 'cause I might use it.

Erum for temporarily though. So-

Karl And I'll give you a review of it too.

Erum [00:04:00] Yeah. Yeah, 'cause I actually was writing about this somewhere where I was like, we gotta get away from subscription models, but I get the recurring reve- no part of it, and maybe it's licensing, annual licensing, or ... And it depends on what it is.

Erum Is it direct to consumer? Is it B2B? But I guess it is a subscription if it's an annual subscription licensing model.

Karl Yeah, I started recently going through making a list of all the things that we're subscribed to, not just on a personal basis, but a business basis, and the list is long. I'm actually surprised, when you sit down and you actually go through it.

Karl I know there's those stupid commercials for like some, financial software that, they do the thing, "How many things do you subscribe to?" And most people don't even know. but look, I agree with you. I kinda hate subscription models, but in some cases they do make sense. Not for everything.

Erum Yes, exactly. Exactly. And then there's, a lot of continued tech innovation, and we can't say tech without saying AI. Sorry, this is probably the 10,000th time you [00:05:00] heard that term today. AI, AI, more and more and more.

Karl Um- Exactly. you flagged some news and I had some AI news, so let's go through it and then we'll get into today's interview.

Erum Yeah. Did you hear about the brain computers in Singapore?

Karl No, I have not heard,

Erum Yeah, so, our friend Steven Ruddy shared a link with me, and then I just started hearing about it a lot. our, our friend Jesse Adler also shared this news with me. I think a lot of people are sharing news because we have this platform to speak about it.

Erum But in Singapore, what they have done is they've taken blood cells, reprogrammed them into stem cells, and then made them into neural cells to do computation. So, in Singapore, they made about 20 computers to compute using neurons, using cells. Some people thought it was to generate electricity. No, it is not.

Erum It's actually computation. And so I am very curious about this. I didn't dig so deep into like- Who's behind it, who invested in these companies is [00:06:00] something we need to look at. But hey, this is biology being used in new ways. it's influencing an industry that you didn't even think could be influenced by biology, but there is a lot of biology being worked on when it comes to data centers.

Erum What do you think about this, Carl?

Karl okay, so I'll bring it back to Neuromancer because I, I did reread the novel, and I know that, there's been some buzz because the Apple series is gonna come out in January. If you pay close attention to the trailer, there is a picture of a circuit with a cell growing on it I'm not sure that that's what that neural interface is gonna look like when it comes to computation, especially something like these brain centers.

Karl But I find this to be very exciting because we know that, Humans and biology in general consume a lot less electricity than a data center does. So really rethinking what a data center looks like using biology, I think is super exciting.

Erum But also, on the flip side, I'm very concerned because what [00:07:00] if these neural cells, achieve consciousness, right?

Karl Liberate themselves.

Erum Brain in a vat. Yeah. But it's not limited to a vat. A lot of our stories today talk about AI and embodiment, so we'll get back to that.

Karl let me do, a couple of news stories that are not so great about data centers, which will also be more reason that we should be looking at them with biology.

Karl So this news came out in March, but I didn't hear it until last week when I was traveling. apparently in a Wyoming data center, they found that the water that was being released also contained a very rare bacterium called Cupriavidus gilardi.

Karl and the contractors found this when they were flushing out a new Meta AI data center in Cheyenne. Apparently, the water in the city was not contaminated, but th-that particular contamination forced water treatment plants in the city to shut down for two weeks.

Karl And, it took a couple months to clean this thing up. [00:08:00] So, why is this weird bacteria showing up in the water that's coming out of a data center? It's just really, very bizarre. And then also kind of on the negative side of the data center, just recently, a New Jersey data center accidentally dumped 5,000 gallons of diesel into the Meadowlands, the New Jersey Meadowlands, which is a kind of a very interesting swampy area, just on the other side of the Hudson River.

Karl And so again, it's another one of these like, well, what are you doing with these data centers? There's been this big pushback against them, and I don't think we've talked about how like people from different sides of the political spectrum are coming together to fight against these things.

Karl And I just wonder, how do we leverage the biology story to actually turn the story around?

Erum Yes. Yeah. We've been hearing a lot of those stories. we'll go deeper into it. I'm sure we can do a whole episode about it. We know people that are creating what you were, mentioning earlier, this idea of a consortia bringing everyone together [00:09:00] in a room to talk about how do we, regulate ourselves if our governments aren't really doing anything, how do we work together?

Erum And, having a company police itself is very challenging, so how does, how do local communities and other associations come together and regulate? but there also has to be, fines. Like, there has to be consequences so that these mistakes don't happen. but then of course what we'd like to talk about is the innovation that can come online to mitigate any risk and perhaps also be a waste to value story, right?

Erum So you have this waste coming out of these data centers. How can that be used to make something else? Because we believe that, waste is a manmade thing, but we can use biology to transform waste into value. So there's gonna be more to be said there and, we wanna talk more about solutions.

Erum And yes, there are horrible things, and we do need to highlight them in order to talk about what those [00:10:00] solutions should be. So more to come on that side

Karl Yeah, and I think this is worth saying, we're not bashing AI. We use it every day in our work. we're fascinated by, like, how it's being applied across biological research.

Karl We know that the big models, the ChatGPT, the Anthropics, they do gate what you're able to do biologically with their models. a few episodes ago, we talked about how Anthropic was recruiting more scientists or making their models more open for scientists. But again, there's gating in place, so it's not like anybody can go in and develop a bioweapon.

Karl And we've also had people on the podcast like Kevin Flyngault of Acklid talking about biosecurity, which is something that we've talked about a fair amount. We can always talk about it more just so you, our audience, know what's going on in the space of biosecurity and how AI impacts that both positively and negatively.

Karl But I think overall what feels to be missing is this kind of, what are the benefits of AI? How's AI gonna help society versus all this doom and gloom? [00:11:00]

Erum So we're not AI bashing, but the whole world is . So, and it is because there are real challenges that have been happening, real threats, obviously the escape.

Erum and one of the things that I'd like to echo what Swisher and Scott Galloway are saying. It's not the AI, it's the people who made the AI that need to be responsible for their AI, right? It's, it's not just this loosey -oosey, self-aware, self-conscious thing that's just going around doing things.

Erum Like the companies that are behind it, which are people behind it, need to be accountable for it. but besides that,there was this article that you flagged by, a writer at Bloomberg that said AI's greatest threat is biological, not robotic.

Erum And so that really is at the interface of what we talk about here, and it's true. s- something that we need to pay attention to, and really think through There needs to be a lot more security protocols and at the very beginnings of [00:12:00] the biological workflow, when it's even inquiring, you know, who's prompting what, to the early stage lab work where you're synthesizing DNA.

Erum as you mentioned, Kevin Flygare of Agly Bio is working on this, and there are others. We've talked to other companies that are looking to put their own AI in place to fight against the nefarious AI, you know? if we're saying we want to use this technology, fine, but we have to have the good AI to combat the bad AI, whichif we are saying that we don't have control over it, we need to build better AI that's,programmed to help human needs and goals, that they're programmed to make sure that we have a great place to live.

Erum We, I'm also wanting to say animals and plants and the environment- All living beings, yeah ... so not just human. You gotta be very specific sometimes with AI, otherwise it's going to achieve a goal [00:13:00] in whatever way, but if a goal is very narrow, it's going to do narrow things that might have consequences.

Erum But anyway,it's in the zeitgeist. We're all talking about it.

Karl Yeah. one thing I just don't understand is why the AI is being programmed don't adhere to Asimov's three laws of robotics. You know, they were like super simple laws from this book from the '60s that became "I, Robot," the book and the movie, and one of the laws is do not harm humans.

Karl So why isn't AI being programmed with those really fundamental laws? I don't understand. so Asimov's law, I can't believe that these AIs are not being programmed, based on Asimov's laws of robotics,

Karl A robot may not harm a human. a robot has to obey a human except for when it might hurt a human, and a robot has to protect its own existence as long as its existence doesn't, hurt humanity. [00:14:00] So those are really, like, the fundamental laws of robotics. If I got them wrong, listeners please let me know.

Karl But the, again, the idea is like, why isn't AI being programmed based on those laws? And maybe it is.

Erum maybe it is, and then it's still not obeying those laws, or maybe it's obeying it but the effects of their work, they just don't know that it is harming.it's not intentional

Erum right? So there might be these knock-on effects that happen and harm happens. which Again, we're saying, okay, well, there needs to be more discussion on guardrails and security, and then how do we use AI to help society? What does the future look like if all the green lights are there and we are a fully functioning autonomous society?

Erum What does that look like and how does it affect us? How does it relate, how does it resonate with us humans to get us excited about it? and what can we do? Like, how can we make our lives different, [00:15:00] and where do we start,

Karl So I think that's a perfect segue to today's guest. we are so excited to have Jason Kelly, the CEO and one of the co-founders of Ginkgo Bioworks on the podcast.

Karl We've been trying to get Jason on from the beginning of this podcast almost four years ago. And just by way of background for our audience who may not know Jason, Jason graduated from MIT with a PhD. He recently served as chair of the US National Security Commission on Emerging Biotech, which we've had on the podcast several members, including Senator Todd Young of Indiana.

Karl And, Jason oversaw the intersection of biological advancements and US Department of War activities. We are huge fans of Jason and the work that Ginkgo has done, and I think with that, let's let Jason take it away

Karl Jason Kelly of Ginkgo Bioworks, we have been wanting to get you on the podcast from the [00:16:00] beginning of the Grow Everything podcast. We are so thrilled to have you here today.

Jason Yeah. Finally made it. I'm so excited.

Karl All right, so let's kick it off with kind of a background, what's going on in the world kind of question.

Karl So, a lot of people still think that biotech is, traditional drug discovery. It's confined to Petri dishes. Ginkgo has, really turned that assumption completely on its head. Can you start us off by painting a picture of what Ginkgo is actually doing today, and how you're building this kind of physical and digital infrastructure to make programming biology as routine as writing software?

Jason the mission at Ginko from the beginning, is to make biology easier to engineer. And the root of that is just a fundamental truth about biology, which is it's the other sort of like coded substrate we have other than computers, right?

Jason So a computer runs on digital code and forms of zeros and ones. That's what's going on down at the bottom. You have all these layers of abstraction on top, and we get the whole world of software and all the variability, the [00:17:00] magic of like installing new code and making that machine do something new. And we only really have one other substrate like that, which is a cell.

Jason And the code in this case is not zeros and ones, it's A, T, Cs, and Gs of DNA, but it is coded. And if you install new code, which is what genetic engineering and biotechnology fundamentally is, starting in 1978 when we moved the first gene, th-this case from, for human insulin into E. coli to, start Genentech, that since then we've been able to install new code into cells.

Jason And the big question is: Are we able to do it well? Like, can we make the cell do what we want? Can we make biology do things that are useful for humans? And that's like my life's work. That's Ginko's whole mission is what's the tooling that makes that easier year over year? And there's different ways to attack that technology problem.

Jason We can talk about them. Ginko has tried a lot, you know, we've spent $2 billion over the last, 10 years, uh, you know, [00:18:00] since doing Y Combinator back in 2014. We've done a lot on laboratory robotics, which we can talk about. We've done a lot on design tools for, genetics, for, designing genomes and pathways.

Jason a lot of lessons learned across those two things. But fundamentally, that's what we've been trying to do. And the thing I try to explain to people, 'cause we spend a lot of time in pharma now, the majority of our customers are these days in the pharmaceutical industry, is that that isn't the only use of a cell, it would be like saying the only use of a computer is, I don't know, computational drug discovery or something, right? Like we obviously understand that it's a machine that could do a million other things, too. A cell is a machine that could do a million other things. And so we have pushed that at Ginko a lot over the years.

Jason We've done projects with, Bayer Crop Science for engineering microbes to produce fertilizer. We did a lot of work in sort of the heyday of animal-free meat with a lot of those startup companies there, cosmetics, nutritional ingredients. We tried a lot of these things. A lot of them have not worked that well, to be honest, commercially, which we can talk about too.

Jason and so a lot of our customers in those sec-sectors don't exist anymore if they're the startup companies or the large [00:19:00] companies. They have programs, but nothing like what you see in pharmaceuticals. So I think we're in this interesting spot where it is still the case that the main app for biotech is drugs But the underlying technology very obviously can do many more things than make drugs.

Jason And that is the thing we have to unlock. And the thing that unlocks that is better tooling. It's as simple as that.

Erum Wow. Yeah, I love it. I mean, just, I would say Ginkgo was very pivotal for us in terms of thinking of how to create this podcast. You guys had a tagline called Grow Everything, and here we are with our podcast called Grow Everything.

Erum And it really helped spark a lot of imagination, in terms of even how you just talked about the possibilities.

Jason you're so close to biology that you don't think of it like a tech. But, like, just imagine that, uh, Tim Cook gets on stage and he's like, "Oh, Apple has a new product. We've invented the seed." Right? you put this thing into the dirt, you cover it up, you add air, water, and sunlight, and [00:20:00] here's what it's gonna do.

Jason it's gonna start pulling carbon atoms out of the air. It's gonna self-assemble, solar panels to collect additional energy to power more carbon capture. it will continue to then do molecular assembly, at a level of atomic resolution like Intel. And, check it out. and we would be like, "This is the most substantial technology breakthrough in the history of mankind," right?

Jason like very obviously, right? Like, what's weird about biology is naturally existing biology is the existence proof that this substrate is capable of absolute magic. Self-repair, self-assembly, creates its own energy. general intelligence. Hello.

Jason like, what the hell are we dealing with here, right? It is magic. but unlike every other engineering field, everything mechanical, chemical, computer science, electrical engineering, humans did not invent biology. So we have this totally bizarre situation that looks nothing like any other engineering field, where you've got absolute [00:21:00] magic and capabilities, but a substrate that you do not understand.

Jason And God bless, we do have the ability to, like, read and write code from this thing, but you're programming a computer you did not design. And so That's it. and it's awesome. Like I, think if you're a young person today, if you like tech, like, biology is the place to go. it is where there's magic, right?

Jason Like, we still have magic there, and it's in the physical world. Which by the way, as, as you guys talk about on this podcast a lot, these are the big markets. like information tech is weird. It kind of collects a lot of, of the world inside of it. But if you really think about what impacts people, it's food, it's medicine, it's building materials, it's fuels.

Jason It's like all the atoms. And the, the cell is the computer of atoms. It is.

Erum Yeah. Yeah. And completely relatable. I mean, we are made of biology, so it's very interesting- We

Jason do ...

Erum to use. It's wild. Right?

Jason Yeah. Yeah, 100%. If you're a longevity bro, this is also the place you need to be hanging out. Yes.

Jason Totally. Yeah. Like, let me tell you, like the AI model [00:22:00] is not the thing that's gonna make you live forever. It's something in the world of biotech, right? we are made of biology. Like, it is figuring this out. That's it. there's no other way.

Erum Yeah, absolutely. Oh my gosh, I just, like, love hearing you speak and your enthusiasm around just not, of course Ginkgo, but just this field in general.

Erum And kind of want to take it back just a little bit, right? you started this company right out of grad school with your classmates and your advisor, Tom Knight. Like, what was it that you did during your PhD years that made you realize that, "I'm going to start a company around this," or, "We're going to start this company"?

Erum Because there's a lot of companies now that are spinning up. It's just like there's so many AI and bio companies, and it is, like, a very clear pathway for a lot of people. But that was not the, necessarily the case when you went through school back in the day. Sorry, I hate to say that.

Erum Yeah.

Karl Back in 2008, right? Back 2008. Oof. Yeah.

Jason I think the thing that, and I'll get back to your question about the, you know, the history there, but we have not gone through a tool revolution like, say, computer science or robotics has over the last 20 years. Like, if you go in [00:23:00] the computer science department grad students at MIT and you look at their tooling compared to when I was in grad school, the power of the computers, the infrastructure, it's completely different.

Jason And like certain areas of, biology, our sequencing is a little cheaper, you know,synthesis is a little cheaper. but most of it's the same. and, and that's a problem, right? like my conclusion now 20 years in is that's the root problem. The root problem is we have not gone hard enough on our tools in biotech.

Jason but what we started Ginko very much with a tools mindset from the beginning. like we were kind of tool nerds coming out of MIT. We were not like therapeutics people at all, right? we had been like, synthetic biology pilled by Tom Knight and Drew Endy, and we started the company with Tom on this idea that maybe you could standardize genetic components and use simple organisms to make biology more predictable.

Jason Okay. That's hard, by the way. That, we did not work as well as I would've liked it to be honest. and also that you could potentially standardize the lab work. That has worked great. so those are [00:24:00] kind of the two things Ginko worked on since founding the company, since particularly the last 10 to 12 years where we had capital, was in those two lanes.

Jason Could you simplify organisms to make them more predictable? And Drew's lab just actually put out this like spud cell, which I think is a great idea. Like, that's like the extreme form of simplification. So there was like that approach to making the organism more simulatable And then the other thing we worked on was could you make the lab work cheaper with scale?

Jason Because if you think of it like a computer scientist, when we compile and debug DNA code, that's a physical process. We actually build the DNA with chemical synthesis, we install it in organisms with cloning, we grow the organisms up in a lab, and then we test them in high-end analytical equipment. It's atoms.

Jason And so if you want that debug cycle, that compiling and debugging of code to be rapid, you gotta automate all that stuff. And so that's been the journey of Ginkgo for the last, 10 years on the lab side has been that. [00:25:00] And then on the other side, we tried a lot on sort of standardizing, and that was tough.

Jason we couldn't simplify the cells enough to make them easy to engineer. That's where we... I've kinda landed on that. And there's a new generation over there with like virtual cells and things like that. If we generate enough data, maybe you don't need to simplify the cell, you can actually manage it in all its complexity.

Jason And so that's sort of the new lane for the people that are trying to, simulate the cell. And in that world, I'm happy to have other people do that. We just wanna make the data for them with the robotics.

Jason 'cause it is still...I'll be honest, it's still hard to start companies like this right out of grad school. it was hard for us in 2008. financial crisis, fine, but we weren't making a drug, which means you make no sense to biotech VCs. And we had a lab, so we made no sense to tech VCs.

Jason oh, a-and we were like kids, which in tech people like, but in biotech they don't.

Jason Yeah.

Jason Right. It's true. I'm now, uh, what, 45? I'm still like a young biotech CEO.

Jason Yeah.18 years, like, like what in what world, right? Like it's so different than tech, right?

Jason That, that said, I'd say the, the two big pieces of advice for like grad students or, um, [00:26:00] deep tech folks tuning in is number one, like don't be afraid of grants. So actually the first like seed money into Ginkgo, the first 3 million or 4 or 5 million bucks we brought in over five years was grants.

Jason ARPA-E, NSF, SBIR, DARPA. Like that was great. It was non-dilutive. You could, you know how to apply for it as a grad student. Everyone's gonna tell you that's lame and slow. Just do it. Okay? And so that's one thing. And then two, once you raise venture capital, if you are anywhere that is not San Francisco, go to San Francisco to raise money And then fly home.

Jason Fly home. Wow. All my labs are in Boston. It's great here. It's like, the labor market scale. There's a lot of great things about being in Boston. The only thing that's bad about being in Boston is fundraising. And, and so just, money travels very easily in airplanes. That's true. Okay, right?

Jason Like, go raise your money in California. Like, it's just a no-brainer. And so we did, we were the first biotech to do Y Combinator in 2014 for that reason. we were sort of like biotech VC rejects, right? Like, like we made no sense on all [00:27:00] axes to our local venture ecosystem, and that's true in Boston.

Jason It's definitely true if you're in Wisconsin or you're in Ohio or you're in, uh, Indianapolis. There's people starting companies all over the place. They should just go to San Francisco to raise startup capital. Don't go to your local VC ecosystem. those are my two pieces of advice. That- that's kinda like what I would say for the first five years of Ginkgo.

Jason That, that's the lesson. Other than that, like don't have a high personal burn rate.

Erum What about New York?

Jason for VC, it's fine, but it's not nearly as good as San Francisco. 'cause it's like, think of it this way. When you take your company public, you do a roadshow, all right?

Jason You, which means you meet a whole bunch of investors in a very short period of time. Do you know where 90% of them are

Erum going public in, oh, in New York.

Jason New York City. Because the industry of New York City is public finance, like on the equity side. That's their industry. and so that's just where they all are.

Jason So you get on a plane and you go to New York City. and there's maybe a few other places that you also meet, but the majority are in New York. The industry of San Francisco is seed capital Okay. There, of course, [00:28:00] there are venture capitalists.

Jason But the industry of San Francisco on the finance side is seed and venture. just like when you go to your IPO roadshow, you go to New York. When you do your seed roadshow, go to San Francisco. It's not complicated, guys. Like,

Jason And by the way, venture, they do not care where you put your company. They're, like, in Tahoe half the time anyway. They don't care. They don't care that it's in San Francisco. if you're doing software there, I think there can be reasons, but if you're doing biotech or other deep tech things, there's no reason to be there.

Jason that's a mis-misunderstanding for a lot of entrepreneurs.

Karl You have made a major strategic shift towards fully autonomous labs and robotics, and this idea of providing lab data as a service for AI models.

Karl AI models, biology to me is like the ultimate data set that all these AI models are looking for. But what's the ultimate economic reality check or operational that drove this pivot for Ginkgo towards this AI-driven biological automation?

Jason Yeah. this is a re-really good question.

Jason And so I'd like to say it's just purely my, entrepreneur intuition, but some of it was beaten [00:29:00] into me by, like, the, business realities of the markets we were in. So what happened to us basically was, we spent 10 years growing on a business model where we were basically doing outsourced R&D for customers and then taking, like, a royalty.

Jason Okay? And that actually worked pretty good, like, in the sense that, like, We had 250 deals like that over 10 years. Yeah, a lot actually. a bunch of with startups, like I was mentioning, the animal-free meat and things like that, but a bunch of them with, like, giant you know, in the chemical industry, worked with Sumitomo, flavors and fragrances, with, Givaudan and IFF.

Jason Like, we worked with, Bayer and, like it was, like, big ag, big chemical And then right before the IPO, we started to just stack pharma companies. We did Biogen, then we did Novo Nordisk, then we did Pfizer for RNA. It was like bang, bang, bang, bang, bang. And, and, and so the issue was, uh, '22 comes along and interest rates go up.

Jason All of the weird biotech companies, so, like, all your, the stuff you guys like, flavors, fragrance, food, all those things, they got wiped, right? Like, SynBioBeta that year was sad, Carl, if you remember. and so, it was tough [00:30:00] because like, the way we do it in the US anyway, is we make our bets on longer-dated stuff when interest rates are low Like we're not China, we don't have like central planning, but we do centrally plan our interest rates relative to inflation.

Jason And so when we were in the ZIRP era, the 0% interest, that's when SpaceX got started and Tesla got started. These like crazy long-dated things could play because money wasn't getting more expensive like it didn't matter what year you got your revenue, you're getting 0% interest on money anyway, and so the people were more patient.

Jason All right? When that went away, all the patience for like non-pharma biotech kind of went away So I instantaneously lost a bunch of customers that like were these startup companies that just were not gonna get another round. All right? And then on the pharma side,

Jason There was just like a post-COVID depression, But like when, when that dropped, a lot of the pharma companies kinda put their arms around their own R&D orgs and, and were like, "Listen, we're gonna cut back on external partnering and sort of protect our people because we're all belt-tightening for the next couple of [00:31:00] years."

Jason And so I lived on external R&D partnerships and that also kind of evaporated. So I had a hard time bringing in additional R&D fees and then, but the real kicker is biotech products take a long time to go to market. And so I was kind of pushing an app store model, which I was hoping would work outside of pharma because things could go faster, right?

Jason Like pharma's brutal, right? Pharma's a seven-year journey, right? like we did a project with like Cronos, that Canadian cannabis company. that went to market. They scaled up and started making stuff, putting it in their gummies for cannabinoids and I got a, something like a $50 million check, right?

Jason like that was awesome. If every deal had looked like that, then the business model would've worked well. It didn't work well enough when all those fast things went away and then I was just left with slow pharma. And so the R&D, I think, loop, it's real tough. Very few companies have been able to pull off that business model.

Jason Like the only one that's even close is Atomab and they mostly did it by like staying low spend and I wasn't low spend. And so I was like, "All right, we're gonna have to adjust." This was like in '22, '23. so we did a, a big layoff. Laid off [00:32:00] half the company, cut costs massively and I basically shifted my business model to...

Jason We still have those types of research partnerships. I still work with Novo, I still work with Bayer, a few others, but increasingly the business model is basically Thermo Fisher. So life science tools, we sell our robotic hardware and we sell what you called lab data as a service, Carl, but like our CRO services.

Jason Like we will just generate data for you, no royalty, no nothing, pay by the data point. We actually call it data points, is our service. And so that's how it works. and so that's been great actually. So like from a business model standpoint, I think that's a good fit for Ginkgo today. I think it might have been harder 10 years ago when we were a smaller company to do that business model, but fine.

Jason then we can talk about the technology side, which is what I'm most excited about because as you mentioned, we went to these kinda bet on lights out total autonomous labs and that has been awesome. but the business, like if you wanna talk the business side of things, if you think of Ginkgo today, like if you talked to us five years ago, some company listening, it would've been about royalties and research partnerships [00:33:00] and now it's like think of us like Thermo.

Jason If you like what we're doing in AI, like our project with OpenAI or, or what we're doing in robotics, you can have that installed into your lab just like Thermo would install it for you, And that, that's the business model today.

Erum Oh, yeah.I have a follow-up then. how autonomous is autonomous, you know?

Jason So this is, this is I think, getting very hot right now, because in Silicon Valley, general purpose robotics is getting hot, so like robotic hands and-

Erum And androids, full on androids in your home.

Karl I think it's 20,000 bucks for the first one. It's, like, not crazy. For what is

Jason it?

Karl The kind of first, robot maid can do your laundry and that kind of stuff.

Jason Yeah, remember "The Jetsons"? Yeah, 100%. Yeah, that's coming, definitely. so okay. I'll give you my analogy for, like, what is, when I say an autonomous lab, what am I talking about.

Jason I'll draw an analogy to the transportation industry. And I think it's important 'cause it's not like we haven't had robots in labs for a long time. We actually have, and so I need to put it in context. All right? So transportation industry, imagine the y-axis is the amount of [00:34:00] automation in the transportation technology, and the x-axis is, like, the level of variability a customer can ask from the technology.

Jason All right? So low variability, high automation in transportation, that's the subway. You sit down. We have had automated transportation for a, a hundred years, Carl, here. We have. You sit there, you do nothing, and you are transported away on the subway. Okay? Right? but you better not expect it to go to your house, okay?

Jason It only goes to these five stops in front of you Not variable. Low amount of automation, high amount of variability, we have that too. It's called the car, and we put our hands on the wheel and our foot on the pedals and we drive it right into our driveway or to the grocery store or the restaurant or wherever we wanna go.

Jason It's totally variable, but it's no longer automated. There's a human in the loop handling all the variability. Does that make sense? That, and that's the transportation system for the last 100 years. Have you been in a [00:35:00] Waymo?

Karl I have not. I've seen them driving around.

Erum I've been in other types, yeah.

Jason Okay. Good.

Jason Yeah, yeah. Th- that's... Okay. So, so it's amazing experience. You should definitely try a Waymo. it is the automation of a subway. In other words, you sit in the backseat like you were sitting in a subway car, but it goes right to your house, okay? So you're getting the variability of the car, but the automation of the subway, and that's so surprising.

Jason You'll notice we don't call it an automated car because that's like an automated door or something. You're like, "It must just go do the same thing over and over." Automation just repeats. Automation doesn't do variability, right? And so the, we call it an autonomous car. We give it a new name. And by the way, it's actually kind of a misnomer, right?

Jason Like, because you think autonomous, it's thinking for you. It... By the way, you are telling it where to go. It's not like the Waymo decides where you're gonna go today. You tell it where to go. all it's, we've done is we have managed the variability of driving so that you no longer need a driver for this transportation technology.

Jason Does that [00:36:00] make sense?

Erum Yeah. And it might, like, decide how to get to your house. Yeah. Like, it might take the long route, the short route.

Jason or not, though. Like, you could control that, too. But like, sure, it could, right? Okay. Now here, here's the other thing, though. miles traveled, cars and trucks versus subways.

Jason Any guess in the United States what the split is?

Karl I'm sure it's, it's probably like 5% and 95%.

Jason 99% cars and trucks. 99. 99 in human miles traveled. not because we don't know about trains, okay? But because we need variability. Like, we need it. Okay. All right. So now let us go to the lab, all right? Same graph, low variability, high amount of automation.

Jason We actually have subways in the lab. We call them automation work cells. And if you've ever seen, like, if you're at a pharma company, somewhere in a basement somewhere guarded by a bunch of automation people is an arm, and it's moving plates through your high throughput screening system to hit a bunch of chemicals against cancer cells and see if you can pull a needle out [00:37:00] of a haystack to discover a new drug.

Jason All right? or if you've been to a diagnostic facility, you might have seen automated systems doing clinical diagnostics over and over again, same test. It is great. It is fully automated. It can run overnight, no people, no mistakes, okay, that the people make at the bench. But you gotta wanna do the same experiment that you did yesterday.

Jason It is totally inflexible Low amount of automation, high amount of variability. Over here, the car, we have that. It's called the lab bench and the manual lab. And it is PhD scientists making up whatever experiment they want to do that day, grabbing reagents from the fridge, interacting with 50 to 100 pieces of benchtop lab equipment in a big room with 20 of their friends, and all kinda doing the work of discovering drugs or finding a new trait for corn or whatever it might be that they're doing, or academic work, training like I would've done at MIT.

Jason like that is our [00:38:00] car. And here's the kicker. If you ask, and I've done this, we've had a bunch of head of R&Ds from pharma companies come by and visit Nebula, our big system here in Boston. I'm like, "Hey, what's your split spending on your work cells versus your, lab benches?" And it's like 97% lab benches.

Jason It-- And just look around it. There's millions of square feet for every major biopharma and biotech company. They have two to four million square feet of lab space filled to the brim with benchtop equipment, filled to the brim with lab technicians, all regulated space and all running, get this, at best 40 hours a week Okay?

Jason We have 168 hours in a week. I don't know if you've done the math, right? So, so like you're literally idling this insane infrastructure. I mean, like pharma companies spend, depending on the which one we're talking about, one to three billion a year on not clinical trials, on those labs. And, and NIH spends 40 a year at all the, at the, the universities on the [00:39:00] same thing.

Jason And maybe they go... I, I used to work on some on the weekends, like grad students work, maybe work a little bit more, but like definitely not working overnight.

Erum Yeah. No, I would-- I did pharma R&D. I was at J&J and GSK at a huge laboratory, and there was like five of us. We had pretty much had like a whole building and there was like this huge building and it was like 20 humans in there.

Erum And there was so much space. There was very big laboratory equipment like the LCMS and things like that took up some space. But yeah, it's just like... I'm not gonna work overnight, Okay. What are you talking about? You got it. Yeah. Nine to five.

Jason Okay. So, so what we're-- When I say autonomous lab, I'm not trying to replace the work cells, the subways at all.

Jason I think they're fine. Go buy them, Thermo sells them, HiRes sells them, Bio cero, these like automation companies. Go buy them from whoever you're buying from. I'm trying to replace the lab benches, okay? I believe that is your actual big spend and, and really where most of your drugs come from.

Jason But to get after that, [00:40:00] I got to kind of do what Lamu did. I got to solve the variability. And that's a super hard problem. Like that's the core of my technical problem. But again, I got to it honestly, because at Ginkgo we had been trying every combination of like semi-automated laboratories.

Jason Like our people plus li- walk-up liquid handling, plus our software on top of the walk-up liquid handling, all kinds of elaborate crap. And I'm telling you, I tried it all. It does not work well enough u-unless you get everyone out. You have to have lights out because the handoffs are too frictional.

Jason And so three years ago we were like, "All right, we gotta go lights out." And we had acquired Zymergen, a Bay Area company who had their own robotics hardware. We made a new generation of that robotics hardware, and we've been working on the software nonstop, and we have all of our people able to use it here.

Jason They're like test pilots. And so the rate that we've been improving that place is pretty crazy. like, if you come to see it now, it is, it's wild. Like, we have 105 robots, 160 [00:41:00] devices, one big integrated system. It runs 24/7. Scientists submit random lab work to it every morning. It's not like we know ahead of time what they're gonna submit.

Jason it can't do every protocol. We definitely run into all kinds of issues still. Like, it's not like we're done here, but like, it's gonna work. and that's wild. , And that's back to my point about like bringing my son to see that lab bench. Like, we gotta close all those benches. and so we just announced today that we're building autonomous labs like the one I have here in Boston for MIT, Caltech, Northwestern, and University of Maryland, through this new NSF White House Genesis Mission program.

Jason so like I, I'm pumped about it. I think this is like the way forward, but boy, have we tried like a million and, one things that can go to make biology easier to engineer. It's still hard. It's still hard to engineer. so I like this is my bet over here, and then I think the, the guys making the big AI models, like the virtual cell stuff is the other good bet right now, and those are the two new things being tried.

Erum Wow. Yeah. That's amazing. just quick question. So what is your car then? Are they robotic arms or are they actual pieces of equipment? And I have seen [00:42:00] videos, so I'm just doing this for our listeners,

Jason Yeah. The, the ba- the basic way to think about it is how do you think about like what a scientist actually does in a manual lab?

Jason Like, what are they really doing? Now, they're doing a lot with their brain. They have a lot of knowhow about what experiments to do to ask a certain scientific question. Really sophisticated, high touch stuff. It's easy to design a bad experiment, okay, as we all know, and then that almost lies to you with the results.

Jason So, so being a good scientist is a lot about what's in your head. But what are they doing with their hands? Number one, they are doing manual liquid handling with pipettes. Number two, they are moving samples, okay, a tube with liquid in it, between different bench top devices. They're putting in that into the device.

Jason And then number three, they're, like, setting the settings on the device.

Jason Those are the things that they're doing, I think, fundamentally. And so we basically have a, a reconfigurable automation cart. It is a cart that you put a lab [00:43:00] device in, one of those benchtop devices that would've been in, your lab,

Jason and when it's in that box, few things are, are true. We have control over the environment, sowe have, a HEPA filter, positive pressure, like, a lot of, things you're doing in biology labs have contamination, so we got that under control. Number two, there's an arm in there. Each device has an arm that can pick up a plate of samples and put it onto the device.

Jason And then number three, we have a track. It's like a maglev track that is part of the cart and can deliver a sample right to that arm. So a sample pulls up like a taxi, the arm takes the sample off, puts it on the device, and then for every one of those lab devices, we've done the work of writing drivers, connecting it to our electronic box so that, like, our software can control it.

Jason So then you can set the settings and run whatever experiment you want, and when it's done, the arm picks up the plate, puts it onto the puck, the little taxi, and it goes to the next device that you would've walked across the lab to next. And that's it. That's, like, what the hardware does. And then the [00:44:00] software does a lot of magic.

Jason it needs to let you say what you want, not by programming the robots because hey, I, don't know, I went to MIT, I did bioengineering. I'm not, like, a great programmer. Like, that wasn't my job. My job was doing lab work and thinking about science. And so I don't want to program to do an experiment.

Jason That's not what I do today. So you need to have the ability to write in human language the experiment you want and have that turn into code on the robot. So that's a thing we've had to build. and w-we obviously benefit from the coding agents, but there's a lot of smarts that we have to put in to have it be good at that.

Jason And then we need to schedule everybody's stuff. You share that lab with 25 other scientists, and you all kind of work around each other. We have to be able to do that too. It can't be that just one person's thing is running on the system. We need to support, in our case, we've done as many as 40 scientists submitting jobs into this one big robot setup in a day.

Jason Okay? So that's cool, right? Like, those are new capabilities. No one's done that in, lab automation before. that's what makes our thing, like, a waymo.

Karl Yeah. I have a kind of a follow-up question to that. I mean, [00:45:00] to me, I've always thought, and you can disagree with me, that scientists, PhD-level scientists moving liquid around, it's like

Karl very expensive manual labor. you're designing an experiment and then you're spending your time moving liquids around and looking for a result. When you move into this autonomous lab world, how does that change? Like, what is a scientist gonna be doing?

Karl To me, it's gonna require a lot more creativity on their part because they're gonna be getting a lot more results, but they have to be much more thoughtful in terms of like what those experiments are gonna be.

Jason Yeah. This is a really good question. So, so I think, what people will feel first is mostly like, kind of speed.

Jason Like, you can run experiments overnight. You can... Okay, right. So sort of things like that. But I don't think that's actually the long-term impact. The long-term impact is lurking behind our rate of progress in biotech today, is the, scarcity mindset of experimental lab work In other words, [00:46:00] your idea has to be good enough to justify the expense and effort of doing the lab experiment And that's not, for example, how writing software works.

Jason Like, yeah, you gotta think about even before the AI models, you had to think about maybe your personal, time writing it, but you didn't have this, like, huge expense to try. Like, you could write some software and th-those debug cycles were cheap, and it was in a coffee shop, and it wasn't this crazy spend There used to be in the mainframe era, but there wasn't in modern computing.

Jason We're, like, in the mainframe era or even the pre-mainframe era. there's, like, an amazing,IBM ad from 1952 that I love, which is, like, the IBM mechanical calculator. it does the work of 150 engineers, and then I swear to God, the picture is 150 guys, they're all guys, of course, holding slide rules.

Jason it, it's like we are going to automate the manual computation that you do today. Okay. [00:47:00] and you might say, like, first impulse might be like, "Boy, we're, all these engineers are gonna lose their slide rule jobs." Okay? and but, like, of course not, because it turned out the limiter on the market size for computation was the cost of doing the computation, not, like, how smart the people were or the ideas they had.

Jason There's actually plenty more ideas. Everything was getting, like, funneled through this, like, crazy expense of manual computation. And that's how I see lab work today. Once you fully automate it, you're gonna be blown out of your mind, like how cheap it is. Reagents are not that expensive. All the cost in those labs, Aram, that you were in are-were the, the people's time, the lab...

Jason Like you just mentioned it, the labs are not dense at all. They're made for humans, so it's huge facilities, cost expense, regulatory expense. It's just woof. Like all-- And then it produces like this much experiments a year, like a little teeny amount. and you're like, "Wow, experiments are expensive."

Jason But now go look at how much you actually were in the [00:48:00] reagents of the tubes you did. It's nothing. Experiments aren't expensive. They have like a huge idiot index if you like know the Elon term, right? Like getting that data is not the cost of the raw materials.

Jason It's some other insane thing, and it's basically the labs and the manual labor. That's it. And so, once we go down this road of total automation, we'll get experiments priced at the cost of reagents, and then scientists can go wild. Wild. Like, right? And, we don't even know what that's like.

Jason We have a whole almost like training infrastructure. This is why I'm excited to be building these at the schools, where we kinda teach grad students the art of being both an excellent experimentalist, 'cause wow, the worst thing to do is blow your experiment, like move the wrong liquids.

Jason And the art of designing experiments in a world of experimental scarcity. And all that's gotta go away. Like, You would have a totally different attitude in a world of experimental abundance, and where like what you're talking about, Carlos, the rate that you can design experiments and parse results and like all [00:49:00] that kind of stuff.

Jason It's a whole new world. but anyway, I do think it, yeah, it's gotta change massively.

Karl It's amazing. Yeah. It's just amazing to think of not just what it'll do for scientists, but for non-scientists to be able to say, "You know what? I've got this idea. maybe it's an, experiment I can run."

Jason And if the price of lab automation brings lab skills and lab,results down to like the level of compute, then, people won't think about it. They'll just be like, "I'm gonna, spend 15 minutes designing and doing an experiment on whether I can, grow this houseplant more effectively," or something, 100%. Or something about their personal health. And, this is a thing that sounds totally crazy today, right? But I don't think it's that crazy, right? like 'cause people will be like, "Oh, like the average person, like they're gonna be a scientist.

Jason Come on." but like let's just get off our science high horse for a minute. like what actually is science? my opinion, it's formalized human curiosity. Right? Like, we are a curious species. That's like, basically the most unique thing about humans.

Jason then, you know, the dolphins are smart, but they're like not curious enough. Like, we're [00:50:00] curious and we came up with a bunch of really good rules for how to like ask and answer questions carefully, right? And now the models, they understand how to take a question you have and turn it into like a decently well-designed experiment.

Jason We actually did a project with OpenAI where like GPT-5 got 30,000 experiments a week on our robot lab, and we like beat scientific state-of-the-art in cell-free protein synthesis by 40%. It's a cool project. But so like the, the model you could say like, "Hey, I have this question. Design me an experiment." And then the autonomous lab could run the experiment.

Jason You're doing science. Like, like, like I don't know what to tell you. That's what science is, right? And so like, would we really not have a lot of people just wanna do science? And everyone's like, "No, no." Again, I think people would still be like, "That's crazy. They won't be interested." But let me tell you what would've sounded absolutely insane in like 1965.

Jason That tons of people, kids even, would be computer programmers. That would've sounded completely insane. Like, do you know what it took to be a computer programmer in 1965? Like, you had to be- A [00:51:00] slide rule ... a literal genius. Like, like you had to understand the hardware, and you had to be like a logician to understand how to design programs, and you had to be willing to like deal with punch cards and wires and the things breaking all the time.

Jason Like, the confluence of those people it l- is like nil. All right? and you were like, "Oh yeah, but no, then someday a 12-year-old's gonna do that." And you would've been laughed out of the room. and so like we shouldn't underestimate what could happen if we could render this stuff scalable and with the AI models accessible to people.

Jason I do believe there's like a fundamental human drive to ask questions, and this is the technology to ask questions. That's what science is. so yeah, I'm psyched about it. I think it'll be a journey, right? Like we're very much in the era where I'd be pretty happy if the pharma companies would be willing to ask questions using autonomous labs, right?

Jason But then eventually as the costs come down, I do think you'll see more and more just everyday people doing it.

Karl Yeah. Just one comment on that. So Hiram heard me talking about this. I went to see "Spider-Man" last night and, there is like this fundamental curiosity, [00:52:00] like, part of the storyline is the spider's taking over Spider-Man.

Karl Peter Parker has to deal with like his changing hormones. But like, There's not enough of that kind of science in the media. But I think that as people see that these kinds of things are available and I think it's up to the creative people in the world to kind of like help people understand what this could look like.

Karl But I think having the tools be as cheap as you're proposing they will be, will also catalyze that.

Erum Yeah. And it's like the idea of like democratizing biology and making it more accessible, just like computation has been. I mean, my son's five and he like designed a video game.

Erum I mean, the UX was very simple, so it was an app and everything. It was drag and drop or slide and he was able to do it and it's amazing. also recently there was this report from the Office of Science and Technology Policy. I'm sure you read that, "The Science and New Era," and it talked about the individual scientist rather than, the institutionalized science, infrastructure and how that could be supported.

Erum you've been working with the government in [00:53:00] different ways. You served as, as the chair of the US National Security Commission on Emerging Biotechnology. We actually had Todd Young, who's a current chair, he was on a few episodes ago. and so you've had this amazing perspective. I would love to know from you, from a defense and national security perspective, what is the biggest thing policymakers in DC still fundamentally misunderstand about biological manufacturing?

Jason Yeah. Well, I'll maybe like telescope to what they don't understand about like science and biotech and then down to manufacturing. Like, I'll give you my like geopolitics of it all, 'cause the point of that commission was basically like, how do we keep up with China? that's the gist, right? So there was a similar commission on AI seven years ago that Eric Schmidt was the chair of, and then five years before that there was like a, one on cybersecurity.

Jason and the idea was like, how does the US, lead in, areas of emerging technology that have national security importance, both for defense uses, but also like economically, right? Like the US [00:54:00] economy right now is sort of living on AI, right? Like we're glad that we got there first 'cause that gives us economic power.

Jason and so this was like, emerging biotech, this new stuff is like gonna be part of that story, as is like I'm sure the law commission on quantum. It's like the, there's a short list of stuff. AI, well, we weren't sure it was, it turned out it was, right? but there are these ones that are like right on the frontier and it's like we gotta watch them.

Jason And how do we make sure, I think this is the most important question, that like the frontier lives in the United States? And I think in the US two things are blind spots for us. One, we've done so well with computers that we have a great sense of the virtual world and the world of bits and how that plays, and we don't have nearly as good of a sense of the world of atoms.

Jason And we felt this first with chip manufacturing because it's the part of the world of atoms that's like most adjacent to the world of bits. And we're like, "Oh, shoot." Like TSMC, the T stands for Taiwan, not Texas. Okay, right? Like, like we messed it up, [00:55:00] okay, right?

Jason like there's that blind spot. And then I think the second one that we have a blind spot on is it's been a long time since we had a near peer competitor in science and technology that was like an adversary.

Jason In other words, was not another like liberal capitalist democracy, potential adversary. All right? And the last time we had it was USSR. All right? And I didn't live through this, like the USSR fell when I was a kid, right? and so like I grew up in the '90s, right? Like it was amazing. There was no competitor to the US.

Jason You could basically offshore anything you wanted and you would kind of by default you'd be offshoring something like low tech. Because there wasn't a place... Or you were offshoring to like a, ally, right? But you couldn't inadvertently offshore a advanced technology to a competing system because there wasn't one that was good enough.

Jason China was still like, Not quite an, like an undeveloped country, but they were like, they had a long way to go in [00:56:00] 1985 or something to be able to do something like advanced chip manufacturing, drones, biotech, all the things that they can do today. All right? And so we kinda lost the muscle over the last 35 years of thinking that we have to choose what we offshore.

Jason That it's not just like an automatic, the world is flat, neoliberalism, hey, it'll be fine, offshore everything and don't think twice. That attitude like sort of sunset, I think with the, President Trump's first election. and so since then it's been like a little bit of a reset and everyone has to say like, "Hey, is this a technology we wanna offshore or not?"

Jason And I think one of the things that has gotten on everybody's radar now is when it comes to science, it's like what we've been talking about here, the work of science is-- Not every field of science, not math, okay? Not computer science. But in many of our fields of science, the way we move those fields forward is empirical lab work, which is [00:57:00] manual.

Jason It's hands. It's PhD hands today. And so we have, number one, started to offshore more of those industries to China because they now have a large number of low-cost, technically trained scientific hands. And that to me is, not the best idea. Like we should not hollow out our scientific infrastructure in the US industrially.

Jason I mean, obviously the, for the most part our government funds our research institutes, but then where do those people go get jobs? They go to the pharma industry. And where has the pharma industry historically done their lab work? Well, Cambridge, South San Francisco, Research Triangle, Houston.

Jason Like there's a few places where we start these companies. Now, increasingly all those startups are moving to China. we've gone from effectively 0% of acquired new drug assets and less than $2 billion worth back in, five years ago coming from China to last year, or $130 billion and 48%.

Jason Like, crazy shift to moving the startup ecosystem to China. And so that's, the big wake-up call. So there's a lot of [00:58:00] activity on this right now. there's a new thing called, INSA, which basically is to add biotech to something called the COINS Act, which is the outbound investment restriction.

Jason So you can't today as a US VC, uh, or US tech company go fund, AI, quantum, drones in China. You can go fund biotech in China And so, the BINSA Act is to add biotech to that list andDoesn't even say you have to necessarily block it, but it should be, like, checked before we just, like, ship all of our capital to China and basically move the ecosystem over there.

Jason And so that I think is, like, how you stem the bleeding. And then alongside that, we need to be more competitive, and obviously I'm talking my book, but I think we need to automate labs, uh, because I just don't see in the long run how we compete with if you keep it hands and you just have more people and you have just as many research institutes, but it's all peer now.

Jason Like that, that was 20 years ago thinking that you would just get all that for free in the US. Like we have to be economically [00:59:00] equal, which I think for a lot of aspects of the world of atoms is gonna mean automation in the US.

Jason And so that I think is coming not just to science, but basically like it's like Travis Kalanick's new company, Atoms. it's like this thing of like we're going to automate atoms in the US is a must-do if we don't wanna have strategic dependency on China, period

Jason let's go back to engineering life or organisms. This is something that's very challenging. it's a stochastic process. engineering living organisms is notoriously non-deterministic, and even, like, a high throughput foundry can hit unexpected roadblocks because biology evolves.

Karl Could you share with us, a spectacular engineering failure or some saltpetre mess that you guys had in your early days that forced a change in how you design experiments and maybe led to where you guys are today?

Jason look, yeah, here's what I would say. there are sort of two technical approaches you can take to solve the problem of engineering cells

Jason [01:00:00] and I, draw the analogy to, like, different other engineering fields. All right? So in our traditional atoms-based engineering fields like mechanical engineering and electrical engineering, like chips, uh, aerospace, like making planes or automobiles, the way we solve the problem of design is through simulation and design rules We don't build a million Boeing airplanes and then fly and see which one flies the best, right?

Jason we simulate when it's a high cost to, prototype Now let's go over to software. Software has a very low cost to prototype. So we don't actually have like really like a big CAD we just get to work, like get writing the software. you build the plane as it's flying and you instantiate it, and you have all these tools like real-time debuggers and things to linters and all these little things like correcting your mistakes as you go.

Jason But the idea is like it's so cheap to prototype that what we have is a fast and cheap,compile debug cycle in software. [01:01:00] And those are both totally fine approaches to, design very complex things. They both work. the question is: where should we put the design and engineering of cells on that continuum?

Jason So will we have a, CAD simulator of a cell? We have one, by the way, now for proteins. If you tell me the shape of protein you want, God bless Google and AlphaFold 3 and David Baker and the whole shebang, right? I can basically write the DNA that makes the protein the shape you want. Now, don't ask me to design you a protein that does a certain thing, Carl.

Jason That, that's a different ballgame. But, but you want to have it have a three-dimensional shape in space, that I could definitely do. Okay? Once you want it to do a thing, that thing better be an antibody binding something. I'm getting pretty good at that with CAD. But it better not be like, I don't know, uh, an enzyme that does some random chemical reaction that no one's ever shown.

Jason I don't know how to design you a protein that does... but I'm just trying to [01:02:00] point out that even at the level of one protein in a tube, we have certain design problems that we cannot simulate. And now we shall go to a whole cell Hundred of thousands, millions of proteins bumping into each other in a bag of liquid, right?

Jason like the, the simulability is tough. Okay? Now that doesn't mean simulators can't help, but you probably shouldn't expect it to look like airplanes. Okay? Like perfect simulation. so that's that half of the equation. Now let's go over to the computer science half. Let's compile debug, baby.

Jason Okay. Well, we pay by the, bit for DNA synthesis. How many As and Ts would you like in your piece of software? 'Cause, you've got to pay for them. Okay, right? So, so you've got this like the expense of the compile and debug makes the like rapid prototype world also a bear.

Jason All right? Now, and there's ways you get around that. mutagenesis is cheaper than synthesis, but less controlled. there is a whole world of interesting engineering theory questions on where you should put the cell between those two bookends.

Jason And no [01:03:00] one knows. No one knows the answer to that question, okay? We all have opinions and we're trying different things and getting to the breakthrough where we start to get, I think the key thing is, like reliably better every year at it.

Jason Like our Moore's law is just like getting better at it in a sort of systematic way where you see a long trail ahead of you. That's the breakthrough. The day you do that, the biotech industry is bigger than the tech industry. That very day. It's just inevitable because you'll know you could eventually engineer a cell to do basically what you want.

Jason It's just a question of what decade it'll be possible in, just like with computers. Like you knew someday, I mean, back in 1950 they wrote like basically papers that described the internet and stuff. Like they knew you'd be able to do it, but what decade would it come? We know we'll replace any organ.

Jason We know we, we'll have self-repairing building materials. We know all these things are totally going to happen and it'll [01:04:00] just be a question of what decade. And, but my God, the market compared to information, Like, the, the market is atoms. Like the, like all the big things are the infinite markets, human health, life and expansion, building.

Jason they're, they're endless. So at that moment, the day we have the ability to have, predictable improvement in our ability to design biology year over year, it, that, that's it. Then we flip. we go from being like a tech, kind of economy to a biotech economy.

Erum Oh, can't wait for that. Had to write down this wor-word, simulatable.

Erum I love that word. And you said simulability

Jason Yeah, that's right. Yeah, and there's lots of ways to attack that problem. I'm super excited about it. I think a lot of stuff that's going on with like virtual cells is very cool. and it'll just be like a spiky front, right? Like it's a little bit like how AI's like amazing at one thing and then just absolutely terrible at this other thing.

Jason That's a little bit the vibes of what I think simulation's gonna be like in cells. It'll be a oh wow, turns out this one, with the right set of data, we can just like, we can treat it like building an airplane, but boy, once you get over here, it's hopeless. Like it's gonna be a little [01:05:00] bit like that.

Erum Interesting. Interesting. So Jason, clearly you have seen the startup ecosystem. You helped create, some startups th- through your efforts, I think ear- early days, and you're still talking to so many other companies.

Erum And then on the flip side, you were at this top level federal policy, part of things in the United States. I wanna know, what is the single biggest policy or infrastructure bottleneck holding back the United States by economy from competing globally? And you cannot say robotics.

Jason I thinkwe today lead in pharma. Like, we're not behind. Like, we lead. I'm, like, trying to not, like, offshore the auto industry. I'm like, I don't want Cambridge to become Detroit.

Jason I'm like, "Guys, we have this right now. There's for no reason to do it." and, like, by the way, there's a super weird phenomena of the US is the only country that pays a fair price for new drugs, because we have a free market system for our, our drug pricing effectively, and everybody else has communism.

Jason And so, like, we are 70% of the drug profits. So, like, it really makes no sense for us to [01:06:00] offshore any part of the technology stack we care about in drugs, 'cause we have complete chokehold control on that market. So that's the biggest policy blind spot. Like, you've got sort of people treating it like, "Oh, we should just offshore to, like, save a little money," when you're really offshoring, like, the frontier labs of biotech by doing that, when you have zero requirement to do that.

Jason Like, you have all the cards because unlike cars or electronics or software or basically any other industry other than defense, which by the way, the US has 80% of our defense industry's profits 'cause we sell to other France and places. So, like, pharma's like the defense industry. It's basically a domestic industry.

Jason Like, we should all be hanging flags. and so, like, with that level of control, we should just drive the technology all here if we care about biotech. that's the big policy shift I would say. And then you do that, it makes it so unfair because that's, really where the app market is for biotech tools.

Jason Like, Thermo's business, 80% of it goes to [01:07:00] pharma. They're a biotech tools company, but really they're, like, a pharma biotech tools company because that's where the money is today. So whoever controls pharma drug discovery essentially controls the tool stack for biotech. And, my opinion is if you control the tool stack for biotech, someday somebody figures out that thing I said, where should we be on the bookends?

Jason we get the breakthrough, and then that's IBM. Like, that's who gets to control that whole stack. Because we got IBM in the '60s is why we have the whole tech industry today. Do not offshore IBM in the '60s. That's what we are starting to do with biotech right now. That is the single biggest mistake.

Jason If we don't do that, whenever biotech comes, when its day comes, and it will, okay, it will be in the United States. But if we offshore it, we're just hoping kinda that the day never comes.

Jason So add it to coins. So like the immediate thing that we need to do is add biotech to coins. that will shift the flow, and the flow will be back to the United States, [01:08:00] not to China as, as we have had a major shift in flow over the last five years.

Karl so speaking about the future, just looking 10 years ahead, what do you think needs to happen for, biofabricated products to become the default standard for everyday consumer materials rather than what they are now, which is a high-cost good?

Jason Yeah. So now let us go outside of pharma, to the world of all the other sad biotech. so which I say that with love. I want to build things out of biology. Uh, I, again, as you mentioned, we like launched a bunch of startups. We did a, like a lot of work over the last 10 years, like trying to get other areas outside of pharma to be new app domains, right?

Jason Because it's a bummer, again, think about computer technology if the only market for computers was drug discovery. it's not like drug discovery doesn't make money, okay? It's a decent sized market. That's not like a nothing market for computers. But it creates a bunch of weird pathologies because of the nature of how drugs work.

Jason Like for example, you don't know [01:09:00] if this drug is gonna work for a long time, so it's very hard to know, is this technology making a big difference or not? And so it's, it's tough to be like a breakthrough technology if your argument is, I impact drug success, if it takes that long to show proof in the pudding.

Jason so all that stuff means I would love, love, love, love, love to see some of these other areas take off. one thing I, would love to see, a-again, back to my argument, like could we make some area predictable? So I think all this protein stuff's amazing.

Jason Like I could not just draw a shape of a, very small like angstrom-y sized object in three-dimensional space and then get that printed by the world's best 3D printer, a cell making a protein. Like, I did not have that power 20 years ago. I didn't have that power five years ago.

Jason What can we do with that? Like, is there a thing we can do with that? 'Cause that one is CADable. Just the shape.

Jason Like, is there anything we can do with making proteins shapes? I don't know. Like things in advanced manufacturing, putting small things around chips. I don't know. find me something, right? because that [01:10:00] we have CAD. We have CAD. So, that's one I'm excited about. I'm excited about all this consumer health stuff because I think, Lilly with the GLP-1 drug has shown like what a market looks like when you sell a consumer product instead of a disease treatment.

Jason And it's insane. Like Lilly is equal to the next 10 pharma companies now or something in market cap. because it's a consumer product. Everybody's a consumer and not that many people are sick, Right? Like, and so it's just, you don't have to charge so much. So a lot of this dynamic of drugs are so expensive is really drugs serve a very small number of people.

Jason That's why they're expensive. It's not more complicated than that. like if they served more people, they would cost less. But disease is a niche I have people in my family that have long-term disease, but like most people spend most of their life not in a state of disease, and they spend all of their life wishing they were like 10 pounds lighter.

Jason And, I'm [01:11:00] serious. That's what a consumer product is. Yeah. and like there's a a zillion of those to make. and so I think those are interesting. Maybe they could be faster to market. But that regulatory, if you unlock that, that would be bigger than pharma.

Jason in fact, it almost already is bigger than pharma. Like look what the kind of companies Lilly's buying now. They're buying a psychedelic company, and they're going into all the consumer adjacent spaces because they're like, "Oh my God, I found a money printer." it was not treating diabetes.

Erum Yeah. That's what I was always saying like, pharma, there's kind of that, you know, you treat disease, don't cure it, because if you cure a disease, it reduces a TAM, right? You want to keep that recurring revenue.

Jason People say that, yeah. I mean, I think, pharma companies still would, but like the, it, the bigger issue is really just that TAM is small if you multiply a small number by the number of customers.

Jason It's really that there's just a small number of customers. that is the thing I think people don't understand about drugs, is just it's not that many people. You come up with a new cancer drug, not that many people have cancer, [01:12:00] right? There's, there's eight billion people on the planet. How many of them have that X type of cancer?

Jason It's small. so like everything has to be expensive. That's why it's expensive. Sorry, I didn't really answer your manufacturing question. uh, what I would say is I think there's like fundamental cost to growing like microbes in a tank.

Jason And I don't know that we've seen a technological breakthrough that has really changed the economics there. And so that really keeps you in a certain band, of like feasible materials. Like, my loose argument is go more complicated. like what biology is like where it's obscene is like the atomic complexity of like an ear of corn At the cost to manufacture the ear of corn.

Jason Like, there, there is no other manufacturing technology that is within four orders of magnitude of that complexity and cost point. That's crazy. So what is up there? Find me stuff up there, right? Like, don't knock off low complexity things. Find me stuff up there, 'cause

Jason there's no other way to make things [01:13:00] like that. The only thing that's even close is semiconductor lithography, and like look at what we spend on one big building to make one thing. Trying to get, yeah.

Karl Like, it's- Yeah.

Jason What are we talking about? Like, that's the on- Like, that's the closest we've got.

Jason Like, it, like it's pathetic in comparison to the capabilities of biology at, at, atomic scale manufacturing of complexity. But once you try to make something that you can basically crack Like, take out of a barrel of oil easily, you're Like, that's not a market.

Erum All right, Jason. We're at our quick fire round of questions.

Erum we, just love talking to you, and this is the 200th episode, so we're giving ourselves a bit of breathing room here.

Jason Love it. Congrats on 200, by the way. Oh, thank you. Yeah. It, it's a big deal. I mean, like, people just try to do these things, but you know, you guys I mean, I listen to your stuff all the time.

Erum Thank you. Oh, very. Thank

Karl you.

Erum Okay, here we go.One everyday item in your house that will be grown in a fermentation tank by 2030.

Jason oh, a bunch of the art, for sure.

Karl The art Wow, that's an amazing answer. Okay, what's the single most overrated trend , in the syn bio startup space [01:14:00] today?

Jason None. Have you seen any other space that isn't syn bio? Like are you kidding me? This is like a classic science nerd attitude of things. go hang out in the tech startup ecosystem and feel what real over-hype is. Yeah. Like we are never--even at the peak of synthetic biology, we were not over-hyped comparatively.

Jason scientists have a very culturally, and by the way, I'm, I come out of this culture. I did PhD for five years. I understand these people. But like the, the very much the culture is if your science was good enough, you wouldn't need to talk about it, that, like that. So we have a super weird vibe on that stuff that's just totally wrong. It's very self, defeating, it's stupid.

Karl I think it goes back to your comment earlier about, you're a very young biopharma CEO, Most of them are like in their 60s or older.

Jason Yeah. Well, it's a little different. It's like, look, the world of atoms is hard. And so if you just march into the world of atoms with like the cocksure attitude of the software people, those, you get buzzsawed, right? That's true. So that's kinda why [01:15:00] the, there's like a natural antibody to like, over-hyping or something.

Jason On the other hand, like if you don't talk about the potential of your stuff, capital is the most undifferentiated asset in the world, okay? like money is money. And so if you're a capital allocator, you can move that, you can put that money in the entire economy Why should you do synbio?

Jason like we have to talk about the potential, right? Like otherwise, forget it. And so the best people are the ones who are good at talking about the potential, but then also can deliver on the atoms. Like by the way, this is why Elon makes so many scientists and hardcore engineers crazy, is because they're like, "Why does he have to be so full of shit?"

Jason And then the reason he makes a bunch of, like other folks crazy is that the rockets land. and that's-- He is the best hard tech entrepreneur in probably the history of the world, and it's because he is so good at both of those things

Jason he is a a great storyteller that pulls capital in the direction of his stories and pulls belief in [01:16:00] the direction of his stories.

Jason And then very importantly, the rockets land.

Jason And you're like, "Oh God, oh God, self-driving was five years late." Who cares? like he was right.And so like, That's what the best looks like.

Erum All right. Go three more really quick. Okay. So if nature had a startup pitch deck, which engineered microbe are you investing in?

Jason Oh, that's a good question. my like nerd project in grad school.

Jason there was this thing that, It's like a, a, a moss, okay? But it, you can grow it in, uh, liquid culture, so it grows like individual cells.

Jason But then when you put it on a plate, like a plate you would put E. coli on, instead of getting a colony, you get like a little moss tree growing on the plate. Yeah, it's the best. and it's like naturally competent. Like you can get DNA in there. It's super cool. Jim,in the UK, the old synbio professor, oh my God, I'm just forgetting his last name.

Jason But anyway, he does, his whole lab does it. So cool. So cool. That's my favorite. It's not really a microbe. It's kind of like a, a plant that can grow as a single cell and then turn into a little moss pile, and it's amazing, and you can [01:17:00] definitely engineer it. It's the coolest.

Karl Very cool. True or false: Writing code for an AI model is easier than convincing a microbe to express a protein.

Jason way easier than getting a protein expressed in a cell today. We need autonomous labs to fix that, but if you're gonna do it yourself, then it sucks.

Jason I spent a whole summer, by the way, my freshman year at MIT, in a lab trying to express one protein in E. coli, and I failed. and that was like, that almost broke me of the whole thing, 'cause like I came into MIT being like, "Jurassic Park, baby, all the way. We're going to the moon."

Jason And I was like, "Oh my God, I can't do the cloning operation invented in 1978," like the one basic molecular biology operation. Like my hands are that bad at the bench. Like it was brutal.

Erum Oh my gosh,

Erum Okay, here we go. Final question. What biological superpower do you wish your automated robotic foundries had right now?

Jason Ooh, that's a good one. I really wanna go like and show you can do [01:18:00] the MIT intro biology lab that I did when I was a freshman all on an autonomous lab.

Jason that's sort of what we teach kids to say, "Hey, you interested in lab work?" And like we could do all that in, on the autonomous lab.

Erum Turning your lab at MIT into the autonomous lab so we can get an upgrade and more people can...

Jason Yeah. And like we're building these labs for MIT, so it's the kind of stuff that'll happen there, but like I'm really excited to see that.

Jason Like, I wanna see kids, getting a taste of being able to have designed an organism without having to pick up a pipette and stand at a bench. I think like we've just never really given anybody that tool yet, and I think that is gonna be eye-opening.

Karl Amazing. Jason Kelly, this has been an amazing podcast episode.

Karl Thank you so much for taking the time today to talk to us.

Jason Yeah, Carl, Iram, thank you so much. Yeah, it was great to be on.

Karl Hiromi, what did you think of that interview with Jason? Well, I'm so happy we finally had him on. He is quite the character. Same here. Yes. Yeah, quite the character.And really it's that the core bet is biology becomes easier to engineer when labs become autonomous.

Erum And the biology we talk about is biology that can make better products, it can make us healthier and happier. and this idea of automation is a very interesting one. I mean, I've worked in labs, and he'd mentioned it too, we talked about it on the [01:20:00] interview. And- it is so helpful because biological experiments can be complex and monotonous, a lot of high throughput.

Erum But, but when you start using autonomous, equipment and engineering, it helps it become more flexible for different types of experiments because when you realize one part of your protocol might not be, the proper one, you might need to keep changing experiments, and that you need to be able to run it continuously without manual handoffs, which can cause variation in data if you're not, doing the lab work in the precise way, and so you're not getting the, right type of data that you need to really get to the discovery that you're looking for.

Erum what did you think about it, Carl?

Karl Yeah, I mean, just based on what you just said, I mean, again, I also said this during the interview, that kind of automation, and I think, this has been part of the discussion, that kind of automation, really forces scientists to be much [01:21:00] more creative in terms of what they're thinking about with their experiments.

Karl they will no longer be these highly paid,people who are moving water from one place to another. You're designing experiments, you're looking at results, and That's a lot harder work than, moving liquid around, from one place to another.

Karl But the other thing was just kind of seeing and having known Ginkgo really from almost the beginning and knowing Jason, I think I met him like back in 2014 at a bioindustrial conference in Philadelphia. just kind of seeing the way that Ginkgo has shifted has been very impressive, going from outsource R&D, to a tools model, to now this robotic hardware and data points as a service.

Karl So all of that I am very impressed by.

Erum Yeah, absolutely. And I love the altruistic democratizing, making science more accessible. I remember on LinkedIn, I saw Jason or someone at Ginkgo post like, "Experiments starting at $39.99." And I was like -

Karl [01:22:00] Right. ...

Erum uh, like $39 and 99 cents. And I was like, this is cool, like to be able to market like that, to make science, not just for scientists that have gone to school for many, many years, but for people that can test their ideas,

Erum we're all using AI to learn, uh, how to make things, and it can take you to a direction of using, biological tools but then where do you go? How do you get access to a laboratory that can do those experiments? And like, I don't wanna be naive and be like, "Yay, rah, rah, rah, biology and robots and AI."

Erum It's like, am I listening to myself? Yes, I am. I am listening to myself. And I think one of the things I wish we were speaking with Jason, was talk more about the security and guardrails and, um, what does that look like so people understand, that this isn't just You know, moving forward and accelerating brute force like the, the narratives we hear about these, [01:23:00] AI foundation models, but that there has been a lot of thought.

Erum You know, Jason has been working with the US government. He was a chair of the National Security Commission for Emerging Biotechnology. So he has been very thoughtful about this. He and his team and committees, I'm sure, are on top of him when he's creating this lab automation. So there's probably a lot there, and I, think we need to have Jason back on to talk about that nuance so people can say like, "Look, biology isn't just a threat.

Erum Biology is an opportunity." And this is what we talk about here on the podcast. So, I'm glad we have another reason to get Jason back on sooner than later.

Karl Yeah, exactly. Yeah, and I mean, I think like, again, going back to this idea of the autonomous labs, these models of cells that, are maybe created in AI.

Karl we know how complicated biology is. a cell is so complicated that we still don't understand how many processes are going on. It's like a crazy supercomputer contained in this microscopic, [01:24:00] shell. It's so small and there's so many things going on there. And, both the AI cell models and the autonomous labs complement each other.

Karl One is, generating data, and the other one is helping the model and designing biology. And I think those things are really important, and they're really gonna move biology forward a lot faster. And so a lot of things that we talk about in terms of the benefits of biology, particularly on the building side and the physical world side of it, are gonna happen a lot faster because of models and products like the ones that, Jason and the team at, Ginkgo Bioworks are working on.

Erum Yeah, absolutely. And, we're gonna get to take a closer look at these automated labs. I mean, I have enrolled in the How to Biomanufacture Almost Anything class by MIT, Harvard, and Wellesley. I just started. I don't know if I'm gonna complete it. I don't know. I mean, I'm busy. You're gonna complete it, Iram.

Erum Okay. Thank you. But, but a part of that class is to first think of an idea that you want to biomanufacture, [01:25:00] how to use cloud labs. So MIT's pioneering this cloud lab model, which I'll talk more about as I learn more about it. there's gonna be a field trip to Ginkgo Bioworks to see how their lab automation works.

Erum there's been, discussion of what it entails, so there's different types of lab equipment or modules, as, um, Jason pointed out. So, there's a centrifuge module and a PCR module, and I'm really curious to see what other types of equipment come online. Like, where's the LCMS module, so liquid chromatography, to be able to look at volatile or like the, the molecules that make up fragrance and taste, to be able to look at that level.

Erum What about, optogenetics and How does light influence biology is very interesting to me, and I think as we start learning more through, quantum computing, biology is a quantum process. what will we learn, and what type of equipment can we make and see how fast it's going?

Erum the class is just [01:26:00] It's so amazing because it's like, use whatever you want, right? Like, use AI to do your homework, just share your prompts, And, like, I started, honing in on what this, project would be, like what I would want to buy and manufacture, and it was like, okay, well, based on your learnings and your readings and, your interest and, what you have access to, you could create this using, this type of microbe which you can find at this place, and it's very, like, specific, and, like, provided clarity on how everything would work, and just felt like seven years of a PhD project boiled down to, like, 10 minutes.

Erum It was, like, insane.

Karl I'm looking forward to having you go through this class. I've listened to some of the lectures and, I want to hear what your project is and the progress that you're making, so that, we can share it with our community, and we can get feedback from them.

Erum Yeah, [01:27:00] absolutely. I am joining virtually, so I am one of 300 people that joined virtually. it's a master class on having a class, first of all, so, and that's shout out to David Kong for running it.

Erum Um, very entertaining, too, when he is teaching his classes. He's also a DJ, which is also makes it fun. and he looks at things not just, technically, scientifically, but also artistically and creatively and, that's something that I really appreciate, that it's not just, let's make, this type of chemical reaction, meh, meh, meh.

Erum It's more like- ... You guys have the creative thinking. bring it, so. Bring

Karl it on.

Erum Bring it on.

Karl that's awesome. I'm really gonna be looking forward to hearing how the class progressed, what your project is, and how you bring that project to fruition. I'm here for it.

Erum If I do it. I don't know. I mean, I might quit, so don't hold me to anything.

Karl Yrem, you can't quit. I'm not gonna let you.

Erum See, that's why you have to have business partners like Carl, everybody. He's mine. You can't have him, but...

Karl Exactly. [01:28:00] So I'm your study partner- Thank you

Karl all right.

Karl With that, let's wrap up this episode. We've got so many exciting episodes coming up, particularly some people working in conservation, a number of science fiction authors which we're very excited about, and some leaders of some large companies. So stay tuned for those interviews. Yrem, do you wanna sign us out?

Erum Yes. Thank you so much for tuning into the Grow Everything podcast. If you have any questions or comments, please reach out to us. Our contact information is in the show notes. and don't forget to subscribe to our Substack. Let's grow.

Karl Let's grow.