Ginkgo Bioworks Holdings, Inc. (DNA) Earnings Call Transcript & Summary

Good afternoon. This is Steven Mah, the tools and diagnostics team, introducing our next company in the synthetic biology space. It's my pleasure to welcome back Jason Kelly, CEO and Founder of Ginkgo Bioworks.

Good to be here.

Yes, thanks. So let's keep it interactive. If the audience has any questions, just raise your hand, and we'll get a mic over to you or you can just e-mail me at [email protected].

Yes, I'm actually very happy to do that. It's an intimate setting.

You keep it interactive, yes. And we might not even need a mic.

Don't need a mic.

Just yell it out. So yes, so Jason, for those new to the story, maybe give a quick introduction to Ginkgo, then we'll kind of dig into your business a little bit more.

Yes. So I can give a little bit of color on what we're trying to accomplish. So I think the sort of major like macro shift that Ginkgo is trying to pull off is if you think about the way we develop biotech products today, you do it on-prem, in other words, on-premises at your lab at a place like Merck, one of our customers, or at a place like Bayer Crop Science if you're developing plant traits in agriculture or Novozymes if you're doing enzymes. And each one of these companies would have a large internal lab. It's going to have a big expense associated with the physical facility. They're going to spend a bunch of money on capital equipment, buying from Thermo Fisher and other places, very expensive analytical equipment and so on. And then they're going to have a bunch of scientists who are PhD trained like me, or expensive salaries. And those folks will stand at those lab benches, using that equipment and pipetting liquids by hand. So they'll be essentially operating at a relatively low throughput in an expensive inefficient way to ultimately generate the products that go into the biotechnology industry. And our view at Ginkgo is that the physical lab work associated with all that should be centralized into large automated facilities like what we've been building down the dry dock. So if anyone is interested in coming to see it in the next few days. We're about 10 minutes away from here, 300,000 square foot, highly automated lab where we think that lab work should be centralized. And so again, like to give you an analogy, if you look at, say, the semiconductor industry over the last 25 years, many semiconductor chip designers have gone fabless and they outsource their fabrication of chips to Taiwan semiconductor. And so my argument is roughly that biotech companies of the future should all be labless. They should be outsourcing their lab work to our infrastructure at Ginkgo to get it done much more efficiently at a larger scale. And so the way to do that today as we basically do service contracts, we have now, Steve we went -- Q4 last year, we had about, I think, a little over 90 active cell engineering R&D partnerships and -- Q4, sorry, '22 and Q4 '23 we had 131, right? And we did that without substantially increasing our OpEx at Ginkgo, and that was because are driving efficiency with scale. So as we add more of these programs to our automation, we mentioned that last year, our unit economic of doing the lab work dropped by 40% to 50% over the course of the year. That's like a factory, right? As you do more work in it, it gets lower at cost. That is not what it's like at the labs of a biotech company. There is no reduction in the unit economic of doing the research if you do more of it. And so that's really our advantage over time, and we need to do more work in our centralized facilities, they get cheaper. Our business model is very much like an AWS, driven by service fees for doing the work. And then depending on the customer will take some type of value share like a rev share either royalty or milestone on the success of their product.

Yes. Well since you mentioned AWS, I mean there was a little bit of a hurdle to get people to do it because they don't want to put their data up into the cloud. There's a similar analogy here like, I have proprietary target, proprietary data, what makes you confident I can share with Ginkgo?

100%. Yes. I mean -- and if folks remember from that era like mid, late 2000s, right? Yes, 100%, Steve, right? It was I don't want to put my data on Amazon service in Seattle. I don't trust their uptime. I am my own IT department. They're really good at this. Amazon is not good at this. And very similarly, I think what we have, we do face that same resistance. And the answer is you sell through it and you keep building scale, which makes your platform better and it makes it more and more inevitable that people will outsource it, that's it. When it comes to the data, we can talk about this to also with regard to AI. We see an opportunity to actually aggregate a subset of data from the industry that is noncompetitive. It's not really what's embedded in somebody's drug or it's not a thing that's going to influence their ability to protect their asset. But that in aggregate allows you to have the kind of training data that is useful for AI and other types of machine learning. We also think our centralized system affords us the opportunity to build those data assets.

Okay. Yes. So you're talking about like a code base and what you're getting from working with them...

100%, yes. Yes, we call it our code base. But the real challenge is, if you look across the biopharma industry, every company has some subset of data they've been generating. Very difficult to put that together, both for intellectual property reasons and just for like technical reasons. The controls that one company uses are different than the controls another company uses. So the exact experimental conditions were never recorded. All these reasons, make it hard for us to create the big pool data assets that ultimately have driven success of large neural net AI models and things like human language, right? Everyone had this gigantic human language database to train on. Everyone had big image databases to train on. What do we have in biotech? We have the protein data bank. We have GenBank. That's about it. And so one of the things we're able to do at Ginkgo is starting to accumulate actually much larger genomic, nonhuman genomic assets that are in GenBank today, label training data, things like that, that we can collect across these projects. And we think, hopefully, we can solve that coordination problem across the industry of exchanging data.

Okay. Yes. All right. And yes, maybe let's talk about -- one of the things we track is like a number of validating partners. And obviously, once you sign a validating part like Novo for instance, that probably helps kind of streamline discussions with other...

Yes.

Counterparties as well? Is that true?

Yes. Yes. Well, maybe I'll talk a little bit about what we've done in the last year, which is a big motion into biopharma. I think for this conference, especially that's of interest. So if you look at the history of Ginkgo, we actually started in non-biopharma biotech. So in -- initially in the consumer goods space, so things like flavors and fragrances, like industrial biotechnology. Then we went into things like animal feed, like companies like Cargill or ADM, then we worked in the ag industry, Syngenta, Corteva and Bayer are all customers of ours. And it's really been in the last 2.5 years that we started to substantially grow in biopharma, we went from, I think, $1 million of revenue in 2020 to $44 million last year from biopharma alone. So this is a brand new -- this is an area that is newer for us. It is by far the biggest market for biotechnology research. So you might ask, why didn't we start there? And the answer is we are an outsourcing business, right? Like we are convincing you to go labless for some portion of your research. And if you have a really good infrastructure, that's a harder sell. So it's actually easier for us to sell outsourced services 8 years ago when my infrastructure wasn't that good to a fragrance company whereas 6 months ago, I did a drug discovery deal with Pfizer, that's a deal I could never have gotten 8 years ago, okay? And it's only because we've been growing the infrastructure, which allows us to basically get more data per research dollar substantially year-over-year. Like we said, we doubled our efficiency last year. That -- it's because of that, that we can now move into the biopharma space. But now that we're here, this is what you're going to see by far the most new deals for us. It's more and more in biopharma. And absolutely, the fact that we've done deals with Novo Nordisk, Merck, Pfizer, Organon all in the last year is extraordinarily helpful for us to get new people on the platform.

All right. That's helpful. So we kind of tracked the Synbio deals across the various industries, therapeutics across all since Synbio is about 1/3 of all deals, today. Is that kind of similar for Ginkgo, is it about a 1/3?

Yes, today, it's about 1/3 of our business, but it's going to go through the roof compared to the others in the near years, in the next 2 to 3 years. And that's just because the research budgets of the biopharma companies are just so much bigger than ag and industrial. Yes.

So what's sort of like your best guess over, I don't know, next 3 years or so percentage of your business being biopharma therapeutics related?

I bet it's 80%.

80%?

Yes.

Yes, in the next 3 years?

Yes. Yes, more than you would think. It's just -- and again, I would encourage you to think of like what Ginkgo is really trying to pull off as that shift from on-prem to cloud, like laboratories for this work. Is like a big, big part of it. And by far, the most on-prem spending is in biopharma. So there's just so much more to get after there, right? Does that make sense?

Yes, that makes sense. And I guess your -- a lot of your existing partners as well expand the scope of their relationship with you as well. So that's a way to kind of grow the therapeutics business as well.

One biopharma company -- we'll have a -- like there's research and development, so there's R&D, Forget the development for a second, which is all the clinical trials, Just a research budgets of $2 billion to $5 billion at some of these companies. I mean one company, right? Like my total cell engineering revenue last year was like $140 million-ish, right? So like I have a lot of room to go, like even one company making a substantial bet ongoing labless would be a huge accelerant to my current revenue.

Right. Yes. I guess now it's a fraction of a percent I guess, right now.

Yes. No one's...

Penetration is almost nothing.

That's a joke. Yes.

Okay. Yes. And let's talk about like partner mix. I'm talking about in terms of -- we've been talking about large pharma. What about like emerging biotechnology companies. Obviously, they're focused on cash preservation, capital markets are a little bit weak still. So how do you view as someone like a new program perspective balance taking so companies which are emerging versus like larger players.

So the small company should actually be an easier sale for me, but they are currently harder for macro reasons. So the reason that would be an easier sale is for the same reason, the very first customers on AWS were start-ups. They don't have labs. Like they're just starting their company. So they could instead of going investing all that money in laboratory infrastructure, they could just go cloud native from the beginning, okay? So I'm actually not just making it cheaper for them to do lab work relative to their current infrastructure. I'm saving them the upfront capital cost of the $5 million or $10 million to build it in the first place. So that's actually like a better sale. The trouble is, if market went like this in terms of funding early-stage biotech. So all of the current people like, basically, we're just focused on keeping the lights on and not as many new companies were getting started, period. And so it's -- so that's why you see over the last 1.5 years, actually, the majority of my growth in biopharma has all been with big guys. Now it's a good thing, like they have a huge research budgets for me to tap into, there's nothing wrong with that. But it's actually a harder sale, right, if that makes sense. So I do look forward to a looser venture capital market because I'm hopeful like on the fundamentals, I have a better sale opportunity to a small guy, so I should sell to them.

And do you think that was the primary reason why kind of like the new program adds have kind of slowed -- that is still growing at a decent pace, but maybe not at the what I would have hoped?

Yes. Yes, we were hoping for more program counts. The bigger -- it is just in terms of number of new customers on our platform, as Steve is asking about. The big challenge there has been these early markets we started in like industrial biotechnology and you know this, like a lot of them what thought of a synthetic biology is often just people mistakenly just call it non-pharma biotech. But things like animal-free meats and cosmetics and nutritional ingredients and all these other applications of biotechnology. Again, interest rates go up and venture capital for that space as to absolutely zero, right? Like it's really tough if you're in those spaces right now. So again, I'm hopeful that you see some change there, but they got hit in order of magnitude harder than even the small biopharma -- so I think that is -- that was probably more of a hit than I was hoping for. But that\s what it is.

That's fair enough. In terms of your kind of...

Like really what have killed us is if we hadn't been able to sell into the big guys, right? So like this was something that I was like stressing a lot more about 18 months ago. But the reality is, now you fast forward to 18 months, we were able to actually get over the bar for the big players. Well, then that starts to get less valuable now because I'm inside these places that have $3 billion or $4 billion budgets and I've only so far sold them a $30 million project, right? Like it's all like -- and once you're in, it's a h*** of a lot easier to add, right? And so I think to some degree, even though like I'm hopeful start-ups come back online, I think you'll just see me doing a lot more at the large biopharma in the years.

So expansion of into different areas...

We already are doing. We had a second deal with Merck within 6 months. anyway, some others that we're working on right now. So it's pretty easy to add.

Easier to paper as well from a contract perspective as well.

Okay. Sort of. So yes, so let me tell you another thing I would love to see happen, shift in terms of how I do business. And again, I'd love to have all this work out already, but it's -- to me, it's a new market, okay, right? Like people do not outsource this right? I'm not offering them an animal study or a med chem library for Mucci or something, right? Like I'm trying to basically do high-end genetic engineering of cells like that is our -- that's the corner of biotech that we think should be outsourced, right? So the way that we sell it today is I negotiate a 2- to 3-year research project with the customer. And we are arguing about the ninth quarter exact research plan before we've signed -- as part of trying to get the contract signed. Extremely annoying, okay? And so that is a big friction to actually getting a deal done. Meanwhile, in their internal R&D department does not plan out 9 quarters and have a big argument of exactly what experimental work is going to happen in the ninth quarter. They kind of go based on what they learned last quarter and then choose their next thing. And they're kind of -- they're headed in a direction, but it doesn't have to be planned out to the 9s, all right? So what I would love to do and I'm working at this, is to have more like a subscription model. So like when you sign up for like a cloud service provider, you basically commit to $10 million of Google Cloud spend or thing, all right? You don't know what you're going to use it on. But because you committed to $10 million, you got a certain pricing. If you commit to $30 million, you get a cheaper pricing. And you're just basically trying to budget, how much cloud is my IT team going to use this year, not what they're going to use it for. That's where I would like to do my contracts to on the...

Licensing mechanism...

Subscription, right. Like you're subscribing -- you are basically buying or like a take-or-pay on a service contract, like you are buying -- if you want to buy $10 million, it's this set of prices. If you want to buy $30 million, it's this set of prices, but we don't have to negotiate a technical plan. Your team will order in partnership with us, what you want from the automation to get what you want done in that quarter, and you just have to leave that of your $3 billion research budget, you should allocate $20 million of it to Ginkgo's infrastructure this year. And your team will decide what to do with it, right? And that has a lot of advantages for me, mainly in speed of closing customer.

Right. Right. Yes. And some of your peers as well. I mean Schroder has a similar model...

They do like a SaaS. They're software, right, yes. So I'm trying to do this in the lab space I see that yes, but you've got conceptually this...

All right. All right. Let's -- we're rapidly running out of time. So let's pivot over -- so you've added new technologies with some acquisitions, Circularis, you added the RAC Automation. Can you point out any particular areas where you're getting more therapeutic interest. It was like gene therapy, is it mRNA, vaccines?

Sure. Yes. So like, for example, we did this deal with Pfizer. The part of what went into that was the fact that we had acquired a company that was doing -- had circular RNA technology called Circularis. We acquired a company called StrideBio that had interesting AAV capsids for targeting different organs with gene therapy. We completed the acquisition of Proof, which has pretty interesting like gene editing technology. We acquired it 6 months ago, we just announced it. And so we are -- we will acquire IP in certain modalities that we can put on top of our automation that offers a more complete product to customers in that area. So like that we will keep doing. And so if there's a small biotech, in your portfolio or otherwise that you know that is selling a lead asset, but has a bunch of technology associated with it. That's just going to get shelved when they sell the asset, Ginkgo would buy that technology, okay? So keep us in mind. We have done that as what we did with Stride. Somebody else bought their drug, but we bought the capsids, all right? Because these to us, are they frequently stranded intellectual property and data that actually could benefit the whole industry, but is basically tied to a single drug asset somewhere. So a capsid that does a good job targeting the lung or something is net valuable actually to a whole bunch of potential drugs, but it's sitting on a company that's pursuing one particular drug. And the whole thing lives or dies with that, transacting it is a pain in the a**. It's much better to have that sitting at a platform like Ginkgo, where I could out-license it to everybody in the industry, right? That's like found value. Does that make sense? And so those are the types of things we've been buying. And then on the automation front, yes, absolutely. The Zymergen stuff is killer, really good. We have flexible automation. If you have not seen it, and you want to see it. I'm telling you it's 10 minutes away, you really need to see -- but it's starting to feel like a data center in there. We just have like basically robots connected by magnetic track -- mag lab tracks that are sending plates all over this room like it's really neat.

We did a challenge check with a user actually when it was former Zymergen when they were a stand-alone company before you bought them. And yes, they square by it. So they've really been enabling flexible automation technology for them. Yes. So that's great.

I see floors full of them.

So speaking of that, that's going to be a focus of your Biofab1. And maybe let's talk about the foundry capacity. I mean you're adding program, maybe not as fast as you wanted, but certainly some of the bigger -- yes, certainly pretty good growth, and also maybe some bigger partners as well. Talk about the capacity of the foundries. You guys rightsize CapEx. I mean talk about Biofab a little bit?

Yes. So I mean we're in a pretty good spot, like I would say my overall focus is just adding more customers on the platform. We think we can do that while maintaining OpEx like flat to down. And so -- and that's because we have a team that's investing in making all the automation infrastructure more efficient, right? And so -- and that's been working, right? Like we really are -- like we run a lab like a factory. And so the more work we put on top of it, the more efficient it gets. And if we just look at the utilization rate of our various equipment, we think we could handle it like 2 to 3x more just on the equipment, okay? That's before you factor in like other efficiency gains. So we think we're in a square spot. We do have Biofab1 opening in '25, it's like a new building. I think that opens up a lot of opportunities. Again, like back to this idea of like subscriptions, with partners and things like that. We would love to dedicate portions of that to certain customers, right, and things like that. You can imagine having a floor it's really like robotic infrastructure that's on demand for your team and isn't used by anybody else. You're always top of queue. There's all kinds of interesting things we can think there. So we're pretty excited about it. But we have some time. So it's still a bit of way before we would be like really moving it.

Would there be an option, and I only mentioned this because some other companies I cover actually have -- when they have partnership, they actually allow their science...

I love that.

To come into their production facilities, help troubleshoot and kind of get products over the goal line. Is that something Biofab1 would...

Would open up the door for? Yes, 100%. Yes. Well, let me say this. The -- another big sales challenge for me is today, it's Ginkgo's scientists using Ginkgo's automation infrastructure to try to deliver whatever, a better-performing RNA construct or a better CAR-T therapy or whatever that particular challenge is for biogen, AAV manufacturing. Like it's kind of like outsourced to us to solve a technical problem for you and get it back some year later or something. And you're getting the check in along the way, but we're really doing the work. Yes. And you're a -- you're going to like that, right? Like you're like a mid-level R&D leader at a company like hey, that's my job. I thought. Okay, I don't want to outsource that to you. And so there's another world where -- and again, not to nerd out like we're like tech industry adjacent. So I will give you a little bit of less -- like the tech industry. If you go to the AWS website, you Google, AWS Microservices, you will see a page that explains to software developers that the old way of developing software was monolithic and it has a whole bunch of consequences about why that's bad to have one whole big system, one big program that runs your whole website. The new way to do it is by combining lots of these little Microservices that all run in the cloud. And it's not so much about AWS. There's actually other cloud service providers, too. But you as a software developer, the implication is you ought to learn how to program with micro services because that's the future of scalable computing. And if you're still program monolithically, you're going to be a dinosaur. You still have a job if you go down the microservices road, but you got to learn how to program in that environment. That's the message I would actually rather have. It's like the era of working with your hands at the lab bench is going to end. You're going to order micro services from centralized laboratory infrastructure, you still need your genius of experimental design, biological know-how, but you're going to have to learn how to do that in this environment if you want to be like scaled up. And it's especially true because that's the beauty and this is really helping us sell. The story of Ginkgo has always been, it's going to be beneficial to generate more data for your problem, right? The criticism has always been, Ginkgo, if you weren't so stupid, you wouldn't need to do so many experiments. I'm a smart scientist, I know just to do the right one experiment. It just so happens I can do it in my hands, but I always do the right one. And you only have to do a lot because you don't know what the right one is. And the great thing about what's going on with AI, generative AI is it's showing the value of large aggregate data assets. And so it is a new way to have that conversation, it has been very enabling for us, okay? And so I think if you're a scientist, the answer is you're not going to be replaced by some place like Ginkgo. This is how you're going to do your science in the future, and you should learn it. And the sooner you learn it, the more valuable you're going to be if this transition ends up happening. Does that make sense? So I'd love to get there, and so we're working on that, okay? And remember, I mentioned that subscription thing earlier where you'd have all those -- that's much more in that vein because your team is deciding where to take it next. If we instead negotiate a 2.5-year project, that's my team doing it, okay? Because that's the only reason we had to have that whole conversation. Does that make sense?

Yes. No, that makes sense. They have a little bit more control of their own project.

Correct. And then it's there -- that also solves a lot of my problems, too, right? Because then it's their choices that they're making, right? So if it's a bad choice. It wasn't my bad choice. It was their bad choice. But at least they got a lot -- they got to try it more cheaply because of my infrastructure, right? So there's a...

But how is it going to work? I mean, how are they going to like see the data coming off? Are they going to physically be there...

No, just log in. You don't need to be there.

This is the beauty of the cloud.

Yes, it's on the computer, right? Yes, you order the experiments you want the data comes back to you, right? Like that's why not, right? And that's how my internal teams work. So when you're the scientists doing like whatever Merck project, Pfizer project, it's actually a relatively small team, logging into their computers like Ginkgo, ordering tons of work from our automated infrastructure, getting the data back, planning the next round of experience, right? Like -- and it's not as clean as just click, click, click, but you see the path, right? And so that's where I would like to ultimately get to that my customer scientists can just use our infrastructure.

Okay. Got it. Got it. And is that related to this recent deal you signed with Google Cloud where you kind of reserved a bunch of compute capacity -- yes, that would be helpful.

Yes, sure. So let me give 2 cents on the AI thing in biology. So like without nerding out on AI too hard, you've got let's take a human language generative AI model like ChatGPT, right? It is a neural net that was trained on human language to learn to speak English and be able to receive English prompts and answer with English word answers, okay, right? And so how did we train the neural net? Well, we took what is a neural net? It's a node, a node, and a node connected by lines, right? It's meant to model like -- this is like for the neuroscientists in the room, somewhat embarrassing. But it's been to be like a human brain, okay, right? So you have a node, a node, connected to another neuron. And if this one fires and this one fires, they send a little message down the lines connected to the third, and each line has a weight on it, it has a number. Let's say it's 0.2 and 0.4, they send their little signals down, you add them together, it's 0.6. If it's above 0.5, this node fires. And it's connected to 3 more nodes beneath that, and you have -- this was the magic of what Sam did at OpenAI, you put 1 billion of these nodes into 1 big neural net. And then you train it on English sentences. What does that mean? You give it a sentence with 10 letters in it -- or 10 words in it, you leave out the eighth word. And it hits the top of the neural net, and the neural net goes, send signals down at the bottom, it predicts the missing word. If it's right, you say, good job, neural net, and you let it be. If it's wrong, you change the weights and you do it again and you see if it got closer. And then you leave out the second word, then you leave out the fifth word. And then you do it for 1 billion sentences and you do it enough times and this thing, which at no point was the neural-net designed to know anything about English, learns English Grammar. It learns Shakespearian poetry. It learns everything it has seen because you are giving it enough sentences and asking it to predict the missing pieces, all right? But nothing in there was designed knowing English. The reason I'm explaining this is if you look at a gene, it is read end to end. It's made up of letters in a line. It's like a paragraph. You can leave parts out, you can feed it into a neural net. You can ask it to predict what's missing. And you can do that billions of times if you have billions of genes, in fact Ginkgo has billions of genes. We have the largest nonhuman genomic collection in the world. Okay. So you leave all that stuff out and you train these models so that they can learn to speak DNA, all right? And then the other missing piece is you would like to know what that gene does. So why did we get alpha fold, first? That was the first, like computational design AI model that actually worked because we had the protein data bank, which coupled the structure of a protein to the gene that encoded it. So you had the DNA model, speaking DNA, and you had labeled data that said this is what this DNA does in domain of protein structure. You put those together and suddenly, you could predict coding structures that I've ever seen before, all right? Same exact idea. You want to have a huge genome collection and then you want to have as much label data as you can to train it against designing a capsid, getting a certain expression level for promotor in a particular organ, like whatever you want, if you accumulate a labeled data, I think, you're going to be able to have generative AI models that are able to design against that particular challenge. And so one of the things I'm excited about and why we did the Google deal was, a, so we have the compute to train where we needed to. But the thing -- and again, for people in the room that are into this stuff, we are good at generating that label data at Ginkgo. And there's a company in the AI space called Scale AI that not everybody has heard of. But ChatGPT, when they train their model after they did all the human sentences, they also paid a bunch of people to put sentences in and say if it was a good or bad -- did it do a good job on the answer. And they actually paid a company called Scale AI to generate all that data. Scale AI also takes images from the front of a car or Tesla and so, hey, that's a dog, that a cone, that's a bike. They do that labeling of the data so that Tesla can train their models. Scale AI's business is just like data for hire. I'm happy to do that for people. So in this world of AI model building, we just announced our technology network also last week, which 25 companies that are kind of adjacent to Ginkgo, a number of them are AI companies. I would love people to be able to focus on building great AI models and just pay me to generate a bunch of data for them. God bless. And we can even figure out ways to give them access to these data assets I have in-house, also great. But the reason we did the Google deal was just to make sure we were building our own muscles on building these models and understanding how to speak the language, people that might ultimately be able to utilize our infrastructure.

But some of the foundational models you're going to be building, how are you going to monetize that? Is that...

Well, in the near years, it would just be part of our cell engineering services. So we would include that as one of the ways we would do a better job designing your RNA, designing your CAR-T, so whatever we would do for a customer. In the long run, same as anyone else what you can charge for inference, right, like you would do that? Or again, other people do that and I backstop them with data generation. I'm open to it, right? Like Ginkgo really -- I think we're at the point now, Steve, we're like -- my cost of data generation is so much better than anybody else's that I just want as many people flowing through it as possible. And I'm more than happy to have a bunch of other companies make money doing that, if I could figure out a clean interface, okay? And that's a little bit the exploration we're doing with the tech network.

Yes. No, fair enough. Yes, data is king. Got it.

I don't want to keep it to myself. I want to like be able to generate it for others. Does that make sense?

Yes. That makes sense, yes. We're out of time. I did want to give the audience a chance to ask a question if they want to do, definitely feel free. Sorry, the time just flew.

I was excited.

A lot of stuff going on. Yes. We didn't even get to biosecurity, unfortunately, but there will be another time. All right.

I appreciate it, and I'll be around if anyone wants to chat. Thanks a lot.

All right. Appreciate it. Thanks.