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

Hey, everyone. Good morning. I'm Tejas Savant, I cover the life sciences at Morgan Stanley. Before we begin important disclosures, please see the Morgan Stanley research disclosure website at morganstanley.com/disclosures. And if you have any questions, do reach out to your sales rep. So it's my pleasure this morning to host Ginkgo Bioworks and speaking on behalf of the company, we have Jason Kelly, CEO. Thank you so much, Jason, for joining us today. Maybe just...

I am filling in for Mark.

I know.

Opportunistic travel change, so glad to be here.

Yes. Maybe just to kick things off, Jason, could you share with us the key accomplishments that you're really proud of in terms of Ginkgo's journey this year. It's been a tough year, lots of macro headwinds and crosscurrents in the portfolio, which we will get to.

Yes.

But talk to us about what you're most proud of in terms of the company and the organization and then we'll go from there.

Yes. Yes. So I can give just a little bit of general context too. So we went through about a 35% RIF earlier this year as part of a general cost take out for the company. Not an easy time. We're obviously very thankful for the employees that got us to where we are today. But a big part of this was really to focus on getting the costs in the company, particularly for our solutions business. And I know you know this well, but the history of Ginkgo is we have been engineering sales on an R&D partnership basis for companies from Bayer CropScience in the ag space, Novo Nordisk, Pfizer, Merck, on the biopharma space and also industrial biotechnology companies like Givaudan in the fragrance industry. And in all those cases, we call those solutions deals. They're basically R&D partnerships. We get some fees and we get some downstream value share. It could be milestones, it could be royalties, all right? And what we don't do is we don't have our own products. We don't have our own pipelines, so a lot of the history of Ginkgo has been figuring out is there more of a tech platform style business model for advanced technologies in biotech R&D that isn't make a pipeline of drugs. All right. And if you look like a company like Pears, companies like a Recursion or something would have a highly automated AI forward like technology platform. But the way they're commercializing it is they're going to develop their own drug pipeline. And that has been a proven way to commercialize these things. We believe at Ginkgo that you could get them to market with, again, a services style business model and that in the long run that creates the kind of feedback loop that drives something like what we've seen in the tech industry, where you get compounding technological improvements, continued investment and ultimately, a really huge market cap industry-changing companies right? So that's why we do it. So what's happened in the last year. I think with our solutions business, we sort of started to hit a limit of a mix of customers being willing to outsource that sort of thing, and also importantly, outside of our first markets, which was industrial biotech, the time lines to getting to royalties and milestones for biopharma in particular, are so much longer that supporting the whole company on the back of milestones and royalties that were in the future was not really compatible with capital markets and just generally like where we were spending. And so what we're doing in solutions, and I am quite proud of this, is cutting costs down so that we can support that work on the fees we receive from customers as close as we can. And then those milestones and royalties, they will come. But when they come, we'll still be here to get them, all right? So that's really what we're doing on solutions, and that was where a lot of our effort was in cost takeout in the first part of this year.

Got it.

What I'm happy to talk about and actually we can get into this is where are we investing for growth and that's really on the tool side, and it's more traditional services businesses like you would think of a traditional CRO by doing different things. We can talk about an equipment, selling some of our robotics. So happy to get into that, but I would say that cost takeout and that focus on the solutions side no small feat. And you saw last quarter, I was quite happy with where we were on revenue. Even in the midst of all that change, the team really pulled through and continued to deliver for our long-term customers, customers we have a lot of repeat business with like Novo like that, that's been really great to see.

Got it. So let's dig in a little bit, start with cell engineering. I think you've got about 140 current active programs there. Food and ag largest followed by pharma and biotech. And just as a point of clarification, is the revenue composition similar to the program count? Or does it vary significantly?

Yes, it's not too far off. And again, I will belabor this, maybe I'll -- I know I'll probably take us off script a little if you don't mind. The -- so I would say, on the solutions business, we're probably about a 1/4; to a 1/3, pharma; 1/4, GOV. And then the remainder is probably it's ag and industrial, but like a majority ag, okay? So that's kind of like how it break down. So I think one of the things that's cool about the solutions business is we have applied it much wider than anyone else. Like you might look at like a company like a Adimab for example, has very similar business model. Fees, milestones in the back end. But what do they apply it to? They apply it to antibodies that's binding discovery basically. They don't go off and help with plant trait discovery or biologics, microbes in agriculture or animal feed, like all these other markets like. So I think Ginkgo has done a nice job showing that, and these are not easy deals to get. We can convince customers that we have something special in the area of research to develop a new microbe or mammalian cell or a fungal cell against their applications. So it's great. I think the spread is important. But I think I'd love to chat with you about it if you're up for it, is sort of where we're headed on the tool side.

Yes. We will get there.

All right, fine, we'll just go down the list.

I want to talk to you about the narrowing of the focus on the cell engineering side. Now it's going to be pharma and industrial solutions and Ag. Can you just clarify like what percentage of your business is new subset of offerings? And then as you think about how this new structure positions you better versus the all-comers approach you've taken earlier, talk to us a little bit about that because I'm sure it involves a little bit of saying no to sort of paid work.

Yes, this is a good point. So one question is like how do you serve the solutions business with less spend right, like wave a magic wand right? So the answer is, the way we previously would sell solutions, you'd walk into a customer, a Merck or something, and it's sort of like what problems do you have? Like give me like a high technology problem you have in the cell engineering, like it wasn't like chemistry, we're not a chemistry company, but if it was in biotech and cell engineering, what do you got. If it's Pfizer, you know, I mean there's RNA to be more stable, like whatever it is, right? And we would say, "Great, let's take that back." We sit down with our scientists at Ginkgo, look at all of our infrastructure, put together a work plan, bring it back to their scientists at the customer, Oh, I like that work plan. Okay, great. It might be a thing we had not done that exact kind of work before. It was going to use pieces of our infrastructure, but we haven't like really run that whole workflow previously. And so that meant that there was going to be a whole set of effort at Ginkgo to sort of like get that really like mainly to a high throughput. That's usually what we spend the most time doing is like getting it really bulked up and high throughput, before we could start the program in real scale. Getting the customer to pay for all that in addition to the running of the program, that's a tough sell on the fee side. So you do it and say, hey, this is a great area. We think there's 10 more deals behind it. We're happy to have the customer pay for any of it on the fee side, we'll make some money on the royalties and milestones to make unit economics of the deal good, plus it's essentially covering some of what is really tech development for me, does that make sense?

Yes.

That's the kind of deal I wouldn't do anymore.

Got it.

All right? Because that is essentially me putting -- we have a -- I mean, look, one of the things I think we're happy we did is work on the cost take out while we still have a large cash position, you know, we ended the second quarter with $730 million, we have no debt, like the -- that -- we have money for growth capital. It's a question of what you want to deploy it on. And I don't want to deploy it anymore on doing a new solutions thing that I haven't done before because the customer is asking for it. So that does reduce the scope of what I can sell. But because we've done such a wide variety of things previously, I actually have quite a few things I can sell. And so we're still -- we have a more narrow set, but yes, absolutely. It's in industrial. It's in biopharma and ag, but it isn't anything in those areas. It's more consolidated around previous work we have done.

So one follow-up there, Jason. In a sense, the key differentiator for Ginkgo has been that Codebase, right? And it's not always about what works is also about reducing the dimensionality of the space for your customers or your marginal customer by telling them, okay, here's the eight things that we definitely know are not going to work, right? And that was largely a creation of that all-comers approach and then sort of going down those rabbit holes, right? So does that now sort of get a little bit deprioritized, if you will, as you try to navigate the macro?

Yes. I'll give you a couple of comments on that. So the two core assets of Ginkgo have always been our Foundry and our Codebase. So part of what the customer is coming for is also access to essentially high-throughput biotechnology in for like sophisticated lab work, right? Like I want to run this one screen a million times, there's a lot of ways to do that. But once you start to say like, oh, I want to do these 9 antibody developability assays at the scale of thousands, it's complicated, okay? And so that Foundry, I don't want to undersell that. That was part of the reason the customers are coming. The other reason they're coming is, like you said, Codebase. And Codebase is sort of our catch-all term for the know-how, the intellectual property, like learnings we accumulated as we did these cell engineering projects, all right? And importantly, when we would do the solutions deals, we would also retain the reuse rights for all that Codebase. That was extremely frictional, being very clear. And I actually think like we needed a little bit of that because some of the absolutely like most core Codebase we were developing in the course of customer projects, and we knew that was going to be reused a lot. And we wanted to ultimately make it available to other people for exactly the reason you said, like we know where the bodies are buried. We've tried it before. We know that doesn't work, right? Well, it's not repeated for you. Let's just incorporate that into the next project, right. But you, at the same time as a customer are like, well, don't give anything to any of my competitors, is going to help them out, if I pay for it, which I actually think is reasonable. So I think we kind of made a mistake that we pushed it too long, hanging on to it, so the change, I think, is basically permanent -- I mean I am just going to say permanent, but like close enough, is you won't see us demand broad reuse on IP for customer projects. You will see us, if we think a piece of Codebase is very general, we'll just develop it ourselves. So that's the short answer on the Codebase. You will see us now on the tool side, though, find interesting ways to start offering Codebase to customers that previously we would have locked up, okay? So in general, on the tool side, what I'm going to do is kind of democratize and open up this platform, both the Foundry and the Codebase that was previously only available to Ginkgo scientists doing a solutions deal with you. I want your scientists again, wherever right, to just be able to access that themselves, right. And I've got a bunch of ways to do that, including for the Codebase.

Fair enough. So as a follow-up to that, Jason, I mean the customer perception around data sharing, it takes time to change, right? I mean we've seen a few examples in the life science.

I don't think it will ever change in life science.

I meant most of the perception if a vendor has been keeping the data, you pivot to not keeping the data anymore, people still think of that vendor as someone who was hoarding all the data, right? And so where are you on that journey? Because it's one thing to put out a press release saying we're not going to do it anymore, it's another thing where customers sort of wake up to that reality, and then second, on the tools program, just walk us through the strategy behind that decision and why now?

Yes. So yes, to your first question about like perception and then just remind me the second one in a second. So the -- I'll give you the perception, like a key point actually, because we had this experience, we recently announced, and we're kind of like soft launching this, that you can buy the robotics directly from Ginkgo, right? This is some of the technology acquired from Zymergen then over the last 2.5 years we've really tuned it up a lot. We have like new versions of all the hardware and everything, and like we put a post up like on LinkedIn or something, and bunch of comments are just sort of like, wait is this your facility or my facility, you know, right? Like because the impulse is like Ginkgo's stuff has been kept tight, right? And so it's good and bad in the sense that like when -- it is like a thing that we have to explain to people because it's not their expectation. But people are excited about it, too, because it's a little bit like getting to go and really wonk at a chocolate factory, you know, right? It's like, I've seen it, but I don't know what's in there, you know, right? Like -- so in that sense, that actually is creating demand for us too, right? Because people are sort of saying like, "Well, great." I mean, maybe I don't know why you didn't do it sooner, and we probably should have, but I think they're generally happy to have it. So -- and I want to be clear, like Ginkgo is a very mission-driven company, and the mission of the company is to make biology easier to engineer. That's -- for example, why you don't see me having a drug pipeline, right, is because my mission is not to cure cancer. At another company, I think it's important mission, this is not my mission, right? My mission is to make biology easier to engineer, and I'm trying to find the way that I can do that with the biggest impact. And early on, we pretty much had to do solutions because we were the ones who really believed that there was a much better way to do the biotech. If you read Biotech Research, if you adopted automation, if you adopted large quantity data science, like all these things, we believed more than other people. And so if I sign an R&D deal up with you, the good thing was, I got to decide how to do the work, and that meant we could do it our way. Okay. We're -- we did like that 10 years ago. We're like 10 years into a scaled version of that. And I have a lot of proof points now that this is actually a better way to do it, and the world has changed. Like look at all these like AI bio companies that are basically like, hey, we're data limited, we need to generate these large data. I mean that is a story that like that dog didn't hunt 10 years ago in biotech, right? Like people just would not spend that kind of money, they don't really want to generate the data and believe it. And it's still early in that, but it's come along enough that when I put these tools out there, there are people that want them. And that -- like the only people that really wanted them 10 years ago that really believed that there was a totally different way to do this stuff was like us, right? Like we had to be our own customers of the infrastructure, does that make sense?

Yes.

And so I am like overstating that like we have fellow weirdos out there. But like -- but that was what it felt like. And that was a big part of the reason we started with solutions. But it's a total mission set. I love to get everybody using our robotics. I'd love to get everybody actions to our Codebase. That means there's more bioengineers, that means biology is being made easier to engineer, you know, right? Like, I don't have a problem with that, that's great.

Fair enough. Does the program...

Ultimately be able to make a killing.

True, expand the addressable market? And what are your expectations for revenue growth from tools in sort of the years ahead?

Okay. So yes, so you finally let me get there. All right. So let me tell you about the tools. All right. So short form of it is we want to take the platform we've already built. And again, I'll tell you on the solution side, I'm trying to like get costs under control there and make sure we can deliver that at a rate that is like paid for by the fees and then make money in the future on the milestones and royalties, keep signing those deals, it's fine. But like where I want to invest growth capital is I want to say, "Hey, here's our platform, I've already got it built. Let me build the channel to sell it to your scientists directly." I don't need to reinvent a whole crazy new thing. I need to take what was already being used by Ginkgo scientists, but I need to actually make it something you can buy.

Got it.

All right. Does that make sense?

Yes.

And that does take some work, right. That's not for free, okay, and so I'll give you a couple of examples. On the robotics side, we have technology. The big problem in integrated robotics. So let me just explain robotics. No one uses robotics in life sciences, that's the first point, okay? It is thinly used. So you have lowest level of that automation is like walk-up automation, a company like Hamilton has a liquid handling robot, you stand in front of it as a scientist. You put plates on it, you interact with their customer software, you tell to do something, and you come back later and take the plate away. What do you take the plate away to, well maybe take them over to this plate reader that you buy from Thermo Fisher or they go to this mass spec or whatever right? And then the mass spec has a little bit of automation built on it, right like an auto sampler or something, okay, right? And it's sitting there, okay. So with automation that's like...

Sporadic?

Yes, okay, the integration of all those things, of all that equipment in the lab, of course, is you. You the scientist. You walk around with the plates and you bring them to the different equipment and you order the integration. All right. So integrated lab automation, you can get from a company like Highres Bio or the high end of Thermo's automation platform. There's like an arm in between a bunch of equipment, moving the plates around. It's pretty cool, okay, right? So now suddenly, you can run this machine overnight. You can move many more plates, right? It doesn't make mistakes, like that seems good. Trouble is those setups are built custom for whatever specific thing you want to do, so like I have these 6 pieces of equipment, and I want to move plates between them in this order. And I want to do that day and night. And okay, here we go, and I'm going to make you a whole system for it. Here's the problem. Six months later, one of your scientists is like, I just read this paper, there's a much better assay for what we were doing, but I need this new piece of equipment, and we need to change the protocol, and you're like, "oh, my God," I just spent $8 million on that integrated automation setup. It took a year to build it, 6 months to teach everybody to use it and now you want to change it. That is a 6-month project, to add a new piece of equipment, reprogram it, retrain everybody, okay, right? And so as a result, knowing that people barely buy it, it's not future proof to changes, okay? And then you only end up really using it in much more constrained automation environments, where you know you're going to use the same thing for like 5 years, does that make sense?

Yes.

All right. So we suffered this exact same problem at Ginkgo because we were deploying automation. Our scientists were changing their minds on what they wanted. We had our own in-house engineering team and everything else, and we're still having to change things all the time, painful, all right? The folks at Zymergen same problem. The invented technologies proprietary to Ginkgo. We used it's hardware, we OEM manufactured it. We do final assembly in Emeryville and it is a cart with a piece of equipment and a robotic arm and a magnetic rail that allows you to move samples down a little track, like a train track. You have a cart, you put them together and the rails connect, right? And the trick is, if you have those 6 pieces of equipment, I sell you 6 racks with the equipment in them. We put them in your lab, they have the rail, a little loop and you can start doing that original idea your scientists had. But now it's that 6 months later. And they're like, "Hey, I got to change one of the pieces of equipment," sell you another rack with that piece of equipment, you pop it on the rail and a week later, you're running the new protocol. Here's really cool. You have now another protocol you just invented that uses the same 7 pieces of equipment, but it's a different order, in or leave it, you can have the software add that workflow to the first line, and now you're running both of them on the same thing, instead of buying a whole second unit, okay. How about you want me to prototype a protocol for you because you don't have the automation engineers? If I had the same 7 pieces of equipment in Boston, I can prototype it. You have them at your place, I just send you the updated workflow and now suddenly, your thing is running it tomorrow, and I did all the work to qualify the biology for you, okay. So this is a different paradigm for deploying integrated automation. So I think it's a really cool thing. I think it's actually better in everybody's hands than just in Ginkgo's hands for a whole bunch of reasons. And so we're super excited about it. But we just started offering it, we just announced it last week, we've been kind of soft selling it for the last 3 or 4 months. But the good thing is Zymergen had actually sold a couple 2.5 years ago. So we have been already serving external customers in National Lab, startup biotech company called Okta. And so we have the experience of serving as an external vendor, repairs, so all that stuff, we've been doing that for last 2 years, so I think this is one that I think we just really had to scale the sales channel.

How proprietary is this?

Very.

Interesting, so a customer who sees this can't just sort of reverse engineer and just start building it on their own.

So number one, it's like an actual custom hardware. So you have to choose to get into the hardware business, which maybe you could, have a tools company, but a customer is not going to do that and then we had a whole software stack which Zymergen started working on this 8 years ago. So like I think it's pretty, we patented all the things, but like -- I'm sure there's other ways to get at this problem. But it's -- I think we are well ahead on having a productized thing here, both in terms of the hardware and the software.

And so explain to us the monetization of this, is there an upfront sort of CapEx outlay and then there's going to be a license fee of some sort.

Yes. So we're in -- it's what we're figuring out right now. We're engaging with a number of customers to get our arms around this. I think you can imagine a few different approaches, a, it's equipment, so you can do a capital expense, people can just buy it that way. If you want to engage with smaller companies, you could like lease it or maybe make the equipment free and just charge for the software. Either way, you're going to pay for software and services on top, like our current customers already do that. So you'll have some recurring kind of SaaS revenue that way as well. So we're flexible. Right now, we're engaging with sort of first customers.

Is the idea now over the next sort of 6 months, let's say, to set up some marquee customers who are running this and have them sort of in a sense, market it for you more broadly?

Yes. I would say that's the plan -- I mean -- in general, I mean, the good thing about Ginkgo like, if you think of this as like a sort of, again, I think the technology is quite mature, but going out and selling it, it's in the early stages, a little bit like a start-up. The good thing is -- I mean Ginkgo is excellent enterprise, Ginkgo has excellent deal teams like we're good at moving contracts and all that, like we have all that other infrastructure in place. So it's really a question of like sales channel and uptake.

I see. Fair enough. I want to talk a little bit about Lab Data as a Service. You launched that offering fairly recently as well at Ferment. Talk to us about the value proposition and particularly the use of the AIML approaches go mainstream in drug discovery. How is it resonating with your customer base?

Yes. So the idea here, we're -- we're actually going to call this data points, that's the idea. And so great example. AI bio company wants to generate a large data set to train a model, I'll give you an example of the kind of data you might want. There's a lot of ways to find an antibody binder out there right? Like yeast display and a hit of mouse or whatever, and you have companies that will do it for you like Adimab and whoever. Then you get your hit and you go send them up to Lucier or somebody to see, to run them through like a set of developability assays or you do them in-house. And you're basically looking to see things like solubility. Is this going to be a good drug. I know it's a good binder. But is it actually going to be something tolerated okay? And there's like, call it, 10 assays you want to do there. Depending on which one it is, it could be pretty challenging assay, the way you do it now is you kind of cross your fingers and hope the ones that were good binders are also good -- well, good candidates on the developability axis. If they're not, you could go grab other ones out of your file, but you don't have a real great sense of like which ones are going to be the ones that are like passing muster. Does that make sense? So there's a lot of interest on the AI side, could we actually use an AI model to predict whether that binder is going to be good on the developability asset. Well, where is the data for that? Because everybody has only got these likes small quantities of developability data. So that's one where -- well, what I'd actually love to have what would be 10,000 or 100,000 data points about different antibodies on these 10 developability assets. So that's an example of like one of our first products in data points is developability at scale. If you want a couple of them, go call WuXi, but if you want 10,000, you call us, right? Same for functional genomics, right? Things like perturb seek, drug seek. You want to basically make edits to mammalian cells with CRISPR or you want to hit them with a compound library and then you want to run a set of high content Omics assays on them, tells what you want, right? And we can again generate that data for you. And it goes back to your data science team not in spreadsheets, right, like it's done cleaned up right? And so because we have in-house on the solutions side, we've been doing large data sets, Gen and ML work and everything else for 5 years, like we have all that stuff. And so it's really that exposing it, pricing it, it's going to be very -- the pricing is transparent like a menu, right? But it's -- but where you would call Ginkgo is if you need a lot of data. Okay. If you need an outsourced set of hands, we're not your guys. Okay. So that's sort of data point. And we want -- and you could see that going to many other types of data in the future. It's a general idea, right? It's sort of like what -- but the question that we're trying to sort out right now is where do we see customers asking us for like large data sets. And you own it. By the way, all that data belongs to you, IP belongs to you, pure fee-for-service business, right? And we'll make money by doing it cheaper than we charge you. That's as simple as that.

Got it. On that note, Jason, I want to run this Nature Communications paper that came out recently talking about AlphaFold not being able to deliver as promised. I think it failed to correctly predict both protein structures and about like 2/3 of the proteins where it had training data. And I think most of the proteins where it didn't have training data, disappointing to see. But can Lab Data as a Service help improve upon that for a customer who's using a data set or an algo like AlphaFold?

Yes. So a couple of areas. So let me just speak to like AI and where like better tooling could potentially help. So there's sort of 2 lab activities associated with an AI model. One is the lab work that generated the data that you use to train the model. And frankly, the biggest data sets are public data sets where that data was generated over years like the Protein Data Bank or GenBank dispersed across many labs, data collected in an organized way. . Those are kind of the big ones, to be honest, PDB and GenBank. And part of the reason is it's easy to measure that type of data in a way where you trust that someone else who did it is giving you decent data. And it's because like everyone kind of agreed on standards a long time ago around the protein stuff and it is like just basically like an atomically physical, like a shape and then the sequence to sequence. But if instead you want a data on, say, like mRNA stability in a certain human cell type, and this guy did it in his lab and she did it in that lab, and you try to put them together, you don't believe that they were done the same way. So there's much less data produced, distributed across labs for training beyond PDB and GenBank. And I'm over simplifying, but that's a little bit of the gist. So one of the places that Lab Data as a Service or data points, as we're calling it, could help is make me from one place a bunch of known comparable data for training, okay, right? And it depends on what you want. Maybe you want antibody developability, but someone else wants mRNA stability, and somebody else wants this, and someone else like great. Someone wants that data in primary whatever neuro cells, like it just depends on what they want, right? But the data sets aren't available publicly at the scale they need because in order to create the scale, you have to jam together a bunch of data sets and you don't -- they're not really comparable, does that makes sense? That's one area. Then the other thing is, okay, I want to, let's call fine-tune my model and so you might be familiar with this in like OpenAI land and in the English language models there's now companies where you can pay a company, they'll take all of the Morgan Stanley in-house documents, take GPT 4, the thing, trained on the whole Internet, but really feed it all the Morgan Stanley documents that it really learns everything. And then when you ask you it a question about company policy, it doesn't pull company policy for Wikipedia, it has mostly learned company policy of Morgan Stanley, that makes sense? That's called fine-tuning. In order to do that, you need a bunch of relevant data and you need to kind of cyclically teach that model, particularly as you generate more of it. Same idea holds here. You could have a really generic model like an AlphaFold, and you would say, well, fine, AlphaFold knows about every protein structure under the sun, but I care about antibodies. So I'm going to -- and people have done this already at David Baker Lab and others. I'm going to take antibody data, and I'm going to really fine-tune it with just that like giving it just the Morgan Stanley documents in the world. Remember, it learned originally on every document. That's why it's English. But like I really want you to pay attention to this, does that make sense? Same idea here. So in that scenario, you could use data points to generate protein data in your area of interest and use that to tune up AlphaFold, okay. In this case, folding is a little secure because the folds are at high throughput, but like other protein properties. Okay. And so that, in general, I think, is also an area that would help. Last but not least, our Codebase. We're going to take a lot of these proprietary large data sets we already have at Ginkgo, embed them in models and make those available to people, too. Okay. Again, just no IP, nothing else fee-for-service, go have fun. And so those are some of the directions you'll see us try to nudge things along. But we're not like -- we're not an AI bio drug developer. We think that's a great area. I'm hopeful that it revolutionizes the drug, I think that's great, right? But what we really want to do is provide tools to people that need large data sets.

Got it. Yes. Fair enough. All right. Almost out of time, but I do want to run a quick numbers question by you on the biosecurity side, right? So recently, there was news around that Traveler-based Genomic Surveillance program. The contract was about $94 million or so. Was that the same thing that you guys were referring to earlier? You talked of potentially getting a CDC contract.

Yes. So I can't speak about this too much publicly just because it's a government project and so everything has to be done together. But yes, there was a contract posted on a government website that has to report out on newly signed contracts for traveler genomics. And our program with the CDC and the TGS program, in general, if you remember, is the -- it's collection of wastewater from airplanes and then you look for viruses and you sequence them if they're there. And so it's pretty neat. And it's not just -- I mean, it obviously got started during COVID, but like mpox, like I'll just say that the whole point of this is to have a radar system like we monitor for the weather for things that are a lot more dangerous than the weather. So it really feels like this is something we should have in place. I'm happy to see that I can put on that website.

Got it. And so what's the cadence of the revenue recognition for you guys like a multiyear period? Or is it sort of quicker than that?

Yes. So in general, with our -- I mean, I'll speak to biosecurity generally. I think what's great about that contract if you see is it's like over a 3-year period. So one of the key things for us in biosecurity has been going from like the episodic revenue of COVID. And by the way, I'll just like -- I think the way biosecurity revenue will end up looking is you'll have a baseline of constant monitoring, airports, other places, maybe animal facilities, like places where disease is born. And then episodically, things will happen. And when that happens, you surge against it, right? And so if there's like mpox right now in Europe, that could be a surge, right? And then you need to go and actually suddenly monitor much more aggressively because you're trying to tamp something out. That's my guess of how that ends up looking at it.

Got it. Fair enough. One final question, and we'll get you out of here. So I don't know when it was, probably like 5, 6 years ago, Jason I remember speaking with you about the approach of horizontal FinBio business model. There are a couple of like failed examples of companies that tried and sort of, knock, knock, essentially ran out of runway. And I remember you saying something to me then that they weren't wrong in taking that approach. They were just too early. And as you look back at some of the challenges that you had at Ginkgo over the last year, 1.5 years or so, has your conviction in that approach evolved at all?

No, I don't think it's the right thing. Yes. horizontals do move. It is an absolutely great question, yes, the problem with vertical is you can't do more than the product that comes off, that one product that comes off. And if you look at like the other great engineering fields and like deep in my heart, I know that bioengineering runs on the same physics as everything else. It happens to have code inside cells. It should match what we've seen in other engineering fields, the greatest companies are the platform companies. And I think, even if you look at tools, companies like Thermo Fisher, like which is really our greatest tools company, $200 billion plus market cap company, they're the horizontal platform for working at the bench. That's what they sell you. And I want to be the horizontal platform for working at high throughput and robotics, right, like for these high content, high-volume data.

Got it, fair enough. Great place to leave it at. Thank you so much, Jason. I appreciate it.

I appreciate it. Good to see you.