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

Good afternoon. I'm Anna Marie Wagner, SVP of Corporate Development at Ginkgo Bioworks. I'm joined by Jason Kelly, our Co-Founder and CEO; and Mark Dmytruk, our CFO. Thanks as always for joining us. We're looking forward to updating you on our progress. As a reminder, during the presentation today, we'll be making forward-looking statements, which involve risks and uncertainties. Please refer to our filings with the Securities and Exchange Commission to learn more about these risks and uncertainties. So we just hosted our annual conference Ferment and all that's geared towards our customers, understanding why customers are choosing Ginkgo is important to our investors. And so we're going to spend some time today recapping some of the themes from that event. As usual, we'll end with a Q&A session, and I'll take questions from analysts, investors and the public. You can submit those questions to us in advance via Twitter #Gingkoresults or e-mail at investors@gingkobioworks.com. All right. Over to you, Jason.

Thanks, Anna Marie. I'm super excited to be chatting with all of you today. We just hosted Ginkgo Ferment, our big meeting. We had about 1,000 people there in person, plus folks on the live stream as well. In my keynote, I reminded the audience that at Ginkgo, we're not spending our cash just on clinical trials or field trials or cosmetic launches. These are sort of end product activities that our customers are doing. At Ginkgo, we're spending our capital on improving our platform for our customers. So a big goal today was learning from our customers about what they want us to build. And I firmly believe that if we deliver on those requests, then we ultimately deliver for all of our investors. We do right by our customers, we do right by all of you. So you're going to hear a lot more from me today about why customers are choosing to sign up for Ginkgo's platform and what I heard at Ferment. When we launched Ginkgo, one of the big criticisms of our whole model was that a general purpose platform would not work in biotech right? It might work in the tech industry, but in biology, the lab work you do to engineer a mammalian cell is just too different from the lab work you do to engineer bacterial cells to get that working on a common robotics platform and automated, for example, or the data and machine learning models that would be relevant in the biopharma industry would never port over to work in agriculture. So I'm happy to say we are proving these people wrong. This is a sampling of our customers at Ginkgo. We have some of the largest biopharma companies in the world now, Novo Nordisk, Merck. We just announced a large deal with BI, chemical majors like Sumitomo, just announced last quarter, Solvay, longtime customers like Givaudan, one of the largest flavor and fragrance companies in the world. Ag majors, Corteva, Syngenta just joined the platform this last quarter and our large long-term customer Bayer in agriculture. Importantly, that's the top of this chart, if you look out at the bottom, you'll see all the startups in the same range of industries, right? And these are the companies that are working to make the new disruptive innovations in these markets. This breadth of our platform and business model being able to work across such a wide range of industries and customer scales, it is a real strength for Ginkgo and I'll talk about that later. One of the best things about this year's Ferment was that the majority of the folks that went up on stage were our customers, right? And so you had amazing customers joining us on panels. We heard 12 lightning talks from customers about what they were building, by leveraging Gingko's platform. And that's really important, right? People in the audience and on the live stream that are thinking about joining Gingko's platform, they might like to hear from Ginkgo folks, but they really want to hear from people like them that are getting value out of using our platform. And so I think we did a nice job of doing that. I really encourage you to watch all those videos that are up on YouTube, and you should check that out. And I think the diversity of what our customers are building with the platform gets folks really excited, and it also gets people thinking about outsourcing from our platform as they hear those different applications. I've shown this flywheel to you all before, but it's important to highlight that our customers help make our platform better. So we improve with scale. And it's a scale economic things like auto manufacturing plant or a chip fab or things like that, right? As we add new customers, we can invest more in our platform and our platform improves. We get larger facilities, and then which drops our cost. And we learned from the data from one project to make another project faster and less risky. And so even though all of you, our investors should be excited when we add a new customer to the platform, I think our customers should also be excited every time they see a new announcement of a customer joining Gingko's platform because they are getting better infrastructure out of that. And so that's why it's so exciting that in 2022, we increased our active program by 60% and the rate of new program additions by 90%. That's why I talk about these numbers. It shows that the flywheel is spinning for our customers. And we expect to keep driving improvements that pay off for decades to come for our customers. Okay. I'm going to hand it over to Mark, who's going to walk you through our Q1 financials, and then we're going to dive in on some of the key themes coming out of Ferment. Over to you, Mark.

Thanks, Jason. I'll start by discussing our Cell Engineering business. As a reminder, we now refer to Cell Engineering revenue rather than foundry revenue as it is more reflective of the business. You'll see that updated throughout our 10-Q. We added 13 new cell programs and supported a total of 97 active programs across 60 customers on the Cell Engineering platform in the first quarter of 2023. This represents substantial growth and diversification and programs relative to the 64 active programs in the first quarter of 2022, with strong growth coming from the pharma and biotech and the food and agriculture segments. We added several large new customers to the platform, including Boehringer Ingelheim, Syngenta, Solvay and a new program with Sumitomo in addition to a good mix of programs with earlier-stage customers across industries. As Jason mentioned, we think both of these customer segments are important. It's an important validation of our capabilities when we add large multinational customers like BI and Syngenta, who have strong internal R&D capabilities but we're also very proud of our ability to enable the next generation of leaders. Cell Engineering revenue was $34 million in the quarter, up 59% compared to the first quarter of 2022. As you can see in the charts at the bottom of the page, this growth was driven entirely by our services revenue with third-party customers and is reflective of diversification in the customer base. Now turning to Biosecurity. Our Biosecurity business generated $47 million of revenue in the first quarter of 2022, a solid result as this business transitions away from K-12 COVID testing services. Importantly, over 20% of this revenue came from what we believe will become more recurring sources, such as federal and international contracts, while that proportion was well under 10% in Q4 of last year. Biosecurity gross margin was 52% in the first quarter of 2023, which benefited from some onetime items. You can see on the right that we're really thinking about this business globally now and not just domestically. We believe there will be strong network effects in this business as biology does not respect borders. We have now collected samples from flights originating in 72 countries through our airport program. We believe this type of infrastructure can provide an early warning system for future biological threats. And now I'll provide more commentary on the rest of the P&L. Where noted, these figures exclude stock-based compensation expense, which is shown separately. Starting with OpEx. R&D expense, excluding stock-based comp, increased from $57 million in the first quarter of 2022 to $115 million in the first quarter of 2023. G&A expense, excluding stock-based comp, increased from $42 million in the first quarter of 2022 to $84 million in the first quarter of 2023. These operating expense items increased year-over-year as expected as we invested in our platform in various functions to support our growth during the past year and layered in the 4 acquisitions we closed in the fourth quarter of last year. Included in these numbers in the first quarter of 2023 is approximately $19 million of onetime M&A and integration-related expenses. Stock-based comp, you'll notice the significant step down in stock-based comp year-over-year. As a reminder, this is because of the catch-up accounting adjustment related to the modification of restricted stock units when we went public is rolling off. Over 60% of the total $75 million stock comp expense in the quarter related to RSUs issued prior to us going public. To help folks model this more precisely, we have provided a new appendix slide in this deck for your reference. Net loss. It is important to note that our net loss includes a number of noncash income and/or expenses as detailed more fully in our financial statements. Because of these noncash and other nonrecurring items, we believe adjusted EBITDA, is a more indicative measure of our profitability. We've also included a reconciliation of adjusted EBITDA to net loss in the appendix. Adjusted EBITDA in the quarter was negative $100 million compared to negative $1 million in the comparable prior year period. The decline in adjusted EBITDA was attributable to both the higher run rate of expenses in Cell Engineering and the as-expected decline in Biosecurity revenue. And finally, CapEx in the first quarter of 2023 was $19 million, reflecting foundry capacity and capability investments as well as leasehold improvements. CapEx was impacted by timing of equipment purchases and projects, and we would, therefore, expect lower levels of CapEx on average in subsequent quarters this year. In terms of our outlook for the full year, we are reaffirming our guidance for 2023, including 100 new cell programs, at least $175 million of Cell Engineering revenue driven by services revenue with additional revenue potential from downstream value share and at least $100 million of Biosecurity revenue. As we shared in our last quarterly update call, we expect our new program additions and revenue to ramp during the year and believe we have a solid backlog and pipeline to support our outlook. In conclusion, we're pleased with our overall progress in the business while navigating a challenging macroeconomic environment. We're adding new programs to the platform in a way that improves the platform and balances both near-term and long-term economics. We are focused on our cost structure with new investments in spend generally targeted to discrete areas such as our mammalian capabilities, and we continue to manage our balance sheet and cash flows to maintain a long runway while retaining flexibility to capitalize on near-term strategic opportunities with $1.2 billion of liquidity at quarter end. And now, Jason, back to you.

Thanks, Mark. It's always exciting to me to see new customers signing up for the platform. And I want to highlight that we actually spend a lot of time talking to our current customers, including running an annual customer survey to learn how they're using the platform and importantly, how we can make the platform better. And so I want to share a little bit about what we've learned on why our customers are choosing to outsource to our platform in the first section. Now, our customers are specialized in their vertical markets, right? They're a pharma company, an industrial biotech or an ag company. We are not, right, I think as a general platform. So I want to talk about these service offerings that Ginkgo is launching so that we can better speak in the language of our customers when we're offering our general platform. And then finally, I'm also really excited about the progress our Biosecurity business is making around the world. And so I'm going to end there with a few comments. Okay. Let's dive in. All right. So I showed this slide before, but I do want to -- I want to pause on it just again for a minute to highlight how unique it is to have this range and breadth of customers on the platform, both in terms of size and range of industries. And so one of the things we wanted to ask is why are these folks getting on the platform, okay? And we did this by again, surveying and talking with folks and things like that. I think this is a really nice quote from one of our larger customers, he's Brian VanDahl at Novo Nordisk and he said, "Science is currently undergoing a revolution. Large-scale data sets, coupled with AI is opening up a greater opportunity space within biology. We no longer have to limit ourselves to the questions that can be addressed by traditional research methods." And we heard more from Brian on a panel at Ferment, I encourage you to watch. I think this is a key idea, right? Pretty much every business right now, regardless of market is asking, is there some large data set that I can train a generative AI model on, it's going to have an impact on my business. That's true if you're a car company, finance, media, whatever it might be. And it's also true if you are running a biotech R&D department right now in biopharma or BioAg, right? And so I think this does represent and highlights one of the -- I want to show is the first example of what we're hearing as feedback from customers. So the -- that we get more data per R&D dollar at Ginkgo using our infrastructure. And this is important to generate those large data sets. Secondly, we access not just that single customer's data, but because of the way Gingko's platform works and our IP works, we're actually able to give a customer access to the data and learning across all the projects that have been happening on Gingko's platform. And then they're not just left on their own to figure out how to navigate that. Many of these companies are new to doing these sort of large-scale data science efforts, they have access to Gingko's data scientists in order to navigate that data. Third, companies want to launch work quickly on the platform, all right? And this is endemic in biotech. The rate and speed the biotech works is too slow. Fourth, if you're a smaller company, there's an enormous upfront cost to building out a laboratory. We want to just cut that out in terms of the cost and spending for new companies trying to do cell engineering. They should be able to use our services rather than build their own lab. And then finally, often, you have a big R&D department that's a big fixed cost sort of using space and using equipment in a facility. We want to replace that with a variable cost service, and we'll talk about why that's particularly important in pharma. So I love this video. This is the new technology that's coming out of -- from our acquisition from Zymergen. These racks, these are basically -- I would consider this to be the best sort of flexible automation platform out there. And the reason I highlighted this at Ferment was, if you are a customer developing a therapeutic drug or you are working on a new agricultural product, you should not have to be a world's expert in laboratory automation, right? If you wanted to use, say, cloud compute, you do not need to be a world expert in data center architecture, right? You leave it up to the cloud computing companies to do that. Our argument at Ginkgo is these are the sort of technologies we are going to focus on so that our customers don't need to and that they have access to the absolute latest in technology for getting more data per R&D dollar. That's on us. And you see this when you look at quotes for example, Marcus Schindler, CSO at Novo Nordisk. He said, they work with Ginkgo because of our ability to rewrite genomes to engineering new bespoke biological system. So being able to make engineer at the scale of a whole genome. That wasn't something they could do in-house, right? Alphonse at Biogen, I was there when we did the deal, a large number of design ideas that Ginkgo could work through and this was to help them with sort of aid the manufacturing. So again, that sort of scale of activity, data per R&D dollar being so much bigger at Ginkgo. Importantly, that's the data from your project. You do want to get more data out of that. But boy, a lot of other biotech work is going on in the world. And it would be really nice if you're trying to train up a model, for example, of your particular system that you were able to draw on data from other people's work. And this is something that the tech industry is benefiting from dramatically. If you look at how these new models are being built, they're being built on huge data sets, across lots of different assets around the Internet and so on. In biology, a lot of that data today, frankly, is being stove-piped across thousands of different companies. And so one of the things Gingko's been doing is accumulating that all in one place so that you can train models on much bigger data assets. And we have many different examples of this. I'm just going to get one. This is some of the proprietary genomic data we have. So sequence genomes largely from microbes and metagenomic sequencing from companies like Radiant Genomics, at Lodo, on Warp Drive Bio and work we've done at Ginkgo. And just in this chart, you can see the size comparison of our proprietary data to what's out there in some public databases, right? And this is -- means that our customers can go and get access to this and find a new protein or a new enzyme or a new natural product and here's a quote from Sumitomo, the great transparency and sophisticated data set was Gingko's strengths, right? So that transparency having access to this, I think, is critical and unique to Ginkgo to be able to get that range of data. Also at Merck, they talk about the professionalism and experience of Gingko's employees, right? So again, having these experts who can help you navigate all this and similarly, similar comments from Givaudan. Great quote up here. So Nicholas Ohler over from Lygos, Chief Technology Officer there. He said, Gingko's entire team was quite talented, but the early results on one of our projects are stunning and supports Lygos' mission of accelerating the world's transition to high-performing sustainable products. Okay. What's interesting about this is this project, if you look at it when we announce it, it was like 6 or 9 months ago, right, like recently. And so how do you get stunning results in that short period of time in biotech? And the answer is you're not starting from scratch. So you're building on both existing hydro but infrastructure that you can turn on immediately and also large code base of what we would call it, like IP and genetic assets that you can make use have to go faster, right? Trent from Microba, SVP Therapeutics, Gingko's expertise and resources have moved our drug discovery project along at a pace that just would not be possible either using internal resources or via a traditional CRO approach, okay? And so again, this is access to a scale and speed that you wouldn't otherwise get, and Bob Reiter at Bayer, Heading Crop Science talking about the open innovation approach, in other words, accessing external service providers instead of doing it in-house, plus bring higher-quality biological solutions and innovative technologies to the market faster, okay? And so I think these types -- this type of speed is one of the key advantages that our customers are seeing on the platform. Next, I want to talk about -- one of the things that gets to be really excited for small companies. So if you have a chance, I would encourage you to go watch Jasmina's talk from Ferment. And so what's cool about Jasmina is she is the CEO of Arcaea. She gave a talk at Ferment -- last Ferment, which was about 1.5 years ago. And at her talk this time she said, 1.5 years ago, I was up here announcing that we had launched the company. I raised money, we're launching Arcaea. And then here I am 1.5 year later, and I'm showing you my first product. And so that type of speed in biotech, even in this cosmetics biotech, I don't really care. That type of speed is really unheard of. And the reason it was possible is normally, when you raise a venture around as a start-up biotech company, the first thing you do is you call Alexandria Real Estate and start being shown very expensive overpriced real estate in Kendall Square and go look for a lab and then you start to go up and buy a bunch of expensive equipment and stock that lab then you start hiring scientists. And 9 months or a year later, you're doing serious work. Here, within weeks, able to deploy high throughput automation at Ginkgo to work on these projects. And that is why we're able to go so much quickly. And also what save Jasmina the cost of needing to build out that lab in the first place. Hugely valuable to small startups in the biotech place. And by the way, this is something that was absolutely seen with Internet companies and cloud computing in the mid-2000s, right, the Perth of AWS and these companies that could start cloud native, unallowed them to save on building out server box. And so I think that's a real similar opportunity here in the biotech sector. Okay. So this is an important point. I think from a standpoint of the industry's efficiency. So if you look at an average small biotech company at the beginning, that dotted line in the middle there is sort of like their R&D teams. They hire an R&D team, they do all that work, they get the lab, they get it going and here it is, okay. Prior to having your drug going into clinical trials and animal studies and so on, like locking your candidate, you wish you had more R&D right? If you have more R&D, you'd get to a better candidate. If you add more R&D capacity, you'd go faster, right? But you're like you don't want to hire too many people and so on, so you kind of keep it at this level and you get to the best candidate you can get. And then off you go, you go into animal studies and clinical trials. Well, now suddenly, you wish you had less R&D spending because you're trying to conserve cash, get that clinical trial result and show that your new application in pharma works. And once you get a good result, well, now you want more R&D again because you want to build out a really robust bigger pipeline in that area. Okay? So that's the sort of sign way there, not efficient to how we're doing it now or either overshooting or undershooting and then importantly, in a tight capital market, that middle dip turns into R&D team layoffs, which is what we're seeing across the industry today for small biopharma companies. So our suggestion would be, wouldn't it be better to be having your scientists accessing that type of capability, a smaller team, accessing that capability as a service. So that when you need a lot at the beginning, you got it. When you need less in the middle, you turn it off and then you turn it back on later. And we have some groups that are doing that. You can see Kristen of Prokarium, Dave of Synlogic, talking about how Prokarium is now dedicated to driving our lead program into clinical trials this year, while leveraging our partnership here with Ginkgo to accelerate our discovery work, right? So I really like that. I think that's a good way to make the whole industry more efficient. All right. So I want to say this and I think that's a message that I gave at Ferment, and I think it was like well received by folks in terms of why you might want to use the platform, right? So let's say you want to -- you hear all that, you're like, "I really want to use Ginkgo platform." Okay. So what do you do about it? Well, the first thing you could do is just go to our website and click on a link that says Work with Us and you'll get an e-mail from our scientists and folks on the commercial team, and they will ask you how you use -- what are you trying to accomplish and how could Gingko's platform be relevant. But some people look at that and say, I don't have time for that conversation. I really want to know is Gingko's technology relevant to my application. I want to know that ahead of time. And so in order to solve that problem, we launched Services. And the first service we offered was Ginkgo Enzyme Services. And I'm going to talk more about that in just a minute. But that was to basically say, if you're doing enzyme work, this is the full suite of things we have at Ginkgo that can help you out and come hear from our scientists and we've got webinars and things to show you how it all works. That was really well received over the last half year or so and is driving a lot of like funnel for us in terms of new customers getting on the platform. So at Ferment, happy to announce 4 new services. Ginkgo Microbe Services, Ginkgo Cell Therapy Services, Ginkgo AAV Services and Ginkgo RNA Therapeutic Services. And you can see here, I highlighted a few recent acquisitions, the Ag Biologicals R&D group from Bayer that we acquired in, StrideBio acquisition, which I'll spend a minute on a second and Circularis and circRNA. Those assets are also available to our customers as part of these services. And so there are virtual events, things coming up, that I encourage you to take a look at if you're interested in these things. I do want to dive in just for a minute on the StrideBio. This is super exciting to me. A, the technology is very cool, and I won't belabor it, but these are folks who are really just leaders in doing the protein structure around these AAV capsids and identifying key regions of the structure that would be worth modulating to work on some of the key challenges in gene therapy. And in fact, a lot of people knew this about Stride, right? So when we announced the acquisition, we had just an enormous amount of inbound interest from folks who are saying, hey, can I get access to these capsids? Can I incorporate this into my gene therapy products? And so we have 30 active conversations going on right now that other folks that called us or folks that we reached out to that we knew would be interested in this sort of thing. And so I do think this idea of -- behind this, it's not just Stride's assets, it's all Ginkgo's automation. You can see that on the left there, and all being able to build many large designs. It is the combination of unique assets plus Ginkgo's platform, plus our ability to go out and sell and do commercial work and be good alliance managed partners. And we just have experience now, more than 100 R&D projects on the platform where we're working with all these different customers in our history. That's a real skill set. It allows us to harvest value from these platform assets where other people wouldn't find it. And then importantly, we also structured these deals in order to best take advantage of that for Ginkgo. So you'll see us aligning most of the cost of the deal being put into things like downstream value share milestones and things like that for the partner. We would love to do more and more of those types of acquisitions. It's something we're actively looking to do. I think the StrideBio is a real case study of what works well for both sides. This asset just does. It's a lot more valuable once it's up on Ginkgo's platform and the folks -- the previous owners of Stride will really benefit from that. Okay. So I want to talk about a really exciting updates on how we're doing some of our services. So the #1 request we get from customers is to improve to better technically derisk cell engineering. So like the 3 thing customers care about, they want it to be less risky, they want it to cost less and they want to be faster. But the one they really want the most is to be less risky, all right? And you see this -- some folks like Nick at Lygos, hey, as a CTO of a growing company, what can I possibly do better than work with Ginkgo to derisk my plans, right? And Keith of Optimvia, the likelihood of success for our project specifically was seen to be improved with the technical capabilities at Ginkgo. So we love that we can -- some people get that we can make things more technically successful. But a lot of people don't, right? Like we'll go to folks and say, listen, in our experience, doing a project like this, we have like a 90% success rate here, it's really going to work, and they'll be like, I don't believe you. And it's not a problem about Ginkgo, it's a problem about biological engineering, right? Like this is considered to be R&D. Like it's just -- it might work and it might not, right? So one of the things that I'm going to -- I'm doing to combat that is for our most mature services, so this is in our Enzyme Services -- Ginkgo Enzyme Discovery Service, Enzyme Optimization Service, Protein Expression Service, we are moving to a success-only payment. In other words, again, these are for shorter duration, high probability projects, and we bet them. If we succeed, you pay us, if we don't, you don't, right? And this is to move away from the model today or if you work with a CRO or someone else in the research space, it's sort of like if you ask someone to build a house for you and on the day that you're going to move in, the house fell down and they said, well, yes, write me the check anyway, like at least I tried, right? And that's what research work looks like. And so we think with our platform, we can start to, again, by treating biology like an engineering discipline and driving scale, move this to more an engineering discipline and then price like an engineering discipline like we get paid when it works. And so we think this is really exciting. I think it's about a week and a half since we announced this. A lot of excitement about this. It is a new way to -- it's a new type of offering on the market really. And we think it's one that Ginkgo could really do well at and our customers are going to love. Okay. Last thing I want to talk about is our progress in Biosecurity. I think we are -- you see this with AI. You see it with just sort of COVID and Bio in general. I think like there's a lot of disruption happening in the world right now. It is important for the folks building these types of technologies to approach them with care, right? And I think if you look at the history in computer science, alongside increasingly powerable computers and network computers and so on, you add the birth of the field of cybersecurity, whose job was basically to ensure that we could do all that type of computing activity with a level of safety, both economic and national security. As the tools for engineering biology, get more sophisticated, more widely distributed. Alongside that, we should be building up the tools of biosecurity. It's very analogous. And we have an interesting thing where fortunately, those tools today are still like mostly centralized and relatively weak. It's not like everybody is able to be engineering cells in their basement or whatever in their garage but that -- I do see that day coming in the future. So we want to be building out biosecurity tools in advance of that. Separately, nature, as we saw with COVID-19 throws off malicious biological code already, right? It is doing a lot more of this than we are as humans. And so these tools are also amazing tools of public health in the interim period, while the tools of bioengineering are falling in costs and becoming more widely distributed. So we can kind of work on a public health problem while also getting ready for sort of the DNA age, just like we had to get ready for the computer age. I'm really excited by the range of expansion of our biosecurity platform. This really originated -- many of you are familiar with the work we're doing in sort of school testing and prisons and things like that. A lot of that work, obviously, is drawing down at the end of the COVID emergency order here in the U.S. But alongside that, we were fortunate to be working with the CDC, for example, in airports in the U.S., doing things like collecting wastewater, anonymous sampling from passengers and sequencing to look for new variants. And many of you remember this, but first sequence BA2 and BA3 coming out of this program to detect in airports. We think of it like a radar system, right? We've been working to expand that internationally as the some of the folks that were either already deployed in or have MOUs with or expanding into with some of the flags up there. This is not the only place that you would like to have persistent monitoring, right? You'd also like to be doing this in places where we're congregating a lot of animals, right? So animal agriculture, honestly, you'd also like to look after it for plants. Agricultural pests as well in the future as well as doing things like hospitals and nursing homes and in crisis settings, like we're doing work in Ukraine, for example. I do see a shift that we're trying to pull off. You can see it on the right-hand side of the slide, where we are increasing the amount of sort of federal and international work we're doing with folks like the CDC and other countries. That's really the thing to watch in the second half of this year is we're going to be working to make that shift from our programs domestically to these international programs where we see good interest. And we see a real opportunity for like a network effect where one country is going to be very interested in the data happening at other airports. And interestingly, what the magic of international airports is flights are coming in from all over the world. So these international airport programs, we see -- we now tested samples and done the sequencing and so on from, I think, more than 60 countries coming into these -- to these airports. So having these nodes out there, having that data globally, it's sort of interest to every country, not just the country that we're testing in. And so we're really excited to see that develop, but it is an early and a new market. All right. So I'd like to end on this slide. I am super excited -- Ferment always going be fired up. But this year was just amazing like to see all of our customers talking about the platform, to hear direct feedback about what it is we can do to make it better. That feedback loop is really working at Ginkgo and it gets me excited. And I think as we're doing all that and making it better by implementing biosecurity globally, we get to do all that with care. So thanks for the time today. And I'll pass it to Anna Marie for Q&A. Thanks.

Great. Thanks, Jason. We'll switch to Q&A in a few moments. Before we do, I wanted to get through a couple of housekeeping items. In my role, I respond to a lot of investor e-mails, and I'd like to make it easier for all our investors to benefit from the questions that are being asked. And there's been a couple of recurring themes. So I've added 2 new slides into the appendix materials that I'm hoping will be helpful. The first, which Mark alluded to, provides more clarity around stock-based comp. And in summary, the vast majority of the stock-based comp we've recorded since going public is related to shares granted prior to going public. That's been a common source of confusion. So hopefully, that will help clarify that as well as provide some modeling tools around what's left. The second slide provides some additional details on stock sales by our founders. This data is all publicly available, but some of the market data providers don't accurately pull our share counts because they sometimes exclude different classes of shares. As you'll see on the appendix slide, our founders still own over 400 million shares. That represents over 20% of the company. They did have some mandatory sell-to-cover transactions when their RSUs were settled and have put in place small 10b5-1 plans. But both of those are dwarfed by their core holdings, most of which sit in illiquid Class B shares. So I'm hopeful that those slides are helpful.

Now we'll move on to Q&A. As usual, I'll start with a question from the public and remind analysts on the line that if they'd like to ask a question to please raise their hands on Zoom, and I'll call on you and open up your line. Thanks all. All right. It looks like everyone has managed to reconnect. So we'll go ahead and get started. The first question, as I mentioned, always comes from retail. This comes from [ Mark De ] on Twitter. Since the number of projects is the best leading indicator for future platform revenues, how do you feel about your original forecast of adding 100 projects for 2023? When looking at the pipeline of projects, are you on track?

Yes. I can take that. I can also talk about the scene change since I am now in Qatar just getting back from a dinner. So I mentioned this at the end of the recorded talk there. We've been expanding our biosecurity business pretty dramatically on the international side. And one of our best sites is actually Hamad International Airport here in Doha, which is just a great regional airport for the area. We have this program with the CDC, where we're collecting wastewater and testing for new variants. I'll just say that the flights into Doha are not overlapping. They're very much of the flights into Atlanta. So it really is a really nice way to get a wider set of data for our biosecurity programs. We're lucky to have the partners here. So to get to the answer to your question, on the programs, yes. So I think one of the key things -- I mean, a, our [indiscernible] was 13% in this quarter, that's down from last quarter. So that's something we're keeping an eye on. I would say we have one disadvantage, we're doing large enterprise sales, which can be a little bit lumpy and unpredictable, like there's just an enterprise sales element to it. The advantage of enterprise sales is you have decent pipeline visibility. So we have a good sense, deals don't close in a week, they close over months. And so we have a good sense of what's in the pipeline. So that's the one reason we have a lot of confidence in that program count for the year. I will say, if I like look across the last year and try to find like actual trends and what's either making it easier or harder to close programs, probably the one thing that's making it harder, I would say, is for start-up companies in kind of non-biopharma biotech, so things like industrial biotechnology. Those companies are having a harder time accessing capital in the sort of tighter capital market. It's one of the areas that venture capitals are putting less money into. And that is making it tougher. It's at least extending deal close time and things like that with programs in that area. On the other hand, again, we think it operates as a general platform, like I mentioned in my remarks there, that allows us to be able to move into other areas that are doing better, like biopharma, for example. So biopharma, you are still seeing a ton of activity, both with start-ups and large companies. We mentioned how much energy we've been getting out of the StrideBio acquisition, but we also have a healthy pipeline in cell therapy applications, mRNA therapeutics applications, things like that. So I think you'll see us shift a little more towards biopharma, but we do have a robust sales pipeline coming up, so I feel good about it.

Thanks, Jason. All right. We'll take a question from analysts now. The first question I'll take comes from Rahul Sarugaser at Raymond James. So let me try to open your line here, although we're actually having a bit of trouble. I may need to ask for a little IT support to give me permission to open the lines. And while we're doing that, I'll go ahead and ask another question. This one actually coming from an employee. So for folks that don't know, any time we do an earnings call, the first sort of investor call that we take after our earnings call is with all of our employees. Our employees as a group are our largest shareholder. And we thought we might share some of their questions with you all as well. So this one came from an employee that chose to remain anonymous. We're increasingly talking about AI at Ginkgo. And so can you provide an overview of our AI and code-based strategy and how we're staffing those efforts?

Yes. So I think this is actually a big deal. So I touched on this a little bit at Ferment, but one of the things that's happening is because of the impact of sort of ChatGPT in the sense that like large data, less generative AI models, equals change in industries, you now have pretty much like every large corporation looking at what the impact of this is going to be on them. And that's auto companies, that's chip companies, media companies, and it's also biotech, biopharma companies, ag companies and so on. And in order for a customer to use Ginkgo's platform, they have to choose to make a change, right? So today, they have an internal R&D department doing work and they're making products and everything else. And I'm saying change some of that, spend some of those R&D dollars on our platform. As sort of like a sales motion, they need to have a reason to want to change. And sometimes, it's -- they're greedy to try to add a new product, sometimes things aren't going well and they want to try something new. And sometimes, something new comes along in the kind of in the atmosphere that makes them think they need to take a look. And that is what's going on with generative AI. So you have people saying, hey, I think I should be looking at what happens if there's big data and models in my space. And the beauty of Ginkgo is we are a great place to generate huge data assets. And so I think AI is a core strategy. It is a very positive wind in our sales here at Ginkgo. In terms of how we're making use of it? Well, we have this advantage that we have been over the last 10 years as we've done all these deals and so on, accumulating a huge data asset. We've talked about this publicly many times, our code-base. That is beautiful, beautiful data to train these types of models. So we're super excited about that. We're already seeing good results. You can see some of this in our webinars about how we do our protein engineering, but expect that to expand to a wider set of activities at the company. And I expect customers to come to us to get access to it.

Thanks, Jason. All right. Rahul, I think we're all set. So I've just opened your line. Please go ahead.

Excellent. This is Michael Freeman on for Rahul today. Congratulations on such a successful Ferment event. That was a really spectacular display with some growing reviews from your customers. So thanks for doing that, you guys. Pleasure to be there. Okay. First question is on the overall IP strategy. I'm wondering what can you tell us about how this year versus perhaps last year, Ginkgo has been leveraging its existing code base for new cell programs versus doing de novo engineering and how much -- perhaps, how much more it's drawing upon that code base now? And the attitude among customers, like I trust in early days, there are some serious pushback among customers saying when -- with Ginkgo's attitude toward holding on to the IP that you develop. So I wonder if you could shed some light on all that.

Yes. Yes, I can touch on this one. Yes, one of the biggest challenges we had with customers over the years was sort of, hey, it seems Ginkgo like you're doing a project in an area for the first time with me. I'm going to fund a chunk of it, and you're going to keep the rights to reuse it and go off and build a business on the back of my investment, right? And short answer was yes, we were doing that in a number of cases. But what's happened over time is we're accumulating assets in all these areas, is we now -- and you can see this what those 4 services I announced at Ferment, each one of those services has specific code base in that area. So when we go and talk to a customer, it isn't saying hey, I don't have anything in AAVs, but I think my robotics could be useful for you. Hey, that would have been true, and I did do sales like that 1.5 years, 2 years ago, they're brutal sales. Now I get to say all my infrastructure and high throughput automation is useful for AAVs. By the way, here's the data to show you. By the way, here's a bunch of great capsids from StrideBio that you can get access to. And by the way, here's some other data capsid work we've done to discover some new stuff and so on, that really, really helps on the sales side. So I would say probably the biggest impact is in selling because a lot of customers, particularly in the biopharma side, want to see data that you've done something like what they're interested in. And then the second order is, like I mentioned, that Lygos project, where we're able to just totally draw on some work we did before to speed a project up. I don't know, in some cases, by years. So I think there's a real -- there's going to be more and more examples of that. But probably the first place we're seeing it is just having a more complete product to offer on these services.

All right. I think as a follow-up, this one will probably be for Mark. Around at the end of last year, we were waiting out some lumpy milestones. We're curious about the timing on that. I'm looking at the Cell Engineering revenue where $1 million of the total $34 million was downstream revenue. Also looking at the appendix of the presentation today where $13 million is noncash consideration of the total $34 million. I wonder if you could just help us sort through those as the definitions and shed light on these things and then perhaps talk about those lumpy milestones.

Okay. So I'll take the 2 points in turn. First on the lumpy milestone. So really, it's the same comment, I think, that we made on our last earnings call, which is, yes, there were the 2 milestones that -- at one point, we had been expecting to hit in Q4, which spilled into 2023. And yes, we are still going after those 2 milestones. We believe the technical work on that is substantially complete. But I think as we had mentioned on the last call, and this is still true, there are aspects of validating the completion of that work that is out of our control. It's dependent on both customer and some third-party manufacturing. And so those are still in play, but timing is just uncertain on that. With respect to the second point that you made on noncash considerations, so yes, first of all, the conclusion that substantially all of the revenue in the first quarter related to services revenue, that's correct. The supplement in the appendix shows you, like you said, the component of services revenue or total revenue that is noncash. So we do in some cases, as you know, and we started doing this last year, we do sometimes take equity from a customer as part of the upfront consideration on a project. So not just for downstream value share, but also for the upfront or the service fee consideration. And so that's why we're giving you that additional sort of date point. Does that answer the question?

It sure does. And I appreciate that. I'll jump back in the queue.

All right. [ Edmond Cha ] from Morgan Stanley. I've just got ahead and opened your line.

Just to circle back on that point, Jason. How do you strike the right balance between leveraging the collective learning sort of code base for the benefit of an individual client versus making sure clients don't feel threatened that their secret sauce is being farmed out for the benefit of other customers. What safeguards do you have in place to make customers feel comfortable?