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. We just hosted our annual conference, Ferment, and while that's geared towards our customers, understanding why customers are choosing Ginkgo is important to our investors. We're gonna 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, #GinkgoResults, or email at investors@ginkgobioworks.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, you know, the audience that at Ginkgo, you know, we're not spending our cash just on, you know, 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, you know, all our capital on improving our platform for our customers. A big goal of the day 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. You know, if we do right by our customers, we do right by all of you. You're gonna 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. You know, 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, you know, the lab work you do to engineer a mammalian cell is just too different from the lab work you do to engineer a bacterial cell to get that working on a common robotics platform and automate it, for example. You know, the data and machine learning models that would be relevant in the biopharma industry would never port over to work in agriculture. I'm happy to say we're proving these people wrong. This is a sampling of our customers at Ginkgo. You know, we have some of the largest biopharma companies in the world now, Novo Nordisk, Merck. You know, we just announced a deal, a large deal with BI. You know, 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, you know, that's the top of this chart. If you look down at the bottom, you'll see all the startups in these same range of industries, right? These are the companies that are working to make the new disruptive innovations in these markets. You know, 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 we'll talk about that later. You know, 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? You had, you know, amazing customers joining us on panels. We heard 12 lightning talks from customers about what they were building by leveraging Ginkgo's platform, that's really important, right? People in the audience and on the live stream that are thinking about joining Ginkgo's platform, you know, they might like to hear from Ginkgo folks, they really want to hear from people like them that are getting value out of using our platform. I think we did a nice job of doing that. I really encourage you to watch, you know, all those videos are up on YouTube, and you should check that out. 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. We improve with scale, you know, and this is a scale economic. Think like a 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 that which drops our costs, and we learn from the data from one project to make another project faster and less risky. 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 Ginkgo's platform because they are getting, better infrastructure out of that. That's why it's so exciting that in 2022 we increased our active programs 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 it to keep driving improvements that pay off for decades to come for our customers. Okay, I'm gonna hand it over to Mark, who's gonna walk you through our Q1 financials, and then we're gonna 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 Q1 of 2023. This represents substantial growth and diversification in programs relative to the 64 active programs in the Q1 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. 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 Q1 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. Turning to biosecurity. Our biosecurity business generated $47 million of revenue in the Q1 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 Q1 of 2023, which benefited from some one-time 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. 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 Q1 of 2022 to $115 million in the Q1 of 2023. G&A expense excluding stock-based comp increased from $42 million in the Q1 of 2022 to $84 million in the Q1 of 2023. These operating expense items increased year-over-year as expected as we invested in our platform and various functions to support our growth during the past year and layered in the 4 acquisitions we closed in the Q4 of last year. Included in these numbers in the Q1 of 2023 is approximately $19 million of one-time 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 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 non-cash income and/or expenses as detailed more fully in our financial statements. Because of these non-cash and other non-recurring 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. Finally, CapEx in the Q1 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. 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. 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. Now, Jason, back to you. Thanks, Mark. It's always exciting to me to see new customers signing up for the platform. I wanna 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 Ginkgo can make the platform better. I wanna share a little bit about what we've learned on why our customers are choosing to outsource to our platform in the first section. Our customers are specialized in their vertical markets, right? They're a pharma company, an industrial biotech, or an ag company. You know, we are not, right? Ginkgo's a general platform. I wanna 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. Finally, I'm also really excited about the progress our biosecurity business is making around the world. I'm gonna end there with a few comments. Okay. Let's dive in. All right. I showed this slide before, I do wanna pause on it just again for a minute to highlight, you know, how unique it is to have this range and breadth of customers on the platform, both in terms of size and range of industries. One of the things we wanted to ask is why are these folks getting on the platform, okay? You know, 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, so this is Brian VanDahl at Novo Nordisk. You know, he said, you know, "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." We heard more from Brian on a panel at Ferment, which 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 that's gonna have an impact on my business?" That's true if you're a car company, finance, media, whatever it might be. It's also true if you are running, you know, a biotech R&D department right now, you know, in biopharma or bio ag, right? I think this does represent and highlights one of the, what I wanna show as the first example of what we're hearing as feedback from customers. The, you know, that we get more data per R&D dollar at Ginkgo using our infrastructure. This is, this is important to generate those large data sets. Secondly, we access not just our, that single customer's data, but because of the way Ginkgo'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 Ginkgo's platform. 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 Ginkgo's data scientists in order to navigate that data. Third, companies want to launch work quickly on the platform. All right? This is endemic in biotech. The rate and speed that biotech works is too slow. Fourth, if you're a smaller company, there's an enormous upfront cost to building out a laboratory. We wanna 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. 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 wanna replace that with a variable cost service, and we'll talk about why that's particularly important in pharma. I love this video. This is a new technology that's coming out of our acquisition from Zymergen. These racks, these are basically, I would consider this to be the, you know, the best sort of flexible automation platform out there. The reason I highlighted this at Ferment was if you are a customer, you know, developing a therapeutic drug, or you are working on a new agricultural product, you should not have to be a world expert in laboratory automation, right? If you wanted to use, say, cloud compute, you would, you know. 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. Well, our argument at Ginkgo is these are the sort of technologies we're gonna 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. You see this when you look at quotes, you know, from, for example, this is Markus Schindler, CSO at Novo Nordisk. He said, you know, they work with Ginkgo because of our ability to rewrite genomes to engineer new bespoke biological systems. Being able to make, you know, engineer at the scale of a whole genome, that wasn't something they could do in-house, right? You know, Alphonse at Biogen was there when we did the deal. You know, a large number of design ideas that Ginkgo could work through, and this was to help them with sort of A, B manufacturing. It was 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 wanna get more data out of that. 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. 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 stovepiped across thousands of different companies. One of the things Ginkgo's been doing is accumulating that all in one place so that you can train models on much bigger data assets. We have many different examples of this. I'm just gonna give one. This is some of the proprietary genomic data we have. Sequenced genomes largely from microbes and metagenomic sequencing from companies like Radiant Genomics and Lodo and Warp Drive Bio and work we've done at Ginkgo. It just, you know, in this chart you can see the size comparison of our proprietary data to what's out there in some public databases, right? This 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. You know, here's a quote from Sumitomo. "The great transparency and sophisticated data set was Ginkgo's strengths." Right? 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 Ginkgo's employees, right? Again, having these experts who can help you navigate all this. Similarly, similar comments from Givaudan. Great quote up here. Nicholas Oler from Lygos, Chief Technology Officer there, he said, you know, "Ginkgo's entire team was quite talented, but you know, the early results on one of our projects are stunning and supports Lygos's 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 when we announced it was, like six or nine months ago, right? Like recently. How do you get stunning results in that short period of time in biotech? The answer is you're not starting from scratch. You're building on both existing high-throughput infrastructure that you can turn on immediately and also large code base, what we would call it, like IP and genetic assets that you can make use of to go faster, right? You know, Trent from Microba, SVP of Therapeutics, you know, "Ginkgo'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, again, this is access to a scale and speed that you wouldn't otherwise get. Bob Reiter at Bayer and Crop Science talking about the open innovation approach, in other words, accessing external service providers instead of doing it in-house will let us bring higher quality biological solutions and innovative technologies to the market faster. Okay, I think this type of speed is one of the key advantages that our customers are seeing on the platform. Next, I wanted to talk about one of the ones that gets me really excited for small companies. If you have a chance, I would encourage you to go watch Jasmina's talk from Ferment. What's cool about Jasmina is she's the CEO of Arcaea. She gave a talk at Ferment, last Ferment, it was about a year and a half ago. At her talk this time, she said, you know, "A year and a half ago, I was up here announcing that we had launched the company. We, you know, I'd raised money, and we were launching Arcaea. And then here I am a year and a half later, and I'm showing you my first product. That type of speed in biotech, even in-- this is 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 round as a startup biotech company, the first thing you do is you call Alexandria Real Estate and start being showed, you know, very expensive overpriced real estate in Kendall Square and go look for a lab, and then you start to go off and buy a bunch of expensive equipment and stock that lab, and you start hiring scientists, and 9 months or 1 year later, you're doing serious work. Here, you know, 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 saved Jasmina the cost of needing to build out that lab in the first place. Hugely valuable to small startups in the biotech place. By the way, this is something that was absolutely seen with internet companies and cloud computing in the mid-2000s, right? That was, you know, the birth of AWS and these companies that could start cloud native allowed them to save on building out server farms. I think that's a real similar opportunity here in the biotech sector. Okay, so this is an important point, I think, from the standpoint of the industry's efficiency. If you look at an average small biotech company, at the beginning, you know, 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 had more R&D, you'd get to a better candidate. If you had more R&D capacity, you'd go faster, right? You're like, you know, 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. Once you get a good result, oh, well now you want more R&D again because you want to build out a really robust, bigger pipeline in that area. That's that sort of sine wave there. You know, not efficient how we're doing it now, where we're either overshooting or undershooting. 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. 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. We have some groups that are doing that. You can see, you know, Kristen at Prokarium, David at Synlogic, talking about how, you know, Prokarium's 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? I really like that, and I think that's a good way to make the whole industry more efficient. All right, I want to say those things, and I think, you know, that's a message that I gave at Ferment, and I think was, like well-received by folks in terms of why you might want to use the platform, right? Let's say you hear all that, and you're like, "I really want to use Ginkgo's platform." Okay, what do you do about it? The first thing you could do is just go to our website and click on a link that, you know, that says work with us, and you'll get an email from our scientists and folks on the commercial team, and they will ask you how you use, you know, what are you trying to accomplish, and how could Ginkgo's platform be relevant? Some people look at that and they say, "I don't have time for that conversation. I really want to know, is Ginkgo's technology relevant to my application? I want to know that ahead of time." 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. That was to basically say: If you're doing enzyme work, this is the full suite of things we have at Ginkgo that could 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, you know, half year or so, and is driving a lot of, like, funnel for us in terms of new customers getting on the platform. At Ferment, we're happy to announce four new services. Ginkgo microbe services, Ginkgo Cell Therapy Services, Ginkgo AAV Services, and Ginkgo RNA Therapeutics Services. 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 in a second, and Circularis and circular RNA. Those assets are also available to our customers as part of these services. You know, there are virtual events and things coming up that I encourage you to take a look at if you're interested in these things. I do wanna dive in just for a minute on the StrideBio one 'cause this is super exciting to me. A, the technology's very cool. I won't belabor it, but these are folks who are really just leaders, you know, in doing the protein structure around these AAV capsids and identifying, you know, key regions of the structure that would be worth modulating to work on some of the key challenges in gene therapy. In fact, a lot of people knew this about Stride, right? When we announced the acquisition, we had just an enormous amount of inbound interest from folks who were saying, "Hey, can I get access, you know, to these capsids? Can I incorporate this into my gene therapy products?" We have 30 active conversations going on right now that are folks that called us or folks that we reached out to that we knew would be interested in this sort of thing. I do think this idea of, you know, behind this, it's not just Stride's assets, it's all of Ginkgo's automation. You can see that on the left there. All the 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. We just have experience now, you know, more than 100 R&D projects that, you know, on the platform where we're working with all these different customers, you know, 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. Importantly, we also structure these deals in order to best take advantage of that for Ginkgo. You'll see us aligning, you know, 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 one is a real case study of what works well for both sides. You know, this asset it makes a lot more valuable on top of Ginkgo's platform, and the folks, you know, the previous owners of Stride will really benefit from that. Okay. I wanna talk about a really exciting update to how we're doing some of our services. The number one request we get from customers is to better technically de-risk cell engineering. Like, the three thing customers care about, they want it to be less risky, they want it to cost less, and they want it to be faster. The one they really want the most is to be less risky. All right? You know, you see this, some folks like, you know, Nick at Lygos, "Hey, as a CTO of a growing company, what can I possibly do better than work with Ginkgo to de-risk my plans," right? Keith at Optimbia, "The likelihood of success for our project specifically was seen to be improved with the technical capabilities at Ginkgo." We love that we can, you know, 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, you know, we have, like, a 90% success rate here. It's really gonna work." They'll be like, "I don't believe you." 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, you know, it might work and it might not, right? One of the things that 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're moving to a success-only payment. In other words, again, these are for shorter duration, high probability projects, and we vet them. If we succeed, you pay us. If we don't, you don't, right? This is to move away from the model today where 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 date of you were gonna move in, the house fell down, and they said, "Well, yeah, write me the check anyway, like, at least I tried," right? You know, that is, you know, that's what research work looks like. 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. We think this is really exciting. You know, in the, I think it's been a week and a half since we announced this, a lot of excitement about this. I, you know, 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 can really do well with and our customers are gonna love. Okay. Last bit I wanna talk about is our progress in biosecurity. I think we are, you know. You see this with AI, you see it, with just sort of, you know, 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? I think if you look at the history in computer science, alongside increasingly powerful computers and network computers and so on, you had the birth of the field of cybersecurity, whose job it was basically to ensure that we could do all that type of computing activity with a level of safety both, you know, 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. We have an interesting thing where fortunately those tools today are still, like, mostly centralized and relatively weak. It's not like everybody is, you know, able to be engineering cells in their basement or whatever, you know, or in their garage. That, you know, I do see that day coming in the future, so we wanna be building out biosecurity tools in advance of that. Separately, nature, as we saw with COVID-19, throws off malicious biological code already, right? You know, it is doing a lot more of this than we are as humans. These tools are also amazing tools of public health in the interim period while the tools of bioengineering are falling in cost and becoming more widely distributed. 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. We, you know, this really originated, you know, many of you are familiar with the work we were doing in, you know, sort of school testing and prisons and things like that. A lot of that work obviously is drawing down with the end of the COVID emergency order here in the U.S. Alongside that, we are, we're 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. Many of you remember this, but like, you know, first sequenced BA.2 and BA.3 coming out of this program to detect in airports. We think of it like a radar system, right? We have been working to expand that internationally. You can see some of the folks that we're either already deployed and/or have MOUs with or expanding into, and 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? Animal agriculture. Honestly, you'd also like to look after it for plant agricultural pests as well in the future, as well as doing things like, you know, hospitals and nursing homes, and in crisis settings. You know, 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, on the right-hand side of this 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 gonna be working to make that shift from our programs domestically, to these international programs where we see, you know, good interest and we see a real opportunity for like a network effect where one country is gonna be very interested in the data happening at other airports. Interestingly, what the magic of international airports is flights are coming in from all over the world. These international airport programs, we've now, you know, tested samples and done the sequencing and so on from I think more than 60 countries coming into these, to these airports. 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. We're really excited to see that develop, but it is an early and new market. All right. You know, I'd like to end on this slide. I am super excited. For me, I always get to 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. You know, that feedback loop is really working. I could go on, and it gets me excited. I think as we're doing all that and making it better, by implementing biosecurity globally, we get to do all that with care. Thanks for the time today and, 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 emails, and I'd like to make it easier for all our investors to benefit from the questions that are being asked. There have been a couple of recurring themes, so I've added two 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. 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'll 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. Both of those are dwarfed by their core holdings, most of which sit in illiquid Class B shares. 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 D. 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? Thanks. Yeah, I can take that. I can also talk about the scene change, since I'm now in Qatar, just getting back from a dinner. I mentioned this at the end of the recorded talk there. We've been expanding our biosecurity business pretty dramatically on the international side. One of our best sites is actually Hamad International Airport here in Doha, which is just a great regional airport for the area. You know, 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 very much with the flights into Atlanta. It, 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. To get to the answer to your question, on the programs. Yeah, I think one of the key things, I mean, A, you know, our program count was 13 this quarter, that's down from last quarter, that's something we're keeping an eye on. I would say we have you know, one disadvantage, you know, 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, we have a good sense. You know, deals don't close in a week, you know, they close over months. We have a good sense of what's in the pipeline. 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 startup companies in kind of non-biopharma biotech. Things like industrial biotechnology. Those companies are having a harder time accessing capital in the sort of tighter capital market. You know, it's one of the areas that venture capitalists are putting less money into, that is making it tougher. It's at least extending deal close time and things like that, with our programs in that area. On the other hand, again, Ginkgo 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. Biopharma, you are still seeing a ton of activity both with startups and large companies. We mentioned how much energy we've been getting out of the StrideBio acquisition, but we also have, you know, healthy pipeline in cell therapy applications, mRNA therapeutics applications, things like that. I think you'll see us shift a little more towards biopharma, but we do have a robust sales pipeline coming up, 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. 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. For folks that don't know, anytime 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. We thought we might share some of their questions with you all as well. 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? Yeah. I think this is actually a big deal. I touched on this a little bit at Ferment. One of the things that's happening is because of the impact of sort of ChatGPT and the sense that, like, large data plus generative AI models equals change in industries, you now have pretty much, like, every large corporation looking at what the impact of this is gonna be on them. That, you know, that's auto companies, that's, you know, chip companies, that's media companies, and it's also biotech, biopharma companies, you know, ag companies, and so on. In order for a customer to use Ginkgo's platform, they have to choose to make a change, right? 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 wanna change, and sometimes it's they're greedy, they're trying to add a new product, sometimes that things aren't going well and they wanna try something new, and sometimes something new comes along in the kinda in the atmosphere that makes them think they need to take a look. That is what's going on with generative AI. You have people saying, "Hey, I think I should be looking at what happens if there's big data and ML models in my space." The beauty of Ginkgo is we are a great place to generate huge data assets, I think AI is a core strategy. It is a very positive win 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, you know, 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 data to train these types of models. 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. 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. I've just opened your line. Please go ahead. All right. Can you guys hear me? Yeah. Excellent. This is Michael Friedman on for Rahul today. Okay. Thanks very much for taking our question. Congratulations on such a successful Ferment event. That was a really spectacular display with some glowing reviews from your customers, thanks for throwing that, you guys. Yeah, thanks for coming. It was a pleasure to be there. Okay, first question is on the overall IP strategy. I'm wondering what can you tell us about how, you know, this year versus perhaps last year Ginkgo's 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 was some serious pushback among customers saying, when, with Ginkgo's attitude toward holding onto the IP that you develop. I wonder if you could shed some light on all that. Yeah, I can touch on this one. Yeah, one of the biggest challenges we've 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 gonna fund a chunk of it, and you're gonna keep the rights to reuse it and go off and build a business on the back of my investment," right? You know, short answer was yes, we were doing that in a number of cases. What's happened over time as we're accumulating assets in all these areas, is we now, you know, and you can see this with those four services I announced at Ferment, each one of those services has specific code base in that area. 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." That would have been true, and I did do sales like that a year and a half, two 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, you know, great capsids from StrideBio that you can get access to. By the way, here's some, you know, other data, you know, capsid work we've done to discover some new stuff," and so on. That really, really helps on the sales side. 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. 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, you know, I don't know, in some cases by years. I think there's a real. There's gonna 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. Thank you very much. I think as I follow up, this one will probably be for Mark. Around, you know, at the end of last year, we were waiting on some lumpy milestones. We're wondering, curious about the timing on that. You know, I'm looking at the cell engineering revenue where you know, $1 million of the total $34 was downstream revenue. Looking at the appendix of the presentation today where $13 million is non-cash consideration of the total $34. Wonder if you could just help us sort through those, at least the definitions, and shed light on these things then, and then perhaps talk about those lumpy milestones? Okay, I'll take the two points in turn, first on the lumpy milestones. So really it's the same comment I think that we made on our last earnings call, which is yes, there were the two milestones that at one point we had been expecting to hit in Q4, which spilled into 2023. Yes, we are still going after those two milestones. We believe the technical work on that is substantially complete. 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. So those are still in play, but timing is just uncertain on that. With respect to the second point that you made on non-cash consideration, yes, first of all, the conclusion that substantially all of the revenue in the Q1 related to services revenue, that's correct. The supplement in the appendix shows you, like you said, the component of services or total revenue that is non-cash. 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. Not just for downstream value share, but also for the upfront or the service fee consideration. That's why we're giving you that additional sort of data point. Does that answer the question? It sure does. Yeah, I appreciate that. I'll jump back in the queue. All right. Thanks, Mike. All right. Edmund Tu from Morgan Stanley, I've just gone ahead and opened your line. Hi, guys. Thanks for taking my questions. 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 benefits of other customers? What safeguards do you have in place to make customers feel comfortable? Yeah, this is a key question, and something we talk a lot about with customers, so happy to share about it. The number one thing is new IP developed in a project for a customer for their application is exclusively licensed to them for that application. If you're developing a, you know, gene therapy against a disease target, whatever it is, you're gonna get the rights to the IP developed with the work done for you for your drug, and no one else can use it for that. They're not gonna get to take what you did and compete with you directly. Where we differ a little bit is we would say, well, if that capsid had use, for example, in some other, you know, disease area, some totally different thing than what you're really working on or just in pharmaceuticals generally, you know, we'd like to be able to reuse that asset. That, and that's where we end up arguing with customers, and kinda and figuring out what's right, you know. I would say the general rule is we're most interested in things that have kind of broad reusability across lots of projects, right? You know, capsids are a good example. Certain internal sequences on CARs are a good example. Like, there's just certain things that we think are... don't make up a whole drug, but boy, if they worked better, they would make it a lot easier to get a lot of drugs to market, right? So that tends to be the kind of thing that we fight hard to make sure we do have broad rights to. If it's something ultra specific to the customer, then that's kind of less relevant. That's how we do it. I think over time, as we accumulate more and more assets, this conversation becomes easier, right? You're sort of coming in and I'm saying, "Listen, I've got 90% of what's necessary for this project, but I, you know, you're gonna have to agree to this for the other 10% that you're gonna add to it, or else we just can't work together, and you're gonna want the 90." Does that make sense? Yeah, got it. That's very helpful. Then Jason, on a separate note, it sounds like you still feel like that the funding pressures are actually driving a push towards greater outsourcing. I mean, clearly you've seen the weakness get worse even with some of the CROs now acknowledging weaker spend at mid-cap biotechs. I just wanted to understand what insulates you more versus the traditional CROs. Just to be clear, like I said, for industrial biotech, my experience is I think it is, like, causing pushback on us, right? I don't think we're seeing more outsourcing necessarily in industrial biotech, we're just seeing less spending in industrial biotech. There I think we see more sensitivity. When it comes to, like, these other areas, I mean, the honest truth is we're not that penetrated into these areas, right? If I was off already serving every biopharma company and they cut their R&D spending 30%, I'd be back 30%. You know, the reality is I'm in, you know, an integer number of biopharma companies out of 1,000, you know, right? We just have so much room to run through, you know, adding new customers. I think we're just not as sensitive to it yet. It does matter if that sector just stops spending on R&D. Mm-hmm Which is a little bit what we're seeing in some of the industrial biotech spaces. In biopharma, that's not the case. There's plenty of opportunity for us. Got it. Thank you for the color and the time. All right. Thanks, Edmund. All right, Gaurav, I've just opened your line. Feel free to go ahead. Awesome. Thanks, guys, for taking my question. I know it's about midnight over there, Jason, so I'll keep it quick. On the new 13 programs, right, this quarter, you know, are you guys able to break out that end market split or even the downstream potential, or is that something that, you know, we should expect only on an annual basis? Mark, you wanna take that? Yeah. Generally speaking, we would only be updating the downstream value share sort of metrics that we had talked about on the last call, we think once, sort of annually. We did announce, just recently a large program with BI, you've got around $400 million of downstream milestone potential from that particular contract. I would just say the 13 programs are spread, like, pretty broadly across the types of downstream value share that we get. I mean, there's a good chunk of royalty-bearing programs in there. There's a few that are milestone-based, a few that are equity-based. It's I think it's just representative of the normal sort of mix. Yep, that makes sense. Thanks, Mark. Just one quick follow-up from me. You know, on the, you know, the new four service offerings, just to make sure I understand it correctly, right? Are these, you know, four new service offering capabilities Ginkgo previously couldn't address on the platform, or are they just, you know, a more structured and focused program version of what they were prior? Yeah. That's an awesome question. Okay. Like, how Ginkgo runs basically is to have a large general platform. It's a mix of software and automation and a variety of genetic and IP and data assets that are all available to a scientist who works at Ginkgo on a customer project to order things from. Like, that's what's happening internally. All right. Mm-hmm. Now, I can walk up to a customer and say, like, "Look at this 300,000 sq ft facility and all these robotics. Could it be useful to you?" Right? They don't know how to translate that. You know, like, they're used to seeing scientists at lab benches working by hand. Like, we do R&D in a very different way. So the point of the services is to speak in the language of the customer. Okay? It is a sales object, right? It is a way for us to say, "Let me just be very clear. This is what we can do in this category. Let me name it for you. You know, Ginkgo does AAV." Like for example, us acquiring StrideBio, in part great assets. People are calling us about the assets. It's also just people are like, "Hey, Jason, wow, I didn't know you guys were working in AAVs." We've been working in AAVs for two years. You know, right? Like, you know, we have an announced deal with Selecta, right? We did the deal with Biogen, right? Still, you know, but the acquisition of Stride was also in part just a marketing activity in the biopharma space so that people knew, right? That's kind of the goal with the services. Like, as a general platform, it's great 'cause our TAM is huge. The downside is people don't understand what we can do for them. Expect more services, right? I'll do as many of these as make any dang sense to customers, frankly. expect to see us experiment there and see where we're landing and having, you know, something to help customers better understand how to leverage the Ginkgo platform. Awesome. That cleared it up. Thanks, Jason. Thanks, everyone, and cheers. Talk soon. Thanks, guys. Thanks. All right. Next question will come from Matt Sykes at Goldman Sachs. Just a reminder to the other analysts on the call that if you'd like to ask a question, please do raise your hand so that I know to call on you. Thanks so much. All right, Matt, your line should be open. Hi, can you hear me? It doesn't look like Matt. This is Elizabeth Koslosky on for Matt. I understand early on, but could you provide any color on how the success only payments has impacted your win rate at this point with customers? Yeah. This is a cool idea, I think. I mean, again, just to restate what we're trying to accomplish here, like the larger mission of Ginkgo, just to be clear, is to make it easier to engineer biology. All right? And by engineer means something, right? Like when you engineer something, there is a predictable set of equations that let you know how to build a bridge or a microchip or whatever, right? When you do research on a cell, like engineering, genetically engineering a cell, people don't think of it really like engineering. You're doing science, right? And you're exploring space, and you don't know if it's gonna work and all these things. We're trying to generally move into engineering. One of the things we noticed was for certain types of projects, doing these certain protein discovery projects and enzymes and optimization projects, certain protein production, it was starting to feel like engineering, right? Like, we were just seeing extremely high success rates. We knew which projects were gonna be hard a priori and which ones were gonna be easy. We would tell customers, we didn't show data, and they'd still be like, "I'm not gonna spend that much on a research project." What we're saying now is, "Fine, it's not a research project anymore, it's an engineering project. You will pay... and you'll pay on delivery." That absolutely is working. We... there's probably seven or eight projects in the sales pipe right now that were previous nos since we announced it. That's, you know, just stuff we had been talking to people about before. Yeah, I think it's a really exciting idea, and we'll see how it plays out over the next couple of quarters, but early looks good. It is good. You know, I mean, look, like, if we're wrong, we're taking a risk, right? You know? Like customers are getting a real value out of this, you know? It's not like we're not offering them something here. But I like our odds and I like our technical success rate in these categories. Yeah. That's super helpful. Thank you. Oh, one last point. Sorry. Yeah. We also are aiming for the shorter projects, right? I'm not, like, you know, expect these projects to be more like 6-12-month projects, not like two or three-year projects where we would be, you know. If it's a longer project, we break it up into smaller success-based pieces. I don't wanna go too far out on a limb, on a project where we're waiting to see if we're technically successful to get paid, if that makes sense. Right. Yeah, that definitely makes sense. On biosecurity revenue, came in much higher than our expectations. I know you talked a little bit about it, but could you talk through your updated strategy as it relates to biosecurity potentially becoming a more durable part of revenue than you might have previously expected? I think you said 20% is recurring, but how should we think about the work down of the non-recurring part, and then also a long-term growth rate of the recurring part? Why don't I start with just to get the numbers kind of straight here. First of all, in the quarter, 20%, roughly speaking, of the biosecurity revenue came from those, what we believe will be more recurring sources. That would not be the state kind of K-12 school COVID testing programs, for example. You'll also note that we didn't change the guidance on biosecurity, what you are gonna see is really one more sort of partial quarter where we do still have some K-12 school testing revenues coming into the numbers. That is expected to drop off pretty dramatically after the Q2. We have sort of little, very little to nothing in the guide for the second half of the year relating to that legacy K-12 business. The Q1 it was solid, like you mentioned, but that is because we were still getting a good chunk of K-12 revenue. We'll get a little bit more in the Q2 and then it's gonna fall off and sort of thereafter. The bulk of the biosecurity business is the sort of the new sources of revenue. You can kind of work, you know, what that sort of will tell you I guess, is that a good portion of the $100 million will be realized in the first half of the year, and then thereafter we're... it's almost like a reset on a lower revenue base that we expect to increase over time that will be largely the new sources of revenue. Okay, great. Helpful. Just to comment on what those will be, that's around things like these airport programs, what we consider to be like persistent monitoring. I think there's a few different places that could happen, but we're probably most excited about what we're seeing in the airports. Great. Thank you. All right. Thanks so much. Final call if there are any other questions to raise your hand. We are just about at time, and so for once, Ginkgo has hosted a call that didn't run over. New KPI for me. We'll let Jason go catch his next flight. I was gonna say, it's good that I can make my flight. Appreciate everyone joining us this quarter and we'll see you next time. Thanks, everyone.