Hi everyone. I'm Anna Marie Wagner. I'm the Senior Vice President of Corporate Development for Ginkgo Bioworks. Thank you so much for joining us today as we announce the business combination between Ginkgo and Soaring Eagle Acquisition Corp. Today, you'll hear from Jason Kelly, our Co-Founder and CEO, as well as Harry Sloan, the CEO of Soaring Eagle, and Dr. Arie Belldegrun, the Founder of Kite Pharma and Allogene Therapeutics, both of whom will be joining our board of directors. Now for the most exciting part of the presentation, I wanted to share that on the call today, we'll be making some forward-looking statements which involve risks and uncertainties. We refer you to the slides accompanying today's presentation and the 8-K filed today by Soaring Eagle with the Securities and Exchange Commission for further information regarding these risks and uncertainties. We'll also be referring today to certain non-GAAP financial measures, including Foundry Billable Revenue, net present value, and adjusted EBITDA that we use in measuring our financial performance. A reconciliation of these non-GAAP measures to their nearest comparable GAAP counterpart, where available, can be found in the appendix of this presentation, which has also been posted to our investor relations website at ginkgobioworks.com/investors. Before getting the Harry, Arie, Jason, and the Ginkgo story, I'll start with a short overview of what we're announcing today and m ore information can be found in the investor presentation. We're extremely excited to announce that we have signed a definitive agreement to merge with Soaring Eagle in a transaction valuing Ginkgo at a pre-money equity value of $15 billion. Upon the completion of the combination, Ginkgo will become a public company and will be trading on the New York Stock Exchange under a new ticker, which I'm going to try to keep under wraps for a few more days, but you can certainly feel free to guess. We expect the transaction to generate proceeds of $2.5 billion, which Ginkgo will use to further scale our horizontal platform for cell programming. These proceeds consist of $1.725 billion of cash in the Soaring Eagle Trust and a $775 million fully committed PIPE, including anchor investments from Baillie Gifford, Putnam Investments, and Morgan Stanley Investment Management. All proceeds generated will go towards building the business. We're very excited about our partnership with Soaring Eagle, and as sponsors of the transaction, they both invested directly in the PIPE and have agreed to convert 30% of their promote interest to an earn-out structure in support of their strong long-term view of Ginkgo's potential. With that, I'm thrilled to turn the call over to Harry Sloan, the CEO of Soaring Eagle, to kick us off. Harry, take it away. Well, thank you, Anna Marie. It's a great pleasure for myself and my partners at Soaring Eagle, Jeff Sagansky and Eli Baker, to be announcing this transaction with Ginkgo today. We've had a number of successful SPAC mergers. What we really prefer and what we think works best for a SPAC are these companies that we call category of one. These are companies that are not only leaders in their field, but actually created the field themselves, and t his is certainly the case with Ginkgo and synthetic biology. Our two previous deals from last year, DraftKings and Skillz, like Ginkgo, also were platforms that benefited from very powerful network effects in their platforms. When we raised the Soaring Eagle SPAC, which was our seventh, it was about two months ago. We were introduced to Ginkgo by one of our board members, Josh Kazam, who's partners with Dr. Arie Belldegrun, who you may know as one of the most successful biotech founders and industry leaders. He's the founder of such companies as Kite, Allogene, and Kronos. He probably has more breakthrough therapies approved by the FDA in the last decade than anybody, including immunotherapy with Kite. Anyway, it was with Arie's very deep knowledge in life science and his excitement about Ginkgo, which caused us to decide to partner, which is kind of unprecedented with a SPAC, where the SPAC partners with real industry expertise, and t ogether, Arie and us are doing this transaction. We're both going to join the board. We're both investing heavily in the PIPE as well. We feel, Arie and I, that by raising this $2.5 billion, the $1.725 from our SPAC and the $775 PIPE, $2.5 billion total, by raising that money, we're going to be able to create, with Jason, a massively scaled company that'll continue to lead the synthetic bio revolution, but also can create deeper moats around the business. It's been a terrific experience for both Arie and I, getting to know the five founders who have been together for 20 years at Ginkgo, but not just them. All of the employees share this sense of mission and purpose. Beyond that, they also have a very specific execution strategy, and it's that strategy that Jason Kelly, the founder, is going to talk to you about. First, I want to turn this over to Arie also to say a few words. Arie? Thank you, Harry, and good afternoon. My name is Arie Belldegrun, and I'm an academic physician and a scientist, and in the past 25 years, involved in building biotechnology companies with the sole focus of bringing innovative and life-saving therapies to patients. In that context, I'm excited to join the Ginkgo family as a board member with the hope of expanding the opportunities to life science by using the state-of-the-art technologies available at Ginkgo to assist biopharma companies and expedite their drug development. We live in an era of revolution in biology and in life science, which is not only driven by better understanding of fundamental molecular genetics, but also by technological advancements of tools like DNA sequencing, gene editing, DNA synthesis, bioinformatics, machine learning, and computational biology. Synthetic biology takes advantage of all of these technologies to engineer cells, whether they are yeast cells or mammalian cells, and reprogram cellular DNA. It has a potential to create technological advancements in everything from therapeutics to food to materials. I believe that the greatest future, however, for this opportunity of synthetic biology is in the area of biotech, drug development, and drug manufacturing. The opportunity for ultra-high throughput screening of compounds, selection of drug candidates, rational therapeutic design, and the opportunity to effectively synthesize therapeutics from cells at scale will increase efficiency of drug development, and significantly reduce cost of production. Areas like engineering living medicines, so-called cell therapy, gene therapy, engineering proteins like cytokines and biologics will all have benefits from synthetic biology. My personal interest in synthetic biology dates back over five years ago when we made our first entry to the space by acquiring technology developed in academia at UCSF, University of California, San Francisco, in the area of CAR T or chimeric antigen receptor T and cell circuits, T cell circuits. It is very quickly became apparent to us that while powerful, the technology is labor intensive and requires significant resources beyond the capability of most small biotech companies. When I first visited with Jason Kelly, the Foundry at Ginkgo, I was impressed by the platform developed there to engineer biology and the investment made in software, in automation, in robotics, while incorporating the latest technologies in gene sequencing, DNA synthesis, and all the rest of technology that I've described. I thought that I would buy into this great vision of the founders and their capable team behind Ginkgo. Harry, I'm so pleased to be working with you together and developing this vision. Arie, thank you for bringing this to us. Now let me turn it over to Jason Kelly, the Founder of Ginkgo. Thanks, Harry, so I'll give a little extra color on the Eagle, folks. You know, obviously, we talked to a bunch of SPAC sponsors, and I think a few things got us really excited to work with the Eagle team. Number one, you know, adding Ari to the team is a huge value driver for us here at Ginkgo. In the last year, we've done more and more in the area of therapeutics, you know, starting with our work in COVID-19 vaccines, and expanding from there into more and more in nucleic acid therapies. Ari is really just has such a brand in cell and gene therapy, but If you want to do the kind of large commercial partnerships that we do with pharma companies, you need breakthrough technology, and you also need credibility with that community. We've got the technology here at Ginkgo, Ari gives us a big speed up in terms of speaking the language of sort of big pharma, big biotech to get these deals done, so t hrilled to have Ari join the team. Also, the Eagle folks invested directly in the PIPE with their personal money. We like to see people putting their money where their mouth is. Finally, they've been doing SPACs for nearly a decade. They're operationally excellent at this and we want to get back to building the business. Velocity in the process here was really important. Harry and Ari and the team, really thrilled to be working with you on this. I'm going to dig in now a little bit on sort of the story of Ginkgo. I'll share a little bit on my background and then highlight some of the key competitive modes around the business, how we're planning to scale the company, and also some of the key KPIs for us as a public business. A little on my background. I was originally technically trained. I did my undergrad at MIT in chemical engineering, stayed on for a PhD in biological engineering, and that was where I actually met the other founders of Ginkgo. There was four of us doing grad school together, and then the fifth founder was a professor at MIT, Tom Knight, and I'll show you a picture of Tom I quite like right here up on the left. This is Tom in 1972, the early '70s at MIT. Tom was a professor in electrical engineering and computer science. To give you a sense of the era, that machine in the middle of the photo there is a minicomputer, r ight? Tom came up doing punch card computing, mainframes, early ARPANET work at MIT, taught the semiconductor design course for many years, dyed-in-the-wool electrical engineer. Mid 1990s, he decides interesting thing to program in the future, not going to be computers, it's going to be cells, and h is core insight was, look, inside of every cell is digital code in the form of DNA, r ight? It's A, T, Cs and Gs. It's not zeros and ones. You can read that code with DNA sequencing, genomics, and you can write that code with DNA synthesis, DNA printing, and If you can read and write code and you have a machine to run it, well, that's programming, r ight? Tom, to his credit, in his 40s, starts taking undergraduate biology lab classes at MIT, sets up a wet lab in the computer science building, and shifts his focus from programming computers to programming cells, r ight? He's still the same guy. When he looked at the biotech industry and how we did this work, you know, he recognized certain similarities to what he had seen in software. When he looked at the computer industry, he noticed the end applications were often market-specific, right. You had certain software for electronic medical records or for something in media or telecom and so on. The low-level tools that you use to program a computer, okay, so think the programming languages, the operating systems, the chips that executed the code, well, those were common across all markets. Tom's view was, well, the reason for that was that at the heart of the computer was common code. All that software at the top all ran on the same low-level zeros and ones on the computer. When he looked at biology, he saw the same thing. At a low level, every cell runs on DNA. It's A, T, Cs and Gs. It's common across all of life, and the underlying machinery in those cells that execute that code, also the same. Why was it that in the biotech industry, you had each company developing its own custom technology stack vertically rather than big horizontal platforms like we saw in computing? Tom's view was, it's common code, we have to build those platforms, and so h e really set us on that journey. There's an old picture of us here at MIT but w e met Tom in 2002. The founder's been working together that whole time on basically the same problem. What is the core technology that makes it cheaper and easier and faster to program a cell, right? What application? Who cares, right? That was never really what motivated us. The underlying platform, what was the motivation? Now today, fast-forward, Ginkgo has a $100 million joint venture with Bayer Crop Science to engineer microbes to produce fertilizer. That's a $70 billion market, 4% of global greenhouse gas, big impact and big market opportunity. We work with Roche in antibiotics. We did a sprint project with Moderna last March, around vaccines. We've worked in the animal-free meat space, r eally exciting end applications that we're quite proud of, but w hat gets us up every morning is the underlying platform that makes all those applications possible, o kay. I wanted to land that because that impacts our business model, how the technology gets built, and everything else is really rooted in this fundamental idea that we're a platform-first company. I'll make one other point about this as to why we're taking the company public now. COVID-19, not going to say this is a silver lining, but there is an impact of COVID-19, which is that biology is on the public's mind, right. I f you look, you know, proteins are on the cover of The New York Times. I was on 60 Minutes three weeks ago. My parents know what PCR is all of a sudden. If I want to explain cell programming to people, I can explain that you got a piece of mRNA code put in your arm, it's going to turn your cells on, make a little protein, and set off your immune system to get us out of this mess, right. That is a visceral opportunity to communicate with the public about cell programming, and it's a great moment for us to come out as Ginkgo and say, "Listen, we're going to make that common platform, that sort of Amazon Web Services, that Windows, that universal infrastructure that makes it cheaper and easier and faster to program cells." I do think it's a unique moment. I'll also point out, computers are amazing, right. They're programmable machines. They get better every year. At the end of the day, they move information around. They move bits. If you look at the industries they disrupted, it was all the information-based industries, you know, media, telecom, finance, advertising, things like that. What they didn't disrupt? Hamburgers, right. Physical goods industries. Biology, on the other hand, is programmable. You put different code in, it does different things, but it doesn't move information around, it moves atoms around. If you think about the industries that are going to get disrupted, it's all your physical goods industries. These are some of the biggest environmental and social challenges we have. If you look at the UN Sustainable Development Goals, they land in these areas of pharmaceuticals, biotechnology, industrial, and environmental cleanup, food and ag, consumer technology, electronics manufacturing, and things like that. All these physical industries are ultimately going to have a much bigger impact from cell programming than we ever saw from computer programming. From a standpoint of ESG and having impact on these bigger problems, I think cell programming is one of the best tools in our toolbox coming up. Here's a slide that explains a little bit about the market. For folks that are deep in the life sciences sector, you can see at the top, a set of our customers. At the bottom, we have our technology suppliers that we would roll into a common horizontal platform, and I'll talk in a minute about what that platform looks like, but j ust to put us in the ecosystem. All right. I want to talk a little bit about the unit of work that we do for a customer. It's what we call a Cell Program. The basic idea, and actually, I'll give you an example from one of our customers. This is Motif FoodWorks, is a company in the animal-free meat space. To give you an idea of what cell programs are capable of. If you're familiar with Impossible Foods, if you ever had a bite into an Impossible Burger, it's a veggie burger, y ou bite into it, and it bleeds. That's a bit weird, right? There's not a lot of blood in plants. Where are they getting that from? What they've done is they found a gene for hemoglobin. It's what makes blood red. It's a protein, then t hey took brewer's yeast, the kind of yeast you would use to make beer, and they take that DNA code of the hemoglobin, they put it into the yeast. They brew it up in effectively a brewery, except instead of beer coming out of it, hemoglobin comes out. They add that back into a burger, and suddenly it smells right and tastes right and cooks right. It's the Impossible Whopper at Burger King nationwide in the U.S. That is, in my view, the first really truly disruptive product in the beef space in probably 100 years, enabled by, at the back end, a cell program that has producing a magical ingredient in there that gives that burger its meaty taste, right. Motif wants to do a similar thing. They want to pursue a whole range of different animal proteins that otherwise wouldn't be available in plants, and then make those available to food developers, so that we can have more Impossible Burgers. They came to us with that spec, "Get me a yeast that produces these animal proteins." We did a one-year and nine-month project where we went looking through hundreds of different proteins, identified ones of interest to Motif, put them into that yeast, optimized the yeast. We made lots of changes to the genome, the code of the yeast to make it produce more of that protein. Then within a year, we were producing enough protein that they could put it into applications testing, like a yogurt or a burger, and then w ithin another nine months, we had tuned that yeast up enough to produce ultimately kilograms of that product in a commercial setting, so in a pilot scale setting. That project, that one year, nine months, that's a cell program, and a t the end of it, we gave our customer two things. One, a tube with that yeast in it, with the genome that has the code that does what they want for their spec, and then a licensing agreement that says, "You can use the IP that's embedded in this code." All right. For that, we get paid in two ways, and so d uring the one year and nine months, we get paid on a usage basis, t hink like a cloud computing provider, you would pay for use of their data center. Same idea, except instead of a data center, we're charging for use of our Foundry, r ight. Right behind me here in Boston, we have about a 200,000 sq ft facility, I'll show you a video of, where we've used automation to do lab work. We charge to do that work during the one year and nine months. Then at the end, in exchange for the code that we've written, we would take a value share on the end product, o kay. That could either be a royalty on the sale of that animal protein, or in lieu of the royalty, in the case of Motif, we could take equity in the company instead for the work that we did in the lab. That gives us some way to reach into the end value of the app. Those are the two ways we make money from a cell program. I'll show you in a minute the kind of TAMs for those opportunities and also how that's looked over time for us in terms of money that's come into Ginkgo. All right. Before I do that, though, why does somebody work with us to do a Cell Program in the first place, right? Why not do it themselves? The reason they work with us is we have two proprietary assets that they want to get access to. One is what we call our Foundry, and so I talked about this already, but it's basically if you want to get a PhD in bioengineering at MIT like I did, it's basically five years of standing at a lab bench, moving clear liquids around a bench by hand, a ll right? It's real work. What we've done is we've taken that work, that lab work, and we've moved it on to robotics and automation, and we've standardized it. I'll show you some stats from that in a minute. Importantly, by automating it, we've created a scale economic. This is similar to being an auto manufacturer or a microchip fabricator. The bigger your factory, the lower the cost to do the work. Same exact idea in our Foundry. We've been roughly tripling the output of that facility and halving the cost for those lab operations annually, and I'll show you some data on that in just a minute. The second asset we have is what we call our Codebase, and it's a data asset. As we do these projects, we learn more and more about how to program cells, and then Ginkgo retains that IP and data so that we can use it for future projects. Every project we do actually benefits customers in the future because we can retain some of that data and learnings and share it across projects. That's why people come to work with us, is to access those two things, and so j ust to show you a little viscerally what's going on in our Foundry, here's a video of our new facility, Bioworks 5. What you're looking at here are these independent cars that can move on these magnetic tracks. It's a material transport system, and it allows us to move that little plastic plate you just saw there, is one example of a piece of laboratory consumable that you would use in doing the lab work of cell programming. That car system brings those little plates over to these green robotic arms that you see here in what we call a work cell. It's basically an arm that can move that laboratory plate ware onto a set of equipment. That equipment could be different things on different work cells. Sometimes it's something simple like a plate sealer and de-sealer, something like a centrifuge, or a piece of analytical equipment. At the end of the day, there is a roughly standardized set of lab equipment you use to get your work done, and we want to make all that equipment available on different work cells around a facility like this. What you'll see as you look down one of these lines here, like you just saw, a line of cars moving on the track, and then alongside that track, a set of work cells. We have three lines like that running through Bioworks 5, it's in about an 18,000 sq ft room and t he key idea here is to take the human error out of the laboratory work associated with cell engineering and to drive scale. This is our newest facility. In fact, in the numbers I'll show you in a minute, Bioworks 5 does not contribute to those yet at all. This is a facility that'll be coming online later this year. The gains we've seen have been actually using what we call walk-up automation. Like you saw some videos of scientists putting plates onto machines, right? That's a higher level of labor utilization. We have probably about 20 to 30 people running one of our current walk-up automation facilities. In a fully automated facility like this, it would be more like four or five operators. We get more leverage out of the infrastructure and reduced operator error. Scientists can spend more time on doing design. That's an example of our newest facility and where we're headed. One of the key drivers for reducing costs, which is automation. I just want to talk for a minute before I move on about our board here at Ginkgo so w e're very fortunate. We have a great set of independent directors. Shyam Sankar, who's the CEO at Palantir, and Christian Henry have both been on the board for more than four years. Shyam is excellent. He's operationally excellent. The way that actually Palantir sells in the market is these big enterprise deals. It's actually quite similar to the way Ginkgo does our work in terms of the commercial side. Christian Henry, longtime CFO at Illumina, more recently the CEO at PacBio. You know, Christian, just longtime public market CFO, has helped us think about what numbers we should be tracking internally, how to have accountability around meeting those, and building up that muscle at Ginkgo over the last four years. They'll both be staying on, Shyam as the Comp Committee Chair, and Christian as the Audit Committee Chair. Finally, our Board Chairman, Marijn Dekkers, was the last CEO of Bayer, and prior to that, CEO of Thermo Fisher. He's actually the fellow who bought Fisher, and sort of created a lot of the structure of what is the modern Thermo Fisher. We're really lucky to have Marijn. He's been a great help with us in the strategic direction of the company. We're adding Arie, like I already mentioned, and we're excited about that, and Harry as well. Okay. I want to highlight just briefly the TAM, the market opportunity for those two sources of revenue for Ginkgo from our cell programs. On the Foundry revenue side, when people pay us on a usage basis during that year and nine months, like I mentioned for Motif, it's usually up to about a three-year project. This is the market we can go after. This is a report from Piper that shows, you know, in 2020, about $33 billion being spent on cell engineering. $21 billion on labor, $12 billion on spending at companies like Thermo on sort of the laboratory tools and reagents. That's kind of like, if I were to draw an analogy, like on-prem IT for servers, and we're suggesting why don't you move that to the cloud? It's just worth noting a big spend across thousands of R&D projects happening in the biotech industry and the chemicals, ag, and pharma biotech, where people are spending money to do this work by hand. We're suggesting that R&D budget could be better spent on Foundry revenue at Ginkgo and utilizing our infrastructure so you get more leverage per dollar. Then the second way, the second TAM for us is, again, we do that reach-in, that value share, on the apps developed on our platform. This is kind of like an app store business model. The market, this is a McKinsey report from last year, is $2 trillion to $4 trillion for bioengineered products like that Impossible Burger, as an example. W e see an enormous opportunity there, and the more our platform improves, the more this market should be able to come into being. W e want to be a big enabler of driving these bioengineered products and take a small piece of that big pie. All right. I want to highlight, you know, one thing that's great about Ginkgo is we have validated the platform over the last six, seven years in terms of showing its relevance in different markets. We started in consumer tech, things like flavors and fragrances, industrial and environmental, a lot of products in the chemicals industry, ag, food and nutrition, pharma biotech. You can see more than 70 programs we've done in partnership with customers over the last several years, and you can see some of those customers here, r ight? We worked with some of the biggest flavor and fragrance companies. We work with Cronos, which has a large ownership position from Altria up in Canada. It's a Canadian cannabis company, t o basically do similar things like that heme, except instead of producing a animal protein, we've been engineering cells to produce these cannabinoids. Instead of growing a big field of cannabis, you would instead just run a brewery and produce those same cannabinoids to help with the cost of goods in that industry and scale. We work with Cargill and Ajinomoto, some of the biggest animal feed players in the world, G enomatica in chemicals. We just announced a deal several weeks ago with Corteva, one of the biggest ag companies. I mentioned that $100 million joint venture with Bayer Crop Science to work in the fertilizer industry. We work with Roche in antibiotics, Synlogic for gut bacteria. I mentioned the project we did last year with Moderna. Enormous range of end markets. For us to work with someone, we don't develop our own products, so a ll of these cell programs are launched in partnership with a third party. That's another way, again, to show that our platform has relevance in a wide range. I want to show some specific numbers from the Foundry so you get a sense of our scale, r ight? On the right-hand side, this is a key metric for us internally. It's what we call a strain test. It basically means I've made a deliberate change to the genome of a cell, so I've changed the code, I've grown it up, and then I've run some tests on it. Every time we do one of those tests on an engineered cell, it counts as a strain test. We went from doing 10 to 100 strain tests per day back in 2015 to 10 to 100,000 strain tests per day now. Roughly, you can see it there, about 3.5x increase annually in the number of designs we can try, and w hy that's important, it's kind of like software developers iterating through different versions of their code. You never get it right the first time. By being able to try more designs per dollar, which is what I'll show you on the next slide here, we get to have a better chance of success for our customers when we take on a cell programming project. If you look, the cost for us to do a strain test has fallen from nearly $1,000 back in 2015, down to the tens of dollars today. That falling cost is, again, a huge advantage for us in terms of value we can give back to customers so that they can get more out of that R&D budget that I was mentioning earlier. All right. The other thing I want to mention, so that's really the Foundry. I also want to talk about the Codebase. The origin of the Codebase is actually when Tom first came into biology from computer science, and he asked the question, "Oh, okay, so you guys are programming." I see that. It's DNA code. You can read and write it. That makes sense. Where are the code libraries, r ight? Like you're telling me you write new code, but you don't build on the preexisting code that other people have written and debugged to make your more complicated programs? The answer is no. The state-of-the-art today in biotechnology is you pull some technology out of an academic lab, and you keep tweaking it at your company, and you take it to market as sort of a one-off project. There isn't this sense that you are accumulating reusable assets that have been carefully curated to make them reusable for someone in the future that didn't develop it in the first place, and t hat's what we've been doing here at Ginkgo. We're very deliberate about how we collect the data and do the work we do in the lab so that we can accumulate assets that we can reuse in the future for projects. To give you an example, I mentioned this project we did for Motif. Well, we made a lot of changes to that yeast to make it be able to produce a lot of these animal proteins. Well, we had another company come to us recently, a company called Kalow, that wants to do a similar thing where you would produce sort of human proteins that might be relevant in cosmetics as an example of a project. What's exciting is we can use that same yeast, right? We could bring that yeast to Bayer, if it's relevant to the projects here, to help reduce the amount of time you would need to do that R&D project. That's because at the end of the day, that intellectual property accumulates here at Ginkgo, rather than having it split and fragmented across hundreds of different companies across the industry. Ultimately, our view is the whole industry benefits from this, because a company in the food space doesn't care if somebody in pharma or cosmetics makes use of some genetic assets that were developed in their projects. It's no harm to them. Today, a lot of that value is lost as that code is spread across all these different companies in the industry. That's a real opportunity. This ends up being the positive feedback loop of the company, is every time we add a new program, our scale goes up in the Foundry, which reduces our costs. Reduced costs leads to more programs signed up by customers, which leads to more Codebase, which increases the odds of success. The more programs we add, the better we get at doing this, the more customers come to work with us. It's that value we give back to customers that allows us to have that sort of app store revenue feature where we can value share on the downstream products. That's really what we're spinning up. I'll highlight how we're going to add all those new programs, because that to me is the real key driver of value for us in the future here at Ginkgo. How do we add programs today, right? These are a couple indicators of how we do our sales at the company. One of the things I'll highlight is inside sales. This is a customer, one of our older customers, and many of our customers are in the last couple of years, like the nature of the scale of the business, but one of our older customers that we did our first project with four or five years ago. We went from that first proof of concept project to now 11 projects with that same customer. One of the ways we're going to grow the number of programs, and you'll see in a minute, I want to grow from 23 new programs in 2021 to more than 500 new programs in 2025. One of the ways we'll do that is we'll do right by the customers we've just signed up recently. We just added Corteva. Listen, there's a big R&D budget in ag biotech companies. If we do well with them on the first projects we do, I hope three or four years from now, we'll be able to grow business with them just like we did with this customer. The other way that we are able to grow business is we demonstrate the utility of the platform in an area we haven't shown it before. This is a great example of this, the project we did with Moderna last year. That was the first time we had ever shown the utility of our platform in nucleic acid therapies. There's a lot of nucleic acid therapies out there. There's a lot in gene therapy. There's other vaccines. They like to follow on work with another DNA vaccine company and so on, manufacturers, th at is because we were able to show utility and then get follow-on business in that category. There are many categories like that Ginkgo has not yet shown where our general purpose platform could be useful, right? Think, again, things like antibodies, Cell Engineering, and so on. Some of the areas we're excited to move into with Arie. Those are all opportunities for us because we haven't proven the platform there yet. We run our sales pipeline. I mentioned Palantir earlier. We have a large commercial team. It's enterprise sales. We have good visibility into our pipeline. This is where we would expect to bring in new deals in 2021. We're aiming for 23 new programs. You can see they're split across those six different markets I mentioned earlier, a l ittle bit of a bias into pharmaceuticals. I think that's likely to be the case the next couple of years at Ginkgo. I see enormous opportunity for us in therapeutics. It's really the biggest R&D budget in biotech today. I think you'll see us doing more and more in therapeutics. We're not picky, right? We don't have to have a strong opinion about exactly what market is going to be the next great market for cell programming. We need to be paying attention and good community builders so that we know when there's a new area for cell engineering and we can go lean in and help out there. That's exactly what we did with nucleic acid vaccines. We see that should happen again and again if we do our job right. I'll mention, we want to get to, just to highlight it, I'll show you the numbers. Go from those 23 new programs in 2021 to 500 new programs in 2025. How are we going to do that? Some of that is going to be showing we can work in an area, show that we can do stuff in antibodies, expanding with existing customers, taking someone who's one program today to 10 programs three or four years from now. Some of it is just the platform getting better, right. As we add more Codebase and bigger scale Foundry, I can just offer more value to customers, so I should drive more demand. The other one is this idea of an ecosystem and, you know, t his is a picture from our annual meeting, Ginkgo Ferment. One of the things, a lesson we learned from folks who have operated in the software industry, so think like the cloud computing companies today or the operating system companies in the '90s who wanted people to develop applications on their platform. To do a good job at that, you have to build community, the community of the app developers that want to build it and are going to do well by building on your platform. Part of that is having the best platform, which I think we will and do. Also, it is all of the other things it takes to develop and launch an app, right? We have an annual meeting. It's similar to what you see in these technology companies, Ginkgo Ferment. We held our first one in 2018, second one in 2019. We have been investing in a variety of ecosystem services to wrap around that platform. We had what we called the Ferment Consortium. This was a $350 million investment consortium among some of our largest investors to back new companies on top of Ginkgo's platform. We help with manufacturing. We helped Cronos acquire a facility up in Canada to be able to manufacture those cannabinoids because they had never done fermentation before. Our team, our deployment team, you know, we have a 30 person to 40 person team there that can help app developers get comfortable with contract manufacturers and get their products made. On the regulatory side, we file a lot of intellectual property. You know, we are able to handle some of those things for smaller companies that then give them a benefit as they're trying to launch these products. Partnerships. We have large companies and small companies at Ferment. There's real opportunities, you know, t he bigger companies want to see new technologies. They're looking for companies to acquire. Trust and credibility. I had an editorial in "The New York Times" four or five years ago around GMO labeling when that was a big issue. We've been doing a ton in biosecurity over the last year, including things like K12 testing and things like that, like t hese are real opportunities to explain to the public how to think about c ell programming as a new technology, just like companies in the '90s explained personal computers to people, or even all the way back to IBM in the '50s explaining computers in the first place. We really see that that's part of the job here if we want to be the ecosystem provider. Okay, those are the ways we really want to scale up and create that additional demand on the platform. We actually already signed in the first quarter of this year, seven new cell programs. Four of them were inside sales and three with new customers. We're well on our way to our targets this year. All right, we'll talk a little bit now about the KPIs for Ginkgo as a public company. As a reminder, there's two ways we make money on those cell programming projects, foundry revenue and downstream value share, right? The foundry revenue comes in during that first, say, one to three years where we're actually doing the cell programming, the development project for the customer. Once we provide the engineered cell and it goes to market, then we would make money via either a royalty or in lieu of a royalty, equity, and we often do this with smaller companies, equity in the company that's launching the product. That's our version of an app store revenue stream. All right, let me give you a little bit of the historical numbers and how that has looked for us in the past. On the Foundry revenue side, this is pretty straightforward, right? One of the things I would highlight that's interesting is if you look at the cost to do a cell programming project at Ginkgo, back in 2014, the deals I did in 2014 and 2015, 100% full burden cost. We were, from customers, getting less than 20% of the cost being paid in revenue from the customer. Ginkgo was bearing 80% of the cost. Now why was that? Well, I was asking for intellectual property rights to go into my Codebase and also royalty or equity as a value share on the backside. My platform, going back four years, substantially worse than it is today, right? I've been basically doubling the efficiency of the platform annually over that period of time. In fact, if you look back on that slide where I showed the numbers, I had a dotted line that showed horizontally the cost for a scientist at the bench doing this work today. Today, we beat that scientist about 5x to 10X cheaper when we benchmark the work. Back in 2014, 2015, I was actually more expensive, so it'd be more expensive than your own R&D team doing this. You can see we held the business model, but we gave on the economics on Foundry. Now, what's happened over the last six years is as our platform has improved, we've been able to capture more of that Foundry revenue spend of the cost of a program covered by the customers, to the point where last year, about 80% of the program cost was paid by the customer. We expect as our costs continue to fall, we can start to actually make a margin on that Foundry revenue in the future while still retaining those intellectual property rights and that royalty or equity value share, a ll right, that's the left side. The great thing about it is it's pretty predictable, right? We are doing these deals are often one to three-year contracts. We have good visibility. The majority of my revenue in 2021 on the Foundry revenue side was actually booked prior to the year starting. I like that as a public market metric for us. On the downstream value, there's two ways we can talk about this, o ne is predictable, and one is unpredictable. I will cut to the chase and say we went with the predictable one. To give you a sense of our choice on what is unpredictable is when is, say, the project with Roche going to complete and give us a royalty on that drug sales? Who knows, right? It could be one year out, it could be five years out, right? There is a lot of variability in that process. Which programs of all the programs ongoing at Ginkgo are going to be the ones that technically succeed and which ones won't? Also unpredictable, right? What is predictable, on the other hand, is how many new programs will I do in the next quarter, right? That sales cycle is a six-month sales cycle on these large customers. We have good visibility into how many new programs we are going to do. We did seven new programs just in the first quarter of this year, right? Four were inside sales, three were new customers, right? We have good visibility into those 23 I'm trying to hit this year. I feel good about our ability to add new programs. What I can also say is, all right, what has a program been worth historically to Ginkgo, so that as we add a new program, investors could give us credit for that implied value of that new program, regardless of which one's going to be successful. If you look at since 2017, I've done 54 deals that we either have a royalty or in lieu of a royalty, equity in the partner company. The royalty ones, the challenge is they take a while, right? I don't get a royalty until not only have I completed the technical work, but the customer commercializes that product, and then I get value back on the sales, r ight? Only some of our earliest programs and things like flavors and fragrances give us royalties today. We don't have great data there. On the equity, what's interesting is when I give an engineered cell to a partner, they can go off and raise money, and those investors will look at the technical asset we provided the customer, look at the end market opportunity in that space, and make a rational decision about what that means for the value increase in the company. We get their embedded knowledge in the valuation of our equity positions in these partners. Across those 34 programs, about half a billion dollars in equity that Ginkgo owns, so t hat works out to about $15 million NPV per program, right? One of the ways we've thought about it is, okay, periodically, as we get more data in these two categories, we can share that out with investors so that they can get a better view over time of the average value of a program at Ginkgo, right? If you go to the next slide, you can see we can share into the future the revenues on the foundry side, and those to us look again like a life science tools company. In the future, we can take life science tools like margins on those. On the program side, we can project the number of new programs. I talked about how we wanted to scale that from those 23 programs this year to more than 500 in 2025, and then with some regularity, provide an update on what has been that historical value, both on the equity and as we get more data on the royalty side as well. That's how we roll up a valuation. One of the things we thought about hard was what is the appropriate valuation to take Ginkgo out? We ultimately settled at a $15 billion valuation. The reason we landed at that was if you just look at the Foundry revenue and put multiples similar to our fast-growing life science tools comp companies for that category of revenue, that gives you about $11 billion of value in the company, depending on what numbers you pick there. We like that that could carry a good chunk of the valuation of the company alone. Then, of course, we have the other half of our business. There's a $2 trillion-$4 trillion TAM in bioengineered products, and Ginkgo's going to capture a portion of that through our app store-like value capture. You can look at those number of new programs, what they've been worth historically, and come to a valuation for that half of our business as well, and add that to the Foundry revenue. That gave us confidence that we were in a good place with that $15 billion. We also have upside to that, both in our biosecurity business, like the work we've been doing in K12 testing nationwide here in the U.S., and then also some of the work we're doing on the vaccine supply chain work. That was how we got to thinking about the valuation. I think I'll end it there. I want to thank you all for your time. You know what? I've been in this field for 20 years, since we were back at MIT, and I think this is a really unique moment. COVID-19 has shown globally the power of biology to make it obvious to everybody. It's happening right alongside the tools to program biology getting easier and easier and easier at places like Ginkgo. The opportunity for good in the world with this technology is enormous, right? It really does unlock, I think, solutions for challenges in climate change, in food security, water security, medicines, down and down the list. Engineered biology, in my view, programming cells, will be more impactful in the next 50 years than computers were in the last 50. We're made out of biology. It is technology that requires careful stewardship, and f or the last 10 years, I've been building this technology here at Ginkgo with a team that has put their blood, sweat, and tears into this. I trust them with this technology. I think they're going to build it out with care. We appreciate you all having the confidence and trust in us to do that as well. Thanks again for your time.