Hi, everyone. Thank you so much for coming to Ginkgo for Ginkgo Bioworks 2023 Investor Day. For those of you that don't know me, my name is Megan LeDuc. I am the Manager of Investor Relations in general, so if you have any questions, feel free to email me. Before we get started, I just want to give you a few reminders that, one, these presentations today will contain forward-looking statements that involve certain risks and uncertainties. And if you have any questions or want to learn more about those risks and uncertainties, please see our SEC filings. And number two, for any of the folks online that are watching, if you have any questions today, please email those to investors@ginkgobioworks.com, and we will try to get those answered throughout the day. Without further ado, please let me hand it over to our CEO and co-founder, Jason Kelly. All right. Thanks, Megan, and excited to have you all here today. Oh, thank you. Okay, already got that. Hey, Bear. Okay. All right, so before we get started, I did want to. We're going to get into the agenda and the plan for today. I do want to start by talking about our subsidiary, Zymergen. There was news this morning that they filed for Chapter 11, so I want to give a little bit of context on that since I have a bunch of you in the room, and I'm sure there'll be a bunch of questions about that later. I will say that we'll be able to talk about this at the Q&A, so you get to ask questions. But first off, you should review our 8-K. We put as much information as we could in there. At the time of the Zymergen acquisition, we were aware of the liabilities at Zymergen and that if they couldn't be resolved, that Zymergen would potentially consider a bankruptcy, so that was known to us. Zymergen has been operating as a distinct legal entity since we did the acquisition and has had arm's length agreements with Ginkgo throughout that time. Just as a reminder, we ended last quarter with $1.1 billion in the bank in cash and cash-like securities. So, Ginkgo is in a strong position. This is really about Zymergen, the subsidiary. Previously, Ginkgo and Zymergen entered into a non-exclusive license agreement to the intellectual property at Zymergen. So Ginkgo can continue business as usual. So the technology we have been using from Zymergen, we still have access to that. So not really a disruption there in terms of our business operations. And then finally, just to help the bankruptcy process along, Ginkgo submitted a bid to purchase the bulk of Zymergen's assets, transfer employees, and assume some liabilities, including one of the leases to the real estate out there. And so I did want to just give a little bit of that context since I figured people would have questions about it before we got into things. And again, feel free, we can talk more about it at Q&A or if you want to go at the breaks or things like that. Okay? All right. Okay, with no further ado, welcome to Ginkgo Investor Day. I want to highlight, what we're hoping you can do today is get a picture of a wider set of the bench here at Ginkgo. You hear a lot from me, on earnings calls. I will obviously be here today. I'm very happy to talk to all of you, but you're going to get to hear, from Barry Canton, our CTO, Patrick Boyle, our Head of Codebase, Anna Marie, who, as of, today, has expanded role as our Head of AI. So you're going to get to hear on, on a number of our technical fronts. Our Jen Wipf, who heads up our cell engineering, Matt in biosecurity. And then in the breakout sessions, you can hear from some of our leaders in certain market areas. So, Kevin Madden on industrial biotechnology, Patrick in agricultural biotechnology, and then Mark, our CFO, and Jason Berndt, who heads up our operations, will be in a breakout around ops and finances as well. Okay, and so what I would encourage you all to do, and I have told the team to do this, to leave time at the end of each of their talks to take questions. And so really what I'm hoping is that we can really dig in. You guys should ask deep questions. The point of the Investor Day, from my standpoint, as compared to an earnings call, is there's a lot more opportunity for a two-way conversation here. So please do take advantage of that. For the folks that are joining us on the webcast, and I know there's a bunch, please do submit questions, and we're happy to work those into the discussion today. And then finally, we're very lucky to be joined by our board chairman, Shyam Sankar, CTO of Palantir. We're going to be doing an AI fireside chat with Barry and Shyam, Dmitriy, who heads up our AI on the technical side, and Anna Marie, in the afternoon. Okay? All right. So, Ginkgo's mission is to make biology easier to engineer. I start a lot of our talks internally at the company with this, almost all of them, as well as our earnings talks. This is a key idea. So this is what I think distinguishes us from most other biotech companies in the industry that might have a mission, like to cure a certain disease or to develop, more efficient crops for growers in the Midwest, right? Or, new fragrances produced with biotechnology. Those are the missions of Ginkgo's customers. Ginkgo does not have a product pipeline. Our mission is to make all of those customers more likely to deliver on their products in biotechnology, in their markets, and we have been committed to that for 15 years. I do think this is what makes us unique. It's why our business model looks the way it does. It leads to a lot of the questions and confusion about Ginkgo, 'cause it is kind of unique in the biotech sector that is very product-focused, but this is where it comes from and why we have that sort of platform services business model.... This is my favorite slide at Ginkgo Bioworks. All right, so this is a, not a subset, not all, of the customers that have come onto our platform, and most of them in more recent years. What I want to highlight is Ginkgo is trying to get people to do R&D differently in the biotech industry. All right? And so if I were a product company, and I wanted to you know, to show that my platform was relevant in, say, RNA therapeutics, I would just start developing my own drug in RNA therapeutics, and the only person I would have convinced that I was any good at doing RNA therapeutics was myself, and my investors who are betting on me to develop that drug. That's not how it works for Ginkgo. If I want to convince you, that my platform is relevant to a certain type of biotech R&D, I have to go get a customer, which means I have to convince, typically, the head of R&D of a company, that what I have and what you'll get to see on the tour today, is either additive or better than what they have in-house for doing that kind of work. If I can't convince them that the logo does not go on the screen. All right? And so that sets a higher bar for us to validate to all of you that our platform is relevant. And so what I'm really excited about is you can see it in so many different areas, right? So Ginkgo started in industrial biotech, you know, some of our earliest customers, like Robertet and Givaudan, the flavor and fragrance industry. Now to places like Sumitomo for large-scale chemicals. In agriculture, you know, we work with Corteva, Syngenta, and Bayer, big three ag biotech companies, all customers of Ginkgo. And then you're going to hear a bunch about this today, but our fastest-growing area right now is in the biopharma space. So you're going to get to hear from Kevin and Patrick on industrial and agricultural biotech, and Jen Wipf, who heads up our commercial on biopharma later today, okay? So I do want to highlight one of the deals. I've highlighted that Pfizer deal. It's a deal we just signed last week. I think this is something investors should be paying a lot of attention to, because this deal is special in a couple of ways. First off, obviously, it's a great big biopharma. Like I just said a minute ago, they have large existing in-house infrastructure, so they had to decide to use Ginkgo instead of, instead of do this in-house. But importantly, you know, here's a comment from Will, Head of Biomedicine Design at Pfizer, Will Somers. It says, you know, "Access to Ginkgo's proprietary platform will help enable Pfizer to search for novel and exciting RNA constructs with improved stability," right? "The progress into that biology, we have the potential to create new RNA treatments." So the key here is this is not about manufacturing therapeutics better. It's not R&D to support that. Like, for example, our deal with Novo Nordisk, our historical deal with Biogen, our first deal with Merck, were all in the area of optimizing R&D, doing R&D to support manufacturing. This is R&D to support drug discovery. All right? And so why is that exciting? That is the biggest R&D market in the biopharma industry. If you think about the markets, the TAMs, that Ginkgo goes after, one of them is the research budget of our customers, so the money they are spending on their labs, on their equipment, on their kits and employees to do research, and then the other is the product revenue that we get a royalty on, okay? In biopharma drug discovery, those are the two, that is the biggest R&D budget market and the biggest product market in biotechnology. So this is a very exciting area for Ginkgo to be getting into. Obviously, this is a deal in RNA. We're hopeful we could get similar deals in gene editors, in, you know, protein therapeutics, in cell therapy. And we're working, and you can talk to Jen and ask questions, we are working to add more discovery deals in those areas as well. Does that make sense? You might ask, why didn't you start in this market if it is the biggest market for biotech R&D and for products? The answer is, you know, eight years ago, when I was doing the first deals in fragrances, there's no way I could have got a deal with Pfizer because our platform, as you're going to hear about today on the technology side, improves with scale. Every year, as we build this place bigger, and every time you see me add a new customer, my infrastructure is improving, and so it has taken us the last eight-10 years of scaling this place to be good enough that this comment is a... You would hear from a R&D leader at a major biopharma company doing drug discovery, okay? I will just point out, the platform you see here is used for all of those projects. So we are nailing now the biggest market for biotech R&D on a platform that can do any type of cell engineering R&D. That generality is why I think Ginkgo is a big deal. It's what makes us special. Does that make sense? Okay. I'm not going to belabor this too much, but you are going to get on the tour. You're going to hear from Barry. I encourage you to ask Barry questions about our Foundry. This is our oldest asset that we started developing first at the company. It is a lot of high throughput automation to do the kind of work I did back when I was at the lab bench at MIT, working by hand on engineering cells and creating research data about how genetic designs perform. Here at Ginkgo, you're going to get to walk through, you know, about 100,000 sq ft of facility here that is automating that lab work, so that we generate more research data per dollar than a Pfizer could in-house with their research teams. That data then goes into our Codebase, which Patrick Boyle heads up, and we keep it. So one of the things Ginkgo does uniquely is when we work out these deals with our customers, we retain the reuse rights to the data for future projects. All right? And that is, again, I, I think, something that's special in the industry, because if you just think of a single product company, they have access to only their data. They don't have access to the data of a bunch of other companies in the industry. But with our Codebase, that's what we're accumulating. So you can push on Patrick. What do we have access to? What are the things that are really reusable? What can, what can cross over customer to customer when we do this work? And so we've been building that. As the scale of the automation went up, we started to generate, organize, and reuse that Codebase. And then, Anna Marie's gonna be on the tour as well. The thing we're doing with the data, the most recent activity, is we announced a partnership with Google Cloud about a month ago, where we're going to be training AI models, particularly in the protein design space, on all this data. Okay, that provides a number of things, but we're hopeful even better predictive ability in terms of what biological experiments you want to run, what type of genetic designs are going to be most effective for a customer project. The key thing at Ginkgo, all of these things get better with scale, okay? The bigger the Foundry, the more data you get per dollar, just like a semiconductor fab or an auto plant, like, it has a physical scale economic. The more data I have from previous projects, when I do a new program, the more likely I can take something off the shelf, and it's relevant from my Codebase. So, like, you know, you wanna do the next RNA project, because I've done a lot of work on the Pfizer project, I will have something that's useful to you. And then finally, the scale of compute we do and the amount of data that we can pump into a single foundation model also has a major scale effect, and you're seeing that in the natural language models today. That's why you have basically three that are coming to dominate the natural language processing space when it comes to foundation models, 'cause there is a scale effect in AI models. That's the kind of technology Ginkgo likes to bet on, general purpose technology that gets better with scale. We're just not doing it in the tech arena, we are doing it in the biotech arena, okay? But these all get better every time we sign up a new customer. And so again, I already highlighted, you're gonna get to hear from all these leaders. The only other point I would make is Jason Berndt, who heads up our operations. He's really focusing on: Can I use the Foundry and Codebase, particularly in the areas of industrial biotechnology, to drive massive efficiencies and make that market into a place almost where, like, we can... When we just get, like, fees from customers, that we are covering the cost of doing that work, that would be ideally where we'd like to get to with that type of project, and that allows us to kinda have as much time as we want to get to royalties. So I think you'll see that type of flywheel spin up first in some of our older areas, like microbial engineering, where we have more built-out scale. Does that make sense? So talk to Jason about that. Okay, finally, you're gonna get to hear from Matt McKnight, who heads up our, our, biosecurity business. The key idea, I think, to take away on biosecurity, and this is work that came out of what we did in COVID, around infectious disease monitoring, so think like radar stations looking for viruses, is that this is moving from a public health market to something that looks a lot more like a national security technology market, okay? Because when we go out and interact with our government and foreign governments around this type of technology, we're often interacting with the national security groups who are trying to understand, "Is something coming to disrupt my country? I need to know about that ahead of time." That's a different kinda solution space than what we've seen in public health, which is often responding to an emergency. Public health is a little more like FEMA, like, I'm, I'm showing up after the problem, okay? The national security people, they're about preparedness. They're like, "I wanna know the missile is coming before it hits us." Okay? That's their job. And so I think that is a-- that's a real shift in how people have been approaching infectious disease. I think we're building a leading brand in this space. We're, again, fortunate to have Shyam. He's obviously done a lot of work at Palantir in building out. I think you are seeing a wave around defense technology right now, companies like Palantir and Anduril leading there. We see an opportunity to lead in biosecurity, similarly in defense. And so you're going to hear about that from Matt. Okay, so I... Since I've told the other leaders of the company that at the ends of their talks, they should, they should leave time to take questions. I'm very happy to take a few questions just right now, and then we're going to go out and take a walk around for some tours, and then come back and do some more talks that folks on the live stream will be able to join. So happy to take a couple questions if anybody has any. Okay, so just so you get a sense of the layout of Ginkgo, this is a building built for World War I, and so it's about 100 years old. Each one of these little squares between the wall behind you and that one over there is an 18,000 sq ft box. All right? The army built this building, so they're all just stamped out, okay? There are eight stories of these, and there are 12 across the building. So if you're a biologist, this is a 96-well plate, okay? And you are currently in A4, Barry? Yeah, A4. Okay, you are sitting in A4. We have, we have filled, I think, seven of these wells along the top of the building. You're gonna get to walk through them. And I think one key point I would make before we jump on the tour with the foundry is to understand that it is an evolving infrastructure, right? So don't think of it like a chip fab or a car plant, where you kind of build it once, that this is the, you know, 6 nm production node at Intel, and we'll build a new one in three years. What goes on in these rooms turns over on probably a couple year timescale. As you'll see, new technologies coming in all the time, and so you'll kinda get to see even different epochs of what we had in the foundry on the tour. So, excited to have you all join us for the tour and looking forward to the day, and welcome to Investor Day here at Ginkgo. Thanks. Okay, for the people in the room, so we have the first two rows, you're going to follow Barry to the back up aisle seven. Yes, you can leave all your stuff here and back... ... Hi, everyone. Welcome back. Hope you all enjoyed your tours. We're gonna jump into some presentations on cell engineering and biosecurity. So without further ado, Jen Wipf, our Head of Commercial for Cell Engineering. Thank you, Megan. Hi, everyone. Great to see you today. I'm gonna talk a little bit about our commercial business in the cell engineering space. So for those of you who had the benefit of going on the tour, hopefully seeing the infrastructure gave you a sense of why customers might be doing work with us. But if I outline a few examples, certainly one of those is the ability to get more data per R&D dollar, so to be doing more work for the same amount of cost. And what that might look like, let's say, in the pharma space, is that maybe you can carry therapeutics longer in your pipeline as opposed to having to narrow those down early on. So your likelihood of success, of sort of finding something of therapeutic importance or finding something that's going to be successful in the clinic is higher. For non-pharma applications, maybe that's just the total likelihood of success of bringing something to market. Another reason is just existing data assets that we have. So that's something you can't see as you walk around the foundry, but as you can see, the scale of which we're producing data, you can imagine all of the data that's behind that. And certainly, some of the reasons here that we're seeing are things that are driving success-based pricing deals, for example. So a lot of times we have a lot of data that's already in existence that helps customers start at a place that's further along the sort of discovery or development pathway, and therefore reach the endpoint much faster. Another reason might be to launch work fast. So I think a shining example of this is Arcaea, right? So the company started in April of 2021. The next day, there was R&D work happening in the Foundry. And fast-forward to today, they now are launching their second product, with an extinct flower fragrance that I think some of you can see here. And so really, the ability to start that work and therefore get to a commercial product much faster is a huge advantage. Also, saving on laboratory CapEx. I've had some customers come and see the space and say, "Oh, I really should think about what I build versus what I maybe consider outsourcing." And along those same lines, sort of replacing fixed costs with variable costs. We know that a lot of companies, particularly small companies, have a really high R&D spend, and then as they get closer to commercialization, they're focused on, you know, driving that product to market, getting into the channels, and that R&D team can sometimes not have enough work. And so the ability to kind of be able to flex that spend and the work up and down is really, advantageous as well. So some of, some of the reasons that we're seeing, and I think those reasons are applied across all of the industries that we're in. Like, all of these companies are struggling with those same kinds of challenges. And so remember, we are an industry-agnostic platform, and so I wanted to give folks a sense of what we're seeing across some of those industries. Today we have 105 programs going on concurrently in the foundry, and the sort of mix of those you can see here along the column. I think the notable thing is that, you know, what we're seeing in pharma and biotech is this growth of the total percentage, and I think that's actually a commentary on really how tip of the iceberg we are in this sector. Remember, we started work in pharma later than the other sectors, and we're continuing to build out both capabilities and physical lab space and data and all of that. And so we're really seeing a lot of momentum picking up in the pharma space. And so you can imagine some of the momentum that's happening there, and I'll talk about it a little bit today. We're still seeing a lot of strong growth in the other sectors as well, and so that's sort of keeping up with our ambitions to grow in those sectors. So in terms of pharma, I want to spend a little bit of time talking about what are we doing in that space? I think it's important to remember that since we're not talking about products, our customers are doing those, we're thinking about that work in terms of modalities, in terms of sort of the scientific problems that pharma companies are trying to solve. So that spans sort of a ton of modalities, cell therapy, gene therapy, biologics, RNA therapeutics. I'll talk about a few of those, but importantly, it also spans from discovery to manufacturing. I think one of the other really interesting things about the solutions that we're providing is the ability to think about manufacturing and scalability alongside discovery, sort of earlier in the development pipeline. A lot of these kind of look like they're in different sections, but really many of them span sort of the R&D development pathway. I wanted to highlight two of these that I think are interesting case studies for what we're doing in pharma. One of those is Merck. Remember, we started with a collaboration that was focused on enzymes, so really biocatalysts for APIs. Again, we're really strong in this space because we've been working on enzymes for many, many years in many, many other industries, and that data is really applicable. You know, biology didn't decide that they were gonna work on enzymes for agriculture or for pharma. Biology is working on a biochemistry solution, right? And so we're able to apply that in many different places of the work. And Merck saw a lot of our strength in the enzyme space, and remember, they are very strong in the biocatalysis space as well, and saw that sort of our data and capabilities would give them a strategic advantage and could help them do more biocatalysis work than their own internal teams could do. So again, expand their pipeline. On the backs of that, as they sort of started to see us delivering in those space, we were also able to sign a second deal that was in the biologics sort of manufacturing space. So again, like, as we're starting to work with these kind of blue-chip pharma companies, they're really learning more about our strengths, we're learning more about what problems they're trying to solve, and we're able to kind of find the next fit of what we can do together. And we're seeing that play out in Merck. We're seeing that play out in Novo Nordisk as well. And so we started a pilot program with them. We reached that over the summer, and now we're expanding into the next set of work with them in sort of a multiprogram biologic space. And so I think what's important about both of these deals and sort of a number of the kind of blue-chip pharma companies is that, you know, now we're in a place where our tech is good enough, right? These are players who are scientifically very strong, have a long history of developing products, and they're seeing us as an advantage to their own R&D teams. So that's one thing that's really important. The second is that these are large companies that are often thinking about a particular therapeutic area, and remember, we're a platform. And so we're learning also how to navigate those companies to get these really big collaborations together, and then from there, finding ways to expand the work that we're doing with them. And so I think you'll find that there are a number of these other sort of logos on the screen, where that same sort of enterprise sales thinking is playing out for us. So one of these kind of new enterprises that we're working with is the Pfizer deal. In the Pfizer space, we're working with them on RNA molecules. Remember, they're thinking about trying to solve clinical problems, get products to market. What they found with Ginkgo is that because we have the scale to kind of explore a really broad set of scientific questions, we're able to also think about not just a particular solution in the RNA space, like circRNA or mRNA, we're actually able to think about the problem that they're trying to solve and then apply whatever tools or modalities might be best to advance that. I think that's a real strength of our platform as well, the breadth can sometimes seems like, well, it's confus-- like, what are you actually doing in the pharma space? But when you're trying to solve a sort of clinical problem, that breadth can be a real advantage, right? You can start to say, "I don't... I'm I'm not looking for a particular solution. I'm trying to get a product into market. I'm trying to discover a therapeutic. I'm trying to make sure it's scalable and manufacturable, and I don't necessarily have a way to do that in mind from the start." And so again, like, this is one where this is really a very much a discovery space. So again, we're moving from a lot of the earlier deals in pharma and the manufacturing space, moving more and more into discovery. I think also we're able to think about not just discovering RNA therapeutics, but think about the path to manufacturing those, the path to making those scalable, the path to bringing those to market really early in the process. So as you walked around the facility, for those of you who are here, you can see how even early on, we're able to run design, build, test, ferment cycles to kind of see where that's going and use that as a part of our, as a part of our process. So again, we're really excited about this particular program, not just for the work that we're doing with Pfizer now, but for the connections that we're making, the other challenges that Pfizer has, and other places that we can potentially expand. Okay, so I talked a little bit about kind of enterprise sales in the context of Merck and Novo Nordisk. Beyond pharma, we have a number of other enterprise customers, right? Bayer in the agriculture space. Sumitomo Chemical has been a long-standing customer that continues to do work with us, Solvay, and others. And so what's really interesting about this is once we have that sort of customer acquisition cost, again, they're starting to understand our capabilities more. We're starting to understand their scientific challenges in whatever industry they're in, be a part of their scientific teams and try to find other ways we can advance their R&D pipeline. Also, I think what's interesting about these deals is, remember, we're signing these big kind of collaboration contracts, which are... You know, they're not sort of, turn the crank, kind of I, I know exactly what the work is, and you do the work, and you deliver it to me, but they're these collaboration contracts. Once you have that, that first one done, from a contracting perspective, from a deal execution perspective, it becomes much easier to do work from there on. So I think one of the things we're unlocking with these customers is the ability to start work with Ginkgo really quickly.... The other thing we're, we are focused on in the sales space is really helping customers understand what it is we can do and how we can deliver that work to them. And so you'll see that in a lot of our kind of service offerings work, so in enzyme and protein services, which we now have the success-based pricing, helping customers really understand what it is that we can do and how to, again, start that work with us really rapidly. And so that is really a driver of helping to make these deals for us execute quickly and bring people onto the platform really rapidly. So we get out of the cycle of, you know, "What do you really need? What can you do?" Enzyme services, success-based pricing, like, "What's your target? Let's go." And oftentimes we have the data to start that already. And so you'll see us, you know, helping to, helping to explain in the marketplace more and more what that looks like and how you can start work with Ginkgo more quickly. Okay, so I think if I, if I sort of summarize a few of key points, and I'll open up for questions after this, I think we continue to sign deals with really kind of blue-chip customers, right? These customers are scientifically, of high value. They're hard to sign deals with. Getting these customers means that, like, our tech is good and is, is good, like it's better than what they have. It is... We've really advanced sort of not just the foundry, not just the automation, but also the data that we have, also the ways we work with customers, also the ways we sign those deals. We continue to have a lot of success with repeat customers, so once we sign that first program, we're delivering on that, we're opening up the doors to more and more work with those customers, and we build out a team to drive the expansion of work with these enterprise customers. Also, we're really focusing on what it is that we do and helping the marketplace understand that, and helping customers come to us with what they're looking to achieve, and finding ways to make that really easy to start work with us. And then we'll have a whole section on AI today, so I'll leave most of that to the team, but I would say this is a real value driver for us. Lots of people are trying to figure out what to do with data, how AI is gonna change their business, and I think we're really seen as a thought leader in that space, and that's driving a lot of conversations with particularly large pharma companies, and opening up new avenues for us to explore and even build off some of the collaborations that we have. So, that's roughly what I wanted to share today, and I know Jason really wanted me to make sure I left time for questions, so hopefully there are some in the room, and happy to open those up for a few before I move to biosecurity. Mark? Mark. Sure. How is the success-based pricing model going? I know it's been relatively early. I know at Ferment, you talked about a well-rounded intro to enzyme services. However, on earnings, you also talked about being willing to expand that, especially if it's gonna bump up your program, so perfect. Mm-hmm. Mm-hmm. Just how's that going, and are you seeing new programs moving on beyond enzyme services? Yeah. So the question was, for the audience, is: How is success-based pricing going? Are we doing that in anything beyond enzymes, and how is that ramping up? So remember, we launched success-based pricing at Ferment, so that was April of last year, and that was really focused in the enzyme space. And we did see a really interesting uptick in sort of interest in that. And we even had a few of our deals that were signed in the second quarter and to be announced in the third quarter come in basically as inbounds from that that we were able to kind of scope and sign basically within a quarter. And those were companies that I didn't even know were sort of in existence. They weren't necessarily on our target list. So I think that's been a really interesting thing in the market to kind of see how broad that application really is in some surprising places. And what we have seen from that beyond enzymes is that we have a real strength also in the protein space, so protein expression in particular. And so we have been doing some success-based pricing in the protein expression space, and I would expect that to continue. Again, it's the same kind of idea, right? We have a lot of code base. We have a lot of assets that are in existence. We know that these are going to be successful. Like, we've changed the sort of, like, ability to engineer biology in those spaces, and so we'll continue to push on that. Yes. So one sort of semi-related follow-up to that, Jen, one of the key things you mentioned, I thought was, you know, pushing more into discovery. Now, discovery is something that pharma likes to hold close to its chest in general. Is that now changing with the advantages of, you know, your scale and the relatively agnostic approach will become more apparent to them? And if that is so, then how does that complicate into this emphasis on success-based pricing? Because at some point, you'd imagine, you know, word gets around that you guys are just potentially somewhere, you know, perhaps there is a larger up-front involvement able to have with you. Yeah, so if I- Can you repeat it for people on the, on the streaming? Yeah. So if I try to summarize the question: So Pfizer kind of represents some work in discovery. What am I seeing in terms of sort of interest in discovery work? Why are pharma companies coming to us for discovery work? Did they, did they sort of realize that we could do that, or how has that played out? Is that about right? Yeah. So I think on top of that has been both building up our internal capabilities across those modalities. So remember, we have been building up our team, we've been building up our physical space in Bioworks 7 to expand to some of those modalities and in discovery space. So I think customers are noticing that. Also, a lot of those customers are already doing work with us in the manufacturing space, and kind of through those programs, are thinking, "Oh, gosh, if I, this could really help me if I thought about it a little bit earlier." And probably these same types of strengths are useful in the discovery space, the ability to really go broad with scale, the ability to really look into sort of the genetic design space. So yes, I think in some respects, like pharma customers are starting to realize that we have a real strength in that space, and it's our job as sort of the sales team, I think, to help show that to them. So I think we'll continue to see more of that. And you said, pharma companies tend to have discovery and kind of hold that close. I think also the sort of changing landscape of what is happening with data and AI, and even the fact that we have a lot of data, is making pharma think about how to stay competitive in discovery. So I think, in particular, our strengths in AI and what we're building are going to continue to help that. Yes? I think before you talked about a 40% reduction in program time to get program started. Can you talk a little bit about how that's impacting your discussion with the customer and sort of getting into adding new programs? Yeah, sure. So the question was the reduction in sort of program launch or program scoping and how that's driving sales. So it's a really important point for us, because some of the hurdle of getting deals signed is figuring out what is the work that we're going to do, what are we gonna deliver to the customers, what are the terms on which we're gonna do that? And so that is probably the biggest linchpin in the sales cycle, is defining the work and making sure we're delivering something to the customer that they want. And so any decrease in doing that is really helping the deal throughput. And remember, that's growing quarter by quarter. And so we're focusing a lot on that. And so one way we can reduce that sort of like, time to start a program is, is some of the sort of, product-based marketing or success-based payments that we have. It's, like, very clear what the work is that we'll be doing. Another is thinking about, sort of the work and the modalities that we're doing and helping to be really crisp on what the deliverables can be for customers. And so that is a focus of the sales team in general, is to figure out how to continue to drive that down. We're doing that at... in order to kind of increase the deal throughput. Yes, Lee? Is there a particular customer type that's more interested in success-based payments? So is it more focused, like, are more interests coming in from, like, smaller startups, or are you seeing larger companies like Pfizer be interested in success-based as well? Yeah. So the question was, like, what types of customers are interested in success-based payments? I would say, to some extent, all types of customers. I would say it leans a little bit more towards smaller customers. It tends to be the case that some of the enzymes that the larger customers are wanting to do are just harder. And so they're not quite the mold of what the success-based structures look like. But they're embedded in those teams. Often there are some sort of simpler, let's say, enzyme programs that are coming from large pharma as well. But SKU's a little small. Can you talk a little bit about, you know, a couple years ago, you had this sort of equity or royalty and milestones, and how does that break out now from each partner? So how does that change from the side of a big company? Yeah. So the question is really around sort of our philosophy on downstream value and the, the breadth of kind of the offerings that we have for deals in downstream value. So the breakdown of royalties, commercial payments, equity. So in general, we've again, we anchor on sort of something in downstream value because we wanna have shared success, and we want our incentives to be in line, in line with the customers, so that we deliver something that goes to market. So we really just anchor on that as a philosophy, and then however that works out with customers, we're often in a lot of discussions with what's best for them, right? And some of them prefer a sort of royalty tale, some of them prefer a payout upon commercialization. Some people are startup companies that have equity more at their disposal than cash, and so we continue to have a lot of open discussions with customers about a fit that works for that, their business. So how has that changed now versus a couple of years ago? I don't know if we're sharing that data exactly. I would say that, you know, as you see more of these kind of blue-chip customers on the board, like, they're not doing equity, right? That's, like, not their preference. It's a pretty healthy mix of both, I would say. I would say we try to manage sort of the portfolio so that we have a mix of, like, near-term payouts and farther-term payouts, and we have a broad portfolio off of which we can do that. So, we're not leaning towards one or the other. Thanks. Alex? I'm just curious if there are, maybe just bars included for, like, what needs to be in, like, the downstream versus upstream bucket. I think you guys believe in the technology, and if you can expand access to it and generate that flywheel to get it- ... I'm just wondering, you have something in the downstream bucket. Is there anything that would have to be required in the upstream bucket? Like, in five years, would you run the boundary below cost, upfront or just to increase accessibility? Or would there, you know, always be a, you know, we need to at least have upstream guaranteed or for the cost of running all these facilities? Yeah. So, so again, we think about our sort of commercial portfolio, like we're managing across a bunch of different industries, a bunch of different project types, a bunch of different paths to commercialization. So in that sense, we're able to really leverage the breadth of the portfolio for near-term payments, for R&D services, and for downstream value. So you can see already that some of what term payments being different and retaining value that we can get later on. You'll also see some sort of licensing deals for assets that we already have, where there, the R&D is sort of done in arrears, and we're able to sort of license some of those assets, and so you'll see us experiment with a lot of those terms. Yes. Yes. On one of your earlier slide, you had a breakdown of active programs by like, you know, pharma, biotech, food, ag, industrial. How do you see that evolving, especially in light of the success-based programs as for late-term? Yeah. So I think if I were to try to figure... You're asking the question is like: What's the mix of industries, and what's that mix going to look like in the future, potentially, and where does success-based enzymes and payments fall in that? So I think if I were to project what that looks like, there's a lot of momentum in biopharma space, so expect the proportion of total biopharma against the rest to be growing. But success-based payments are embedded in all of those, right? And so success-based payments are part of how we're expanding with pharma. They're part of how we're doing more work in industrial biotech, how we're doing more work in ag. And so consider those kind of in all of those spaces for a certain type of program with customers. Is that helpful? All right. Okay, I think we'll close there, and I will hand over and introduce my... Wait, I lost the clicker here. One, one- Yeah, sure. So, yeah, maybe one quick thing I would add on the success space, since it got asked a lot. In the near term, it's a lot of what Jen was talking about, like, it's another tool at our disposal to get deals done with customers. The most important thing for Ginkgo is to add new logos and add repeat programs with existing customers, because it drives scale, makes the platform better, makes our lives easier next quarter. Okay, no, no question about that. The long-term thing is all biotechnology product development is considered R&D. Like, we would not say that about, like, electronics, okay? It is not considered an R&D project to develop the next iPhone. We appreciate that it has got business risk in it, and it is an engineering project that involves a bunch of engineers laying out the spec and scope and understanding what the iPhone can do, but we don't consider it a R&D project like we consider drug development or trait discovery, things that just might not work. So a big part of success-based pricing is to price what today we think of as biotech R&D, like engineering. In other words, you don't pay for your engineering design on the iPhone if it doesn't freaking work, right? Like, so that is actually the macro shift that we're like in the long arc of Ginkgo, in making biology easier to engineer, we're trying to accomplish. We want these things to become predictable product development by biotechnology companies, like there's predictable product development for electronics companies, 'cause that is what blows the market wide open, okay? And so success-based pricing is a pricing scheme stepping in that direction. Everything you just toured is a technological scheme stepping in that direction. But you're starting to see us starting to align the business model in that direction. I'm really excited about that. Separately, it also gives the enterprise sales team more weapons to get deals done, which is great, but that's really the longer-term nudge I would push you to, that we're trying to pull off here, I think, uniquely at Ginkgo. Does that make sense? Okay. Next up will be Matt McKnight, our General Manager for Biosecurity, who can talk about how we're growing that business, and particularly moving it towards national security. So- Thanks. Thanks, Jen. Thanks, Jason. Cool. I'm excited to be here. We... This is something we haven't had a chance to talk about as much, publicly, so it should be a really good discussion. I am going to endeavor to do about 15 minutes, and so please do ask questions. This is definitely a topic that there are a lot of questions about, and just in, in how the market is developing, so it's super good to hear how people are thinking about it. I'll do, like, two halves. I'll do a little bit of, like, footing of how we're thinking about what is happening in biosecurity, biodefense, like, in the world that we are all living in, and then very specifically, go into, like, how we're thinking about product development, where we're deploying, what we are deploying as a product, and how we're thinking about sales. Maybe the first piece, Ginkgo, we've had two choices, basically, and, like, it, it's actually important to identify these as two choices. The first choice is when you're building a cell engineering platform, you're on the forefront of biological engineering. You can choose to care deeply how that platform is deployed, or you can choose not to, right? And I think very clearly you've heard from us, we care very deeply how people use this technology. It is a transformational-... set of capabilities for the world. So we've chosen both to do that in how we talk about it in the world, how we engage in everything from policy to our own decisions internally, but also with technology. So the first piece is build biosecurity technology so that people on our platform are able to build things responsibly, and that's a 25-year objective. The second one, which is a little bit less kind of obvious as a choice, is you could pick that and make sure everybody that does things on the Ginkgo platform is doing things in a biosecure or a safe and secure manner with technology. Or you could also say, we need to make those technologies available to the world, because also, by the way, in the world, biology does not respect borders. Every country in the world is going to need to deploy these capabilities, likely deploy cybersecurity. But that's, it's a conscious choice, and I would say that we've made that conscious choice to build this business, to make it available globally, not just for companies on the Ginkgo platform. And, you know, I, I think the, you know, the very blunt like, why to do that is if you look at what happened in cybersecurity. Cybersecurity was not a market category that any of us would have like in 1960 been like, "Oh, you know what? There's going to be a huge market category of technology companies across the world building amazing capabilities in cybersecurity." And today, obviously, it's something that is, you know, hugely thought of as a, an important category within the computer engineering ecosystem. Obviously, to us, we believe there's going to be, and why we use like the little b not on this slide. Little b biosecurity is because we believe it'll be an industry category. There's going to be a huge ecosystem of technology companies building biosecurity capabilities in a bioengineered future. And Ginkgo absolutely should be the leading company building into that market, and I don't think it's 10 years. I think it's two, three, five, seven years, you're going to see this category emerging. It is just kind of like the historical fact as you invent new engineering disciplines, that you also invent the security components of those. So kind of, one, we've chosen to make this a global business. Two, we believe it's a big market coming up. I think the second one, and Jason alluded to this, like the reality here is that the threat, kind of envelope that the world is seeing from biology, now that's natural man-made and engineered, is changing dramatically. And like if you spend time talking to people in D.C., policymakers, everything from, you know, ChatGPT plus bio, all the way to the kind of massive diffusion of capabilities, both in scale, and kind of distribution and power, what nation-states are doing behind the scenes with, investing in high security laboratories. The threat envelope of bio is being identified as clearly not just... and, and, and certainly there's going to be benefits to public health, but not just an episodic pandemic boom and bust. We need to be prepared, kind of, and it is being seen by the places where, like, annual budgets are spent to do preparedness, i.e., DoD, national security type communities, not just in the U.S., but everywhere. This is becoming a massive category of concern, and you can see in the documents that have come out. But I think that the key point to think about there is that when in every point of history, when you've had a new threat category turn into a national security concern, whether that was nuclear weapons through to cyber, the persistent budgets, we spend over $1 trillion a year in the United States of America on DoD plus the intel community. The persistent budgets for preparedness change the market dramatically. And this is my favorite. So this is the National Biodefense Strategy, and you can just go back in the last 18 months. The number of policy documents that have a decidedly different tone on how to think about biodefense is pretty substantial. And so this last line, this is actually new, in like the vision for how DoD thinks about this. It used to be like, "Oh, these are the seven bad things, and only these bad things we should worry about." This is, "It comes from anywhere, and we seek to create a world free from catastrophic biological incidents." Like that, the kind of the only other statement of that is that's an acknowledgment that there's basically two things that, and not to overdo it, but like two things that can kill tens of millions of people in a very short period of time, and it's nuclear weapons and bio, and that is the national security community saying, "We're not going to let that happen." Once that flows through the system, that changes the following, which is they look for technology to answer that problem, because it's not just a policy problem. And so this is where this, like, big emerging, you know, kind of Shyam Sankar, Palantir are doing this for complicated data problems. Anduril is doing it for robots, right? This is where the defense tech ecosystem, where American technology companies need to provide... This is the way our system works, need to provide capabilities for these big categories of threat, becomes a symbiotic relationship between government and the private sector. And that's certainly what we're thinking about on the biosecurity side. How do we build into that demand for, like, cutting-edge technologies in biodefense? So you ask, okay, like, what is that in reality? Like, what does that look like from a—what is the tech stack? What is the product stack for biodefense or biosecurity? The cool thing is you don't actually have to, like, invent anything new here. Like, this is a lesson that has been learned over and over again in the national security community. Like, it's the defensive targeting cycle, essentially. If you are looking for, Jason used the analogy before, if you are worried about somebody shooting a missile, what you are doing is you're constantly watching. Every day, every second, persistent, pervasive monitoring. If you're worried about cyber threats, you're monitoring every zero and one at all times. Most of them are boring. Usually, you're not finding anything. You're persistent, pervasively monitoring. You are then prioritizing and characterizing those threats into what is concerning. By the way, this is especially in bio, this is where AI is going to be incredibly important over time. If you're trying to shorten the time from detection to mitigation, which is how national security thinks about this, you have to be able to monitor data and figure out what's important very quickly. And then you get to what we're actually like, relatively better at, which is where almost all the resources are going today, which is medical countermeasures, vaccines, therapeutics, right? We think a lot about how to develop drugs and other things to mitigate the thing that we don't know is showing up. And so the big change moment, we believe, is this is what's missing. If you wanna think about it from a DoD tech stack standpoint or from a national security tech stack standpoint, what we are missing is per, persistent, pervasive monitoring so that you can enable rapid response. So all three of these things need to be built, by the way, right? Where we see the, the need really, and what is, kind of in significant demand, relative to where there's a lot of expertise, is in that first piece. Also, we are not insane. If you read all those documents, like, the national security community of the world, like, has figured this out, right? And the most recent... This is the Biodefense Posture Review. I love it. They actually, I, I thought it was very, for the first time, very thoughtful about saying, essentially, we need to build early warning around genomic sequencing. Early warning in genomic sequencing. Why? Because that is what the, the, and I'll talk about in a second, that's the high-fidelity data asset that you can get novel information from. And then second, the bottom one is the U.K., this summer, launched their Biological Security Strategy. Super neat, because it's language that we've been using for 18 months. Their kind of top priority is launch Bio- Threats Radar. Turns out that in the physics engineering era of the 1930s, the U.K. were the first to launch radar. This is the same thing. It's literally launch Bio-T hreats Radar to get early warning. Okay, so like, this is my favorite analogy. Just to go, like, nerdy for one minute, right? So in the 1950s and 1960s, we invented the high-altitude camera, right? So we had the ability to take pictures from high atmosphere or space of land. That was like... That was a new, a new technology, like snap picture, have cool picture of ground, right? And what was neat, Edwin Land, who was the CEO of Polaroid at the time, said: "Wow, this is going to be a new domain of comprehensive intelligence that we don't have, and it's gonna be useful for national security purposes. Oh, by the way, we're gonna be able to see where the Soviets have nuclear weapons and whether, whether they're spinning them up or doing something weird with them. But also, it's going to be super useful for economic purposes down the road." And then they're kinda like: "Well, also, we just need to figure out how to get these cameras up high. That's the issue." So if you look at the, like, history of the U-2, and if you go to, like, the National Air and Space Museum, and you actually look at a U-2, you're like, it's, it is an amazing plane, but it is like a piece of metal built solely to get a camera up really high. It is the system around the first-generation high-altitude cameras so that we could take pictures over the Soviet Union above surface-to-air missile height, and then play that through. You get satellites, better system, better cameras, and then ultimately, over the course of three decades, you get this really substantial industry, which is commercial companies running satellites, or to mix metaphors, radars, taking pictures of things, feeding data for national security purposes. Almost the vast majority of national security imagery today is not owned by the government. It is commercial satellites feeding national security organizations and also feeding the private sector. So if you can think about that, that's a high-fidelity data asset, pictures turned into a really substantial business model. What I love about what we're building is this transition to product. So you would not have built this except for COVID, but we spent a lot of time and effort building what is essentially very neat, the operating system, the U-2, if you will. Does not look like an airplane. It would be cooler if it did. The operating system around what is today, the genomic camera, the sequencer. So the sequencer, you know, 20 years of genomics revolution, still super nascent. Illumina, Oxford, everybody else has done a great job getting those two places. Almost every country that we visit in the world, and I spent basically the last 24 months on the road globally, because governments are the customer, have sequencers, but they use them for very niche purposes. It is not the high-altitude version. They are using them for clinical, they are using them for, you know, the high-end diagnostics, but they're not using them as a general-purpose aperture to take pictures of DNA and RNA on the planet, where the bio threats will emerge from. And so what we've been able to build is essentially an end-to-end software and services system, the software that runs a radar station and stamp it around sequencers around the world and work on biosecurity, biosurveillance programs with countries that are looking to utilize their installed assets more effectively. So when we go in, we don't need to buy assets, we don't need to buy sequencers. We are building a system end-to-end, which collects data from airplanes, that's our CDC program, moves it through to a lab, and feeds genomic data off the other side into a usable form for bio-threat detection. And that is the, that is the core set of assets that we're able to take, and deploy. Now, what we think of this as is much more like a software problem than a wet lab biology problem. So now, once we have sequence data coming off, each day, we are just versioning newer and newer assays and analytics into the system. So, folks have read about... We have one program that we did with IARPA, which is the intelligence community's DARPA, where we're essentially a good old-fashioned AI version of comparing sequence data against known known natural and known engineered sequences, where we can determine if something has been genetically engineered or not. That's just a software package that goes on top of version of a radar station, and we're constantly thinking about what are the next detection algorithms that we can put on top of this network. And that gives us a lot of stickiness with our partners, countries around the world, who are constantly looking for better and better detection because they all have this security mandate in their own countries, and are also interested in preventing preventing what frankly happened over the last three years or what they're all seeing in the threat surface coming up. We've taken a very laser-focused approach to deploy this country by country by country. Again, because for the short near future near term, countries are the customers because they have the national security mandate. That is who spends resources to secure their populations first. So we're operating eight international airports. We've announced 11 MOUs. We're monitoring essentially just over 100 countries because we're getting flight origin, and that gives us unique data that nobody else has about how pathogens are flowing around the world. I'll give you a sense of so you can, like, understand our press releases. This is essentially our enterprise sales model. What we do is we go in, and we, we look for countries that are interested in, in investing in biosecurity overall, and we sit down with them. We, we talk to them about what is it going to look like to deploy biosecurity capabilities in the 21st century. We start scoping that out. We generally, the way it works with countries, sign an MOU to then explore things further in a program design mode. We're almost always starting with airport programs, but not, not exclusively. And so this is an example of what we were doing in Botswana, which we've announced. We signed an MOU. We've had teams going back and forth, scoping out, monitoring programs, and we'll be launching that one, and then they move into operational programs. These have no money associated with them. This is where you get into paying programs, and our view is that we should be the biosecurity infrastructure and partner for allied nations around the world. And that's essentially how we're thinking about the process of expanding our footprint and expanding our biosecurity network. To kind of come back to the beginning and close, I think there's a very cool synergy that happens over time, and it's not a long period of time, between biosecurity at Ginkgo and cell engineering. To build that defensive targeting cycle that we're talking about, detection to prioritization, characterization, to building mitigation or enabling mitigation, medical countermeasures, vaccines, therapeutics, you have to have the whole arc. You have to have the network of the whole arc, and over time, we very much see both sides of Ginkgo's business feeding each other to kind of close the loop from bio threat detection to mitigation and response. This is certainly like how you think about detection, deciding, and responding over time, enabling a network of partners, something we are playing for. But today we are laser focused on how do you build that network that we just talked about? How do you deploy the bio radar systems most effectively? I think that's all I have, and happy to answer questions. Can you give a couple of examples of, in the last six-nine months, that you have detected things and you have done something to prevent it? It seems things a lot is reactive. Yeah. I think our favorite example, which was an early proof point, when BA.2 Omicron came out, we detected it 42 days before it was found clinically. Because this is the big—so, like, the big transition is not that you should replace current surveillance systems, current monitoring systems, which are mostly or most often clinically based, right? Somebody shows up, they're sick, flag it. It's that you should build a layered system. So that was a very cool example. We were able to flag that to CDC. They were able to characterize it, understand, you know, what the flow in and out of the country was, and what— In that case, it's a public health use case, right? It is, "Let me make nursing homes aware that a new, highly transmissible variant is coming." This was a very cool example of an end-to-end, where we were still serving nursing homes kind of during the outbreak mode, and we were able to notify nursing homes in our network with kind of 42-day, 43-day notice that there was this variant that was showing up on the shores. They were able to reinstitute masking policies, push vaccines, et cetera, in their own communities. So that's a public health use case, is just a little bit more early warning. What we're starting to see now, we're working. We've done pilots with the CDC flu program. You can imagine identification and collection of flu variants from south, from the southern hemisphere, being able to go directly into the vaccine manufacturing process, and that's something that's been explored just in kind of a pilot mode. So just on this flu issue, how much can people with all these interests identify exactly what strain they need to do? Because everyone is like in the midst, which strain is going to work. So with this, how much? Yeah, like, our view, like. So I think the 100 Days Mission is like, very lofty. But I also think that from a technology aspiration standpoint, we should be thinking about how we collapse that even more. And our view is the biggest gains are in how early you detect all the variants that are circulating. And then there's a separate piece of how you model those variants, right? But I don't have a great answer for like, what the art of the possible is, but kind of like in every other domain, we've been able to take and shorten timelines far more dramatically than I think our aspirations are today. One last question. Yeah. What's the pushback you get from customers when you roll out kind of sell your service? Yeah, I still think it's. I still think we fight through just, this is not about COVID, right? So this is about understanding that you've got to be multipathogen detection. A lot of times people will say, "Well, if it's coming through an airport, there's delay five, six days before we get sequences. Like, what are you gonna do?" And our general view is that. This, two things. One, you have to start with five or six days. That gives you a lot more early warning than you used to have, i.e., people showing up in the clinic. My favorite analogy on this, by the way, side note, is when we launched regular radar in the 1930s, the first use case was like flying airplanes to look for U-boats that were hitting Atlantic shipping going between England and the U.S. And we were like, I don't know what the exact date is, we were like, successful 5% of the time. Nobody was like: "You know what? We should stop detecting 5% of the U-boats. We should just get better," right? And so a lot of the times you get the pushback from folks who are like, "Sequencing is too slow. It is. It's only detecting these three, these three..." When we first launched, we usually did COVID flu. "It's only detecting these two or three pathogens. Like, we want to do it all now." And we're like, "Okay, but you have to start laying infrastructure, and then you have to build this plan that we were talking about to, to deploy that over a number of years, so you actually have comprehensive detection." But we fight this battle of like, it's not, it's not the all-seeing eye today, but it will be, and trying to get people activated even around that. Yeah, that's cool. I love that second question. Well, the first one question, too. Oh, I'm sorry, I'm sorry. Yeah, I apologize. I was informed that I needed to do that, and then I failed miserably. The question was, first, like, how should we measure how well this is going? And then the second was, are you still just doing wastewater, or how do you think about other modalities like air, et cetera? First one is, I think the best—I mean, look, we're this is a market that's building, right? And we're watching governments change their biodefense posture, like monthly. But I think the right way to think about it is that, like enterprise sales pipeline, if you will, like, we're able to announce MOUs, and every one of those MOUs we're in engagements, some fast, some slower, talking to people about what their program design is. Sometimes countries are willing to announce the operational program. It's really not on us. We'd love to announce all of them. Sometimes they want to hold and like, see it working, but it's really like, we can show you that top of the pipeline of the MOUs and then kind of those flow through. That'd be the best answer I can give you on that one. On the modality side, this is why we think about as infrastructure. And Ashish Jha, who has been a, like, great thought partner for us, both before he went in government and in government, you know, he, he said this really, really well about the CDC program. He's like: "Look, is it wastewater? Is it airplanes? What we're laying is biosurveillance infrastructure." And so that's exactly how we think about this operating system. We are sample agnostic. Like, right now, it's a lot of way to answer your question directly, but it's a lot of wastewater. But we're also doing in Rwanda, U.S., other places, we're also doing different types of samples, all anonymous, focused on non-human biological data. So we're very. We have a very hard line on that. But, and then we're starting to pilot air in airplanes. Like, why wouldn't we go to that? But it's really the key thing is like, you got to get to sequences. It's not about yes, no detection. We're like, really focused on getting to sequences. So, this year you guys talked about $100 million+ biosecurity revenue, percent of which is recurring. As you acquire platforms, one application of which would be COVID testing, how do you actually delineate between what's recurring and what's not? Yes. No, you know, I think the big picture way to think about that is the three years of COVID response dollars, which essentially ended on June 30th, versus long-term national security programs. That's the most detail I can give you right now, but you can look at it. June 30th was the... It's all public. It was the last day, officially, of kind of the federal dollars that ended with the May eleventh public health emergency being over. And so it's really how do you think about the transition to long-term national security? Always with the infrastructure in place, if something else happened, that we can spin it up super fast, right? Like, that's the... Can't think about what that looks like over time, nor is it the foundation of the business, but there is that kind of, you know, the kind of emergency response capability that could spin up super fast. Like 50% would be kind of PCR testing, the other half is sequencing and infrastructure partnerships. I'll, I'll leave that for later. Yeah.[audio distortion] Yes. Last question, by the way, was how do you disambiguate between non-recurring and recurring revenue, plus or minus? How do you disambiguate between those two, PCR testing to sequencing and bio radar type work? Just to repeat the last question. So this question is, if I say back to you, like, first one is Why wouldn't anybody just do this? Like, what's unique about what you guys are doing? Are you just comparing these to databases and finding new variants? And then the second half, say the second half again. Just around the government side. Oh, yeah, yeah. Governments putting their head in the sand. Governments not wanting to know the information. Yeah. So on the first half, like, one of the, like, really cool things that we've been working very hard on, and it, it's like the, the outward-facing version of what we do every day here at Ginkgo, like the massive, highly complex bioinformatics and computational bio, kind of expertise. We've kind of, for the first time at scale, basically turned a separate version of the team, which lives inside of biosecurity, and pointed that out. So we're now, we work very closely inside on everything that you're gonna hear about AI later, et cetera. But it's not just about can we run, can we run sequence data and compare it to variant databases? They're constantly looking for novel variants, running custom computational tools against that, against the data that's coming across. For many of these countries, in partnership with the smaller, kind of, by definition, less capable bioinformatics teams, you know, in each country, we're essentially running remote bioinformatics for a lot of our customers. So it's, it is, it is very much a core kind of piece of our value proposition. When I say, like, versioning the operating system, that's very much what I'm talking about. On the second side, it's, like, an interesting. It is clearly a dynamic that happens. Like, do governments, like, hesitate to know? I think we've got, we're, we're seeing that, like, go away pretty rapidly. It's like a, it is, it is the previous question of, like, what do, what do governments push us on? Like, oh, do we wanna know? We can get through that pretty quickly, and as the network has gotten big enough, it, enough places are able to kind of show their, share their data, so it's not. And we see how global air travel works. Like, people have realized that it's not like you can stop it from going one place to another. You actually wanna know early enough, and one country is, like, willing to share their data 'cause they will know it, they will know it earlier. And there's, there is still, like, a public good component to this that people, people are pursuing. We haven't. I think that where the big, where the big question marks will come in is when we, you know, when you get into the, the really serious, you know, really serious diseases, and you start finding those in places you didn't expect. Generally, people realize that they should, they really do wanna know that information. But it is, it's a question that people ask a lot. So, yeah? How about the market dynamics that have come up globally? Because the MOUs that you have are particularly, mostly in the Middle East and in Africa. Is that because, you know, like U.K. or Europe or other countries are not interested, and do you think you'd be able to expand geographically? Yeah. So there's, like, three layers of this question. This kind of integrated service for biosecurity, globally, the market dynamics, to be really direct, where we run into other companies, it's really BGI in building strategic biosecurity, biodefense relationships with countries. That's kind of who we see when we're talking about this category. That's a global market dynamic. Separately, I think you will, and I can't talk about all of it, today, you will see, kind of the more technically developed economies coming online with similar versions, and I think we see a market there for sure, even being an American company. It's just a developing market, and they also have homegrown capabilities that are more substantial. Whereas in a lot of the more kind of nascent biotech markets, people are starting from scratch, and they know they're not gonna develop homegrown solutions. So they're like, "Okay, we're gonna—we want to invest in this, like, we invest in cybersecurity. We know we had to buy cybersecurity from Norton and McAfee and whoever. We know we're gonna have to buy biosecurity from somewhere. Where do I wanna buy it from? Do I wanna buy it from Ginkgo? Do I wanna try to buy some Illumina sequencers and hire some bioinformaticians and do it myself, or do I want to buy it from, do I wanna buy it from BGI, which is a, which is a solution that is available?" And so that's kind of like the choice that we're seeing. The question was, what are the market dynamics? I'm, like, doing good afterwards, Megan. We have one question from online, from Eric at Kahuna Capital Biotech. For biosecurity, which I agree needs more love from investors, do you see any addressable markets in the private sector, or will governments be the exclusive consumer of this product line for the foreseeable future? Yeah, it's a super good question. So, oh, yeah. Well, yeah, so for those in the room, the question was, biosecurity needs way more love from investors. I think that was the first part. But the second part was, do you see private sector customers emerging, or will the foreseeable future be government customers only? And I think the short answer, I showed that, the Edwin Land, kind of, g- how did GEOINT develop as a dual-purpose data, data assets that you could turn into dual-purpose national security, governments, and private sector tool? I mean, we don't have Zillow today without that, right? Or Google Maps, et cetera. 100% do we believe that the, like, DNA and RNA of the planet will be a high-fidelity data asset that will be, commercializable both by governments and private sector. It is the foundation of what we think about here from a cell engineering standpoint at Ginkgo, like, absolutely. And I think the nearest term use cases in biosecurity, you would see that, is like things like supply chain management, right? Like, what happens when CVS stocks out of Tylenol in Arizona because they didn't know RSV was gonna spike? That's insane. Like, we should know that, we should know that that's happening. That's a very pretty basic, like, biosurveillance and monitoring projection question, right? Like, and you know, you looked at the supply chain kind of management that happened via HHS and friends of ours during COVID, that the infrastructure for, like, sharing how you should distribute medical products around the U.S. already exists. We just don't have the data feed on the front end to do it proactively. Now, that's all well and good. We do think that that's a little further out. There's a lot that needs to go into that to make it a really viable data feed. So I'd say not for the next 10 years, but, like, next, you know, two, four years, like, the 100%, like, the major customers are governments. Like, that, that is without hesitation, and that's certainly where we're focusing our time. National security almost always comes first before commercial applications of this stuff, if you look at, like, kind of the history of deployment of them. Great. Thanks, everybody. Thanks, everyone. Thanks, Matt. We'll be taking a break for lunch, so for the people online, we will be back at 1:30 P.M. for a presentation and fireside chat on AI. We'll see you then. For the folks in the room, we will be taking a break for lunch. We will do breakout sessions. So, Hi, everyone. Welcome back. I hope you enjoyed your breakout sessions and got some, some quality time with, with our execs and our great exec team. Our next panel and presentation is going to be from our Head of Corporate Development and new Head of AI, Anna Marie Wagner. All right. Thanks, Megan, and really appreciate everybody coming out for this. I know it's a, it's a full day, but it's great to, great to see you all, and I know the rest of the team enjoyed getting to meet you. As I've been spending more time in AI, one of the things that has been sort of remarkable to me is just thinking about the last couple years and just how much sort of public appreciation of science has evolved in that time. Like, a couple of years ago, we were all remarking that, you know, PCR was on the front page of The New York Times, and now our parents are talking about large language models and neural networks. And I think one of the things that's really appealing about AI today is that it's actually quite accessible. You know, transformer architectures are relatively easy to understand. There are rules that we understand about how this kind of stuff works, and, you know, you've got 26 letters that make 200,000 words or so, at least in the human language, and you can assemble those into sentences that, you know, with enough diversity, you know, can sort of represent human understanding. Biology is also a language. You've got four base pairs in DNA. Those four base pairs combine to make 64 kind of words or codons, and you can assemble those codons in by making amino acids into proteins that follow some biochemical rules, fold up, and then that one little thing creates all this diversity of life that we see around us. But there's one really important difference. We created human language. We understand the rules. We know what good looks like. Biology created us, and so when, you know, when we think about the last couple hundred years of biological innovation, it's really been an area of discovery, right? Scientists, you know, bread goes moldy. Hey, we've got antibiotics now. How do bacteria defend themselves? Oh, look, we just discovered restriction enzymes in CRISPR. So all of this, like, these major scientific breakthroughs, have really been a process of discovering what nature created for us. But here at Ginkgo, we're trying to make biology engineerable, and to make this engineerable, we actually have to understand these rules. We have to understand what takes this into this. We need to understand how these things work, how they function. That's something we can do in human language. It's not something we really know how to do yet in biology. One of the things I'm most excited about as we think about AI is, it can really help us deal with the sheer complexity of this problem. It's not intractable. Like, there are rules that govern this stuff. We're still living in the rules of physics and chemistry and math, but it's just so complicated that we can't look at the rules and figure it all out. And AI, with enough data, with enough compute, can really help us start making those types of advances. Now, if we think about human language, it's sort of in this interesting spot. We're really good at human language. Again, we wrote the rules. We know what good looks like. Most of us can speak it, and so interestingly, that gives us actually a really high bar for AI. It takes a lot for us to be impressed with AI. You know, most of the news articles about ChatGPT are like, "Huh, isn't it funny? It can't add 2+2," and we feel good about ourselves because we're still smarter than AI. We get frustrated when we try to bring an AI assistant on board, and it can't really replicate our tone of voice when it's writing an email for us. So it's really hard to make AI models that actually make us better. And what's been so remarkable, you know, over the last year or so, is we have seen that with enough data, enough compute, these AI models are starting to do something that looks a little bit like reasoning. They're starting to connect the dots. I'd like to believe that wisdom is still the domain of humanity and not AI, but it's, it's starting to look a little bit like that. You know, this is one of my favorite examples from learning grammar as a child, but you can plug this in. It knows that pandas are not murderous dinner guests and that they just like bamboo. It's kind of impressive. It's starting to impress us. I'll leave this one for you, just because I still like feeling good about myself and my own wisdom, and so I tested ChatGPT on that little cartoon, and I will leave that for you, for your enjoyment when you have more free time. But we're here to talk about biology. So biology is more complex than human language. We don't understand all the rules. And oh, by the way, we don't all speak biology today. Only a few folks that have gotten PhDs are really capable of making the types of discoveries that are advancing the field in meaningful ways. So we're living in a very different part of this, of this curve. And again, I would posit that AI has the potential to be really impactful here. There's a lot of data that we sometimes delude ourselves into thinking we have about biology. We've got sequence data, but I can assure you that none of you know what that sequence makes, even though it's only 613 letters long. I can even tell you what amino acids that make. I can tell you how it folds, like plugging in an AlphaFold. We still have no idea what this protein is. No one in this room does, and I can assure you no one at Ginkgo would be able to look at this and say, "I know exactly what that protein does." What you really need is you need functional information. You need to know: Where does this show up? Is it secreted? What metabolites are created when this thing is around? What does it bind to? Where else have I seen this, and what can I deduce from that complexity, from that level of information? And this is the type of information that Ginkgo is generating every single day in service of our programs. By the way, this protein is the reason we can all walk. It's the reason we have balance. It literally creates little calcium crystals. It makes little rocks in your ears. It's amazing. Tiny, little protein. So in, in Ginkgo's world, we are generating this kind of labeled training data, that functional data that answers the questions of what does this thing do and why, in service of the hundreds of programs that we are working on. And I can't emphasize enough the cultural shift that's happening here. When I was in your shoes about five years ago, and I was doing diligence on Ginkgo, in that case, deciding to leave a cushy investing job to come here, you know, the critique that I heard about Ginkgo was, "Ginkgo just throws spaghetti at a wall and sees what sticks. You know, they're just doing brute force experimentation to figure out the result. They're not. It's not the art of biology." And now, you know, I was walking into work last week, and I listened to a podcast, and you have the CTO of Bristol Myers Squibb saying, "You know what? These models are saying we should put a compound together, and any of our scientists who knows anything knows that that's not gonna work, but we're gonna do it anyway. It's still worth doing because understanding how and why that experiment fails is gonna make our model better, and that model is an important component of what is making us better as a company." And so he's, he's highlighting this big cultural shift, and, and I can't emphasize enough just how much we're seeing that cultural shift across our customer base. Now, because Ginkgo's been generating all this data for so long, we've been thinking about how to apply that data for AI and ML programs for many years. For those of you that are new to this space, Josh Dunn, who's our Head of DNA Design at Ginkgo, wrote a great little kind of summary paper on machine learning across kind of biological engineering applications with some folks at Lawrence Berkeley National Lab. He also is the technical lead for our ENDAR program, which, for those of you who joined our biosecurity breakout session or spent any time around Matt, understand is part of our kind of biosecurity infrastructure around identifying genetic engineering in biological samples, kind of genetic tampering, you might think of it that way. And then our protein engineering team is a really remarkable use case of this technology. We do protein engineering across basically every program that we do at Ginkgo. You know, each of these lines, those are protein engineering programs, and each of those colors are different classes of enzymes we've had to engineer for those programs. So the reason that we've been able to to offer some of these, like, really game-changing kind of value propositions to customers, like success-based pricing, that's unheard of in our world. The reason we're able to do that is because we have now gotten so good at predicting whether or not a program's gonna work and reducing the cost of doing that work in the lab through computational design, through applying these tools, that we're able to do something really quite different. All right, so we've talked a little bit about data. We've talked a little bit about the AI models. What I want to emphasize is that Ginkgo thinks this needs to come together. Like, we will be consuming this data into our models as fast as we can create it, and there are different levels of data, and there are different levels of models that matter here. What really makes the difference in kind of next generation AI tools today is if you have a really strong foundation. So think, this tells me what proteins look like. You know, proteins that work, this is kinda what they look like. Stick just a ton of protein data in there, and you've got a model that understands generally, "All right, that's a protein." That's not good enough to tell you, "I need a protein that binds to this organ and doesn't create this immunogenic response and is thermostable because this is my supply chain," and, and, and, and, and. For that, you need these task-specific models, and to answer those questions, you need the kind of data that is coming out of our Foundry, that labeled functional data. And so we really view this as an, in kind of an interactive process. It's the same flywheel we've been talking about for as many years as you've known us, but AI is really a tool that allows us to take advantage of all that data that we're generating, and not just the data for the successful experiments, but also the data for any experiment we do that really helps us understand how and why things work or don't work. The most common question we've been getting since we announced our Google collaboration is, all right, what models are you building first? And how much data do you need, and, and what is that data? Jason mentioned at the beginning of the day that our first set of models are gonna be in the protein world, so a protein foundation model and set of applications on top of that. The reason for that is it's at that nice intersection of we have a lot of data, and we have a lot of customer demand in that area, and so it's a great place to get started and start building the foundations. But we absolutely see the value and intend to be building models across, if you will, the central dogma of biology. There is a lot of value in the work we do to understanding the rest of the genome and how, how DNA works, not just how proteins work. And then if you wanna think about the data that we have that's going into these models, yes, we also benefit from discovery. There's a lot of natural genetics that are out in the world. We have 2 billion proprietary sequences at Ginkgo that we can combine with all the public databases to create a really rich data set to start training these models. But we also focus on creating diversity so that we can start testing new things that maybe aren't showing up or aren't showing up very often in nature, and we can start understanding those new functions. We then can apply a whole set of measurements on that, right? What are the genetics? Is it being transcribed? What metabolites is it making? What proteins is it making? What are those proteins binding to? Et cetera, et cetera, et cetera. That is the data that is then training all of these kind of applications that sit on top of the foundation model that we're building. I think by now you're all familiar with our Google deal, but certainly happy to speak more about it. You know, we've got the data. We've got the sort of biological insight. What we needed, though, in this new world of how do you build foundation models with lots of data, is you need compute that'll scale with you. So we were looking for a partner that would give us that asset. In the same way that we've built a scalable foundry that can, you know, work on many different programs at low cost for customers, we needed the same asset for compute. And so really happy with our partnership with Google there. But then what was so interesting about creating this relationship with Google was that they saw in us a couple things. One was what we've just been talking about. Ginkgo has a lot of data, and the thing they're hearing over and over from their potential customers in the life sciences is, "Yeah, we'd love to use AI, but we don't... Like, how? You know, we don't really have enough data to make it super useful." And so it's been really hard for them actually to go penetrate big pharma with compute as the product. And so when they look at Ginkgo, the way they think about Ginkgo is, "Well, maybe compute isn't the product. Maybe Ginkgo's model is actually the product. And what we wanna do is we wanna help Ginkgo build better models so that Ginkgo can then help bring, you know, the Pfizers of the world, the big pharmas of the world onto AI, onto the cloud via their models." And so that was really the basis for Google, you know, giving us funding to really accelerate the development of our models. We announced that about a month ago. It's been a really, really great relationship getting started with them, and we're sort of off to the races, so that's been fun. I'm gonna turn it over to a panel in just a second. I'm really fortunate to have three great thinkers in AI with me here. But knowing that this is an investor day, I did just want to nod to the how do you make money in AI question. Obviously, this is going to be a part of our platform. Platform improves with scale. AI improves the platform. We will deliver better value to our customers, we hope, and we will generate value in that way. But one of the reasons I'm particularly interested in spending more of my time in this space is I do think that there are new opportunities that are emerging with AI today. Again, there's this kind of cultural shift happening, especially in biopharma and especially in biosecurity, thinking through what, what is our AI strategy? What is our data strategy?...Who do we work with? Who helps us figure that out? And I want Ginkgo to be the partner that is helping all of these customers figure it out. And that could be, how do we help create data together that helps answer these questions? And it could be, how do we create models that are useful for the types of problems that you're interested in? Certainly, many of these models we will keep internally, but we do plan to release models broadly. And you can also imagine models that we would be developing in close collaboration with partners as well. So more to come here. I'm very, very excited about this space. But wanted to give just that quick introduction, before launching our panel here. And so while I do introductions, I would invite our panelists to come up, and we'll get some chairs set up here, and there will be time at the end for questions. Don't worry. But joining me is Shyam Sankar, who's been on Ginkgo's board since 2015. Very happy, he recently agreed to serve as our chairman. But he's better known as the CTO of Palantir, which he joined in 2006 as the first business hire, and has, as far as I can tell, led just about every function in that company at some point. And so as we've been building Ginkgo, any new function we had to take on, Shyam has been a wealth of knowledge and experience and advice over the years. We've got Barry Canton, many of you were fortunate to tour the foundry with him earlier today. Barry is Ginkgo's co-founder and CTO. Just a little side note, something I've noticed is like a superpower of Ginkgo's, is that we have five founders, all of whom are still with the company after 15 years, somehow. They all really complement each other. Barry, who is the mechanical engineer of the bunch, he's really responsible, as he is fixing his microphone, for those of you who can see that. We'll see if he can figure it out. He actually was a mechanical engineer. That was not just a really well-timed joke. He is responsible for figuring out, like, how we were gonna make this, like, messy, wet, unpredictable field of biology scalable and standardized. I think, honestly, we're here talking about the potential of AI at Ginkgo because of many of the decisions that Barry has made to create the framework with which we could then generate biological data at scale. And then last, but certainly not least, we've got Dmitriy Ryaboy. Dmitriy is Ginkgo's VP of AI Enablement, and is responsible for our long-term technical strategy in AI. Today, he's focused on building our AI infrastructure, optimizing our model architectures, and working with our scientific teams to design, train, and assess our AI models. I found this out recently. So Dmitriy has a long history of working at the intersection of biology and computation. He started at Lawrence Berkeley National Lab in the late nineties, meandered through the internet boom, including building a lot of Twitter's data architecture. And then most recently, he led the digital technology organization as CTO of Zymergen. So please welcome this panel, and we'll get kicked off. All right. I feel very far away from you guys. I'm gonna scooch up a little bit. So the first question here is... I'm gonna give it to Barry and Shyam. So both Palantir and Ginkgo have something called a foundry. I think they look pretty different. But I'd be curious, where did the term come from, and what does it represent to you? We'll start with you, Shyam. Great. Yeah, yeah, they are, in fact, quite different, but I think they probably share a philosophy in common. So when you know, at Palantir, when we think of data, we see fuel, not exhaust, and that's the raw material to decisions and the decision-making process. And really thinking about the flywheel effect you get from doing that right. You know, if you visualize an institution, it's not one decision. It's kind of a chain of decisions. And anywhere you're touching and poking this pressure system, you have an ability to affect how the institution runs. And so you want this factory that allows you to make and produce those decisions as effectively as possible. I think one of the things that's exciting with AIP and AI is taking that same philosophy from how do I turn data into decisions to how do I help customers build AI-enabled application forges to do that across their business? Yeah, I would like my answer to be data is fuel, not exhaust, as well. But I'll add some of the Ginkgo-specific stuff. Yeah, I think a lot of what Shyam said applies to Ginkgo from a philosophy level and at an abstract level. But to give you a little bit of Ginkgo-specific context, obviously, we are a platform company. Our platform is about transforming how R&D is done anywhere that biology is used. And the physical facility, the lab, is at the center of how R&D is done. And we wanted to transform, and continue to want to transform the vision for how that works. A part of that was to kind of establish a break and use a different name, a different term, and lay out a different vision, that kind of makes a break from the conventional notion of what a lab is. For us, the Foundry, you know, by analogy to a semiconductor foundry, was the perfect term for us to settle on, at a number of levels. First of all, as you heard from Jason this morning, the separation of the physical activity of the R&D from the design activity is a separation that we wanted to create and that wasn't really present at all in the life sciences when we started building all of this. Once you have that separation, now you can start to have a lot of design activities centralize on a shared general purpose, a physical platform for doing the work. The foundries that we toured this morning, and you get all kinds of operational efficiencies from doing that. Further, once you centralize it, you can start to automate it. You can increase the operationalization. It becomes capital intensive. It becomes all of the things that semiconductor fabs are, and we see a lot of analogies there that we wanna—that we are continuing to push after. So yeah, great term from our persp ective. ... for what we're trying to do. Appreciate it. All right, Dmitriy, when you were at Lawrence Berkeley in the late 1990s and early 2000s, again, I found this out recently, and it's been fun. You know, the people you work with closely, you don't know them until you introduce them for a panel. You developed something called the VISTA Genome Browser 2.0. What was that? And could you predict then what we would be doing at the intersection of computation and biology 25 years later? The internet remembers. I then took, like, a more than a decade off of biotech and went into consumer internet. So I came back. I came back. So it was the second version of the VISTA Genome Browser, obviously. The VISTA Genome Browser was, this was happening when. So I was an undergrad, and I just happened to get a programmer job I was looking for, you know, pay my tuition at Eddy Rubin's lab in LBL, and that was right around the time when the Human Genome Project was just about to finish, and then I was there as it published, both of them published, and a lot of other sequences started coming online. So you got human, you got monkey, you got pig, and then you started getting microbes, and all kinds of things. So there's this explosion of data that was just coming at a very different pace than the field had been used to. And that was before NGS came along, next-generation sequencing. And people were doing a lot of very interesting comparative genomics. Compare the human genome to the mouse genome, find areas of the genome that are very similar, get a multiple genome alignment, and that tells you where, where the genes are, right? Like, that was a problem. Figure out what's conserved between through evolution. And so it turned out that the actual Genome Browser was like a Java applet, if you remember those. JavaScript wasn't quite, quite right yet for providing the results of these alignments to scientists across the U.S. who could kinda navigate them and look at them. But behind that was the more interesting part to me, which was how do you actually get those alignments? Because now we were at a place where the data was way too big for us to do it the way people were used to doing it. So we had to get into distributed computing. We literally built a rack, like a wire rack, not... if you've seen server racks, they don't look like wire racks anymore. It was a literal rack, like you've seen around here, with beige boxes on it, all wired together in a literal broom closet. We had to keep the door open so it wouldn't get too hot. And so we built all that up and wrote up a bunch of software to have these things talk to each other so we could run these alignments. And so that was kind of my introduction to distributed computing and to what became known as big data. And then that actually set me on the path of doing big data for internet companies. They have a lot of data, and eventually for returning here. Could you predict then what we're doing now? Right! The second part of the question. I think because it was I think it was the seeds of what we're doing now. It was right at the point where things leapt into data is much bigger, it doesn't fit in a single computer, and all of these problems that were before kind of more pure science problem, lab problems, became, "Oh, my God, like, we really have to do a lot of computational analysis of this data, and that's how we're going to move the field forward." So it was a seed, maybe an acorn, and now we're like in oak territory, and with AI, we're heading into sequoias and beyond. But there were glimpses. All right, Shyam, so the last 12 months have really, I think, like, rocked the world of a lot of corporations. They're all kind of struggling to figure out, okay, like, what do we do about AI? Didn't think AI was relevant for them before, but suddenly they're feeling the basis of competition sort of change under their feet. I would guess Palantir sometimes sees this coming before the rest of the world does. And so I'm curious how you think about product development in a world where your customers might not necessarily know what they need and what they want. Do you approach it like, "Hey, we know best, and we're going to develop the product that's right," or do you find that it's still a very kind of collaborative and consultative product project? That's, like, the hardest question, 'cause I think the answer is that you have to find a way of doing both. You have to both meet the customer where they are today and what they understand. But if you haven't already developed meaningful conviction in what you think they're gonna need two years, five years from now, then, you know, it's not actually gonna work. And the way that we've squared this—what I'm quite excited about with this, and part of this, I think, is just that people now have an expectation that software is supposed to work fast. If you just take that as a rebaselining of the amount of energy people are putting into this, what we've really seen work now is getting people with their hands on their keyboard. This is not the sort of problem or technology that you can admire and think your way through. Like, you have to actually experience it and iterate with it. So, like, getting multiple customers in a room for a week to actually build something where they're gonna exit that boot camp with something they can put in production, has been so efficacious. 'Cause I can scream until I'm blue in the face that, like, chat is a limiting paradigm and not how you should be thinking about applying this in the enterprise, that LLMs are statistics, not calculus, right? Any of these like deductive frames, and it's just like, okay, conceptually, maybe interesting. Maybe. But really it's like: Oh, I just built something that saved my users 50% of their time in a day. Like, it. You. They get it. I think squaring those two things is, is the art of this. Barry, maybe turning to you. We talked just in the introduction a little bit, just at the highest level, the difference between foundation models and task-specific applications. And so as we think about building product at Ginkgo, you know, either for our own internal use, for our scientists or, you know, more broadly, where are we spending our time, and how are we thinking about the technology that our customers and our scientists are gonna need? ...Well, I think the answer is that we have to work on both of those things. So as has always been true in the history of Ginkgo, we have to think about the platform, and then, as Shyam said, we have to think about meeting the customers where they are today and helping solve the problems that they have today. And so the way I think about the foundation models that we're building is analogous to the foundry that we've been building and the code base that we've been accumulating. It's a general purpose asset that gets better with scale, and the more broadly useful it is, the better it will get. So we are absolutely using the data that we already have access to, both public data, but also the proprietary data that we have to train Foundation Models that we hope will be broadly useful across markets and projects. Second, on the task-specific model side, you know, our partners have very specific needs. They need a particular protein to be more active or more soluble or expressed at a higher level, or they need a promoter that has greater tissue specificity. You know, whatever it might be, these are very specific problems, and to solve those, it is not sufficient to have a Foundation Model. You need to have a task-specific model that can address those particular questions. To be able to build a task-specific model, you need relevant data. You need data with the right kinds of labels, tissue specificity, labels for promoter sequences, for example, stability data for proteins. If you think about our Foundry, what we've been building here is a way to generate labeled data sets for very particular problems that are commercially relevant, and we've been working on doing that now for 15 years. So we have the engine, and we have the capability to collect data to train fine-tune models, and so we'll absolutely do that to help solve customers' problems today. And the last thing I wanna say is, I would like to zoom out a little bit, because while there's an enormous amount we can do with better modeling of protein and DNA using the language models that are emerging over the last couple of years. To some extent, the most powerful thing in biology are cells. You know, these are fully featured little machines that can do incredible things and that self-replicate each other, and that we can't, you know, we can't build with any other technology. I think the language model tools that we're all able to use today are gonna make it easier to understand and program cells at the cellular level, but I don't think they're gonna be sufficient. We're gonna need better AI tools, and we're gonna need to be able to integrate mechanistic modeling techniques in order to be able to model and predict at the cellular or tissue level. That's where a lot of the true value in the future is going to be. Proteins and small molecules and DNA-level work can be extremely valuable today. I think the broader potential of this technology is gonna be at the cellular and tissue level, and we're gonna need to do a lot of new things on the modeling side and on the data collection side to be able to enable those. Sounds like a lot of complicated, messy data. Yeah. Dmitriy, you have spent the last 25 years or so working on lots of complicated, messy data. How do you think about building the infrastructure for Ginkgo that can handle that complexity, that diversity? What allows us to start thinking about those bigger questions that Barry is outlining? Yeah. You have to be thoughtful about the foundation. I don't mean the foundation model, but the foundation of the foundation model. Because there's a lot of engineering that goes into sort of the enabling those things. That's why my title is AI enablement. That's the tricky problem. So organizing the data, making sure the data is captured in the right ways, the data is relevant, you can actually interpret it later, you can look at your models and find out where the data that went into those models came from. You can figure out when your results are off, and you can build a feedback loop. And that's all kind of abstracted from what exactly is the data. Is it the data from the LCMS? Is it the data from our HCS screens? And then how do you organize that information so that it can provide appropriate input to a model that sort of fundamentally you don't wanna overspecify to the individual types of input. So there's a lot of mess in there. Fortunately, to some extent, this new paradigm for how we build AI models lets you get away with a fair amount of mess, right? Like, human language is messy, images are messy, and yet we're able to extract meaning out of them. So... And the models really shine in sort of a domain that's very expressive. You can express pretty much anything with language. That's what we do. Images are very expressive, right? So that's a very good kind of problem for these models, where it's something that's very expressive and very complex, right? But there is an internal structure to it. It's not random. And they're able to elucidate that structure internally. And that's the kind of data we're dealing with in biology, and that's why, fundamentally, we think this, this is gonna work. So I think we biologists like to think we're special and feeling like data is all that matters, and you know, it's the big, hard problem. Shyam, do you run into the data problem in your world, and if so, what does that look like? ... Yeah, I think one of the exciting things about the current approach with LLMs, in particular in the enterprise, is that there's all sorts of data that nobody used to even think was economical to capture that you now can. And part of this is actually elucidated by trying to solve problems. So if you are trying to use something like retrieval augmented generation to service very high-end equipment to automate and build a copilot for maintenance, the first thing you're gonna go to is like the maintenance manuals. Except the reality is, those 10,000-page PDF documents are wildly out of date. No one maintains them, and you kinda have to mark to market as soon as you try using them, that it doesn't actually have the source of truth. But you know what does have the source of truth? The Slack rooms, the Jira tickets, the audio recordings of the video conference calls you're using in your incident response to debug these things. That is otherwise historically treated as ephemeral and useless data that is unstructured and irrelevant, but it's actually completely trivial to structure now, and it is actually the most relevant data. In fact, all the canonical historical sources are known to be inaccurate. And so if you kind of string this together where it's not just how do I solve the end part of this, but what are the new sorts of data that actually are much higher fidelity, but historically harder to capture? You get a lot of value, and I suspect there would be analogy there to the sorts of data you're able to capture to the Foundry. You agree, Barry? Yeah. I, yeah, I think that, that is absolutely true for us. A lot of the, a lot of the insights, a lot of the interpretation have been treated as, as being ephemeral, and, and now they can be integrated with the kind of the harder, more structured data, and that's exciting. Actually, one of the things that Dmitriy is working on is how to, how to, surface and structure that information to make it easier to get at. Several. Several of those. All right, so, shifting gears a little bit, Shyam, this one's still for you. So you spend a lot of time working with government customers. We obviously have a biosecurity business also working with that type of customer. How do you see them thinking about AI? Are they viewing it more as a threat or an opportunity? And, how do we build that trust? I think folks in government—thinking is the right word. They're doing a lot of thinking about AI and maybe not as much acting on it. I think that's created a lot of opportunity for companies that are incorporating this into their products, so just taking ground, manifesting the facts on the ground by rolling out their products that incorporate these things. But it speaks to the underlying reality that AI is this experiential technology. You're not gonna be able to think your way through it, and the advantage is gonna accrue to the people who are experimenting most rapidly with it. So I don't wanna paint the government in broad brushstrokes. There are pockets of deep innovation where people are going fast, but by and large, there's a lot of admiring how exquisitely interesting the problem is and not enough hands on keyboard. Yeah. Well, I think, you know, at least when I, when I hear folks talking about the dangers of AI, biology is often one of the things that they bring up, right? Like, well, what happens when we can start messing with biology? It's a-- It's sort of a touchy subject. So, Barry, you know, I'd love to get your thoughts on, on just, you know, these concerns that we sometimes hear about the risks of moving too fast in AI, especially as it relates to biology. And, and so how do you think about reconciling the, the huge opportunities? You know, as I think about biology, it is probably the only technology that can deal with hunger and health and climate in a, in a real scalable way, but with, you know, the, the potential for misuse or, or risk. Yeah, I think, you know, why we started Ginkgo and why all of us are still here is because of that potential to solve global scale challenges with biology. So I would say that in the biological domain, we have 50 years of history now of dealing with really kind of monumental breakthroughs that have raised questions of biosafety and security. And so actually, the community has spent a lot of time wrestling with those questions and thinking about them, going all the way back to the discovery of recombinant DNA in the 1970s, the publication of the human genome, the discovery of CRISPR. You know, we have been, you know, deployment of engineered crops. These are questions that society and experts have worked through. Are the systems perfect? Are the regulations perfect? Obviously not. Would we like things to move faster? Obviously. But we do have a kind of a multilayered protection system in place, everything from the level of DNA synthesis screening to labs being able to operate through the controls around how engineered biology is deployed. So, I think we have a lot going in our favor. I think it's really gratifying to hear every time I hear from Matt about the progress on the biosecurity side, because I think that's like filling a huge gap. Also on the biology side, we've spent a lot of time wrestling with what data it makes sense to have in the public domain versus to be kept private, both from a commercial perspective, but also from a biosafety perspective. So I see AI as being an accelerant here to what we're able to do. But I think that we've been working on a lot of these questions for a long time with other scientific breakthroughs, and I think that, you know, we will be able to use a lot of that in this case, too. Well, one of the things I think is sort of most interesting as we think about this problem is some of the same things that we think about purely for commercial reasons, right? How do we protect our IP? How do we protect our data? How do we protect our insights? Are the same types of questions I think you need to wrestle with when you're also thinking about kind of these security implications. You know, biosecurity, it's still, you know, how do you, how do you regulate access? How do you regulate access you know, data, et cetera? And so it feels like we have a sort of a unique place in the world just by virtue of the way that we've built the business to try to wrestle with with a lot of these these hard challenges. Yeah. I mean, I think the way the commercial incentives will line up is that it's fast-moving commercial entities working with proprietary data and data generation platforms that are gonna make the most progress. I think efforts to build large, federated public databases are gonna run into all kinds of misaligned incentives and structural problems, and will just move very slowly, and the speed and the progress will come from commercial entities operating in a kind of an agile and focused way. The other point I forgot to mention before is, with biology, unlike, say, software, even if we said, "Hey, we're gonna, you know, slow down technology. We're gonna try to somehow use social, cultural, and political approaches to slow down the rate of technology change," evolution is out there, you know, running billions of experiments every day, finding it- It's sort of humbling, isn't it? Right. Yeah. It's, you know- What's the foundry throughput of Mother Nature? Yeah. It's huge, right? And it's just doing penetration testing every day, right, against our immune systems. And so the cost of inaction, the risk of inaction is just really high, I think, in this area, so. All right. So I want to keep an eye on time, and I don't have a watch, so I'm really probably failing at doing that. I do wanna leave some time open for questions. So I've got a little rapid fire for the crowd here just to warm things up. But if you do have questions, I think we'll bring a mic around, so please get those ready in your head. All right, rapid fire. We're just gonna go down the list. You have 10 seconds or less. I mean it. All right, favorite use case for ChatGPT, minus bedtime stories for my kids. Lord of the Rings limericks to celebrate Palantir anniversaries. I can't possibly read books fast enough, and I need AI to help me go faster. Recipes, but you have to be mindful of the amounts that it gives you, so- All right. It's only for advanced users. We'll go the other direction now. Dmitriy, you start. All right. Most surprising thing about our lives, I don't know, 30 years from now. Somehow Twitter managed to survive 17 attempts to disintegrate and is still around. X, you mean, I think. Might get renamed, I don't know. Okay. Ginkgo's business model will be a completely logical and obvious thing, and everyone will look back and say, "That made total sense." So yeah. Too soon! That we're gonna really miss being able to talk to everyone in real time, to our friends in Mars. Oh. Oh, Oh, yeah. Yeah. All right, Shyam, you start. All right, name a task that AI, or I'll give you AI plus robots, will never be able to perform as well as a human? Dances awkwardly as me. Caring, but, Dmitriy is... I think I've heard Dmitriy talk about this. It's great. Oh, I think AI will never be able to wrap its neural networks around a child crying desperately about wanting cereal for breakfast, while there's a bowl of cereal right in front of them. I think AI might do better than me at that job, but next. All right, Dmitriy, you're starting on this one. What's the most amusing AI mistake or misunderstanding that you have witnessed? I tried to self-ChatGPT, or how do you—I don't know what the term is. We're gonna come up with one. Look myself up, and it thought that I'm Chip Huyen, who is a really prolific author of, like, ML stuff. So I'm glad that we're in the same vector space, but I'm definitely not as cool as her. Every interaction I have with Siri is unfortunately challenging, so... I did say ChatGPT, but I'll give it to you. ChatGPT. All right, Shyam. I have to occasionally just do math problems to remind myself that I'm still good for something. 2 + 2? Yeah. All right, last one. What will you do when AI can do your job? Easy. I'm gonna go look for John Connor and join the resistance. Yeah, that, that sounds right. I was gonna go with Morpheus, but, yeah, you can go with John Connor, too. I took it to a dark place. I've been working myself out of a job for 25 years, and it's still rolling, so I... It'll, it'll be cool. It'll be fine. I might ask AI to teach me, calligraphy. I think that'll be really fun to take up in my old age. I've learned so much about Dmitriy in this panel. I don't know how much time we have, but for whatever amount of time we have... Oh, great! Lots of time. I was doing so well. If there are any questions, I'm happy to run around with a mic and take some of those questions. Yeah. You want my mic? So the- You're good. So the comment of data is fuel, absolutely agree, and obviously that's, you know, one of the wonderful things that you guys have built. But compute is still a part of the equation, just because there's presumably large amounts of things that can be brute forced, figuring out which information is the most useful, the associations between it. If you were to think about your business and you had access to all of the compute available in the world today, how would that supercharge what you could do, and how should we think about that? I can start. I mean... For me, it comes back to the fact that we need better data, and we need more of it. I think just having more compute than we have right now, I don't think it would actually be. I think we've we have a lot of compute available to us now. So we need better data. We need to. Internally, at Ginkgo, we have some expertise and capability building to do here. I think we are good users of AI today. I don't think we are good developers of the fundamental technology, and we need to get better at that and are working hard on that at the moment. And then I think the last thing is, I think it's still pretty early in the development of model architectures and the breadth of problems they can be applied to, and how to integrate multimodal data together. And that is, that's all hard thinking work rather than just computing work, and that's where I think we need to focus. So, but, Dmitriy? Yeah, I think the Google deal in large was specifically about that, because... So basically, my answer to you is nothing different than what we're doing now, because that was why we did the Google deal, to get effectively unlimited compute as far as, as far as our ability to consume it goes. I'm sure I'll be singing a different tune in like three years, and I'll say, "I need more compute. Like, give me more budget, Anna Marie." But, but for now, like, the strategy, this is why we did it. We want as much compute as we can eat, and that's what we got. So this is what we would be doing. I would just say that, you know, it, all of the advancements of Gen AI don't change what problems are valuable to solve. The same problems are valuable to solve. You can just go much faster with it. So what do you do with more compute? You go faster against the things you know are already valuable. You spend less time optimizing. So do you guys, you know, do you see tech companies becoming, you know, a, I guess, a long-term partner in the drug discovery space, or with their investments, you know, increasing Google with Isomorphic Labs, for example, to see them kind of sneaking their way to being a potential competitor in the space? So just to repeat the question for the for folks online, the question there was, how do we think about the relationship with the tech companies going forward? Are they a partner? Are they a competitor? You know, I think we'll, we'll see. No doubt, they are building very useful technology and capacity today and infrastructure, and there's obvious opportunities to partner, and we'll continue to do so in the in the mode of the the the Google deal that we that we announced. What their commitment to making biology easier to engineer will be in the future is unknown, right? I think most of the efforts that they have had in those areas so far are like kind of more like side efforts, right? Not central to the core game by any means. So I think, you know, I'm glad they've done those projects. I'm glad AlphaFold exists. I'm glad ESM exists, and we'll look to leverage those advancements where we can. But I think, you know, I think we need to make sure that we are building the platform for making biology easier to engineer. I think we're the mission-driven company in this space here. And so we're gonna be the ones who are going to make the investments that, that have to be made for, for, to achieve our mission. And, to the extent that that aligns with what the big tech companies, care about, that's fantastic. But they, you know, we, we don't know where that, where their focus will be, so. Yeah. I'd probably just say, like, I think on the one hand, like, these are also just really hard problems. And so I do think sort of to Barry's point, like, we will benefit from investments that others are making and technologies that are generally useful to understand biology. I think also to Barry's point, the nuance I'd make is a couplefold. One is, we tend to see when tech companies are getting interested in biology, because it's such a kind of complex, intractable seeming problem, they focus in on a relatively narrow domain, and it becomes very, very rational then to also say, "Okay, let me solve a very narrow problem. Let me make a drug." And so most of the companies that we see that have come out of this, sort of tech background are, are really therapeutics companies. And again, those are, those are companies that we think we could support. And, you know, one of the most common questions we get is: how do you deal with the data grounding problem in biology? And then for those of you who don't spend as much time in AI, basically, how do you make sure what your AI is training on and spitting out means anything at all? And the way that you know that is you have a foundry that can actually test the things that are coming out of your model, and you're constantly reinforcing your model in that way. And that's not an area where we've just seen that much interest, candidly, from those companies in building that type of infrastructure in-house. And in fact, when we were talking to Google in the early days of putting this partnership together, I think one of the things that they really appreciated was a gap that the DeepMind team has historically had is exactly that. You know, they've built absolutely incredible technologies that we use regularly at Ginkgo, but what sort of got them oohing and aahing was, "Oh, if our model spits out 1,000 predictions, you can actually go run those pretty trivially and let us know how the model did." And that's just not a capability that they have or, candidly, that I think they really want to have in-house. It's just... It's not the area of highest ROI for them. Thanks. Any other questions from the group? I've got a couple more I can throw in. Can you talk about your new role and Ginkgo specifically, how you're thinking about—I mean, you've done a good job talking about AI and the science, but structurally, how, how, you know, how are you thinking about this across all the platforms? And talk a little bit more about that from an operational standpoint. Yeah, sure. So the question is really around: how are we organizing around AI? And so I think, again, maybe just going back to one thing I said earlier, I think at the core, AI is a tool. AI is a tool that we will use as broadly as possible to make our platform stronger. At the same time, I think we've all recognized that there are real new business opportunities for Ginkgo to be a thought partner to our largest customers, to our government partners, in figuring out AI as a strategy. And that requires a little bit of flexibility outside of our normal kind of commercial program structure. And so we wanted to create the flexibility to explore those opportunities, because I do think, you know, Ginkgo wants to be the place where you come to figure out hard biological problems. And I think, again, AI will at its core, and the models that we're building right now, are really designed to support our broader platform. But we did want to create a little bit more focus at the commercial level to open up some of those larger kind of strategic partnerships and opportunities. Any other questions? Yeah, we've got one more here. Go. On that point, and also your question of what would you do when AI replaces you? You know, a lot of engineers in the low-code, no-code world are actually being laid off, and I'm the optimist that thinks those people who are, you know, very intelligent, are going to move towards harder problems, of which healthcare is one of the hardest and most important problems that we can think of. Have you started to see that major shift, where that population is expressing a lot more interest in the cross themes of AI and healthcare? And how do you expect that to change the talent pool and your recruiting capabilities going forward? Dmitriy, you want to go first? I'm not sure about the sort of low-code, no-code tailwinds for that. I haven't explored exactly the full pipeline. But I would say that earlier and now still, there's a healthy amount of interest in... among engineers across the board in tackling problems that matter, and moving away from sort of getting an incremental improvement in ad click-throughs and into things that actually affect people's lives. No offense to anybody here who is investing in the ad techs of the world. That's what brought me back. That's Dmitriy, who built Twitter's data architecture. Right. I'm fairly intimately familiar with that problem. There's no shortage of interest in solving really hard problems and working on things that matter. So, yeah. It is interesting when we announced the Google partnership, I got a couple notes from folks at Google who weren't part of our collaboration, just saying the Google internal, you know, kind of chats and conversations just completely lit up when this partnership was announced because they were just so excited to be able to work on problems that we're focused on. There, there's been competition on who gets to work on Ginkgo's, you know, Ginkgo's collaboration within Google, and so that has definitely been really encouraging to see, although there's definitely still a war on AI talent that, you know, I think we all face. Yeah. We definitely have people who are, like, have the freedom to pick their projects at Google. I'm seeing a few of those people show up in, in our, in our meetings. We've got Mark in the back here. Great panel. Maye one for Barry. Obviously, the Google, Google partnership is a multi-year collaboration. I'd be curious, how should we measure your progress against that? Obviously, seeing new programs is certainly one obvious way. You know, is there one particular major contract that you think you could sign, perhaps with a pharma company, that would validate the investment in the AI? So obviously, the Google partnership does come with a meaningful cost. I'm just curious how quickly you think you can prove that the cost certainly justifies the commitment. Yeah. Yeah, it's a great, it's a great question, and we, I... It's something we're thinking about a lot internally. I mean, I think the way we see the power of AI today for us is compressing R&D timelines and reducing the cost of R&D projects. So compressing R&D timelines by allowing us to eliminate entire cycles of experimental cycles of design, build, and test. Reducing R&D budgets, both by that first factor, but also allowing us to look at potentially smaller numbers of individual designs within a particular round of testing. So, you know, 500 designs instead of 1,000 designs, 500 designs instead of 10,000 designs. So, we think all of those factors will shorten R&D timelines, reduce the budgets for projects. So you'll see things, you know, like success-based pricing for particular deals will become easier through the use of technologies like this. It's certainly possible that through the adoption of this technology, there will be new categories of deals that are enabled, and Anna Marie was talking about that at the end of her opening remarks. So we'll see where that goes. That's obviously an exciting part of this, so yeah. All right, I think we have... We have time for one more question. I've got one in case nobody else does. All right, I've got one. This has been a topic that is just absolutely fascinating to me, and maybe we can just get a quick take, Barry and Shyam, from each of you. We're seeing some really interesting debates about IP in the world of AI. And IP is, you know, been a sort of constant theme in biotech land, especially. How do you expect that to evolve, this kind of IP copyright debate? Can AI drugs be patented, for example? I guess I can start. So I think primarily we'll see AI as accelerating the development of of IP. Our understanding is that it's somewhat of a settled legal question that an AI cannot invent a drug by itself. Or cannot patent a drug by itself. It's gonna require human enablement. I think the reality in our field, in biology, is that there needs to be human enablement anyway. And so I... It's kind of a moot point. I think there will continue to be people driving the innovation supported by AI, then you know, copilots essentially for invention. So, maybe it's going to accelerate and change who some of the players are in generating IP, but it's not yet clear that the rules of the game are going to change. The patent office is certainly looking at how they can foster AI-enabled invention, so we're watching that, but too early to say if anything is going to is gonna come out of that yet. The other big problem that's happening, obviously, in consumer tech is that the models are getting trained with a huge massive data, some of which may well be or is copyrighted. We don't really run into that problem. The data that we're using to train our models is either publicly non-copyrighted data, that or it's proprietary data that we have generated or we've generated with our partners, and so we're kind of able to sidestep a lot of those kind of challenges that are being wrestled with in the consumer space at the moment. Everything I'm seeing agrees with what Barry's saying there. I would say that in practice, obviously, the technology is so powerful, but empirically, at the coal face, all the value comes from an elegant integration of generative AI with human thought and traditional software. So it is a moot point in the sense that the things that will be created are gonna have some sort of complicated mix. Okay. Well, appreciate everyone again coming to join us. I think we have a Q&A session with the whole executive team scheduled right now. So we'll take just a couple of minutes to get everybody in the room and rearranged and be back with you shortly. Thanks, everyone. ... All right, welcome back. We have our excellent executive team here to answer any questions. For the people online, if you have a question, please email it to investors@ginkgobioworks.com. Does anyone in the room have a question to start out? Well, let's maybe Jason kick off a few words. Oh, sure. Yeah. Well, what I'll do is I'll fill in a few things since I kind of bounced around the different breakout sessions. I know everybody didn't get to get to all of them. So, we still have Jen up here. Good. Okay. I think, Jen, one of the things that came up in or just the discussions on the sort of biopharma and cell engineering was around this balance between what I would say is like enterprise sales at Ginkgo and then, like, product marketing, sort of how you described it. And, I'll give my two cents on it, and I'd love to get your thoughts, too. So, one of the key points was the bulk of our efforts today are still very much in, like, the enterprise sales area. So, like I say, about 85% of our effort is go out and try to sell, and this is for the benefit of the folks that are on the call, too, that weren't in these meetings, sell large biopharma companies on. And others, you know, large ag and large industrial, on doing what I would call, like, bespoke, high-end, high-technology product development deals. And I would, I would draw the comparison that in the biopharma industry, and a lot of the folks in this room are analysts in the tool sector and so forth, you do have companies that are like traditional CROs, contract research organizations, that offer what I would call, and when we talk to heads of R&D, they describe it this way, like, straightforward services. In other words, work that you could do yourself, you have the infrastructure, and you-- the main reason you're outsourcing it is you trust a third party to do that, because it's kinda obvious work to do. You don't think they're gonna screw it up. And that's the bulk today. I think there's some edge cases, but that is the bulk of the, of, like, the contract research services offered. And I think what Ginkgo is trying to do is offer these high-end discovery and high-end R, manufacturing R&D services that are more commonly associated with a small biotech, with a proprietary technology, doing a one-off deal with a large biopharma, like a CRISPR Therapeutics, doing a one-off deal in, in gene editors. I think what the team that Jen has built is, you know, 50 people running around, selling those kinds of deals at a throughput that's closer to the type of throughput you would see in a more traditional CRO setting, where you're selling, like a lower value, more, you know, standard product. I think, like, we shouldn't sell short that. I, I personally think that's a huge advantage for us. It's a unique asset in the market. Jen, maybe, like, if you look into the future and you look at what we're trying to do on the productization side, how do you see that complementing with this enterprise sales engine we've built? Is it just kind of ride the enterprise sales engine for a while and see about the products later? Like, how do you think about that balance? You know, I think they're really symbiotic in a lot of ways, right? In many cases, the enterprise sales kind of machine that we built has opened up some doors for the product sales, right? Like, a lot of what some of the large companies want to do are going to be or are the, the products that we're trying to build. Like a productized offering for Ginkgo. Yeah, I think, I think one would be protein expression, right? We have a lot of strength in protein expression. We've been doing that in industrial biotech for a long time, and what's particularly interesting about that is that in industrial biotech, margins are important, right? Those kinds of products are competing against petrochemical-based products, and so we've built up a lot of capabilities for really high protein expression systems that meet those kinds of COGS. Well, that's particularly interesting in pharma as well, where you have a large biologic, and to some extent, over the years, maybe pharma wasn't worried about, let's say, margins, because those margins are really high. But today, we see a shift where a lot of those products are huge products for these companies, and they have an issue in supply. They can't make them enough demand. And so that's become a really interesting application for those companies where we have a real strength. We've been thinking about high-producing large proteins for a long time, and now it's applied to pharma, and now we have those kind of relationships with those pharmas. We can do that enterprise sales. We can understand what their, what their targets are and bridge those two things. So it's an example of kind of the productized sale happening in, ... Thank you. And I would add maybe one, like, more key to color to that from the industrial biotech breakout session. So there were some good discussion around again, like success-based pricing, things like that, around like, how can we offer as we go to these more productized offerings that are more standard, what allows us to do that? And I think the key takeaway from that discussion was it's the code base, right? It is that project looks a lot like projects we've done before. So that protein expression that John's talking about, we have done many projects that involved overexpressing a protein in a fungal host, right? And so when the next one comes in, it's natural for us to try to move that into a success-based pricing. Then importantly, from a different one of our sessions, around operations, Jason Berndt, who's not up here, is heading up our foundry operations. If it's similar work, we can really drive costs down. And so that also allows us, again, like the advantage on this productized side is lower costs on the programs, better, more aggressive pricing for customers. But I would highlight, that's probably 15% of our commercial effort- Yeah. versus 85%, where I think we do have this enterprise sales engine selling what are really these bespoke R&D deals at scale, which I think is frankly unique in the R&D services market. Okay, so I'll pause for a sec. Happy to hear of questions on any of the topics from today. Yeah. Thanks, Mark. Thanks for a good analyst day. This is maybe a question of minutiae, but at one point, Zymergen indicated plans to potentially sell the RAC carts on their own. Is that something you're considering, assuming the dust settles with the recent news this morning? Yeah. So repeat the question. At one point, Zymergen had thought about selling RAC carts. I don't know, Barry, do you want to comment a bit about this? I can speak to the business model or it's up to you. Go ahead. Fire away. I was going to follow up. Okay, got it. Yeah. Thanks. Mechanical engineer. Chemical engineer. Much. Yeah, great question, Mark. So first of all, you know, hopefully everyone on the tour this morning saw that automation technology. We're very excited about it. We are trying to roll it out as quickly as we can across Ginkgo platforms. We think it gives us a lot of advantages. I think when it comes to third-party sales, I think that's something we're going to continue to look at and see how that makes sense with respect to our kind of the broader business model of selling cell programming as a service. And you can imagine that there could be places where it would be very interesting to deploy that technology with, with partners of, of ours, partners around data generation, partners in, in, in other spaces. I would say our primary focus today is making sure that we are able to leverage the technology as much as possible, but we, we know it's an asset, and we'll continue to look at how to, how to exploit that more broadly as well. Yeah, and I do think I would add on, around on the, like some of the AI discussion, I think it would be great to be able to offer, like Anna-Marie mentioned this at the end of her talk around, like, data as a service. Like, we think that's a cool idea. You know, it's a—I would say the general challenge with biotech as compared to consumer tech or software tech, is like the interfaces aren't as clean. The customers don't quite trust, data from one place to be compatible with another. Like, it doesn't have the clean APIs that the software industry has and has built up over the last, really, honestly, since the rise of cloud computing, I think, really took this to the extreme, and you now have, like, all these different players with technologies talking to each other. You know, it's amazing. We're very early on that journey in biotech. Ginkgo would love to see that happen, just to be clear. And so we're, you know, I think we will explore that kind of stuff with partners, both on the technology side and on the customer side, but it's, it is early. Yeah. In the meantime, it's helpful for us to even just show it's useful ourselves, because that then helps prove to others that those types of data assets may make a difference in, in biotechnology. Yeah. Thanks. Second question. On the biosecurity side of the business, right, you know, are there any catalysts upcoming that we could point to just looking forward as kind of a barometer to say, "Here could be a potential update on the business?" And then as a follow-up to that, you know, this was kind of discussed in the breakout room, but is there potential for that business to get back to those COVID levels of revenues in the relative near future? Is it still a little bit too early to be thinking about that? Thanks. Yeah. So the two questions were, are there any catalysts coming up that we should be looking towards, just from a business growth standpoint or a business expansion standpoint? And then, kind of like, what's the timing to see how we can get back to the COVID level of revenues, was the second question. I think on the first one, we talked about it a bit earlier. I mean, very much how we are looking at this is country-by-country relationships. So, I don't know that I would, you know, say one way or the other to look for, you know, outsized catalysts. This is something that we obviously continue to work on strategic relationships with countries. Countries are big entities, so you could imagine, you could imagine big things happening. You could also imagine it being a very kind of measured process. We're trying to build that system that we talked about earlier, where we are the strategic partner, and there's 195 nation-states on the planet. And so, you know, you can start filtering how many are available for U.S. technology companies to partner with and kind of go down that list. That, that, that's how I'd look at it. On the second one, I think it's, it's an interesting moment, right? We've had three years of trillions of dollars dumped into a, like, domestic emergency response. We were able to build infrastructure far more rapidly us and others than you would ever have imagined it being built before. Look at how fast we went from, mRNA vaccines being essentially a DARPA R&D project to billions of doses scaled across the planet. So I would say on, on that front, that is a unique time. We fully believe that the market for biosecurity, biodefense technology products in the, in the kind of ecosystem that we live in today is large today and getting much larger very fast. So like a time, a temporal prediction, you know, I think that's probably. I think we're probably too soon for that. But we are very excited about this transition to a long-term, sustainable business, providing the technology tools that these national security organizations are gonna need going forward. You start looking, we talked about a little bit in the breakout session. You can kinda just start timing these things. You can look at the policy statements, the FY 2024 budget for DoD's immediately talked about a $812 million reallocation towards a number of things in biosecurity, biodefense, of which wastewater monitoring is in there. That is just our immediate reallocation on top of what they already spend. Next year, we'll have. We expect that there will be continued investment in this category, so you can start pacing those things out. It's not immediate with governments, but once it hits, it is something that doesn't generally go away. Is there another question? Thanks. I appreciate the color you gave on the different types of foundational models you'll be releasing. Are you willing to give any sort of expectation around cadence of releasing those models? And when these model-as-a-service or data-as-a-service revenues eventually come in, is this a whole new business segment, or will these be booked in cell engineering revenues? Yeah, maybe I can take the second part of that question, and then, Barry, you can, you can tackle roadmap a bit. You know, I think the, the technical roadmap is in many ways easier for us than the commercial roadmap, because we want to be very thoughtful about how we release these models for a number of reasons, our own platform security, biosecurity, et cetera. So I don't think we're prepared at this point to, to give guidance on, on when we would be, releasing a, a public-facing model. In terms of some of the other partnerships, I think that is something that, you know, we'll, we'll be working on in, in the reasonably near term. These are big enterprise collaborations, just like any other large deal we do. They take a long time to nurture and mature, but those are conversations that we're actively having now as part of, you know, the broader, broader enterprise sales work that Jen's team does. This is one more, really powerful tool, that we can use when we talk to these customers. And so that is, I'd say, a nearer term catalyst or signal, I think that you can look for in terms of the both usage and development of our AI capabilities. Maybe, Barry, you wanna just chat a little bit about overall technology roadmap on the AI side? Yes, I can. Well, first, if you look at the consumer tech companies, so the cadence there is a you may have better data on this than me, but my anecdotal observations would be a new version of the underlying model every six months to a year. It's gonna take us a little bit of time to get up to our full speed. We're not there yet. We're building... We're still building capabilities and infrastructure. I wouldn't expect that we will be iterating any faster than that cadence of, you know, six months to a year on the foundation models. As you may have heard during the Gen AI chat, we're also going to be balancing our efforts across foundation models and fine-tuning, and so you may expect that we will be, yeah, we will be shifting that focus depending on where we think that there's the greatest impact to be had. So I don't know. It's a little bit early to say exactly when the next, you know, big advance is gonna come on our foundation models. But, yeah, 'cause we're gonna be going back and forth between those, and a lot of it will be deployed against internal commercial programs. Maybe just one other piece I'd add. You know, Jen was talking a little bit about productization. One way that we can imagine using some of these AI tools is helping with some of those interface challenges that we otherwise sometimes deal with with our customers, and how can you simplify the interface of what a customer wants to what the foundry can deliver? I think those are the sorts of products that are AI-powered, that you could imagine us releasing on a much shorter cadence. It's, it's not quite as dependent on, you know, the, the bigger infrastructure challenge. It is more around how do you translate what a customer needs to something that our foundry can understand while bypassing a lot of the normal kind of program management, in infrastructure and architecture. I think that that might be a quicker, a quicker win. Yeah, very much. In general, right, again, I mentioned Jen's built this great enterprise sales team. I think it is a unique asset on the market, but it requires us to go out, interact with a scientific team, work out a joint project, have our scientists run that project here on top of the platform you toured today, plus the computational tools. And so that is a, like... It's clear we are delivering value. We are signing up deals of that sort. It is clear, I think, we uniquely can go out and close deals like that at scale because we're doing more of it than anybody, but it is a frictional thing to have to sell through. It would be a lot better if their scientists could just leverage all this stuff, right? That I'd like to do it is technically difficult today, right? You know, it is just using this place requires a whole level of training that isn't the same training, you know, you get when you're doing your PhD or even if you've been working at a more traditional pharma biotech or an industrial biotech that doesn't have access to our scale of automation and infrastructure. And so I can't train the customers on that. ... and thus they can't really use it, and so here we sit, right? But it's potentially true that maybe with these types of tools serve as an interface, like Sean was mentioning, that basically connect, you know, it's a better interface into that platform. That would be a better way of, potentially a better way for us to sell. I don't know if that makes sense to you, Jen. Yeah. And what I wanted to comment on is just the importance and the value of what we can use today that we're building. So there's one world where we think about what we release externally or how we change that interface, but we're going to, and we are, and we will be, like, applying those advancements to our internal projects that we are doing already to customer programs, that we have planned revenue, that we're working on delivering now. And I'm really excited about that because I think it brings some acceleration to those efforts- Yeah. that customers will see soon. There's a nice... This is why I wanted you to see the entire executive team today. So, there's a nice balance here where Jen appreciates we need to deliver on revenue targets, we need to deliver on program targets. We have a way to sell these things to our enterprise sales team, and the AI tools are going to help that. They're going to help us get meetings to sell. They're going to help our program teams move more efficient today. Separately, as Barry was joking about earlier, it would be nice to unlock a less frictional interaction to our platform over time in the biotech industry, just like AWS was able to ultimately do with cloud compute and the software industry. We should work on those things, too, because those pay off massively, you know, on a five-year timescale, right? But we should also make sure we keep adding customers and scaling. So thank you, Jen. We have one question coming in online. "Ginkgo is loaded with cash in a very interesting time. You have said in the past that having cash opens opportunities. Are you seeing opportunities present themselves that look interesting?" And this comes from Mark Didovick. So Anna Marie, in addition to being our new head of AI, is still heading up our M&A efforts. So do you want... So the question was, we have a lot of cash. It is a particularly opportune time in the biotech markets, I would say especially. There's a lot of assets that have been radically repriced over the last couple of years. You know, do we see things we're interested in? How do we think about that? Sure. So I do think, you know, maintaining this kind of conservative balance sheet does give us the ability to make some of these big bets that are transforming our business. It is what gives us the ability to really lean into AI and open up these opportunities, and certainly allows us to take advantage of a market like the one that we're in today, which isn't fun for anybody, but for somebody that's well-positioned with a strong balance sheet, it does give us the opportunity to play. And what we're seeing is that, you know, when cash is tight, companies tend to focus very quickly, and they focus on, in the case of biotechnology companies, their lead asset. And that creates a real opportunity for Ginkgo to bring on board platform technologies that have broader potential, at very attractive, attractive prices. And so that is definitely still an area where we're spending a lot of time. Jen and I collaborate very closely together to identify the areas where we are seeing a lot of customer demand and where we are facing the choice between we could go run, you know, an internal R&D program to do a giant screen of CAR T-cells or something, you know, to help create a data set that a customer, you know, would find really valuable, or, you know, would it be faster to go bring in an asset that would help unlock that segment of the market a lot faster? And so that to me is still a real opportunity that we're seeing in the market right now. How are we on time? Wrap up. We're right on time. Okay. Believe it or not. I would maybe just add one other takeaway I thought was interesting. You know, I think from the AI panel, it was interesting, Sean, to hear about sort of this boot camp for customers idea. I do think like that, again, general direction of how do we make all this technology more accessible for our customers is something I think AI will open up. And I think also, Anna Marie, you made this point in your talk, and I think I'll leave you with this takeaway, as exciting or scary as it might be. You know, all these AI models that are being trained on the English language, as Anna Marie said, are competing with us in our own domain, right? So, like, they are competing with a lawyer at Ropes & Gray, with 15 years of experience working in contracts, a thing humans invented on top of the English language, which co-evolved with our own brains, actually, over the period of times that language developed. And we're expecting these computer brains to be as good at us at that, and they're not. Biology, DNA is sequential code. It looks a lot like a book. There are an enormous number of said books out in nature that are fully written over the last 4 billion years. Humans did not write them. Okay? We cannot read them. This is the best place in the world at writing that kind of language, and we're still not very good at it. So these models may become better than us, may become sort of superhuman in this domain much sooner than they do in the natural language domain. And I think that could be quite exciting for biotechnology broadly, because then we spent a little bit of time touching on this today. Biotechnology is living in a world where the consequence of the fact that we didn't invent the medium is that every product development project, like developing a product as a company, is considered research. I don't know that there's any other industry like that, where, like, the product development cycle is considered a research cycle, where it just might not work at all. All right? That is not our fault. It's not biologist's fault, bioengineer's fault. It is the fault of working with a substrate that was not invented by humans. We're the only engineering field like that. All the rest were built by humans. And so I think AI, and its ability to understand complicated things not designed by us, could be the chance for biotechnology to ultimately turn into something more predictable, where product development is actually a product engineering process. It may be that these tools help. Frankly, it is the thing in combination with large scale data that I would say, Barry, since we got involved in synthetic biology 20 years ago, I think it's the most inflective thing since some of the very early theories, like abstraction and automation. Yeah. And so it'll be fun to see. We appreciate all of you joining us for Investor and Analyst Day, our first one here. I'm so glad you got to meet the wider management team. I want to give a special thanks to Megan and the team for organizing all this.