DNA - NYSE

Good evening. I'm Megan LeDuc, Manager of Investor Relations at Ginkgo Bioworks. I'm joined by Jason Kelly, our Co-Founder and CEO; and Mark Dmytruk, our CFO. Thanks, as always, for joining us. We're looking forward to updating you on our progress. As a reminder, during the presentation today, we'll be making forward-looking statements, which involve risks and uncertainties. Please refer to our filings with the Securities and Exchange Commission to learn more about these risks and uncertainties. Today, in addition to updating you on the quarter, we're going to dive deeper into a few case studies of how we're seeing our mission to make biology easier to engineer, come to life as well as provide further details on our diverse program pipeline and the growth opportunities we see in biosecurity business. As usual, we'll end with the Q&A session, and I'll take questions from analysts, investors and the public. You can submit those questions to us in advance via Twitter at #GinkgoResults or e-mail us at [email protected]. All right. Over to you, Jason.

I'm super excited to be chatting with you all today. I always start with a reminder that our mission at Ginkgo is to make biology easier to engineer. As we dig into the strategic section, you'll see the progress we're making on that mission, particularly with our AI efforts and our strong pipeline of active programs. We pursue this mission on behalf of a diverse group of customers. This is one of my favorite slides, having a customer list that ranges from agriculture to consumer goods, to chemicals to therapeutics is common for a horizontal tech platform, but it's pretty unique in biotech. It makes sense because all these diverse programs benefit from the scaling of the same underlying technology at Ginkgo. We've added programs with several new customers this quarter, including smaller companies like Nosh Biofoods in the industrial biotech field and Exacta Biosciences in the ag space, as well as large companies like Pfizer in pharma in addition to new programs with many of our existing customers. We took a view early on at Ginkgo that scale would be needed to drive our mission. And you see that reflected in our business model as a platform service provider. We had 116 active programs on the platform this quarter, representing 36% growth over last year, and our highest active program count ever. As our foundry scales, our data generation capabilities scale in turn. You can see this on the slide comparing some of our internal assets to public data assets. Our ability to generate data at scale for customers, paired with our existing code base, is a big part of the reason customers choose to work with Ginkgo, particularly as leveraging generative AI becomes a bigger priority for our customers. For those of you that turned into our -- tuned into our Investor Day, and I encourage you to watch the YouTube recording if you didn't, you know that we're using this data to build AI foundation models and fine-tune applications for biological engineering. Our recent partnership with Google is helping fuel this in the last quarter, and I'm proud of the progress the team is making on track with our plans. In fact, we've already achieved the first milestone in our partnership with Google. The reality is that biology is getting easier to engineer. We're really excited about that, what that opens up for our customers, better medicines, more resilient food systems, cleaner industry, but it has not lost on us that the advancement of biological engineering tools, particularly when coupled to advancements in AI, creates risk. We're sitting at the intersection of several exponentially improving techs, and the world is grappling with how to keep up with the pace of change and limit the risks these technologies create. Our biosecurity business works hand in hand with our cell programming business to address this challenge. Helping build early warning systems and decision support for national security and public health is going to be critical to protecting against the potential misuses of these technologies, along with anything Mother Nature throws at us. I can't emphasize enough the synergies between these businesses. It's clear that there's an unmet need for biosecurity, but the question is, who is best positioned to grow into this large addressable market? We think Ginkgo is well positioned to do it as the tools we're building in our cell engineering business help provide the foundation for biosecurity. Likewise, our close connection to a biosecurity platform that is highlighting emerging threats allows us to be a better partner to our vaccine and therapeutic partners, ideally enabling us to make more effective countermeasures earlier in the risk cycle. And you're going to hear from me a bit in the strategic session about how we're building all that in biosecurity. All right. Now let me hand it over to Mark to give a little more color on our financial performance this quarter.

Thanks, Jason. I'll start with the cell engineering business. We added 21 new cell programs and supported a total of 116 active programs across 76 customers on the cell engineering platform in the third quarter of 2023. This represents a 36% increase in active programs year-over-year, with significant growth in the biopharma and the food and agriculture verticals. Notably, we added 10 new biopharma programs in the quarter, a record number of new programs for any particular market segment in 1 quarter. Cell engineering revenue was $37 million in the quarter, up 51% compared to the third quarter of 2022, driven by our significantly expanded customer base. Now turning to biosecurity. Our biosecurity business generated $18 million of revenue in the third quarter of 2023 at a gross margin of 62%. Both revenue and gross margin benefited in the quarter as we close out our last remaining K-12 COVID testing contracts. We're continuing to gain traction on an international scale, now totaling 14 countries with either active programs, pilots or MOUs. And Concentric is also progressing its bioradar offering with multipathogen detection and new efforts in zoonotic disease monitoring while also building a suite of next-generation biological intelligence capabilities, including AI-based epidemic forecasting. And now I'll provide more commentary on the rest of the P&L, where noted, these figures exclude stock-based compensation expense, which is shown separately. Starting with OpEx. R&D expense, excluding stock-based comp, increased from $74 million in the third quarter of 2022 to $123 million in the third quarter of 2023, representing growth and capabilities, particularly from our acquisitions in the fourth quarter of last year. G&A expense, excluding stock-based comp, increased slightly from $59 million in the third quarter of 2022 to $62 million in the third quarter of 2023, supporting the growth of cell engineering revenue and the integration of prior year acquisitions. You will also see that we recorded a $96 million noncash impairment charge on a

Thanks, Mark. This is a solid quarter for Ginkgo. Our deal with Google sets us up well to lead in the application of AI to design DNA and proteins. While our deal with Pfizer is a real signal of commercial progress that I'm going to be digging in on a second. So however, I want to address why we're taking down cell engineering guidance. We're building our relationship with you all as a young public company. And so while we want to have ambitious but achievable goals, we also want to update them as the year progresses and tighten ranges as we get close to year-end. So we're revising our guidance on the cell engineering services components of our revenue to $140 million to $145 million, down from $145 million to $160 million. Generally, this is for the reasons I provided on the last call around industrial biotech venture capital drying up, and also -- and reducing the size of programs that we're seeing in that sector as well as our new program counts being lower than hoped for in Q3, which impacts Q4 revenue. Now I do want to spend some time on the program counts being lower because this is a critical metric for us that demonstrates our flywheel spinning up at Ginkgo, we get better with scale. And it's one that I pay a lot of attention to internally here. So we had 21 new programs this quarter, which was less than I hoped to get. But at the same time, our enterprise sales infrastructure is stronger than it's ever been at Ginkgo. And in particular, I want to call out our new program with Pfizer and explain why it's an important demonstration of our commercial capabilities here. So this is a drug discovery deal in mRNA therapeutics. And that's important, first, because drug discovery is a harder sell than manufacturing R&D deals, which is a number of the previous deals we've done in biopharma. And the reason for that is discovery work is more closely held by our customers. Remember, we have to convince customers to outsource work to Ginkgo's platform that they would otherwise do themselves. This is the kind of work that they tend to think they should do themselves, all right? And then secondly, mRNA is a new modality. It's a new type of drug, right? And so it's emerging, it's high tech. And Ginkgo is proving that we can lead, right, because customers have to choose to work with us in an area like mRNA as a general platform. In other words, the same platform that's doing mRNA biotech is doing agricultural biotech. And all this is to say that this is not an easy deal to close, especially with several hundred millions of dollars of potential downstream value attached to it, which is why it's worth pointing out that I wasn't involved heavily in closing this deal nor was Jen Wipf who heads up our commercial team. This really came out of a normal sales process from our commercial and deal teams here at Ginkgo. And this is a big deal because as much as I like to think myself as good at sales, I'm not scalable, okay? Jen is not scalable. Our enterprise sales team is scalable, and the types of deals Ginkgo is doing, involving fees during technical work, plus hundreds of millions of dollars downstream milestones or royalties, are typically negotiated by the CEO and leadership team of a small biotech company if they were partnering with a large biopharma like Pfizer, like that type of deal you see popping up all the time in industry press and so on in the pharma industry. Ginkgo being able to do a deal like that in a routine manner is a huge strategic advantage for the company and the result of great work and team building, led by Jen, who heads our commercial team over the last 2 years to really build an enterprise sales engine here at Ginkgo. So I'm thrilled to see that. And if we hit the high end of our updated guidance of 85 programs, that would work out to 30 new program starts in Q4, which would be a great further signal of how we're scaling this enterprise sales infrastructure, and that's something I'll be watching with the commercial team coming up. Finally, with the updated guidance, we're still looking at 36% to 44% growth in new programs and 32% to 37% growth in cell engineering services revenue over the last year. Scale helps Ginkgo, and so I'm happy to see that rate of growth. Okay. So let's dive in on our 3 strategic topics. So first, I want to share some recent customer case studies, where we apply our AI technology, so you get a little more sense of what we're doing there. Second, we often get asked, actually, get asked a lot about what programs we're most excited about and what programs are most advanced, sort of our program pipeline. I'm not going to pick favorites. But I'm going to share a lot more data around that pipeline, so you get a better understanding of where all those active programs are and just how much diversity there is in those programs. And then finally, I want to share a little more about how we're thinking about the future of our biosecurity business as a defense technology business, as part of national security infrastructure, and how importantly it relates back to cell engineering. Okay. Let's jump in. Okay. So first, I want to talk about how AI fits into the other assets of Ginkgo and show some case studies of its application. So we talked a lot previously about our foundry and code base at Ginkgo. A reminder, our foundry is our automated laboratories here in Boston that generate data at lower costs as they grow in scale, okay? Think like a factory for testing genetic designs. This data is organized into what we call our code base, which is reused across many different customer programs. In other words, we can use data from one project to help speed the development of a second project from a different customer. And this is, again, another important asset that gets better at scale. So what's exciting is that these data assets can also be used to train large AI models that then inform the sort of experiments we should do to better train those very models, okay? That's a very exciting feedback loop, and it's making our models better every day. It's something we've been making good use of at Ginkgo. We did just announce this quarter, our partnership with Google Cloud. That is going to enhance our development efforts here at Ginkgo. And as a reminder, this partnership with Google gives Ginkgo scalable compute capacity and attractive prices to train large foundation models, but it also represents a commitment by Google to fund our model development efforts upon completion of certain milestones. We're already well on our way to building out those models as we have already achieved our first cash milestone in this deal and expected to earn the second in relatively short order. One way to measure making biology easier to engineer at Ginkgo is by reducing the cost to get to a successful result for our customers. And that cost is a function of 3 things. First, the cost per unit operation, right? So this is like the various operations happening in our foundry, and we drive that down through investments in increased scale automation, miniaturization of liquid handling, so we can use less reagents and so on. Second, the number of unit operations that we need per design cycle. In other words, each round of engineering we do, how many -- how much work do we need to do in the factory. And this one is tricky because it requires judgment. Sometimes you want to run a giant campaign or we try tens of thousand designs. And that's the right thing to do, and it's something that Ginkgo can do uniquely because of our scale. And those early large campaigns can really increase how fast you learn. But to the extent that AI models increase the quality of those designs, we could reduce the size of those libraries, which would save a lot of money, and also mean we get more scale out of that automation, right? If you can use less per program, you can do more programs on the same infrastructure. So it's very exciting. Finally, and I think this may be the most important, is the number of cycles that we need to do for a project. Again, the ability to do reinforcement learning from prior results, in other words, take what we learned from something and feed it back into the model is a key part of why customers are working with us. But in certain areas, we've developed so much depth. In other words, we've done enough projects like that, that we can exceed a customer spec in just the first design cycle. And this is really critical to the customer because the number of cycles, reducing that significantly speeds up programs and often customers, especially in biopharma, care much more about speed than they do about budget. All right. So I want to share a couple of case studies that highlight how we're seeing those variables move, right? So the first case study, that's really cool. This is an enzyme engineering program that we started earlier this year. A customer came to us with an enzyme that had been produced for them by another service provider and wasn't sufficient to meet their need in the market. On the left, you can see the various enzyme designs we tested. So that black dot in the middle is the starting sequence. It represents a starting sequence our customer gave us. Dots closer to that are protein sequences that are closer, that are more similar. And the further way you get, the less similar the sequences to the original. And so this is where the combination of AI and our foundry become really powerful. First, we can afford because of the foundry to test much more broadly than is typical. We see over and over again that minor tweaks to an enzyme is not sufficient to get the kind of big step change improvements customers want. So adding -- but adding all that diversity, all that change in the protein is risky, right? Many of -- if you change sequences a lot, they tend to have less success. And so because we can screen enzymes so much more efficiently, we can search that much wider space and find that kind of needle in a hay stack. Second, our AI/ML models are getting extremely predictive. So ultimately, here, we tested a 500-member library comprising both known enzymes in our code base as well as custom-engineered novel enzymes. Each member is represented by a dot on the left. And in the first cycle, we discovered an enzyme that was 21x better than the original from the customer. And the big win here is speed. Yes, we were also able to use a highly efficient workflow with a relatively small library, those 500 members versus, again, sometimes we do tens of thousands in a cycle. But what really unlocked it was improved accuracy of our AI/ML models predicting sequences that would work, far exceeding our customers' expectations in just the first cycle of design. So that's really exciting. The second customer case study is around production rather than optimizing that enzyme. These are a customer that wanted to figure out how to produce a small molecule compound at higher titers, basically, like how much of it you get out of the fermentation putting in this big tank. And they had a goal here to do this over the next 3 to 5 years, which will be important in a second. And so here, we were able to deliver a better outcome than what the customer asked for. Originally, they just wanted us to try a couple of different host strains, and we did it faster and cheaper than they wanted. So on our first experiment, we were able to improve the titer by 12.5-fold. This is what the customer wanted us to get done by the end of the first year, and we did that in the first experiment, and it was building on knowledge that we already had in our code base. So this is less about the AI and more of that we had genetic elements and we knew which ones worked well in this organism, and we could just take them off the shelf. Okay. That's very powerful, again, strength of scale. Because we have that big code base, we could take something off the shelf that another company that didn't have that wouldn't have on the shelf. And then by the end of the program, which only took us 10 months instead of 3 to 5 years, we were able to deliver 50-fold titer improvements, which is almost double what the customer was originally working towards. The second round of improvement was driven by machine learning and driven enzyme improvement similar to the last case study. And again, this is something that makes Ginkgo unique. We draw on a wide range of tools. So in this case, genetic elements off the shelf, as well as protein design enabled by AI, putting those together, gave that outsized outcome for the customer. So these are a couple of examples of how we're integrating our AI tools into customer programs. I cannot emphasize we're just getting started with this. I'm super excited about AI's ability, like I said, in that equation, to improve efficiency of all those different steps, hopefully reducing cost and speeding time lines for customers. Okay. Second topic. So let's take a look at the rest of the pipeline of customer programs. So again, as a reminder, I want to be very clear about this. Ginkgo does not have its own product pipeline. Like I do not have my own drug development pipeline here at Ginkgo. So when I'm saying pipeline, this is a pipeline of customer programs. In other words, we had to negotiate and sign up a customer and get them to outsource this work for us -- to us to get on this program list. In Q3, we had our highest number of active programs on our platform of all time, with pharma making up the largest percentage of those programs. But an additional piece of color I want to give you today is where those programs stand in terms of their maturity. In other words, how are they progressing through the technical work? All right. And again, I'm happy about that pharmaship. I don't want to undersell it. I do think that's really important, especially as industrial biotech has gotten tighter. It's really great to see that. That is, again, a real strength of being a platform, right? If a certain area gets thinner, we can move to areas that have more demand. As long as biotech in general is moving, we've got something to do. Okay. So this is a fun chart to me. As a traditional product-based biotech company would often show a pipeline like this for maybe 5 or 10 drug assets that are sort of moving through preclinical and clinical trials. Here, we have so many of these programs that we can't fit them on 1 slide. This page is just a programs that are over 50% complete. And we'll show the rest on the next slide. This is the point I was making earlier of why I'm still excited to see 21 programs being added in the quarter, even if it was less than we were hoping. It's just a really great amount of scale on a relative basis in the biotech industry. And to give you an overview of the chart, each horizontal bar here represents a program, and the dark portion of the bar, like on the right-hand side, represents the progress made on that program year-to-date as a portion of the total program. And I cannot tell you the number of times we get asked for this. So I'm very happy to be sharing it with all of you. We will try to do this again, if people ask for things, we try to clean it up and get it out there is a good example. So as you can see at the top, there are a number of programs that are at 100%, okay? So that means that Ginkgo's program were concluded on that program in Q3. So we do this again for Q4, it would be gone, right? And on the next slide, you can start to see some of the shift in program mix. So if you -- again, the colors are tricky. But if you look at the colors here, given more of our recent efforts into biopharma, you'll see that a lot, just under half of our newer programs that are early in development are in biopharma. So again, I'm happy to see that. All right. After those programs hit 100%, if they do, and some fail before they do, but if they hit 100%, then the customer -- and the customer chooses to move forward with them, they enter commercialization. And so you can see we have on the bottom 15 programs that are being actively commercialized now, meaning the customer is moving forward with taking them through regulatory like Synlogic in Phase II trials or they're going into scale up, like Centrient. Many customers don't announce this. Again, this is like product development. So it can be held close to the vest, but we've shared a couple where they have. And eventually, that commercialization process finishes. And we have 6 programs that are kind of fully commercial. In other words, they're giving us royalties, or it's equity on a program that the customer has put into the market. So we're very excited to see the pipeline you saw on the last couple of slides, hopefully move into these buckets as the programs get to 100% complete, and we're extra excited to add many more programs to the pipeline in the coming quarters. So we need many more slides. And the scale of all this is what makes Ginkgo special as a platform rather than a product company in biotech. I'm really, really proud of this scale. It's pretty awesome to see it all in one place. Okay. I'd like to cover our last strategic topic for the day, which is about the national security priority that's emerging around biosecurity and Ginkgo's position in this emerging space. So just the past few weeks, have seen a ton of discussion around the convergence of AI and biology. I've linked a bunch here, articles here, which are good reading for those interested in the space. Last week, Anna Marie, our new Head of AI, which is why you got to see Megan at the start of this presentation instead of Anna Marie, and Matt McKnight, the General Manager of our biosecurity business, were in London during the AI Safety Summit to discuss this. And we've been spending quite a bit of time down in Capitol Hill discussing both how to accelerate U.S. leadership in AI in biotech, but also how to advance these technologies responsibly. So while biosecurity needs to exist, irrespective of its potential for misuse by humans. And the reason for this is, whether we do it or not, Mother Nature is throwing off epidemics and pandemics on our own. And so we do need biosecurity regardless for public health. We're seeing that biology is becoming a more clear national security priority with the advancement of AI tools, which is driving more global government focus on what needs to be done to ensure the technology is deployed responsibly. We've seen real momentum in defense technologies recently, I'd say, as a category. And with particular leadership from our Board Chair, Shyam Sankar at Palantir, you can read a really nice blog post of Shyam's link there. There is not yet a defense tech business for biology, but it is increasingly clear that the defense community believes we have a critical gap when it comes to biology. Ginkgo has been building biodefense tools for years now to protect our platform, to respond to COVID in a big way and, more recently, mapping out what a more scalable biodefense ecosystem might look like, which I'll talk about here. Okay. So Ginkgo plays across the tech stack for biodefense, but we see a significant investment and product gap in the area of monitoring and analytics as most of the investment to date has been on this third box at the bottom, response, so things like vaccines and therapeutics. And that's in part because kind of our approach to infectious disease has been, "Wait until it gets out of control and then do something about it." And with -- work hard if we can to put a damper on it, but hey, things are going to happen, and we got to be able to respond after the fact. That's been the overwhelming investment as opposed to prevention and early detection. I think the impact of COVID has changed that calculus. You have countries looking and saying, hey, the national security effects of this mean we can't just let it happen and clean it up afterwards. We need to be able to detect and respond. And you can think about this a bit like cybersecurity, right? Your computer is constantly monitoring for something dangerous, characterizing it, addressing the threat right as it comes about. In biosecurity, we want to do the exact same thing. We want to build that infrastructure to be constantly monitoring leverage AI to reduce the time to threat detection and then mitigation. And it could be even a different kind of mitigation than vaccines. If you detect it early enough, you just snuff it out, okay? So our bioradar product, where we collect samples from wastewater on planes and anonymously from voluntary swabs from passengers on airlines, is exactly the type of infrastructure we're building to do this type of monitoring. This bioradar product enables continuous data collection. So not just on COVID, but we just announced we're expanding to over 30 pathogen targets. We announced an expansion of our partnership with the CDC just this week. Some of these pathogens have actually little to no genetic data publicly available in recent years. So we're really tackling some big blind spots with this expansion into more disease. We're up and running in 9 international airports so far, and that means we're already getting visibility into flights originating from over 100 countries. In addition to airports, we're working in complex zones. And this quarter, we've made a lot of progress with new efforts to monitor agricultural and animal samples from zoonotic spillover, including partnering on 2 new USDA-funded projects. And we're now progressing to deepen our analytical insights by integrating AI-based tools with our bioradar data. And we're examining historical epidemic data and routinely use common AI methods for bioinformatics, genetic engineering detection, I've talked about before, our work with IARPA there, and modeling. And these will continue to get better as our AI platform gets better. But right now, we're really excited to build new prediction capabilities, and we're working with a consortium of partners funded by the CDC Center for Forecasting and Analytics to find new ways to tell when is the disease about to spike? And what measures should you be take -- should be taken against it. And again, I would highlight we already do this sort of thing for weather, right? Like when is that hurricane looking like it's going to come in, right? Like where is it going to land and all that sort of stuff? This is exactly the kind of infrastructure we should have for infectious disease. So all of these data and analytic capabilities are at the foundation of our novel BIOINT so things like biointelligence product for national security as biological risk accelerates and intersects with global conflict and geopolitics, BIOINT will represent a critical component of intelligence capabilities. So this is now things like satellites, okay, yes, they're looking for hurricanes, but they're also looking for missile launches. All right. So we also want to see if someone doing something, some type of misuse, we want to be monitoring to detect that. We're working to build out a BIOINT platform that can support attribution, scenario-based response planning and medical countermeasures. And through this work, BIOINT will be able to address critical questions for security decision makers such as what threats and outbreaks are on the horizon? How dangerous is a new threat? Where does it emerge and how? What can I do about it? And the key here is that last part of what can you do about it, we'll need BIOINT if we want to effectively neutralize biothreats before they cause a lot of damage. It's a lot easier to put out that fire when it's small than when it's too late. All right. Finally, I want to touch on why I think it's so valuable to have Ginkgo's Biosecurity business alongside our Cell Engineering business. These things make sense together. With our bioradar product and BIOINT, we can provide metagenomic data to feed our cell engineering platform. This is more data for training. And the tools we build for understanding biology in our cell engineering platform can be reused to make analytics better on the biosecurity side. Hey, I want to understand what this protein does is a useful thing for cell engineering. It's also useful for an emerging pathogen. Finally, our biosecurity platform could also provide early warning information to help develop new countermeasures, vaccines and therapeutics that our cell engineering platform could help build, right? Our customers develop vaccines and therapeutics, right? So both our biosecurity and cell engineering offerings enable one another, and we believe they'll continue to grow, especially through the use of AI. Okay. In summary, I'm really excited about the great work we've done this quarter, especially the demonstration of our commercial sales engine with the Pfizer program I mentioned and our strategic relationship with Google and AI. And I'm looking forward to continuing our growth in this space. All right. Now I'll hand it back to Megan for Q&A.

Great. Thanks, Jason. As usual, I'll with a question from the public. [Operator Instructions] Thanks all. Okay. Welcome back, everyone. Our first question comes from @cliffordmlong on Twitter, formerly known as X. What milestones will be tracked to measure Ginkgo's success in building DNA's AI? What metrics can you share that will track the accuracy improvements when building AI over time?

Sure, I can take that one. Yes. So I think one of the things that's very interesting about Ginkgo is we have a lot of ongoing programs today. And so I think the first place we'll see the application of AI is in driving efficiency of all of that ongoing work. So you saw some examples of that in the slides I showed. But a big part of what we're trying to do next year is add lots of new programs while keeping a lid on our operational expenses. You will see that, in part, driven by the efficiencies we're going to gain in AI. And then secondly, I think in the longer term, AI represents an interesting interface to our platform. So I think we'll ultimately be able to open it up more directly to customers through AI tools. And so that's something we're excited about and part of the model building we're doing with Google.

Great. Thanks, Jason. We'll start opening it up to analysts now. Tejas from Morgan Stanley.

Good evening. Can you hear me okay? Perfect. So Jason, one quick question for you. Just in terms of the later program adds here and the implied sort of cell engineering guide for the fourth quarter, how should we be thinking about 2024? Consensus has you doing about $300 million in cell engineering revenue, but you guys are sort of in that $40 million to $50 million quarterly run rate at the moment. So are there any sort of like missing pieces there that we should be thinking about as we think about the year-over-year progression?

Yes. So we, obviously, are not sharing guidance yet on 2024. I'd say mainly the -- we have an aggressive push around expanding the scale of our enterprise sales efforts and our ability to add new programs to the platform. I'm really happy to see like if you look at the number of active programs going up on the platform, there's our ability to handle more work has gone up a lot. And so I think that's part of what gets us excited for next year. But we will be sharing, obviously, guidance at the next call.

Fair enough. And then one on just the tech licensing evaluation deals that you mentioned. Obviously, early days still, and you're not including them in the program count, but can you just give us some context around how meaningful a contribution this could be? And over what time frame do you expect sort of some early wins based upon your conversation so far?

Yes. Mark, do you want to talk a little bit about how we think about those tech licensing?

Yes. So I would think sort of single-digit millions in terms of potential licenses or kind of lower double-digit millions and potentially some wins, certainly, within the next 12 months.

Got it. Fair enough. I appreciate it.

And maybe the only thing I would add to that, I think it will be an interesting thing for us to think about in the long term. Obviously, we have certain definitions for what makes for a major program at Ginkgo that gets added to our program count. I kind of hope over time, we have more assets in our code base that can more easily be directly licensed into customers. That's obviously great. It's revenue back to us without a bunch of work. So I think those are nice things to see. But as we get more of them, I think we'll want to figure out how to communicate that to you all better.

Next up, we have Steve Mah from Cowen.

Great. Can you hear me? Great. With regards to the new program adds, can you give us a sense if there's any particular partner class, which is harder to get over the deal signing goal line? And what specifically are you guys going to be doing to improve the deal closing time line? If I heard correctly, it looks like you said your enterprise sales team is rightsized. So if it's rightsized, what exactly are you doing to kind of improve the deal closing timing?

Yes. So I'll speak to it generally. Mark, if you want to add anything, go for it. I know the timing is something you think a lot about. So first thing I would say is I don't know that we're like -- I don't think we're rightsized on the total size of the enterprise sales team. I think what is exciting to me is if you look at the new programs, 10 out of 21, were in biopharma this quarter. And so -- and Steve, we talked about this previously, but like if you look across different industries for biotechnology, the largest R&D budget spend is in biopharma. So in terms of our ability to expand into a market is the one I'm the most excited about. Now we started in industrial biotech even before we got into ag, and that's in part because that industry had less in-house infrastructure, right? At the end of the day, Ginkgo is convincing a customer to outsource to our platform, something they might otherwise do in-house. And that was an easier argument to a start-up industrial biotech company versus Pfizer, okay, say, 5 years ago. Now what's happened in the interim is the venture capital ecosystem around industrial biotech has gotten really tight so that has been headwinds for us in terms of adding new programs there. But we've built out more of our enterprise sales team, like I mentioned on closing that Pfizer deal in biopharma. Expect us to make that team bigger. We see a lot more opportunity there. I think we're fundamentally limited by the number of people we have out talking to customers right now. And so I think that's one of the ways we're going to grow program counts next year is growing that sales team. But now I'm confident, like we have the right thing to grow, right? If you were around the clock 2 years and I just threw on a ton of folks to try to sell into biopharma, we didn't have the reps that we do now in terms of knowing what it takes to get deals, what are the right kind of people to hire and all that sort of stuff. I feel much more confident about that now. So it's the right time to scale that team.

Okay. Got it. And then with regards to any particular partner class being harder or easier to get over the goal line? Can you give any color on that?

I think start of industrial biotech. It was one, I think that used to be like a real strength for us just because we had good reputation, a lot of good examples of stuff there. It's just a market that is like kind of in shellshock right now because a lot of the venture funding is right up there. So that's one, I think, that has been tough. Now I like it in the long term. It's one of the more interesting markets. Remember, our mission here is just to make it easier to engineer biology. And so what's exciting about industrial biotech is unlike a therapeutic that ends up in a human, a microbe or yeast or whatever that you're going to do for industrial biotech ends up in a steel tank. So the path to predictability in industrial biotech is more obvious. In therapeutics, I think we'll get better at designing drugs. But at the end of the day, there's still a fundamental unpredictability, putting something inside a human. That's going to be hard to muscle through. Industrial biotech is going to get to engineering a lot faster. So that's exciting to me, but it's still like -- we still have to deal with the ebbs and flows of capital market interest.

Okay. Cool. And then maybe a quick one on biosecurity. Mark, on the gross margins, we've noticed that they ticked up as you're exiting and the mix shift goes away from the K-12 testing. But how should we think about the go-forward run rate ex K-12 testing?

The go-forward run rate on margin or on revenue?

Gross margin?

Yes. So I would more or less ignore what happened in Q3 as you think about go forward. So we benefited on both revenue and gross margin from the closeout of some legacy K-12 contracts. And so there was some, what I would just call, like onetime revenue, and some of that came through a good gross margin that hit in the first half of the quarter. And so that's why you saw the pop in Q3. It's not because the newer business, the new federal and international business is sort of a higher portion of the mix and is somehow higher gross margin. It isn't. So just to kind of reiterate what I've said in the past, we don't know how the gross margin will evolve, but we're certainly targeting something around that 40% range once we get to kind of an appropriate scale. But as you saw, when we were building the business to begin with, the gross margin did fluctuate quite a bit until we got to the right sort of scale. But we certainly think about our target margin in that 40% kind of plus or minus range. We'll see sort of how it evolves over time.

Thanks, Steve. Next up, we have Derik De Bruin from Bank of America.

So, Jason, you've added a number of new programs considerably. I guess how should we think about, 2 points, like, one, what's your related party revenues exiting this year? And I'm sure it's down quite a bit, I'm sure. Just a little bit clarity on that. And any preliminary color on sort of like cash burn, particularly as you sort of like get rid of the legacy

Yes. I might kick that to -- those to Mark and then pick it up at the end and give a little extra color on it because I think, Mark, will have the numbers.

So the exit rate on related party revenues is going to be like a substantial decrease from anything you've seen in prior years. It fluctuates a bit. If you were to look at it this year, quarter-to-quarter, and we disclosed those figures, so you've got them. You'll see it moves around a little bit. But in the aggregate, it's certainly much less than it was last year. And I would expect -- yes, I mean, that mix shift has largely at this place or at this time taken place.

Do we have the percent this quarter? It was on....

Yes.

We'll pull it up for you, Derik, but sorry. But keep going, Mark, and we'll get it back.

In the appendix to the earnings deck.

Okay.

The related party mix, I mean it was still sort of in the -- yes, it was about 25% of revenues in the third quarter. So you can see compared to the past, 70% or something, it's gone down quite a bit. I would expect it to even be less than that. So yes. So -- and then -- sorry, the second question on cash burn, if you could maybe just restate the question.

No. Just sort of wondering, you've added a lot of programs. Just wondering what the -- how are you sort of thinking about cash burn? I mean, you've got your cash balance and just sort of thinking about R&D expenses and things evolving next year? Yes.

So if you think about how we think we'll finish this year, we don't guide to cash burn. But I think if you take the Q3 year-to-date cash flow statement and just extrapolate it and then there is going to be some stuff that happens with the deconsolidation of

Got it. And then one final one, if I can. Significant expectations for more downstream value in 2024?

Sorry, I missed the beginning of the question. Would you mind....

Yes. You had about $4 million that you're including in terms of like downstream value this year. Does that number go up next year? There are more milestones. And sort of going back to Tejas' question on trying to get a revenue number, which I know you're not going to answer, but I got to try.

Yes. So we're not guiding on downstream value share even in year you see us. We did say where we're at right now, but we're not even guiding for the rest of this year. And that's in part because it's really not a thing that's under our control in a very direct way. It basically depends on those commercializing programs when they hit certain points for customers that can trigger downstream value share for us or, in the longer term, things like royalties. So I think we're going to stick with that model. I know it's not ideal, but we're sharing more things like the program pipeline we shared today. And I think over time, as we get bigger numbers on stuff, hopefully, we can give you a little more to work with there, Derik. But yes, I understand that, that's something people want to see. Maybe the only thing I would add is a couple of things. On the -- in part of the related party, if you look back in time, a lot of that was like, again, new companies getting started on the platform, things like that. And so I would highlight as venture capital has gotten tighter in other words, higher interest rates, that whole line of customers, like new company starts, we had entrepreneurs and residents at Ginkgo that were launching companies. That just isn't there right now in the market we're in today, which is why I'm -- even though I know we're up on our program counts, the ability for Ginkgo to have pivoted into selling from EIRs that are launching a company on the platform being a lot of our demand 3 years ago to Pfizer and Merck and Novo Nordisk and Boehringer as our customers -- like that's a pretty different sale. And I think it also reflects the flexibility of having a platform business model. This is one of the reasons I like us our ability to survive in changing markets, especially in this earlier stage of the company, where we're still spinning up scale. I like -- strategically I like that flexibility. I think that was borne out this year. So I do want to just highlight that. And then on the cash point, obviously, were -- at the end of quarter was $1 billion plus. The -- we're very sensitive to cash. And we appreciate that Ginkgo gets better with scale, and we also have all this downstream value share that we want to get to. But to get there, we have to not run out of money. And so that is internally really one of the big things that we do all our planning around. So that's not something we won't pay attention to, I assure you.

Thanks, Derik. Next up, we have Michael Freeman at Raymond James.

I really appreciate also the -- you guys putting in that swimmers plot on project maturity. I think that casts -- sheds a lot of light on what's going on inside the Ginkgo platform. Now one blind spot in the data visualization and I trust for Ginkgo is what happens between 100% completion and commercial. So I wonder how -- what sort of work Ginkgo can do to help its partners undertake whatever work needs to be done between 100% and commercialization?

Good question. I mean the sort of flipping answer is have enough programs that it like kind of comes out in the wash. In other words, like we can't be responsible for animal-free meat go-to-market to cannabinoid to new pharmaceuticals to agricultural traits, like the range of products just makes it tough for us to really be a major player in ensuring that those steps downstream of the cell engineering are successful for our customers. So now that said, I mean, as Ginkgo gets bigger and more of the world is running on our platform, investments that generally help biotech products make it through that will pay off in big ways for us. But I would say today, I'm more focused on just getting more people on the platform and just kind of -- it's up to them to do that part. I think realistically in terms of where Ginkgo needs to put our resources, it's making the platform more efficient so that I can do better on fees versus our spending and make it through the downstream value share. So right now today, we're not spending a lot on that, Michael.

Got you. Got you. Now another key feature of a biotech or a biopharma swimmers plot is how many patients die or how many programs die. You mentioned, of course, some programs don't get to 100%. Curious how can we get a sense of how programs fail and what proportions -- what proportion of programs might fail? I guess like what does it take for you and a partner to agree that a program is done?

Yes. So I would actually kind of like to share that over time. Right now, we just -- I want to get like a little more out the pipeline so that I have like a better -- like kind of a better set of data there, I would say, is like the major thing holding me back on that. But I think that is something ultimately we'll be able to share with you. And what -- in terms of what hits it, I mean, we set technical milestones negotiated with each customer because they're going to pay us on hitting those typically. And so that is what sets is it a 100%, right? Ultimately, the -- it's some agreed upon technical target with the customer. Now the other obvious challenge is like it changes, right? Like in other words, like program to program, they're different. So it is also a little bit like certain programs are going to be harder than others, right? Like we're not making widgets here. So I think that's another thing that we're probably just stuck with in terms of making it tougher to model. My long-term goal here is like be a utility, right? Like we really want as much of the world running on our platform as possible and it will be fine, right? But I appreciate that in this era. People are trying to handicap programs. And -- but hopefully, as the numbers go up, it gets a little easier.

Thanks, Michael. Next up, we have E.V. From Goldman Sachs.

Just filling in for Matt tonight. Following up on new programs, could you maybe just give an update on what you're seeing in the sales funnel from customers. I think in the past, you said there's a lot of potential of new programs in the funnel. Are you seeing some of these conversations being pushed out due to capital conservations as we've seen many headlines from pharma, R&D cuts? Is there anything else you're hearing from customers on program cancellation?

There maybe 2 different questions there. There's sort of -- you asked program cancellation at the end, but I'll defer that for a minute that's like a slightly different topic. But in terms of like sales pipeline. No, again, it's very strong. And Mark touched on this a little bit in his comments that like one of the challenges we have is like the timing of the close. So we do -- we end up starting certain programs like in advance of closing -- signing with the customers so that we get started a little faster and things like that. We do that as we get very close to being across the line with the customer from a deal standpoint. We have lots of those right now. So like I like where we're at going into the upcoming quarters. I like our sales infrastructure, I like our pipeline. Like those things are good. I think we will see quarter-to-quarter variability on what gets across the line. Sometimes that will break in our favor, and sometimes it won't. I think that's kind of Mark's -- not to put words in your mouth, Mark, general point about the timing challenges of complex enterprise sales. But I overall like it. And then on -- does that answer your question? I just want to make sure.

Yes, that's helpful. And then on program cancellations. I mean more on like the biotech programs or projects not like your programs, does that make sense, kind of the difference there?

In other words, like once I hand something off to a customer and then they fail in a trial and shut down the commercialization, is that what you mean?

Yes. Or if we're seeing less discovery work being done, so more prioritization of like later-stage projects.

I think that's true across the industry, yes. The good thing about biopharma is Ginkgo's penetration into that industry today is like rapidly small. So like even though that is true, we're still talking to like companies that have never even talked to us before. So like it's not as if, oh well, we've got this level of penetration and they're backing off. And it's also not like industrial biotech, where it's kind of gone to, right, like it's really tightened up a lot. There's still a good amount of funding, certainly at the large biopharmas, but even at the small ones, people are still pursuing research and most of them have not given Ginkgo a serious look yet. So that all bodes well for our enterprise sales team to go around and talk to people and show what we got.

Okay. Great. That's super helpful. And then one more. You gave a lot of detail on biosecurity at the Investor Day. Nice to see the guidance raise there. Could you talk through the competitive environment in that market? Obviously, it's very new and emerging. But has anything come up when talking to customers with other players you're seeing in that space?

No, I've shown those 3 boxes of like kind of like monitor, decision-making and then response. Obviously, in response, there's time, right, like response is just the whole biopharma industry vaccine developer. And so -- but Ginkgo's focus has been on the monitor and decision-making. And in that area, there is some -- you might have seen there's like people doing like Verily is doing like a wastewater in the U.S., like municipal wastewater. So that's something that's happening and that was with another smaller company Biobot. So those are folks that are kind of like at least doing some monitoring. On the airport side, right now, there's not really a lot of that going on. We're more fighting like convincing people that this is a good thing to have out in the world, and I think we're making good progress on that. But it's a little more fighting like getting the infrastructure built in the first place and getting it funded versus like it being a blood red competition. So I'd say, overall, that's the bigger thing, it's just increasing the profile of biosecurity as a category. It's not as competitive at the moment.

And I think we have time for one more question from the public. This one comes from the investor inbox. Please provide more details on the recent Pfizer deal, specifically, what does Ginkgo need to do to earn the $330 million mentioned in the recent press release?

Yes. So I can give extra color on this. So I mean what we had in the press release is basically what we can actually say about the deal, but it does highlight that we get research payments as well as milestones, which are like once we've handed off the asset to the customer, and they're going to move it through things like clinical trials and so on. And then there's also potential for royalties. I will say, in general, I think you'll see this a lot -- across a lot of biopharma deals. We will typically have some type of milestone payments as a -- if we're doing drug development, R&D, it's different if we're doing, say, manufacturing R&D, which we've done with partners like Biogen and Novo Nordisk. But for drug development, you will see things like, okay, if it gets through Phase I, Phase II, Phase III clinical trial, those will be types of things that would typically provide milestones if you're doing this type of research, that sort of stuff. And then obviously, if there is a royalty that's on once it's gone commercial.

Great. Thanks, Jason. That about closes it out. Do you have any closing thoughts for us today?

No, other than to say, like I mentioned, I'm really quite proud of the infrastructure that's been -- being built up on the enterprise sales side. I think that is unappreciated that how difficult that is because it is -- we're selling a different thing, right? Like there are CROs out in the world that are selling, what I'd call, like straightforward research services, like give me something that you pretty much know you could do or anybody else could do, and I can do it cheaper or whatever. Ginkgo selling like high-end drug discovery. It's a much more complicated sale. So to be able to do that at scale, I think, it's going to really be valuable for us in the long run. So I'm happy to see that.

All right. Thanks so much. That concludes this quarter's earnings call. Talk to you all next quarter.

Thanks, everybody.