Good evening. I'm Joseph Friedman, Director of Communications and Corporate Affairs here at Ginkgo, where it's my fifth year. During that time, I've had the pleasure and the privilege of working with our investor relations team, usually behind the scenes of these earnings calls. And so it's exciting to be supporting you today in this more front-facing role. I'm joined by Jason Kelly, our co-founder and CEO, and Mark Dmytruk, our CFO. Thanks, as always, for joining us. We're really looking forward to updating you on our progress today. As a reminder, during the presentation today, we will be making forward-looking statements. These involve risks and uncertainties. So please refer to our filings with the SEC to learn more about these risks and uncertainties, including in our most recent 10-K. Today, in addition to updating you on the quarter and the full-year results, we're going to provide updates on our path towards adjusted EBITDA break-even, as well as customer progress in our Cell Engineering business across both our services and tools offerings, and the latest offerings in our Biosecurity business. As usual, we'll end with a Q&A session, and I'll take questions from analysts, investors, and the public. You can submit questions for that to us in advance via X. Please tag your post with the hashtag #GinkgoResults or email us. Our inbox is investors@ginkgobioworks.com. I'll be checking that throughout the call. All right, over to you, Jason. Thanks, Joseph, and thanks, everyone, for joining us. We always start with our mission of making biology easier to engineer. And similar to last quarter, our focus for that mission is on these three key objectives. First, we want to reach adjusted EBITDA break-even while maintaining a cash margin of safety. And we ended this quarter with $562 million in cash and no bank debt and significantly exceeded our original cost-cutting target for 2024. You're going to see this reflected in a dramatically reduced level of cash burn in Q4 versus Q3. And I'm really happy to see this. A cash margin of safety is what protects Ginkgo from having to raise capital in conditions that aren't favorable. And we want to keep our cash war chest large while reducing cash spending and expanding our sources of revenue. This is a simple strategy, right? You know, like, just drive the cost down and keep expanding. And we executed on it really well in the second half of 2024. And we will keep pushing on it in 2025. That is not changing. Second, while we cut costs, we need to keep serving our current customers and adding new customers. Q4 saw us achieve a record number of technical milestones in a single quarter, showing our team's ability to continue to deliver great new science for our customers. I'm really proud of that delivery across the team. Finally, we want to grow our Cell Engineering revenue and continue to expand our tools offerings, which I'm going to talk a lot more about in the strategic section. Okay, I'm excited to get into all of that. But first, I want to hand it over to Mark to discuss the financial results for the quarter. Thanks, Jason. I'll start with the Cell Engineering business. Cell Engineering revenue was $35 million in the Q4 of 2024, up 29% compared to the Q4 of 2023. This increase was primarily driven by growth with large biopharma customers and government accounts, partially offset by declines with smaller customers in the industrial biotech segments. As we've discussed previously, this customer mix shift has been a headwind to growth in prior quarters. And so we're very pleased to now see the positive impact of the shift in this quarter's revenue. On a full-year basis, Cell Engineering services revenue was $174 million in 2024. As a reminder, in the Q3 of 2024, Ginkgo recognized $45 million in non-cash revenue from a release of deferred revenue relating to the mutual termination of a customer agreement we had with Motif FoodWorks, one of our platform ventures. Excluding this impact, Cell Engineering revenue was $129 million in 2024, down 10% compared to the full year of 2023. This decrease was driven by the customer mix shift discussed previously, along with commercial changes related to the restructuring. In the Q4 of 2024, we supported a total of 138 active programs across 85 customers on the Cell Engineering platform. This represents a 5% increase in active programs year-over-year. As we discussed on our previous 2024 earnings calls, the nature of programs that we take on with our customers has evolved significantly following our adjustments to commercial terms and the launch of our tools offerings. As such, going forward, we are no longer going to report a new program metric. However, we are going to provide additional perspective on active programs in the quarter, which I will discuss in a moment. Before I do that, I will close out 2024 by noting that we added a total of 31 new programs and contracts in Q4 of 2024, of which 14 were generally comparable in size and scope to historically reported new programs and were included in the current active program count on the prior slide. In addition, we commenced 17 other customer contracts in the quarter that represent a variety of small deal archetypes. These are generally much smaller in scope and shorter in duration. We are very pleased that we've been able to continue the momentum with our biopharma customer base, including five new large pharma logos and seven new Datapoints contracts. Now, going forward, we're going to provide you with a revenue-generating active program count metric that we think will be more useful to analysts that are using this to model revenue. This metric will include all programs that generated revenue in the quarter, including smaller programs that I refer to as other contracts on this slide. Further, in this metric, we will only include programs that were revenue-generating in the quarter. So, for example, at any point in time, we have a significant number of programs that are either just starting or are wrapping up, and so they aren't generating any meaningful revenue. We'll exclude those for you, which will give you a better indication of revenue per program in the quarter, and you can measure how that trends over time in a more meaningful way. In the appendix, we've provided you with a preliminary look at this new metric for the past four quarters as a reference, and we welcome your feedback. Now, turning to Biosecurity, our Biosecurity business generated $9 million of revenue in the Q4 of 2024 at a gross margin of 17%. Revenue and gross margin were down quarter-over-quarter, and as you'll note, lumpy during the course of the year due partly to the timing of signing of a customer contract in Q2 of this year. On a full-year basis, Biosecurity revenue for 2024 was $53 million, down 51% from $108 million in 2023. As a reminder, our K-12 COVID testing contracts ended in the Q3 of 2023, and the business has now moved entirely towards building out both domestic and international infrastructure for Biosecurity. And now I'll provide more commentary on key items for the rest of the P&L. We've changed the presentation of this slide this quarter to align with our segment reporting disclosures. We believe this will give you more insight into the underlying profitability of our two segments. And specifically, we'll give you more information on the cost structure and how that is changing as we undertake our restructuring. A full reconciliation between segment operating loss, adjusted EBITDA, and GAAP net loss can be found in the appendix. Segment operating expenses. So starting with the more significant items in segment OpEx, in the Q4 of 2024, Cell Engineering R&D expense decreased 31% from $73 million in the Q4 of 2023 to $50 million in the Q4 of 2024. Cell Engineering G&A expense decreased 49% from $40 million in the Q4 of 2023 to $21 million in the Q4 of 2024. These decreases were driven by our restructuring efforts. On a full-year basis, Cell Engineering R&D expense decreased from $336 million in 2023 to $272 million in 2024. G&A expense decreased from $171 million in 2023 to $115 million in 2024. Net loss. It is important to note that our net loss includes a number of non-cash income and/or expenses as detailed more fully in our financial statements. Because of these non-cash and other non-recurring items, we believe adjusted EBITDA is a more indicative measure of our profitability. As noted, we are now showing you adjusted EBITDA at the segment level so that you can more clearly see the relative profitability of Cell Engineering and Biosecurity. The significant improvement in Cell Engineering segment operating loss in the Q4 of 2024 compared to the comparable prior year period was due to the previously discussed drivers of improved revenue and reduced operating expenses. Moving further down the page, you'll note that total company adjusted EBITDA in the Q4 of 2024 was negative $57 million, which was up from negative $101 million in the Q4 of 2023. The principal differences between segment operating loss and total company adjusted EBITDA in the Q4 relate to the carrying cost of excess lease space, which you can see was $9 million in Q4 and $26 million in the year. This cost represents the base rent and other charges relating to lease space, which we are not occupying, net of sublease income. We will continue to break that out for you going forward since that is a cash operating cost that is not related to driving revenue right now and can be potentially mitigated through subleasing. On a full-year basis, total Ginkgo adjusted EBITDA was negative $293 million, which was up from negative $365 million in 2023. This improvement in adjusted EBITDA can be attributed to the fact or the impact of the previously mentioned Q3 non-cash deferred revenue release, as well as the restructuring implemented over the last three quarters. And finally, I'll just make one additional comment relating to cash burn in the quarter. Cash burn in the Q4 of 2024 was $55 million, down significantly from $114 million in the Q3 of 2024. This significant decrease in cash burn sequentially was a result of the restructuring and was further impacted positively by higher revenue, which was partly driven by the successful completion of a number of technical milestones in the quarter, as mentioned by Jason. We would further expect to reduce the cash burn run rate significantly from this level by the Q4 of 2025, though we expect some lumpiness in the progression during the year due to timing of working capital and one-time payments. Jason will discuss our increased target for OpEx reductions later in the presentation. As we did last year, we'd also like to provide you with some updated Datapoints relating to downstream value share. On the left-hand side of the chart, you can see that as of the end of 2024, we have the potential to earn up to $1.7 billion in milestone payments based on customer collaborations previously entered into, with a majority of those payments dependent on successful commercialization of a product. This figure does not include potential royalties. On the right side of the page, we are showing you the full total of programs for which we currently have downstream value share potential, including those with royalties. While it is harder to estimate the total potential value on royalty deals, you'll see the volume of programs there remain substantial. You'll also see that the decrease in potential milestone payments was approximately $700 million when comparing 2024 to 2023. We do expect reduction in milestones if a program doesn't meet a technical goal or a customer changes commercial direction. Previously, we made up for these decreases with new milestone-bearing programs. But as a result of the commercial changes we implemented in Q2 and our focus on tools offerings that generate near-term revenue, we did not book a significant amount of new potential milestones in the year. Now, I'd like to provide some commentary on our outlook for the full year 2025. Before I get into the Cell Engineering revenue numbers, there are two important points of context. Firstly, I want to reiterate that our primary objective is to reduce cash burn. We like Ginkgo's competitive position and are very encouraged by what we're seeing with our tools offerings. However, we are still selling into a challenging biotech R&D market and are being diligent with respect to new business that we take on. Secondly, the government segment has been a source of growth for us in the past year, and our momentum there has been very strong. However, given current uncertainties in this area, we are baking that risk into the low end of our guidance. That said, then, our Cell Engineering revenue guidance is a range of $110 million-$130 million. We believe we are taking a conservative approach in providing this guidance range. We could see potential upside to this range coming from our new tools offerings, where we have a solid BD pipeline and, as discussed earlier, closed a number of new biopharma deals in Q4. Our Biosecurity revenue guidance for 2025 is at least $50 million. As we have in the past, we are guiding to our approximate current level of contracted backlog for the year, including an expected mid-year program renewal, and have a pipeline of opportunities we are pursuing beyond that. Also, to clarify, this business is almost entirely dependent on government funding, which is a risk we have been managing since we started operations in the Biosecurity space. And so we are providing guidance here under the assumption that our government contracts continue. Finally, we expect total revenue for the year 2025 to be in a range of $160million-$180 million. In conclusion, we're pleased with our overall execution of the restructuring thus far, as evidenced by the reduction in cash burn in Q4. We are very encouraged by the early traction we were seeing in tools. However, we also acknowledge that we continue to operate in a very uncertain macro environment. We believe we are taking a prudent approach to managing both our risks and growth opportunities along a path to adjusted EBITDA break-even by the end of 2026 and maintaining a cash margin of safety. One final footnote. On the financial reporting front, I can confirm that we remediated our SOX material weakness and wanted to express my appreciation to the team for all the work that went into addressing that. Back over to you, Jason. Thanks, Mark. Similar to the last two quarters, I'm going to use the first strategic section to focus on Ginkgo's efforts to reduce costs and update you on our restructuring efforts. Next, I'll cover our expansion into life science tools and services. This is a big change we made last summer, and we now have some data on how it's going. We're seeing a market trend in the biopharma industry towards needing more data for AI models, where customers haven't been well served by traditional life science tools companies and CROs, and I think there's a nice opening for Ginkgo there. Finally, I'll share more details on the specific tools and service offerings in Ginkgo Datapoints and Ginkgo Automation, where we've been getting good traction with customers over the last six months. Okay, let's get started. All right, so when we announced our restructuring less than a year ago, we set a $100 million spending cut target for 2024 with an additional $100 million reduction set for 2025. In our Q3 call last November, we reiterated those targets, having achieved a considerably accelerated site consolidation and cost takeout. Today, we've achieved a $190 million annualized run rate reduction through Q4 when compared to Q1. That's partially offset by a headwind where we have increased rent charges related to the BioFab1 facility, and you can see that reflected in our quarterly cash burn in Q4 2024 being reduced down to $55 million. As I said, having a cash margin of safety is very important to me, as that is how you avoid having to take on a diluted fundraising when you don't want to. And so I'm absolutely thrilled to see the progress on our cash burn here while still having over $560 million in cash and cash equivalents with no bank debt at the end of Q4. Among our peers pursuing advanced technology and AI and biotech, we're in a really strong position, and we plan to keep it that way. I want to thank the Ginkgo team for the incredibly hard work in 2024 while undergoing a lot of change to get us to where we are today. There's still a lot of work to do in 2025, but I do really like our position from here. You can see also our progress reflected between Q1 and Q4 of 2024. I really like this chart comparing total revenue to cash expenses, and that's inclusive of the cost of sales. My goal in 2025, like I said, is to keep this trend going. As Mark mentioned, we are being conservative with our revenue guidance in 2025, given uncertainty around government R&D funding, obviously a lot of changes there. But we've already taken steps to further drop our cash expenses, and I'm hopeful we'll do even better than the $60 million annual improvement that we have as our target shown there. Honestly, this is getting easier as we see what is working and not working throughout the business based on the changes we made last summer. We can move very quickly, I think, though. We showed that in 2024, and we're on an even more solid footing now, so the rate that we can make changes should only improve in the following year. I'm also really proud of the team for their quick work in spinning down and moving out of the various sites you see on this map. In particular, their work on the Lesaffre acquisition of Altar ensured that we retained critical access to technologies, and overall, the operational team's ability to maintain customer delivery while moving equipment and people has been really impressive. So, you know, we were in too many sites. You know, those are mistakes that we made over the past couple of years, and being able to, you know, in the face of drawbacks in biotech, move out of those quickly and consolidate around what was working, again, has been really impressive. I will say, please don't hesitate to reach out if you are growing biotech looking for space in Cambridge or Boston. I am the friendliest biotech landlord here. We've been making headway on subleases, but we still have a lot of lab space available that we'd love to get you into. I want to note as well that despite our site consolidation, we still maintain technical and programmatic teaming and corporate entities in Europe that are at work and delivering on our European contracts, including a very exciting new one that I'll mention later today, the RANGER program. Okay, all right, so let's move on to the next topic. All right, so, you know, I mentioned Ginkgo's been expanding outside of sort of our original solutions business into tools and services, so I want to give a little more color on that for where we're headed in 2025, so the first bit here, as you can see, are our historic solutions activities. We were really selling to sort of the head of R&D of a large biopharma, right? And so this was a deal kind of like the deals you would see negotiated at JPMorgan. They took a long time to close. They were large programs, multi-year, big checks, and they included downstream value in the form of milestones or royalties. The big change in 2024 was, in addition, we're still doing those solutions deals. We started selling tools, and for tools the customer could, it's sort of not the head of R&D, but someone that reports to the head of R&D or maybe reports to the person that reports to the head of R&D, and this is really, if you look on this chart that I quite like, at the high end, we have sort of a lot of customization and technical risk taken on by a biotech company. So, you know, an extreme form of this would be a small biotech in Cambridge developing a unique drug and hoping to license it to a large biopharma partner. So that sort of asset licensing model is at the high end of technical risk, the high end of customization. You also get a lot of value for that, right? You can get bought out for, you know, you see these billion- and multi-billion-dollar deals for preclinical or at least clinically proven drug assets that are pre-commercial. As you go down that curve a bit, you get to our solutions business at Ginkgo. These are highly customized, right? We do these deals with Novo Nordisk, and Merck, and Pfizer and so on to support drug discovery or manufacturing R&D. We are getting large milestones and royalties, and Mark shared sort of the aggregate numbers there, you know, more than $1.7 billion of milestones and then a whole bunch more royalties on top of that. However, again, highly customized, and we are taking on some technical risk. We met more technical milestones than ever in the last quarter, but sometimes we don't meet them, and then that hurts us on the revenue side, and certainly, we're dependent on customers to commercialize to see the really big value in the downstream, and so that's great. I will talk about, as I'll talk about in a second, it just takes a while to get to. That dotted line in the middle sort of represents where you go to the point where you're not customizing as much, you're not taking technical risk, and you lose that reach through into the customer's revenues on their products, but you get what amounts to a more easily scalable business. And so we're going to talk about Datap oints, which is our business that's most akin to like a traditional CRO, contract research organization, generating large data assets for customers to order, often for AI model training. I'm not going to talk about as much today, but we've released AI models up on the web that folks should try out. And then finally, the other business where we're seeing a lot of traction is we're an equipment business. We're selling our automation platform here at Ginkgo, and that has been going really, really well. So again, on the left-hand side, you know, we've got larger but longer-term potential upside where we get a piece of a customer's products and we get to show every technology at Ginkgo rolled up to do that R&D project for a customer. And then on the right-hand side, it's really the customer scientist driving. We get nearer-term fee revenue, faster sales cycle, and a much wider swath of customers. So I think these are very complementary. Us having the solutions business is really helping us sell tools, but they are very different. And so it was, you know, a risk jumping into this last year, and so I'm really happy to see that that has paid off so well for us. And the reason it has is that we're seeing customer interest in large data assets to enable AI models in the biotech industry. And if you go around and talk to heads of R&D or even the CEOs of pharma companies, you're going to hear that AI is sort of one and two on their priority list, both internally to drive efficiency in the organization, but also to help them develop new products. You know, this was really led, you know, kicked off by, it was a partnership between Genentech and Recursion a few years ago that sort of demonstrated the applicability of this in particular in the area of target discovery, which I'll talk about later in Datapoints. I think this kind of was a little bit of a kick in the butt for the industry, and you saw a lot more people getting interested in this. As the AI models have improved from the tech industry, that's only driving more of that. I want to frame like, what does this mean for the life science tools industry? You know, I think on the left-hand side, you can see, and this is a slide I showed, you know, a couple of quarters ago as well, you know, the traditional life science tools market, right? It's serving a scientist at the bench. It is maximally flexible. So that scientist has a hypothesis about an experiment they want to run to answer a question that they have based on reading a lot of literature and previous results that they've seen in their work, and they want to run that perfect experiment, all right? And that perfect experiment, they want to run it tomorrow. So they need all these reagents to be able to show up on very short notice, and they need to have a bunch of equipment and assets available to them right on site. And they want it to be exactly the experiment they want. So, you know, you have these huge catalogs from the big tools providers with, you know, thousands and thousands of SKUs so that that scientist can do exactly what they want. By the way, I think this is a really important thing, right? I think we've developed, you know, all of our drugs, biotech, and biological learnings have come from this tools infrastructure. It's just different than the infrastructure you need if you want to generate data for AI model training. And if you want to generate data for AI model training, what you really care about is the cost per data point generated, because these models get a lot better if they see a lot more data. And so in that case, you don't want infinite flexibility. What you want is sort of infinite scale with less flexibility. In fact, you want a lot of the experiments to be done similarly so that the data can be compared well. And so, you know, I think this sort of classic research approach versus what we're calling a data Foundry research approach, again, I think they're very complementary. I don't think we're going to stop doing hypothesis-driven, discovery-based science, but I think you're going to have a complement to it where we really need, at all the major biopharma companies, all the major bio research institutes out there, you need the ability to generate large sets of data in order to train models and approach it with this AI-based approach. So I think you're going to see a lot of energy in this direction. We think we're extremely well positioned for this at Ginkgo, but that is the underlying driver that's made adoption of our tools products go so well in the second half of last year. Okay, so now I want to talk a bit more about the specific, the two products where we've seen the most traction, which is our Datapoints product and our automation product. So if you go to Datapoints here, the very first thing I'll point out is this is a different business model than we have in our solution. So, you know, when customers are seeing our Datapoints offering, they own all the intellectual property. There's no royalties. There's no milestones. We're really generating large data assets on a fee-for-service basis for those customers. There's going to be two Datapoints products that are already launched, the functional genomics and antibody developability. So I'll talk about functional genomics first. This is really exciting. This is pretty similar in spirit to what I mentioned with that Genentech, Recursion partnership, where you're really using AI models to do target discovery, to find new targets to develop drugs against in disease models. And often a customer will have, for example, a cell type that they've maybe developed in-house that's relevant to their disease, or maybe they want to take a standard one that's used out in the academic literature, and they want to perturb that cell type. And what I mean by perturb is they want to either hit it with a chemical library, so think, you know, 10 or 30,000 chemicals that they might have in a compound library, or they want to perturb it genetically. In other words, they want to make CRISPR knockouts of 1,000, maybe 1,000, maybe 10,000 genes in that host cell. And each time you knock out one gene or hit it with a compound, you would like that cell to be in a well, and then you would like to read out a high-complexity measurement on the performance of that cell. And we have a few different types of measurements we can do, but most popular so far has been transcriptomics. And so we are able to give you a readout of the transcriptomic profile, but we can also do high-content imagery, both cell painting and bright field on that cell to give you back those sort of many thousands of cells with these high-content outputs. We've done this now in 100 different human cell types that we've worked with at Ginkgo, but you can see the ones that have already been onboarded for that high-content transcriptomics drug screen on the left there. You know, we've been doing for customers, you know, skeletal myoblasts, normal skin fibroblasts, HeLa cells, CAR T- cells, so on and so forth. And we don't really have an issue bringing cells online. We've had a really good hit rate doing that. So again, we're happy to work with customer cells or cells that we have here at Ginkgo. Also, a thing that people have really liked is we've just gone ahead and put some data sets that we paid for and developed here at Ginkgo just online so that folks, AI and ML teams can just work with them and see the format of data that you get out of Datapoints. And so I'll draw your attention to the GDP x2 data set. It's our biggest data set we've launched. It's nearly a terabyte of data. There's 85 chemical compounds here. I think six or seven of them are sort of controls, and the rest are drug compounds. We then hit four different cell types with six different concentrations of these drugs and four replicates of that, again, in 384-well plates. We do a transcriptomic readout of that. We put all that data online. It's about 200 GB per cell type across those cells. So download it. You can hit that QR code and download and play around with it. That's been a really good way to bring in new customers for that product. It shows people exactly what they're going to get. The next product I'll talk about in Datapoints is our antibody developability product. Here, the idea is you can send us, you know, 100, you can send us 1,000 antibody sequences. We'll synthesize, express those antibodies, and then run a battery of developability assays on them. And so you can see a list here, but these are things like thermostability and heparin binding and polyspecificity. Some of these are really complex, difficult-to-run assays. And so being able to get these at a throughput of, you know, thousands and get that data back to you again in a very clean AI, ML-ready format over the cloud, I think is a unique offering in the market. And so we're really excited to have that out there, and we've seen good uptake. And to give you a sense of sort of timing and what programs look like, these are just two example projects. The first would be, you know, an oncology target ID project where you could see the types of cells that we would go after there. This is 2,000 compounds in five different cell lines, and that's a 10-11-week project, and then you get that data right back. The next one over would be for an Alzheimer's target ID here, interested in a genetic perturbation. So doing those CRISPR knockouts, in this case, of 1,000 genes across three cell types, and that's a 9-10-week return on that data asset. And you can see on the right-hand side, the, you know, illustration of the different types of data formats that come back to you. So again, really excited about this. The only thing I will mention is upcoming out next will be RNA data services, and we also recently released an RNA AI model. So you can see the types of design tools that you can generate by using these types of data assets. But again, this is something that'll be coming up, and we encourage folks that are working in the RNA AI space or RNA drug discovery, if they're interested to give us a call. It'll be exactly the same as the other two offerings in terms of IP rights and no royalties, fee-for-service. So if you're looking for large data sets in RNA, also give us a call in addition to the sort of functional genomics and antibodies. Okay, so that's what I wanted to say on Datapoints. I'm going to move on next to Ginkgo Automation. I was in, I guess, near the end of January in San Diego for the Society for Laboratory Automation and Screening SLAS conference, and it was really exciting to be there because there's actually a ton of interest in our reconfigurable automation carts, our RAC technology. You might remember this came in through an acquisition of Zymergen a few years ago, and this is one of the big reasons we acquired that company was we felt that they had really unique automation assets. This had been started, I want to say, seven or eight years ago now, and then they had a previous generation of these carts were available when we acquired Zymergen, and then over the last two and a half years, a lot more work has gone into making it simpler, easier to manufacture this new generation of the carts, and so we were really happy to launch them at SLAS and get the reception. You know, just so you understand how these work, we have inside the cart a piece of laboratory equipment, so this is coming from, you know, any lab vendor you want, right? So this is a piece of lab equipment that's been developed at the lab bench or for the lab bench. And then there's a robotic arm, okay? And then there's a piece of magnetics called MagneMotion track, which basically allows you to move a lab plate along a rail, all right? And that's all in the cart, all right? Including the rail. And then what you can do is you can stitch parts together. You can see three of them put together in this image here. And the samples can move to any one of the three arms on those three carts and then get delivered to the piece of equipment, okay, inside that cart. And then importantly, the equipment is connected. There's a lot of electronics hardware in the bottom of the cart, and all of that goes back to our cloud software. So we can control both the delivery of the samples, the arm putting the sample onto the equipment, and then the equipment itself is controlled by our software. And you can see we're rapidly adding new sort of off-the-shelf hardware integrations. In other words, these are pieces of equipment that we've already put into RACs, either based on customer interest or Ginkgo interest, because we are users of these systems, and they're already plugged into the software. We're adding new things to this all the time. So if you're interested in integrated automation and a piece of equipment you want is not on this list, just let us know, and we'll get it added. We did, so prior to acquiring Zymergen, they had actually started to sell this hardware. In my infinite wisdom, I stopped them from selling Ginkgo, from selling that automation once we acquired the company. Happy to be doing that now. But in the meantime, it was actually sold to both a national lab and a pharma start-up called Octant. And Octant's a good case study. I mean, this is a, you know, it's about an 80-person company, 100-person company. And they have a very small automation team. It's like one automation engineer and a couple of operators. And they're running, you know, and this is, you know, numbers that they've shared, 400,000 samples a month, recently hitting 150,000 samples a week. This is a 7x throughput increase over their manual processes and an 88% reduction in hands-on time. And it's let that lean team really be able to generate a scale of automation or sort of a scale of data generation that would traditionally require a pretty large dedicated automation team and a work cell that's designed to really do, you know, one very specific reaction. And in this case, Octant's been able to use it to do a number of different things. You can also see our setup here in Boston. Unique among automation equipment vendors, Ginkgo uses our own automation. So because of our solutions business and because of Datapoints, we do a lot of biotech R&D here in our facility in Boston. So these, you can see, these are actually the older generation of the automation carts that we have been bringing into Ginkgo at Boston over the last couple of years since the Zymergen acquisition. This is now the schematic of what all the different pieces of equipment are in our setup here in Boston. What's really neat about it is we originally, you know, aimed this to do NGS library prep. That was the first thing that was running on the rack automation. We were able to add DNA assembly and PCR prep and all of the same system, but just adding more pieces of equipment, Lego block style, onto those rails. This is something that people were really excited about at SLAS because the traditional integrated automation vendors basically sell you a one-off custom engineering project. You're like, "I want a high-throughput screening setup to look for whatever, you know, a drug compound. This is the assay I want to run. I want to run 100,000 a week." Okay, great. You need this equipment. There's a whole 3D file. There's a custom software build, and everything is built to really do that one setup, and then it rolls out six months later, and then if two years later, say you change your mind and want a different protocol running on there, you basically are scrapping the thing to build a new one, and so, whereas with the RACs, we've been able to expand the system now several times to add new workflows onto one big system here in Boston, and so that is a unique selling proposition that folks were really excited about at SLAS. As you can see, this is our booth at SLAS. One thing I'll mention is because we decided like mid-year to start selling the equipment directly, we were like late to signing up at SLAS. Our booth was like at the back next to the bathrooms, and it was mobbed the entire time. So I do think people are really excited about this idea of modularity built into the hardware itself. Okay, so this isn't like just an improvement in software or something else. These carts took a ton of physical engineering effort for us to get them to the place they are today where we can just rapidly add them together. And to give you a sense of how rapid, up at the top right, Recursion had a party at JPMorgan, and they were nice enough to invite us to have our automation there. And so there's a cocktail party. We actually set those RAC carts up that afternoon and had the system running, moving plates around, getting picked up, put onto the equipment, all just in a few hours. And so that type of speed of a build-out is really enabled by the modular automation. You know, I don't think people need to set up an automation system in three hours, but it's nice to know that you can. And importantly, it's nice to know and really economically impactful that you can expand and adjust and change it. The idea I like to describe it as is an automation core rather than an automation work cell. So instead of doing one thing, it's really an automation site that you're going to expand and grow as you need new AI protocols or whatever you need to do at high throughput at your company or university. Speaking of selling systems, so we just announced back at the end of January that we sold a system to Great Lakes Bioenergy Research Center. Really excited about this. Super excited to see these systems going out in the wild. We have a great pipeline, both before, but importantly, after SLAS, you know, a ton of people interested in evaluating these systems now. So please reach out to us if you're interested in either planning to currently build an automation work cell or this idea of an automation core is of interest to you. I'm not going to get into it today, but all the software that operates the RACs is cloud-based. We have a constraint-based scheduler, which we really think is leading in the industry. We have a form we basically monitor with cameras all the systems, and we can do a lot of at a distance debugging, not just of the tracks and the arms, but also of the equipment itself on the system. That's really neat, and early customers have liked that. And the last thing I'll say is, look, the big dream here on this stuff is that this could become a horizontal platform that sort of standardizes integrated lab automation across the industry. So one of the things I'm seeing is you have all these tech folks that are really excited about AI, applying AI to science and biotechnology, and they sort of run into this issue that there's not a lot of data, number one. And so they want to be able to generate more data. They go look at how data is generated. It's very expensive and done by hand. And they're immediately jumped to the idea that, well, why don't we use robots? Why don't we automate this? And the big challenge is there's just so much variability in the work of doing science. And so I think as an industry, we need to start to come up with standards that allow us to sort of modularize equipment, make it all be easy to connect. Yes, at the software level, but frankly, software is easy. It needs to be easy to connect at the hardware level. And that is what the RACs do, and I think they're going to be ahead. I'm really excited about it. Okay, the last thing I want to talk about is we're not going to have a ton of time today to talk about Biosecurity in general, but we are getting started on some very exciting Biosecurity work funded by the European Health and Digital Executive Agency, HaDEA, through the EU4Health program, linked to the key priorities of the Health Emergency Preparedness and Response Authority, HERA, at the European Commission. We'll be leading an international consortium for a project worth up to EUR 24 million to make what I've been thinking of as sort of like a bit of a Star Trek nerd, tricorder for disease. The idea here is to have sort of point of care sequencing and metagenomic NGS available to look and say, "Hey, I'm sick. What virus do I have?" The best way to do that would just be to look at the genome. Today, that's not really available. There's too many steps between that sample and you actually getting the result. It's not really point of care. The idea here is, could we start to build out some of those technologies? I think this is really neat. This is, you know, ultimately the direction that Biosecurity needs to go in, right? Like eventually, Biosecurity should feel almost like a smoke detector, right? Like I would like to know if there's Ebola in this room right now. That should set off an alarm just as much as I would like to know if there was smoke, and so that really requires a lot of breakthroughs over time and being able to monitor circulating DNA and viruses and so on, and I think it is technically feasible, but it's a lot of engineering work, and I'm excited to be kicking off that project in Europe. Okay, so in conclusion, I'm extremely proud of our execution this quarter as we continue to cut back our costs while still delivering on our revenue targets. We continuously deliver for customers across all our offerings, and I'm excited for the opportunity we have ahead of us as we continue to strive towards adjusted EBITDA break even by mid-2026. All right, now I'm going to hand it back to Joseph for Q&A. Great. Thanks so much, Jason. As usual, for the Q&A, we'll start with a question from the public. I'll remind the analysts on the line that if you'd like to ask a question, please raise your hand on Zoom, and I'll call on you and open up your line. Thanks, everybody. All right, let's get started with a question from X. So this was in response to our post. It comes from Bisbee. The question is, what are the ideal customer personas Ginkgo needs to close for new client acquisition? And what functional group is the ideal point of entry within these prospects, i.e., C-suite, R&D, strategy, product development? I'll go with. I saw Jason's hand up first after that. Yeah, happy to take that. Yeah, I think this is actually a key strength of Ginkgo, just given our experience in the solutions business. We've engaged with kind of folks all around the R&D stack in these companies. So the short answer is it varies depending on what we're selling. So a solutions deal itself, basically our ideal customer persona there is the head of R&D. If it's a large biotech, biopharma like a Novo Nordisk or a Merck, and if it's a small biotech, it's the CEO, because we are doing basically an outsourced research project. We're almost like an outsourced R&D team for that R&D leader. If we're selling Datapoints, it's going to, if it's a drug company, really a lead for a drug program. And so that means there's a lot more of those than heads of R&D. So that's exciting to me. It gives us kind of a wider set of folks to engage with, but there's still sort of like one or two levels below that head of R&D. And then finally, if it's automation, it's the kind of folks that are at that SLAS meeting. It's really the kind of automation leads in these companies who are usually responsible for building out a new work cell or integrated automation setup. That's what I think that's going to change over time. Like, I'm knock on wood, I'm sort of optimistic that the RACs are such an innovation in the industry that we could ultimately see it just requiring the addition of a new cart to an automation core to bring online a whole new workflow. And so then you might hear from just someone on the technical team who wants to automate something, and they give us a call to add that piece of equipment to their existing core. But for today, when we're doing these first sales, it's really sort of the heads of automation at these various large and mid-sized companies. Thanks so much. Jason, I've opened up your line from Morgan Stanley. Hi, this is Jason Kritzer. Thank you for taking our questions. So I guess starting off, maybe just the question, the 2025 guide. What are your assumptions in the 2025 guide for some of the new offerings Ginkgo has launched, namely tools, Datapoints, and Ginkgo Automation? Are you anticipating material contribution from any of these offerings in 2025? You want to take that, Mark? So I'd be happy to take that. So we are, I think, being relatively conservative in the guide with respect to the new offerings. For context, in 2024, your sort of baseline is single-digit millions of contribution from that. And so in the guide, I think we're anticipating at least getting into double-digit millions in terms of revenue contribution. And so what you should maybe think of is there is certainly upside opportunity from the tools offerings, but that within the guide, we're being relatively conservative there. Yeah, and the other thing I'll add to that is, you know, we're coming from basically a standing start on that in mid-2024, and it's gone well enough that you're seeing me direct more of our kind of sales and commercial effort in that direction. So we'll see. I mean, the AI thing is real wind in the sales for Datapoints, especially in the near term and automation on a slightly longer term. So, you know, knock on wood again, but we are trying to be conservative given it's a new business. Got it. That was helpful. And then maybe just if I could ask a follow-up question, just so you reiterate expectations to reach adjusted EBITDA break even by year-end in 2026. Where does Ginkgo need to finish in 2025 from an EBITDA perspective for us to feel comfortable about reaching this target? And then could you provide some color on some of your assumptions that you have embedded regarding achieving this target? So any assumptions regarding the range of revenue you need to achieve is the success of either tools or Datapoints or Ginkgo Automation criteria to achieve this target? And is the OpEx run rate, once the current restructuring initiative is completed, sufficient, or do you need to pull on additional levers on the OpEx front? Thank you. Yeah, so I'd be happy to take that one. So I think a good way to frame it is to look at where we finished in Q4 of 2024. So that was adjusted EBITDA of negative $57 million. So we talked, so the levers that we have are really revenue costs and then sublease. On the cost front, we talked about increasing the cost takeout run rate by another $60 million by the end of this year. And so on a sort of quarterly basis, Jason, you would see at least a $15 million contribution to adjusted EBITDA just from that alone. And I'll say, largely speaking, the actions that get us that 60 have already been taken or are in motion. We will also continue to push on the cost lever. So we're not saying we're sort of done there. And so through 2025 and 2026, we think there would be additional opportunities on the cost front. So that's part of the equation to getting to adjusted EBITDA break even by the end of 2026. Then you've got sort of revenue growth, of course, would be we would expect to be a contributor. And then you've got sublease sort of mitigating the $65 million on an annual run rate basis of excess space cost. Now, on an annual basis, about $8 million of that just naturally expires in 2026. The rest of that we would have to deal with through sublease income. It's a challenging market, and so we're not banking necessarily on being able to do that, but that's another sort of piece of the equation. And then I guess the only sort of final point, Jason, would be that downstream value share in 2026 is a real opportunity. There's a fairly significant amount of the programs that we have that would be in play in 2026. Again, we're not necessarily banking on that to get us to adjusted EBITDA break even, but that would be another opportunity that would be in play for 2026. So yeah, so hopefully that sort of frames it for you. Yeah. Yep. My only addition to that, Jason, is the, I think if you look at biotech market today, right, it's generally like a tough market. You're having a hard time, less investment in R&D and so on and so forth. And we have launched these new products, which again, I'm happy they're going well, but they are new for us. So you see us being conservative on revenue guide because from my standpoint, costs are under our control, right? So as we march to break even, I'm going to be looking at that, right? If we're doing better on revenue guide, if we're not, we'll take out costs. You know, it's not that that's the equation. It's not more complicated than that. And I do think we have plenty of levers. That was helpful. Thank you, guys. Yep. Awesome. Thanks, Jason. Evie from Goldman, you're up next. Your line's open. Hi, thank you for filling in for Matt tonight. How should we think about the margin profile differences between the traditional Foundry Datapoints and automation? I understand it's early days and revenue is limited at this point, but just trying to get a sense for where growth will help push you towards EBITDA break even. Yeah, so on the Datapoints businesses or the tools offerings, we are really targeting the way we think about margins is we really target what you would see in kind of a solid sort of CRO services market, so 40% + gross margins. I will just say the business is still not even close to being at a scale where you can really understand sort of what's possible in terms of gross margin. We do think our offering there is pretty differentiated. We also think we have a way to sort of scale into growth in a cost-effective way. So that's generally how we're targeting it. Compared to the traditional solutions offerings, you'll remember that we were using, to some extent, downstream value share there as part of the thesis in terms of getting to margins. And so we were willing to do business at a sort of much lower kind of gross margin profile. And that, of course, has evolved. We're no longer sort of willing to do that. And you see that kind of in the cost structure coming down. And so, but that's how we're thinking about the tools business, Evie. Okay, that's super helpful. And then what are you seeing in your sales funnel now that you've opened up your sales process towards more tools? Are you seeing the volume pickup you sort of expected through the less customized, less risky offerings? Yeah, this is something I'm super excited about. I mean, I think Mark mentioned it, but you know we had, I think, eight or sorry, seven new Datapoints deals in the last quarter. We had five new logos to Ginkgo, right? Like that, what we found with the Datapoints, in fact, I had a customer here today who was like, Oh yeah, I talked to you all a year ago or a year and a half ago. And you know we kind of left saying like, Well, we can't really get behind the royalties and milestones for this kind of a project. And so I assume you listened to us and that's why you've updated your business model, you know, right? And so it sounds great to me. So like you know we have these new interactions, even with places we've talked to before now where like they're basically introducing us to procurement now and we're treated much more like a straightforward kind of CRO interaction. It's way faster to close. And then what we're finding with Datapoints is we can do kind of a smaller proof of concept. People want to try it out. Like I encourage customers listening in on the call, you can do a relatively small project, $100,000 here, $200,000 there, to generate a data set and see how it works in your area, in your cell type and so on. And then if it looks good, you can generate a much larger data set specifically for model fine-tuning or other ML. And so that, again, it feels pretty good. It feels very different than when we were selling solutions, certainly than, like, when in my commercial sales. So yeah [crosstalk]. Automation's a different crew. Like I would say that that is really, it is more of a sales cycle where you're going through. They're planning to do a build-out. At least for our first installs, you'll see us go in with kind of a like a traditional automation sales cycle into like a work cell type build. However, I think again, what's unique about the RACs, and we're even seeing this with companies like Octant, you can just add to them in small batches after that, right? So if we can just get in places, I think then we'll have a much quicker sales cycle than is traditional and integrated automation. Okay, Mark. Awesome. Yeah, Mark from BTIG, your line's open. Great. Thanks, guys. Can you hear me? Yes, we can. Excellent. So I know the new administration has been in office not terribly long here, but I'm just curious. In your Biosecurity guidance, you assumed no change to any government contracts. Obviously, there could be some changes, right? So help us to just better understand who these contracts are, which agencies they're with, and you know give us a sense, maybe just remind us if you think there are synergies remaining between your Biosecurity business and your core Cell Engineering business. Yeah, I can give a few comments there. So we do have government contracts both on the Cell Engineering side and the Biosecurity, as you're mentioning. The Cell Engineering contracts are much more like traditional R&D contracts, again, places like DARPA and ARPA and so on. And there, I think you have seen with this administration coming in and making changes. Certainly the research universities are being impacted pretty quickly by changes to how they're approaching research funding. I will say, like, and this is part of the reason why we also were conservative on the guide for this year. We don't want, you know, like the, we don't want to count on things. We'll see what happens. I will say, I think we're in the era with this administration, certainly my interactions in DC, where the new leads have not, you know, they're either just getting in or aren't even yet in for a lot of these agencies, and certainly their deputies are not in place yet. And so all you're seeing in advance of that is cutting. What I think is going to be interesting about this administration is once those leads are in, I think you're going to have some people that do have certain vision for things is going to be potentially different than the visions of people that have come before, and they're going to want to move on it just as aggressively and quickly as you've seen them move on the cuts. And that represents an opportunity, I'd say less of an opportunity on the research side. I think research will just be research. I do think research money will keep flowing, and there it's maybe just questioned project by project. But on the Biosecurity side, that could really go just in wildly different directions depending on what the administration's plans are. Obviously, we've gotten out of the WHO. There's a big impact on CDC, one of our contracts with CDC. Where do they go with all that, right? You know, are they going to have an alternative to WHO? Some of the programs we have, these are even programs outside the U.S. like we do, like we mentioned before, monitoring in airports in Africa and in the Middle East and so on. Those could be real assets for some of the things that previously were being relied on, just voluntary reporting from countries in the WHO. We could now maybe monitor those with hard technology, and I think that's something that could appeal to this administration, but it's early days because we don't really have the leaderships in place yet for in some of those. Okay, great, and by the way, you've done a nice job on the cash burn reduction. Clearly, you've made a lot of changes to the company in the last several quarters. But Mark, you know, you talked about how you'll no longer report a new program metric. Maybe just remind us what we should be looking for. Obviously, we can track your Cell Engineering revenue, but remind us how we can sort of, what other ways can we track the health of your funnel and your business going forward? Yeah, so what you'll get is a, I would say, an enhanced version of the current active program metric. And if you look at the appendix of the presentation, you'll get a preliminary view for the last four quarters of what that metric looks like and would be happy to talk about it with you offline further and get your feedback on that. But you'll get sort of an enhanced current active program metric that will give you a better sense of what the real revenue per program at Ginkgo is. Because, as I mentioned on the call, the number, the metric you get right now includes a lot of programs that are either starting or stopping and therefore aren't contributing revenue. And so when you try to take revenue divided by that program count and do the, I know the analytics that you all do, you're not really getting a sense of what revenue per program is. So this will give you a better sense of that. And because you'll have four quarters of history, you'll be able to see how that's trending over time. And that's what I would use to model revenue. But we can talk about that in a little bit more detail offline. Okay, thanks, guys. Thanks, Mark. Thanks, Mark. And yeah, I appreciate the compliment on that. Again, I'll give a shout out to the team for an enormous amount of work to get that cash burned down in Q4. It is top of mind and a lot of what you see. I showed that slide there where we're, you know, get the revenue up, bring the cost down, and they line up and there we go. I like Ginkgo's position, as I mentioned, in terms of, you know, from a technology basis, the things we've accumulated, the talent we have here. If you look across the industry, it's tough times in general. I think if we can get to that point where we're even close to break even in the near term, that allows us to be sort of like a port in that storm. So I do think that's a really important thing. And that's why we're putting so much effort into the cost takeouts. Brendan. Yeah, thanks, Mark. Yeah, Brendan from TD. No one's open. All right, great. Thanks for taking the questions, guys. Maybe just piggybacking on some of the AI commentary, I actually wanted to ask how you're thinking about growth trajectory of some of these newer offerings. I understand, fully understand it's early days here. But you know, just from a high-level strategy perspective, should we anticipate any shift in priority and maybe timing for kind of expanding these newer products, just given all the headlines in recent months? And then I guess, honestly, related to that, just wondering if you see any potential synergies with your Biosecurity offerings there moving forward. Thanks very much. Yeah, so I think, you know, I mentioned this a bit in the talk, but the interest in AI from the, and it's really the mid and large kind of biopharma biotech companies is what's driving all the interest in Datapoints and even like on a second-order basis, a chunk of interest in the automation, so I do think that'll keep going, right? Like it's not going away. You know, and I think you're seeing the success of the models just in driving efficiency across things like clinical filings and all this stuff, making it very obvious to leadership that it would be nice to see similar gains in their research outputs, and so I think that's what that's kind of driving it. I think then, like I mentioned, you quickly run into this realization that even at these large biopharma companies, the existing data sets you have across the many, you know, hundreds of drug development programs you would have done over the years, were all done in like slightly different ways with different controls and different data standards. And then also they were done at the lab bench. And so very smart science was done, but maybe not huge data assets were generated. And so everyone I think was kind of originally hoping, I'll just go mine all this data we already have. And surely that will let me train these models. And then the realization has set in that actually you mostly need to generate new data. And so that I would say that like, hey, we're interested. Oh, we've done the nine-month cycle to look at our existing data and realized we want to make new stuff or even new just relevant to our new drug programs. It's kind of clicked through and that's why you're seeing this new demand. So I do think that will keep going. When it comes to Biosecurity, I think, yeah, I mean, one of the things, look, I cannot emphasize how poor we are as sort of a species at establishing Biosecurity so far, but you can absolutely imagine a future world where you have basically rapid at-site DNA sequencing, that sequencing is getting sent up into the cloud and it's being analyzed across thousands of collection sites by AI models that are basically looking for things that look bad. You know, and this is roughly the analogy would be like what you see with cybersecurity, right? You know, places like Microsoft and others who are monitoring across many, many endpoints see an anomaly. They're like, oh, that's a new virus and then are able to try to contain it and then also push patches and update. We got to get to this place when it comes to viruses. It's just literally crazy. And so I do think that type of infrastructure, getting that in place is absolutely going to evolve AI analysis of just the raw amount of genomic data. But the first step is start collecting the genomic data. And that's really what we've been putting in place to begin with. So I think there's a little bit of like, you know, I think the data collection is probably the driver initially for this stuff in both cases. All right, great. Thanks, guys. Appreciate it. Yep Yeah, Matt from William Blair, your line is open. Hey, Matt. Hey, good afternoon. Or evening, Jason. A couple of times you might have lost him. We've got to come back.[crosstalk] We see you again. You want to start that question again? Yeah, sure. Do you have me? Yep. Sure. So a couple of times you've referenced an effort to be conservative with guidance. And obviously, a challenge over the last couple of years was in these very long sales cycles, given the complexity of the deal that you were targeting. And you've done a lot of work to simplify that and, you know, really work with customers to reduce sales cycle, kind of meet them where they're at. Can you just help us when you say more conservative? Does that just reflect shorter sales cycles, so more data around close time? What is kind of the early Datapoints around the tools and Datapoints offering in terms of close time? How do you really arrive at, you know, confidence this is conservative guidance? So yeah, I'll start by saying, Matt, on the solution side, remember we do programs that in many cases are multi-year programs. So we're coming into 2025 with a pretty substantial backlog of business that we need to burn. And this is on the sort of commercial side of the company with lots of like good customers and long-standing customers. So there's sort of a base of business that's already baked in. When we say conservative, we're really talking about some uncertainty around the government side of solutions. Government has been a source of growth for us when you just look at the numbers in 2024 and was expected to be a source of further growth in 2025. Just given sort of the current environment, very specifically, we thought we should be conservative on that piece of solutions. Then finally on tools [crosstalk] like I said, that was maybe in 2024, that was what, 15%-20% or something? Yeah, of revenue. So you can look at the, you'll see it in the 10-K, the percent of revenue by industry segment, but government was sort of roughly speaking about $20 million of revenue for us in solutions in 2024, and we would have expected that to grow in 2025. So that's really, Matt, what we mean by conservative. On the tool side, I think it just, you heard me say it in one of the earlier questions, but we starting from a base of effectively zero, have sort of modeled in the guidance, getting into kind of double-digit millions of revenue, but not beyond that. We do see potential just based on pipeline to do better than that in the year, but that's not in the guidance on the tool side. Okay, thanks. Jason, you know, the vision of, you know, building a company that can accelerate and make R&D more efficient, maybe it looks a little different today with Datapoints and with tools, but that's still really where you're headed. I would think that, you know, over time, there could be a number of customers that might, you know, work with Ginkgo in a number of different ways, both on the automation side, maybe doing some Datapoints projects and then larger enterprise efforts. Do you have any of those examples yet? Is that something over time you think you can get to? And what's kind of the path from here to there? Yeah, there is sort of like an interesting [flyer]. So yeah, Ginkgo's mission is to make biology easier to engineer. Datapoints and tools very clearly fit into that. It's infrastructure we built. We can go to market with it quickly. We can sell it in a new way. All that, you know, again, was a hypothesis last summer that has now been proven true. So I'm really excited about that. It takes something like robotics, right? I mean, that robotics infrastructure obviously is relevant to biotech research, but it's really connecting laboratory equipment into an integrated setup. You could use it for other types of laboratory work as well. Certainly, we expect demand and have already seen demand in our funnel from things like diagnostics companies and things like that. I think you'll absolutely see that as a base of customers for us in automation. But you're also seeing some efforts from like the large tech companies. Like I was on a panel with the head of R&D for Microsoft. And like they're doing like, you know, AI for science. Google, who is a strong partner of ours, they just came out with a co-pilot for scientists, which is actually pretty amazing. And so, I think you are seeing the frontier AI labs say, Well, these reasoning models at the high end, one of the use cases could be changing how we do scientific and engineering discovery in the world. And if you want to do that, then those models need to interact with the real world. You know, they need to be able to run experiments. And so again, I feel like automation could be a really big part of that. You know, that's not a big part of my, you know, revenue plans in 2025, although we do have some, we have a contract just on data generation with a major tech company. But it is, I think, a thing to watch. And if that becomes an actual big area of investment among the major tech players, you can expect Ginkgo to be right there. So for example, we'll have our RACs at the big NVIDIA event. So if you want, if you're happening to go to that, you can check them out in person there. Yeah. Very good. Thank you. Yep. Thanks for bearing with me there, Matt. Jason from Morgan Stanley, if you have any other questions, happy to take a second one. But if not, I'll close with a retail question. Okay. So a retail question, this comes from Cabalis. The question is, is Ginkgo revenue impacted at all by avian flu, potential pandemics, et cetera? Matter of personal concern. So I appreciate you guys answering that. So I think, I mean, like H5N1, well, here's what I'll say about Biosecurity. We've been building out the infrastructure, obviously, to support persistent pervasive surveillance in between certainly pandemics, but even in between various regional outbreaks. Like there's some horrible thing that came out in like DRC, I just saw today, right? So you're going to have these, you know, you have regional stuff that creates spot demand and then the occasional larger, more global thing. I mean, H5N1 has pandemic potential, like end of story, right? So it's just one of these things you have to watch at the human transmission level, and if it does, you should absolutely again expect us to be right there like we were during COVID to just lean right into that and grow, but then there is an interesting angle around, I mean, like, you know, egg prices are up, right? There's a reason for that. And so, you know, I do think there's also an angle there potentially with Biosecurity just around, you know, preserving food security and food pricing, which I think is something that's of interest to this administration and just all, you know, everyday folks. And so I think that that's another angle for Biosecurity in the future, but obviously we pay the most attention to the human stuff. Thanks so much. Thanks to everybody for joining us. Yeah, thanks everyone for the time today. Appreciate [the] questions.