i'm daniel marshall senior manager of communications and ownership i'm joined by jason kelly our co founder and ceo and steve cohen 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 will be making forward-looking statements which involve risks and uncertainties please refer to our filings with the sec to learn more about these risks and uncertainties including our most recent 10k Today, in addition to updating you on the quarter results, we're going to make the argument that autonomous labs are an imperative for American science. We're also going to provide insight into how we are going to scale the capabilities of Nebula, our autonomous lab in Boston, and share an update on how we are getting autonomous labs like Nebula into the hands of the next generation of scientists. As usual, we'll end with a Q&A session, and I'll take questions from analysts, investors, and the public. You can submit those questions to us in advance via X, hashtag GinkgoResults, or email investors at GinkgoBioworks.com. All right, over to you, Jason.

Thanks, Daniel. We always start with our mission here, which is to make biology easier to engineer at Ginkgo. In 2026, our goals remain the same. We want to focus and invest to win in this new category of autonomous labs. We want to focus Ginkgo's efforts really on the technology side, largely into autonomous labs, and we're going to invest to extend our lead there. Second, we want to demonstrate the capabilities of an autonomous lab by using our big system here in Boston, Nebula, which I'll talk about today, that we, in the last quarter, expanded that substantially so that we can sort of move the majority of our work onto that system over the course of the year and into the future. That's a great chance to both improve the economics of our services and also demonstrate to other potential buyers of autonomous labs just what you can do with a system like this. And so I want to talk a bit about that today as well. And then finally, I want to book new sales of autonomous labs in biopharma, national labs, and as I'll mention today, research universities, which we're very excited about. We have made a lot of headway, as you know, and we've been talking about for a couple of years now on improving our cash burn. You can see that in the second half of this year, we intend to improve on that burn even further than we did in the first half of the year. And that is really work we've been doing in the first half of the year, sort of paying off and bearing fruit. So really excited. This gives us this plus our $302 million in cash and cash equivalents, as well as we have an additional $87 million that we've set aside for restricted cash for various customers and certain operating activities puts us in a really nice spot going into the second half of this year and the future to really have the capital we need to continue this growth into Autonomous Labs. So with that, I'm going to pass it over to Steve in order to dig into the financials, then you'll hear from me again in the strategic session. Thank you.

Thanks, Jason. Before I walk through our financials, I want to remind everyone that following the previously announced transaction that closed on April 3rd, the divestiture of biosecurity is classified as discontinued operations within our financial status. Accordingly, we have and will retrospectively recast all prior periods presented to conform to this presentation. The former biosecurity results are now reported as loss from discontinued operations below loss from continuing operations. All of our financial commentary I will provide today relates exclusively to continuing operations, where we now operate as a single sector. With that, I'll now discuss our Q2 results. Revenue was $20 million in the second quarter of 2026, down 48% compared to the second quarter of 2025. For the first six months of 2026, revenue was $40 million, down 49%, compared to the same period last year. As previously disclosed, revenue in the first six months of 2025 included $7.5 million in non-cash revenue relating to the mutual termination of the Biomedic Agreement. Excluding this, revenue for the first six months of 2026 was down approximately 42% from the prior year period. It is important to note that our net loss includes a number of non-cash and other non-recurring items 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. A full reconciliation between adjusted EBITDA and gap net loss from continuing operations can be found in the appendix. In the second quarter of 2026, R&D expense decreased 4% from $31 million in the second quarter of 2025 to $30 million in the second quarter of 2026. G&A expense decreased 26% from $16 million in the second quarter of 2025 to $12 million in the second quarter of 2026. These decreases were primarily driven by our restructuring efforts, which was substantially concluded at the end of 2025. Net loss from continuing operations was $57 million in the second quarter of 2026, compared to a loss of $53 million in the prior year period. Moving further down the page, you'll note that adjusted EBITDA in the second quarter of 2026 was negative $36 million compared to negative $25 million in the second quarter of 2025. It is important to note that adjusted EBITDA includes the carrying cost of excess lease space, which you can see was $14 million in the second quarter of 2026, up from $12 million in the prior year period. This cost represents the base rent and other charges relating to lease space, which we are not occupying, net of sublease income. This is a cash operating cost that is not related to driving revenue right now and can be potentially mitigated through subleasing. And finally, cash burn in the second quarter of 2026 was $45 million compared to $38 million in the second quarter of 2025. For the first six months of 2026, cash burn was $93 million, dollars down from 96 million dollars in the same period last year a three percent decrease as previously reported we paid google cloud 14 million dollars in the first quarter of this year relating to the 2025 amended commitment which increased our cash burn for the period resetting the commitment reduced our future minimum commitments by more than 100 million dollars compared with the original terms and extended the commitment term from three to six years. Excluding this payment, cash burn reflects a significant decrease in the first half of 2026 compared to the first half of 2025, which was a direct result of the restructuring. During the second quarter, we raised $17 million through our at-the-market equity program. Consistent with our methodology, these related proceeds are excluded from cash burn for all periods present. Now turning to guidance. As we discussed earlier this year, 2026 is about continuing to be cost efficient while investing in our AI robotics and software to bring autonomous labs to our bioscience customers, including the build out of our Frontier Autonomous Lab in Boston. We have turned the page from focusing on restructuring actions to focus this year not only on cost efficiency but on investing in what we see as our opportunities while continuing to provide our customers the advanced services they have come to expect. For these reasons, we believe Cash Burn best reflects our continuing services and tools and further investments in autonomous labs. In terms of outlook for the full year, we are reaffirming our overall Cash Burn guidance for 2026 totaling 125 to 150 million dollars this range reflects a firm balance amongst cost efficiency continuing services and tools and further investments we are making in conclusion we are pleased with the continued improvements in cash burn efficiency and our business pursuits for 2026. and with that i'll hand it back over to you jason Thanks, Steve.

As I said, Ginkgo's mission is to make biology easier to engineer. We're going to have three strategic topics today to dig in on. First, there's been a lot of activity in sort of U.S. science, a new report coming out of Office of Science and Technology Policy I'm going to touch on. Autonomous labs are becoming a real imperative for the U.S. to stay competitive in science and particularly in biotechnology versus China. So I'm going to speak to that. Second, Nebula, our large autonomous lab here in Boston, is the largest in the world. It's growing rapidly. I want to showcase what we've been doing with it. And then finally, we are using that lab and all our infrastructure here at Ginkgo to offer up competing services to offshore CROs that are quite economically competitive for customers. And I want to highlight one of those in particular. All right. So let's dig in on the autonomous labs. There's been a lot of news in the last quarter in particular. An article coming out in Stat magazine that highlighted featured Ginkgo quite heavily about this question within the biotech industry of should we be offshoring our work to China for the discovery of drugs? And is that a concern in a world where there's sort of increasing geopolitical tensions between the two countries? Ginkgo is featured around how our automation could be a counterweight to lower cost labor in China. But this is a hot topic. And the reason it is, is I highlighted in that Wall Street Journal article where you've seen the number of newly acquired drug assets. In other words, drugs bought from startup biotech companies go from almost none coming from Chinese startups about five years ago to last year was 48 percent. And the first quarter of this year was more than 50 percent. And then that's obviously borne out in our sort of jobs ecosystem and our technology ecosystem. system. This is a post in Reddit on the biotech forum. I'm an extremely frustrated bench scientist having no luck finding work in six months after layoff. I did get an interesting suggestion of one biopharma startup CEO told me he doesn't hire for any bench work in the States, outsources at all to China. He said, have you considered working in China? And this person says, you know, is that a good idea considering I only speak English? I don't think that's a great idea. I don't think Our scientists should be moving to China in search of biotech jobs. I think the U.S. needs to become competitive with China. And the way we're going to do that is we're going to automate the laboratory work at the lab bench. And you're seeing a lot of energy around this. There's an absolutely great report out of the Office in Science and Technology Policy from Director Mike Kratzios there, highlighting the new strategy for science in the United States. This is partially under the umbrella of the Genesis mission, which I'll talk about, to bring AI into science. but also highlights NSF's new program to spend $400 million on a national network of cloud laboratories. And if you look in the document, you'll see this section on autonomous experimentation. Closed-loop autonomous laboratories can collapse discovery timelines by orders of magnitude and enable science at a truly industrial scale. Focused investments in robotics and automated laboratories leveraging industry demand and federal R&D to ensure our scientific equipment industrial base is built on the world's best hardware and software and leads the charge in the coming scientific revolution. This is awesome. It's really great to see a call to action like this out of the OSTP. It's exactly what they should be doing. If you see here on this next slide, Yinko's been building the first autonomous lab for a national lab here in the US. I had the chance to ribbon cut the first 13 of our racks at Pacific Northwest National Lab with the Secretary of Energy, Secretary Wright in December. On the right-hand side, you can actually see all the expanded 97 RAC system that will be building a schematic of that, that this is going to be expanded into coming up under the Genesis mission. So really excited to be a part of that, but very excited to announce just yesterday that we had been selected to build autonomous labs for MIT, Caltech, Maryland, and Northwestern. Caltech, Maryland, and Northwestern as part of this NSF program and mit uh through a separate grant this this is really exciting because we're getting um autonomous labs in the hands of graduate students you know people with my sort of training so that they're learning how to do science on top of robotics rather than how i was taught which was sort of slaving away at a lab bench doing experiments by hand we have to think about the practice of how we do this work alongside the underlying technology of robotics so they develop together. And so I think this program is super important. I think it's a big part of how the U.S. stays competitive. I'm quite proud we're a part of it. So I wanted to, again, I'm going to highlight a few slides I showed last time, but I think it's an important point to make. When I say autonomous lab, what do we even mean by that? And I'll draw an analogy to the transportation industry. So on the y-axis of this chart is sort of the amount of automation of a given transportation technology. And on the x-axis is the flexibility of a request from a user of that automation that the technology will allow so low amount of flexibility high amount of automation top left that's the that's a subway that's our red line t here in boston it's totally automated you sit down it takes you away but you better want to go to one of the stops on the subway it's not going to pull up in front of your house low amount of automation a high amount of flexibility is a car you put your hands on the wheel your foot on the pedals and you can go straight to your house or the grocery store you go wherever you want it's highly flexible but you have a human in the loop to manage the variability. And that's the transportation system for the last 100 years, unless you've been in a Waymo, which is what we call an autonomous car. You'll notice we don't call it an automated car because automated sounds like automated door or something. It's just doing the same thing over and over again. But an autonomous car magically goes wherever you ask it to go without a human in the loop. And here's the kicker. If you look at miles traveled in the United States, subways versus cars and trucks, it's 99% cars and trucks. Because you need the flexibility. It's not like we don't know about railroads and tracks. It's that people need to go where they need to go in their lives. And so that's why this is such a disruptive thing coming with Waymos, is they're going to go after the 99%. It's going to automate the overwhelming majority of the transportation ecosystem, which is what subways never got to. Here's what it looks like in the lab low amount of flexibility high amount of automation we actually have our subways they're called work cells and they're used for things like high throughput screening and pharma companies or for running diagnostic tests at a clinical lab where you've got the same experiment being run over and over again and they're wonderful because they're fully automated you can walk away you can run them 24 7 you don't need a person in the middle uh but they are not flexible you cannot read a new experiment in a paper and then have it running on your work cell tomorrow Low amount of automation, high amount of flexibility. This is that car, right? It can do whatever you want, but you have to have a human in the loop. This is the lab bench and the manual laboratory, all right? And again, much like cars and transportation, the bench is 95% plus of the $60 to $80 billion a year that pharma companies spend on research, not clinical trials, but their research labs, and the $40 billion a year that the NIH spends on doing research laboratory work. And so all that money is going towards the benches, and almost none of it today is going to robotics, not because we don't know about robots, but because the robotic systems so far have not been flexible enough to do science and to do drug discovery. That's what we're trying to build at Ginkgo. We're trying to make our version of a Waymo, that top right corner. It should have the automation of the work cells. You should be able to walk away and run it 24-7, but the flexibility of the lab bench. That's a much bigger prize than the work cell prize, but a much harder technical challenge. The ROI for an autonomous lab is quite clear. If you compare it to our manual labs at Ginkgo, which we have plenty, you can see some very obvious differences. For starters, you cram the same amount of equipment that you would have spread out around a manual lab with humans moving through it into about a third of the space. So it's much smaller. And then additionally, our lab is running 24-7. So Nebula, the autonomous lab, is running 24-7. If you haven't done the math on a week, work week for a lab technician is like 40 hours and there's 168 hours in a week. So you're getting a fourfold increase in the hours that that big sunk cost laboratory is being used. Finally, repeatability, traceability, electronic records are all just right inside of an autonomous lab without even having to work for it. AI-driven science is going to need these things. I think it's going to be hard to connect that into the manual infrastructure. The way that we're going to do that is through robotic labs. It's intrinsic to those systems. I get asked a lot, and I started this off with a bench scientist worried about a job. that are autonomous labs going to exacerbate the problem of scientists having a hard time getting jobs in the United States? I don't think so. This is our advertisement from IBM back in 1952. I love this ad. It says, hey, here's the IBM mechanical calculator. It's actually predated the computer. It can do the work of 150 extra engineers. And there they are, these engineers with their slide rules, right? And this was the era before computation had been automated. And And you might have said, oh, well, this machine over here will, of course, replace these 150 gentlemen with their slide rules. And that is not at all what happened. In fact, we had an enormous explosion in the number of engineering jobs. And the reason was the actual limiter on the market size for computation was the fact that we were doing it manually. Once we automated computation, it turned out there was a vastly bigger market for computation than we thought there was. And that what was really valuable was what was in those engineers' heads, their knowledge of practice in computation, their knowledge of the problems you want to solve with computation. And once you could get a much better ROI on that through the automation of computation with computers, that field exploded. That's really what I see as the opportunity for us in biotechnology. We're being limited by our manual labs. Our scientists' jobs are limited by the manual labs. And manual science jobs in particular are being offshored as fast as possible. the way to stop that is with laboratory automation. Okay. Let's talk about an actual existing autonomous lab that we have here in Boston. I love this video. So Nebula is the name for our autonomous lab here in the seaport. We have now 105 racks on it. It really is huge and awesome to see in person. If you remember how this works, we have a track system that's moving samples from device to device on the system. And then the arms pick up the samples, put it onto to that particular device, and then that device does whatever particular lab protocol, step in the lab protocol is asked for by the scientist that submitted the job. One thing I'll highlight is we actually roughly doubled the size of the system. We added 50 new racks, basically over a three-week period, right? We had built the racks in advance in manufacturing, but just to put them in, connect up all the hardware, do like a cycle of debugging on things that broke on the software when we expanded to be that big. And we had it up and running doing experiments about three weeks later. That in the world of like subway work cell automation is just crazy. Building a new automation system with 50 new devices on it and having it up and running over three weeks is just not a thing that happens. So I do think we're really benefiting from the fact that we've productized through our rack carts what has up till now been a custom process of integrating devices in an autonomous lab. We now, like I said, have 105 racks. This is running day and night. I'll just point out, you know, like an average-ish day would be 30 unique protocols coming from scientists, more than 100 if you start, if you count copies of protocols running across those 100 devices. I don't think there's anything else like this running in the world today where new experiments are submitted by scientists, not automation engineers, but scientists every day onto the system. And the system just handles that variability and manages it. This is that like Waymo phenomena, like being able to handle the variability at scale is pretty crazy. And it's not like we don't have bugs, like we don't have issues to work through. But just even being able to do that is pretty nuts at this point. And it's running 24-7. There's a picture of our scheduler. The colors are different protocols. X axis is time. Y axis is all the different racks on the system. And you can see how we have to sort of jigsaw puzzle in different protocols. And so if you submitted a new job to the system, it would check to see is the device you need available in the times that you need it? And could you fit your particular set of protocols into this jigsaw puzzle? If so, you would get to go in. And so a lot of the work we're doing is on improving the scheduler and improving robustness of the system and all kinds of really interesting stuff. But it's very much engineering work to continue to drive up the variability that scientists can put on the system, as well as increase the total number of protocols we can run at any given time. So really exciting engineering work. You should come take a tour of Nebula. We've had a lot of people come through now, many hundreds of people in the first half of this year. There's lots of really fun videos on Instagram and TikTok and everywhere else. it's a neat system to see in person. We do tours three days a week. Anyone's welcome to sign up for it. Please do. We really love to have people come by and see it. If you're sort of a pharma company or even an academic scientist or someone who has a particular protocol that you really would get value from automating, but you've never automated it before, if we have the same equipment that you use in your manual lab, we're happy to try your protocol on Nebula. We just have one of our scientists submit it as their protocol that day, and we would see how well it would work. And so you can kind of do this sort of try before you buy on integrated automation. That's, again, not a thing that happens with the subways. They're sort of built, you test them with water, and then you ship it over and cross your fingers, and the customer kind of hopes that what the vendor showed works with clear, you know, with water runs ends up playing out in practice with biological runs once they get it in-house and it's their job to debug it. If not, we're able to bring that sort of debugging work earlier in the process. So if that's of interest to you as a buyer of automation, we're finding people really like that.

Okay.

Lastly, so we are using our autonomous lab. One way we do business is you could buy it. But the other way we do business is we run our labs as a service, as a CRO, contract research organization. And increasingly, we're, you know, we've always done that for sort of like very high-end specialized services at Ginkgo, most notably our solutions business. You know, we have these large projects with like Bayer or Nova Nordisk, where we're doing like multi-year research projects using our infrastructure. That's not what I'm going to talk to you about today. I'm going to talk to you today about going straight at the traditional CRO work that pharma companies have been offshoring to scientists in China, companies like Wuxi for over the last 20 to 25 years. Once you have a lab that doesn't have people in it, we really think we can compete on a cost basis very well with those offshore CROs. This is not unique to Bio. There's a company I really like. It's called SendCutSend, where you can, I don't know if anybody has done this, but you can order sort of custom sheet metal fabrication. And this is, again, back to that graph I drew of throughput and automation level and variability. This is custom sheet metal fabrication, which means we basically offshored it because it was a labor-intensive custom process to cut this in the particular way that a customer would want them to cut it to. And so we lost this industry over the last 50 years. It's really exciting to see this coming back via SendCutSend. And that's through a mix of some automation, but also through really smart software to turn customer requests into smart geometries of how they're doing it and basically use technology to bring costs back in line with what you would have got by offshoring the old generation of approaches to lower cost labor overseas. I think this is how the US is going to bring back the world of atoms, right? Like we should not just be a country that only does information technology and services. We should also be able to build things. And in order to do that, we need to rethink the way that we work with atoms. And that's the only way I think you bring atoms back versus lower cost manual labor. We're coming after that when it comes to these CROs. So these contract research organizations, most notably WUSHI, has really been sort of the centerpiece of offshoring, starting with chemistry, but then increasingly biotech CRO services over the last 20 to 30 years. We launched a service now about six weeks ago called ADME-1. ADME stands for absorption, distribution, metabolism, and excretion. This is sort of a standard panel of, in this case, five tier one assays that are run on small molecules, so chemical drug candidates to see how good they are on these sort of, not drug properties specific to your disease, but just these general drug properties about like how your body processes the small molecule. And to give you a sense, you can buy these. These are very standard assays. You can get them from Western CRO vendors for $2,000 to $5,000 for the panel, or from Chinese CRO vendors for $1,000 to $2,500 for the panel, or you can get them from Ginkgo data points for $199. And that's not just the assays. We've also partnered up with Inductive Bio and Tangible Scientific to handle both a PK projection as well as compound management for your small molecules. So you're getting sort of a whole kit and caboodle here for, you know, close to a tenth a price. We've done a lot of work to validate these assays. I'll just flip through a few slides, but you can also go check this out on our website, both internal QC as well as very importantly, we've compared two external vendors. So we had the same sample go get tested by this ADME panel at external vendors and compared it to what we were seeing with our robotic automated approaches to doing ADME. We've seen really great results. I'll just flip through a few of these on kinetic solubility. On the left, you can see that, you know, how we rank. So it's like Spearman coefficient, like how well do we put the molecules in the same order that our industry period on this particular assay, and then as well as this binning low, medium, high. And we have good agreement there for kinetic solubility, also for permeability. Again, same set of assays for microsomal stability in human microsomes, same set of assays, P450 inhibition and plasmid protein binding. And we do have done this also for a very popular small molecule library called LOPAC, 320 different compounds. We went ahead and tested all those across three of our tier one assays and put that data set up on the web. So you can download that and then you can use that to compare to the literature. Since this is up online, it means other people have been able to go download it and check it out. There's a company called Inflex AI that did a bunch of work with this data set and they published, you know, the platforms technically clean and talked about our replicates and in assay controls and so on. So, you know, we really encourage folks to check it out themselves. We think we stand up very well to Wuxi in terms of technical capability and throughput, and we kick their butt on price. So I don't know why you couldn't use us. What's coming soon? This is another thing Wuxi does well, which is chemical synthesis. So being able to build the molecules in addition to test the molecules. ADMI is about testing. So we'll bring online plate-based chemistry. We already actually do a lot of chemical purification historically at Ginkgo because of all our work in natural products. We're really just bringing that into an automated environment. And then finally, we want to have inner atmospheres, in other words, like anaerobic chambers to do chemistry in. Here, we're fortunate because the first system we delivered to Pacific Northwest National Lab with our racks in it that I mentioned earlier with Secretary of Energy, that was actually an anaerobic system. And so we've already had a lot of experience getting our robots into an anaerobic environment. And so we're going to be doing that, but pointing it towards doing chemistry. And so if you wanted to sort of beta test that with us, give me a call if you're interested in sort of the chemistry half of things. This is a natural complement to the biological assays we've developed at Ginkgo over the A lot of times in drug discovery, you're either making a chemical or you're making a protein drug, but depending on the disease you're going into, they're both funneling into a similar set of biological assays about either that disease area or whatnot. And we already have a lot of those assays running at high throughput on our automation. So adding chemistry is a really natural match for us. And it's a bigger fraction of the CRO business today in China. If you want to learn more about any of this, you can go to datapoints.ginco.bio. There's a banner at the top and you can check out our ADME1 service. Okay. I want to end just as a reminder, you can buy an autonomous lab from us. So if you really like this, or even like the types of assays we're doing, many customers might want to run their ADME internally, right? Maybe you want to build a service, whatever it might be, we're happy to sell an autonomous lab to anyone that wants to use it to offer whatever types of products and services they want to develop. Or if you want to get experience trying one out, please try our lab services and do consider reshoring your work if you're concerned about this offshoring trend. We want to keep adding more and more of the services you're currently getting from offshore CROs to our offerings in Datapoints and Ginkgo Cloud Lab. Okay, let's go to the world we want to see. My email's up there. Always happy to get emails from folks if you have more questions and happy to do Q&A.

Thanks, Jason. As usual, I'll start with a question from the public and remind the analysts on the line that if you'd like to ask a question, please raise your hands on Zoom and I'll call on you and open up your line. Thanks, everyone. All right, let's get started. So just a reminder, I'm going to start with some questions that were sent in beforehand, but if any of the analysts on the line would like to ask a question, they can raise their hand, I'll mute you and put you on one. so we're going to start with uh two questions from brendan uh from td the first question is what can you confirm in terms of revenues for the rack slash autonomous lab segment and the ai data points how should we think about order funnel backlog revenue recognition for both moving forward jason i think you might be uh muted by accident sorry there we go thanks um

So as a reminder, we're not doing revenue guidance this year. So forward looking, we don't have. We also aren't currently breaking out the revenue we're bringing in to date. We do have pretty different rev rec for automation versus data points and our other services as well. So, Steve, are you up for sharing a little bit on just how we approach that?

Sure. Give a little insight. Mike. So from the large government deal, we did have a preliminary contract with them. And from that standpoint, there's some small amounts of revenue, but the larger deal that everyone's talking about is that revenue will come about when we deliver and complete the install. And right now we're really in the planning coordination phase with that. So that'll be at a point in time with regards to data points. Data points is very much like the solutions business where we recognize revenue over time uh reminder smaller projects than we've seen in the past uh a good growth level we're very very happy with what we're seeing from growth in that uh but uh it's spread out over multiple quarters uh from that standpoint so a reminder most of those projects take anywhere from three to nine months maybe it's a little bit longer again smaller deals compared to what we're used to but it'll spread out and so some of that's reflected in the numbers for q2 for sure yeah so play that back uh the revenue on the data points business looks similar to what you would have seen before uh but all these automation deals including like the new academic deals we just signed uh with

these four universities those really are for the hardware part of it it's recognition on delivery i will point out we also have like an ongoing services and sas revenue for those so once they're deployed uh that would come in more regularly but you have to wait for deployment for that to show up and you have to wait for the uh deployment for the uh revenue rec to show up even if we get cash earlier exactly so brennan's second question was how should we think about the cadence of revenues to be recognized as part of the emsl project at pnn basically which is smart yeah that that's the big national lab project steve was just mentioning so i think we covered though.

Yeah. Sounds good. All right. Let's move on to X. So our first question is from at Busy Gong Dole. And this question is, for data points and Cloud Lab solutions, what is the customer repeat order rate? And what is the average follow-on order value as a percentage of the initial order value?

Yeah. So we're not, again, we're not breaking it out in that much detail. But what I will say is the way we typically end up having these deals happen is we'll get an initial proof of concept deal, and then a much larger expanded deal if people are happy with it, and then some amount of regular recurring work. It isn't as much, I'd say there's probably like two categories, like the add me work that, you know, I'm really excited about these new, I think, what did we say, 16 customers? A lot in the first six weeks is very exciting. And these are like new, you know, some of these are new logos for Ginkgo, which is great. But add me is something that pharma companies are sort of just ordering off a conveyor belt a little bit as they're developing new molecules all the time. And that's why it's been sort of like a foundation of part of Wuxi's CRO business. The work we're doing on data points where we're, say, generating data for an AI model, that might come in campaigns where we're making a whole bunch of data. We do a proof of concept. We do some amount of data gen. Maybe the customer says, hey, I actually want more data for further model training. We generate more. And then maybe they're like, OK, the next model I want to train on something else. Or it gets into some sort of pattern where they're actually using it a little closer, like Admi, where they're designing constructs on the regular and they want more and more data of that sort. But it can be a little more campaign-y if it's for an AI project versus some of these traditional CRO services, which are like on and on and on. So I am pretty excited to get into, I like both those areas. I mean, the AI stuff is really taking off recently in general, but I'm also pretty excited to go after the traditional CRO because it's just a reliable source of demand.

But we got to prove ourselves we're new in that area um but i do like our odds there looks real good all right so we have um two questions there's another question that's also about like revenue recognition from x um but i wonder if we kind of bundle that with another question that we got which is about the announcement that we made today about uh the nsf uh announcements where four new autonomous labs are going to be built at universities across the country um so i'll sort of ask both of these and uh one question How did the recent autonomous, sorry, the recent announcement regarding autonomous labs at universities across the U.S. impact your outlook for other new academic labs? Is this just a product of the NSF investment, or do you see this becoming more of a trend across the board? And how will revenue work with all that stuff, too?

Yes, I can, I can speak to the, to the sort of demand, and then Steve's going to chat on the, on the RevRec. So the, so what I'm excited about on these is, I think this is the beginning of showcasing that the academic research infrastructure, which by the way, you know, NIH alone spends $40 billion a year out to our academic medical and academic research institutes in doing biological research. NSF spends on top of that. DARPA spends on top of that. So there's actually a good amount of money that flows through this community. It's sort of a attempt at a paradigm shift for that group that at least some chunk of that work. And what's pretty interesting is we have like really great partners in this and so if you look at the the group at caltech they're focusing on a cloud lab you know an autonomous lab uh that does like basically chemical structure data generation from chemicals or originating in the natural world uh if you look at the um group at northwestern it's protein engineering if you look at the group at MIT, it's for education uses, like training people on these things. So really, it's pretty cool to see. Oh, and at Maryland, it's biomanufacturing. So those are like four disparate areas of biology research, but they're all running on the same underlying autonomous lab platform underneath. That's what I'm most excited to demonstrate is what we've been saying all along is this is an alternative to the lab bench. And across all those different labs doing very different things at academic research universities they've all got lab benches they often have 60 or 70 percent the same equipment and then maybe you know 30 or 40 that's a little bit specialized in their area but it's not an infinite list of equipment and the proposal is there should be a giant automation autonomous lab core in every biology department and you could kind of close most of the labs down and that would be much less expensive uh you'd have way more output from the graduate students it would feel a little more like takes buying time on a data center and I think that's a I don't know we'll see and so I think depending on how this first batch of uh NSF labs go I think you will see a good amount of FOMO among other research institutes that don't have these uh if it goes well and then that should I think lead to both just immediate demand um or new grants which you heard um from uh Director Kratzios at OSTP there's a push in this area but even without directed funding to buy them remember the universities they have these overhead they're spending to maintain all these labs so you could also say well hey listen if i could offset a bunch of my lab spending by adopting an autonomous lab there may be money within the university for that or donors that want to see it go in this direction there's a lot of ways for universities to get money for i think interesting projects like this so uh so i'm actually kind of bullish that it won't just be associated with new grants for robots but i also think there will be new grants for robots Maybe last but not least, I do think it also trains a set of, you know, you're sort of also starting to train the next generation of scientists with this approach to doing science, which I think is particularly important. So I'm really excited about this program. I think it's going to be great for us. Steve, did you want to comment? I don't know if there's more to say on the rhetoric, but yeah.

So bridging off what we just spoke about a few minutes ago about revenue and like, I should clarify, our legacy has been services where we get paid for the work over time. and that's still true as we mentioned with data points. With regards to the big block is when we deliver the equipment, install, but that also comes with services and I'm not going to get into the details of these contracts or the others, but we do get paid services whether it be custom work, we absolutely have support services after the install and for which we have a long tail of revenue coming from that. So we look at it, you have to think about that business model as equipment and support. And the support could come in the front end. The support would definitely come in the back end on maintenance, support, and access and the like. So that's sort of the model, but not getting into specifics. There's a twist on different contracts for what piece is what. But that's what you should think about. Equipment delivery, that's when we recognize the bulk of revenue. Might be services up front.

Absolutely services out to the back yeah and that's inclusive of software licensing as well on the back end so yeah all right i think that's all we got um just a reminder to everyone you don't have to wait for earnings to ask us questions you can send us emails at investors that can go buy works.com and we'll respond i hope everyone is