I'm Daniel Marshall, Senior Manager of Communications and Investor Relations. I'm joined by Jason Kelly, our Co-Founder and CEO; and Steven Coen, 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 10-K. 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 them to enhance the next generation of scientists. As usual, we'll end with the Q&A session, and I'll take questions from analysts, investors and the public. You can submit those questions to us in advance via X, #GinkgoResults or e-mail, investors@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, we want to book new sales of autonomous labs in biopharma, national labs and, as I mentioned 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—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 3, the divestiture of biosecurity is classified as discontinued operations within our financial statements. Accordingly, we have and we will retrospectively recast all prior periods presented to conform to this presentation. The formal 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 segment. 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 6 months of 2026 revenue was $40 million, down 49% compared to the same period last year. As previously disclosed, revenue in the first 6 months of 2025 included $7.5 million in noncash revenue relating to the mutual termination of the BiomEdit agreement. Excluding this, revenue for the first 6 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 noncash and other nonrecurring items that are detailed more fully in our financial statements. Because of these noncash and other nonrecurring items, we believe adjusted EBITDA is a more indicative measure of our profitability. A full reconciliation between adjusted EBITDA and GAAP 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 for 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 6 months of 2026, cash burn was $93 million down from $96 million in the same period last year, a 3% decrease. As previously reported, we paid Google Cloud $14 million 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 compared with the original terms and extended the commitment term from 3 to 6 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 presented. 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 million to $150 million. 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 U.S. science, a new report coming out of the Office of Science and Technology Policy, and I'm going to touch on that. 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 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 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 highlighted in that Wall Street Journal article, where you've seen the number of newly acquired drug assets—that is, drugs bought from start-up biotech companies—go from almost none coming from Chinese startups about five years ago to last year, it was 48%; in the first quarter of this year, it was more than 50%. And then that's obviously borne out in our jobs ecosystem and our technology ecosystem. This is a post in Reddit in the biotech forum. I'm an extremely frustrated bench scientist having no luck finding work in six months after a layoff. I did get an interesting suggestion. One biopharma start-up CEO told me he hasn't hired for any bench work in the States, outsources all of it to China. He said, have you considered working in China? This person says, "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. So there's an absolutely great report out of the Office of Science and Technology Policy from Director Mike Kratsios, 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 the 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, Ginkgo has been building the first autonomous lab for a national lab here in the U.S. I had the chance to ribbon cut the first 13 of our RACs 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-rack system that we'll be building as a schematic of that that is going to be expanded under the Genesis Mission. So really excited to be a part of that. But—very excited to announce just yesterday that we have been selected to build autonomous labs for MIT, Caltech, Maryland and Northwestern. Caltech, Maryland and Northwestern as part of this NSF program and MIT through a separate grant. This is really exciting because we're getting autonomous labs in the hands of graduate students, 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 that they've developed together. 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 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 important to make a point. 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 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 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 to a grocery store—you can 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 an 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 Waymo—Waymo is 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 in pharma companies or for running diagnostic tests in 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, but they are not flexible. You cannot read the new experiment in the paper and then have it running on your work cell tomorrow. Low amount of automation, high amount of flexibility—this is that car. 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. And again, much like cars and transportation, the bench is 95% plus of the $60 billion to $80 billion a year that pharma companies spend on research—not clinical trials, but their research labs—and the $40 billion a year 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 because 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 at 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 of, 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 one third of the space. So it's much smaller. And then additionally, our lab is running 24/7. Nebula, the autonomous lab, runs 24/7. If you haven't done the math on a work week for a lab technician, it's 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. All right. I get asked a lot, and I started this off with a bench scientist worried about a job that our autonomous lab is 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. And this was the era before computation had been automated. 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 what was really valuable was in those engineers' heads—their knowledge of practice and computation, their knowledge of the problems you want to solve with computation. And once you can 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 of our autonomous lab here in the Seaport. We have now 105 RACs 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 that particular device and then that device does whatever particular lab protocol step in the lab protocol is asked for by the scientists that submitted the job. One thing I'll highlight is we actually roughly doubled the size of the system. We added 50 new RACs basically over a three-week period. We had built the RACs in advanced manufacturing, but just to put them in, connect up all the hardware, do 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 subway or 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 are really benefiting from the fact that we productized through our rack carts what has until now been a custom process of integrating devices in an autonomous lab. We now, like I said, have 105 RACs; this is running day and night. I'll just point out an average-ish day would be 30 unique protocols coming from scientists and more than 100 total jobs. If you count copies of protocols running across those 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 Waymo phenomenon—being able to handle the variability at scale is pretty crazy. And it's not like we don't have bugs, we do; we have issues to work through, but just even being able to do that is pretty remarkable at this point. And it's running 24/7. It's a picture of our scheduler—the colors are different protocols, the x-axis is time, the y-axis is all the different RACs 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 needed 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 to 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 are lots of really fun videos on social media. It's a need-to-see system in person. We do tours three days a week. Anyone is welcome to sign up for it. Please do. We really love to have people come by and see it. If you're a pharma company or even an academic scientist or someone who has a particular protocol that you would really 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 in Nebula. You would just have one of our scientists submit it as their protocol that day, and we would see how well it would work. So you can do this sort of try-before-you-buy on integrated automation. And that's again not a thing that happens with subways—you test them and then you ship them and cross your fingers that what the vendor showed works with water tests translates to biological runs once they get it in-house, and it's their job to debug if not. We're able to bring that 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, we are using our autonomous lab. One way we do business is you can buy it. But the other way we do business is we run our labs as a service, as a CRO. And increasingly, we've always done that for very high-end specialized services like our solutions business. We have large projects with companies like Bayer or Novo Nordisk where we're doing multiyear 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 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 called SendCutSend where you can order custom sheet metal fabrication. This is, again, back to that graph I drew of throughput and automation level and variability. This is custom sheet metal fabrication, which had largely been offshored because it was a labor-intensive custom process. It's really exciting to see this coming back via SendCutSend. And that's through a mix of automation but also really smart software to turn customer requests into manufacturable geometries; it's a way 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 U.S. is going to bring back the world of manufacturing. 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 manufacturing. And that's the only way I think you bring manufacturing back versus lower-cost manual labor. We're coming after that when it comes to these CROs—so these contract research organizations. Most notably WuXi has really been the centerpiece of offshoring, starting with chemistry but then increasingly biotech CRO services over the last 20 to 30 years. We launched a service about six weeks ago called ADME-One. ADME stands for absorption, distribution, metabolism and excretion. This is sort of a standard panel of, in this case, five Tier 1 assays that are run on small molecules, so chemical drug candidates, to see how good they are on the sort of general drug properties—not disease-specific properties but just 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 Datapoints 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 the whole kit and caboodle here for close to a tenth of the price. We've done a lot of work to validate these assays. You can also check this out on our website, both internal QC as well as, very importantly, we've compared to 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 how we rank. This is like a Spearman coefficient—how well do we put the molecules in the same order that our industry peer would on this particular assay—and then as well as 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 plasma protein binding. And we 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 1 assays and put that dataset up on the web. So you can download that and then you can use that to compare to the literature. This is up online. It means other people have been able to go download and check it out. There's a company called Inflexa AI that did a bunch of work with this dataset and they published that the platform is technically clean and talked about our replicates and assay controls. 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 beat them on price. So I don't know why you wouldn't use us. What's coming soon: another thing which we do well is chemical synthesis—being able to build molecules in addition to testing molecules. ADME is about testing. So we 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, and we're bringing that into an automated environment. And then finally, we want to have inert atmospheres, in other words, anaerobic chambers to do chemistry in. We're fortunate because the first system we delivered to Pacific Northwest National Lab with our RACs had anaerobic capability, and so we've already had a lot of experience getting our robots into an anaerobic environment. We're going to be applying that to chemistry. So if you wanted to test that with us, give me a call if you're interested in the chemistry half of things. This is a natural complement to the biological assays we've developed at Ginkgo over the years. 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 similar sets of biological assays about either that disease area or what it might be. 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.ginkgo.bio. There's a banner at the top and you can check out our ADME-One 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. 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 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. My e-mail is up there. Always happy to get e-mails for folks if you have more questions, and happy to do Q&A.
Thanks, Jason. As usual, I'll start with the question from the public and remind the analysts on the line that the operator provided instructions. 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. The operator provided instructions. So we're going to start with two questions from Brendan from TD. The first question is, what can you confirm in terms of revenues for the RAC/autonomous lab segment and the AI Datapoints? How should we think about order funnel, backlog, revenue recognition for both moving forward? Jason, I think you might be muted by accident.
So as a reminder, we're not doing revenue guidance this year, so forward-looking we don't have guidance. We also aren't currently breaking out revenue we're bringing in to date. We do have pretty different revenue recognition for automation versus Datapoints and our other services as well. So Steve, you look to share a little bit on just how we approach that.
Sure. Give a little insight. 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. Right now, we're really in the planning and coordination phase with that. So that will be recognized at a point in time upon acceptance. With regards to Datapoints, it's very much like the solutions business where we recognize revenue over time. Reminder: smaller projects than we've seen in the past, good growth level, we're very, very happy with what we're seeing from growth in that, but it's spread out over multiple quarters. Most of those projects take anywhere from three to nine months, maybe a little bit longer for smaller deals compared to what we used to, but it will spread out. And so some of that's reflected in the numbers for Q2 for sure.
Yes. I'll add that the revenue on the Datapoints business looks similar to what you would have seen before. But all these automation deals, including the new academic deals we just signed with these four universities, those really are—for the hardware part of it—recognized on delivery. I will point out, we also have ongoing services and SaaS revenue for those. So once they're deployed, 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 deployment for the revenue recognition to show up even if we get cash earlier.
Exactly.
So Brendan's second question was, how should we think about the cadence of revenues to be recognized as part of the EMSL project at PNNL? Basically, which is similar. We also received another question about the announcement we made today about the NSF awards, where four new autonomous labs are going to be built at universities across the country. How did 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 world? And how will revenue work with all that activity?
I get to pick up on the sort of demand and then Steve, you want to chat on the revenue recognition. So the thing I'm excited about on these is I think this is the beginning of showcasing that academic research infrastructure—which, by the way, NIH alone spends $40 billion a year at academic and medical research institutes on biological research; NSF spends on top of that, DARPA spends on top of that—there's a good amount of money that flows through this community. This is an attempt at a paradigm shift for that group that at least some chunk of that work could migrate to autonomous labs. What's interesting is we have really great partners in this. If you look at the group at Caltech, they're focusing on a cloud lab autonomous lab that does chemical structure data generation from chemicals originating in the natural world. Northwestern is focused on protein engineering. MIT is focused on education uses like training people on these systems. Maryland is focused on biomanufacturing. So those are four disparate areas of biology research, but they're all running on the same underlying autonomous lab platform. That's what I'm most excited to demonstrate: what we've been saying all along is this is an alternative to the lab bench. 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% the same equipment and maybe 30% or 40% that's a little specialized, but it's not an infinite list of equipment. The proposal is there should be a large automation platform in every biology department and you could close much of the manual infrastructure down. That would be much less expensive and you'd have way more output from the graduate students. It would feel a little more like buying time on a data center. I think depending on how this first batch of NSF labs go, you will see a good amount of FOMO among other research institutes that don't have these if it goes well. And then that should lead to immediate demand or new grants, which you heard from OSTP—there's a push in this area. But even without directed funding to buy them, remember, universities have overhead and spending to maintain all these labs. If you can offset a bunch of lab spending by adopting an autonomous lab, there may be money within the university or donors that want to see it go in this direction. There are many ways for universities to get money for projects like this. 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, it also trains a set of scientists—you're starting to train the next generation of scientists with this approach, which I think is particularly important. So really excited about this program. I think it would be great for us.
Yes. So bridging up what we just spoke about a few minutes ago about revenue, I should clarify our legacy has been services where we get paid to work over time, and that's still true with Datapoints. Now with regards to equipment sales, the big block is when we deliver the equipment and install it. But that also comes with services, and we do get paid for services. There will be custom work; we absolutely support services after the install and we have a long tail of revenue coming from that. So you should think about the business model as equipment and support. Support could come in the front end and definitely comes on the back end for maintenance, support and access. That's sort of the model, but I'm not going to get into specifics on contract terms. Equipment delivery is when we recognize the bulk of revenue; services can be recognized over time.
And that's inclusive of software licensing as well on the back end.
All right. I think that's all we got. Just a reminder to everyone, you don't have to wait for earnings to ask us questions. You can send us e-mails at investors@ginkgobioworks.com and we'll respond. I hope everyone is having a great evening, and we'll see you next quarter.