Ginkgo Bioworks Q3 2026 Update & Business Review - GINKGO BIOWORKS HOLDINGS INC

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 uncertainty including our most recent 10k Today, in addition to updating you on the quarter's results, we're going to show how Ginkgo is now two businesses built on one autonomous lab platform at a moment when the US government is starting to treat autonomous labs as national infrastructure. We'll share how our autonomous labs business is growing, including our first autonomous lab sale to a pharma company, Novo, alongside new government wins with ARPA-H and the NSF. We'll also share how cloud lab business is going with new pharma logos, including 22 customers this quarter, and with AI data generation for Frontier Labs. 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. As we always say, our mission here at Ginkgos is to make biology easier to engineer. Over the last few years, we've been going through a transformation with this focus on autonomous labs. And that was really our goal in 2026. I will say, I think that that succeeded very well. And this quarter is really good evidence of that. We've been focusing our efforts on moving more and more of our internal lab work onto autonomous labs that continues and improving the product, but a real breakthrough in the last quarter in terms of external customers adopting our systems. And so we will be all happy to talk about some of the new customers we have for autonomous labs, both in universities and the biopharma sector. And we've been explaining the services that we're offering on top of our big system in Boston here in Nebula. So really happy about this. I think we made the right choice in betting on this technology. So you're going to hear more from me on that in the strategic section. Also, our financials have been going in the right direction. We made a big push to cut back on spending while still making major investments in research and development around autonomous labs, AI, and robotics in the laboratory. And that has also paid off. You can see our year-over-year change here, pretty substantial improvements in net loss from continuing operations, as well as our total adjusted EBITDA. So this is what we were aiming to achieve. And really congrats to the team on pulling this off. It's hard to do these two things at once, cost-cutting and being really the technology leader in an emerging tech area, especially one next to AI. All right. Last thing I'll mention, we have still quite a nice cash cushion. And this is, again, thanks to those cost-cutting efforts, $285 million in cash and cash equivalents with no bank debt and another $86 million on top of that that we keep as restricted cash for some of our government projects and leases. So overall, I like our position from a cash position. I like where we are with cash spending. And as you're going to hear from me in the strategic section, I think we're doing a nice job externally selling and improving really a new category of product in Autonomous Labs. So back to me in a moment, but I'm going to hand it to Steve to a little more depth on the financials. Thanks, Jason. Before I walk through our financials, I want to remind everyone that our biosecurity results are classified as discontinued operations within our financial statements, where we have and will retrospectively recast all prior periods presented to conform to this presentation. All of the financial information I will cover today relates exclusively to continuing operations where we operate as a single segment. With that, I will now discuss our Q3 results. As Jason mentioned, revenue was $28 million in the third quarter of 2026, down 6% compared to third quarter of 2025. for the nine months of 2026 revenue was 67 million dollars down 37 compared to the same period last year as previously disclosed revenue in the first nine months in 2025 included 7.5 million dollars of non-cash revenue relating to the mutual termination of the biomedic agreement excluding this revenue for the first nine months of 2026 was down approximately 32 percent from the prior year and no as noted in our release revenue in the third quarter was higher than last quarter and this was primarily from a cumulative catch-up on one of our larger long-term contracts which was modified to realign the work plan resulting in us recognizing some revenue soon 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 non- recurring items, we believe adjusted EBITDA is a more indicative measure of our profitability. A full reconciliation between adjusted EBITA and GAAP net loss from continuing operations can be found in the appendix. In addition, in discussing our R&D and G&A expenses, we exclude stock-based compensation and other separately captioned items. In the third quarter of 2026, R&d expense decreased 48% from $51 million in the third quarter of 2025 to $27 million in third quarter 2026. G&a expense decreased 32% from 14 million dollars in the 3rd quarter of 25 to 9 million in the third quarter of 2026. These decreases were primarily driven by our restructuring efforts, which was substantially completed by the end of 2025. Moving further down the page, net loss from continuing operations was $41 million in the first quarter of 2020. And the third part of 2027 compared to a loss of $73 million in the prior year, a 44% improvement. You will note that adjusted EBITDA in the second quarter of 2021 was negative $22 million compared to negative $53 million in the third quarter of 2025, a 58% improvement. It is important to note that adjusted EBITDA includes the carrying cost of excess leased space, which you can see was approximately $14 million in both periods. This cash operating cost represents the base rent and other charges relating to unused leased space net of suddenly sitcom that is not driving revenue right now and can potentially be mitigated through subleasing and finally cash burn in the third quarter of 2026 was 27 million dollars compared to 28 million dollars in the 3rd quarter of 2025. for the 9 months of 20 26 cash burn was 120 million dollars down from 124 million dollars and the same period last year a four percent decrease as previously reported we paid google cloud 14 million dollars earlier this year as part of the favorable restructuring of that contract late last year excluding this payment cash brand reflects a significant decrease in the first nine months of 2026 compared to the first nine months in 2025 which was directly result from the restructurings partially offset by investments we are making during the third quarter we raised an additional net seven million dollars through our at the market equity program consistent with our methodology these related proceeds are excluded from cash burn for all periods presented as previously reported cash burn excludes the restrictions we made last quarter to secure a surety bond on our pnl contract We ended the quarter with $285 million in cash, cash equivalents, and mockable securities, and no bank debt, plus $86 million of restricted cash, cash equivalence, and markable securities for customers and operating activities. 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-up of our frontier autonomous lab in Boston. For these reasons, we believe cash burn best reflects our continuing services and tools and further investments in autonomous labs. In terms of the outlook for the full year, we are reaffirming our overall cash burn guidance for 2026 totaling $125 to $150 million. This range reflects a firm balance amongst cost efficiency, continuing services, and tools, and further investment we are making. In conclusion, we are pleased with the continued improvements in cash burn efficiency in our business pursuits for 2026. And with that, I'll hand it back over to you, Jason. Thanks, Steve. Our mission here at Ginkgo is to make biology easier to engineer. As I mentioned, we've been doing that by expanding our technology and autonomous labs and bringing those to market. I will say they are currently having a moment, a big push on the government side, a lot more attention from private sector biopharma. And as I'll talk about it, even frontier AI labs are getting excited about the potential of robotic laboratories. We sell those in two different ways, both hardware, we'll install an autonomous lab for you, or you can leverage our autonomous lab as a cloud. And so those are going to be my two sections. You're going to hear about sort of the blue chip early adopters we're seeing on selling our systems as well as the growth in the services. All right. But it is a moment. So I do want to just highlight a few of the good things that have been moving in our favor over the last quarter. I just a few weeks ago was at President Trump's Golden Age of Science Summit. This was to announce a report out of the Office of Science and Technology Policy. I've mentioned this before, but there's a little clip in there from the report about accelerating autonomous experimentation. And we've seen real money coming behind this, $400 million going to the National Science Foundation to invest in this. The Genesis mission, which is partially what funded our big project at the Pacific Northwest National Lab. I think you will continue to see more of this coming from the White House as we expand AI in science in the United States. But importantly, our bigger customer base ultimately, I think, is the biopharma companies. Dave Ricks, the CEO of Lilly, posted on X just a couple weeks ago, AI is going to help us conquer disease, but this is going take time, effort, and lots and lots of data that doesn't exist yet. And I would add that robotic labs will begin to make scaling data more tractable. That's a big deal. Robots, just if you don't know it, have been sort of relegated to the basement of biopharma companies. They run high throughput screens, some diagnostic testing at the diagnostic labs runs on robotics. But by and large, robots have not been a major part of the overwhelming laboratory investment at pharma companies, which is really manual labs. And so it's exciting to hear that from the CEO of Lilly. I think Dave is great. We just sold a system to Novo, which I'll talk about, but Dave should probably come by and visit our site in Boston. Anthropic also, if you can believe it, announced that they had set up a biology lab in San Francisco as part of their AI drug discovery efforts. Super exciting. That is not a robotic lab yet, but I do think if you look at the way that those companies think about the world, very natural to go for models that can ultimately run the lab experiments themselves, which would be robotics. So, okay. As I mentioned, we have two ways to buy the labs. You can either get your own installed or you can use ours in the cloud. Alongside that, we had just had two great events in September, one for our automation group and people that wanted to buy the systems, and then also for our data point summit just the next day. And the reason I mentioned this, and we did these back to back, there was maybe about a 10% overlap in the crowd between the days. It's really, there's a real nice additive effect of Ginkgo running services using our robotics infrastructure, because we were able to learn faster than we would if we were just building systems for external customers. And so I think that was a wise choice, us setting the business up that way. We will continue to do that. And I actually want to migrate more and more more of our services onto an ever bigger nebula here in Boston. And you'll see more of that in the next year. Okay. Let's talk about those new customers, because this is the thing I'm most excited about. A number of blue chip early adopters. I'll start with the universities. So we were very fortunate to be selected by MIT, Caltech, Maryland, and Northwestern. Caltech Maryland, Northwestern is part of this NSF program I mentioned to build out. In the case of the NSF, There's close to $50 million flowing through this program, autonomous labs at those schools. We also have a couple other schools that we're talking to where it's not coming from a grant. So I think there's sort of two ways we're going to sell to the universities, either as part, you know, being a subcontractor or equipment provider on a grant or directly from the infrastructure budget, kind of the overhead budget, the department budgets in, say, a major biology R&D department at a large school. So I see both of those roads working out. I think we will continue to sell systems into academic environment. That's super exciting. A lot of the most exciting new protocols are developed in academia. I'd love to see that work happening directly on autonomous labs from the beginning. I want to see grad students do their whole bioengineering PhD without touching a pipette. You know, these are dreams that we've had for a long time at Ginkgo. It's going to be really fun to seethat taking off. And what an awesome set of first customers for us in the in the university space. The other thing I'm very excited to announce, Nova Nordisk selected Ginkgo to build an autonomous lab at their new R&D site in Waltham. Very excited about this. This is our first sort of big pharma announcement of an autonomous Lab. That's that sends a signal to all the other companies in the industry. You know. This ist hardware. You know, this is infrastructure. There's a big switching cost for people to go with new vendors or to try new players. There are a number of existing robot, you know, kind of laboratory robotics companies. And I won't give my usual comparison of Waymos and Subways, but there's a lot of Subway sellers who sell robotic systems that do the same thing over and over again and that have a lot of existing relationships with the large pharma and a lot trust. We're coming in as a new vendor. We got to have something new to offer if we're going to unseat some of those existing vendors. And what we're offering is a system that can do variable work, something closer to what you get from your lab benches. That's why I call it a Waymo. And so it's really exciting to see NovaNordis valuing that enough to bring us in as a new automation vendor. We obviously have a great relationship with them on the services side, too. But this is the first hardware contract we have with them. The other thing I was really excited to see, ARPA-H selected us as part of, again, a subcontract on a 17.5, up to 17. 5 million dollar award. This is very cool because this is using robotics for manufacturing of genetic medicines. And these sort of, if you're familiar with like the Moderna Merck partnership around cancer vaccines, these are a vaccine, but it's being built just for you. So it's sort of a custom manufacturing process. That's a great fit for robotics and especially Genco's robotics. And so what I think is uh great about this is we're going to through this grant start to demonstrate our robotics in glp gmp environments and so that could be very exciting for ultimately having these as part of people's manufacturing processes in biopharma so uh you know stay tuned on that but a very exciting project uh last but not least i've mentioned this before um you know we were selected to build out a 97 rack system for pacific northwest national labs uh we just wanted to say say congratulations to our partners at PNNL. They broke ground just a month or so ago on the new building that's going to contain this big system. So we're working away building and validating some of the new instruments that they want on there that we haven't put on racks before and building some of the subsystems for the big install so that the minute that building is, the door is open, we can roll our racks in. So very excited though that they're doing that. Okay. And maybe on that note of building these systems, this is just a video from our lab here in Boston. And the thing I'd like to convey is we want the experience of adding new equipment to an existing robotic lab. So we put in a robotic lab, say, at Caltech. It's going to initially be five or 10 racks or something, right? Within, in this case, less than a day, you can add a new device onto that rack system. It's very similar to if you just bought a new piece of equipment for your manual lab and put it on a bench. It is not a big project. It isn't a giant 3D modeling exercise or anything else. You can just add it quite quickly, turn it back on, and now you have a new capability with all of the other equipment you already have on the system. That is, again, unique to our setup. Nobody else's stuff does that anywhere near our robustness or scale. I am really excited that as we enter all these places, we will, you know, customers, and we've already seen this, will want to grow those systems and they can do that incrementally. It's not like another giant project to bring them in. They can just order another one or two pieces of equipment that they might want to add. So that's very exciting for how we'll grow once we're in at these customers. You know, we, as I mentioned earlier, because we run our services, we actually have the largest autonomous lab in the world, 105 of our racks here in Boston. Customers can use it as a cloud lab, but very importantly, we're able to learn a lot faster because we are developing on this system. So when PNNL bought 97 racks, we expanded this from 50 to 105 because I wanted, well, you know, 18 months in advance of them getting a big system, I wanted us to have tried everything, found all the bugs, squeezed them out, right? You know, nobody's really pushed autonomous labs and robotic automation and labs to the scale Ginkgo has. And so we want to make we get there before our customers do that's the purpose of nebula and frankly it is amazing what we've been able to show on this system um we're doing you know more than 100 protocol these numbers are actually kind of dated now but more than a hundred protocols more than 30 unique in a single day a hundred you know 100 plus now i think it's 160 lab devices on that setup and it's running 24 7. right so that is again that scale of devices that range of variability especially has not been shown on a laboratory robotic system previously um so really excited about this uh and maybe the last thing i'll make we started to look at like how many equivalent technicians would it take to like run around and move the plates onto the the different devices like so if like a plate moves across the room you know a person would have had to kind of walk between those two pieces of equipment in a manual lab and if it spins in a centrifuge for five minutes the person would to spend five minutes kind of staring at that centrifuge so so we add all that up uh and our peak day so far has been 500 uh technician hour equivalents there but on like an average they were at more like two to three hundred so that's pretty good i mean if you think of it as a five hour in the lab sort of per technician that's something like 100 you know 50 to 100 technicians uh capability right um and then the question is like how do you design experiments for 50 to to 100 technicians to be running on an automated system, you know, worth of work happening across these devices. And that's where AI comes in. I'll talk about that later. But I think there's a big overlap between AI needing autonomous labs and in fact, autonomous labs needing AI. So I'll touch on that. Okay. But either way, you should come visit. Don't take my word for it. There's a sign up link here. We've had, I don't know, probably more than a thousand people come visit the lab now. So you should definitely sign up. It's great. We do tours every week. Seeing is believing on this. It's really wild. Okay. Lastly, I want to talk about our cloud lab services. These have been growing really well with biopharma customers where we're operating really like a CRO and large data gen. And then we're also seeing a little bit from the frontier model companies, which I'll talk about. Okay, so I love this post. This is a post from one of our customers up on X. One of our agents flagged that Ginkgo Data Points has been outperforming others for protein biophysical data production ahead on cost and quality as well as tat yeah so cheaper faster and higher quality uh and they use their ai agent to sort of compare us to some of the other vendors they've been using uh i think this is going to surprise people i when you do things more robotically they can be competitive with work we've been offshoring to low-cost scientific labor in china frankly over the last 30 years in the pharmaceutical industry and so if you look at for example our admi services at Ginkgo Data Points. It's $199 for five tier one ADME assays. These are very common assays being used as part of small molecule drug discovery every week. If you want those same assays from WUSHI, it's like $1,000 or more, depending on how you're doing it. And so that's really exciting. I think this is the potential of laboratory automation. And by the way, I think this applies for automation around a lot of sort of small batch, I'll call it manufacturing in the United States. If we want to bring it back, this is how you're going to do it. But it's working. I mean, we're at it, you know, we added 20 new logos to Ginkgo. Like these are biopharma, biotech customers that have never engaged us, whether on the equipment side or our old services, you knowsolutions type services that we did in the past, just new customers in the last quarter. So this is really exciting and people should try it out and it's cheap to try. So give it a shot. The other thing I'll mention on the data point side is we launched our antibody developability consortium. So there's been a lot of work in antibody binding. So, there's a few different companies, Swiss has been doing this, Adaptive, others, we offer this as well, where you can synthesize a antibody and see it binds something. And that's great. And people have been training really large frontier biomodels on protein design for binding things to use in drug discovery. But that's not all you need to know to make an antibody drug. You also have to check how developable it is. In other words, does it make for a good drug? Is it soluble, right? Or is it going to all just clump up in your blood? Is immunogenic? Is that going to freak out your immune system? There's a whole list of assays that you run on antibodies that aren't just about binding. You can have an antibody that binds great, but ends up being a bad drug. And so as we're starting to win on the binding side with these models, we believe the other key assay that everyone's going to want will be developability. And this is a much harder thing to do high throughput. It's much more complicated assays, more steps. It really benefits more from what we've been doing here at Ginkgo on the integrated robotics side. So we have this up and running. It's really great. Ours is the best in the industry, I'm sure. And we have launched AbbVie, Argenix, Lundbeck, and Takeda as our sort of founding members of our antibody developability consortium. They're each submitting antibody designs. We build, test, and give back and make this big developability data set. What's really cool is that then gets what our partner Aferis put into a federated learning environment where nobody gets to see each other's designs, but everybody gets to train models on the full data set. I think this is a super exciting way to, again, these are more expensive assays than binding. Binding is pretty cheap. Being able to do developability is harder. And so this is the way to make that cost lower on a per company basis, even though everyone's generating a lot of data. So we're really excited about that. If you're interested in this consortium, if you're a biotech or biopharma listening, you should definitely reach out. If you add yourself as a new member, you'll get access to historical data as well. So very good opportunity. And I really like this model. Also, we announced in the last quarter a partnership with Lily Toon Lab, where our kind of suite of Ginkgo Data Point services is available to members. These are a bunch of startups and others that are kind of affiliated with Lily Tune Labs, just making that super easy for people to order our services. OK, last thing I'll talk about. The question I get asked the most by investors is, are the Frontier AI labs buying biodata? And the answer is yes. Yeah, we're working with a few of them. um the what i would say is you're what you're seeing is a realization that the smartest models are going to have value in a bunch of these advanced engineering and science areas like things like chip design things like drug discovery things like new materials and energy uh and so it but in order for them to get good at that like they got good at math you need to tell them if they're doing a good job with their ideas right so the reason things work so well in chess and math is the model can come up with something and then go get validated reinforcement learning and see hey is my math proof right is this code i wrote right did i play a good game of chess and the beautiful thing is you can answer all those questions inside of a data center right so those are like compute computable answers that's not true in a lot of these other areas and so in these other areas what you're going to want is basically the feedback of laboratories so that you can make your model smarter and so as long as i think the frontier ai labs want to keep pushing the boundary of their capabilities in these physical areas i do think they're going to want to train on physical data in our case uh biological and chemistry data so uh so super excited about this i do think it's a um a very cool area and we're happy to be participating in this along with others all right uh i'm I'm happy to take questions about all that. As always, we should grow the world we want to see. We appreciate your interest in your investment, those of you who are investors in Ginkgo. And my email is up there if you ever want to email me and ask questions offline. All right, thanks a lot. 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 your line. Thanks all. All right. First, I just want to thank everyone for sticking with us through those technical difficulties earlier. We're extra excited to be there with you tonight. I want to start with a question from Brendan who sent it in from TD. He actually sent two questions. We'll start with the first. How should we think about evolution of the Cloud Lab offering how Ginkgo plans to monetize this moving forward? Is the idea to ultimately become a kind of AI model training data generation service? And if so, what kinds of data is Ginko able to supply that customers might not be able to get elsewhere? Yes, I can take that. Good. So yeah, I talked about this a little bit at the end there around the antibody developability consortium. So So I think the sweet spot for Ginkgo on this, and by the way, I would say this is for Cloud Lab, data points are basically our services that we run on top of the platform. And so we think for these particularly complicated assays, that's a really strong fit where we can generate data that you're going to have a hard time getting from other CROs. So things like binding, there's now a lot of players doing that. I think our hat is in the ring there. That's fine. I think we'll be able to play there. But things like developability, there is really not. it's much more sparse because those are harder to do. The other area is in target discovery. So we offer our functional genomics offering where you can take your favorite cell line and we can make either genetic perturbations. So like CRISPR edits to the genes, you want to say knock out every gene in that genome one by one, we can put those in wells and then give you a functional genomics. So like an RNA readout of what's going on inside that cell. Or we can add small molecules. So we can do a small molecule perturbation by adding thousands of those to the cell line. That sort of stuff is much more complicated. And so those are the types of things I think as we get past this like initial phase where you kind of go after the easy stuff and it's good for storytelling and you move more into the phase of, boy, I would love to have the models that ultimately give me all the data I need to develop a drug or to find a new target against a disease that's been resistant to having a good target. Those types of big activities that happen across the industry, I think AI is going to play there too. And that I think will be quite favorable for us. I think we'll be unique in that respect. Cool. All right. Second question from Brendan. I'll just remind all the analysts on the call that if you have a question, just raise your hand and i'll call on you after this um so the next question is what is the expected timeline to reporting revenues from the sale of the rack and catalyst systems from both the recently confirmed partnerships and any potential future deals yeah steve do you want to talk about that a little bit uh yes i just want to remind uh everyone uh we're not giving guidance on revenue uh and we're not going to give guidance on 2027 yet i think the right time we will bring the investors along as as we feel it's important to know uh we'll be resetting expectations for 2027 uh in february and our guidance and we will look at uh whether or not that information is meaningful for that at that point in time so and then we'll bake it into our guides yeah the other thing i'd add is as we build these systems so we'll recognize revenue when the system is installed right so that's what we would we would get the sort of like capex revenue for the system and then we have on an ongoing basis in these sales we have a sort of services and sas software licensing uh that's recurring and so you'll see those over the life as we get a larger fleet deployed that'll sort of ramp up and stay up hopefully uh as people keep these systems for a long time we hope Yeah, that's exactly right, Jason. As we mentioned in the last meeting or one before, it is multifaceted, you know, delivering CapEx equipment, large deploy. That takes a good bit of time, depending on the size of the lab or is it a green space lab. And then, of course, we have all the front end services and the back end services. A lot of the back-end services are, you now, more SaaS-like. So that's why it takes time to build out a lab. That's why we'll be looking at it and we'll provide the guidance when it's improved. Yeah, maybe the other thing I would add, we are taking a different approach than people have traditionally taken to integrated automation. So the normal mental model, again, for like a higher-es or a thermo or biocero, the folks that build these subway work cells, is that each one is unique to a customer. So as a customer is saying, I have this protocol I want to run at high throughput and the vendor is going to basically do some non-recurring engineering, generate a 3D model, design the system, build it all for you over a series of months and then install it. And so there's sort of a six to 12 month period where there's like kind of custom engineering happening. That's not how the racks work. So the good thing about our system is we're producing rack enclosures every week. We can accumulate an inventory of rack enclosures so that over time, cut orders come in and we just need to integrate equipment and ship them. And so I do think what will happen as we get more mature in this area is the sort of time it takes to get a system installed will speed up relative to what you've seen in historical integrated automation, just because our offering is more productized. It is a standard thing that we're manufacturing on the regular rather than a system we've build one-off against a customer spec. Thanks, Jason. All right. Next question. Can you give a little more color on the AI frontier labs ordering bio data? Do you think that is durable? Does that help your business with traditional biopharma companies in any way? Yeah, this is super interesting. I mean, I think there's a little bit of a, what's your thesis on, you know, open AI and anthropic getting into the pharmaceutical industry. And there's a lot of talk about that. You're certainly hearing about it a lot from Dario, for example. Obviously, Anthropic invested in that wet lab. So that's sort of the, I would say, the big overlay. What I'm most excited about, I think there's like two lanes. There is generate bio data to train models that understand biology. So the protein models that you've seen from people, the virtual cell initiatives, the types of data we were just talking about with the Developability Consortium. This is all biological data training, biological models, where like AlphaFold is an example from Google, was an example of a protein biological model. The other thing we have, obviously, is the reasoning models. And the reasoning models I'm quite excited about because I believe they will be able to operate robotic labs and they essentially need to learn to write protocol code, for lack of a better word so you can think of them as a coding agent programming robots and laboratory protocols to do science and that's also a type of training data that we expect labs are going to want to purchase and we are excited to see models get better at doing that and the reason i'm excited about that is because i watch our system at ginkgo nebula and i look at at protocols coming onto that system. And it's not perfect, but it looks a little bit like a workday, right? So scientists are coming in in the morning, they're submitting new protocols based on their project. You see our number of protocols running simultaneously going up, you know, midday, it's quite high in the afternoon, it is high. And then by like, you know, nine o'clock at night, it starting to drop and by three in the morning, you know, there's a tenth as many protocols running as we're running at noon. That makes basically no sense, right? You know, you have an autonomous lab that's running 24-7. There's no reason you wouldn't want to utilize that asset. And so what I want to get to is increasingly stuff like we've been doing with OpenAI in the Announce project, a gentic generation of protocols so that really just to keep the whole thing busy. And I think our customers, when they buy our autonomous labs over time, they're going to have that same need. They're going want a gentick science happening on the system because it feels like a waste not to be using it all hours once you have a system not going to actually do variable work all hours. And that's just a change, right? Today, the manual labs run 40 hours a week, not 168 hours like the autonomous lab. So filling that is, I think, going to require some new tools. And so those are the two areas. That's why I say the AI companies are interested in autonomous labs for data generation, but the Autonomous labs are also interested in frontier AI reasoning for the purposes of protocol coding. We need each other. All right. Next question. What size of autonomous labs are customers starting with? And how are you all thinking about product moving forward? Yeah. So sort of, I would say we're at like kind of a, if you're building a small system, it's harder to be smaller than like five. and something more like 10 to 15, I think is like a healthy, like initial system. And then if something is closer to what you'd normally buy a work cell, like if you were going to go buy a system to do some high throughput screening, or, you know, would be a couple high throughput screening systems, that's more like 30 just to give you a ballpark. And, and so one of the things I'm excited about on the product side is sort of multi-purpose work cells, right? So a work cell that could do, you know, 10 or 50 different protocols so that instead of having to buy three work cells to do three different things, you could just combine all of that into one autonomous lab that does what you would have previously used three work sales to do. That gives us, that gives us actually an advantage even in the, in the work cell space before you go all the way to the lab bench Waymo. And so I'm pretty excited to just demonstrate to more pharma partners, the, the what's possible there because we're certainly seeing gains of that sort when we run our own services internally at Ginkgo. All right. Next we have, how is Ginko able to price Admi services and data points below Wuxi and how durable is that cost advantage as competitors respond? Robots. I think there's just like a lack of awareness of how much of the cost when you're doing manual lab work as in the technician doing the work and the lab space you need to do it. And that's true in China too. I mean, scientists are less expensive, but they're not 10 times less expensive. They're more like half, you know? And so there is actually a lot of room, you know, as we get closer to the cost of the services being majority reagents, then I think there's less of a reason to offshore. Also, offshoring is expensive, right? You're kind of dealing with this, like the shipping and the timing and all this stuff, right? Like the, so there's a, it's pretty interesting moment right now. You're also seeing a lot of companies wanting to split where there used to be a moat that Wushi had, which was, hey, you're making the molecules there. You should test them there because that's more convenient and faster. Like they're doing the chemical synthesis, which is still fairly resistant to automation. Why not just take it down the hall and do the assay? And it turns out the reason not to is if the same organization knows the molecule structure, which is like secret IP of the pharma company, plus which of the 10,000 molecules you tried performed really well as a drug. Well, that's all you need to walk out the door and start a competing biotech company in China based on that molecule. and so for you know and because you're seeing this now more and more of these companies that are started it's like wow it looks a lot like the molecule that uh you know was shipped to a week ago uh that uh is driving companies to split where they do it so they're doing molecule synthesis one place shipping plates of molecules not saying what the molecules are to a different place to do the assays just so that that information is not held by one party that's good news for us. Right. So great. Do your chemical synthesis wherever you want and ship it to us for the assays. I'm happy to do it. Thanks, Jason. All right. There's one more. It's somewhat similar to one of the ones that Brendan asked earlier, but I'm just going to ask it again in case there's sort of anything else to add. This one is we saw a lot of inbound interest this quarter in our autonomous lab offerings. And so the question is like, how should investors think about converting that interest into revenue and kind of over what timeline should they be thinking about. Yeah, I think that's pretty close to what we covered before. Again, I think we want, we'll be as public as we can. Not every customer wants us to announce it, but we like to announce that we sell systems because it helps improve our brand. So you'll see us announcing as we get systems sold. And then I think you're going to see a period of time, again, it depends on the customer. They're often expecting a system installed like six months later or something, right? Because they're six to 12 often because the normal cycle they're on is like, okay, we're, we're planning well in advance of this because that's what it normally takes to get an automation system in. And so we've got a room and it's going to get cleared out or we're building a building and it'S going to open and I'm on the team that's putting in the automation. So there is that kind of lag today. Again, I don't know what will happen as we start to have, if I can be like, sure, but I've got these enclosures and if you have the equipment, I can integrate it in about a month and we could just install. I don't know if that would like change the dynamic or people are sort of like, nah, it's okay. Like I already started asking for this with an RFP six months in advance anyway. So please bring it in six months. So we'll see. But I would say that's pretty typical, six to 12 months. All right. I think that's all the questions that we have for today. I just want to thank everyone again for joining us and remind you that you can always reach out to us at investors at GinkgoBioworks.com. Jason also shared his email earlier, Jason at Ginkgobioworks.com in case you want to contact him directly. And yeah, thank you all so much. And you'll hear from us next quarter. Yep. Thanks, everybody.