Manager of Communications and Ownership at Ginkgo. I'm joined by Jason Cowley, 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'll 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 be providing insight into how we believe AI models will impact biotechnology, how our tools are positioned to support those impacts, and how those tools are winning us new deals with customers. 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.
All right, thanks, Daniel. Ginkgo's mission is to make biology easier to engineer. We always start with that. I want to highlight the three big objectives for us going into 2026. And I'm going to give you a little more detail on these today. The first is to deliver the robotics and software that bring autonomous labs on-prem, in other words, at our customer sites so that they can run them themselves through our tools business. And we really grew into that sort of tools business model last year. But this robotics and automation and AI controlling it, I think, is having a big moment right now. And I think we've got the right tool stack to bring that to customers. Second, we want to expand sort of our frontier autonomous lab here in Boston. We have the largest rack install in the world. I want to keep it that way. We'll be continuing to expand that even as our customers build larger systems as well. And we want to use that to be able to show just the art of the possible to customers, What you can do when you have ultimately hundreds of pieces of equipment all connected in a single robotic setup that can be controlled by AI. And so I'll show a few photos and what we're doing there coming up. And then finally, our two big services, our CRO services, solutions, and data points. We want to offer best-in-class services, best-on-the-market services to customers there by leveraging that in-house robotic infrastructure. And that helps us kind of, again, demonstrate what's possible with those robotics and also offer great services to customers. So you're going to get to hear about all three of those things later from me. What you're not going to hear as much about in 26, but I'm very proud of us pulling off in 25, is this chart. Dramatic reduction in our quarterly cash burn over the last year, doing all that while still maintaining a strong margin of safety in our cash position. So after Q3, we have $462 million in cash and cash equivalents and no bank debt. So I think this is really, again, particularly in what's been a tough biotech market over the last few years, puts us in a very, very strong spot as a growing tools company. And so, again, very proud of the team for doing that. You're going to hear less about cost takeouts in 26 and a lot more about our investments for growth and what we're doing for customers as we expand in AI and automation. All right, with that, I'm going to pass it to Steve, but looking forward to giving you more detail in a moment.
Thanks, Jason. I'll start with the cell engineering business. Cell engineering revenue was $29 million in the third quarter of 2025, down 61% compared to the third quarter of 2024. As previously disclosed, cell engineering revenue in the third quarter of 2024 included $45 million of non-cash revenue from a release of deferred revenue relating to the mutual termination of a customer agreement with Motif Foodworks, one of our platform ventures. Excluding this, revenue in the third quarter of 2025 was down 11% from the prior year period. In the third quarter of 2025, we supported a total of 102 revenue-generating cell engineering programs. This represents a decrease of 5% in revenue-generating programs year-over-year. This decrease can be primarily attributed to the ongoing program rationalization as part of our restructuring activities. Turning to biosecurity. Our biosecurity business generated $9 million of revenue in the third quarter of 2025 at a segment gross margin of 19%. As a reminder, segment gross margin excludes stock-based compensation. Turning to the next slide. 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 segment operating loss, adjusted EBITDA, and gap net loss can be found in the appendix. In the third quarter of 2025, cell engineering R&D expense decreased 8% from $55 million in the third quarter of 2024 to $51 million in the third quarter of 2025. The 2025 period R&D expense included a $21 million shortfall obligation related to our multi-year strategic cloud and AI partnership with Google Cloud. In October 2025, we amended and reset the annual commitments in future years and settled the shortfall obligation for $14 million. Cell engineering G&A expense decreased 47% from $23 million in the third quarter of 2024 to $12 million in the third quarter of 2025. These decreases were all driven by our restructuring efforts. Cell engineering segment operating loss was $37 million in the third quarter of 2025 compared to a loss of $5 million in the comparable prior year period. The increased loss year over year was due to two factors. First, as previously mentioned, the third quarter 2025 expense included a $21 million shortfall related to a Google Cloud contract that was subsequently settled. Second, as previously mentioned, the third quarter of 2024 included $45 million of non-cash revenue from the motif contract termination. Biosecurity segment operating loss improved 21 percent in the third quarter of 2025 compared to the prior year comparable period moving further down the page you'll note that total adjusted even in the third quarter of 2025 was negative 56 million dollars which was down from negative 20 million dollars in the third quarter of 2024. again this year-over-year decline can be attributed to the previously mentioned google cloud shortfall expense recorded in the third quarter of 2025 as well as the motif-related non-cash revenue in the comparable prior year period. So, turning to the next slide, we show adjusted EBITDA at the segment level to show the relative profitability of our segments. The principal differences between segment operating loss and total adjusted EBITDA related to the carrying cost of excess lease space, which you can see was $14 million in the third quarter of 2025. This cost represents the base rent and other charges related 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 potentially be mitigated through subleasing. And finally, cash burn in the third quarter of 2025 was $28 million, down from $114 million in the third quarter of 2024, a 75% decrease. Cash burn does not include the proceeds from ATM sales during the quarter. The significant decrease in cash burn was a direct result of the restructure. Now, turning to guidance. In terms of outlook for the full year, we are reaffirming our overall revenue guidance for 2025, totaling $167 to $187 million, with cell engineering revenue to be $117 to $137 million, and biosecurity revenue expected to be at least $40 million. In conclusion, we're pleased with the continued improvements in cash burn and cost reduction. In the fourth quarter, we will continue to execute against our core objectives while navigating continued uncertainty in the macro environment. And with that, I'll hand it back over to you, Jason.
Thanks, Steve. All right, so we'll start the strategic review. There's three topics we want to cover today. The first, I believe AI models are going to impact biotechnology fundamentally in two big ways. And I think Ginkgo is well positioned to sell tools into both of those. So I'm going to talk about that. Second, we are continuing to offer that research solutions business on top of our in-house robotics platform at Ginkgo. And we had two big wins in the last quarter. I want to touch on that briefly. And then finally, we are expanding our sort of frontier autonomous lab here in Boston, the big rack setup. So I'll show you some photos and a little bit of background on what we're doing there. And please do come visit. I'll mention that when we get to that section. But if you want to come see it, you're very welcome. All right. So let's dig in on really how AI is impacting biology. Before I do that, I do want to remind, you know, we made, again, over 25 and the second half of 2024, we made a big shift in the business where we went from just offering research solutions, which is the left-hand side of this chart here. These are these types of research partnerships. We get fees and we get downstream value share. We get royalties or milestones in the sort of ultimate end products that our customers are developing, leveraging our platform. It's a very close partnership with a customer. There's a lot of our scientists involved, as well as our robotics. We've done about 250 of those R&D partnerships over the last eight to 10 years. That is a business we will be continuing. but in the last year and a half we expanded into the tool space with our data points automation and reagents businesses and so i want to spend a minute talking about how ai uh and what's really been coming uh down the pipeline i think offers us a nice niche and entry point into the tools market where we really have i think the sort of category defining technology so first why is why is ai important right now in sort of uh sciences in general and bioscience in particular so uh this This was America's AI action plan, came out of the White House in the last few months. There's one specific section I draw your attention to, which was investing in AI-enabled science. And the general idea here is to have AI reasoning models leveraging, and they highlight automated cloud-enabled labs, and that's why I'm excited to share more on what we've been building here in Boston, which I think is a great example of one of these cloud-enabled labs, that if you connect those two things together, you could potentially change how science is done. And the idea is the reasoning models could be thinking and the labs could be doing that lab work. And I'll talk about that more in a second. And the reason this is important is shown here. I think we're particularly in the biosciences are going to be the first sort of battleground for AI enabled science if you look at what's happening between US and China. So there was a New York Times editorial just a few months ago saying China's biotech is cheaper and faster. I think that's largely true if you think about the traditional way we're doing biotech today, which is you basically have well-trained scientists working by hand in laboratories here in Boston. It's in the Kendall Square area here down the street. It's also in South San Francisco and California, San Diego, Research Triangle, North Carolina, a few hubs in the United States where you have sort of scientists working by hand doing biotechnology research. For a long time, if you go back, you can stay back a slide. For a long time, that was in, we had an advantage over China just in the sense that our people were better trained and we had access to sort of like better facilities and things like that. That advantage has largely evaporated over the last 10 to 15 years. There are just as good academic institutions, just as good startup ecosystem and so on in China. And there are more scientists trained, and they're paid less, frankly. And so I don't really see where we have an advantage on physical labor anymore versus China. And so I was really excited to see Senator Young, who's sort of heading up that National Security Commission on Emerging Biotechnology, put in a number of bills around this topic. NSF launched $100 million AI programmable cloud labs initiative. And the big theory behind these things is, if we're going to compete with China in biotechnology, technology. We need to do it with robotics rather than hands at the bench. And if we don't do it, I think you're going to see what we've seen over the last two or three quarters, where an increasing number of the early stage biotech startups that are being acquired by large pharma or invested in by USVCs are based in China. And so I think if we're going to turn that around, both for biotechnology and for science at large, we need to do it by investing in robotic infrastructure. and i think that's not lost on the u.s government uh and i think um ginkgo if you go the next slide has exactly the right technology for that uh and so i've shown these before but these are reconfigurable automation carts or rack carts and this is the first big area where i think ai is coming into biotechnology and so this is around reasoning models so again think like gpt5 from openai and so on these are in gemini from google these are these models that are able to think think over a period of time, come to sort of a conclusion based on what you've asked them to do, and either they can write code, they can do other things, they can kind of use browsers and tools to go off and do sort of a multi-step operation and come back and bring a result to you. I think the first big frontier here is going to be connecting those reasoning models to physical automation in the lab. And the reason this is necessary is if you think about how science gets done outside of areas like math or theoretical physics that are purely kind of people thinking about stuff it's purely intellectual the majority of science experimental physics experimental chemistry experimental biology and so on is moved forward by lab work right like we have a hypothesis scientist has a hypothesis about how some disease works or whatever but they only way they really know the answer is to go off and run uh carefully constructed laboratory experiments and so if you want these models to really be AI scientists, and you're seeing, you know, Future House just had a great new model come out yesterday, or now called Edison Scientific, super excited about that. Those models need to be able to do experiments. And if you go to the next slide, the way they're going to do experiments is using the technology like what we built at Ginkgo. This is our reconfigurable automation carts. Each cart has a piece of lab equipment, a robotic arm, and a plate transport track. And I'm going to spend a minute later showing you these in action. But basically, what it allows you to do is sort of Lego block together. If you go to the next slide, five of these in a linear setup, 20 of these in a circular setup, or here's a setup. We actually just sold one of these systems with 97 carts on it in one giant setup. And so the idea here is to be able to connect ultimately hundreds of pieces of lab equipment, Lego block style, into a huge setup where the whole thing is software controlled. And the reason it's important that it's software controlled is just like these reasoning models can write code for, you know, Python or whatever, right, for a website, they're also able to write code to run this automation and design and execute experiments and interpret data. And so if we want to have these sort of AI controlled science, these cloud enabled labs, this is what they look like. And you really need a new hardware technology like what we've built with the racks to do that. So I think we're extremely well position for this. And you'll see us leaning in heavily here in 2026. The second area where we're seeing AI applied to biotechnology is in using the same kind of like math and compute that was used for the reasoning model. So large neural networks, GPUs, that whole infrastructure, except instead of training those neural nets on human language and human reasoning and code and programming, things that humans kind of read and understand and interpret, you train them on biological language. So DNA, amino acid sequences from proteins, the language of life, the language of living organisms. And you do the same type of training, the same infrastructure, but these things learn to speak biology. And so this is a more nascent area compared to the reasoning models when it comes to AI and biotech, but I think it's also going to be extremely important. and with our Ginkgo Data Point service, we really want to build the community in that area. So we highlight here our antibody developability competition. This is just, I think, at the end of November going to wrap up. So you should, if you go to the next slide, you should check it out. You can go to datapoints.gingo.bio. You can sign up. We have more than 200 teams now competing in that competition. And the idea there is build a model like the one I just mentioned, like train a model on data for the developability of antibodies. In other words, is this antibody sequence going to work well as a drug? Will it be soluble? And so forth. Is it not immunogenic? That is a very valuable feature set for biopharma companies. So if you're a bioinformatician, or you're a startup that has a great new AI model, I encourage you to compete in our competition here. We basically generate a large amount of developability data. We shared some of that with the community. We kept some of it back as the competition set. And your job is to predict the held back data and we'll rank who does the best. The other thing we're doing to help build the community is we're releasing data sets for free. Again, you can go to our website there and download these sort of AI ML ready data sets. They're an example of the sort of data that we generate on a fee for service basis for customers through our data point service. So go download those, play around. If you wanted to buy data from us, we're very happy to do that. And we're really here to build a community of folks who are trying to train AI models using biological data. And so real excited about this as a sort of a nascent area for AI applied to biology. All right. Second thing I wanted to talk about. So those are the two big buckets for AI. Again, reasoning models, controlling robotics in the lab, and then basically neural nets trained on biological data. And they're both involving AI, but they are different. And so Gingo will play there through our automation in the first one and our data points for the second one. All right. So next category. This is now going back to that left-hand side of this chart, the business that Gingo sort of like primarily focused on over the last 10 years, our research solutions business. We are still doing these. if you are looking for sort of breakthrough research in any of the areas that could basically leverage like high throughput biotechnology, I think it goes still a very good call. If you go to the next slide, we won a couple of great deals in the last quarter. BARDA awarded us and our partners $22 million around the manufacturing of monoclonal antibodies, bringing that back in the US, making that cheaper, particularly around producing key medical countermeasures. So I national security and also important for reducing the cost of manufacturing drugs, particularly biologics drugs. And you heard the administration talking about this recently on the regulatory side to try to lower the cost of biologics. This is a technical approach to dropping the cost of biologics. If you go to the next slide, in the agricultural sector, very happy to extend our partnership. The partnership's been on for five years. We're really working on engineering microbes, if you go to the next slide, for the production of fertilizers. And if you remember, this is actually, I think, a pretty amazing story. So if you think about elementary school biology, you learned about crop rotation, right? So you would rotate in a legume, like soybeans or peanuts or things like that, and they would re-fertilize the soil. And then you'd plant something like corn, and corn largely takes fertilizer out of the soil. So that's sort of how we used to do it. And then in the early 1900s, we invented the Haber-Bosch process where you take nitrogen out of the atmosphere by burning natural gas and combining the nitrogen with that and producing synthetic ammonia. And then that goes out to the tune of many billions of dollars a year and about 4% of global greenhouse gas and so on. So it's a big, big chemistry industry and it's largely based in China. That's a huge input into things like corn farming. Well, those crops that you rotate in, like soybeans and legumes, they're able to re-fertilize the soil because they have microbes on their roots running that Haberbosch process, taking nitrogen out of the air, fertilizing the crop. So I'm really happy to see this project continuing. I think it's the kind of world-changing stuff that only biotechnology can do in the physical world. And so really excited to keep that going. All right. Again, if you're in agriculture, industrial biotech, biopharma, you want to try large-scale biotech on your problem, I encourage you to call us up and we're happy to have our scientists work with yours to leverage the infrastructure here at Ginkgo to deliver that. I really like this photo. This is two of my co-founders, Rachel Van Austin, in the lab just a few weeks ago. The reason I bring this up is Rachel Van Austin had not been in the lab prior to a few months ago for like the last, I don't know, 10 or 15 years since he started the company. And the reason they're back in the lab is because what we've been doing on the automation side at Ginkgo, building out our rack setup here in Boston, has gotten sort of ridiculously exciting over the last six months or so. So if you go to the next slide, I want to talk about what we're building with our Frontier Autonomous Lab. We're getting a ton of interest in this right now, both from customers and even just internally. So we've been expanding our setup here in Boston. So you can see our rack carts there in the photo inside of one of our kind of big foundry bays here in Boston. If you go to the next slide, we're going to have about 45 instruments, 46 instruments on this setup. A lot like 10 carts are getting installed right now to bring it up to 36 racks. Ultimately, I'd like to get it in that room to about 100 racks. You can see a photo on the left of one of the racks going in. That's pretty exciting, right? So this is us putting a new piece of equipment on. That video is sped up, but it takes just a couple hours really to get that device on the setup. This is because we have invested in productizing the cart hardware so that we have greatly simplified. And if you're not in the laboratory automation business, you may not know this, but integrating equipment into laboratory setups right now is done as a custom job. You basically pay an engineering firm and they spend months making CAD designs and they build you this kind of Rube Goldberg machine device. We've taken all that and standardized it with cards, turned it into a product that you can just buy off the rack and install in these big setups. And so we're really excited to be building this out. The picture in the middle there that's running is actually a rack inside of an anaerobic chamber. We built this for Pacific Northwest National Lab, PNNL. It's like, I think, 14 or 18 of our robotic arms and rack setups inside of an anaerobic chamber where people can't go in because there's no air. And so very exciting, big setup. We're excited to see more customers bringing those in house. If you go to the next slide, I just want to kind of show like what it looks like. So each row in that is a different piece of equipment. Those red bars are when a sample is interacting with that piece of equipment. So that's sort of like the timeline of a protocol being submitted. So a plate, and in this case, this is a standard piece of lab where that little plastic rectangle you see moving on our track system is a 384 well plate. So there's 384 samples in there. It's being put onto a centrifuge in this video here. So that plate goes in and then that centrifuge is going to spin. This plate now is then after the centrifuge step being delivered to an echo liquid handler. This is an acoustic liquid handler that's able to move liquids with sound. And what it's going to do is it's going to set up the reaction conditions on each of those 384 well plates, as programmed by the software that is telling the system what to do. And importantly, again, to nerd out a little bit, each piece of equipment, this is like a Bravo liquid handler, that was the Echo, each one has its own piece of sort of proprietary third-party software that's kind of a pain to deal with, honestly. And so what we've done as part of the RAC system on the software side is we have connected into each piece of hardware with our software. So you're able to write a multi-step protocol. So like what you're watching here, this particular protocol is protein, cell-free protein expression. What you're able to do is connect many different pieces of equipment in a single protocol where you're controlling in a parametrized way each piece of equipment. This is a shaker. And then it's going to go on finally to a piece of assay equipment, a thermocycler to go kind of complete this reaction. And so all of those steps are encoded in the Ginkgo software. And then the scheduler and larger system goes and talks to all the equipment in a seamless way. So your scientists aren't dealing with 18 different types of software to do an 18 equipment run. That's a really big deal. And it also means it can be connected back to reasoning models to do that type of design of experiments as well. If you go to the next slide, we are able, like I mentioned, to set these up quickly. So, you know, this is these 10 cards that have been coming in. This is like literally from last week. And so if we've already have the equipment that's relevant, and again, we're at 45 pieces of equipment now on this setup for the protocol you want to do, if you go to the next slide, we are able to then demo it for you in pretty short order. So if your group has been thinking about just automation in general, you can try our system. If you want to see what it's like as a scientist to interact with a system through a language model that we have a human language interface now to that setup so you can play around with that and then finally if you wanted to have an ai reasoning model controlling this setup to work on a problem of interest to you we can do that too and what's exciting is we do all that just on our setup here in boston it's very inexpensive for you you're not buying a bunch of equipment or anything else and you can see if it works you know like try it before you buy it right if it works then we're very happy to install this in your lab so that your labs could have the same sort of just very latest scale in terms of automation and AI that we're running here at Gitko. And I'm telling you, it is very, very exciting. It's working really well. So I do think folks should come and try it. And if you just want to come visit, you know, please do just shoot me a note and we're happy to do that and have you come by. All right. That's what I had today. I have to answer questions about all that, but super excited. I think we've done the team again, a big round of thanks for 2025. It's a very difficult year, bringing down our costs in a huge way while maintaining that sort of large margin of safety. And that's what's allowing us to really now invest for growth in the future, particularly in this area of applying, you know, building out basically the automation and AI tooling for biosciences. And I think that's going to be the niche that we grow into in the coming, you know, five to 10 years in a big way. So excited for
your questions and thanks again. Great. Thanks, Jason. As usual, I'll start with the question from the public and remind the analysts on the line that if they'd like to ask a question to please raise their hands on zoom and i'll call on you and open up your line thanks everyone all right uh let's get started so um the first question was one that we got on twitter uh from an account at david ju tweets uh and this question is can you comment on the extent of ginkgo's exposure to u.s government business and how that has been impacted by the shutdown
yeah i can touch on that um so uh short answer on the shutdown has not had a big impact on us uh so sort of the areas the grants and funding there keeps flowing during the shutdown um the i would say in general though we have a good amount of exposure to the government overall so between our um biosecurity business and then things like the new barter awards you'll see us announcing uh some recently also our page awards uh we've been doing very well i guess i would say with bringing in research partnerships with the government. So overall, I think hopefully we're even doing more in the future with some of this sort of cloud labs work and investments I hope to see from sort of government labs around automation, but the shutdown doesn't impact us. Cool.
All right. And our first question from Brendan from TD Securities, he writes, how do you see the broader development or rollout path ahead for the RAC system over the next 18 months, are there any additional validation steps or accounts to land that you expect could really unlock this opportunity and widen the commercial funnel for this over the near term? Yeah, I can
touch on that too. So first off, I think what's super exciting about the racks, and again, I tried to mention this, but there's sort of like walk-up automation, like companies like Hamilton and so on, where you're getting like a liquid handling deck, and that is a very productized offering. But then there's integrated automation, which basically means there's a robotic arm in the middle of a bunch of equipment. And the key there is one piece of equipment maybe does the liquid handling, but then you got to take your samples to the next piece of equipment. And you saw in the video, the plates moving on that track and getting delivered to six or seven different pieces of equipment in that single protocol. You might have protocols that interact with 15 different pieces of equipment. And a human, by and large, is doing that in 99% of the labs that are out there. uh there is a small niche industry around integrated automation for things like high throughput screening where you put an arm in the middle of 15 pieces of equipment that is built basically application specific in other words it's a design of a setup just for the one thing you want to do our carts are not like that they are they're productized they're you know they're coming off the line the same and then we're just connecting them so that you have whatever equipment you want initially and then actually able to expand that equipment over time into bigger and bigger setups. So that's something you just cannot get with the traditional integrated automation. So what I'm excited about on a rollout basis is continuing to scale up our manufacturing of these cards, bring the cost down, like turn that again into more and more productized offering. But then on the sales side, it's basically getting folks to see this distinction between application-specific work cells that they buy today and general purpose autonomous labs, like what I was showing you there with our frontier lab here in Boston. It's that adoption, this idea that automation isn't a thing you build for one application and then literally decommission and throw away three or four years later. That's what happens with these systems. But something that just keeps expanding over years and then ultimately replaces hopefully tens of thousands, hundreds of thousands of square feet of laboratory benches, because we're just going to move off that system. We have to move away from the bench as the general purpose laboratory infrastructure to the automated bench, to the autonomous lab. And that's the transition that I want to drive. So if you're looking for milestones, I want internal milestones at Ginkgo. So one of the things I want to see is 50 plus scientists internally at Ginkgo ordering simultaneously from our automation system in a single day. That's the thing I think I can have happening in 2026. That's something that's never been seen with an automated lab previously. So there's internal milestones. And then what I would love to see, we're starting to see this on the government side, but I'd also like to see it in the private sector, ideally with large biopharma, a similar, like a purchase of a very large system with an intent for a general purpose autonomous lab. And so those are kind of my two big things I'd love to see in 2026. Us demonstrating just what you can do with already having one of these kind of autonomous labs and then a large biopharma leaning in and making a purchase for one. We'll still sell opposite the work cells. That's what we're selling today. But I would love to see someone kind of lean in on the dream of the big general purpose autonomous lab. I think it's the time for it. And we're going to prove it either way at Gingo. But I think our customers will be sort of adopting that mindset soon too, is my view. It's gotten so much easier to use automation with the AI stuff. And so I do think that's going to just bring the barrier down massively for this in the industry.
Cool. All right. And then Brendan had one more question, which was, as you look at the current revenue mix between cell processing, cell engineering and biosecurity, and then consider your internal assumptions about the AI tools and RACs rollouts, what do you see as the ideal revenue mix for Ginkgo by 2030? What has to happen to get there?
2030. Okay, yeah, that's interesting. I mean, so my dream by 2030 is we're starting to put a bunch of benches to bed. And so my expectation, like if I think about the balance between, let's leave biosecurity, I'll come back to that in a second, but between like the sort of tools business, in other words, like robotics, software on the robotics, reagents going into all that infrastructure, devices, that whole ecosystem of like our tools business versus is the services offerings that we offer on top of our setup, like data points and solutions, that tools versus services, I would say is like 80-20 in the tools side of the house in terms of our revenue mix in 2030. My hope would be we're largely taking over the general purpose R&D infrastructure and being that provider of the tools into the whole industry. So that should be dominant um when it comes to biosecurity there it's very dependent on how things play out it's like a very interesting time right now so you know cdc is getting rebuilt um there was a great post from matt mcknight who heads up our biosecurity business today i encourage folks to read about sort of like what a rebuilt cdc looks like uh you know i i think fundamentally uh you need uh persistent pervasive monitoring of viruses to as the as like foundational layer for biosecurity in future whether you're in an outbreak or not just all the time uh and so if that type of infrastructure gets built uh here in the us and worldwide then you know who knows uh about security could be 50 50 with the rest of the business um but it does depend on whether we see that adoption of sort of like monitoring technology as the core one of the core pillars of uh a biosecurity that works a cdc
that could stop the next COVID. Cool. So we got a question for Steve. So Steve, you mentioned in October, 2025, Ginkgo reset the annual commitments and its contract to Google.
Can you provide a little more color on that? Sure. When we were negotiating the Google Cloud contract, obviously we had a shortfall to solve for in Q3. We talked about that. We reset going forward. In my view, very favorable terms for Ginkgo. We're able to reduce our go forward commitment by over 100 million and extended out the period by 2x. So going out over six years over the prior three years. From that standpoint, I think that puts us right where we
want to be. Yeah, just a little extra color on this. We had made that investment on the Google cloud side around remember i mentioned the two areas of ai the sort of reasoning model based ai and the bio model based ai uh it was originally made with a a mindset of that bio-based ai was going to grow quickly and i think what we've seen in the industry is it's being adopted but it has not grown at anywhere near the rate that that the reasoning models have uh and so this is more a reflection of kind of how we see uh the deployment of like really like training needs internal to Ginkgo in the future. It's a much more smooth ramp over a longer period of time compared to if you were seeing massive investment across bio AI models. And that just hasn't been at the rate we were expecting back then. So I'm very happy that this was cleaned up very nicely by Steve and the team and our great partners at Google have worked with us on this. So I'm really happy
about where it landed. All right. The next one's for Jason. Jason, you mentioned FutureLab's new announcement of its next-gen AI scientist, Cosmos. Can you say more about how your experience at Ginkgo kind of informs your viewpoint on AI, not just analyzing data, but also designing experiments,
et cetera? Yeah. I mean, it's worth folks checking this thing out. I mean, so Future Labs, it's now called Edison Scientific. It used to be a nonprofit sort of doing the open AI thing, becoming a for-profit. But what they're doing is they basically built up a model that's read all the scientific literature, you can kind of ask it like a scientific question. It'll run for several hours and then kind of come back with either like kind of hypotheses or predictions or learnings or conclusions. And they were able to show this model making several like, frankly, new scientific discoveries just from reading the literature. So that's already very exciting. Like I think, and it's sort of this indicator that we're on this like inevitable path, or I think like the logic of the of the models like their ability to just do complex reasoning is going to work it already works frankly i i think the limitation will then move to what tools can you give access to these models and the big one we believe is important in the realm of science like i mentioned earlier is hands in the lab that's just it it's hands in the lab and so that type of a model with the ability to then say well what i actually believe i should do to really answer your question based on everything I read in the literature is run these 10 experiments or these 100 experiments, see what I learn, and then run another 100 and do that a few more times, and then I'll come back to you with the answer. I mean, that's what a PhD does. I mean, that's what I did for five years at MIT in my PhD. It's like, yep, I got this question I'm trying to answer. I'm going to run some experiments. I'm going to look at the results. I'm going to interpret them, and I'm going to go around that loop. And a lot of it is understanding what other people have done in the literature. I think that's what this model does from Future House, Edison. and then the other half is kind of just not basic logic but not the world's most complex analysis of what you're seeing in the lab it's really your ability to conduct and design the experiments and then interpret the results just the craft of that is what keeps a lot of people out of science and i think that can just be replaced now i think with programming and a robotic interface to the lab and i don't know what that does i mean that might blow open access to asking hard scientific questions in a wide number of areas, which would be very exciting. So we'll see. But we want to provide the hands. That's our role in that. And we're very happy to have other places build those
genius models. So the next question is kind of a follow-up to that one, actually. And so the question is, how do you see this AI plus robotics platform changing the R&D landscape, sort of at large? And what has the initial feedback been from potential tools customers? Yeah. So I think
what like if you think commercially how this can make a big difference right so what the way like say drug discovery for example right like you're uh you have an idea you've read about um again you've read the literature you're an expert in this area you have a hypothesis about a certain disease and how it works and you're looking for an interesting drug target around your hypothesis so you would sort of plan a line of experiments you and a team of researchers would go conduct that over a period of six months or a year year and a half and then try to get to an answer on your hypothesis. I think what's exciting is that first, you know, maybe those original hypotheses, maybe stuff like future house can just come up with those who cares, even if they can't, you always have a longer list of hypotheses, then you have the resources to go out and test in the lab based on the number of scientists you have, like fundamentally, like that, that is the limit. And so if instead, you could basically spider these models out and say, Hey, I want you to pursue my top 100 hypotheses instead of my top three. And for each one, again, it's not just one experiment, it's got to do some lab work, interpret the results, and then plan some more lab work and keep going down that trail. You know, you could be running that across 100 or 1000 hypotheses in parallel, as a single researcher, potentially with access to robotics to go spider and then have it just come back and tell you when it gets interesting results. And that is just, I mean, I don't even know. That's a fundamentally different way to pursue a goal around, say, how does this disease work? Fundamentally, what is limited is reasoning and experimental hands. And if we can take both those off the table, then I think all the cost just turns into like reagent costs. It's like literally the consumables you're going through, which is just crazy. Like that is not at all the cost right now. The costs now are 100% dominated by basically human time in all these areas, really. And like laboratory space, like just like literally square footage. And both of those could compress massively with automation plus AI. It's really exciting.
All right. That's all the questions that we have for tonight. A reminder, you can always ask questions by emailing us at investors at ginkobuierworks.com. And also, as Jason said earlier. If you're interested in coming by and seeing some of this equipment, reach out and we'll make it happen. Great. Thanks, everybody. Appreciate the questions.
