Ginkgo Bioworks - Earnings Call - Q3 2025

Executive Summary

• Q3 2025 revenue was $38.84M, down 56% YoY due to a $45M non-cash deferred revenue release in the prior year; ex that, revenue fell 11% YoY. Adjusted EBITDA was $(55.7)M, and GAAP net loss was $(80.8)M.
• Versus S&P Global consensus, revenue was essentially in line ($38.94M est. vs $38.84M actual), but EPS missed (−$1.24 est. vs −$1.45 actual) and EBITDA missed materially (−$22.29M est. vs −$74.10M actual); management cited a $21M Google Cloud shortfall expense in Q3 as a driver of profitability pressure (settled for $14M in Oct). Revenue*, EPS*, EBITDA* estimates and actual EBITDA* from S&P Global.
• FY25 outlook reaffirmed: total revenue $167–$187M, Cell Engineering $117–$137M, Biosecurity ≥$40M (Biosecurity guide was lowered to ≥$40M in Q2 from ≥$50M in Q1, and maintained in Q3).
• Cash burn improved to $28M in Q3 (vs $114M in Q3’24; −75% YoY) and period-end cash, cash equivalents and marketable securities were $462M, supporting continued investment in AI-enabled automation tools.

What Went Well and What Went Wrong

• What Went Well

  • Cash preservation and burn: “Cash burn in the third quarter of 2025 was $28 million, down from $114 million… a 75% decrease,” driven by restructuring.
  • Strategic wins: BARDA award up to $22.2M to develop mAb biomanufacturing innovations and produce an anti-filovirus countermeasure; Bayer multi‑year partnership extension in ag biologicals.
  • AI-enabled automation narrative: CEO emphasized AI “reasoning models controlling lab automation” and Ginkgo’s “reconfigurable automation carts,” positioning for on-prem autonomous labs; showcased Boston “frontier autonomous lab” scaling to 46 instruments on 36 RACs.

• What Went Wrong

  • Profitability miss: Adjusted EBITDA deteriorated to $(55.7)M (vs $(20.0)M in Q3’24) and EBITDA to $(72.3)M, with CFO calling out a $21M Google Cloud shortfall expense in Q3 and lapping prior-year $45M non‑cash revenue.
  • Segment softness: Cell Engineering revenue fell to $29.4M (−61% YoY; slightly down ex-$45M prior-year non‑cash item); Biosecurity declined to $9.5M (from $14.0M).
  • Program rationalization: Revenue-generating programs declined to 102 (−5% YoY) due to ongoing restructuring/program rationalization.

Transcript

Moderator (participant)

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 10-K. 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 Ginkgo Results, or email [email protected]. All right, over to you, Jason.

Jason Kelly (Co-founder and CEO)

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 site so that they can run them themselves through our tools business. We really grew into that sort of tools business model last year. 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 will be continuing to expand that even as our customers build larger systems as well. 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. I will show a few photos and what we are doing there coming up. 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. That helps us kind of, again, demonstrate what is possible with those robotics and also offer great services to customers.

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 2026, but I'm very proud of us pulling off in 2025, 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. After Q3, we have $462 million in cash and cash equivalents and no bank debt. 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. Again, very proud of the team for doing that.

You're going to hear less about cost takeouts in 2026 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.

Steve Coen (CFO)

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 GAAP 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 our 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% in the third quarter of 2025 compared to the prior year comparable period. Moving further down the page, you'll note that total Adjusted EBITDA in the third quarter of 2025 was -$56 million, which was down from -$20 million 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. 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. 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 restructuring. Now, turning to guidance.

In terms of outlook for the full year, we are reaffirming our overall revenue guidance for 2025, totaling $167 million-$187 million, with cell engineering revenue to be $117 million-$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. With that, I'll hand it back over to you, Jason.

Jason Kelly (Co-founder and CEO)

Thanks, Steve. All right, we'll start the strategic review. There are 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's 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. We had two big wins in the last quarter. I want to touch on that briefly. Finally, we are expanding our sort of frontier autonomous lab here in Boston, the big rack setup. I'll show you some photos and a little bit of background on what we're doing there. 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 2025 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, which 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. I want to spend a minute talking about how AI, and what's really been coming 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. First, why is AI important right now in sciences in general and bioscience in particular? This was America's AI Action Plan, came out of the White House in the last few months. There is one specific section I draw your attention to, which was investing in AI-enabled science. The general idea here is to have AI reasoning models leveraging, and they highlight automated cloud-enabled labs.

That is 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. If you connect those two things together, you could potentially change how science is done. The idea is the reasoning models could be thinking and the labs could be doing that lab work. I'll talk about that more in a second. The reason this is important is shown here. I think we are particularly in the biosciences, going to be the first sort of battleground for AI-enabled science if you look at what's happening between the U.S. and China. 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. 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 10-15 years.

There are just as good academic institutions, just as good startup ecosystem, and so on in China. There are more scientists trained and they're paid less, frankly. I don't really see where we have an advantage on physical labor anymore versus China. 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 a $100 million AI programmable cloud labs initiative. The big theory behind these things is if we're going to compete with China in biotechnology, we need to do it with robotics rather than hands at the bench. 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 U.S. VCs are based in China. 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. I think that's not lost on the U.S. government. I think Ginkgo, if you go to the next slide, has exactly the right technology for that. I've shown these before, but these are our reconfigurable automation carts, our rack carts. This is the first big area where I think AI is coming into biotechnology. This is around reasoning models. Again, think like GPT-5 from OpenAI and so on. These are in Gemini from Google.

These are these models that are able to think over a period of time, come to sort of a conclusion based on what you've asked them to do. 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. 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, scientists have a hypothesis about how some disease works or whatever, but the only way they really know the answer is to go off and run carefully constructed laboratory experiments. If you want these models to really be AI scientists and you're seeing 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. If you go to the next slide, the way they're going to do experiments is using the technology like what we've 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. I'm going to spend a minute later showing you these in action.

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 is a setup. We actually just sold one of these systems with 97 carts on it in one giant setup. 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. The reason it is important that it is software controlled is just like these reasoning models can write code for Python or whatever, right, for a website, they are also able to write code to run this automation and design and execute experiments and interpret data.

If we want to have these sort of AI-controlled science, these cloud-enabled labs, this is what they look like. You really need a new hardware technology like what we've built with the racks to do that. I think we're extremely well positioned for this. 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 math and compute that was used for the reasoning models. 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. DNA, amino acid sequences from proteins, the language of life, the language of living organisms.

You do the same type of training, the same infrastructure, but these things learn to speak biology. This is a more nascent area compared to the reasoning models when it comes to AI and biotech. I think it's also going to be extremely important. With our Ginkgo Data Points service, we really want to build the community in that area. We highlight here our Antibody Developability Competition. This is just, I think, at the end of November, going to wrap up. If you go to the next slide, you should check it out. You can go to datapoints.ginkgo.bio. You can sign up. We have more than 200 teams now competing in that competition. 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. 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. 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 Points service. Go download those, play around. If you wanted to buy data from us, we're very happy to do that. We're really here to build a community of folks who are trying to train AI models using biological data. We're really excited about this as a sort of nascent area for AI applied to biology. All right. Second thing I wanted to talk about. Those are the two big buckets for AI: reasoning models controlling robotics in the lab, and then basically neural nets trained on biological data. They're both involving AI, but they are different. Ginkgo will play there through our automation in the first one and our Data Points for the second one. All right.

Next category, this is now going back to that left-hand side of this chart. The business that Ginkgo sort of primarily focused on over the last 10 years are 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 high throughput biotechnology, I think Ginkgo is 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 U.S., making that cheaper, particularly around producing key medical countermeasures. I think this is both important for 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. This is a partnership that's been going on for five years with Bayer. We're really working on engineering microbes. If you go to the next slide for the production of fertilizers. If you remember, this is actually a pretty amazing story. If you think about elementary school biology, you learned about crop rotation, right? You would rotate in a legume like soybeans or peanuts or things like that, and they would refertilize the soil. You'd plant something like corn, and corn largely takes fertilizer out of the soil. That is sort of how we used to do it.

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. That goes out to the tune of many billions of dollars a year and about 4% of global greenhouse gas and so on. It is a big, big chemistry industry, and it is largely based in China. That is a huge input into things like corn farming. Those crops that you rotate in, like soybeans and legumes, are able to refertilize the soil because they have microbes on their roots running that Haber-Bosch process, taking nitrogen out of the air, fertilizing the crop. I am really happy to see this project continuing. I think it is the kind of world-changing stuff that only biotechnology can do in the physical world.

Really excited to keep that going. All right. 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. 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 and Austin, in the lab just a few weeks ago. The reason I bring this up is Rachel and 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 we started the company.

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. 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. We've been expanding our setup here in Boston. 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. 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? This is us putting a new piece of equipment on. That video is sped up, but it takes just a couple of 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. 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 carts, turned it into a product that you can just buy off the rack and install in these big setups. 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 Laboratory, 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. Very exciting, big setup. We're excited to see more customers bringing those in-house. If you go to the next slide, I just wanted to kind of show what it looks like. Each row in that is a different piece of equipment. Those red bars are when a sample is interacting with that piece of equipment.

That's sort of like the timeline of a protocol being submitted. A plate, and in this case, this is a standard piece of labware. That little plastic rectangle you see moving on our track system is a 384 well plate. There are 384 samples in there. It's being put onto a centrifuge in this video here. 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. 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.

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. What we've done as part of the rack system on the software side is we have connected into each piece of hardware with our software. You're able to write a multi-step protocol. It's like what you're watching here. This particular protocol is self-read 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 parameterized 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 thermocycling to go kind of complete this reaction.

All of those steps are encoded in the Ginkgo software. The scheduler and larger system goes and talks to all the equipment in a seamless way. Your scientists are not dealing with 18 different types of software to do an 18 equipment run. That is a really big deal. 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. This is these 10 carts that have been coming in. This is literally from last week. If we have already had 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. 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, we have human language interface now to that setup so you can play around with that. 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. 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. You can see if it works. 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 Ginkgo. I'm telling you, it is very, very exciting. It's working really well. I do think folks should come and try it. If you just want to come visit, please do just shoot me a note. We're happy to do that and have you come by. All right. That's what I had today. Happy 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. That's what's allowing us to really now invest for growth in the future, particularly in this area of building out basically the automation and AI tooling for biosciences. I think that's going to be the niche that we grow into in the coming 5-10 years in a big way. Excited for your questions and thanks again.

Moderator (participant)

Great. Thanks, Jason. As usual, I'll start with a 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. Let's get started. The first question was one that we got on Twitter from an account at David Zhu Tweets. 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?

Jason Kelly (Co-founder and CEO)

Yeah, I can touch on that. So short answer on the shutdown has not had a big impact on us. Sort of the areas that are grants and funding there keeps flowing during the shutdown. I would say in general, though, we have a good amount of exposure to the government overall. So between our biosecurity business and then things like the new BARDA awards, you'll see us announcing some recently also ARPA-H awards. We've been doing very well, I guess I would say, with bringing in research partnerships with the government. 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. The shutdown does not impact us.

Moderator (participant)

Cool. All right. Our first question from Brendan from TD Securities, he writes, how do you see the broader development or rollout path ahead for the rack 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?

Jason Kelly (Co-founder and CEO)

Yeah, I can touch on that too. 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.

That is a very productized offering. There is integrated automation, which basically means there is a robotic arm in the middle of a bunch of equipment. The key there is one piece of equipment maybe does the liquid handling, but then you have to take your samples to the next piece of equipment. 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. A human, by and large, is doing that in 99% of the labs that are out there. 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 productized. They're coming off the line the same. We are 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. That is something you just cannot get with the traditional integrated automation. What I'm excited about on a rollout basis is continuing to scale up our manufacturing of these carts, bring the cost down, like turn that again into a more and more productized offering. On the sale 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 is that adoption, this idea that automation is not a thing you build for one application and then literally decommission and throw away three or four years later. That is 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 sq ft of laboratory benches because we are 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. That is the transition that I want to drive. If you are looking for milestones, I want internal milestones at Ginkgo.

It's like one of the things I want to see is 50+ scientists internally at Ginkgo ordering simultaneously from our automation system in a single day. That's a thing I think I can have happening in 2026. That's something that's never been seen with an automated lab previously. There are internal milestones. 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 a large biopharma, like a purchase of a very large system with an intent for a general-purpose autonomous lab. 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 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. 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. We're going to prove it either way at Ginkgo. I think our customers will be sort of adopting that mindset soon too, is my view. I don't know, it's gotten so much easier to use automation with the AI stuff. I do think that's going to just bring the barrier down massively for this in the industry.

Moderator (participant)

Cool. All right. Brendan had one more question, which was, as you look at the current revenue mix between cell processing, as we said, cell engineering and biosecurity, and then consider your internal assumptions about the AI tools and racks rollouts, what do you see as the ideal revenue mix for Ginkgo by 2030? What has to happen to get there?

Jason Kelly (Co-founder and CEO)

2030. Okay, yeah, it sounds interesting. I mean, so my dream by 2030 is we're starting to put a bunch of benches to bed.

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 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 our tools business versus 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 are largely taking over the general-purpose R&D infrastructure and being that provider of the tools into the whole industry. That should be dominant. When it comes to biosecurity, there it's very dependent on how things play out. It's like a very interesting time right now.

CDC is getting rebuilt. There is a great post from Matt McKnight who heads up our biosecurity business today. I encourage folks to read about sort of what a rebuilt CDC looks like. I think fundamentally you need persistent, pervasive monitoring of viruses as a foundational layer for biosecurity in the future, whether you are in an outbreak or not, just all the time. If that type of infrastructure gets built here in the U.S. and worldwide, then who knows? Biosecurity could be 50-50 with the rest of the business. It does depend on whether we see that adoption of sort of monitoring technology as one of the core pillars of a biosecurity that works, a CDC that could stop the next COVID.

Moderator (participant)

Cool. We got a question for Steve. Steve, you mentioned in October 2025 Ginkgo reset the annual commitments in its contract to Google. Can you provide a little more color on that?

Steve Coen (CFO)

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 were 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.

Jason Kelly (Co-founder and CEO)

Yeah, and 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. It was originally made with a mindset of that bio-based AI was going to grow quickly.

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 the reasoning models have. This is more a reflection of kind of how we see the deployment of really 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. That just hasn't been at the rate we were expecting back then. I'm very happy that this was cleaned up very nicely by Steve and the team. Our great partners at Google have worked with us on this. I'm really happy about where it landed.

Moderator (participant)

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, etc.?

Jason Kelly (Co-founder and CEO)

Yeah, I mean, so it's worth folks checking this thing out. I mean, so FutureHouse is now called Edison Scientific. It used to be a nonprofit sort of doing the OpenAI thing, becoming a for-profit. 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 kind of hypotheses or predictions or learnings or conclusions. They were able to show this model making several, frankly, new scientific discoveries just from reading the literature. That's already very exciting. It's sort of this indicator that.

We're on this inevitable path where I think the logic of the models, like their ability to just do complex reasoning, is going to work. It already works, frankly. I think the limitation will then move to what tools can you give access to these models? 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. That type of a model with the ability to then say, 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. 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. I'm going to go around that loop. A lot of it is understanding what other people have done in the literature. I think that's what this model does from Edison Scientific. 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. I think.

That can just be replaced now, I think, with programming and a robotic interface to the lab. I do not 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. We will see. Where we want to provide the hands, that is our role in that. We are very happy to have other places build those genius models.

Moderator (participant)

The next question is kind of a follow-up to that one, actually. The question is, how do you see this AI plus robotics platform changing the R&D landscape sort of at large? What has the initial feedback been from potential tools customers?

Jason Kelly (Co-founder and CEO)

Yeah, I think if you think commercially, how this can make a big difference, right? The way, like say drug discovery, for example, right?

You have an idea. You've read about. 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. You're looking for an interesting drug target around your hypothesis. 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 or a year and a half and then try to get to an answer on your hypothesis. I think what's exciting is that first. Maybe those original hypotheses, maybe stuff like FutureHouse can just come up with those, who cares? Even if they can't, you always have a longer list of hypotheses than you have the resources to go out and test in the lab based on the number of scientists you have. Fundamentally, that is the limit.

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." For each one, again, it is not just one experiment. It has got to do some lab work, interpret the results, and then plan some more lab work and keep going down that trail. You could be running that across 100 or 1,000 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. That is just, I mean, I do not even know. That is a fundamentally different way to pursue a goal around, say, how does this disease work? Fundamentally, what is limited is reasoning and experimental hands.

If we can take both those off the table, I think all the cost just turns into reagent costs. It's like literally the consumables you're going through, which is just crazy. 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 laboratory space, just literally square footage. Both of those could compress massively with automation plus AI. It's really exciting.

Moderator (participant)

All right. That's all the questions that we have for tonight. A reminder, you can always ask questions by emailing us at [email protected]. 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.

Jason Kelly (Co-founder and CEO)

Great. Thanks, everybody. Appreciate the questions.