Earnings Call Transcript - DNA Q3 2025

Daniel Waid Marshall, Manager of Communications and Ownership

Manager of Communications and Ownership at Ginkgo. I'm joined by Jason Kelly, our Co-Founder and CEO, and Steve Coen, our CFO. Thanks for being with us today. We are eager to share updates on our progress. During the presentation, we will be making forward-looking statements that involve risks and uncertainties. Please refer to our filings with the SEC for more details on these risks, including our most recent 10-K. Along with discussing the quarterly results, we will provide insights on how we believe AI models will influence biotechnology, how our tools are set up to support those changes, and how these tools are helping us secure new customers. As usual, we will conclude with a Q&A session where I will take questions from analysts, investors, and the public. You can send those questions to us in advance via #ginkgoresults or e-mail investors@ginkgobioworks.com. Now, I will turn it 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 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, 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 our frontier autonomous lab here in Boston. We have the largest RAC 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 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.

Steven 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 noncash 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 noncash and other nonrecurring items as detailed more fully in our financial statements. Because of these noncash and other nonrecurring items, we believe adjusted EBITDA is a more indicative measure of our profitability. A full reconciliation between 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 multiyear strategic cloud and AI partnership with Google Cloud. In October 2025, we amended and reset the annual commitments in future years and settled this 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 noncash 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 negative $56 million, which was down from negative $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 noncash 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 leased 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 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 to $187 million with Cell Engineering revenue to be $117 million 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.

Jason Kelly, Co-Founder and CEO

Thank you, Steve. Let's begin the strategic review. There are three main topics to discuss today. First, I believe AI models will significantly change biotechnology in two major ways, and I think Ginkgo is positioned well to provide tools in both areas. I'll elaborate on that shortly. Secondly, we continue to offer our Research Solutions business alongside our in-house robotics platform at Ginkgo. We had two significant wins in the last quarter that I want to briefly mention. Finally, we're expanding our frontier autonomous lab here in Boston, which includes a large RAC setup. I'll share some photos and details about our progress, and I encourage you to come visit us if you’re interested. Now, let's delve into how AI is influencing biology. Before proceeding, I want to remind everyone that back in 2025 and into the second half of 2024, we made a significant shift in our business model. We transitioned from solely offering research solutions—which involve close partnerships with clients providing fees and downstream value sharing—to also entering the tools market through our data points, automation, and reagents businesses over the past year and a half. I want to take a moment to discuss how the incoming AI advancements create an excellent opportunity for us in the tools market, where we possess category-defining technology. So why is AI currently pivotal in scientific fields, particularly in bioscience? Recently, the America’s AI Action Plan released by the White House highlighted the importance of investing in AI-enabled science, emphasizing automated cloud-enabled laboratories. I'm excited to share insights into what we've built in Boston, as it exemplifies this concept and could transform research methodologies. The idea is that reasoning models could advance decision-making while laboratories handle the physical experimentation, which I'll discuss further. The significance of this has become evident, especially in biosciences, which may very well become the frontline for AI-enabled research, particularly in the competitive landscape between the U.S. and China. A recent New York Times editorial noted that China's biotechnology is becoming cheaper and faster. This is largely accurate when considering traditional methods involving well-trained scientists conducting experiments manually in laboratories across the U.S. For a considerable time, we had an edge over China due to superior training and facilities; however, that advantage has largely diminished over the past decade to 15 years. China now boasts comparable academic institutions and a larger pool of trained scientists who are compensated significantly less. I was encouraged to see Senator Young, heading the National Security Commission on emerging biotechnology, push forward several initiatives on this front, including a $100 million NSF-funded AI programmable cloud labs initiative. The premise is clear: if we want to maintain our competitive edge in biotechnology against China, we need to rely on robotics rather than manual labor. Failure to adapt will lead to a trend we’ve observed recently, wherein an increasing number of early-stage biotech startups are being acquired or funded by U.S. VCs, but based out of China. To make progress in biotechnology and science, we must invest in robotic infrastructure. I believe the U.S. government recognizes this need, and Ginkgo is equipped with the right technology to capitalize on it. I have shared details previously about our reconfigurable automation carts, or RAC carts, where AI is starting to make waves in biotechnology through reasoning models. These AI models, such as GPT-5 from OpenAI and Gemini from Google, can process information over time, drawing conclusions based on given tasks. They can code, browse, and execute multi-step operations, ultimately returning results. The initial frontier will focus on integrating these reasoning models with lab automation. Science is predominantly driven by experimental work. Researchers form hypotheses about diseases, and the only way to confirm those hypotheses is through meticulously conducted laboratory experiments. For AI to effectively partner in scientific work, it needs the capability to perform experiments. The technology we are developing at Ginkgo, specifically our RACs, is designed to support this endeavor. Each RAC features lab equipment, robotic arms, and plate transport systems, allowing for versatile configurations to meet various research needs. The second area where we're witnessing AI application in biotechnology is training neural networks—not for human language but for biological language, focusing on DNA and amino acid sequences. This represents an emerging segment in AI applied to biotech, and with our Ginkgo data points service, we're eager to foster collaboration in this domain. We recently launched an antibody developability competition, and it's a great opportunity for bioinformaticians and startups to engage in this burgeoning field. The competition involves predicting the effectiveness of antibody sequences as drugs. We are also committed to building community resources by releasing datasets for free to support AI model training efforts using biological data. Returning to our Research Solutions business, we are still actively engaged in breakthrough research. We recently secured a $22 million contract with BARDA for domestic monoclonal antibody manufacturing, which is essential for enhancing national security and lowering drug production costs. We are also pleased to continue our five-year partnership with Bayer, focusing on engineering microbes for fertilizer production. It's thrilling to witness our co-founders return to the lab, inspired by our recent advancements in automation. We're expanding our setup in Boston with significant interest from customers and internally. We're on track to have 46 instruments at this facility, with plans to scale to approximately 100 RACs. We've made strides in standardizing our cart hardware, allowing quick integration of new equipment, and making lab automation more accessible. We believe this RAC system represents a significant leap in laboratory efficiency, enabling seamless coordination among various pieces of equipment. Our goal is to provide scientists with a streamlined experience where they don’t need to navigate through multiple software programs for complex procedures. We aim to offer demos of our systems, allowing scientific teams to explore automation and AI solutions that could enhance their research capabilities. Thank you for your time, and I’m eager to address any questions you may have. I'm excited about the progress we've made and our ability to invest in future growth opportunities in automation and AI for biosciences.

Daniel Waid Marshall, Manager of Communications and Ownership

Great. Thanks, Jason. As usual, I'll start with a question from the public and remind the analysts on the line to ask a question by raising their hands on Zoom, and I'll call on you to open your line. Thanks, everyone. All right. Let's get started. So the first question comes from Twitter and is about Ginkgo's exposure to U.S. government business and how that has been impacted by the shutdown.

Jason Kelly, Co-Founder and CEO

Yes, I can touch on that. So short answer on the shutdown has not had a big impact on us. So sort of the areas the grants and funding there keeps slowing during the shutdown. I would say, in general, though, we have a good amount of exposure to the government overall. So between our cloud security business and then things like the new BARDA award, 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. So overall, I think hopefully, we're even doing more in the future with some of the sort of cloud labs work and investments I hope to see from some of the government labs around automation, but the shutdown doesn't impact us.

Daniel Waid Marshall, Manager of Communications and Ownership

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?

Jason Kelly, Co-Founder and CEO

Sure, I can elaborate on that. What’s really exciting about the RACs is the blend of different types of automation. For instance, there are companies like Hamilton that offer liquid handling decks, which is a very standardized product. On the other hand, integrated automation features a robotic arm coordinating multiple pieces of equipment. Often, one piece handles the liquid management, but samples must be transferred to another piece of equipment. In the video, you saw plates moving along a track to be delivered to six or seven different tools within a single protocol, and in many labs, a person is managing this process nearly all the time. There is a niche in integrated automation for high-throughput screening, where an arm connects with multiple pieces of equipment, designed specifically for distinct tasks. Our carts are different – they are standardized, produced consistently, and can be linked with any equipment you need initially, with the flexibility to grow the setup over time. This is something traditional integrated automation doesn’t offer. I’m enthusiastic about scaling up our cart manufacturing and reducing costs to develop more standardized offerings. On the sales front, it’s crucial for people to recognize the difference between application-specific work cells they currently buy and the general-purpose autonomous labs we propose, such as our frontier lab in Boston. The goal is to shift the perception of automation from being something built for a single application that is discarded after a few years to a system that expands over time and ultimately replaces extensive laboratory bench space. We need to transition from traditional bench setups to automated benches and then to fully autonomous labs. For our internal goals at Ginkgo, I envision having over 50 scientists simultaneously ordering from our automation system in one day by 2026, a feat never achieved before with automated labs. Externally, I hope to see a large biopharma company make a significant investment in a general-purpose autonomous lab system. While we will continue to sell work cells, I’d love for someone to embrace the concept of a large general-purpose autonomous lab. I believe the timing is right, and we're prepared to demonstrate this potential at Ginkgo. I anticipate our customers will start to adopt this mindset soon, particularly since using automation along with AI significantly lowers the barriers within the industry.

Daniel Waid Marshall, Manager of Communications and Ownership

Cool. All right. And then Brendan had one more question, which was, as you look at the current revenue mix between cell processing, as he said, 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 by 2030?

Jason Kelly, Co-Founder and CEO

My vision for 2030 involves significantly progressing in our operations. I expect that the revenue mix will lean heavily towards our tools business, which includes robotics, software for robotics, and reagents, constituting about 80% of our revenue. My goal is for us to dominate the general-purpose R&D infrastructure and supply tools across the entire industry. As for biosecurity, its future is uncertain and depends on the ongoing developments. The CDC is undergoing a transformation, and I recommend reading insights from Matt McKnight, who leads our biosecurity division, about this evolution. Effective biosecurity will require continuous monitoring of viruses, whether we're facing an outbreak or not. If the necessary infrastructure for this monitoring is established both in the U.S. and globally, it's possible that biosecurity could eventually account for half of our business. However, this will depend on the acceptance of monitoring technology as a vital component in making biosecurity effective and preventing future pandemics like COVID.

Steven 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 6 years over the prior 3 years. From that standpoint, I think that puts us right where we want to be.

Jason Kelly, Co-Founder and CEO

Yes. I want to add some context here. We invested in the Google Cloud focusing on two areas of AI: reasoning model-based AI and bio model-based AI. Initially, we expected bio-based AI to grow rapidly. However, what we've observed in the industry is that while it is being adopted, the growth rate is nowhere near that of reasoning models. This reflects our expectations for deployment and internal training needs at Ginkgo. The ramp-up will be much smoother over a longer period compared to what we anticipated with significant investments in bio AI models, which simply hasn't materialized as expected. I'm very pleased with how Steve and the team have handled this, and our partners at Google have been a great support. Overall, I'm satisfied with the outcome.

Daniel Waid Marshall, Manager of Communications and Ownership

All right. The next one is for Jason. Jason, you mentioned Future Labs' new announcement of its next-gen AI scientist Kosmos. 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?

Jason Kelly, Co-Founder and CEO

Yes, more people are checking this out. Future Labs, now known as Edison Scientific, has transitioned from a nonprofit doing something like OpenAI to a for-profit organization. They have developed a model that has read all existing scientific literature, allowing users to pose scientific questions. This model takes several hours to process and then returns hypotheses, predictions, learnings, or conclusions. It has already demonstrated the ability to make new scientific discoveries just by analyzing the literature, which is very exciting. I believe we are on an inevitable path where the models' reasoning capabilities will effectively function, and this capability is already in place. The challenge will shift to providing these models with the tools they need. In the scientific field, as I mentioned earlier, this primarily means having hands-on experiments in the lab. This model can assess everything it has learned from the literature and then suggest running a certain number of experiments to derive answers. This mirrors what a PhD candidate does—formulating questions, conducting experiments, analyzing results, and iterating based on findings while considering previous literature. This model from Future House, Edison, fulfills that role. Additionally, the model emphasizes the importance of conducting and designing experiments and interpreting results, which are crucial skills in science. The challenge of these skills has historically kept many people out of the field. However, I think we are now in a position to use programming and robotic interfaces to conduct experiments in the lab, which might greatly enhance access to tackling challenging scientific questions across various domains. We will see how this develops, but we aim to provide the necessary hands-on experience, and we are pleased to support other entities in creating advanced models.

Daniel Waid Marshall, Manager of Communications and Ownership

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?

Jason Kelly, Co-Founder and CEO

I believe that commercially, this approach can significantly impact drug discovery. Imagine you have a hypothesis about a specific disease based on your expertise and literature. You would typically plan a series of experiments with your research team over several months to test that hypothesis. What’s exciting is that perhaps systems like Future House could generate those initial hypotheses. Regardless, researchers often have more hypotheses than they can realistically test due to limitations in resources. However, if you could effectively utilize these models to explore your top 100 hypotheses simultaneously, rather than just your top three, you could conduct multiple experiments for each hypothesis, interpret results, and continue refining your approach. This could allow a single researcher to run tests on hundreds or even thousands of hypotheses in parallel, especially with the help of robotics. This is a completely different strategy for investigating diseases. Traditionally, the limitations are in reasoning and the hands-on work required. If we could alleviate those constraints, costs would primarily shrink to just the price of reagents and consumables. Presently, most expenses stem from human labor and the physical space required for laboratories. Both of these costs could be drastically reduced through automation and AI, which is incredibly promising.

Daniel Waid Marshall, Manager of Communications and Ownership

All right. That's all the questions that we have for tonight. A reminder, you can always ask questions by e-mailing us at investors@ginkgobioworks.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.

Jason Kelly, Co-Founder and CEO

Great. Thanks, everybody. Appreciate the questions.