Earnings Call Transcript - DNA Q1 2026

Daniel Marshall, Senior Manager, Communications & Investor Relations

Good evening. I'm Daniel Marshall, Senior Manager of Communications and Investor Relations at Ginkgo. I'm joined by Jason Kelly, our Co-Founder and CEO; and Steven Coen, our CFO. Thanks, as always, for joining us. We're looking forward to updating you on our progress. As a reminder, during the presentation today, we will be making forward-looking statements, which involve risks and uncertainties. Please refer to our filings with the SEC to learn more about these risks and uncertainties, including our most recent 10-K. Today, in addition to updating you on the quarter results, we're going to provide insight into how and why we see autonomous labs like Nebula, our autonomous lab, replacing the lab bench, which is where nearly all of biological science is done today. As usual, we'll end with the Q&A session, and I'll take questions from analysts, investors and the public. You can submit those questions to us in advance via X using #GinkgoResults or e-mail [email protected]. All right. Over to you, Jason.

Jason Kelly, Co-Founder & Chief Executive Officer

Thanks, Daniel. We always start with this. Ginkgo's mission is to make biology easier to engineer. And I mentioned this at the last earnings call, but in 2026, our focus will be on investing to win the category of autonomous labs. And I'm really excited; even since we just spoke a few months ago, this category has been growing in attention, with new companies in Silicon Valley pursuing this, a lot of interest from the AI frontier labs about the application of AI models in science via autonomous labs, and government talking more about this. So I do think we're on to the right track with this focus for the company. The two big ways I'm going to be pursuing that goal in 2026: the first is to take our services and solutions, datapoints and cloud lab, and run them on top of our autonomous lab here in Boston that we call Nebula. That's a chance to prove out the capabilities of our system with real-world activities. The second big area of activity will be getting early adopters of autonomous labs out in the world to buy our systems like we've done already with Pacific Northwest National Labs that I talked about last time. So I'm excited to pursue both of those, and you're going to hear more about it from me in the strategy section. We also, in the last quarter, were able to close on a deal I talked about extensively last time, which is the spin-off of our biosecurity unit into a new company called Perimeter. I want to say congratulations to the team in biosecurity at Ginkgo on pulling that off: $60 million and a lot of great new investors coming into that focus firmly in the area of defense tech and building a biosecurity prime. Ginkgo is a shareholder in that company. We're super excited to see it succeed. I think this is really nice, as I talked about last time—a great opportunity both for Ginkgo to keep our focus on autonomous labs and for the team at Perimeter to grow under their own brand with a new set of defense-tech-focused investors. Our focus over the last couple of years was very much on getting these numbers where they are today, bringing down our cash burn in the company. We guided towards this, and Steven will touch on that in his section. But again, happy to have a very strong cash position, $373 million with no bank debt as of Q1 2026. You'll hear a little bit more from Steven on this. This sets us up very nicely. We're well capitalized to pursue autonomous labs. We have base service businesses to build on top of and the lead in developing the technology, and put all that together, I think we're by far the best bet in this sector. All right. I'm going to pass it on to Steven to dig into the financials.

Steven Coen, Chief Financial Officer

Thanks, Jason. Before I walk through our financials, I want to take a moment to frame an important change in how we are presenting our results beginning in Q1 2026. As we announced in February, we entered into a definitive agreement to sell our biosecurity business, which was previously reported as a separate segment. As Jason noted, we closed that transaction on April 3. The transferred assets met the criteria under U.S. accounting to be classified as held for sale and the financial results are reported as discontinued operations as of March 31, 2026. This is the first quarter in which biosecurity is reflected as discontinued operations within our financial statements. To comply with accounting rules, we have and will retrospectively recast all prior periods presented to conform to this presentation. That means the revenue, operating expenses and cash flows previously attributed to the biosecurity business are removed from each line item of our continuing operations and cash flows as the prior period information is presented, including for Q1 of last year. The former biosecurity results are now reported as a single net line loss from discontinued operations, below loss from continuing operations. To be clear, all of the financial commentary I will provide today relates exclusively to continuing operations. We will not be discussing the biosecurity business further in our prepared remarks. On April 7, 2026, for your information, we filed a current report on Form 8-K that includes pro forma financial information for fiscal years 2023, 2024 and 2025 on a continuing operations basis. Following the biosecurity divestiture, we now operate as a single segment. With that, I'll discuss our Q1 results. Revenue was $19 million in the first quarter of 2026, down 49% compared to the first quarter of 2025. As previously disclosed, revenue in the first quarter of 2025 included $7.5 million in noncash revenue relating to the mutual termination of the BiomEdit agreement. Excluding this, revenue in the first quarter of 2026 was down 37% from the prior year period. 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 items, we believe adjusted EBITDA is a more indicative measure of our profitability. A full reconciliation between adjusted EBITDA and GAAP net loss from continuing operations can be found in the appendix. In Q1 2026, R&D expense decreased 38% from $49 million in Q1 2025 to $30 million in Q1 2026. G&A expense decreased 35% from $20 million in Q1 2025 to $13 million in Q1 2026. These decreases were driven by our restructuring efforts. Net loss from continuing operations was $76 million in Q1 2026 compared to a loss of $83 million in the prior year period. The reduction in loss year-over-year was due to restructuring. Adjusted EBITDA in Q1 2026 was negative $42 million, down from negative $44 million in Q1 2025. Since we are now operating in a single segment, we present a single measure of adjusted EBITDA. It is important to note that adjusted EBITDA includes the carrying cost of excess lease space, which was $16 million in Q1 2026. Previously, this cost would not have been included in the former presentation of segment adjusted EBITDA. This cost represents the base rent and other charges relating to lease space which we are not occupying, net of sublease income. This is a cash operating cost not related to driving revenue right now and can be potentially mitigated through subleasing. Finally, cash burn in Q1 2026 was $48 million, down from $58 million in Q1 2025, a 17% decrease. As previously reported, in October 2025, we amended and reset the annual commitments with Google Cloud for $14 million. Resetting the commitment reduced our future minimum commitments by more than $100 million compared with the original terms and extended the commitment term from three to six years. We paid this $14 million in Q1 2026, which is reflected in our cash burn for the quarter. Excluding the payment to Google Cloud, cash burn reflects a significant decrease in Q1 2026 compared to Q1 2025, which was a direct result of the restructuring. Now turning to guidance. As we discussed in February, 2026 is about continuing to be cost efficient while investing in our AI, robotics and software to bring autonomous labs to our bioscience customers, including the build-out of our Frontier Autonomous Lab in Boston. We have turned the page on our pure focus on restructuring actions to focus this year not only on cost efficiency, but on investing in what we see as our opportunities while continuing to provide our customers the advanced services they expect. For these reasons, we believe cash burn best reflects our continuing services and tools and further investments in autonomous labs. In terms of outlook for the full year, we are reaffirming our overall cash burn guidance for 2026, totaling $125 million to $150 million. This range reflects a firm balance among cost efficiency, continuing services and tools, and further investments. In conclusion, we are pleased with our continued improvements in cash burn efficiency and our business pursuits for 2026. With that, I'll hand it back to Jason.

Jason Kelly, Co-Founder & Chief Executive Officer

Thanks, Steven. I'm going to dive into the strategic section. Our mission is to make biology easier to engineer. We believe the bottleneck fundamentally is the laboratory work associated with bioengineering, and I'm going to explain why autonomous labs will be replacing the lab bench. I want to highlight what we're doing with Nebula, our system, because we have some news this month about expanding that system. Then I'll discuss the services we put on top of Nebula—our cloud lab, Datapoints, and Solutions—which I liken to a Starlink for autonomous labs. If you think about SpaceX, a large portion of their launches were Starlink, their own product; that allowed them to scale their launch platform. In the coming year, the ability for us to scale up autonomous lab services and demonstrate that you can make money on services without human technicians in the middle of the lab is a real highlight and will help drive sales of our systems into the world. I'll walk through these three areas. First, an analogy to transportation: on the y-axis is the amount of automation and on the x-axis is request flexibility. Low request flexibility and high automation is a subway: highly automated but inflexible. Low automation and high flexibility is a car: flexible but requires a human in control. Recently, autonomous cars like Waymo combine high automation with high flexibility, enabling new use cases. For labs, low flexibility and high automation is a work cell—fully automated devices built for a specific protocol. Low automation and high flexibility is the lab bench—scientists manually connect devices to execute protocols. Research budgets are concentrated at the bench—over 95%—because scientists need flexibility to explore hypotheses. We're trying to build a system that reaches the top-right corner: an autonomous lab with the flexibility of the bench and the automation of a work cell. That means scientists can order whatever experiment they want and have the protocol run end-to-end without being present. The value proposition is clear: reduce overhead costs, dramatically reduce laboratory space, increase research productivity as AI needs more data, and enable AI scientists to run lab-in-the-loop experiments. A large pharma may spend $1 billion to $3 billion a year on research across a million-plus square feet of benches, while spending well below $100 million on automation. I believe those numbers should flip, with more capital going toward automated laboratory work rather than manual benches. Comparing a traditional manual lab and an autonomous lab, you get about a threefold space improvement because equipment is packed closer in automated setups, and a fourfold utilization improvement because manual labs typically operate about 40 hours a week while Nebula runs 168 hours a week. The technical challenge is achieving high automation and high flexibility without humans in the loop. At Ginkgo, we designed for equipment-centric labs rather than protocol-centric work cells. Our hardware solution is rack carts—reconfigurable automation carts with a robot wrapped around each laboratory device, HEPA filtration, a six-axis robotic arm, and magnetic motion tracks that let us LEGO-block these carts into large setups. We're at 50-plus racks in the lab and expanding to over 100. In our project with OpenAI, GPT-5 controlled the lab and we documented samples moving through racks for a protocol: plates move in SBS format, arms pick them up, transport them to devices like acoustic or Bravo liquid handlers, and the final analytical device runs qPCR or sequencing. Any device that accepts SBS plates can be integrated into the racks. Adding a new device generally takes one to one-and-a-half months. Most runs on Nebula return data to human scientists, but we expect a mix of scientists and their AI agents ordering experiments. We started Nebula with about eight racks doing NGS prep and expanded to over 100 racks. What's unique is we're not just a hardware company; we run BSL-2 labs in Boston and do scientific partnerships with many large biotech, ag-biotech, and industrial biotech companies, so we can show what real science looks like on the system. In the last quarter, we've run over 100 protocols with more than 30 unique protocols submitted by scientists—not automation engineers—on a system with 50-plus devices integrated where you can move samples point-to-point between devices. There's nothing else like Nebula today doing open-ended science at this scale. It's proof that autonomous labs are feasible. We still encounter failures as we scale, but we're confident this will outcompete manual labs. Key requirements for a lab that would replace a lab floor at Takeda, Merck, Novartis, Bayer Crop Science, or similar: connect 100-plus devices in a single automation setup; run dozens to hundreds of unique protocols in parallel; and have scientists—not automation engineers—submit protocols. We expect about 100 devices is the right order of magnitude. This week we'll be turning on 103 racks in one big setup. Our Catalyst scheduler manages the complex scheduling problem—biology is sensitive to timing and the scheduler checks whether a new protocol can fit in without disrupting others. We've had peak days with over 400 scientists submitting protocols, which is unprecedented. We're leveraging AI coding tools with custom harnesses to translate scientist intent in natural language into automation code. We must avoid forcing scientists to become coders; these AI tools help bridge that gap. We're expanding from 50 to 105 racks by the end of this month; 103 racks will be coming online in about a week. The scheduler is nontrivial; it's coordinating many protocols across many devices in time. We see strong policy and government interest, including involvement in initiatives like the Genesis mission and NSF funding for cloud laboratory networks. There's momentum in national labs and government research. For example, we signed a $47 million contract with Pacific Northwest National Labs to install nearly 100 RACs in a new building over the next few years. ARPA-H toured Nebula and we have a great project with them. Having autonomous labs accelerates scientific projects for NIH, NSF-funded labs, and academic research universities by allowing many more hypotheses to be tested. Nebula showcases what's possible and attracts early adopters; we're building autonomous labs for those early adopters. We've had over 600 visitors in the first quarter, and we host weekly tours. Now, on services: Cloud lab, Datapoints, and Solutions act as a recurring revenue business we can run on top of Nebula—similar to how Starlink created demand for SpaceX launches. We launched cloud.ginkgo.bio recently; on the estimate tab you can type the protocol you're interested in, it will check equipment availability and estimate price to run that protocol in a cloud lab. People are often surprised at how inexpensive it can be, which reflects how expensive manual lab work is due to low equipment density and low utilization in costly lab space. Our cloud lab can change that economics. In our OpenAI project, after six rounds of design we improved the cost of cell-free protein synthesis by 40% over scientific state-of-the-art. That opened eyes about whether models can design experiments and interpret data with that level of sophistication. We see this as an 'AI scientist' using autonomous lab resources. We've also added three new channels to our delivery business for cloud lab and Datapoints services focused on antibodies: Amazon BioDiscovery from AWS, Benchling, and Tamarind Bio. These platforms allow access to antibody design models, and when designs are ready, they can be sent to a cloud lab to be tested, with data flowing back to models to iterate. Benchling is a leader in electronic lab notebooks and presents another potential channel where a scientist could design an experiment and hit 'go' to run it in a cloud lab. For Datapoints, we're working with 10 of the top biopharma companies in the world in the first year. The revenue unlock is repeat business as customers scale up model training and demand more data. We're running competitions and initiatives such as a virtual cell pharmacology initiative to test compounds. In Solutions, over the last decade we've run more than 250 research partnerships across pharma, industrial biotech, and agricultural biotech—work ranging from engineering microbes associated with plant roots to mRNA therapeutics and enzymes for industrial biotech. This semi-automated work is the majority of customer spend, and migrating that work onto Nebula—replacing the manual lab bench—is a critical demonstration. We bring customers through Nebula to show them scientists submitting new protocols every day. That has been compelling for heads of R&D. If you're interested in visiting, sign up for a tour. If you want to follow up directly, e-mail me at [email protected]. Happy to take your questions now. Thank you.

Operator, Operator

And enzymes for industrial biotech. This semi-automated work is the majority of customer spend, and migrating that work onto Nebula—replacing the manual lab bench—is a critical demonstration. We bring customers through Nebula to show them scientists submitting new protocols every day. That has been compelling for heads of R&D. If you're interested in visiting, sign up for a tour. If you want to follow up directly, e-mail me at [email protected]. Happy to take your questions now. Thank you.

Daniel Marshall, Senior Manager, Communications & Investor Relations

We have one to start off submitted from Brendan at TD. We got it over e-mail. He has two questions. First: How should we think about the potential impact to revenues this year from the AWS and Benchling announcements? How have the launches gone thus far? What is baked into your assumptions for the rest of 2026 for these new platforms?

Jason Kelly, Co-Founder & Chief Executive Officer

Yes, I can take that one. We talked about AWS and Benchling, and also the Tamarind Bio partnership. I'm super excited about this. This is the first time I've seen this sort of cloud layer talking directly to labs as a sales channel. It's definitely new, and we've seen some inbound because of the channels, which is exciting. I'm most excited that it's starting around antibodies because there are several AI models and providers in the antibody space. With our cloud lab, we're not limited to testing antibody binding; we posted eight to ten protocols and add new ones weekly—mass spec, metabolomics, and more. You can request a protocol and if we have the equipment, we'll add it. I'd love for this to become a direct channel from an electronic lab notebook or similar, where a scientist designs a protocol, gets a price from cloud.ginkgo.bio, and then runs the experiment. That would feel closer to the AWS compute model. Right now these channels are more narrow around antibodies, which is an exciting place to start. I'm excited to expand that and see it become a broader channel that scientists can access for custom work.

Daniel Marshall, Senior Manager, Communications & Investor Relations

Next question from Brendan: What are you hearing on Datapoints and the collective AI-driven offerings? Are Ginkgo's offerings especially attractive for customers as biotech and pharma companies continue to roll out their own AI capabilities? In other words, what kind of demand dynamics are you seeing here? Are there any potential revenue funnel unlocks we should watch for over the coming quarters from this part of the business?

Jason Kelly, Co-Founder & Chief Executive Officer

We launched Datapoints about 18 months ago, and having 10 top pharma companies as customers in the first year is very encouraging. The revenue unlock will come from repeat business as customers that start with pilot and data generation projects decide they need more data to improve their internal models. These are specialized models trained on biological data, not general reasoning models. It's common in this field for organizations to build their own tuned models based on their proprietary datasets. As these internal models show performance gains with more data, customers will come back for more. That pattern mirrors what we saw in early image and language model markets: when companies see improved performance from additional data, they buy more. We'll be watching repeat demand from customers as their in-house models mature and require more data.

Daniel Marshall, Senior Manager, Communications & Investor Relations

On the theme of AI, someone on X asked about your project with OpenAI: How much efficiency improvements did you see after using GPT-5? Any idea how much space is left for improvement, and will this be a transitional factor?

Jason Kelly, Co-Founder & Chief Executive Officer

We had a project with OpenAI where GPT-5 controlled the lab; note the project used GPT-5 throughout. Over six rounds of the model running with about 100 384-well plates designed per round, we achieved a 40% improvement over state-of-the-art for the specific scientific objective we were optimizing. There are open questions: how much further can you push this? Are there diminishing returns in certain areas? Can models produce breakthrough ideas that lead to new techniques? As models improve, we expect additional gains—whether GPT-5.5 or subsequent models will perform better is something to test. We're planning to do more work with OpenAI. The core functions are experimental design and experimental analysis: the model proposes experiments, the lab executes them, and the model analyzes results to propose new experiments. This approach could let individual scientists operate with the throughput of larger labs by leveraging agents plus autonomous labs. If an individual scientist can run multiple agents on an autonomous lab at similar cost, that would change the rate at which science is done and could materially increase scientific output. That's why initiatives like the Genesis mission are investing in this field—the goal is to accelerate scientific output materially, and our industry, including pharma, would be transformed if we could increase throughput two- to three-fold.

Daniel Marshall, Senior Manager, Communications & Investor Relations

Next we have a bundle of questions from DK in South Korea about how moving to Nebula has changed the science Ginkgo is doing: How does use of Ginkgo's automated lab affect overall costs? Are there meaningful differences in speed or turnaround time for experiments? Have you observed improvements in success rates, reproducibility, or scalability since moving to the autonomous lab?

Jason Kelly, Co-Founder & Chief Executive Officer

On cost, the clear ROI for customers is the combination of about a threefold reduction in space requirements and a fourfold increase in available lab hours because an autonomous lab runs 24/7. Those two factors—space and scientist time—are the largest cost drivers in research. On speed, an individual protocol's run time may not be dramatically shorter than at the bench, but the availability of running experiments around the clock lets you start an experiment at 4:00 p.m. and have results in the morning, effectively shaving a day off many iterative workflows. So you can see meaningful practical speedups by taking advantage of continuous operation. For reproducibility, automation inherently improves reproducibility because of audit trails and machine-controlled steps. When errors occur on automated systems, you can trace them; manual bench work often has undetected variation. Through automation, we reduce variability and improve reproducibility. On throughput and scalability, cloud.ginkgo.bio shows how much lower per-sample costs can be once labs are automated and utilized better; scientists who understand that will order many more experiments. Automation historically leads to huge increases in data and throughput across fields, and I expect the same for biotech. We're continuing to demonstrate these gains on Nebula and sharing results with visitors and customers.

Daniel Marshall, Senior Manager, Communications & Investor Relations

You mentioned you're trying to get to 100 RACs in Nebula. When do you expect to get there?

Jason Kelly, Co-Founder & Chief Executive Officer

We've been installing RACs over the last three weeks. The new RACs were built by our team in Emeryville and came in on trucks; we rolled in an additional 50 and they are fully connected in the lab. The original system is running and the new 50 are running as another loop. The connection between the two loops will be turned on imminently; I expect it to be active next week on the 14th. So we'll be at around 100—103 or 105 racks—next week. It's been a fast install and a unique effort in laboratory automation, and I'm excited to see it come together.

Daniel Marshall, Senior Manager, Communications & Investor Relations

If you want to follow us on that journey, you can go to X or LinkedIn and keep watching. We'll have a lot of content coming about the unveiling of the new full system. As always, if you have questions, you can reach out to us at [email protected]. Thanks so much, everyone, until next time.

Jason Kelly, Co-Founder & Chief Executive Officer

Thanks, everybody.