Ginkgo Bioworks Holdings, Inc. (NYSE:DNA) Q2 2025 Earnings Call Transcript

Ginkgo Bioworks Holdings, Inc. (NYSE:DNA) Q2 2025 Earnings Call Transcript August 8, 2025

Daniel Waid Marshall: Good evening. I’m Daniel Marshall, Senior Manager of Communications and Ownership. I’m joined by Jason Kelly, our Co-Founder and CEO; and our new CFO, Steve Coen. Thanks, as always, for joining us. We’re looking forward to updating you on our progress. As a reminder, during the presentation today, we’ll be making forward-looking statements, which involve risks and uncertainties. Please refer to our filings with the SEC to learn more about these risks and uncertainties, including our most recent 10-K. Today, in addition to updating you on the quarter results, we’re going to provide updates on our path towards adjusted EBITDA breakeven and dive deeper into the new deals and launches in Ginkgo’s Tools businesses, which continue to establish themselves as critical tools in AI-powered bioengineering.

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 #Ginkgoresults or e-mail investors@ginkgobioworks.com. All right. Over to you, Jason.

Jason Kelly: All right. Thanks, Daniel. We always start with our mission here at Ginkgo, which is to make biology easier to engineer. Our objectives are very similar to what you’ve heard from me over the last few earnings calls. We’re trying to reach adjusted EBITDA by the end — breakeven by the end of 2026, while maintaining a cash margin of safety, and I’m going to update on that in just a sec. We’re cutting costs while serving our current customers. And then very importantly, we’re expanding from an R&D solutions business into the life science tools space. And in the strategic section, you’re going to hear a lot about that from me today. Before I get to that, I do want to touch on that maintaining a cash margin of safety and the cost cutting.

So you can see our numbers here for the quarter, really happy about this. We’ve been aiming, and I told you this about a year ago, to get to a $250 million annual run rate cost savings by Q3 of this year of 2025. I’m happy to say we hit that target a quarter early. This was a tremendous amount of very painful work by the team at Ginkgo. And so I want to say thank you to folks and sort of congratulate them on that progress and getting there early. That is very strategically important for us. So because the earlier we do it, as you can see, we have $474 million in cash and cash equivalents with no bank debt. And that’s where that margin of safety comes from, having that large cash position while also getting burn under control means that we don’t get pushed into needing to raise in a situation we don’t want to or from someone we don’t want to.

We can be strategic about engaging with capital markets, which is really important. And then it also means we can start to take our focus from just purely cost cutting to — which we are still going to be cutting costs, but from purely cost cutting to also just really how we want to grow the business into 2026. And so you’re going to hear a bunch from me today on that in the strategic section. Before that, I do want to hand it to Steve to go through the numbers, and I want to say congratulations to Steve, our new CFO. We mentioned this when we announced it, but Steve has been with the company over the last 2 years. He worked very closely with Mark throughout that time, particularly over the last several months to really shadow and be a part of everything that Mark was doing.

Q&A Session

And so it made that transition super smooth. And so really delighted. We’re very lucky to have Steve in the CFO seat, and I’ll pass it to him to go through the numbers.

Steven P. Coen: Thanks, Jason. I’ll start with the Cell Engineering business. Cell Engineering revenue was $39 million in the second quarter of 2025, up 8% compared to the second quarter of 2024. In the second quarter of 2025, we supported a total of 120 revenue-generating programs. This represents a 10% increase year-over-year. Turning to Biosecurity. Our Biosecurity business generated $10 million of revenue in the second quarter of 2025 at a segment gross margin of 18%. 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 more indicative of our profitability.

A full reconciliation between segment operating loss, adjusted EBITDA and GAAP loss or GAAP net loss can be found in the appendix. Now that we’ve completed a year of restructuring, you can see the very substantial cost reductions and improvements in profitability compared to the first quarter of 2024. In the second quarter of 2025, Cell Engineering R&D expenses decreased 63% from $84 million in the second quarter of 2024 to $31 million in the second quarter of 2025. Cell Engineering G&A expense decreased 57% from $33 million in the second quarter of 2024 to $14 million in the second quarter of 2025. These decreases were all driven by our restructuring efforts. The significant improvement in Cell Engineering segment operating loss in the second quarter of 2025 compared to the same prior year period was due to the previously discussed drivers of improved revenue and reduced operating expenses.

Biosecurity segment operating loss was impacted by the timing of programs in the second quarter. Moving further down the page, you’ll note that total adjusted EBITDA in the second quarter of 2025 was negative $28 million, which was improved from negative $99 million in the second quarter of 2024, a 72% improvement. We show adjusted EBITDA at the segment level to show the relative profitability of each. The principal difference between segment operating loss and total adjusted EBITDA in the second quarter relates to the carrying cost of excess lease space, which you can see was $12 million in the second quarter of this year. 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 that is not related to driving revenue right now and can be potentially mitigated through subleasing. And finally, cash burn in the second quarter of 2025 was $38 million, down from $110 million in the second quarter of 2024. The significant decrease in cash burn was a direct result of the restructuring. Now turning to guidance. In the terms of the outlook for the full year, we are reaffirming our total 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 substantial improvements in cash burn and cost reductions when looking back over the past year, where we achieved our targeted $250 million run rate cost takeout 3 months earlier than planned.

In the third 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 to you, Jason.

Jason Kelly: Thanks, Steve. The 3 topics we’re going to cover today in the deep dive is, one, our continued restructuring and the cost takeout. And then in sections 2 and 3, I want to go through automation and datapoints and our newly launched reagent product, which are really our 3 big motions into the life science tools space. So really excited about this today. Okay, so let’s dive in. So first, I mentioned this already. I’m really excited to see these numbers, that $250 million cost reduction, getting that done ahead of schedule is very strategically important for the company. So the whole reason we’ve been focusing on this, and the team has put in an absolutely enormous amount of work and pain around this is we wanted to be able to do this motion of moving into the life science tools space with a margin of safety.

In other words, with enough cash in the bank and no bank debt to allow us to not be forced to take money for people who don’t want to or raise in circumstances where we weren’t happy. And so having that large cash balance relative to our cash burn is really a critical piece of putting us in a good position when and if we engage with capital markets. And so really happy that we’re there on that. You can see here our burn rate getting down to $28 million, if you go to the next slide of adjusted EBITDA for this quarter. So really, again, a testament to the team and strategically important to Ginkgo. Okay. All right. So now I want to talk a little bit about our automation and datapoints offerings, and then we’ll talk at the end about reagents. So to give you some macro context, and I spoke about this before, but Ginkgo’s business over the last decade has really been what we call solutions.

So in other words, selling to the Head of R&D of a large company or the CEO of a small or midsized company and basically being an outsourced research team, Ginkgo scientists using Ginkgo tools to deliver them a research product, right? That was the Solutions business. Last year, about a year ago, alongside a restructuring in the company, we started to offer Ginkgo’s tools and services that we had previously had in-house just for our scientists, directly to the scientists at our customers. And that has been going really, really well. And so again, I want to give a little more context on that. So if you go to the next slide, you can see on the Y-axis here, we have what I’ll say is like our customization and technical risk we’re taking for the customer.

So when that is high, like it is in research solutions, in other words, we’ll have a big milestone that will only get paid if we’re technically successful, the customer is willing to give us downstream value share. In other words, a share of the future value of their products, either a royalty or success-based milestones like that technical milestone that I mentioned and so on. That’s really in exchange for the level of customization and risk we’re taking. So as we go down that Y-axis, we go to the right-hand side of this chart where we’re not able to get royalties and downstream value share. So that’s a downside, okay? But the upside is we’re selling something much more off the rack. In other words, a more standard scalable product to the customer.

And if you go to the next slide, what we’re seeing here is the Solutions business has that big upside, it takes a while to get to it. So I think there’s a really nice complement here where our tools, offerings are able to give us near-term revenue, smaller batches, wider customer set, opening new markets. We’re going to talk about the reagents. This first kit is a $2,000 kit, scientists can order it with a credit card. So that is really allowing us to have a faster cycle time going to market. It’s a good complement for the Solutions business and it’s the right time to do it. All right. So I’m going to jump in and talk a little bit about our automations offering, and then we’ll get to our datapoints, which is more of a traditional CRO and then finally, reagents.

All right. So when I talk to customers about automation, I’d like to show this slide, which is that Ginkgo, in addition to selling automation, has been a user and builder of automation over the last decade as we’ve been doing these solutions partnerships. And this is where that Solutions business really complements life science tools. We’re almost unique among life science tools vendors in really being primarily doing high-end science using our tools over the last decade, which means we have an enormous amount of familiarity with what’s out there in the market, what works and what doesn’t. And we built a lot of our in-house tools to fill gaps in what we couldn’t get from vendors on the market today, which is what makes our Tools business so exciting because when we launch these things, they’re immediately stepping into a gap in the market because if it hadn’t been a gap, we would have been buying it already from the life science tools companies.

And so if you go to the next slide, this is what I think is the core challenge if you look across the industry today. So when we talk to life science leaders, heads of R&D and so on, the #1 thing you’re hearing is there is a demand for more output from the same R&D resources. And this is a combination of factors, sort of economic pressure in the industry over the last 3 or 4 years with interest rates up. But it’s also competition from biotech companies in China, where you’re seeing lower cost labor, sort of lower-cost infrastructure and so on, creating pressure on the research infrastructure here in the United States and in Europe and others. And so how do you solve that problem? Well, part of the issue from my standpoint is the majority — the overwhelming majority, 95% plus of the research work done in the sciences and in commercial biotech and agriculture is done at the lab bench.

And that picture on the left is basically what every lab bench looks like if you go into any one of these companies, right? So there’s pipettes at the bench. I did my PhD in bioengineering, that’s 5 years of picking up one of those pipettes and moving liquids around working by hand at the bench, buying things from the Thermo Fisher catalog reagents. It’s very variable. Like you can do almost anything you want, but you do it at low throughput. And as you do more of it, it does not get cheaper, right? It’s not like making cars or making semiconductor chips, whereas you do more, the cost falls per unit. As you do more research, it’s just as expensive as the last unit as you do more because it’s being done by hand. So sort of the obvious thing like if you’re a tech person, is like, well, let’s just automate it, right?

Like if we automate it like semiconductors and automobiles, you’ll get a much lower cost per unit operation in the lab. And this is even more acute because you’re seeing demand around AI for these large data sets. And I’ll point out, we are not the only ones thinking this way, like, let’s automate it, right? So President Trump put this out just last week, Winning the Race, America’s AI Action Plan. And I would really recommend you read this document. It’s great. It’s very focused on the actual things to do in order for the United States to make strategic choices in AI. And one of the categories is invest in AI-enabled science. And you should read the dock, but I’ll just call out 1 specific part where it says, through NSF and DOE and so on and other federal partners, there should be an investment in automated cloud-enabled labs.

And what they’re saying there with cloud-enabled is think like a data center, right? When we say cloud computing, we think of a big data center that can do lots of different stuff and it’s accessible and gets cheaper with scale and improves the technology. Can we make the lab bench more like the data center cloud? That’s the provocation from this sort of AI action plan, and I think we can. And if we go to the next slide, I’ll show you why it’s been hard historically in the industry. So on the Y-axis here, and this is going to be my like automation nerd out slides, so bear with me. So on the Y-axis here is a term of art and automation called mix, okay? So a low mix environment is like an automobile plant, all right? You’re making the same car over and over again.

It’s a low mix of output. A high mix of output is like a fine chef at a restaurant, all right? Lots of different orders coming in from the menu, variations, people are requesting all kinds of stuff. You have a common set of tools, but you use it in very different ways to produce different high mix of outputs. Okay, that chef is very analogous to the scientists at the lab bench today, very analogous, right? They have a common set of tools, common set of equipment on those benches. They’re using their hands and they’re doing a very high mix of work. And they are very well served by Thermo Fisher, Danaher and a long tail of equipment and reagent vendors over the last 50 years that are selling them all kinds of stuff to work at that bench, all right?

It actually works pretty good. It just doesn’t scale. It really does not get cheaper with scale, and that’s what we’re seeing with the increasing price per new drug discover and everything else. All right. On the other hand, on the low mix side, more like an auto plant and a high throughput on the X-axis, we have what we call automation work cells, and I’ll show you a picture of one in a second. But these are where automation has been used in life sciences today, things like high throughput screening and compound management, places where diagnostics where you’re doing the same protocol over and over and over again. And their automation does work great in the lab. And there’s companies like Thermo Fisher and HighRes Biosolutions that will sell you these customized work cells.

The trouble is they just do those 1 or 2 protocols. They don’t have anywhere near the flexibility of the bench. And so the question is, can we get to high mix, high throughput or at least like media mix, medium throughput, something that’s closer to the bench, but sees a scale economic. And that’s what we’re trying to achieve with Ginkgo automation we believe is possible with our reconfigurable automation carts, our racks and our software on top of them. And so I’m going to talk a little bit more about that. So to give you some context, on the slide here, you can see a picture of, if you go to the next slide, a workcell. And so this is that traditional low mix, high throughput automation workcell. This is actually one that we got built for Ginkgo, right?

And those 2 white towers in the middle are robotic arms. They can pick up a plate and move it to all the various benchtop lab equipment that’s jammed into that thing. You can see everything kind of stuck in there and on top of each other and everything else. If it’s not obvious, that is a very custom object, okay? It’s not standardized. It is built just for you, right? And it has a relatively low return on investment because the entire value of that workcell has to be justified by the 1 or 2 lab protocols that it’s able to conduct, right? And that means that, back to my comment earlier, 95% of the lab work is happening at the bench, and less than 5% is happening on workcells like this because it’s only the most repeatable work that can justify that return on investment.

So if you go to the next slide, this is our solution to that. They’re reconfigurable automation cart. This is technology invented at Ginkgo. We’ve been building this up over the last 10 years. There is a — in this box basically is a piece of lab equipment. You can see an orange centrifuge there inside the box, in the cart. There’s a robotic arm, and there’s a piece of MagneMotion track. And what this track does is allows you to deliver a plate of 96 or 384-well plate to that robotic arm. The robotic arm picks up the plate, puts it on to the piece of lab equipment, and we have, I’ll show you in a minute, now 50-plus lab equipment integrated, puts it on the equipment and the software tells the equipment, run your experiment. And when it’s done, the arm picks up the plate and puts it onto the track.

And what’s great about this is once that custom piece of equipment is inside this box and we integrate directly with the equipment to our software, it’s now basically like a standard unit, all right? And if you go to the next slide, you can see we can stitch these together, we put unit, unit, unit, and we’ve now connected 3 pieces of lab equipment all into 1 setup, and we can move the plates among those equipment on that magnetic track. And with the arms, we can deliver the samples to the equipment, and it all just works if it’s on that integrated setup. And we have now like I said, 50-plus pieces of equipment. They’re not all shown here, integrated into these setups, and we’re adding more every day. If a customer wants a new piece of bench equipment inside our setup, we do that at our cost, and then have it integrated in the future for future customers, okay?

And you can put together many of these. This is a picture of our lab, if you have the next slide here in Boston. And again, unique among automation vendors. We use our own automation in a BSL-2 lab. This is a 20-plus RAC setup and inside it you have all these different pieces of equipment. And you can, again, run protocols that connect any piece of equipment to any other piece of equipment in that setup. You go to the next slide. This modularity is really exciting. Customers are loving it. This is just us at a few vendor trade shows. I really like the picture up on the top right. Recursion had an event at JPMorgan. They invited us to come and we actually set our RAC system up with like a 5-cart system in an afternoon and had it running for the cocktail party, all right?

So the ability to quickly build the system and then very importantly, expand the system is unique to our hardware. If you’re building that kind of Rube Goldberg machine with the arms in the middle and everything else, that is a custom job that takes a long time to do, and it’s again built one-off for the customer. With this, we can really print these cards and allows customers to quickly scale their infrastructure. And if you go to the next slide, we have a great existence proof of this, which is our setup that we’ve been using at Ginkgo to do research work for customers over the last several years. You can see here highlighted in blue, a number of pieces of equipment that were originally put on our setup for next-gen sequencing prep of samples, okay?

And so having all those on that setup allows a sample to get prepared and go on to our sequencers. That was the original investment. That was the ROI. We’re going to do tons of next-gen sequencing, so that justified it. But then very importantly, our scientists came along, and if you go to the next slide, they requested a protein quantification asset. It’s a HiBiT assay from — made by a company called Promega, and they wanted to run this at high throughput instead of at the bench. And so we developed a protocol that would be 7,600 samples in 6 hours like a very high throughput protocol. And if you look, and we want to now add this to the RACs, on the next slide, we were able to reuse now the blue on here, our machines from the NGS protocol that are relevant to the HiBiT protocol.

So we don’t need to buy those again. They’re already on the setup. In fact, in order to add this HiBiT protocol, we only had to add the PHERAstar, that 1 pink highlighted piece of equipment at the top was added in order to enable a whole new protocol. So that’s the ROI, right? Like we had to just add 1 piece of equipment and all this existing investment and these things — these workcells and things can cost $1 million plus when you make the one-off and you can’t expand it. By adding just 1 cart to this, we’re able to have it do a whole other protocol. And then importantly, as you add enough carts, it costs no more to do more protocols. It’s just software changes because you have enough equipment in 1 big setup in order to make that possible.

And this is — if you go to the next slide, what I’m really excited. I think this is the direction that the U.S. government is headed with these cloud-enabled labs. This is the direction that I think heads of R&D absolutely have to have on their radar if they’re looking to reduce research costs, which is to have many, many, many pieces of equipment, all in 1 big setup that can basically do whatever protocol you want in the future. And this is a setup we just announced a week ago that we had nearly complete for Pacific Northwest National Labs. It’s an 18 piece of equipment set up. And what’s really amazing about this, if you go to the next slide, it is all of our sort of like arms and tracks are inside of anaerobic chambers for the system. So this is an environment that humans can’t go in.

It’s air free. So it’s really difficult. You see those like arm things. Normally, people are doing experiments with their hands in glove boxes and all this crazy stuff. Instead here, those arms are really just to service the equipment that you see on this setup and all the samples that are going to move among the equipment are going to run through our automation. And if you go to the next slide, we believe this is the largest automated anaerobic system in the world now. Really excited about the Department of Energy investing in this. I think it’s exactly what the President is looking for, in the next slide, in these sort of cloud- enabled labs initiatives. And so I think you will see more of this and really excited about this. I think Ginkgo’s technology is perfect for this.

And by the way, I think 18 instruments in 1 setup is going to be looked at as small in the future. Really, we should have 100, 200 instruments all in 1 big setup that allows you to ultimately submit protocols to do anything you could do at the bench. And that ultimately — we’re not there yet. There’s a lot of technology between here and there, but that’s really the dream here is to be able to have that same level of flexibility or something near it but with the scale economic of automation. And that is absolutely essential if we’re going to have AI-enabled science without question. It’s just not going to happen at the lab bench. All right. One more thing on this. The software side, I’m not going to be able to dig in today, but I’m excited to tell you more about it in the future.

I will just say for customers that are tuning in, Ginkgo has been doing lab-in-the-loop AI-enabled science, having reasoning models, interacting with this robotics, really, really cool stuff. We’d love to share it with you. And we have the whole both — obviously, the hardware I spoke a lot about today. But importantly, the software stack, the modern APIs cloud-based software, everything that makes that all really feasible. MCP servers accessing all these equipment. So if you’re really ahead of AI looking to bring that into your biotech company, you should give us a call, both for the hardware and the software layer. So that’s much I want to say about automation, but I really see that as being extremely strategic for Ginkgo going into 2026.

And as we’ve gotten our costs more under control, you’re going to hear me go more in this direction, right? It’s going to be more about what can we invest in for growth in the future. And one of those big areas is going to be automation in AI. Beyond that, I want to talk about our push into the CRO services market, we call this Ginkgo Datapoints. We have a number of different services now, perturbation response profiling, specialized high throughput screening, antibody developability, which I’ve talked about before, but we just launched our small molecule developability or ADME service. And you can do lots of different things with these services. They are available. Just to be clear, there’s no royalty. There’s no milestone. It’s just like engaging with a CRO like a WuXi or whoever fee-for-service basis, you own all the IP and data as the customer.

But we’re able to do this at very large scale because of our automation expertise. And so one of the things I’m really excited about, we announced this in the press release of the ADME service, is if you have a quote from another vendor in the CRO space, like, for example, a Chinese vendor and you want to onshore that back here to the United States, just send us a quote. We’re happy to meet it. And that goes for ADME, but generally, you should send us the quote anyway. We’re happy to see it across any of our services and meet vendors. And so please do keep that in mind if you’re looking at datapoints. This is why I’m excited about datapoints in the long run. I think it is exciting to go after the traditional CRO market. I think there’s good business there.

It’s also not that high throughput. A lot of what places like Wuxi have done has basically gotten cheaper hands at the bench and then offer that as a service. So like that buys us whatever, 40% cost reduction on the big problem of reducing R&D and getting scalability, but then it kind of runs out because it’s just not getting cheaper. I think, across the board, if we want to get cheaper, the answer is automation. And so Ginkgo has been doing this work really in an automated fashion, and that allows some unique offerings to customers. So I’ll just highlight this funnel here, where this is traditional drug discovery, you’re going to identify a target, then you’re going to run some high throughput screen maybe on a robotic setup, maybe in some sort of pooled assay in the lab, either way, you’re going to screen a bunch of lab work to pick a few hits.

And then you’re going to take those hits into a much more expensive series of experiments in order to validate if they’re good drugs, all right? And it’s those set of more expensive experiments that we’ve been focusing on trying to make high throughput on our automation at Ginkgo and offer as a service through datapoints. And what’s exciting about that, for example, say, antibody developability, you find these binders, which you can do at a high throughput, really cheap. But then you got developability and it’s expensive. Is it soluble? Is it immunogenic? These are things that you have to do these more expensive experiments. And so you only try them on your top hits and you kind of cross your fingers. What we are able to do with our throughput is let you apply those developability assays back much earlier in your hit finding so that you look at a much wider range of potential candidates against not just whether they bind, but also are they developable.

And if you generate enough of this data, maybe we can even have computational models and AI that can predict developability. And so that’s where we’re seeing a lot of excitement. That’s kind of our niche to get off the ground in the CRO space. And this is the DPMTA, the design, predict, make, test, analyze cycle in pharmaceuticals. We’re really focusing on scaling up that test step for these high complexity assays. And I think that’s something we’re very, very good at, at Ginkgo. So you should expect us to launch more products. And this is just that ADME service, kind of start to finish, project scoping, chemical library and so on. I will highlight we’re using Echo MS, Echo mass spec, to do that sort of high quality, but also high throughput.

Actually, that’s what allows us to get cost that can really compete with doing it with low-cost labor overseas. All right. Last but not least, I want to talk about reagents. I’m super excited about this. I’m always excited when I see Ginkgo move into a new market area because if we do pick up traction there, there’s sort of like a lot of clear vistas in front of you to get into. So this is our first reagent product. And just to understand kind of the theory here. Again, over the last decade, Ginkgo has been a big, big consumer of life science tools. We have bought various services. We have bought a ton of equipment like those custom workcells I mentioned, and we bought a lot of reagents. And where we can get something great on the market, we’ll use it.

But what we found is there are certain gaps in areas that were important, maybe very important to us for our cell engineering that weren’t widely available or the products weren’t really up to our level that we needed on the market. And so in those areas, over the last decade, we developed our own stuff. We just never sold it to anyone because it was part of our solutions offering and we kind of wanted to keep it proprietary. So what’s really fun here in reagents is we’re getting to launch a bunch of these, what had previously been in-house assets at Ginkgo. And in fact, we had Ginkgo employees who left, went to other companies and were like, “Hey, will you just like give me that reagent or thing we used to have at Ginkgo because I want it.” And so we heard that enough times that we decided we might as well try to sell it.

And so this is our first product, the cell-free protein synthesis. So cell-free protein synthesis is basically instead of, if you want to produce a lot of protein, taking your gene of interest and moving it into a live cell like an E. coli or a yeast, and then growing that live cell, producing the protein and extracting it. Instead, you start with the live cells like the E. coli, you grow a bunch up, you pop them open, you [ lice ] them, you take the contents out, you make that into your reagent. Then you add the DNA straight to that reagent mix, and it’s got all the components of the cell, it’s just not alive. And so it will make protein. Now there’s some downside that the cell keeps everything in a little, small container, so it had like a high density, which is helpful for production.

But you don’t have this extra step of growing the cells and everything else. So for a number of applications, cell-free really does stand out, and we had a lot of those applications at Ginkgo. So we have — our product here has twice the yields for half the cost compared to market leaders for certain protein constructs, and you can get $2,000, you can get a 10 ml kit, which is a great offer on the market today. And in fact, we launched this just last week. We’ve already got some early sales, which makes me very excited. But importantly, we also had like a free sample. So we have over 100 people have requested samples. And what I think is just — I wanted to highlight was a large fraction of that was actually in the academic research market.

This is a market that Ginkgo has basically never sold anything to until selling a kit recently because we haven’t had anything to offer. They’re obviously not going to outsource research to us. That’s really like all they do for a living. So our Solutions business never made sense. And then we had a certain scale of CRO services with datapoints that were really pointed at the commercial market. So I’m pretty excited to see this. I think the academic research market has been a huge market for life science tools companies like the sequencing companies and companies like Thermo Fisher. So us being able to get into that market here with reagents is very exciting. Okay. So that was kind of what I want to walk through. Again, big takeaways. We’re coming in a quarter early on that cost takeout target.

That’s very strategically important. We’ve done that with a good amount of cash and margin of safety still in the bank, that $474 million in cash equivalents and no bank debt. That sets us up very well to look to the future, and we are doing that. So you’ll see and hear more from us on the life science tool space, I shared some of that today, but expect Ginkgo to really be focused on growing into 2026 from here on out. So super excited to hear your questions, and thanks very much for your time.

Daniel Waid Marshall: 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. Getting started. We’ll start with a question from x.com, I confess I’m not sure how to pronounce it, so I’ll read the whole user name out for you, YEPINY471. And so this question is about automation. Could you please share whether Ginkgo Automation is expected to become a primary driver of the company’s revenue? And I ask if Ginkgo is considering acquiring additional companies in the near future. Could you elaborate on the strategic significance of Ginkgo RNA solutions for the company?

Jason Kelly: Sure. I can take that one. So yes, it’s good to get a question about automation. The — obviously, I spent a lot of time about this on the earnings call. I do think automation is going to be a huge part of our future business. And I tried to convey this idea that what we’re really trying to solve for with our technology is general purpose automation, right? And the market for general purpose automation we think, ultimately, is something like the market for the lab bench, right? The lab bench has been the general purpose kind of like platform for doing laboratory work. And there’s obviously lots of ways to sell things into the lab benches, reagents, consumables, benchtop equipment, services and so on. And so the real question is, are we able technologically to make automation as general as a lab bench or even somewhere along that arc?

If so, then yes, it will be the majority of our business in the future if we can pull that off because the lab bench has been such a huge market in the life science tools space. So that’s what we’re going to see. I’m certainly optimistic that we could pull that off. But yes, absolutely, like automation, writ large, when it is that generic, absolutely would be, I think, ultimately, that the majority of the revenue of the company would flow through something like that automated bench. You asked about acquisitions also. We don’t have anything immediately planned. It’s a tough market for life science right now, life science tools in particular as well. So there are things kind of popping up on the market all the time. If something was a really great fit and a good opportunity, you might see us do it, but nothing immediately planned.

And then, well, the last thing was RNA solutions. Is that right?

Daniel Waid Marshall: Yes, RNA solutions.

Jason Kelly: Yes. So we announced — I didn’t talk about this on the earnings call. We announced a product called RNA solutions. Best way to think about this is taking some of our expertise in the solution space. So like a solutions project, again, is a customer outsources a whole, usually like a 6-month, a 3-year R&D partnership. Our scientists are doing the work using all the tools available at Ginkgo to deliver ultimately a scientific result to the customer. Maybe it’s a better drug candidate or a new agricultural product, whatever. As part of that, we have a whole bunch of kind of capabilities in there. And some of them, like I was mentioning, we can turn into a reagent. Some of them are turning into hardware products and some of them we can turn into services.

And so with datapoints, we’re doing that in a few specific areas, but RNA solutions is an example of us offering a service like that, radiating out of our work, doing RNA discovery. You might remember I had partnerships with places like Pfizer and others doing that. So that’s just us turning that into a kind of off-the-shelf service. So I’m excited to see that. I think there’s more things like that in the Solutions business at Ginkgo. So expect to see more things like that.

Daniel Waid Marshall: And for our callers, you can just raise your hand and I’ll open your line. I have another e-mail question, which I can get to in the meantime while we’re waiting. So this is from Brendan with TD Cowen. There’s 2 questions.

Jason Kelly: Folks, there’s like a whole bunch of earnings calls today. So we had some folks tell us that they were not going to be able to make it. So we apologize for scheduling it on top of everyone else. We’ll try to do better next quarter. But yes, go ahead. It’d be great to hear from Brendan.

Daniel Waid Marshall: Sure, sure, yes. Okay. So the first question is, could you provide some more color into your ADME data generation software? And are you planning to develop any of your own models on the generated ADME data as a separate build-out for customers? And how does the meter beat pricing work in terms of licensing over the course of a contract’s lifetime? And are you pushing the service to any partners that house their own RAC systems?

Jason Kelly: Okay. So maybe I’ll go in reverse order and then maybe you’ll give me that first one again, that would help me out. So on the RAC systems, yes, I mean, one of the things I’m excited about is having us demonstrating capabilities through our service offerings on the RAC hardware at Ginkgo in Boston. And then if a customer wanted to have that infrastructure in-house, and there can be a lot of reasons for that. Maybe they want to apply the technology against a cell line that’s very proprietary that they don’t like to have lead the building or whatever. There’s lots of reasons, you could imagine it. We would have kind of proven that technology out on the RAC modular automation hardware. And the great thing about that hardware is I can just install those systems at your site and the protocols should run the same as they run for me, right?

And this is the advantage of Ginkgo having a bio lab, where we run our own automation and we do these high throughput services, it does mean that we can actually kind of lift and shift those services right onto your premises if you want them. So I think there’s an opportunity for us to do work as a service, show people it’s valuable, install RACs that do that work so that we have that business in the future with a customer. As far as we’re concerned, whatever makes the most sense, for our biopharma, bio ag, industrial biotech customers, if they want to do it in-house or through services is fine with us. So I think you will see that crossover between automation and datapoints in the future. The meter beat. Yes, so I think the key — I mean, the idea is very simple.

Like there’s a lot of vibes, I would say, around, hey, we need to have these CRO services in China because they’re so cheap, and if you take them away, we won’t have these cheap services. And we just want to try to take that off the table and offer CRO services. That costs the same thing. So now there’s not really an excuse to not have it onshore in the United States. And so that’s the whole point with the meter beat. It’s really to send that signal to the industry that there will be providers here in the United States that can match prices with Wuxi and other CROs overseas. And the first question — sorry, the ADME one, Daniel?

Daniel Waid Marshall: Yes, the first question was whether we were planning to develop any of our own models on the generated ADME data as a separate build-out.

Jason Kelly: Yes. So you are seeing folks working on this problem. It’s a few start-ups right now. They’re like a liver tox one and some others. The basic idea is if you’re going to generate all this data, like the ADME data, a lot of it is around like kind of the developability of a small molecule. Could you then turn that data set into an AI model that is then just available as a model to customers that they can include that in their design of drugs in the first place? I mean I think it’s a great idea. I think it’s a tough — the business model for that has not really been worked out well in the biopharma space, that sort of history of software. There’s been places here and there — I’m sorry, I’m just basing on the name, but there’s a well- known sort of drug modeling company that has made an okay business out of this.

But it’s generally been tough to be like a pure-play software-type service. So I think it is like an add-on we could add, but the primary activity, the thing we think customers do have a willingness to pay for is generating data. And so if it’s data for their proprietary molecules, for their libraries and whatnot, that’s data they need. And if we can generate that data for them more efficiently or at a scale, they can’t do it in-house, that it’s data they’ll pay for. So we like that just as a business model. But I do think there’s an opportunity as those big data sets kind of get produced, whether we do them with partners, whether we do them in-house that you could develop models. The one thing I will say is we do release data sets. We do these data drops where we’ll post — actually put them up on Hugging Face now.

So you can go to Hugging Face and Google for Ginkgo’s data sets. We have antibody developability, we have functional genomics, terabyte size data sets. So if you’re tuning in from a customer, again on the AI side or high throughput biology, you should go download those data sets. It will let you see the kind of data that we make from the datapoint service in a nice clean format and you can play around with it. And if you like it, then you can just order more for your specific areas of interest. So I think you’ll see us do data drops and then maybe depending on the market over time, we could do models. But we’re also happy to enable other people that want to do models, right? If they want to generate a huge data set and make an awesome AI model and then sell that model, we are here for it.

So I think there’ll be an ecosystem in the market.

Daniel Waid Marshall: Cool. There was one more question from TD Cowen, and that was about biosecurity. So on the lower biosecurity guide, are you seeing any areas that are particularly exposed to geopolitical pullback or tensions? Are there any end markets that are seeing particular exposure as well?

Jason Kelly: Yes. Sorry, I meant to mention this in my talk. As Steve had mentioned and shown in the numbers, we’ve gone from a $50 million plus to a $40 million plus on biosecurity, brought that down. This is basically because in biosecurity, we’ve always tried to guide to like what we had in the bank as much as possible. We try to be conservative about it. We’re still like — it’s basically on the international side is the short answer to this question. So we’re seeing certain contracts that we were hoping to have in place by now, not be in place. I don’t think we’re — they’re not like totally off the table, but at this point, I just wanted to be more conservative because that had been kind of the attitude we’ve taken with the markets on biosecurity.

Whether that’s like a macro trend or an anecdote, not totally clear. I think we are certainly seeing a lot more focus in the U.S. on like defense technology. And I think biodefense, and this is like the companies like the Andurils and the Palantirs of the world, I think there’s little question that there should be sort of like a biodefense prime, right? That’s the thing that should exist in the market. How that gets built and what are the first types of contracts and so on, I think are still like open questions, but I think biosecurity business is well positioned to lean into that. But we have to kind of just see the market as it develops.

Daniel Waid Marshall: Cool. Thanks, Jason. All right. Any questions? I have another one from online, if you’d like for me to go that direction.

Jason Kelly: Yes, sure. Go ahead. I’ll do one more. And if no one else is there, it’s busy earnings today. Go ahead.

Daniel Waid Marshall: So this question is from Trip90501 on x.com. Regarding your target of adjusted EBITDA profitability next year, could you walk us through the key levers you’re focused on to bridge the gap from today? Specifically, where do you see the most significant impact coming from? Is it increased foundry automation, AI-driven efficiencies or disciplined SG&A management?

Jason Kelly: Steve, do you want to touch on that?

Steven P. Coen: Yes. I can start it, maybe you speak to maybe some trends, Jason. If you just level set what we just accomplished in 4 quarters, we’ve succeeded in taking $250 million out of our cost run rate. And we have effectively 6 quarters to go before we get to our target goal. So just looking at what we’ve done in the last 4 quarters, that’s going to roll forward positively for the next 6. In addition to that, we still have some cost levers to take out. We need to be strategic about that, as company-wide and holistic as we just accomplished, but there’s absolutely more opportunities on the cost side. And then we have to drive revenue. And a lot of the drivers of revenue and what we’ve been talking about all along, we need to see solutions, contributions from tools.

And we really see — a lot of what Jason talked about is going to roll in, in some successful way in revenue. That said, our biggest risk and opportunity still remains the sublease situation that we have. We have a significant amount of underutilized rent space, lease space. And so you’ve seen that we’ve taken out of the segment adjusted EBITDA of the unused space because we’re not using it to contribute revenue right now. So the most important element of that is we’ve succeeded in doing what we said we were going to do. We were going to shrink our footprint as far as our work level, revenue production level. We’ve done that successfully, and we’re out marketing. The tough side and the risk side is the fact that the Boston market and the other markets around are just soft at this moment, but we’re continuing to focus on that.

Jason, I don’t know if you have any views on revenue drivers.

Jason Kelly: No. I mean I think the big one is just a continued shift into tools. So I think we’ll watch how fast we can get to pick up on the automation in particular in datapoints. It could be very swingy, I mean we’re seeing a lot of interest because of the AI work in beginning to automate labs. And I do think we have the sort of the best technology in the market for that. If you’re really talking about general purpose lab automation and connecting it to AI reasoning models and this lab-in-the-loop concept and all these types of things, I really think we’re well ahead on that. And so we’ll see. That would be the one that’s the most swinging where we could really get ahead on things. But it is a new area for us. And so I don’t want to overstate it. But I’d say that’s the place where I see the most like upside potential on revenue in 2026.

Daniel Waid Marshall: Cool. Thanks, Jason. All right. I’m not seeing any other questions right now. I know folks are on other calls as well. So just a reminder that you can always reach us at investors@ginkgobioworks.com, and we’ll get back to you as soon as we can. I want to thank everyone for tuning in today.

Jason Kelly: Yes. I appreciate it. Thanks for the questions.