Ginkgo Bioworks Holdings, Inc. (NYSE:DNA) Q1 2025 Earnings Call Transcript

Ginkgo Bioworks Holdings, Inc. (NYSE:DNA) Q1 2025 Earnings Call Transcript May 6, 2025

Ginkgo Bioworks Holdings, Inc. misses on earnings expectations. Reported EPS is $-1.58 EPS, expectations were $-1.23.

Operator: [Call Starts Abruptly] live on air. I’m joined by Jason Kelly, our Co-Founder and CEO; and Mark Dmytruk, our CFO. Thanks, as always, for joining us. We’re looking forward to updating you on our progress. As a reminder, during the presentation today, we’ll be making forward-looking statements, which involve risks and uncertainties. Please refer to our filings with the SEC to learn more about these risks and uncertainties, including our most recent 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, traction with our government clients as well as new offerings and opportunities emerging for our tools businesses. As usual, we’ll end with a Q&A session and I’ll take questions from analysts, investors and the public. You can submit those questions to us in advance via X, #GinkgoResults or e-mail, investors at investors@ginkgobioworks.com. All right. Over to you, Jason.

Jason Kelly: Thanks, Daniel. We always start off with our mission here at Ginkgo, which is to make biology easier to engineer. And then we had three objectives. And I first showed these are close variants of these about a year ago when we announced that we were going to be doing a major restructuring of the Company. And these three objectives were to reach adjusted EBITDA breakeven by the end of 2026. And importantly, doing that while maintaining a cash margin of safety. In other words, we didn’t want to get in a position where we’re going to need to fund raise when we didn’t want to, right? We wanted to be doing that we needed to fund raise from a position of strength, but ideally not even need to fund raise. Second, we wanted to cut costs while importantly, serving our current customers.

We had a lot of amazing customers, large pharma, large ag biotechs, industrial biotechs as well as the government. We want to keep serving those customers well while at the same time focusing the Company. And then finally, we wanted to expand the way we sold our platform, and I’ll talk more about this in the strategic section. But from not just R&D solutions, where we do an end-to-end research project, but also directly as a tools business like a traditional CRO or an equipment vendor would. These were new ways to go to market to a wider set of potential customers than we had with our solutions business. So those were our three objectives, and I’m very happy to say we made progress on all of them. But after a year, we’ve just made unbelievable progress on taking out costs while still serving our customers.

So we’re — I’m very happy to say we’re at a $205 million reduction in our annual run rate between Q1 2024 and Q1 ’25. You might remember, the target I had set was $200 million, I think, by Q3 or something of this year, like halfway through this year. We already beat that. We’re moving — we’ve taken actions in the first quarter that are going to improve this even further. Mark will mention. And so, I really think sets us up to be in an incredibly strong position. And importantly, because we did it faster, we’re at this place while still having $517 million in cash and cash equivalents on the balance sheet and no bank debt. So that, among our peers in sort of the advanced sort of platform technology space in the market today, I think, is a uniquely strong position.

And look, biotech on the capital market is going through a tough time right now, that is challenging for the Company is in it. It’s also opportunity, I would say, for investors. And from my standpoint, the companies that can make it out the other side of that are in a particularly strong position as a biotechnology is, I think, a fundamental industry that’s not going away. And so, this sets us up to be in a place to do that. And I want to be just give my thanks to the team for what’s been an incredibly difficult, challenging ton of work last year to get us to where we are, but it puts us in a very, very strong spot going forward. So, with that, I’m going to hand it to Mark to go off over this quarter’s financials.

Q&A Session

Mark Dmytruk: Thanks, Jason. I’ll start with the Cell Engineering business. Cell Engineering revenue was $38 million in the first quarter of 2025, up 37% compared to the first quarter of 2024. The first quarter this year included $7.5 million in noncash revenue from a release of deferred revenue relating to the mutual termination of a customer agreement we had with BiomEdit, one of our platform ventures. Excluding this impact, Cell Engineering revenue was $31 million, up 10% compared to the first quarter of 2024. This increase was primarily driven by strong growth with biopharma and government customers. In the first quarter of 2025, we supported a total of 123 revenue-generating programs on the Cell Engineering platform. This represents a 32% increase in revenue-generating programs year-over-year.

As discussed on our last earnings call, this quarter represents the first time we are reporting the new revenue-generating program metric and are no longer reporting the original program metrics. As a reminder on the rationale here, the nature of programs that we take on with our customers has evolved significantly following our adjustments to commercial terms and the launch of our tools offerings in 2024. This new metric includes all programs that generated meaningful revenue in the quarter, including smaller programs that were previously reported as other contracts and further excludes programs that did not generate meaningful revenue in the quarter, which typically would be those programs either just starting or in final stages of completion.

We believe the new metric will be more useful to analysts who are using this to model revenue. We have also updated the 2024 comparables using this new metric in the appendix. Now turning to Biosecurity. Our Biosecurity business generated $10 million of revenue in the first quarter of 2025 at a segment gross margin of 28%. Segment gross margin excludes stock-based compensation. Turning to the next slide, I’ll provide more commentary on key items for the rest of the P&L. Now that we are almost a year into our restructuring, you can see the very substantial cost reductions and improvements in profitability that we have executed when compared to the first quarter of 2024. As a reminder, a full reconciliation between segment operating loss, adjusted EBITDA and GAAP net loss can be found in the appendix.

Starting with the more significant items in segment OpEx. In the first quarter of 2025, Cell Engineering R&D expense decreased 41% from $82 million in the first quarter of 2024 to $49 million in the first quarter of 2025. Cell Engineering G&A expense decreased 53% from $38 million in the first quarter of 2024 to $18 million in the first quarter of 2025. And while smaller in amount, you can also see a decrease in Biosecurity operating expenses by 33% year-over-year. All these decreases were driven by our restructuring efforts. Net loss. It is important to note that our net loss includes a number of noncash income and/or expenses 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.

And we are now showing you adjusted EBITDA at the segment level so that you can more clearly see the relative profitability of Cell Engineering and Biosecurity. The significant improvement in Cell Engineering segment operating loss in the first quarter of 2025 compared to the comparable prior year period was due to the previously discussed drivers of improved revenue and reduced operating expenses as well as the noncash deferred revenue release within the quarter. Biosecurity segment operating loss also improved significantly due to the primarily cost reduction efforts. Moving further down the page, you’ll note that total company adjusted EBITDA in the first quarter of 2025 was negative $47 million, which was up from negative $117 million in the first quarter of 2024.

The principal differences between segment operating loss and total company adjusted EBITDA in the first quarter relates to the carrying cost of excess lease space, which you can see was $12 million in Q1 this year. This cost represents the base rent and other charges relating to leased space, which we are not occupying, net of sublease income. We’ll continue to break that out for you going forward since that is a cash operating cost that is not related to driving revenue right now and can be potentially mitigated through subleasing. And finally, I’ll just make one additional comment relating to cash burn in the quarter. Cash burn in the first quarter of 2025 was $58 million, down from $104 million in the first quarter of 2024. This significant decrease in cash burn was a result of the restructuring.

We expect to further reduce the cash burn run rate significantly from this level by the fourth quarter of 2025, though we expect some lumpiness in the progression during the year due to timing of working capital. In terms of outlook for the full year, we previously issued guidance for total revenue of $160 million to $180 million, Cell Engineering services revenue of $110 million to $130 million, and Biosecurity revenue of at least $50 million. We update this previously issued guidance solely to reflect the impact of the previously mentioned $7.5 million noncash deferred revenue release in the first quarter. With this in mind, we now expect our total revenue to be $167 million to $187 million, Cell Engineering revenues to be $117 million to $137 million and Biosecurity to remain the same of at least $50 million.

In conclusion, we’re pleased with the substantial improvements in cash burn and profitability when looking back over the past year. In the first quarter, we continued to execute against our core objectives while navigating significant uncertainty in the macro environment. And with that, I will hand it back over to you, Jason.

Jason Kelly: Thanks, Mark. So, in the strategic section, we’re going to cover three topics today. The first, I want to touch again on our continued restructuring efforts and how well that’s going on the cash take outside in our path to sort of EBITDA breakeven by the end of next year. Second, there’s been a lot of changes in the administration in the U.S. government here in terms of sort of approach to research spending and Biosecurity and things like that. And I want to just highlight that I think biotech remains a critical emerging tech in the U.S., and Ginkgo’s well positioned for it. And then the third topic, I want to talk about our tools businesses Datapoints and Automation. This has been our big motion over the last year is expanding into the tool space, and that’s going really well, and I want to give an update on that.

Okay. So first, I talked earlier, I’m really happy to have that highlighted in the middle of that $205 million of annualized run rate cost takeout that we’ve achieved in the year since we announced the restructuring. Our goal, of course, is to get to adjusted EBITDA breakeven in 2026. And so I really like this chart on the left, you can see back in Q1 2024, where we were on the cash expenses and total revenues. And what we want to do is just shrink that gray bar and grow the green bar and eventually get those to be the same size. And so, we are pushing at that is sort of the relentless focus here on the team. Again, I’m really happy to see the progress. It’s going in the right direction. We already have made changes in the first quarter that you’ll see reflected in the coming quarters to continue to take costs out.

And hopefully, our efforts in the tools space will keep growing sales as well. I will mention, if you look and see the segment breakout here in Biosecurity, again, Q1 2024 — or sorry, Q4 2024 to Q1 2025., we’re at $5 million on a run rate burn. That’s one where we’re hoping to really get Biosecurity to breakeven this year and then Cell Engineering, you can see the enormous progress we’ve made since Q1 of 2024 over last year. But need to continue squeezing on that in order to reach adjusted EBITDA breakeven next year. So, I think we’ve got a path to it. It will be a serious amount of work, but I’ve been extraordinarily impressed again, kudos to the team here at Ginkgo and all the work to date to get to the strong position we’re in today. And again, I don’t have it on these slides here, but over $0.5 billion in cash in the bank.

This has been a tough market for biotechnology. The companies I think that make it out the other side of it will be in an especially strong position. And so the fact that we’re so well shored up is thanks to the team’s efforts over the last year. All right. Next, I want to talk about the new administration and what the U.S. government is doing in biotechnology and Biosecurity. There was actually a great speech. I really encourage you to either watch it or read it from the President’s Science Adviser, Michael Kratsios. It’s a cabinet position as of the last administration was turned into a cabinet position. And he had a great speech where we talked about sort of how the administration was going to invest in technology and science and he said, whether in AI, quantum, biotech or next-gen semiconductors, it’s the duty of the government to enable scientists to create new theories, and power engineers to put them into practice.

And so what’s important there is that’s your short list of critical technologies for the U.S. AI, quantum, biotech and chips, all right? So it’s good to see biotech on that list. It’s been on the list for a while, certainly in the last administration as well. And so I’m happy to see it’s still there from the President’s Sciences Adviser. This is a report that came out. You might remember, I was actually sharing this commission. I’m very thankful that Senator Young is now the Chair that’s a load off me. And Michelle Rose, the Vice Chair, the final report from this National Security Commission on emerging biotech just came out a few weeks ago. I highly encourage folks to read it. Just to quote here, we stand at the edge of a new industrial revolution, one that depends on our ability to engineer biology.

So, this is a bipartisan commission, obviously, Senator Young’s Republican. I see, again, a push here really coming on the legislative side for improved reduction in regulations, new sources of funding. I think you will see this administration fund things differently than the previous administration. But I think you will still see funds continue to go out the door towards biotechnology. And importantly, our solutions business at Ginkgo is a trusted R&D service provider to the U.S. government. So, we have 28 government projects across both Cell Engineering and Biosecurity, about $180 million plus of contracted backlog or unfunded potential This is it sort of like options on some of our contracts, depending on how things go. And just to highlight a couple of wins since President Trump selection.

We won a brand called ARPA-H REACT. This is in partnership with Carnegie Mellon, about $9 million programs sort of for bioelectronic devices in disease treatment. But then I really wanted to highlight a new program we just announced a few weeks ago called WHEAT, a $29 million funded program. And if you go to the next slide, the applications here is really around how do we bring manufacturing of critical raw materials in the pharmaceutical industry back onshore. And I think there’s a ton of work to do here. You’re already starting to see motions happen here, pharmaceutical company companies investing in manufacturing plants. That’s usually around their newer drugs. We also have a lot of critical drugs that are antibiotics, a lot of sort of frontline medications that have really been moved overseas over the last 20, 30 years.

I think some of those, we do want to bring back. So, some of that’s just going to be building manufacturing plants. But what this program is, is actually to make use of weak germs. So, this is — it’s an extract that comes as a byproduct of growing WHEAT. And what’s cool about it is you are able to essentially take that wheat germ extract, which has all the components like the low-level components of cells, right? So, this is part of the magic of biology. Our cells, weed cells, insect cells, bacterial cells at the lowest level, the DNA, the ribosomes, the mRNA, that’s all the same. And so, you can actually reuse the material that comes from that wheat germ, add in a piece of DNA that say, encodes for like human insulin or another therapeutic.

And then in that cell-free system in that extract actually produce that therapeutic drug. And this is not a technology that’s coming out tomorrow, but this is a much lower cost source for this sort of cell-free extract than what you can currently get on the market today, if we’re able to be successful in this research project for ARPA-H. And so, this is a time, I think, is, obviously, I’m excited that we’re being a part of this, but I’m just glad to see the government funding this. And this is the type of thing that can go solutions business where we do these end-to-end projects and deliver a scientific result. This is an example of where we’re doing that for the U.S. government. And I expect we’ll see more of those. Okay. So, I want to talk a little bit now about Ginkgo Biosecurity, which is the other big area where we work with the U.S. government.

We really have two big offerings, product offerings here. The first we call Canopy. And you might remember, we — I’m not going to go into great detail, but we collect wastewater from plans, inbound planes into international airports. We collect metadata, where did that plan come from. And then we look in the wastewater for a whole panel, I think we’re up to like 60 now different infectious diseases. If we see them, then like if we see a virus, we can sequence it and get that variants, genomes, remember all the COVID variants, we can get the variant sequence and then give that back to the government or whoever is having us do that particular work. And so that’s the actual physical collection of data. And then our Horizon platform is when we take all that data and we try to give actionable information back to decision makers.

And you can imagine there’s a lot of great opportunities for AI and sort of automated learning and data parsing there on the Horizon platform. These types of — we think of these like almost like radar stations for monitoring for infectious disease. I mentioned airports, but absolutely, we should be doing this on ships, should be doing this at mass gatherings, military installations, embassies, BSL-3 and four labs, like this is a very obvious thing to me, like we ought to be monitoring the effluent like what’s coming out of these labs and the surrounding area around these labs just to keep an eye on if there’s a leak or things like that. This type of, really, we consider a passive monitoring, like you’re just looking all the time. We think it’s going to be actually critical in terms of having a strong Biosecurity defense network here in the United States.

And I think this is particularly salient coming up because, as you know, the United States has stepped out of the WHO. And if you look at how the WHO did its work, that was based on voluntary information sharing. So, in other words, there’ll be an outbreak in a country, and that country is equivalent of the CDC, which share that information back with the WHO. WHO would disseminate information globally. I think that’s getting outdated in this era. One, there’s a lot less cooperation among countries at the moment. Number two, post COVID, it’s very clear the economic impact of these things. So once a country has an outbreak, they got — there’s often a reticence to share that information. We even saw that with COVID itself. And also, the technology has just changed a lot in the last 20 years.

And passive monitoring, like I talked about in the last couple of slides can turn like a political problem where you have to ask people for things and have the politics to have them give it to you. It’s a technological problem where we’re just looking. And if something happens, we see it. And just to be clear, that’s how we approach cybersecurity. That’s how we approach Missile Defense. We don’t ask did you launch a missile. We have the satellites up there looking all the time for them. And that’s really how we should move to a platform like that for monitoring infectious disease. And I’m hopeful there’ll be opportunities to do that as the U.S. considers how to build our infrastructure outside of the WHO. Okay. I want to now talk about Ginkgo’s Datapoints and Automation offerings where I’ve seen new deals and opportunities emerging.

Okay. So, about a year ago, I showed this slide for the first time. So, Ginkgo is historical, the way we brought our platform to customers was through what we call Solutions, where our customer is really the head of R&D. So, this is the person who is in charge of, say, drug development at a company like Merck or Pfizer, Nova Nordisk, some of our customers. And Ginkgo is an outsourced scientific team with access to a highly automated, unique platform here in the 200,000 square foot lab over here next to me in Boston. And we would give that customer back a scientific result. So good example is that WHEAT program I just mentioned. The customer there is a program manager sort of a Head of R&D for the government at our page, and our job is to give them back a scientific result over a period of one to two years with milestones along the way.

Very similar to our commercial relationships. All right. About a year ago, we said, “Hey, we’re going to keep doing that. We’re going to do it in a more focused set of areas.” That’s a lot of how we took the cost down. But we’re also going to start offering that very same platform, the same robotics, the same integrated systems directly to customer scientists. Okay, to the many scientists that are at a Nova Nordisk or at Merck and give them tools so that they could do the job of scientific discovery. And that was a new way to go to market. I really like this chart, this curve here, I’ve shown this before, but on the Y axis, the idea here is as you go up the axis, you have increased customization and technical risk for the customer. And the reason I’m highlighting this is because there’s sort of a business model shift in the middle of this chart.

So, at the extreme left end of this, I’m designing a custom drug. I’m taking all the risk on it, and I’m hoping that when I get great Phase 2 results or Phase 3 results, I can sell it to a large pharma company. I make an enormous amount of value, all right? But I take a lot of risk, and it’s very custom. As you go down the chart, you have our Research Solutions business. So, we are doing custom work, like every one of these customer projects is different. And it is a technical risk, like we get paid if we are successful, and we hit certain technical milestones. And so, as a result, we’re able to get royalties and milestones. We’re able to get a piece of the customers product revenue, essentially, okay, in one form or another. That’s sort of on the left-hand side of that dotted green line.

On the right-hand side, you have our tools offerings. And here, we are not taking a royalty. We’re not taking any milestones from the customer. And what we’re really offering is sort of fee-for-service work for that customer so that they can ultimately develop their own products. And that’s either going to market with sort of a traditional like CRO style business model with Datapoints or via an equipment business model with automation. And this really does change if you go to the next slide, the solutions business is really based on sort of longer term, but bigger upside per project. We’re getting a piece of that drug value, for example, in the long run, it just takes a long time. The advantage of our tools business is its near-term fees. It’s a faster sales cycle, and we have many, many more potential customers for that product in any organization.

And the reason I’m excited about this is Ginkgo has worked out the hard challenges over the last 10 years, of building out our own automation and software stack, if you go to the next slide. And this is not just hardware, it’s also our code base, our operations, our data stack and everything else. And we’ve done this over 200 R&D projects in agricultural, industrial and pharma biotechnology. So, we have the scars of knowing sort of what works and what doesn’t work when you’re doing this work at a high throughput. And if you go to the next slide, our interest from customers is really around sort of large data set generation for AI. This has been wind in our sales as we’ve opened our platform up companies like Genentech and Recursion have been showing that you can use these AI models in service of drug discovery.

That’s meaning a lot more companies are interested in sort of automated data generation, and that’s exactly what we’ve built the reps doing over the last 10 years. And so that’s been an excellent conversation to have with customers. Now if you go to the next slide, Ginkgo’s technology is shown on the right here, that’s our facility in Boston with our automated RACs. I will highlight, the left-hand side of this chart, the lab bench with the Fisher catalog to order whatever reagents you need is actually a very effective way to go do drug discovery, right? It’s a very effective way to go discover plant traits and things like that. It has — it allows scientists to order what they need, when they need it. It’s very quick. You get turned around in 24 hours, massively customizable.

There’s nothing wrong with the left-hand side. It just is not great at generating low-cost data points. In other words, if you want to make a lot of data an AI model or for high throughput screening or things like that. The bench is not your friend. You do need to move on to something like robotics. And I have that on the next slide as well. These are just two different approaches that are quite complementary, right? It isn’t like one has to replace the other. That’s a lot of the conversations we have with customers. It’s really that, particularly as these AI models are gaining in prominence. You’re going to want to have what we call a foundry basically a generalized automated facility that can be quickly reprogrammed to make new large data sets support your AI and ML teams alongside of the benches where your scientists are still doing small batch very hypothesis-driven research.

And by the way, what you learn over here from the foundry and the AI models is going to inform those scientists hypotheses. Absolutely. That’s exactly what we’ve seen with the Recursion and the Genentech of the world, where you can use the foundry per se, target discovery and then get in the lab at the bench and go test those targets out quickly by hand. So that type of feedback loop, I think every major pharma, every large research institute will ultimately need to have sort of a foundry type setup to complement their lab benches. All right. So, Ginkgo Datapoints is our first offering in this area. I’m not going to spend and talked a lot about it at the last earnings call. Just a quick update on this. We launched actually just yes well, Monday, our GDP A1 data set.

And these data drops are very valuable for the community and they showcase the output of our data point services. So, this is a really great one. There’s a new preprint that came out. You can see on the right. There’s a link at the bottom to go download the data set but 246 different therapeutic antibodies. And you can see these 10 different developability assays listed on the left. And then importantly, all that data is in a really clean format for your AI or ML team to go play around with it. And so, we’re going to keep doing this. So, you’ll see us keep putting out data sets. The data scientists love these. It creates new customer demand for us, and it showcases just what our platform can do. Okay. So I want to spend a chunk of time real quick talking about Ginkgo Automation and some of the interest we’ve seen around, in particular, AI reasoning models and connecting those to automated platforms in the lab.

First, I want to mention, we had a big win. So, we announced a week or two ago that we — our pet partner and sold the system to Aura Genetics. This is a diagnostics company building out a new facility. This is really exciting to me because on the next slide, we’ve obviously had a lot of success with early customers like Octant in the drug discovery space, 7x, throughput increase, 88% reduction in hands-on time. They’ve been using the system for two years. They were kind of our original drug discovery beta customer. A lot of conversations with high throughput screening, pharma company is definitely going to buy this. But diagnostics companies is really a new market for Ginkgo. So, I’m really excited to see the automation going there. And I think this is one of the exciting things about Ginkgo’s platform going out as tools, okay?

When we’re offering solutions, we had sort of like a much more narrow window of where we could apply, say, our automation, right? It was ultimately going up through this kind of window of a research project associated with Cell Engineering. Now the automation could really go anywhere to any lab that would benefit from integrated automation. And you can see that with our RAC carts in the next slide. The idea behind the Ginkgo Automation is we’re basically creating a standardized physical wrapper around a piece of laboratory, essentially benchtop hardware. So that’s a center fuse there, that orange thing inside the RAC, then we have a robotic arm. And then we have a piece of MagneMotion track, which is kind of like a little railroad track that can move material along it and deliver a 96 or 384 whatever well plates to that robotic arm, the arm picks it up and puts it on to the — in this case, center fuse.

All right. And so, what you’ve done is you’ve taken a piece of lab equipment that today is very custom, right? Like it’s coming from some particular vendor, it’s got its own software. You’ve got to walk up to it and interact with it and you put it inside this box. And once you’ve done that, if you go to the next slide, you can stick that RAC card together with as many other ones as you want in a line. And let’s say, you add 10 pieces of equipment you wanted to integrate. We would send you the 10 carts with that equipment, you would stick them in a line and then you would use our cloud software to control it. And suddenly, you don’t need to be in the weeds in the software on all 10 pieces of lab equipment because our software has parameterized control of all of them.

And we didn’t have to like a traditional integrated automation vendor would basically do a big custom design for you, a custom engineering project that would take a year or something to ultimately design and build and get it shipped and installed for you. If we had these 10 RACs available, we could send them over and put them together in a very short period of time. And so, a matter of weeks. And so that is really exciting and a big change to how you build out integrated automation. The other big change other than just speed to deploy is it’s expandable. So when you normally build a custom integrated setup for automation, it’s built to do one thing, right? With these RAC systems, for example, this is a system we have in Boston, we had five of these to start with, I think, are six doing NGS prep, that was like the original application.

And then we were able to keep adding more RACs. We now have 25 RACs on this setup. And we have a whole range of different. You can see it here, equipment that have been integrated into these systems. We have three different sizes of RAC so that we can integrate this equipment. This is out of date. We keep adding stuff. Whatever customers want in their set up, if there’s a piece of equipment haven’t yet integrated, we can get it integrated in a few weeks. And so really excited about this kind of general concept. And customers are loving this as well. You can see our booth here at the SLAs show, on the next slide. And what I like about this actually the top right corner, there was — this is actually a JPMorgan Recursion at a party. And we said, “Hey, can we bring the RACs and so we were able to set them up in a few hours in the afternoon before the cocktail party and have them moving plates around”.

So that’s the kind of speed in terms of deploying an integrated automation setup that you just really don’t see with other technology. This next slide is a picture of our facility in Boston. That’s a — it is an older picture, but that’s a 25 RAC setup I was mentioning. And another thing that’s unique about Ginkgo is that we actually run our own automation. This is our BSL-2 lab here in Boston to do high throughput data generation for these research projects we’re doing for customers. So we have a lot of experience understanding sort of bio validation and moving these high-throughput protocols on to integrated automation. So, one of the things I’m really excited and we have customers reaching out to us about this system, if you go to the next slide, is this application of what people are calling lab in the loop or sort of physical AI in the lab.

And so just to give you an example of this, if you were to go on to ChatGPT and click that little deep research button, and you ask it a question, instead of getting an answer in five seconds, it’s going to give you an answer in like five minutes. And the reason for that, and you can even ask if you want to see it doing this, you can say, “Hey, show your thinking”. And you’ll see what’s called chain of thought reasoning. And so, what the model does is it says, okay, based on your — what you’ve asked me to do. I’ve broken this problem into pieces. And for piece number one, I’m going to go call up a web browser and do some research on the Internet. And then based on that information, I get back, there’s a bunch of numeric data in there, so I’m going to write a Python script to analyze that data.

And based on the results of the Python script, I’m going to do some more thinking and analysis and I’m going to right to a summary. And it goes and does all that. It’s absolutely fabulous. Like you really should see it if you hadn’t. But what’s gotten people excited is that type of reasoning and analysis, what if you were to then connect a reasoning model like that into the physical world. And so, there’s a lot of activity right now, a lot of start-ups getting funded to do like robotic hands, to like pick things up and hold shirts or some electronics. But what I think is really exciting is could we give that reasoning model hands in the lab. And that’s how we see our RACs. They’re actually like a perfect fit for this. We’re able to integrate — I mean you could integrate 100 pieces of equipment.

We have one project where we’re scoping that with RACs. But in Boston, for example, we already have a setup with 25 pieces of lab equipment set up. And if you go to the next slide, a reasoning model could go ask that set of equipment to run some experiments and then get back really rich data, time series data, raw data files, the RACs give a whole bunch of event data, LIMS metadata about exactly what’s going on inside that experiment. A lot more data than you would get if you were doing the experiment by hand at the lab bench. These are just things that you wouldn’t be collecting, just wouldn’t be collecting them because you’re doing a lot of small things as you work at the bench that aren’t really being recorded. But everything is being recorded when it’s being run on automated setups.

And if you go to the next slide, you can see we’ve already demonstrated — this is a 24-hour protocol without any human intervention, 10,000 Q PCR reactions. These are the types of things we can do. This is just one example of sort is a large data set on a complex protocol being run over a long period of time. So, these sort of like long, continuous experiments, ideally with a reasoning model, controlling it and talking, being able — having those hands in the lab is something we’re really excited about, and we have a lot of customers excited about, too. And so if you go to the next slide, I’ll just say for customers tuning in, again, this is what’s special about Ginkgo compared to a traditional automation vendor. Over the last 10 years, we have been building and running a highly automated lab.

And that’s not just having the automation set up and doing one thing over and over again. It’s doing many things, collecting the data off that automation, getting it cleaned up and back to the scientists. There’s a whole software and data stack needed to really make the most out of these sort of automated data foundries. And so if you’re tasked to bring AI into your research department or deploy these sort of lab and the loop models, we’re more than happy, not just to engage with you on the automation, but really on a consultative basis, to help you build out your whole technology stack internally. And we are — we’ve actually started doing that for some large pharmas now as well. So these are, I think, just some of the things I wanted to update on.

I think this whole push on the reasoning model and AI side is really exciting. And again, I want to just highlight and thank the team for an enormous, enormous amount of work over the last year. For us to be in the position where we are today, where we have growing opportunities on the tool side. We have world-leading automation. We have over $0.5 billion in the bank and our spending is under control is a far cry from where we were a year ago. And so again, kudos to the team for pulling that off and look forward to hearing your questions. Thank you so much.

A – Daniel 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, please just raise your hand on Zoom, and I’ll call on you and open up your line. Thanks, everyone. All right, getting started. So, our first question is from x.com, and this question is from That’s Brendan. The question is, does Jason think there’s a possible opportunity for Datapoints to evolve into a SaaS cloud computing product tool to compete with traditional companies in the space like Veeva Systems?

Jason Kelly: Yes, it’s actually a good question. So, I mentioned this a little bit at the end, but we’ve been starting to do more of these like kind of, I’ll call it, consultative almost like tech-enabled consulting for a large pharma company where we’re actually coming in and helping them think about their data architecture and like how you should — if you’re going to run a big automated lab, what does it look like? What software do you need to have in place to do that? What kind of data systems you need to have in place to do that and so forth. And I think this is something that Ginkgo has a lot of very uniquely specialized expertise in. There’s a question of like how to go to market with that? Like do you actually want to go all the way to offering like SaaS software?

Do you want to just do consultative work and then be able to bring in things like our automation technology alongside that and so on, we’re sort of figuring that stuff out. But certainly, companies do make plenty of money in the sort of cloud and SaaS base. So, if we saw an opening there, I would say the research side of the house is a little different when it comes to commercial where I think you already have a lot of these tools in like pharma sales and things like that. But when it comes to the research side of the house, many companies are not — don’t already have in place large sort of data infrastructure. And so again, most of the work is done at the bench. Most of it is not large data sets with the exception of things like high throughput screening.

So, as they build out more of that, I do think there’s an opening. We’ll have to see if it’s a business for us, but definitely it’s a place we can help, I would say.

Daniel Marshall: All right. Now for some questions from our analysts. The first question is from Michael Ryskin from Bank of America. Michael, your line is open.

Michael Ryskin: Yes. I wanted to ask kind of a two-parter, but on the same topic. One is on the ARPA-H news you provided. Just sort of wondering, if you could provide a little bit more details on some of the economics beyond that. We have the headline number in terms of the $29 million contract, but just thoughts on how and when that will be recognized, how that contributes to revenues? And then related to that, you had a couple of slides where you talked about government contracts, government relationships, things like that. Just wondering what the latest on those is given the current environment with DOGE cutting back a lot of that funding? Have they been reviewed already? Are those sort of how safe are those? And if you could talk about any take-or-pay considerations there, that would be helpful.

Mark Dmytruk: So, Jason, I’d be happy to take the first part of that. So, the ARPA-H just in terms of kind of economic side revenue flows. So, it’s a $29 million two-year contract. So, you can sort of expect sort of generally revenue to be recognized over two years. And then I guess the second point I would just make is that from our perspective, that really significantly derisks the guide for the year. And so — yes, so I think it was very good news to be getting that from the perspective of this year.

Jason Kelly: Yes, just sort of speak to the second part, yes, that was one of the ones we’re keeping an eye on that. We had sort of gotten awarded — I knew we were going to get it, but we hadn’t closed on contracting. So, I do think, in general, like I mentioned, I think biotechnology is still on the short list of critical emerging technologies. So, I’m generally hopeful that any additional programs that we have that aren’t quite contracted yet will move through. But importantly, that they’ll still continue to be funding, like Mark said, and we were sort of the big bogey on that for us for this year. But in general, what’s more important is like do we keep seeing the funding of advanced research in this area, we think so. And then certainly, on the Biosecurity side, there was a couple of executive orders just today related to Bio.

I don’t know if you saw that, but there was one on regulatory relief to promote domestic production of critical medicines. So that’s right in line with sort of the wheat project and generally onshoring. It’s basically reducing regulation for people building out manufacturing for pharma, not something we do, but just to speak to this being a critical priority. And then second is improving safety and security of biological research. This is around like not funding gain of function work. But again, speaks to, I’d say, Biosecurity being on the list of things that are not being written off is not important. So, I’m cautiously optimistic, but of course, you never know.

Michael Ryskin: If I could — I guess just a follow-up, Mark, on the first point on the ARPA-H. Just clarify, there’s multiple partners in that, right? So is there any clarity out of time how that $29 million gets split up or…

Mark Dmytruk: Yes. So, we’re the prime, which means we will recognize all the revenue on that, and you would then see sort of in the cost of sales or in the R&D expense and the effect of the subcontractor costs on that, but we would be recognizing the full amount of revenue.

Daniel Marshall: All right. Our next question is from Mark Massaro, who’s from BTIG. Mark, your are live.

Mark Massaro: So, I wanted to just ask a question about revenue-generating program metric. Maybe this could be for you, Mark. Just help us think about how we should be tracking the economics per program. So, if I’m doing the math right, it looks like the revenue per program might be down in the quarter. Can you just give us a sense for how we should think about that? Is that something that should grow? Or is it because you’re onboarding newer programs that are just starting to generate revenue, it takes some time to move up?

Mark Dmytruk: It’s actually a little bit of a mix shift from sort of the bigger solutions deals. To data point deals. When, in the metric, went from about 10 to 20 when you compare Q4 to [Technical Difficulties].

Mark Massaro: Okay. And then the other one is just, and I recognize the new reporting structures is early days. But if I have this right, looks like your revenue to think about them, progressing throughout the year. And then, I would just be curious if you could just give us any color as to sort of, like, the flavor of some of these prized due in the court?

Mark Dmytruk: So, you’re going to have programs that complete and programs that onboard. And so, the kind of net impact of that means you probably are not going to see a net plus 20 sort of quarter after quarter after quarter after quarter. You’re going to have some quarters where we finish a bunch of programs. And so again, still early days, Mark, with the metric. But I think we’re all sort of keeping our eye on that trend line. The flavor in terms of what’s in there. So, there’s still — I would say, like, generally speaking, the solutions deals of course are bigger. The Datapoints deals are smaller. There are just a few Automation deals in the mix right now because that’s really the newest of the tools offerings. We did sign a bunch of ag solutions deals in Q1, which is new.

I think it might have been the most new programs that I remember in that sort of part of the business in a single quarter. That said, they’re all relatively small in size. And so you can think of those as almost pilot in terms of scale relative to the solutions side of the business. So yes, I would just say probably like the flavor is good diversity in terms of what we’re seeing with big programs, small programs across ag, biopharma, et cetera, data points, solutions.

Daniel Marshall: All right. Next question from Tejas who’s coming from Morgan Stanley. Your line is open.

Tejas Savant: So maybe a sort of predictable question for you, Jason, just on the continued pressure on pharma and biotech here, the commentary from service providers through the starting season has gotten incrementally more cautious, I guess? Yes. And I guess, a lot of sort of nebulous concerns out there, things like tariffs and reference pricing, this afternoon, we heard of the appointment of a new head of [Cyborgenic] with a pretty vocal stance and accelerated approvals and surrogate endpoints and whatnot. So, I’m just curious as to what you’re hearing from your customers, especially over the last like six to eight weeks when things seem to have sort of ratcheted up a little bit, if you will?

Jason Kelly: Yes. I mean I would say, overall, it is, like there is a lot of sort of hesitancy in general around R&D services. So, like I think you’re just seeing, a, less outsourcing of stuff. People are like more protective of their internal teams. So that’s like been headwinds for us on the solution side. I think if you look across the industry broadly, the like there’s less demand going to like basically all CRO and equipment and tools vendors. I think that’s — and you’ve seen that reflected in the pressure on all of them. I mentioned this before, Ginkgo’s new in the tools industry. So unlike, say, us or somebody that’s like highly penetrated across the whole industry and sort of moves with the total demand. We’re more winning pie from others.

And so, I think there’s a lot of opportunity for us still uniquely in tools, but I would say the whole sector is definitely under pressure. I think that probably means places invest less in new advanced technologies, right? Like I think you’re less likely to see a lot of the current players like doing some big project to expand in a new area right now. So, that’s maybe good for us because we’re sort of an innovative new entrant. And so — but I would say across the industry, I’m hearing from people what you’re hearing, which is pull back on things. But I don’t know that Ginkgo maybe gets affected a little less on that when it comes to our tools business. I do think on solutions, that makes it tougher for us.

Tejas Savant: Got it. That’s helpful. A couple of quick cleanups for Mark. Mark, can you just share some color or just ballpark numbers to help us bridge from your old program ad metrics, the new revenue-generating programs? And then I think on the last call, you guys had talked about a little bit of potential upside from tools offering and a number of pharma deals that closed in the fourth quarter. So just curious as to how those opportunities have evolved since? And then on this sort of pharma reshoring point, Jason, which you alluded to a little bit earlier, is there an opportunity beyond sort of the work you’re doing with ARPA-H here in your mind for Ginkgo to participate in?

Mark Dmytruk: All right. So why don’t I start there? So, in terms of bridging the old metric to the new. So, the new metric very importantly, excludes programs with de minimis revenue in the quarter. So that would typically be a program that is either just starting or is in the final stages of completion. And we really, in any particular quarter in the past had quite a lot of those, like we might have had just like rough numbers 20 to 30 programs that were in kind of a start mode and maybe similar number that we’re in a final stage of completion mode. And so those would be kind of out of the mix right now completely. However, it’s not a straight subtract because we’re now including programs or wouldn’t have been included in the past because they didn’t meet the sort of definition of what we thought of as a major program.

And so, a lot of the sort of smaller Datapoints programs or even small solutions programs wouldn’t necessarily have been included in the past at all under the definition. And so those are now kind of in the mix. So that’s sort of like the rough bridge here. Now if you look in the appendix to the presentation, we have included a restatement of — or I would just say the historical comparables that you need on the current metric so that you can see what it was in each quarter of last year. So that will help you kind of bridge the old to the new. On your second question, upside on tools and pharma. So, I think probably the best way to put it is in terms of the revenue guide, yes, the guide is still being, I would say, relatively conservative in terms of what we’re expecting from tools this year.

So still kind of in that low double-digit million-dollar contribution on a full year basis. And just to put that in context, in the first quarter, tools contributed sort of low single-digit millions in terms of revenue. So less than 10% of the Cell Engineering revenue in Q1 came from the tools offering, and then we would expect that number to increase as we get sort of through the year. But it’s still early days, so we’re being conservative there, but I would say there’s upside potential on the tools side of the business, particularly on data points, I think what we’re learning on automation is that is a longer sales cycle. With respect to some of the bigger — like your point on biopharma, yes, I mean, I think we’re happy with how we’re executing on those deals right now.

And I think we’ll be looking to see whether or not we can kind of expand the relationships with some of those biopharma as we execute on some of the first projects that we have with them in data points.

Jason Kelly: Yes. I would say I think one of the things that the biopharma is we can get in with a proof of concept. We were adding and continue to add like new logos there, which is always exciting for Ginkgo because we have a variety of things we can sell to people. And so, getting in improving ourselves and getting set up with the procurement system at these places is all just like wins for us. So that continues to work like the proof-of-concept deals. The hope is that those then grow into larger programs and datapoints or maybe an automation purchase. And then when it comes to the onshoring and asked about us data. I think one application there for us would be using the automation for some of the QC on — so it’s usually a variety of different assays that are being included in the quality control for therapeutics coming off the manufacturing.

Those are often like can be pretty complicated experiments and can be a good fit depending on the drug for some of our automation. So that’s one place I think we could play. We obviously don’t do. We’re not a manufacturer. So, you won’t see us like building a new site. We’re not like [Lonza] or something. But I do think on the automation side, we could play.

Daniel Marshall: All right. Next up, we have Matt Sykes from Goldman Sachs. Matt, your line is open.

Evie Koslosky: This Evie on for Matt. So, the first one, great to see the deal with Aura. How do you view the longer-term opportunity within the diagnostics for RACs? And then are you seeing any interest from customers in this space on a broader scale, especially given the durability of that end market versus earlier-stage R&D spending?

Jason Kelly: Yes. We think it’s a great, great fit for diagnostics. I mean, what makes the RACs unique compared to like a traditional integrated setup is that it is expandable. So, when you’re getting a work cell set up to do whatever your particular diagnostic reaction like you’re trying to predict how much demand you have, that work cell has a certain capacity. If you start to exceed that, the current industry standard is basically build a whole another work cell. Whereas with the RACs, you could take whatever piece of equipment it is that your current bottleneck on your diagnostic process and just add a second one to the setup and potentially alleviate that bottleneck. So that’s really exciting. It’s also like not something that sunsets in the event that your mix of diagnostic demand changes in the future, right?

So, what’s great about the RACs is some of the equipment is likely to be common across different protocols you’re running. And you could — if you had, say, a new protocol you brought online, let’s say you wanted to add some sort of like NGS diagnostic to your current setup, you could then add a few pieces of equipment to the very same RAC setup you had running your first protocol and add a second one. So, this is — I mean like we’re obviously biased, but like we really think of it like you’re building an automation core that can be used for lots of different things rather than a work cell, that’s meant to do one thing. And that’s really compelling and totally changes the ROI calculation for people building out integrated automation. So, we think we should be on the RFP and the look for anybody building a new automation facility.

We got to get the word out in the market, but it’s really exciting to me to see us getting this first diagnostics deal, like we really see ourselves able to play in that space.

Evie Koslosky: Okay. Great. And then on the EBITDA breakeven target for the end of 2026. What are the areas — are you — have you uncovered any areas of upside as you work through the cost-cutting exercises? And then on the flip side, how are you able to balance the spending to make sure that the new offerings get good initial traction commercially while also meeting the profitability goals?

Mark Dmytruk: So, Jason, I’d be happy to take the first part and maybe hand the second part over to you. So, we do still have room to go on cost, and that’s why we upped the target to $250 million. I would say, Evie, yes, there’s probably still some room after we get to that level. We largely, at this point, have taken the actions that we need to take in order to get to the $250 million. There’s still a little bit of work to do there. So, you’ll start to see the impact of that part of the cost reduction roll through the kind of Q2, Q3 numbers. But we are, I think, sort of being careful at this point. And I’ll let Jason just talk about the kind of new opportunities. I wouldn’t say, though, Evie, there’s some like very large sort of silver bullet kind of upside cost reduction opportunity other than, of course, subleasing the excess space, which we all know is a challenge in this market.

Other than that, we’re much more into the weeds on looking at sort of small dollar kind of line items. And that’s sort of where we’re at right now. There aren’t like these big chunks of upside anymore.

Jason Kelly: Yes. And just a comment on the tool side. I mean you won’t see us do anything pathological if that’s the question, right? Like so the — if we see opportunity where by investing in — for example, just today, we got another sort of data drop from Datapoints for cell painting. This is like imaging-based data set so you can have bright field, you can do the cell painting. These are becoming like common high content data sources for people doing like AI/ML for drug discovery, right? Like that’s the first time we put out a data set like that. We’d love to do stuff like that. We already have a ton of like interest in that, a lot of people downloading it. You’ll see us keep doing that, investing in new areas for Datapoints.

No-brainer. As long as we — when we put out a new one, we see new customers, you won’t see me stop investing and that sort of thing. Same look like on the automation side, we see opportunities to demonstrate particular workflows and show people we put out these sort of like white papers and demonstrations of what you can do on the RACs. I see a lot of upside in all those things. You won’t see a slowdown there just to meet an EBITDA target. But all things equal, we do see a good line of sight to getting to it by the end of ’26. So, it is a focus. But a lot of it is really just tightening up on the solutions side so that we have room to invest in tools. That’s really the big motion.

Daniel Marshall: I think we have one last question. I saw another pop up, but I think we probably just have time for one more from Matt Larew, who’s coming to us from William Blair.

Matt Larew: Jason, if I think back to ’23, ’24 when there were perhaps different, but in some way similar macro constraints with respect to biotech funding, you really were selling…

Daniel Marshall: It’s been sometime.

Matt Larew: We have three weeks of positivity at the beginning of this year. You’re really selling one or at least just a couple of solutions. The contract structure was perhaps more onerous to get deals done in terms of longer lead times. And there was maybe more of a focus on biopharma, but I know that remains today. If I then transport it today and maybe we’re entering more macro uncertainty from a variety of different angles, you now have a number of different product offerings that span content, tools, automation, et cetera. You’ve opened up the way you’re thinking about doing deals and different sizes and structures. But you also alluded to in your earlier comments, this sort of inherent push-pull between the willingness to outsource.

It does the same dollar amount mean that less work gets done? Or does it — which people to do more to work more efficiently, right? So, some of these tensions. And so, I’d just be curious, either in the first couple of months of the year or what you’re seeing and hearing from customers how you think Ginkgo can fit into if macro situation deteriorates or may uncertain, how you fit into the picture differently this time than perhaps a couple of years ago?

Jason Kelly: Yes. I think the biggest thing is with the tools offering, like you can like take smaller bites and engage with us, right? Like the what I would have had ’23, ’24 was there the elongated deal with in was the sort of big R&D projects. That type of big outsourced R&D work across the industry, like pick your favorite small biotech in Cambridge. It’s not getting like a large research partnership right now with like major pharmas or midsize pharmas. So that — that’s just the reality, right? So I think the thing we’ve done well in the last year, and again, I think this continues to speak to, a, the flexibility of the platform we’ve built at Ginkgo, we’re really like the core heart of it is sort of a lot of this automation and software infrastructure and approaching a like a factory, that’s proving like very durable to be able to take in different directions, all the way from industrial biotech when we first would have been talking to you through ag to pharma to now different styles, whether you’re selling it to solutions and tools, I think like we’re tough to kill, right?

I think that’s sort of what’s been proven over the last couple of years here at Ginkgo. And so, I think that, that speaks to the strength of the platform. I would — I do think there is some looking for silver linings, like whenever there is churn, like right now, like, for example, at the FDA, you’re seeing a lot of interest around changing how we approach things like toxicology, with the government in general, a lot of issues around how we’re approaching other countries, particularly China, doing our work. I mean, look, if Wuxi gets nuked, that’s like that’s a huge opening for us, right? Like if it’s like, hey, you can’t use them. then there’s a lot of new CRO business to be had, right? So, there are things there that could change the macro for Ginkgo specifically.

We’ll see, right? But I think what we’ve shown is Ginkgo’s resilient, Ginkgo will change as the market changes around us and we’re not going to die, right? So that, I think, remains the case ’23 to now. And certainly, as the biggest change is us going to market with tools, which is much more favorable for this current environment in terms of what customers are up for buying. Smaller chunks, more arm’s length is what the market demands right now.

Matt Larew: Got it. And then maybe a follow-up to that. Curious the lead generation and closes for the newer programs, datapoints tool, the tools offerings. Are most of those internal in the sense that perhaps you were actively engaging with the customer will be on a broader different project and then that didn’t work, but, hey, something else popped up you can do or more to those external at this point as people get more awareness about them?

Jason Kelly: It’s a lot more stuff coming externally now. That’s great about the tools business. Like we put these data drops up like the one we did today, and people just download it and get us the e-mails we follow up, and we’ve gotten chunks the business that way. It’s pretty neat. So that’s exciting. Within our close accounts, we certainly are able to expand, right? So, like there’s RFPs out right now for automation systems with customers that we’ve had multiyear engagements with on the solution side. That’s obviously puts us in a really nice spot. So, we do see some of that, but it’s not the only source of leads. We are — again, with the tools business. And ideally, I’d actually like the tools business to keep enabling smaller and smaller bite-size chunks, right?

So, watch us over the course of the year, hopefully be able to launch other things that let people bite off even smaller bids because I just think that’s what’s still selling. And so — but yes, but it is definitely, we’re also getting external inbound now, which is nice.

Daniel Marshall: Unfortunately, I think we’re out of time…

Mark Dmytruk: Daniel, I think I see Brendan with his hand up. So, I think we can fit in one more.

Daniel Marshall: Yes, for sure. Sure. Go ahead, Brendan. Mark, I don’t really know how you do that. That’s amazing. That’s beyond my Zoom screens. So yes, Brendan, I appreciate the question. Go ahead.

Brendan Smith: Always happy to impress. Yes. So maybe just quickly kind of expanding on a couple of the questions previously. Really, on the AI tools offering, can you speak maybe just a little bit more on kind of the training data you’re using for some of these models and really just where you’re sourcing some of that from us because we’re starting to get questions on where as there are more offering kind of across the sector where people are kind of sourcing a lot of this stuff from — are you able to use partner or licensee data sets to trade back in your models or everything kind of proprietary and generated in-house, really more so understand trying to understand how scalable some of those could be over the longer term for you guys?

Jason Kelly: Yes, I can speak to that. So yes, we’ve got a few experiments with that. So just so you know, like the models we’ve put out like a, zero, for example, which is like an ESM style model trained on our both the public data plus our internal where we acquired like Warp Drive Bio and Radiant Genomics Zymergen, there’s a lot, AgBiome. There’s always like genomic assets we’ve kind of piled up over time that are actually larger than the public data set, I believe, at this point or at least comparable. We trained up on that. So that is proprietary data. I will say like I think that market is early, right? Like we’re not like the ability to go to market as just a pure model company right now in the way that like the tech companies have been able to do on the language side, I don’t think is really bearing out in the market yet.

So that served as like kind of like an appetizer to help people come in for datapoints to like generate their proprietary data at the large mid-sized pharmas. But like just going to market like purely as a model, I don’t really see it working in the market today. Certainly, people are trying it and we tried it, but it hasn’t been a big revenue driver for us yet, unfortunately.

Daniel Marshall: All right. I think that’s all for tonight. Just a reminder, if you have any other questions, you can always e-mail us at investors@ginkgobioworks.com. Thanks for joining us.

Jason Kelly: Yes. I appreciate all the questions, everybody. Thank you.

Mark Dmytruk: Thank you. Bye.