Manager of communications and ownership. I'm joined by Jason Kelly, our co-founder and CEO, and Steve Coen, our CFO.

Thanks as always for joining us. We're looking forward to updating you on our progress.

As a reminder, during the presentation today, we will be making forward-looking statements, which involve risks and uncertainties. Please refer to our filings with the SEC to learn more about these risks and uncertainties, including our most recent 10-K.

Today, in addition to updating you on the quarter's results, we're going to show how Ginkgo is now two businesses built on one autonomous lab platform, at a moment when the US government is starting to treat autonomous labs as national infrastructure. We'll share how our autonomous labs business is growing, including our first autonomous lab sale to a pharma company, Novo, alongside new government wins with ARPA-H and the NSF.

We'll also share how our cloud lab business is growing, with new pharma logos, including 22 customers this quarter, and with AI data generation for frontier labs. 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 email [email protected]. All right, over to you, Jason.

Thanks, Daniel. As I always say, our mission here at Ginkgo is to make biology easier to engineer.

Over the last few years, we've been going through a transformation with this focus on autonomous labs, and that was really our goal in 2026. I will say, I think that that's succeeded very well, and this quarter is really good evidence of that.

We've been focusing our efforts on moving more and more of our internal lab work onto autonomous labs, that continues, and improving the product. Real breakthrough in the last quarter in terms of external customers adopting our systems, and so we will be happy to talk about some of the new customers we have for autonomous labs, both in universities and the biopharma sector, and we've been expanding the services that we're offering on top of our big system in Boston here in Nebula.

Really happy about this, I think we made the right choice in betting on this technology, you can hear more from me on that in the strategic section. Also, our financials have been going in the right direction.

We made a big push to cut back on spending while still making major investments in research and development around autonomous labs, AI, and robotics in the laboratory, and that has also paid off. You can see our year-over-year change here, pretty substantial improvements in net loss from continuing operations as well as our total adjusted EBITDA.

This is what we were aiming to achieve, and really congrats to the team on pulling this off. It's hard to do these two things at once, cost-cutting and being really the technology leader in an emerging tech area, especially one next to AI.

All right. Last thing I'll mention, we have still quite a nice cash cushion.

This is, again, thanks to those cost-cutting efforts. $285 million in cash and cash equivalents, with no bank debt, and another $86 million on top of that that we keep as restricted cash for some of our government projects and leases.

Overall, I like our position from a cash position. I like where we are with cash spending, and as you'll hear from me in the strategic section, I think we're doing a nice job externally selling and improving really a new category of product in autonomous labs.

Back to me in a moment, but I'm going to hand it to Steve for a little more depth on the financials.

Thanks, Jason. Before I walk through our financials, I want to remind everyone that our biosecurity results are classified as discontinued operations within our financial statements, where we have and will retrospectively recast all prior periods presented to conform to this presentation.

All the financial information I will cover today relates exclusively to continuing operations, where we operate as a single segment. With that, I will now discuss our Q3 results.

As Jason mentioned, revenue was $28 million in the third quarter of 2026, down 6% compared to the third quarter of 2025. For the nine months of 2026, revenue was $67 million, down 37% compared to the same period last year.

As previously disclosed, revenue in the first nine months of 2025 included $7.5 million of non-cash revenue relating to the mutual termination of the Biogen agreement. Excluding this, revenue for the first nine months of 2026 was down approximately 32% from the prior year period.

As noted in our release, revenue in the third quarter was higher than last quarter, and this was primarily from a cumulative catch-up on one of our larger long-term contracts, which was modified to realign the work plan, resulting in us recognizing some revenue soon. It is important to note that our net loss includes a number of non-cash and other non-recurring items as detailed more fully in our financial statements.

Because of these non-cash and non-recurring items, we believe adjusted EBITDA is a more indicative measure of our profitability. A full reconciliation between adjusted EBITDA and GAAP net loss from continuing operations can be found in the appendix.

In addition, in discussing our R&D and G&A expenses, we exclude stock-based compensation and other separately captioned items. In the third quarter of 2026, R&D expense decreased 48% from $51 million in the third quarter of 2025 to $27 million in the third quarter of 2026.

G&A expense decreased 32% from $14 million in the third quarter of 2025 to $9 million in the third quarter of 2026. These decreases were primarily driven by our restructuring efforts, which were substantially completed by the end of 2025.

Moving further down the page, net loss from continuing operations was $41 million in the third quarter of 2026 compared to a loss of $73 million in the prior year period, a 44% improvement. You will note that adjusted EBITDA in the third quarter of 2026 was -$22 million compared to -$53 million in the third quarter of 2025, a 58% improvement.

It is important to note that adjusted EBITDA includes the carrying cost of excess leased space, which you can see was approximately $14 million in both periods. This cash operating cost represents the base rent and other charges relating to unused leased space, net of sublease income, that is not driving revenue right now, and can potentially be mitigated through subleasing.

Finally, cash burn in the third quarter of 2026 was $27 million compared to $28 million in the third quarter of 2025. For the nine months of 2026, cash burn was $120 million, down from $124 million in the same period last year, a 4% decrease.

As previously reported, we paid Google Cloud $14 million earlier this year as part of the favorable restructuring of that contract late last year. Excluding this payment, cash burn reflects a significant decrease in the first nine months of 2026 compared to the first nine months of 2025, which was directly result from the restructuring, partially offset by investments we are making.

During the third quarter, we raised an additional net $7 million through our at-the-market equity program. Consistent with our methodology, these related proceeds are excluded from cash burn for all periods presented.

As previously reported, cash burn excludes the restrictions we made last quarter to secure a surety bond on our PNNL contract. We ended the quarter with $285 million in cash, cash equivalents, and marketable securities, and no bank debt, plus $86 million of restricted cash, cash equivalents, and marketable securities for customers and operating activities.

Now turning to guidance. As we discussed earlier this year, 2026 is about continuing to be cost-efficient while investing in our AI, robotics, and software to bring autonomous labs to our bioscience customers, including the build-out of our frontier autonomous lab in Boston.

For these reasons, we believe cash burn best reflects our continuing services and tools, and further investments in autonomous labs. In terms of the outlook for the full year, we are reaffirming our overall cash burn guidance for 2026 totaling $125 to $150 million.

This range reflects a firm balance amongst cost efficiency, continuing services and tools, and further investments we are making. In conclusion, we are pleased with the continued improvements in cash burn, efficiency, and our business pursuits for 2026.

With that, I'll hand it back over to you, Jason.

Thanks, Steve. Our mission here at Ginkgo is to make biology easier to engineer.

As I mentioned, we've been doing that by expanding our technology and autonomous labs and bringing those to market. I will say they are currently having a moment.

Big push on the government side, a lot more attention from private sector biopharma, and as I'll talk about it, even frontier AI labs are getting excited about the potential of robotic laboratories. We sell those in two different ways, both hardware, we'll install an autonomous lab for you, or you can leverage our autonomous lab as a cloud.

Those are going to be my two sections, you're going to hear about sort of the blue-chip early adopters we're seeing on selling our systems as well as the growth in the services. All right, but it is a moment, I do want to just highlight a few of the good things that have been moving in our favor over the last quarter.

I just a few weeks ago was at President Trump's Golden Age of Science Summit, this was to announce a report out of the Office of Science and Technology Policy. I've mentioned this before, but there's a little clip in there from the report about accelerating autonomous experimentation.

We've seen real money coming behind this, $400 million going to the National Science Foundation to invest in this, the Genesis Mission, which is partially what funded our big project at the Pacific Northwest National Lab. I think you will continue to see more of this coming from the White House as we expand AI in science in the United States.

Importantly, our bigger customer base ultimately, I think, it's the biopharma companies. Dave Ricks, the CEO of Lilly, posted on X just a couple weeks ago, "AI's going to help us conquer disease, but this is going to take time, effort, and lots and lots of data that doesn't exist yet."

I would add that robotic labs will begin to make scaling data more tractable. That's a big deal.

Robots, just if you don't know it, have been sort of relegated to the basements of biopharma companies. They run high-throughput screens, and some diagnostic testing at the diagnostic labs runs on robotics, but by and large, robots have not been a major part of the overwhelming laboratory investment at pharma companies, which is really manual labs.

Exciting to hear that from CEO of Lilly. I think Dave is great.

We just sold the system to Novo, which I'll talk about, but Dave should probably come by and visit our site in Boston. Anthropic also, if you can believe it, announced that they had set up a biology lab in San Francisco as part of their AI drug discovery efforts.

Super exciting. That is not a robotic lab yet, but I do think if you look at the way that those companies think about the world, it's very natural to go for models that can ultimately run the lab experiments themselves, which would be robotics.

Okay. As I mentioned, we have two ways to buy the labs.

You can either get your own installed, or you can use ours in the cloud. Alongside that, we had just had two great events in September, one for our automation group and people that wanted to buy the systems, and then also for our Data Points Summit just the next day.

The reason I mentioned this, we did these back-to-back, there was maybe about a 10% overlap in the crowd between the days. There's a real nice additive effect of Ginkgo running services using our robotics infrastructure, because we are able to learn faster than we would if we were just building systems for external customers.

I think that was a wise choice of setting the business up that way. We will continue to do that, and I actually want to migrate more and more and more of our services onto an ever-bigger Nebula here in Boston, and you'll see more of that in the next year.

Okay. Let's talk about those new customers, because this is the thing I'm most excited about.

A number of blue-chip early adopters. I'll start with the universities.

We were very fortunate to be selected by MIT, Caltech, Maryland, and Northwestern. Caltech, Maryland, Northwestern is part of this NSF program I mentioned to build out, in the case of the NSF, there's close to $50 million flowing through this program, autonomous labs at those schools.

We also have a couple other schools that we're talking to where it's not coming from a grant. I think there's sort of two ways we're going to sell to the universities, either as part, being a subcontractor or equipment provider on a grant, or directly from the infrastructure budget, kind of the overhead budgets, the department budgets in, say, a major biology R&D department at a large school.

I see both of those roads working out. I think we will continue to sell systems into academic environment.

That's super exciting. A lot of the most exciting new protocols were developed in academia.

I'd love to see that work happening directly on autonomous labs from the beginning. I want to see grad students do their whole bioengineering PhD without touching a pipette.

These are dreams that we've had for a long time at Ginkgo, it's going to be really fun to see that taking off, and what an awesome set of first customers for us in the university space. The other thing I'm very excited to announce, Novo Nordisk selected Ginkgo to build an autonomous lab at their new R&D site in Waltham.

Very excited about this, this is our first sort of big pharma announcement of an autonomous lab. That sends a signal to all the other companies in the industry.

This is hardware. This is infrastructure.

There's a big switching cost for people to go with new vendors, or to try new players. There are a number of existing kind of laboratory robotics companies, and I won't give my usual comparison of Waymos and subways, but there's a lot of subway sellers who sell robotic systems that do the same thing over and over again, and that have a lot of existing relationships with the large pharmas and a lot of trust.

We're coming in as a new vendor. We got to have something new to offer if we're going to unseat some of those existing vendors, and what we're offering is a system that can do variable work, something closer to what you get from your lab benches, that's why I call it a Waymo.

It's really exciting to see Novo Nordisk valuing that enough to bring us in as a new automation vendor. We already have a great relationship with them on the services side, too, but this is the first hardware contract we have with them.

The other thing I was really excited to see, ARPA-H selected us as part of, again, a subcontract on a $17.5 million, up to $17.5 million award. This is very cool, because this is using robotics for manufacturing of genetic medicines and these sort of, if you're familiar with the Moderna-Merck partnership around cancer vaccines, these are a vaccine, but it's being built just for you, it's sort of a custom manufacturing process.

That's a great fit for robotics, especially Ginkgo's robotics. What I think is great about this is we are going to, through this grant, sort of demonstrate our robotics in GLP, GMP environments.

That could be very exciting for ultimately having these as part of people's manufacturing processes in biopharma. Stay tuned on that, but a very exciting project.

Last but not least, I've mentioned this before, we were selected to build out a 97-rack system for Pacific Northwest National Labs. Just want to say congratulations to our partners at PNNL.

They broke ground just a month or so ago on the new building that's going to contain this big system. We're working away building and validating some of the new instruments that they want on there that we haven't put on racks before, and building some of the subsystems for the big install so that the minute the doors open, we can roll our racks in.

Very excited though that they're doing that. Maybe on that note of sort of building these systems, this is just a video from our lab here in Boston.

The thing I'd like to convey is we want the experience of adding new equipment to an existing robotic lab, we put in a robotic lab, say, at Caltech, it's going to initially be 5 or 10 racks or something, right? Within, in this case, less than a day, you can add a new device onto that rack system.

It's very similar to if you just bought a new piece of equipment for your manual lab and put it on a bench. It's not a big project, it's not a giant 3D modeling exercise or anything else, you can just add it quite quickly, turn it back on, and now you have a new capability with all of the other equipment you already have on the system.

That's again unique to our setup. Nobody else's stuff does that with anywhere near our robustness or scale.

Really excited that as we enter all these places, customers, and we've already seen this, will want to grow those systems, and they can do that incrementally. It's not like another giant project to bring them in, they can just order another one or two pieces of equipment that they might want to add.

That's very exciting for how we'll grow once we're in at these customers. As I mentioned earlier, because we run our services, we actually have the largest autonomous lab in the world, 105 of our racks here in Boston.

Customers can use it as a cloud lab, but very importantly, we're able to learn a lot faster because we are developing on this system. When PNNL bought 97 racks, we expanded this from 50 to 105.

Because 18 months in advance of them getting a big system, I wanted us to have tried everything, found all the bugs, squeezed them out, right? Nobody's really pushed autonomous labs and robotic automation in labs to the scale Ginkgo has, and so we want to make sure we get there before our customers do.

That's the purpose of Nebula. Frankly, it is amazing what we've been able to show on this system.

We're doing more than 100 protocols, these numbers are actually kind of dated now, but more than 100 protocols, more than 30 unique in a single day, 100 plus, I think it's 160 lab devices on that setup, and it's running 24/7, right? That is, again, that scale of devices, that range of variability especially has not been shown on a laboratory robotics system previously.

Really excited about this, and maybe the last thing I'll make, we started to look at how many equivalent technicians would it take to run around and move the plates onto the different devices, just like a plate moves across the room, a person would have had to kind of walk between those two pieces of equipment in a manual lab. If it spins in a centrifuge for five minutes, the person would have spent five minutes kind of staring at that centrifuge.

We add all that up, and our peak day so far has been 500 technician hour equivalents there, but on an average day we're at more like 200 to 300. It's pretty good.

If you think of it as a five hour in the lab sort of per technician, that's something like 100, 50 to 100 technicians capability, right? Then the question is, how do you design experiments for 50 to 100 technicians to be running on an automated system, worth of work happening across these devices?

That's where AI comes in, and I'll talk about that later, but I think there's a big overlap between AI needing autonomous labs, and in fact, autonomous labs needing AI. I'll touch on that.

Either way, you should come visit, don't take my word for it. There's a sign-up link here.

We've had, I don't know, probably more than 1,000 people come visit the lab now, you should definitely sign up. It's great, we do tours every week.

Seeing is believing on this, it's really wild. Okay.

Lastly, I want to talk about our cloud lab services. These have been growing really well with biopharma customers, where we're operating really like a CRO and large data gen.

Then we're also seeing a little bit from the frontier model companies, which I'll talk about. I love this post, this is a post from one of our customers up on X.

"One of our agents flagged that Ginkgo Data Points has been outperforming others for protein biophysical data production, ahead on cost and quality, as well as TAT." Cheaper, faster, and higher quality.

They used their AI agent to sort of compare us to some of the other vendors they've been using. I think this is going to surprise people.

When you do things more robotically, they can be competitive with work we've been offshoring to low-cost scientific labor in China, frankly, over the last 30 years in the pharmaceutical industry. If you look at, for example, our ADME services at Ginkgo Data Points, it's $199 for five tier one ADME assays.

These are very common assays being used as part of small molecule just drug discovery every week. If you want those same assays from WuXi, it's $1,000 or more, depending on how you're doing it.

That's really exciting, and I think this is the potential of laboratory automation. By the way, I think this applies for automation around a lot of sort of small batch, we'll call it, manufacturing in the United States.

If we want to bring it back, this is how you're going to do it. It's working.

We added 20 new logos to Ginkgo. These are biopharma or biotech customers that have never engaged us, whether on the equipment side or our old services, solutions-type services that we did in the past, just new customers in the last quarter.

This is really exciting, and people should try it out. It's cheap to try, give it a shot.

The other thing I'll mention on the Data Points side is we launched our Antibody Developability Consortium. There's been a lot of work in antibody binding.

There's a few different companies, Twist has been doing this, Adaptive, a few others. We offer this as well, where you can synthesize an antibody and see if it binds something.

That's great, and people have been training really large kind of frontier biomodels on protein design for binding things to use in drug discovery. That's not all you need to know to make an antibody drug.

You also have to check how developable it is. In other words, does it make for a good drug?

Is it soluble, right? Or is it going to all just clump up in your blood?

Is it immunogenic? Is it going to freak out your immune system?

There's a whole list of assays that you run on antibodies that aren't just about it binding. You can have an antibody that binds great, then ends up being a bad drug.

As we're starting to win on the binding side with these models, we believe the other key assay that everyone's going to want will be developability. This is a much harder thing to do high-throughput.

It's much more complicated assays, more steps, really benefits more from what we've been doing here at Ginkgo on the integrated robotics side. We have this up and running, it's really great.

Ours is the best in the industry, I'm sure. We have launched AbbVie, argenx, Lundbeck, and Takeda as our sort of founding members of our Antibody Developability Consortium.

They're each submitting antibody designs. We build, test, and give back and make this big developability data set.

What's really cool is that then gets, with our partner Afero, put into a federated learning environment, where nobody gets to see each other's designs, but everybody gets to train models on the full data set. I think this is a super exciting way to, again, these are more expensive assays than binding, binding's pretty cheap, being able to do developability is harder.

This is a way to make that cost lower on a per-company basis, even though everyone's generating a lot of data. We're really excited about that.

If you're interested in this consortium, if you're a biotech or biopharma listening, you should definitely reach out. If you add yourself as a new member, you'll get access to historical data as well.

A good opportunity, and I really like this model. Also, we announced in the last quarter a partnership with Lilly TuneLab, where our kind of suite of Ginkgo Data Points services is available to members.

These are a bunch of startups and others that are kind of affiliated with Lilly TuneLab, just making that super easy for people to order our services. Okay.

Last thing I'll talk about, the question I get asked the most by investors is, are the frontier AI labs buying biodata? The answer is yes.

We're working with a few of them. What I would say is what you're seeing is a realization that the smartest models are going to have value in a bunch of these advanced engineering and science areas, things like chip design, things like drug discovery, things like new materials and energy.

In order for them to get good at that, like they got good at math, you need to tell them if they're doing a good job with their ideas, right? The reason things work so well in chess and math is the model can come up with something, and then go get validated, reinforcement learning, and see, hey, is my math proof right?

Is this code I wrote right? Did I play a good game of chess?

The beautiful thing is you can answer all those questions inside of a data center, right? Those are computable answers.

That's not true in a lot of these other areas. In these other areas, what you're going to want is basically the feedback of the laboratories, so that you can make your model smarter.

As long as I think the frontier AI labs want to keep pushing the boundary of their capabilities in these physical areas, I do think they're going to want to train on physical data, in our case, biological and chemistry data. Super excited about this.

I do think it's a very cool area, and we're happy to be participating in this along with others. All right.

I'll be happy to take questions about all of that. As always, we should grow the world we want to see.

We appreciate your interest and your investment, those of you who are investors in Ginkgo. My email is up there if you ever want to email me and ask questions offline.

All right. Thanks a lot.

Thanks, Jason. As usual, I'll start with a question from the public and remind the analysts on the line that if you'd like to ask a question, please raise your hands on Zoom, and I'll call on you and open your line.

Thanks, all. All right.

First, just want to thank everyone for sticking with us through those technical difficulties earlier. We're extra excited to be there with you tonight.

I wanted to start with a question from Brendan, who sent it in from TD. He actually sent two questions, we'll start with the first: "How should we think about evolution of the cloud lab offering, and how Ginkgo plans to monetize this moving forward?

Is the idea to ultimately become a kind of AI model training data generation service? If so, what kinds of data is Ginkgo able to supply that customers might not be able to get elsewhere?"

If I can take that. Good.

I touched about this a little bit at the end there around the antibody developability consortium. I think the sweet spot for Ginkgo on this, and by the way, I would say this is for cloud lab, Data Points, or basically our services that we run on top of the platform.

We think for these particularly complicated assays, that's a really strong fit, where we can generate data that you're going to have a hard time getting from other CROs. Things like binding, there's now a lot of players doing that.

I think where our hat is in the ring there, that's fine, I think we'll be able to play there. Things like developability, there's really not.

It's much more sparse, because those are harder to do. The other area is in target discovery, we offer our functional genomics offering, where you can take your favorite cell line, and we can make either genetic perturbations, like CRISPR edits to the genes, you want to say knock out every gene in that genome one by one, we can put those in wells, and then give you a functional genomics, like an RNA readout of what's going on inside that cell, or we can add small molecules.

We can do a small molecule perturbation by adding thousands of those to the cell line. That sort of stuff is much more complicated.

Those are the types of things I think as we get past an initial phase where if you kind of go after the easy stuff and it's good for storytelling, and you move more into the phase of, boy, I would love to have the models that ultimately give me all the data I need to develop a drug or to find a new target against a disease that's been resistant to having a good target, those types of big activities that happen across the industry, I think AI is going to play there, too, and that I think will be quite favorable for us. I think we'll be unique in that respect.

All right. Second question from Brendan, and I'll just remind all the analysts on the call that if you have a question, just raise your hand and I'll call on you after this.

The next question is: What is the expected timeline to reporting revenues from the sale of the RAC and CATALYS systems, from both the recently confirmed partnerships and any potential future deals?

Steve, you want to talk about that a little bit?

Yes, I just want to remind everyone we're not giving guidance on revenue, and we're not going to give guidance on 2027 yet. I think the right time, we will bring the investors along as we feel it's important to know.

We'll be resetting expectations for 2027 in February in our guidance, and we will look at whether or not that information is meaningful for that at that point in time. Then we'll bake it into our guidance.

The other thing I'd add is as we build these systems, we'll recognize revenue when the system is installed, right? That's when we would get the sort of CapEx revenue for the system.

Then, on an ongoing basis in these sales, we have a sort of services and SaaS software licensing that's recurring. You'll see those over the life as we get a larger fleet deployed, that'll sort of ramp up and stay up, hopefully, as people keep these systems for a long time, we hope.

That's exactly right, Jason. As we mentioned the last meeting or one before, it is multifaceted, delivering CapEx, equipment, large deploy, that takes a good bit of time depending on size of lab, or is it green-space lab, and then, of course, we have all the front-end services and then the back-end services, and all the back-end services are more SaaS work.

That's why it takes time to build out a lab. That's why we'll be looking at it, and we'll provide the guidance when it's input.

Maybe the only other thing I would add, I think we are taking a different approach than people have traditionally taken to integrated automation. The normal mental model, again, for like a HighRes or a Thermo or Biosero, the folks that build these subway work cells, is that each one is unique to a customer.

The customer's saying, "I have this protocol I want to run at high throughput," and the vendor is going to basically do some non-recurring engineering, generate a 3D model, design the system, build it all for you over a series of months, and then install it. There's sort of a 6- to 12-month period where there's kind of custom engineering happening.

That's not how the RACs work. The good thing about our system is we're producing RAC enclosures every week.

We can accumulate an inventory of RAC enclosures so that over time orders come in, and we just need to integrate equipment and ship them. I do think what will happen as we get more mature in this area is the sort of time it takes to get a system installed will speed up relative to what you've seen in historical integrated automation, just because our offering is more productized.

It is a standard thing that we're manufacturing on the regular rather than a system we build one-off against the customer spec.

Thanks, Jason. All right, next question.

Can you give a little more color on the AI frontier labs ordering biodata? Do you think that is durable?

Does that help your business with traditional biopharma companies in any way?

It's super interesting. I think there's a little bit of a what's your thesis on OpenAI and Anthropic getting into the pharmaceutical industry, and there's a lot of talk about that, you're certainly hearing about it a lot from Dario, for example.

Obviously, Anthropic invested in that wet lab. That's sort of the, I would say, the big overlay.

What I'm most excited about, I think there's two lanes. There is generate biodata to train models that understand biology.

That's the protein models that you've seen from people, the Virtual Cell Initiative, the types of data we were just talking about with the Developability Consortium. This is all biological data training biological models, where AlphaFold from Google was an example of a protein biological model.

The other thing we have, obviously, is the reasoning models. The reasoning models, I'm quite excited about, because I believe they will be able to operate robotic labs, and they essentially need to learn to write protocol code, for lack of a better words.

You can think of them as a coding agent programming robots and laboratory protocols to do science. That's also a type of training data that we expect labs are going to want to purchase.

We are excited to see models get better at doing that. The reason I'm excited about that is I watch our system at Ginkgo, Nebula, and I look at protocols coming onto that system, and it's not perfect, but it looks a little bit like a workday, right?

Scientists are coming in in the morning, they're submitting new protocols based on their project. You see our number of protocols running simultaneously going up, midday it's quite high, in the afternoon it's high, and then by 9:00 at night it's starting to drop, and by 3:00 in the morning, there's a tenth as many protocols running as were running at noon.

That makes basically no sense, right? You have an autonomous lab that's running 24/7, there's no reason you wouldn't want to utilize that asset.

What I want to get to is increasingly stuff like we've been doing with OpenAI in the announced project, agentic generation of protocols so that really just to keep the whole thing busy. I think our customers, when they buy our autonomous labs, over time, they're going to have that same need.

They're going to want agentic science happening on the system, because it feels like a waste not to be using it at all hours once you have a system that can actually do variable work all hours. That's just a change, right?

Right today, the manual labs run 40 hours a week, not 168 hours like the autonomous labs, filling that is I think going to require some new tools. Those are the two areas.

That's why I'd say the AI companies are interested in autonomous labs for data generation, but the autonomous labs are also interested in frontier AI reasoning for the purposes of protocol coding. We need each other.

All right. Next question: What size of autonomous labs are customers starting with?

How are you all thinking about products moving forward?

Sort of, I would say we're at like kind of a if you're building a small system, it's harder to be smaller than like five. Something more like 10 to 15, I think, is like a healthy like initial system.

Then if something is closer to what you'd normally buy a work cell, like if you were going to go buy a system to do some high-throughput screening or, would be a couple high-throughput screening systems, that's more like 30, just to give you a ballpark. One of the things I'm excited about on the product side is sort of multipurpose work cells, right?

A work cell that could do, 10 or 15 different protocols, so that instead of having to buy three work cells to do three different things, you could just combine all of that into one autonomous lab that does what you would have previously used three work cells to do. That gives us that gives us actually an advantage even in the in the work cell space before you go all the way to the lab bench Waymo.

I'm pretty excited to just demonstrate to more pharma partners the what's possible there, because we're certainly seeing gains of that sort when we run our own services internally at Ginkgo.

All right. Next we have: How is Ginkgo able to price ADME services and data points below WuXi, and how durable is that cost advantage as competitors respond?

Robots. I think there's just a lack of awareness of how much of the cost when you're doing manual lab work is in the technicians doing the work and the lab space you need to do it.

That's true in China, too. Scientists are less expensive, but they're not 10 times less expensive, they're more like half.

There is actually a lot of room, as we get closer to the cost of the services being majority reagents, then I think there's less of a reason to offshore. Also, offshoring's expensive, right?

You're kind of dealing with just the shipping and the timing and all this stuff, right? There's a pretty interesting moment right now.

You're also seeing a lot of companies wanting to split where there used to be a moat that WuXi had, which was, hey, you're making the molecules there, you should test them there because that's more convenient and faster. Like they're doing the chemical synthesis, which is still fairly resistant to automation, why not just take it down the hall and do the assays?

Turns out the reason not to is if the same organization knows the molecule structure, which is secret IP of the pharma company, plus which of the 10,000 molecules you tried performed really well as a drug, well, that's all you need to walk out the door and start a competing biotech company in China based on that molecule. Because you're seeing this now more and more of these companies that are started, it's like, wow, this looks a lot like the molecule that was shipped to WuXi a week ago, that is driving companies to split where they do it.

They're doing molecule synthesis one place, shipping plates of molecules, not saying what the molecules are, to a different place to do the assays, just so that that information is not held by one party. That's good news for us, right?

Great, do your chemical synthesis wherever you want, ship it to us for the assays. Happy to do it.

Thanks, Jason. All right, there's one more, it's somewhat similar to one of the ones that Brendan asked earlier, but I'm just going to ask it again in case there's sort of anything else to add.

This one is: We saw a lot of inbound interest this quarter in our autonomous lab offerings, and so the question is, how should investors think about converting that interest into revenue, and kind of over what timeline should they be thinking about?

I think it's pretty close to what we covered before. Again, I think we'll be as public as we can.

Not every customer wants us to announce it, but we like to announce that we sell systems because it helps improve our brand. You'll see us announcing as we get systems sold, and then I think you're going to see a period of time, again, it depends on the customer, they're often expecting a system installed 6 months later or something, right?

Because they're 6 to 12 often, because the normal cycle they're on is like, okay, we're planning well in advance of this, because that's what it normally takes to get an automation system in, and so we've got a room and it's going to get cleared out, or we're building a building and it's going to open, and I'm on the team that's putting in the automation. There is that kind of lag today.

Again, I don't know what'll happen if I can be like, "Sure, but I've got these enclosures, and if you have the equipment, I can integrate it in about a month, and we can just install," I don't know if that would change the dynamic, or if people are sort of like, "No, that's okay, I already started asking for this with an RFP 6 months in advance anyway, please bring it in 6 months." We'll see, but I would say that's pretty typical, 6 to 12 months.

All right, I think that's all the questions that we have for today. I just want to thank everyone again for joining us, and remind you that you can always reach out to us at [email protected].

Jason also shared his email earlier, [email protected], in case you want to contact him directly. Thank you all so much, and you'll hear from us next quarter.

Yep. Thanks, everybody.