Thanks, Daniel. We always start with this: Ginkgo's mission is to make biology easier to engineer.

I mentioned this at the last earnings call, but in 2026, our focus will be on investing to win the category of autonomous labs. I'm really excited.

Even since we just spoke a few months ago, this category has really been growing in attention, new companies in Silicon Valley pursuing this, a lot of interest from the AI frontier labs about the application of AI models in science via autonomous labs, government talking more about this. I do think we're on to the right track with this focus for the company.

The two big ways I'm going to be pursuing that goal in 2026: the first is to take our services in solutions, in Data Points, in Cloud Lab, and run them on top of our autonomous lab here in Boston that we call Nebula. That's a chance to prove out the capabilities of our system with real-world activities.

Then the second big area of activity will be getting early adopters of autonomous labs out in the world to buy our systems, like we've done already with Pacific Northwest National Labs that I talked about last time. Excited to pursue both of those, and you're going to hear more about it from me in the strategic section.

We also, in the last quarter, were able to close on a deal I talked about extensively last time, which is the spin-off of our biosecurity unit into a new company called Perimeter. Want to say congratulations to the team at biosecurity at Ginkgo in pulling that off.

$60 million and a lot of great new investors coming into that, focused really firmly in the area of defense tech and building sort of a biosecurity prime. Ginkgo is a shareholder in that company.

We're super excited to see it succeed, and I think this is a really nice, as I talked about last time, opportunity both for Ginkgo to keep our focus on the autonomous labs and for the team at Perimeter to grow under their own brand with a new set of defense tech-focused investors. Our focus over the last couple of years was very much on getting these numbers where they are today, bringing down our cash burn in the company.

We guided towards this, and Steve will touch on that in his section. Again, happy to have a very strong cash position, $373 million with no bank debt as of Q1 2026.

You'll hear a little bit more from Steve on this, but this sets us up very nicely. We're well-capitalized to pursue this area of autonomous labs.

We have these base service businesses to build on top of and the lead in developing the technology. You put all that together, and I think we're by far the best bet in this sector.

All right, I'm going to pass it on to Steve to dig into the financials.

Thanks, Jason. Before I walk through our financials, I want to take a moment to frame an important change in how we are presenting our results beginning in Q1 2026.

As we announced in February, we entered into a definitive agreement to sell our biosecurity business, which was previously reported as a separate segment. Further, as Jason noted, we closed that transaction on April 3rd.

The biosecurity transferred assets met the criteria under US GAAP to be classified as held for sale and the financial results reported as discontinued operations as of March 31st, 2026. This is the first quarter in which biosecurity is reflected as discontinued operations within our financial statements.

In accordance with accounting rules, we have and will retrospectively recast all prior periods presented to conform to this presentation. That means the revenue, operating expenses, and cash flows previously attributed to the biosecurity business are removed from each line item of our continuing operations and cash flows as the prior-period information is presented, including for Q1 of last year.

The former biosecurity results are now reported as a single net line: loss from discontinued operations, below loss from continuing operations. To be clear, all of the financial commentary I will provide today relates exclusively to continuing operations.

We will not be discussing the biosecurity business further in our prepared remarks. On April 7th, 2026, for your information, we filed a current report on Form 8-K that includes pro forma financial information for fiscal years 2023, 2024, and 2025 on a continuing operations basis.

Following the biosecurity divestiture, we now operate as a single segment. With that, I'll now discuss our Q1 results.

Revenue was $19 million in the first quarter of 2026, down 49% compared to the first quarter of 2025. As previously disclosed, revenue in the first quarter of 2025 included $7.5 million in non-cash revenue relating to the mutual termination of the BiomEdit agreement.

Excluding this, revenue in the first quarter of 2026 was down 37% from the prior-year period. It is important to note that our net loss includes a number of non-cash and other non-recurring items, as detailed more fully in our financial statements.

Because of these non-cash and other 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 the first quarter of 2026, R&D expense decreased 38% from $49 million in the first quarter of 2025 to $30 million in the first quarter of 2026. G&A expense decreased 35% from $20 million in the first quarter of 2025 to $13 million in the first quarter of 2026.

These decreases were all driven by our restructuring efforts. Net loss from continuing operations was 30-- was $76 million in the first quarter of 2026, compared to a loss of $83 million in the prior-year period.

The reduction in loss year-over-year was due to our restructuring efforts. Moving further down the page, you'll note that adjusted EBITDA in the first quarter of 2026 was -$42 million, which was down from -$44 million in the first quarter of 2025.

Since we are now only operating in a single segment, we only present a single measure of adjusted EBITDA. It is important to note that adjusted EBITDA includes the carrying cost of excess leased space, which you can see was $16 million in the first quarter of 2026.

Previously, this cost would not have been included in the former presentation of segment adjusted EBITDA. This cost represents the base rent and other charges relating to leased space which we are not occupying, net of sublease income.

This is a cash operating cost that is not related to driving revenue right now and can be potentially mitigated through subleasing. Finally, cash burn in the first quarter of 2026 was $48 million, down from $58 million in the first quarter of 2025, a 17% decrease.

As previously reported, in October 2025, we amended and reset the annual commitments with Google Cloud for $14 million. Resetting the commitment reduced our future minimum commitments by more than $100 million compared with the original terms and extended the commitment term from 3 to 6 years.

We paid this $14 million in Q1 of 2026, which is reflected in our cash burn for the quarter. Excluding the payment to Google Cloud, cash burn reflects a significant decrease in the first quarter of 2026 compared to the first quarter of 2025, which was a direct result of the restructuring.

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

We have turned the page on our pure focus on restructuring actions to focus this year not only on cost efficiency, but on investing in what we see as our opportunities while continuing to provide our customers the advanced services that they have come to expect. For these reasons, we believe cash burn best reflects our continuing services and tools and further investments in autonomous labs.

In terms of outlook for the full year, we are reaffirming our overall cash burn guidance for 2026, totaling $125 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 our 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. I'm going to get dive in on the strategic section.

I'm excited to go into this today. Our mission is to make biology easier to engineer.

The way we're really aiming to solve that problem, we believe the bottleneck fundamentally is the laboratory work associated with bioengineering. I'm going to dig deep today and talk about why autonomous labs will be replacing the lab bench.

I want to highlight some of what we're doing with Nebula, our system, because we have some news this month in terms of expanding that system. Then finally, the services that we've put on top of Nebula, Cloud Lab, Data Points, and solutions.

These are sort of like we call it like our Starlink, right? If you think about SpaceX, 70% of the launches last year were actually Starlink, their own internal product.

In the coming year, the ability for us to scale up an autonomous lab and showcase that you can make money on services without having people in the middle of a lab doing those laboratory services, I think, is a real highlight, and it'll help drive sales of our systems into the world. I'm going to talk about all three, and let's dive in.

Okay. I gave this analogy.

I'm going to do it again because I think this is, for new folks listening on the call, it's worth understanding what we say when we say autonomous lab as distinct from traditional lab automation. I'm going to give an analogy from the transportation industry.

On the y-axis here, we have the amount of automation for a certain type of transport, and on the x-axis, the request flexibility. In other words, the users asking the transportation system to do something different or not.

For low request flexibility and a high level of automation, that's your subway, right? It's the Red Line here in Boston.

You sit down in the subway and it takes you away. You don't have to do anything.

It is high level of automation, totally automated transport, but it is very inflexible. You have to want to go to one of the stops on the Red Line.

Low amount of automation, high amount of flexibility, that's a car, right? You get your hands on the wheel, your foot on the pedals, and it'll take you right take you right to your house or to the grocery store or anywhere you want to go.

That's roughly what the transportation system has looked like for the last 100 years. Let's go to the next slide.

You've been to California in the last four or five years, or now LA or Austin, or soon in Boston, and you've sat in the backseat of a Waymo, and it is amazing. It is like sitting on a subway.

You don't have to do anything, but it'll take you right to your house. It has the flexibility of a car.

It's those two things together, that that sort of flexibility plus a high level of automation, that mean we actually give it a new name. We don't call it an automated car, we call it an autonomous car, because up till now, you've needed a human being with our brain in the loop in order to manage that amount of flexibility into the system.

All right, if you look at the next slide, you'll see the miles traveled by cars and trucks versus subways and trains in the United States. It's more than 99% in cars and trucks.

That's not because we don't know about subways. It's that we need that flexibility to do our day-to-day lives.

It's required for the transportation that humans need. All right, now let's go into the lab bench and into the lab.

Low amount of flexibility, high amount of automation, we have actually automation in the lab. It's called a workcell.

That's a 3D schematic of a of a workcell we have here at Ginkgo. Companies like HighRes and Thermo and Biosero make these.

They're they're like a subway, right? They're great.

They're fully automated. They'll do an experiment without a scientist in the loop, but it better be the experiment that you ordered yesterday.

It's not going to do a new experiment for you today. Low amount of automation, high amount of flexibility, we have those two in the lab.

It's called the lab bench. You, as a scientist, are basically the human glue connecting all of these different devices in the lab together to do whatever protocol you want to do.

Again, here's the kicker. If you look at research budgets between laboratory workcells, which are used in things in pharma companies like high-throughput screening or combinatorial chemistry or things like that, versus at the lab bench, it's about 95% plus at the bench.

Again, it's not like we don't know about workcells. It's that scientists need flexibility to explore all the different hypotheses they have for discovering a new drug or developing a new crop trait or whatever type of biotechnology they're doing.

This is what we're trying to build at Ginkgo. We're trying to get up to that top-right corner and make a Waymo.

We're trying to make an autonomous lab that has the flexibility of the bench, scientists can order whatever experiment they want, but the automation of a workcell. In other words, they don't have to be there to do each and every step and move the samples among the different equipment and program the equipment.

They can just hit go and have that protocol run end to end for them via the automation, but whatever they want to order that day. That's the target.

Look, if you go to the next slide, the value prop here, I think, is very clear for getting rid of the lab bench. Massive overhead cost savings.

We've heard a lot about overhead costs at academic research labs and things like that. That's really paying for, ultimately, millions of square feet of laboratory space.

50 million square feet of laboratory space just in the Boston area. You can you can dramatically reduce that.

You can increase the research productivity of your human scientists as we need more data from AI. I'm going to talk about that in a minute.

Then we can enable AI scientists to run these lab-in-the-loop experiments. We just announced a project with OpenAI a few months ago where GPT-5 ran our lab.

That's that kind of lab-in-the-loop experiments that we're seeing increasingly also in the biopharma industry. To put a point on it, like a typical large pharma, biopharma, biotech spends $1 to $3 billion a year on research, not clinical trials, but spending on the on, a million-plus square feet of lab benches.

If you look at their spending within that on automation, it's well below $100 million, usually much less. Frankly, I think those numbers should flip.

I think really the majority of the capital should be going towards automated laboratory work rather than manual benches. The reason for that is, I think, a relatively straightforward calculation.

On the next slide, you can see a comparison between a traditional manual lab and an autonomous lab. It's about a threefold space improvement when you take all these this equipment that's often very spread out in a normal human-operated lab, because people need to get around it and safety reasons and all these things.

In an autonomous lab, you can jam all that equipment right next to each other, about as tight as you can make it for the arms to work and things like that. It's about a threefold space reduction.

Then the manual labs really are just run 40 hours a week, right? It's when people come in to the lab and humans are there and they've got to be there, and that's when you can you can get people to work.

There's almost never multiple shifts in these types of sort of high-end research labs. It's really a 40-hour work week.

Our system here, Nebula, is running 168 hours a week, so, 24/7, and it's a fourfold improvement in sort of hours available for utilization of your laboratory. I think a real clear threefold, fourfold on two different axes.

It is a clear value driver. If you go to the next slide, I think there's little question the ROI is there.

I think the big question with autonomous labs is a technical one. It's how do you get that high level of automation and the high level of flexibility, that top-right corner, without a human being in the loop, right?

It's the same question of the Waymo: How do you get it to navigate all these different environments and different roads without a human in the loop? If you can do it, it's an obvious win.

If we can do this in the lab, it's an obvious win. All right, let me talk through a little bit the design constraints that we focused on at Ginkgo.

On the next slide, you can see, a workcell, one of those subways, that the way it's designed is it's designed against a particular protocol. If you're a biopharma company and you want to build a high-throughput screening workcell and you cold-call a traditional automation vendor, they're going to The first question they're going to ask you is, 'Tell me about your protocol, and tell me what throughput you need it run at.

How many samples do you want to get through every week?' Because they're building you a subway line.

It's going to be built to do that protocol for you. If you're a, a new facility head who is opening a lab in Central Square or, in Kendall Square here in Cambridge, and you are building it for a scientific lead, you don't ask that scientific lead, 'Hey, what's the protocol you're going to run in this lab that 30 of your scientists are going to use to do work?'

You say, 'What kind of science are you doing, and, very specifically, what equipment do you want me to install in that lab so that your scientists can be productive over the next 3 to 5 years as they use the lab?' It is oriented around the equipment rather than the protocol.

That's sort of a subtle point, but it has a huge amount of consequences when it comes to how you design the hardware and software that responds to this challenge. If you go to the next slide, you can see our hardware solution here is what we call our RAC carts, our Reconfigurable Automation Carts.

We have It's basically a robot wrapped around each laboratory device, and we have control over that environment. There's a HEPA filter on the top, which is important for a lot of biological work where you have opportunities for contamination and things like that.

We have a 6-axis robotic arm and a piece of MagneMotion track that allows you to, if you go to the next slide, Lego-block these together into ultimately very large setups. We're at 50-plus right now in the lab, and it's growing quickly.

I'll show you some photos in a second. We have 103 RACs will be coming online just in about a week, all in one big setup here in Boston.

If you go to the next slide, we can I want to show this is this is actually a video of the OpenAI protocol. We did this project with OpenAI where GPT-5 controlled the lab, and we made a video of one of the samples just moving through the various RACs for that protocol.

This just gives you a sense of, like, how does it work, right? You have these tracks, and we're able to move like our our one sort of constraint on the system is that we pass things in what's called SBS format.

That little rectangle you saw there is like a 3x5-inch square rectangle, and it can contain a 96- or this is a 384-well plate or 1536-well plate. It can also carry consumables like tips and other things.

The arm picks up that piece of plasticware or samples or tips or whatever it might be and then puts it onto a particular device. In this case, it's going, just went through an acoustic liquid handler, now it's going on to a Bravo liquid handler.

You saw, again, that first thing that got put down there was actually the plastic tips that are now getting picked up by the liquid handler, and the other two plates are sort of sample and destination plates. We're we're in this case for the OpenAI project, we're we're picking up some synthetic DNA and we're putting it into reaction mixes that were designed by the GPT-5 model at the time, all right?

Again, key feature here is any device that accepts those SBS plates, we can integrate into the RACs. It takes us usually like a month to a month and a half if it's a new device.

We've now got 80-plus devices on there. We're adding new devices all the time.

If a customer asks us for one, if we want to add one to Nebula, we just bring it online. Now that plate is going to get shaken up and put onto ultimately thermal cycler or this final analytical device to run the qPCR reactions and and give a readout on the performance of each one of those samples back to, in this case, an AI model.

That data at for most of the runs on Nebula is going back to a human scientist as opposed to an AI scientist, but we expect there'll be a mix of both as science goes forward, we'll have both scientists and their agents ordering experiments on autonomous labs. Go to the next slide.

A great thing about this system is it can expand. We started Nebula, I think, with about eight RACs doing NGS, like next-generation sequencing prep for our samples here at Ginkgo, and expanded ultimately now up to over 100 RACs on the system.

Let's dig in a little bit. I want to go to the next section.

That that was sort of the theory, like how we design the hardware in order to solve some of the challenges of the autonomous lab and what autonomy means. Now I want to dig in a little bit on Nebula specifically, because I think what's really unique about Ginkgo is we're not just a hardware company.

We actually run BSL-2 labs here in Boston and and do scientific partnerships with some of the largest biotech, ag biotech, industrial biotech companies in the world. We can actually show what it looks like to do real science on a system like this.

If you go to the next slide, one of the things I'm quite proud of that we've been able to show in the last quarter is over 100 protocols, with more than 30 of them being unique, submitted by scientists, and I'll mention this, but these are not being submitted by automation engineers or experts in robotics, being submitted onto our system, Nebula, here in Boston, which has 50-plus lab devices all integrated together, where we can send point-to-point samples from any device to any other device as as requested by those scientists. There is nothing else like Nebula in the world today doing sort of open-ended scientists like this science like this at this scale with this number of unique protocols and and users.

It's proof that autonomous labs are feasible. There's work to do, and talk about that.

Things break as we're scaling this up, that's for sure. It is it is evidence, in my view, that this is this is going to land, like we are going to beat the manually operated lab.

If you go to the next slide, I want to walk through a few of the key things that you've got to show if you're going to take out, one of those laboratory floors at Takeda or Merck or Novartis or wherever or Bayer Crop Science, or any of these companies that do a lot of laboratory work. First, you want to connect 100-plus devices in a single automation setup, all right?

It can't just be 5 or 10. A scientist expects to have access to many different devices in order to do whatever protocol they might read about in a scientific paper, this week.

I think about 100 is the right number. We've been able to This week, we'll we'll find out, we're turning it on in about 5 days, 105 all in or 103 RACs all in one big setup.

The reason we can do that is because of that RAC productized hardware, that cart I showed you. We just rolled in, they came in off a truck, and we rolled another 50 in, and those have all gone in the actual install over the last 3 or 4 weeks.

It's pretty fast to put that many new devices on a on a automated setup. We'll see if it works.

Second, we have run 10 Now, like I said, 30-plus unique protocols, 100-plus different proto- 100-plus total protocols. That's kind of you've got to get in that, I think, 50 to 100 to maybe 200 unique protocols all running on the autonomous lab at the same time.

We do that with our Catalyst. This is our software.

Our scheduler that we've built is a very complicated scheduling problem. It's really easy to mess this up.

Biology is very sensitive to timing. Things break all the time as we keep driving the scale up here.

We're getting to do that quick cycle of debugging and improving the system, but that scheduler is really the key piece of software driving that. Then finally, scientists, scientists, not automation engineers, and I think on a peak day on Nebula, we had board 39 or so scientists submitting.

That's really exciting, like to have that many different scientists submitting protocols on one automation system. Again, I don't know of any automation system in the world that's been able to do that before.

The and we're able to do that in part by leveraging AI coding tools with custom harnesses wrapped around them that basically understand how to transfer a scientist's intent in human language into code to operate the autonomous lab. That is a big unlock.

We're very thankful for what's going on with all the coding agents. That's a real help for improving the ease of use, because at the end of the day, to make robots do something, you have to program them.

To walk up to a lab bench and do your work by hand, you don't. We have to solve this problem of we can't make it so that scientists have to become coders to do their job, and we've really just been given a gift by these AI coding tools, again, like the Codexes and the Claude Codes and things like that, that can sit inside other tools that are specific to the automation to get this done.

Those are the three big ones, and I'm pretty happy with the progress on all of them. If you go to the next slide, as I've mentioned several times now, we're going from 50 to 105 RACs by the end of this month.

It's going to be awesome. It's a really cool system to see, and people should come out and visit it.

If you go to the next slide, that scheduler is not trivial. This is an example of our scheduler, I think, running 17 or 20 different protocols at the same time.

Each color is a different protocol. Each row is a different device on the system.

The x-axis is time. You can imagine, if you want to add a new protocol to that and you're like, 'Okay, I need to use the device on row 3, the device on row 7, and the device on row 9, and I need the device on row 3 for the first 3 minutes, then I'm willing to tolerate a up to 1-hour gap, then the second device for 15 minutes, then up to a 30-minute gap, then the last device.'

It'll check, can it fit you in? If it can fit you in, or if it can fit you in by moving a couple other things that doesn't disrupt them in a way that breaks the protocol, it'll fit you in.

That's awesome, right? That's very very much not how the traditional lab automation, the subways, work.

They're running a batch, that subway line is showing up at a certain time, you can't just jump in and insert yourself in the middle, but you can with our our scheduling software here. On the next slide, the little green one, it's hard to see, but that that column, third column over from the left over there, has the names of all the different scientists submitting.

I really love this. I love that we're seeing different people submitting different orders for for protocols every day.

It's really exciting. Again, I think it's unique.

We're also seeing a lot of energy on the US government side. If you go to the next slide, a lot of new policy, act action here.

There's the Genesis Mission, which we're fortunate to be a part of, from the White House to bring AI into the national labs. There's a big motion right now where we're seeing an increasing amount of drug discovery work moving to China from Kendall Square, I was talking about earlier, here in the Boston area.

That's because, simply, Chinese scientists are paid a third as much and they're doing equal work to what's happening here in the US. Like they're, they're just as good, they're just as smart.

I think if the if we want to remain competitive, we've got to think about doing our research in a fundamentally different way in the United States. I don't think we can just rest on our laurels of having the only smart scientists in the world in this area, or at least versus China, and I think that era is over, firmly over at this point.

We've got to think about a new way to do it. I'm pretty heartened to see activity out of, the National Science Foundation is funding $100 million for a network of cloud laboratories and autonomous labs.

There's a new bill introduced by Senator Young to sort of do more of this cloud labs and autonomous labs. Hopefully we see more here, but I'm I'm encouraged by what we see already.

If you go to the next slide, we were obviously very fortunate. I had a chance in December to sort of ribbon-cut the first 18 of our RAC robots going in at Pacific Northwest National Labs and signed a new contract for $47 million, much larger autonomous lab setup, nearly 100 RACs, going in a new building in a in a couple years at PNNL.

This is really exciting, and I think sort of highlights the direction I'm I believe our our national labs will go, our our scientific research in the country. If you go to the next slide, we were lucky to give ARPA-H a tour of Nebula.

We have a great project with them. The work is accelerated by having these autonomous labs available to our scientists at Ginkgo.

I think this is something that makes a lot of sense for a lot of labs at the National Institutes of Health, for example, or NSF-funded labs, or academic research universities. They would all be accelerated if our scientific talent could get many more of their hypotheses tested than are today due to the limitation of the manual lab.

Next slide. Listen, Nebula is showcasing what is possible, and that means that early adopters are getting excited about it.

We are also building autonomous labs for that left end of the chart here, the very earliest adopters, the people that are excited to try this out as a different way, as an alternative to their lab benches. We'll keep leaning in there, building those systems as that demand comes in.

We are seeing, if you go to the next slide, a lot of interest. We've had, 600-plus visitors in the first quarter.

I'll show it at the end, we have a great like a little sign-up. You can we do tours weekly if any of you want to sign up who are listening in.

We're very happy to give you a tour. That's Nebula, and that's the dive on that.

All right. Now I want to talk a little bit about our our service businesses: Cloud Lab, Data Points, and solutions, which I think of a little bit, like I said, like our Starlink, right?

Last year, 70% of the, launches at SpaceX were Starlink. If you go to the next slide, that's a huge advantage for SpaceX.

That means they get to basically be creating an asset, a money-making asset in the form of Starlink, while also getting to test over and over and over again their launch platform. Their launch platform, ultimately, I think in their view, is the big product, right?

That they can have that sort of transportation layer to space. Today, they're 70% of the demand for that platform, right?

I see a similar situation with the autonomous lab. We are able to have a big system here in Boston and basically prove out moving over our work from Data Points, Ginkgo Cloud Lab solutions, even our reagents business, onto that platform.

If you go to the next slide, I'm really excited. We we got our Cloud Lab off the ground just in in the last quarter.

It's really been exciting. This is from The Times in London: 'Do you want to run an experiment for $39?

Robots will do it for you.' Go check out cloud.ginkgobioworks.com.

You can go in the estimate tab at the top, type in whatever protocol you're interested in. It'll look up and see, do we have the equipment needed to do your protocol?

If so, it'll make an estimate of what the price would be to run that protocol on a cloud lab. People are, I think, pretty surprised at how inexpensive it can be.

That is a reflection of where all the costs lie in doing lab work, which is in manual lab work done 40 hours a week, done at low equipment density, low equipment utilization, in laboratories that cost a fortune to run. That then flows through, and it means all of the CRO services you order and and so on are very expensive.

We think we can we can solve that problem through automation, and the cloud.ginkgobioworks.com, our Cloud Lab service, is really a great way to do that. If you go to the next slide, this is what OpenAI took advantage of when we did this project where GPT-5 ran the lab.

We had an awesome result back in February. We showed that after six rounds of design, we had improved the cost of cell-free protein synthesis by 40% over scientific state of the art.

That that opened a lot of eyes. I think people weren't really We didn't know ahead of time whether the models would even be able to design experiments and interpret data at this level of sophistication.

Really excited about that. Really excited about future work we'll be doing to keep proving this out with OpenAI.

It's it's it's a neat line of work. I would say it's distinct from the autonomous lab.

I'd call this really an AI scientist using the autonomous lab, using a cloud lab to get its work done, but it is also really an important thing to watch if you're following kind of how AI is changing science. On the next slide, also excited, just in the last quarter, three new channels coming to our delivered business to our Cloud Lab and Data Points service.

Amazon Bio Discovery got launched by AWS, which is basically a platform to allow you to design antibodies. All three of these are sort of in the antibody space.

Benchling similarly, and then Tamarind Bio. The These are Tamarind and Amazon are sort of ways for pharma companies to access these frontier bio models.

If you think of things like AlphaFold, which got the Nobel Prize for Demis at Google, those that was like one of the earliest protein design models. There's many more now.

They're computationally intensive, they're interesting, and they help drug discovery scientists come up with a design for an antibody or protein for their drug. Then you've got to test it, right?

Like we don't know if these things work in biology unless you go into the lab. The idea is, could you have these layers where you access the latest models and all the compute to power them, and then, when you're ready to do your experiment, you hit a button and it kicks the designs to a cloud lab to do it for you, and the data flows back very nicely, well-packaged, right to the model, and you can run that loop as many times as you want?

That's sort of what's going on with Amazon and Tamarind. Then Benchling is really the leader in electronic lab notebooks, and it's a similar idea.

If you're in your ELN as a scientist and you've designed this experiment, could you ultimately hit go and kick it off to a cloud lab? We partnered there with our Data Points service, again, around antibodies.

Super excited to see these. I think this is this is like early indications of a way that could become a norm for how scientists do their work in the future and kind of order their laboratory experiments.

Okay. I'll I'll just say a couple more quick things about Data Points.

Really excited about the progress here, working with, 10 of the top biopharma companies in the world just in the first year of running it. It's a good mix of pharma and government and even tech companies and tech-bio companies.

We've done a nice job, on the next slide, of really being a community leader here. We're running competitions.

There's the Virtual Cell Pharmacology Initiative where we'll actually test compounds for free. People should definitely check that out if you're in the in the small molecule drug discovery space.

Really neat opportunities, and we host these summits and things like that. It's been good.

I think AI, as applied to the design of of drugs, is a big area, and with Data Points, we're sort of operating almost like a Scale AI, like creating those just big data packages that train the models. All right.

Next slide. We have had a long-standing business in solutions, more than 250 of these research partnerships over the last 10 years.

It's gotten us to work with the R&D groups of some of the largest companies in pharmaceuticals, industrial biotech, and agricultural biotech. Uniquely at Ginkgo, it is a huge range of different kinds of research, from, microbes associated with the roots of corn and trying to engineer them to produce fertilizer, to mRNA therapeutics, or antibody development in pharmaceuticals, to enzymes for industrial biotech.

Really wide range of different types of genetic engineering and biotech lab work that has happened at Ginkgo in sort of a not totally automated way, in other words, not like no people in the lab, but like semi-automated. Human interacting with a liquid handling robot and a human interacting with various benchtop devices that can, take a lot of samples at once.

We're sort of like not all the way to an autonomous lab, but we're doing a lot of variable work over the years in in semi-automated setups. If you go to the next slide, I'm I'm most excited to move this kind of work onto Nebula.

It is the hardest work to move, right? This is the stuff that really is that that car I mentioned earlier, the lab bench.

It's totally variable, it's really different, it's not just doing the same experiment over and over again like you would at a traditional CRO. If you remember my slide, it's where 95% of the spending is going at all of our customers.

They're, they they spend a bit with us, but they mostly spend on huge internal research labs to do this kind of work. If we want to replace the manual lab bench, migrating the work from our solutions business onto Nebula is is a really critical demonstration.

I'm excited about the progress there. We're trying to share that publicly even yet.

We bring people through. If you go to the next slide, one of the best things we do is we bring people through, show them the lab, let them talk to our scientists, see how scientists are submitting new protocols every day.

This has been really exciting to bring research leaders from, I've had, I don't know, three heads of pharma or ag R&D come through to visit just this year, right, to see the system. If you just want to visit, there's the link.

You really should come by. I think Nebula and our services on top of it is a truly unique asset to demonstrate what we think fundamentally is a better way to do biotech R&D, and we would love to get it in at every company out there and and replace their benches.

If you go to the next slide, that is the world that I want to see. Please, if you're interested, you can email me at [email protected].

Happy to follow up, and happy to take your questions now. Thank you.

Great,

thanks, Jason. As usual, I'll start with the 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 up your line.

Thanks, everyone. All right.

Thanks for joining us, everyone. Just a reminder to the analysts on the line, if you have a question, feel free to raise your hand and I'll call on you.

We have one to start off submitted from Brendan at TD we got over email. He has two questions.

The first one is: How should we think about the potential impact to revenues this year from the AWS and Benchling announcements? How have the launches gone thus far, and what is baked into your assumptions for the rest of 2026 for these new platforms?

I can take that one. Yes, we talked about AWS, Benchling.

The other one in that same category is the Tamarind Bio partnership as well. I'm super excited about these.

This is the first time I've seen this sort of kind of cloud layer talking directly to labs as a sales channel, excited to see where it goes. It is definitely new, right?

It's not like seeing a flood of inbound there. We are seeing some people reaching out to us because of the channel, so that's exciting.

I'm most excited that it's starting around antibodies, right, because that's just kind of naturally there's a number of these AI models associated with antibodies and so on, and because there's a few different providers that'll do these antibody services for you. The one I'm most excited about is, with our Cloud Lab, we're not limited to testing antibody binding, right?

If you look already on the, I don't know, 8 or 9, 10 protocols we've posted, and we're posting a new one every week, it's a pretty wide variety of stuff. We're doing mass spec, metabolomics, like all kinds of things.

You can come and ask for a protocol in Cloud Lab, we'll add it. I'd love that to turn into a channel straight from an electronic lab notebook or whatever where a scientist is like, 'This is the protocol I want, price it.'

You get a price back from cloud.ginkgobioworks.com and then you can run your experiment. That feels a lot closer to AWS and sort of like what we saw as successful with cloud compute than where these are today, which is really much more just in a more narrow lane around antibodies, which I think is an exciting place to start, but I am super excited to fan that out.

I think that then that could become a really quite an interesting channel and something that scientists just don't have access to today at the end of the day. You can't get custom stuff done.

I think that that's what I'm most excited about there. Cool.

All right,

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

We launched Data Points, what was it, a year and a half ago now, and to have 10 of the top pharma companies as customers now is really exciting. I think the revenue unlock is just repeat business from those customers.

We are starting to see that. What we saw with sort of like pilot project, data-gen project, and then now you've got, again, because you are seeing people trying to build in-house models.

Now, remember, these are not reasoning models. These are not like in-house versions of Claude or Codex, or Opus or whatever or GPT-5.5.

They are models trained on biological data. They're much more specialized.

I do think it makes sense, actually, in the field that you're going to see a lot of people having their own datasets, their own models that are sort of tuned-up versions maybe of various protein models. That's not going to be uncommon at all, much more common than I think you'll see in the reasoning model and coding space, because these things are very different and people have different datasets.

I'm sort of hopeful, as their people are building these models, we'll keep seeing this sort of repeat demand as they're like, 'Okay, I found one. I like what I'm seeing in terms of return on data and performance of my internal model.

Give me more data.' That's the revenue unlock.

The more that we see, then I think we become sort of like a default provider. That's certainly what happened with Scale and other places in the early days of image models and then language models.

When people saw, 'Oh, I'm seeing performance increase with more data,' they turned around and bought more data. That's what we're going to be watching as these protein models and other types of models come out, too, in the future.

I think that's the lane for Data Points.

Cool. On the theme of AI, there was someone who was on X who asked us a question, I think this is sort of based on our project with OpenAI: How much efficiency improvements after using GPT-5.5?

Any idea for space left for improvement? Will this be a transitional factor?

We did this project, and just to remind that we announced back in February with OpenAI as our first project with them, where we had GPT -- it was actually not 5.5, it was 5, because we started much earlier and that was when that was the model that was out, and we kind of kept the same model through the whole thing for more scientific paper purposes. We were able to show over a series of six rounds of running the model, with 100 384-well plates designed by GPT-5 per round, a 40% improvement over state of the art in the scientific goal we were trying to achieve.

I think there's real interesting questions: a, how much further could you push that, sort of what does actually diminishing returns look like in some of these scientific areas? Can the model have sort of breakthrough ideas that create really new ways of doing this?

TBD. Then, as the models have gotten better, yeah, would 5.5 be better than what we got with 5, right?

I think that that's all going to be exciting stuff to test. We're excited to do more with OpenAI, and we're planning to.

I think this is a open terrain in terms of how good the reasoning models can be at basically experimental design and experimental analysis. Those are the two things it's really doing.

It's like, 'Here's an experiment I want to run. Give me back the data, Cloud Lab, autonomous lab.

Give me back what are the results of my experiments I just designed, and then I'm going to analyze them and design more experiments.' We'll see.

I think that's real exciting to watch what's going to be capable of there. It's a new way to do science.

It really is. I won't belabor this too much, but I think the access to a model like that plus an autonomous lab can let individual scientists operate closer to how a principal investigator of an academic lab or a head of a drug discovery group who has a lab of 8 people or a lab of 30 people and is sort of assigning hypotheses to different people and kind of pursuing that over time, an individual could push that out for probably close to the same cost as they're currently costing to be themselves at a lab bench in terms of just their utility costs and everything else and low utilization of equipment.

They could push out 5 agents on top of an autonomous lab to go pursue a bunch of experiments. That is real exciting if that works.

I think it really fundamentally changes the rate we can do science. That's why you see the Genesis Mission in the US investing in this sort of stuff, because their goal is to 2x the output of US science.

These are the ways that that'll do it. Our science-based industries, of which pharma is the biggest, will be completely changed by this if you can 2x or 3x the rate.

No question about it. All right.

Our next question is really a bundle of questions from DK, who's writing from South Korea. These questions are all about how the move on to Nebula, our autonomous lab, has changed the science that we're doing.

The questions are: How does the use of Ginkgo's automated lab affect overall costs? Are there meaningful differences in speed, for example, turnaround time for experiments?

Have you observed improvements in success rates, reproducibility, or scalability since moving to the autonomous lab?

On cost, I try to touch on this a little bit in the talk, but the -- I think like the clear ROI, not just for us, but for any one of our customers looking at an autonomous lab, is about a threefold reduction in space utilization compared to a manual lab and a fourfold increase in the time. In other words, like the amount of time the lab is being used to do lab work, right?

From that 40 hours to 168, 24/7 week. That's really -- that that those improvements is where it's going to yield the cost reduction.

That is a huge amount, because those are really the two -- like sort of people time and space time are the two big things we spend money on in research. On the speed front, yeah, it's interesting.

An individual protocol doesn't really get shorter like then necessarily you would do it at the bench. You could imagine ways to do that in the future, rebuild protocols differently.

The first thing scientists are going to do is just take work they're doing at the bench and move it on to the autonomous lab. In that world, it does not need to get faster in terms of like end-to-end time for the protocol.

However, it, in practice, can get faster, because you can start a protocol at 4:00 PM in the afternoon where you never would have planned to spend the next 7 hours in the lab, kick it off, and have the thing run overnight. In that world, you took an experiment that you would have started tomorrow at 10:00 AM and start it at 4:00 PM and have the results by tomorrow at at 8:00 or 10:00 AM.

That that can shave a whole day off. I think you will see actually a massive speed up, because scientists will start taking advantage of the 4x more time that they have available every week.

If they plan it right, in theory, you could see a fourfold improvement in a lot of the times, depending on how serialized your experiments need to be. I think that's really exciting, and our scientists in at Ginkgo really like that.

Then on the -- just sort of like improvement and like the -- I would say I would call this like the quality of the experiments, I think reproducibility is inherently advantaged on automation. That's and that's mainly to do with like the audit trail.

Like you kind of if an instrument errors, if a liquid handler makes a mistake, like what -- these are all tracked. You kind of know like those those experiments that you don't catch at the manual lab bench, you catch if there's such a mistake on the autonomous lab.

If you saw a really, 'Wow, that's a surprising result,' you might go back, look at your experiments, be like, 'Oh, oops, I see what I did there. I like designed this experiment in a way that was like a little silly, and that's actually what's giving me this result,' as opposed to assuming you did the experiment you wanted to do and that was the origin of this like amazing result you got.

I think, yeah, that's a common thing that can happen for no nefarious reasons for scientists at the bench. I think that you will see a big improvement in reproducibility.

Then the other thing that got brought up there was throughput. The throughput increase is going to be insane.

I I think people are surprised when they go to cloud.ginkgobioworks.com, which I encourage people to do, and type in a protocol and see how much it costs. Because I'm basically pricing that protocol based on reagent use and equipment time and a markup on that, and it is not the insane cost that you have when you're have a whole team doing this work at the bench.

It's just not. Like like so so if scientists really understood just how low-cost each sample could be in an experiment, and they didn't -- in order to multi- to do many more, they just hit a button rather than have to slave in the lab for 3 days doing 1,000 experiments, they're going to just order 1,000 experiments.

I think you will see an explosion in the in the amount of data. This is 100% what happened in every other field that's ever been automated.

Right? It it's the like the beginnings of the automation of computation, right?

Like when we went from slide rules to to automated computation, an explosion in the amount of compute used and and a massive increase in the return on investment from what people who understood how to design computation could do. That's what I want to do for for the scientists, for the drug discovery leads when they have access to an autonomous lab compared to the to the ROI and the throughput that they can get out of manual labs.

It's just no -- going to be no comparison. I think I think all three you're going to see big gains on.

The cool thing is we're going to keep showing this on Nebula. We, just had a, again, head of R&D through today, and we went through with his team and showed all the all the gains, and it's it's a, yeah, it's really exciting right now.

I think we'll end on a note kind of related to that, which is you guys mentioned in the call, you've mentioned other places that you're trying to get to 100 racks. When do you actually expect to get there?

It's been pretty fun. We're going to put some behind the scenes videos up, but we have been installing racks for the last 3 weeks here at Ginkgo.

There's just showed up on trucks built by our team in Emeryville, and we just added the additional 50. They're all fully connected now in lab.

Just took a tour of it, it's insane. We can run them now, like the original system is running, and now the new 50 is running.

There is a connection between the two, and that connection is going to get turned on, I think, on the 14th, I don't know, next week. It is imminent.

As I'm really excited to see it all come together. We already have it up now running as two separate loops.

To put in 50 new pieces of equipment in 3 weeks, again, these are just things that no one's ever done in laboratory automation. I do think we are doing a very unique thing here at Ginkgo.

That's the bet. That's certainly what I'm leaning in on the company, it's what we're investing our capital into, it's where our new customers are coming from.

If you like that idea, I think that's a really exciting time to get involved with the company in any way. Yes, we're going to be at 100 next week.

103 or 5, I got to count them up. Yep.

Thanks. All right, and if you want to follow us on that journey, you can go to X or LinkedIn, Instagram, and keep watching.

We'll have a lot of content coming about the unveiling of the new full system. As always, if you have questions, you can reach out to us at [email protected].

Thanks so much, everyone. Until next time.

Thanks, everybody.