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 onto the right track with this focus for the company. The two big ways I'm gonna 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. 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 gonna 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. I wanna 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 focus really firmly in the area of defense tech and building sort of a Biosecurity prime. Ginkgo as a shareholder in that company, we're super excited to see it succeed. 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. 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, best bet in this sector. All right. I'm gonna 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. As Jason noted, we closed that transaction on April 3rd. The biosecurity transferred assets met the criteria under U.S. accounting 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 the accounting rules, we have and will retrospectively recast all prior periods presented to conform to this presentation. That means the revenue, operating expenses, and cash flows previously attributed to the biosecurity business are removed from each line item of our continuing operations and cash flows as the prior period information is presented, including for Q1 of last year. The former biosecurity results are now reported as a single net line loss from discontinued operations below loss from continuing operations. To be clear, all of the financial commentary I will provide today relates exclusively to continuing operations. We will not be discussing the biosecurity business further in our prepared remarks. On April 7, 2026, for your information, we filed a current report on Form 8-K that includes pro forma financial information for fiscal years 2023, 2024, and 2025 on a continuing operations basis. Following the biosecurity divestiture, we now operate as a single segment. With that, I'll 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 $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 lease 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 lease space which we are not occupying, net of sublease income. This is a cash operating cost that is not related to driving revenue right now and can be potentially mitigated through subleasing. 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 three to six 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. Turning to guidance. As we discussed in February, 2026 is about continuing to be cost-efficient while investing in our AI robotics and software to bring autonomous labs to our bioscience customers, including the build-out of our frontier autonomous lab in Boston. We have turned the page on our pure focus on restructuring actions to focus this year not only on cost efficiency, but on investing in what we see as our opportunities while continuing to provide our customers the advanced services 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 million-$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 gonna 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 gonna dig deep today and talk about why autonomous labs will be replacing the lab bench. I wanna highlight, some of what we're doing with Nebula, our system, 'cause we have, some news this month, in terms of expanding that system. Finally, the services that we put on top of Nebula are Cloud Lab, Datapoints, 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 will help drive sales of our systems into the world. I'm gonna talk about all three, and let's dive in. I gave this analogy. I'm gonna do it again 'cause 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 gonna give an analogy from the transportation industry. On the Y-axis here, we have the amount of automation for a certain type of transport. On the X-axis, the request flexibility. In other words, the user's asking the transportation system to do something different, or not. For a 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 wanna 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, it'll 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. Unless, go to the next slide, you've been to California in the last four or five years, or now L.A. or Austin or soon in Boston, and you've sat in the back seat of a Waymo, and it is amazing. It is like sitting on a subway. You don't have to do anything, it'll take you right to your house. It has the flexibility of a car. It's those two things together 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. If you look at the next slide, you'll see the miles traveled by cars and trucks versus subways and trains in the U.S. 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. 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 workcell we have here at Ginkgo. Companies like HighRes and Thermo and Biosero make these. They're like a subway, right? They're great. They're fully automated. They'll do an experiment without a scientist in the loop, it better be the experiment that you ordered yesterday. It's not gonna 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 wanna 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%+ 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, so 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 wanna 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. There's 50 million sq ft of laboratory space just in the Boston area. You can dramatically reduce that. You can increase the research productivity of your human scientists as we need more data from AI, and we'll talk about that in a minute. 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 billion-$3 billion a year on research. Not clinical trials, spending on, you know, 1 million+ sq ft of lab benches. If you look at their spending within that or 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, 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 this equipment that's often very spread out in a normal human-operated lab 'cause 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? I mean, it's when people come into the lab and humans are there and they gotta be there, and that's when you can get people to work. There's now almost never multiple shifts in these types of sorta high-end research labs. It's really a 40-hour work week. Our system here in Nebula is running 168 hours a week. You know, 24/7, that'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'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, you know, a workcell, one of those subways. The way it's designed is it's designed against a particular protocol. If you're a biopharma company and you wanna build a high-throughput screening workcell and you call a traditional automation vendor, the first question they're gonna ask you is, "Tell me about your protocol and tell me what throughput you need it run at. How many samples do you wanna get through every week?" 'Cause they're building you a subway line. It's gonna be built to do that protocol for you. If you're 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 gonna run in this lab that 30 of your scientists are gonna use to do work?" You say, "What kind of science are you doing? Very specifically, what equipment do you want me to install in that lab so that your scientists can be productive over the next three to five years as they use the lab?" So it is oriented around the equipment rather than the protocol. That's a 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. 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 six-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+ 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, I wanna show. 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 racks 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 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 in sq, rectangle, and it can contain a 96, or this is a 384-well plate or 1,586-well plate. It can also carry consumables like tips and other things. The arm picks up that piece of plastic ware or samples or tips or whatever it might be, and then puts it onto a particular device. In this case, it's going, you know, just went through an acoustic liquid handler. Now it's going onto a Bravo liquid handler. You saw, that first thing that got put down there was actually the plastic tips that are now getting picked up by the liquid handler. The other two plates are sort of sample and destination plates. We're, in this case, for the OpenAI project, 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, right? Again, key feature here is any device that accepts those SBS plates, we can integrate into the racks. It takes us usually, like, a month to month and a half if it's a new device. We've now got 80+ devices on there. We're adding new devices all the time. If a customer asks us for one, if we wanna add one to Nebula, we just bring it online. Now that plate's going to get shaken up and put onto ultimately this thermocycler or this final analytical device to run the qPCR reactions and give a readout on the performance of each one of those samples back to, in this case, an AI model. That data from most of the runs on Nebula is going back to a human scientist as opposed to an AI scientist. We expect there will be a mix of both as science goes forward. We will 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. You know, we started Nebula, I think, with about eight racks doing NGS, like, Next-Generation Sequencing prep for our samples here at Ginkgo and expanded ultimately, now up to over 100 racks on the system. Let's dig in a little bit. I wanna go in the next section. That, that was sort of the theory, like how we designed the hardware in order to solve some of the challenges of the autonomous lab and what autonomy means. Now I wanna 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 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. 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+ lab devices all integrated together, where you can send point-to-point samples from any device to any other device as requested by those scientists. There is nothing else like Nebula in the world today doing sort of open-ended science like this at this scale, with this number of unique protocols and users. It's proof that autonomous labs are feasible. There's work to do and talk about that. Things break as we are scaling this up, that's for sure. It is evidence, in my view that this is, this is gonna land. Like, we are gonna be the manually operated lab. If you go to the next slide, I wanna walk through a few of the key things that you gotta show if you're gonna 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 wanna connect 100 plus devices in a single automation setup. It can't just be five or 10. You know, a scientist expects to have access to many different devices in order to do whatever protocol they might read about in a scientific paper, you know, this week. And that, I think about 100 is the right number. We've been able to this week, we'll find out. We're turning it on in about five days. 105 all 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, you know, they came in off a truck, and we rolled another 50 in, and those have all gone in the actual install over the last three or four weeks. It's pretty fast to put that many new devices on a, on an automated setup. We'll see if it works. Second, we have run 10 now, like I said, 30+ unique protocols, 100+ different, 100+ total protocols. That's, you gotta 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 built. This is a very complicated scheduling problem. It is 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 are getting to do that quick cycle of debugging and improving the system. That scheduler is really the key piece of software driving that. Finally, scientists, not automation engineers, I think on a peak day on Nebula, we had 439 or so scientists submitting. That is really exciting. Like, to have that many different scientists submitting protocols on one automation system, again, I do not know of any automation system in the world that has been able to do that before. 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 Code and things like 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 gonna be awesome. It's a really cool system to see. 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 wanna add a new protocol to that and you're like, Okay, I need to use the device on row three, the device on row seven, and the device on row nine, and I need the device on row three for the first three minutes, then I'm willing to tolerate a up to one-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, and 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 and insert yourself in the middle, but you can with our scheduling software here. The next slide, the little green one, it's hard to see, but 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 protocols every day. It's really exciting. Again, I think it's unique. We're also seeing a lot of energy on the U.S. government side. If you go to the next slide, a lot of new policy 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. They're doing equal work to what's happening here in the U.S. Like they're, you know, they're just as good, they're just as smart. I think if we want to remain competitive, we gotta 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. I think that era is over, firmly over at this point. We gotta 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 encouraged by what we see already. If you go to the next slide, we're obviously very fortunate. I had a chance in December to sort of ribbon cut the first 18 of our RACs going in at Pacific Northwest National Laboratory and signed a new contract for a $47 million much larger autonomous lab setup, nearly 100 RACs going in a new building in a couple of years at PNNL. This is really exciting and I think sort of highlights the direction I believe our national labs will go, 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, you know, 600+ visitors in the first quarter. I'll show it at the end. We have a great, like a little sign-up. We do tours weekly if any of you wanna sign up who are listening in. We're very happy to give you a tour. Okay. That's Nebula, and that's the dive on that. Now I wanna talk a little bit about our service businesses, cloud labs, data points, and solutions, which I think of a little bit like I said, like our Starlink. 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 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. That they can have that sort of transportation layer to space. Today, they're 70% of the demand for that platform. 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 Ginkgo Datapoints, Ginkgo Cloud Lab Solutions, even our reagents business onto that platform. If you go to the next slide, I'm really excited we got our Cloud Lab off the ground just in the last quarter. It's really been exciting. This is from The Times. "Do you wanna run an experiment for $39? Robots will do it for you." Go check out cloud.ginkgo.bio. 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, and if so, it'll make an estimate of what the price would be to run that protocol in a cloud lab. People are, I think, pretty surprised at how inexpensive it can be, and 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, it means all of the CRO services you order and so on are very expensive. We think we can solve that problem through automation and the cloud.ginkgo.bio, our Ginkgo 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 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're gonna be doing to keep proving this out with OpenAI. 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. 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 deliver business to our cloud lab and Ginkgo Datapoints 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. 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 a protein for their drug. You gotta test it. 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, 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. 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 Datapoints service again around antibodies. Super exciting to see these. I think 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 just say a couple more quick things about data points. Really excited by 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 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 drugs, is a big area, and with Datapoints, 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. We have 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. A 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 were sort of like not all the way to an autonomous lab, but we were doing a lot of variable work over the years in semi-automated setups. If you go to the next slide, 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 car I mentioned earlier, the lab bench. It is totally variable. It is really different. It is not just doing the same experiment over- and- over again like you would at a traditional CRO. If you go remember my slide, it is where 95% of the spending is going at all of our customers. 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 a really critical demonstration. I'm excited about the progress there. We're trying to share that publicly. Vignettes, 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, and this has been really exciting to bring research leaders from, three heads of pharma or ag R&D come through to visit just this year. To see the system. If you just wanna 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 replace their benches. Go to the next slide. That is the world that I wanna see. Please, if you're interested, you can email me at jason@ginkgobioworks.com. Happy to follow up and happy to take your questions now. Thank you. 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 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. I mean, this is the first time I've seen this sort of kind of like cloud layer talking directly to labs as a sales channel, I'm excited to see where it goes. It is definitely new. We're not like seeing a flood of inbound there. We are seeing some people reaching out to us because of the channel. That's exciting. I'm most excited that, it's starting around antibodies. That's just kind of naturally there's a number of these AI models associated with antibodies and so on, because there's a few different providers that'll do these antibody services for you. What I'm most excited about is with our cloud lab, we're not limited to test an antibody binding. If you look already on the, I don't know, eight or nine, 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, all kinds of things. So you can come and ask for a protocol on Ginkgo 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, you know, cloud.ginkgo.bio, and then you go run your experiment. I think 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. I am super excited to fan that out. I think that then it could become 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's what I'm most excited about there. Next question from Brendan. What are you hearing on Ginkgo Datapoints 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? Are there any potential revenue funnel unlocks we should watch for over the coming quarters from this part of the business? We launched Datapoints, what was it, a year and a half ago now. To have, 10 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 was 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, like 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, so 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 the 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 of that we see, I think we become sort of like a default provider. That's certainly what happened with Scale AI 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 it does not just protein, other types of models come out too in the future. I think that's the lane for Ginkgo Datapoints. Sort of 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, 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, 'cause we started much earlier, and that was when that was the model that was out, and we kinda 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? Like sort of, what does actually diminishing re-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? As the models have gotten better, it would 5.5 be better than what we got with 5. I think that's all gonna be exciting stuff to test. We're excited to do more with OpenAI, and we're planning to. I think this is 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 wanna 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 gonna analyze them and design more experiments." We'll see. I think that's real exciting to watch what it's gonna be capable of there. It's a new way to do science. It really is. I won't belabor this too much, 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 eight 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 their utility costs and everything else, and utilization, low utilization of equipment. They could push out five 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 U.S. investing in this sort of stuff because their goal is to 2x the output of U.S. science. These are the ways that that'll do it. Our science-based industries, of which pharma's the biggest, will be completely changed by this. If you can 2x or 3x the rate, no question about it. 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 onto Nebula, our autonomous lab, has sort of 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 tried to touch on this a little bit in the talk, but I think, 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 four-fold increase in the time. In other words, 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 those improvements is where it's gonna yield the cost reduction. That is a huge amount, 'cause 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, it's interesting. An individual protocol doesn't really get shorter, like, than necessarily you would do it at the bench. You can imagine ways to do that in the future, rebuild protocols differently. The first thing scientists are gonna do is just take work they're doing at the bench and move it onto the autonomous lab. In that world, it does not need to get faster in terms of, end-to-end time for the protocol. However, it, in practice, can get faster 'cause you can start a protocol at 4:00 P.M. in the afternoon where you never would have planned to spend the next seven hours in the lab, kick it off, and have the thing run overnight. In that world, you took an experiment that you would've started tomorrow at 10:00 A.M. and start it at 4:00 P.M. and have the results by tomorrow at 8:00 A.M. or 10:00 A.M. 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 four-fold 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 at Ginkgo really like that. On the just sort of, improvement and, I would call this, the quality of the experiments. I think reproducibility is inherently advantaged on automation, and that's mainly to do with, the audit trail. Like, you kinda. If an instrument errors, if a liquid handler makes a mistake. These are all tracked. You kinda know, 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 experiment, and say like, "Oh, oops, I see what I did there. I designed this experiment in a way that was, 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. 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. The other thing that got brought up there was throughput. The throughput increase is gonna be insane. I think people are surprised when they go to cloud.ginkgo.bio, which I encourage people to do, and type in a protocol and see how much it costs. 'Cause 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 have a whole team doing this work at the bench. It's just not. Like if scientists really understood just how low-cost each sample could be in an experiment, in order to do many more, they just hit a button rather than have to slave in the lab for three days doing 1,000 experiments, they're gonna just order 1,000 experiments. I think you will see an explosion in the amount of data, and this is 100% what happened in every other field that's ever been automated. It's like the beginnings of the automation of computation. Like when we went from slide rules to automated computation, an explosion in the amount of compute you use and a massive increase in the ROI from what people who understood how to design computation could do. That's what I want to do for the scientists, for the drug discovery leads when they have access to an autonomous lab compared to the ROI and the throughput that they can get out of manual labs. It's just going to be no comparison. 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 gains and it's real 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 RACs. When do you actually expect to get there? It's been pretty fun. We're here to put some behind the scene videos up. We have been installing racks for the last three weeks here at Ginkgo. They just showed up on trucks, built by our team in Emeryville. We just added the additional 50. They are all fully connected now, and the 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, and there is a connection between the two, and that connection is gonna get turned on, I think, on the 14th. I don't know. Next week. It is imminent. Really excited to see it all come together, but we already have it up now running as two separate loops. To put in 50 new pieces of equipment in three 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 now is a really exciting time to get involved with the company in any way. Yes, we're gonna be at 100 next week. 103 or 105. I gotta count 'em up. Thanks. All right, if you wanna 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 investors@ginkgobioworks.com. Thanks so much, everyone. Until next time. Thanks, everybody.