Thanks, Steve. The three topics we're going to cover today in the deep dive is, one, our continued restructuring and the cost takeout. And then in sections two and three, I want to go through automation and data points and our newly launched reagent product, which are really our three big motions into the life science tool space. So really excited about this today. So okay, so let's dive in. So first, you know, I mentioned this already, I'm really excited to see these numbers, that $250 million cost reduction, getting that done ahead of schedule is very strategically important for the company. So the whole reason we've been focusing on this, and the team has put in an absolutely enormous amount of work and pain around this is we wanted to be able to do this motion of moving into the life science tool space with a margin of safety. In other words, with enough cash in the bank and no bank debt to allow us to not be forced to take money for people we don't want to or raise in circumstances where we weren't happy. And so having that large cash balance relative to our cash burn is really a critical piece of putting us in a good position when and if we engage with capital markets. And so really happy that we're there on that. You can see here our burn rates getting down to $28 million. If you go to the next slide, I've adjusted EBITDA for this quarter. So really, again, a testament to the team and strategically important to Ginkgo. Okay. All right. So now I want to talk a little bit about our automation and data points offerings, and then we'll talk at the end about reagents. So to give you some macro context, and I spoke about this before, but Ginkgo's business over the last decade has really been what we call solutions. So in other words, selling to the head of R&D of a large company or the CEO of a small or mid-sized company, and basically being an outsourced research team, Ginkgo scientists using Ginkgo tools to deliver them a research product. All right, that was the solutions business. Last year, about a year ago, alongside a restructuring in the company, we started to offer Ginkgo's tools and services that we had previously had in-house just for our scientists, directly to the scientists at our customers. And that has been going really, really well. And so, again, I want to give a little more context on that. So, if you go to the next slide, you can see, you know, on the y-axis here, we have what I'll say is like our customization and technical risk we're taking for the customer. So, when that is high, like it is in research solutions, in other words, we'll have a big milestone that will only get paid if we're technically successful, the customer is willing to give us downstream value share. In other words, a share of the future value of their products, either a royalty or success-based milestones like that technical milestone that I mentioned and so on. That's really in exchange for the level of customization and risk we're taking. So as we go down that y-axis, we go to the right-hand side of this chart where we're not able to get royalties and downstream value share. So that's a downside. But the upside is we're selling something much more off the rack. In other words, a more standard, scalable product to the customer. And if you go to the next slide, what we're seeing here is the solutions business has that big upside. It takes a while to get to it. So I think there's a really nice complement here where our tools offerings are able to give us near-term revenue, smaller batches, wider customer set, opening new markets. We're going to talk about the reagents. This first kit is a $2,000 kit. Scientists can order it with a credit card. So that is really allowing us to have a faster cycle time going to market. It's a good complement for the solutions business, and it's the right time to do it. All right. So I'm going to jump in and talk a little bit about our automations offering, and then we'll get to our data points, which is more of a traditional CRO, and then finally reagents. All right. So when I talk to customers about automation, I like to show this slide, which is that Ginkgo, in addition to selling automation, has been a user and builder of automation over the last decade as we've been doing these solutions partnerships. And this is where that solutions business really complements life science tools. We're almost unique among life science tools vendors in really being primarily doing high-end science using our tools over the last decade, which means we have an enormous amount of familiarity with what's out there in the market what works and what doesn't and we built a lot of our in-house tools to fill gaps in what we couldn't get from vendors on the market today which is what makes our tools business so exciting because when we launch these things they're immediately stepping into a gap in the market because if it hadn't been a gap we would have been buying it already from the life science tools companies uh and so if you go to the next slide you know this is what uh i think is the core challenge if you look across the industry today. So when we talk to life science leaders, heads of R&D and so on, the number one thing you're hearing is there's a demand for more output from the same R&D resources. And this is a combination of factors, sort of economic pressure in the industry over the last three or four years with interest rates up. But it's also competition from biotech companies in China, where you're seeing lower cost labor, sort of lower cost infrastructure, and so on, creating pressure on the research infrastructure here in the United States and in Europe and others. And so how do you solve that problem? Well, part of the issue from my standpoint is the majority, the overwhelming majority, 95% plus of the research work done in the sciences and in commercial biotech and agriculture is done at the lab bench. And that picture on the left is basically what every lab bench looks like if you go into any one of these companies, right? So there's pipettes at the bench. I did my PhD in bioengineering. That's five years of picking up one of those pipettes and moving liquids around, working by hand at the bench, buying things from the Thermo Fisher catalog, reagents. It's very variable. You can do almost anything you want, but you do it at low throughput. And as you do more of it, it does not get cheaper, right? It's not like making cars or making semiconductor chips, whereas you do more, the cost falls per unit as you do more research it's just as expensive uh as the last unit as you do more because it's being done by hand so sort of the obvious thing like if you're a tech person is like well let's just automate it right like if we automate it like semiconductors and automobiles you'll get a much lower cost uh per unit operation in the lab uh and this is even more acute because you're seeing demand uh around ai for these large data sets uh and i'll i'll point out we're not the only ones thinking this way. Like, let's automate it, right? So, you know, President Trump put this out just last week, Winning the Race, America's AI Action Plan. And I would really recommend you read this document. It's great. It's very focused on the actual things to do in order for the United States to make strategic choices in AI. And one of the categories is invest in AI-enabled science. And you should read the doc, but I'll just call out one specific part where it says, you know, NSF and DOE and so on and other federal partners, there should be an investment in automated cloud enabled labs. Okay. And what they're saying there with cloud enabled is think like a data center, right? When we say cloud computing, we think of a big data center that can do lots of different stuff and it's accessible and gets cheaper with scale and improves with technology. Can we make the lab bench more like the data center cloud. That's the provocation from this sort of AI action plan. And I think we can. And if you go to the next slide, I'll show you why it's been hard historically in the industry. So on the y-axis here, and this is going to be my like automation nerd out slides, so bear with me. So on the y-axis here is a term of art and automation called mix. Okay. So a low mix environment is like an automobile plant. All right. You're making the same car over and over again. It's a low mix of output. A high mix of output is like a fine chef at a restaurant. All right. Lots of different orders coming in from the menu, variations, people are requesting all kinds of stuff. You have a common set of tools, but you use it in very different ways to produce different high mix of outputs. Okay. That chef is very analogous to the scientists at the lab bench today. Very analogous. All right. There's a common set of tools, common set of equipment on those benches. They're using their hands and they're doing a very high mix of work. And they are very well served by Thermo Fisher, Danaher, and a long tail of equipment and reagent vendors over the last 50 years that are selling them all kinds of stuff to work at that bench. All right. It actually works pretty good. It just doesn't scale. Okay. It really does not get cheaper with scale. And that's what we're seeing with the increasing price for new drug discovered and everything else. All right. On the other hand, on the low mix side, more like an auto plant and a high throughput on the X axis. Okay. We have what we call automation work cells. And I'll show you a picture of one in a second, but these are where automation has been used in life sciences today. Things like high throughput screening and compound management, places where, you know, diagnostics, where you're doing the same protocol over and over and over again. And there, automation does work great in the lab. And there's companies like Thermo Fisher and Hi-Res BioSolutions that'll sell you these customized work cells. The trouble is they just do those one or two protocols. They don't have anywhere near the flexibility of the bench. And so the question is, can we get to high mix, high throughput, or at least like medium mix, medium throughput, something that's closer to the bench, but sees a scale economic. All right. And that's what we're trying to achieve with Ginkgo Automation. We believe is possible with our reconfigurable automation carts, our racks, and our software on top of them. And so I'm going to talk a little bit more about that. So to give you some context, on the slide here, you can see a picture of, if you go to the next slide, a work cell. And so this is that traditional low mix, high throughput automation work cell. And this is actually one that we got built for Ginkgo, okay, right? And those two white towers in the middle are robotic arms. They can pick up a plate and move it to all the various benchtop lab equipment that's jammed into that thing. You see everything kind of stuck in there and on top of each other and everything else. If it's not obvious, that is a very custom object, okay? It is not standardized. It is built just for you, all right? And it has a relatively low return on investment because the entire value of that work cell has to be justified by the one or two lab protocols that it's able to conduct. All right. And that means that, back to my comment earlier, 95% of the lab work is happening at the bench and less than 5% is happening on work cells like this because it's only the most repeatable work that can justify that return on investment. So if you go to the next slide, this is our solution to that, the reconfigurable automation cart. All right. This is technology invented at Ginkgo. We've been building this up over the last 10 years. In this box basically is a piece of lab equipment. You can see an orange centrifuge there inside the box in the cart. There's a robotic arm and there's a piece of MagnaMotion track. And what this track does is allows you to deliver a plate, a 96 or 384 well plate to that robotic arm. The robotic arm picks up the plate, puts it onto the piece of lab equipment. And we have, I'll show you in a minute, now 50 plus lab equipment integrated, puts it on the equipment, and the software tells the equipment, run your experiment. And when it's done, the arm picks up the plate and puts it onto the track. And what's great about this is once that custom piece of equipment is inside this box, and we integrate directly with the equipment to our software, it's now basically like a standard unit. All right. And if you go to the next slide, you can see we can stitch these together. We put unit, unit, unit, and we've now connected three pieces of lab equipment all into one setup and we can move the plates among those equipment on that magnetic track. And with the arms, we can deliver the samples to the equipment and it all just works if it's on that integrated setup. And we have now, you know, like I said, 50 plus pieces of equipment. They're not all shown here, integrated into these setups. And we're adding more every day. If a customer wants a new piece of bench equipment inside our setup, we do that at our cost and then have it integrated in the future for future customers, okay? And you can put together many of these. There's a picture of our lab, if you go to the next slide, here in Boston. And again, unique among automation vendors, we use our own automation in a BSL-2 lab. So this is a 20-plus rack setup. And inside it, you have all these different pieces of equipment, and you can, again, run protocols that connect any piece of equipment to any other piece of equipment in that setup. You go to the next slide. This modularity is really exciting. Customers are loving it. This is just us at a few vendor trade shows. I really like the picture up in the top right. Recursion had an event at JP Morgan. They invited us to come. And we actually set our rack system up. It was like a five-part system in an afternoon and had it running for the cocktail party. So the ability to quickly build the system and then, very importantly, expand the system is unique to our hardware. If you're building that, you know, kind of Rube Goldberg machine with the arms in the middle and everything else, that is a custom job that takes a long time to do. And it's, again, built one off for the customer. With this, we can really print these carts and allows customers to quickly scale their infrastructure. And if you go to the next slide, we have a great existence proof of this, which is our setup that we've been using at Ginkgo to do research work for customers over the last several years. So you can see here, highlighted in blue, a number of pieces of equipment that were originally put on our setup for next-gen sequencing crap of samples, okay? And so having all those on that setup allows a sample to get prepared and go on to our sequencers. That was the original investment. That was the ROI. We were going to do tons of next-gen sequencing, so that justified it. But then very importantly, our scientists came along, and I have to go to the next slide. they requested a protein quantification assay. It's a high-bit assay made by a company called ProMega. And they wanted to run this at high throughput instead of at the bench. And so, we developed a protocol that would be 7,600 samples in six hours, like a very high throughput protocol. And if you look, and we want to now add this to the racks on the next slide, we were able to reuse, now the blue on here, our machines from the NGS protocol that are relevant to the HiBit protocol. So we don't need to buy those again. They're already on the setup. In fact, in order to add this HiBit protocol, we only had to add the Ferristar. That one pink highlighted piece of equipment at the top was added in order to enable a whole new protocol. So that's the ROI, right? Like we had to just add one piece of equipment and all this existing investment and these things, these work cells and things can cost a million dollars plus when you make the one-off and you can't expand it, by adding just one cart to this, we're able to have it do a whole other protocol. And then importantly, as you add enough carts, it costs no more to do more protocols. It's just software changes because you have enough equipment in one big setup in order to make that possible. And this is, if you go to the next slide, what I'm really excited, I think this is the direction that the U.S. government is headed with these cloud-enabled labs. This is the direction that I think heads of R&D absolutely have to have on their radar if they're looking to reduce research costs which is to have many many many pieces of equipment all in one big setup that can basically do whatever protocol you want in the future uh and this is a setup we just announced um a week ago that we had um nearly complete for pacific northwest national labs it's an 18 piece of equipment setup and what's really amazing about this if you go to the next slide it is all of our uh our sort of like um arms and tracks are inside of uh anaerobic chambers for this system. So this is an environment that humans can't go in. It's air-free. So it's really difficult. You see those arm things. Normally, people are doing experiments with their hands in glove boxes and all this crazy stuff. Instead here, those arms are really just to service the equipment that you see on this setup. And all the samples that are going to move among the equipment are going to run through our automation. And if you go to the next slide, we believe this is the largest automated anaerobic system in the world now. I'm really excited about Department of Energy investing in this. I think it's exactly what the president's looking for in the next slide in these sort of cloud-enabled labs initiatives. And so I think you will see more of this. I'm really excited about this. I think Ginkgo's technology is perfect for this. And by the way, I think 18 instruments in one setup is going to be looked at as small in the future. Really, we should have 100, 200 instruments all in one big setup that allows you to ultimately submit protocols to do anything you could do at the bench. And that ultimately, we're not there yet. There's a lot of technology between here and there, but that's really the dream here is to be able to have that same level of flexibility or something near it, but with the scale economic of automation. And that is absolutely essential if we're going to have AI-enabled science, without question. It's just not going to happen at the lab bench. All right. One more thing on this, the software side, I'm not going to be able to dig in today, but I'm excited to tell you more about it in the future. I will just say for customers that are tuning in, Ginkgo's been doing lab-in-the-loop AI-enabled science, having reasoning models, interacting with this robotics, really, really cool stuff. We'd love to share it with you. And we have the whole, both obviously the hardware I spoke a lot about today, but importantly, the software stack, the modern APIs, cloud-based software, everything that makes that all really feasible. You know, MCP servers accessing all these equipment. So if you're really, you know, ahead of AI looking to bring that into your biotech company, you should give us a call both for the hardware and the software layer. Okay. So that's as much I want to say about automation, but I really see that as being extremely strategic for Ginkgo going into 2026. And as we've gotten our cost more under control, you're going to hear me go more in this direction, right? It's going to be more about what can we invest in for growth in the future. And one of those big areas is going to the automation and AI. All right. Beyond that, I want to talk about our push into the CRO services market. We call this Ginkgo Data Points. We have a number of different services now, perturbation response profiling, specialized high throughput screening, antibody developability, which I've talked about before, but we just launched our small molecule developability or add me service. And you can do lots of different things with these services. They are available, Just to be clear, there's no royalty. There's no milestone. It's just like engaging with a CRO like a Wuxi or whoever fee-for-service basis. You own all the IP and data as the customer. But we're able to do this at very large scale because of our automation expertise. And so one of the things I'm really excited about, we announced this in the press release of the AdMe service, is if you have a quote from another vendor in the CRO space, like, for example, a Chinese vendor, and you want to onshore that back here to the United States, just send us the quote. We're happy to meet it. And that goes for AdMe, but generally you should send us the quote anyway. We're happy to see it across any of our services and meet vendors. And so please do keep that in mind if you're looking at data points. This is why I'm excited about data points in the long run. I think it is exciting to go after the traditional CRL market. I think there's good business there. It's also not that high throughput. You know, a lot of what places like Wuxi have done is basically gotten cheaper hands at the bench and then offer that as a service. So, like, that buys us, you know, whatever 40% cost reduction on the big problem of reducing R&D and getting scalability. But then it kind of runs out because it's just not getting cheaper. I think across the board, if we want to get cheaper, the answer is automation. And so Ginkgo's been doing this work really in an automated fashion, and that allows some unique offerings to customers. So I'll just highlight this funnel here where this is traditional drug discovery. You're going to identify a target. Then you're going to run some high throughput screen, maybe on a robotic setup, maybe in some sort of pooled assay in the lab. Either way, you're going to screen a bunch of lab work to pick a few hits. And then you're going to take those hits into a much more expensive series of experiments in order to validate if they're good drugs. All right. And it's those set of more expensive experiments that we've been focusing on trying to make high throughput on our automation at Ginkgo and offer as a service through data points. And what's exciting about that, for example, say antibody developability, you find these binders, which you can do at high throughput, really cheap. But then you get to developability and it's expensive. Is it soluble? Is it immunogenic? These are things that you have to do these more expensive experiments. And so you only try them on your top hits and you kind of cross your fingers. What we are able to do with our throughput is let you apply those developability assays back much earlier in your hit finding so that you look at a much wider range of potential candidates against not just whether they bind, but also are they developable? And if you generate enough of this data. Maybe we can even have computational models and AI that can predict developability. And so that's where we're seeing a lot of excitement. That's kind of our niche to get off the ground in the CRO space. And this is the DPMTA, the design, predict, make, test, analyze cycle in pharmaceuticals. We're really focusing on scaling up that test step for these high complexity assays. And I think that's something we're very, very good at at Genco. So you should expect us to launch more products. And this is just that ADME service kind of start to finish, project scoping, chemical libraries, so on. I will highlight we're using ECHO MS, ECHO mass spec, to do that sort of high quality, but also high throughput assay. That's what allows us to get costs that can really compete with doing it with low cost labor overseas. All right. Last but not least, I want to talk about reagents. I'm super excited about this. I'm always excited when I see Ginkgo move into a new market area because if we do pick up traction there, there's sort of like a lot of clear vistas in front of you to get into. So this is our first reagent product. And just to understand kind of the theory here, again, over the last decade, Ginkgo has been a big, big consumer of life science tools. We have bought various services. We have bought a ton of equipment like those custom work cells I mentioned, and we bought a lot of reagents. And where we can get something great on the market, we'll use it. But what we found is there are certain gaps in areas that were important, maybe very important to us for our cell engineering that weren't widely available or the products weren't really up to our level that we needed on the market. And so in those areas over the last decade, we developed our own stuff. We just never sold it to anyone because it was part of our solutions offering and we kind of wanted to keep it proprietary. So what's really fun here in Reagent is we're getting to launch a bunch of these, what had previously been in-house assets at Ginkgo. And in fact, we had Ginkgo employees who left, went to other companies, and were like, hey, will you just give me that reagent or thing we used to have at Ginkgo? Because I want it. And so we heard that enough times that we decided we might as well try to sell it. And so this is our first product, the Cell-Free Protein Synthesis. So Cell-Free Protein Synthesis is basically, instead of, if you want to produce a lot of protein, taking your gene of interest and moving it into a live cell like an E. coli or a yeast and then growing that live cell, producing the protein and extracting it. Instead, you start with the live cells like the E. coli, you grow a bunch up, you pop them open, you lyse them, you take the contents out, you make that into your reagent, then you add the DNA straight to that reagent mix. And it's got all the components of the cell, it's just not alive. And so it'll make protein. Now, there's some downsides, like the cell keeps everything in a little, you know, small container. So it has like a high density, which is helpful for production, but you don't have this extra step of growing the cells and everything else. So for a number of applications, cell-free really does stand out. And we had a lot of those applications that can't go. So we have, you know, our product here has twice the yields for half the cost compared to market leaders for certain protein constructs. And you can get, you know, $2,000, you can get a 10 mil kit, which is a great offer on the market today. And in fact, we launched this just last week, we've already got some early sales, which makes me very excited. But importantly, we also had a free sample. So we have, you know, over 100 people have requested samples. And what I think is just I wanted to highlight was a large fraction of that was actually in the academic research market. This is a market that Ginkgo has basically never sold anything to until selling a kit recently. Because we haven't had anything to offer. They're obviously not going to outsource research to us. That's really all they do for a living. So our solutions business never made sense. And then we had a certain scale of CRO services with data points that were really pointed at the commercial market. So I'm pretty excited to see this. I think the academic research market has been a huge market for life science tools companies like the sequencing companies and companies like Thermo Fisher. So us being able to get into that market here with reagents is very exciting. Okay. So that was kind of what I wanted to walk through. Again, big takeaways. We're coming in a quarter early on that cost takeout target. That's very strategically important. We've done that with a good amount of cash and margin of safety still in the bank, that 474 million in cash equivalents and no bank debt. That sets us up very well to look to the future. And we are doing that. So you will see and hear more from us on the life science tool space. I shared some of that today, but expect Ginkgo to really be focused on growing into 2026 from here on out. So super excited to hear your questions. And thanks very much for your time. Great. Thanks, Jason. As usual, I'll start with
