Good evening. I'm Megan LeDuc, Manager of Investor Relations at Ginkgo Bioworks. I'm joined by Jason Kelly, our Co-Founder and CEO, and Mark Dmytruk, our CFO. Thanks, as always, for joining us, and we're looking forward to updating you on our progress. As a reminder, during the presentation today, we will be making forward-looking statements which involve risks and uncertainties. Please refer to our filings with the Securities and Exchange Commission to learn more about these risks and uncertainties. Today, in addition to updating you on the quarter and full year, we are going to dive deeper into Ginkgo's evolution as a data generator and systems integrator within the biotech R&D ecosystem and biosecurity. As usual, we'll end with a Q&A session, and I'll take questions from analysts, investors, and the public. You can submit those questions to us in advance via Twitter at #Ginkgoresults or email investors@ginkgobioworks.com. All right, over to you, Jason. It's been a busy and exciting week here at Ginkgo, and it's a great week to remind everyone of our mission, which is to make biology easier to engineer. We don't take this mission lightly, and achieving it requires not only our own infrastructure investments here at Ginkgo, but also truly to drive change across the culture of the industry, fostering collaboration over competition, especially among tool developers. I'm going to spend a bunch of time today talking about that. I'm really excited about our progress, and as we'll dig into the strategic sections, you'll see how we're building and integrating a set of capabilities we believe could really revolutionize how biotech R&D is done. Before we get to that, I want to say, if you want to imagine a little bit of what a world would look like when biology gets easier to engineer, it looks a little something like this. So engineered biology becoming an everyday part of our lives, not just something we experience when we're sick in a hospital or things like that. And this photo is a real photo of the Firefly-branded petunias that one of our customers, Light Bio, just launched to the public. And we're working with them to make these plants an order of magnitude brighter. Today, to see them, you have to be in a really dark room. But now you may not think that bioluminescent flowers are going to sort of change the world like a blockbuster drug. But the reason I got into biotech was really because of movies like Jurassic Park. And if you look at some of the younger employees here at Ginkgo, they were inspired by things like Avatar. And this idea that we could start to really design biology and build more beautiful things in the world, I think, is going to be an inspiration, especially for kids that want to get into biotechnology. And I promise you, if you fast forward 10 years, you're going to see high school students designing their own flowers in their DNA programming classes. And I think this first product from Light Bio is the start of that. Okay. Now, the road to that consumer biotech world, though, leads through this road. And Ginkgo continues to lead in B2B sales to large R&D groups at both big and small biotech companies across the three major industries of biotech: industrial, agriculture, and most importantly, for our conversation today, biopharma. I want to really take a minute and focus on our progress in biopharma in 2023. We added several new biopharma programs in 2023, including with Pfizer, Boehringer Ingelheim, Novo Nordisk, Merck, as well as successfully completing our first project with Biogen. All of these are hugely important for Ginkgo. First, it is a wide range of different projects we are doing with these companies. We're doing manufacturing R&D for Novo, biocatalytic enzyme development for Merck, RNA drug discovery for Pfizer, small molecule natural product drug discovery for Boehringer. That breadth is a huge vote of confidence for Ginkgo's sort of core thesis of being a platform business model. We believe that our robotics, our data, and you're going to hear today, our AI models, will be relevant to product developers across all biotech, either modalities of drugs or different industries in biotechnology. These deals represent that thesis being confirmed by customers in the biopharma industry, right? This is not Ginkgo getting into enzymes or Ginkgo getting into RNA drugs. These are customers who are experts in those fields choosing to look at our platform and see that it gives them leverage. And for potential customers on this call, I want to be very clear. We do not have our own product portfolio or drug pipeline at Ginkgo. Everything we build, we build to serve you, our customers, like the ones on this page. And we're looking forward to continuing to build the best platform we can to serve your needs going forward. Speaking of what's going to propel our business in 2024, we plan to expand our capabilities. You've heard us talk about our Foundry and Codebase, which is really our robotics and our data at Ginkgo. And we're going to continue to build that out with new RAC deployments. This is the robotics technology from Zymergen, which I'll speak more about today, and the building of BioFab1, which I'll get into more in the strategic section, our new big integrated facility. And yesterday, we announced three new M&A deals, as well as our Technology Network that brings together over 25 companies from different parts of the industry, particularly focused on AI and biopharma, where we're seeing the most sort of inbound research demand. We made the choice many years ago to be a horizontal platform technology company. And inherent in that choice, it doesn't mean we're not going to be able to specialize in everything. Our value really comes from scale and integration. To bring together these more specialized technologies through M&A and partnership, we think, is key to driving success across the industry. You're going to hear a lot from me about that a bit later. Before I get too deep into that, I want to hand it over to Mark to discuss our financials. Thanks, Jason. I'll start with the cell engineering business. We added 23 new cell programs in the fourth quarter of 2023, which brought us to 78 new cell programs for the full year 2023. This represents a 32% increase over 2022. Importantly, we continue to be successful in adding new programs with large enterprise pharma customers. We will provide some additional detail in a moment on our penetration in biopharma, as this trend provides a critical new perspective on our aggregate new programs metric. We supported a total of 131 active programs in the fourth quarter of 2023 across 80 customers on our platform. This represents substantial growth and diversification in programs relative to the 96 active programs across 54 customers in the fourth quarter of 2022. On a full-year basis, cell engineering services revenue was $139 million in 2023, an increase of 31% compared to the full year of 2022. As discussed in prior quarters, this services revenue growth was offset by a decline in downstream value share from equity milestones achieved in 2022, resulting in total cell engineering revenue of $144 million in 2023, being approximately flat compared to 2022. When looking just at the fourth quarter of 2023, cell engineering revenue was $27 million, down 49% compared to the fourth quarter of 2022, primarily due to the decline in downstream value share just mentioned. Services revenue, which excludes the impact of downstream value share, was also down year-over-year. As discussed in the past, we often see interquarter lumpiness in services revenue, even though the underlying foundry platform output is more level. That is because of the timing of revenue recognition, the achievement of certain technical milestones, contract-specific factors, and related accounting adjustments. So, for example, the actual platform work that we did for customers in Q4 was comparable to Q3, even though we had a significantly different revenue result. Now, turning to biosecurity briefly. Our biosecurity business generated $8 million of revenue in the fourth quarter of 2023 at a gross margin of 15%. Biosecurity revenue for the full year 2023 was $108 million, with a gross margin of 50%. As a reminder, our K-12 COVID testing contracts ended in the third quarter, and the business has now moved entirely towards building out both domestic and international infrastructure for biosecurity, highlighted, for example, by our CUBE-D announcement earlier this week and our expanded CDC Multipathogen Surveillance Program announced in Q4. Before getting into the rest of the P&L, I'd like to talk about how we're managing cash flow and cash expenses in this environment. We finished 2023 with nearly $950 million of cash on hand. From our balance sheet, you can see that our total use of cash in the year was about $370 million. Both of these figures were favorable to our internal targets, despite the revenue shortfall relative to our original guidance. Over the past 18 months, we have been assessing every area of cash spend with the goal of identifying categories that should be decreased, categories that should be constrained, and areas where targeted investments are appropriate. In 2023 and in our planning for 2024, we took the following actions. The completion of our integration efforts relating to the 4 acquisitions that we had closed back in the fourth quarter of 2022 has resulted in cost synergies at this point, particularly as relates to DNA expenses and the Zymergen transaction. You will see that reflected both in the fourth quarter of 2023 as well as in 2024. We are also reducing certain DNA spend in supporting functions such as finance, people, and legal by decreasing professional services and consulting spend and bringing more capabilities in-house. Due to improved equipment capacity, we rationalized CapEx in 2023. The majority of the spend in 2023 related to various smaller projects. In 2024, the majority of the CapEx relates to the buildout of the new BioFab1 facility to support future growth and efficiency. And then, building on the operational improvements discussed on our second quarter earnings call, we are constraining OpEx in our microbial platform, which is our most mature platform capability. As we increase revenue in these programs, we are driving higher productivity. Now, partially offsetting these cost savings actions, there are also key areas where we are investing. We are expanding the pharma business development team by about 50% and are investing in our mammalian platform capabilities. And we are increasing spend related to AI, which Jason will be speaking about in more detail later in the presentation. These investments are critical to both near-term and long-term growth. Collectively, these actions have resulted in a net spend reduction when you compare the fourth quarter of 2023 to prior quarters and are expected to decrease OpEx in 2024 relative to 2023. The combination of our expected revenue growth and decrease in OpEx is expected to drive an improved cash burn level in 2024. Successful execution here would also put us on a trajectory for further improvements to cash burn in 2025. Now I'll provide more commentary on the rest of the P&L. Where noted, these figures exclude stock-based compensation expense, which is shown separately. Starting with OpEx, R&D expense decreased from $109 million in the fourth quarter of 2022 to $90 million in the fourth quarter of 2023. DNA expense decreased from $78 million in the fourth quarter of 2022 to $72 million in the fourth quarter of 2023. On a full-year basis, R&D expense increased from $314 million in the full year 2022 to $432 million in the full year 2023, while DNA expense increased from $228 million in the full year 2022 to $299 million in the full year 2023. These operating expense items increased year-over-year as expected as we layered in the 2022 acquisitions. These expenses also include one-time charges, both cash and non-cash, relating to M&A, integration, and other costs as detailed more fully in our Adjusted EBITDA reconciliation. Stock-based comp, consistent with prior quarters, in 2023, you'll notice a significant drop in stock-based comp in the fourth quarter and in the full year 2023. As a reminder, this is because the catch-up accounting adjustment relating to the modification of restricted stock units when we went public has substantially all rolled off at this point. While the bulk of that adjustment is done, just under half of the total stock comp expense in the quarter is still related to RSUs issued prior to us going public. Additional details are provided in the appendix to this presentation. Net loss. It is important to note that our net loss includes a number of non-cash income and/or expenses as detailed more fully in our financial statements. Because of these non-cash and other non-recurring items, we look to Adjusted EBITDA as a more indicative measure of our profitability. Adjusted EBITDA in the fourth quarter of 2023 was negative $96 million compared to negative $76 million in the comparable prior year period. The decrease was driven by lower revenue, partially offset by lower operating expenses year-over-year. Full year 2023 Adjusted EBITDA was negative $355 million compared to negative $173 million in the prior year. The decrease was driven by lower revenue and higher operating expenses year-over-year. A full reconciliation of adjusted EBITDA is provided in the appendix to this presentation. Finally, CapEx in the fourth quarter of 2023 was only $3 million. CapEx in full year 2023 was $41 million, down from $52 million in the prior year. This reflects our spend prioritization efforts. CapEx will be higher in 2024 due to the buildout of BioFab1. As discussed, this is one of our targeted areas of investment. Before I get into our guidance update for the year, I'd like to give you a bit more color on the evolution of our program mix. We have talked about how we are shifting our focus to biopharma, and these two charts on the left show that progress. The most important thing to note is that 65% of our new biopharma programs this year were from large enterprise customers. That is a meaningful shift and something we think you should watch going forward. On the revenue side, we have gone from our biopharma being really just a rounding error a few years ago to making up almost a third of our overall cell engineering revenue this year. We expect this kind of growth rate to continue based on the larger deals we signed in the past 2 years with the likes of Pfizer, Merck, and Novo Nordisk, along with the pipeline of opportunities our now much expanded biopharma business development team is pursuing. This is an enormous market, and as we continue to deliver for our early customers, we see significant growth potential ahead. As we did last year, we'd also like to provide you with some updated data points relating to Downstream Value Share. On the left-hand side of the chart, you can see that as of the end of 2023, we have the potential to earn up to $2.4 billion in milestone payments based on customer collaborations previously entered into, with the majority of these potential payments being linked to successful commercialization of a product. This figure does not include potential royalties. One clarification on this chart, you'll see that the increase in potential milestone payments was relatively small when comparing 2023 to 2022. We actually added nearly $1.5 billion in new milestone potential in 2023. However, we also saw some specific programs fall off during the year, and so we have removed those milestones from the total. As you'd expect, most of the milestone potential is coming from biopharma customers and, in 2023, from large pharma in particular. On the right side of this page, you can see how our downstream value share mix has continued to shift over the years. Just a few years ago, the bulk of our downstream value potential was in the form of equity in relatively young companies. As our customer base has shifted significantly towards larger, more mature companies, our downstream value mix has shifted towards milestones and royalties. Now, I'd like to provide some commentary on our outlook for the full year 2024. We expect to add in the range of 100-120 programs in 2024, which represents a growth rate of 41% at the midpoint over 2023. Our cell engineering revenue guidance is a range of $165-$185 million, which we expect to ramp over the course of the year and excludes the impact of any potential Downstream Value Share. This represents a services growth rate of 26% at the midpoint, excluding potential Downstream Value Share over 2023. As discussed earlier, we're seeing the beginning stages of real penetration into the large pharma customer segment and are expecting biopharma to be a key growth driver in 2024. We expect that will favorably impact the composition of our program and revenue base, while the industrial biotech vertical is still dealing with unfavorable macroeconomic conditions. In addition, we also expect the government vertical to be a strong contributor to growth in 2024 based on a record pipeline we have there. Our biosecurity revenue guidance range for 2024 is at least $50 million. As we have in the past, we are guiding to our approximate current level of contracted backlog for the year and have a pipeline of opportunities we are pursuing beyond that. Finally, we expect total revenue for the full year 2024 to be in a range of $215 million-$235 million. In summary, we are pleased with the overall direction of progress. While we still believe that scaling the number of programs we can launch and execute is an important indicator of long-term value and our focus on driving growth there, we have placed more emphasis on our revenue targets for our team internally to complement our efforts to drive OpEx efficiency in our path to profitability. Over the past few years, the business has been evolving from a customer base that was predominantly industrial biotech and earlier stage companies to a customer base that now comprises more larger enterprises along with increased biopharma industry penetration. The government vertical has also emerged as a driver of growth. We think this evolving customer profile is attractive on many dimensions. We continue to manage our balance sheet and cash flows to maintain a multi-year runway with nearly $950 million of liquidity at year-end. Now, Jason, back to you. Thanks, Mark. A couple of things I want to reiterate from Mark's section. So you'll see us giving a pretty broad range for our service revenues and new program count guidance. The reason for this is that infrastructure services is really new to the biotech industry. So we're blazing a bit of a new trail here, and that adds a little bit more unpredictability, in my opinion. Mark mentioned how small our current services revenues are relative to the research budgets in the biopharma industry. The reality today is that, by and large, infrastructure tools for product development in biotechnology are done on premises. They're done in-house at these companies. And that's why getting these first deals with Merck, Novo Nordisk, Pfizer, Boehringer, and others is so important. It's a chance for those companies to get experience really with outsourcing core research infrastructure to sort of cloud infrastructure services like Ginkgo as an alternative to doing it in-house. And the rate that those R&D decision makers make that switch from doing things in-house to outsource service providers is going to be what will drive big changes in Ginkgo's revenue over the next couple of years. That's really the driving factor. And it's less about whether we can scale into it. I'm confident we can scale into it. It's at what rate do they turn that knob? And there's unpredictability to that as it's fundamentally a question of new sales. But we have a 90-person commercial sales team now, and we have a big focus in biopharma. So we're putting our backs into it. Now, since the big question for Ginkgo coming up is what is that rate that biopharma leaders will adopt outsource infrastructure services, I'm going to use the first two parts of my strategic review to really dig in on that. Why are those folks interested? What are we doing that's new to make that happen? And so in the first section, Ginkgo has invested close to $1 billion in software and automation, what we call our Foundry, that is flexible enough to handle the variety of lab work needed for cell engineering across a wide range of biotech products. And this is critical for large-scale data generation that's needed for applying, in particular, AI and biotechnology. The second section I'm going to dig in on is we want to see a robust infrastructure services industry grow in biotech. Our view is that a rising tide will lift all boats in the industry, including Ginkgo. We're working to make our automation, our Foundry I'll talk about in the first section available to other service companies so they don't need to repeat our investments as they build a business doing this in the industry. Finally, we'll give an update on biosecurity where we recently expanded our strong relationship with the government of Qatar. Okay, let's dive in. Okay, so what do I mean when I say infrastructure services? All right? On the left side of this chart, you can see the magical world of the tech industry. Okay? On the top of each of these boxes, you see a large company building on top of the infrastructure services of an often even larger company underneath it. To give you an example, new trillion-dollar companies like NVIDIA are built entirely on top of other companies' infrastructure like TSMC. I want to emphasize NVIDIA, trillion-dollar microchip company that does not manufacture its own chips. Right? Similarly, Salesforce, Netflix, many of the other software as a service SaaS applications that have really bloomed over the last 15, 20 years are all built on top of things like Amazon Web Services and Google Cloud, the big cloud compute providers. Every app on your phone is enabled by the iPhone or Android OS ecosystem, depending on which ecosystem you're in. This is very powerful. It's very enabling. In my opinion, it's why we see trillion-dollar tech companies is you're getting this unbelievable rate of innovation and people building on each other's core infrastructure. This is not how the biotech industry works, whether we're talking about ag, industrial, or biopharma, and we engage all areas of those different industries. Large customers typically have their own in-house infrastructure with vertical integration from R&D, often all the way through to manufacturing. Okay? And our view, my view in particular, is that part of the reason that on the left-hand side, it all works so well in the tech industry is it's fundamentally a code-based industry. Right? People are moving zeros and ones around. It is digital. So you have a very clear opportunity to have interfaces and standards across players where there isn't confusion. Right? You're able to tell someone exactly what you want and get back exactly what you want. Well, in my opinion, biotech is also a code-based industry. Right? DNA code is common across all the products of biotechnology, whether it's a trait in corn or RNA gene therapy going into human. I'm hopeful that means that we could structure our industry, our biotech industry, similar to tech and see similar gains. My hope is that perhaps one of our customers could be the first trillion-dollar biotech company. Okay, now, since there are a lot of biotech investors that tune into our call, I will mention that canonical wisdom in biotech is that even if you have this great, robust platform technology that can do lots of things, you always end up vertically integrating into becoming a drug company in the biotech industry. A famous example, one that I like a lot, is Millennium, really from the genomics boom in the early 2000s, huge platform story, amazing leadership, great technology. And here's their CEO, Mark Levin, explaining, "We can't afford to remain a research company." And if you look at the history of Millennium and play it out, ultimately, they did move towards creating specific drug assets before being acquired by Takeda in 2008 for those drug assets. Right? And we even hear this from potential customers of our infrastructure services. Right? They'll say, "Oh, it's fine, Ginkgo. I like the story. But inevitably, you're going to develop your own drugs and compete with us." All right? So again, I want to be emphatic here for our potential customers on the call. Ginkgo today is 1,200 people. We have close to $1 billion in cash in the bank. We're very protective of that cash. Our revenues are increasing, as you heard from Mark, while our cash burn is falling and our rate of new customers is going up. So we are not going to end up developing our own drugs. I think we are uniquely at our scale in terms of the tech and infrastructure we built and the scale of service revenues and the number of customers on the platform. I think we are now sort of in our own category when it comes to really sticking with a platform infrastructure services business model in biotech. I'm biased, but I think we're going to make it. And so again, for our customers, I want them to know that we're sticking to that and don't intend to compete with them. Okay, so a key component of our infrastructure today is Ginkgo's investment in flexible lab automation. All right? I mentioned earlier, we spent close to $1 billion on this. Why do you need automation? Right? Why is that important in biotech? Why is it important to biopharma R&D leaders? The reason is that in order to generate the large data sets that are driving modern biotech R&D, the cost per data point generated really matters because we are talking about millions and millions of data points. I went into this more in my talk at JPM, which I encourage you to listen to. But you can see on the left here, Ginkgo's in-house genomic data library that has about 10 times the number of genes relative to the large public data sets. Importantly, on the right, we've run millions of assays on genes from that library and tested their performance. That chart on the top is actually each row in that is an EC number. So this is like a class, different enzyme class. We have hundreds of thousands of data points for each of these different EC classes. That gives us an enormous set of label data, as we'll talk about later, when it comes to AI, as well as the raw large DNA data asset. This aggregate data is important because it's needed to develop, down here at the bottom in gray, AI Foundation Models in biotech. Right? If you are an AI company watching this right now and salivating over these data assets at Ginkgo, give us a call. Right? As you're going to hear in the next section, we really want to enable others to build on top of principally our automation because we've invested a lot there. But also these data assets that are starting to accumulate at Ginkgo, we think they can be resources for tool developers in the industry. But I'll just point out, now kind of the upper part of this graph, most of the data in biology has yet to be created. Okay? So we do have great starter assets. Ginkgo's got bigger ones, I think, than most places. But what really we want to do is generate more data using the automation so our customers and partners can do things like fine-tune an AI model offered by, for example, another service company without that company needing to develop their own automated lab. And so you'll hear about that in a second with our partnership with Cradle. That'll talk about the next section. But some companies will also want to generate their own huge data assets to build, for example, maybe a proprietary foundation model in a certain disease cell line that they're interested in, as an example. We can do that too. This is similar to the business model of Scale AI in the tech industry. So on that tech chart, I showed OpenAI on top of Scale AI. Right? So OpenAI pays Scale AI to generate a lot of the data they use to train things like ChatGPT. Tesla pays Scale AI to basically analyze images and highlight, "That's a dog. That's a pedestrian," right, to help train their models for self-driving cars. It's a company in the business of generating labeled data to feed into other companies' machine learning and AI models, absolutely happy to do that at Ginkgo at large scale for customers. Okay, so one of the best investments we've made was bringing in Zymergen's proprietary software and automation technology to dramatically enhance both the scalability and flexibility of data generation at Ginkgo. You can see within a year of the acquisition, we had installed the technology at our site in Boston. This is one of the things we were so excited about because they had this automation software team that had been supporting Zymergen's efforts, and so they could drop right in. Since then, we've actually evolved. We call these RACs, reconfigurable automation carts. So without nerding out too much, what's exciting about this is we can easily plug in new equipment into a big centralized automated system without needing to do a whole new automation rebuild. If you live in the world of lab automation, when you have a great idea for some new thing to automate, you can start the clock, and you'll have a big automated system ready six months to a year and a half later. Okay, with the RAC system, if you have a new piece of equipment you want to add to an integrated automation workflow, we can pop that right into the system and then, using software, add that equipment into the workflow relatively quickly. And importantly, we are now able to manufacture these RACs much more inexpensively. We've increased our ability to manufacture them fivefold while keeping our hardware team flat. And with this increased production capability, we plan to deploy three times as many RACs in 2024 as we did in 2023 at Ginkgo. And we aren't the only ones that believe data generation is important. Just to give an example on the government side, just a few weeks ago, we hosted the House Select Committee on the Strategic Competition between the United States and the Chinese Communist Party. Congressman Mike Gallagher, who chairs the committee, noted that they came to Ginkgo, "To talk to experts and figure out the right strategy so that we and not the Chinese Communist Party can dominate AI and set ethical rules of the road." So what did we talk to them about? Well, we talked to them about the facility you can see here, BioFab1. This is actually a render, but I can look out the window over here and see it. And I took a tour of it last week. And that tall top floor is actually filled with things like huge cooling units and other hardware that's needed for running a building that is really meant to be filled largely with automated labs with these RACs I just showed you. This looks and feels like a data center. Right? If you went and saw an Amazon data center and things like this, it is built-to-purpose buildings that are going to be filled with a certain set of hardware and have the infrastructure to enable that. That's very much what we're doing here, except instead of a bunch of servers, what we're going to have is a bunch of RAC automation, hopefully generating the data that powers infrastructure services across the biotech industry. All right, on to section two. Okay, so I'm super excited about this as we had the chance to announce our technology network with its 25 inaugural members just yesterday. We want to make infrastructure services the way that R&D work gets done in biotechnology. Ginkgo cannot do that alone. Ideally, we want hundreds of new tools and service companies to flourish and to move that percent of R&D spending currently going to outsourced R&D services. Mark showed some numbers of the total R&D spending at biopharma companies compared to, for example, our revenues today. My estimate is across the entire sort of research services industry, we're maybe at less than 5%. It could be even as low as less than 1% of total research spending going to outsourced infrastructure. It's mainly going to on-prem work. Right? People doing work by hand in labs, people buying reagents and things like that. Really, I would love to see that shift into infrastructure services like it has in tech. We are big believers that brilliant technology is being invented outside of Ginkgo. There's not a big "not invented here" culture at Ginkgo. We partner deeply with many life science tools companies to integrate their tech in our workflow. To give a specific example, we were the first large DNA synthesis big contract for Twist when they were a very small startup back in the day. We're still one of their largest customers today. We have also conducted over 15 M&A transactions over the last several years, including three we announced yesterday, to bring certain technologies in-house. Quick update about why we're excited about those three: Patch Biosciences, Proof Diagnostics, and Reverie. So Patch is going to bring in large data sets ready to deploy ML models and downstream assays for promoter and RNA stability and expression. And so we do a lot of work in RNA right now, so that's very exciting and a quick and good fit. Proof offers massive libraries of Obligate Mobile Element Guided Activity, the OMEGAs for short, with RNA programmable nucleases and nucleases that will enhance our Codebase and overall offerings to pharma companies. This is more sort of microbiology technology sort of in the kind of editing space. And then importantly, also a great data asset. So it's more to add to that big data pile I was mentioning earlier. And lastly, Reverie is an AI company focused on leveraging computational chemistry and machine learning to accelerate drug discovery programs. And this acquisition will allow Ginkgo to significantly accelerate our own build-out of AI program development. And I'm excited about the technology, but in all of these cases, I'm especially excited about the teams that are coming over in these acquisitions. It's a real speed-up in terms of our human capital build in those areas and just awesome people. Okay, but the thing I want to highlight today is our technology network. Just as I highlighted, several of our suppliers and acquisitions, we are now partnered with over 25 different companies that can benefit our network. All of those companies bring something different to the table, whether that's a focus on pharma, AI, enzymes, manufacturing, or biosecurity. We believe that this network is just the start of driving a cultural change, like I mentioned earlier, in biotech R&D from doing things in-house to doing them with outsourced infrastructure services. I'll give you a couple of examples so you get a sense of what might be possible. Cradle is an exciting company developing AI models for DNA design in the area of enzyme engineering. Let's say a customer comes to Ginkgo to improve the activity and titer of an enzyme. Then after they find it, they want to express it in high titers in a cell, for example. That would be a big end-to-end project at Ginkgo, take a little bit. I'll give you one part of that where we'd be integrating in Cradle. We'd first go through our normal steps of designing and screening a metagenomic library to find an ideal candidate for our customer. Remember that big 2.7 billion gene database I showed you earlier? Great, we source an interesting seed sequence from that library. Now here's where we can leverage Cradle's generative AI platform to then computationally create a library of protein designs and their respective DNA sequences based on that seed sequence. And then the magic, those designs could then be synthesized, probably at Twist, given how Ginkgo works, and then cloned and tested using Ginkgo's automated labs in our Foundry and eventually in BioFab1. And once we're happy with the results, the leading candidates would be transferred to our customer. And we might go through that loop a few different times for a customer project. And importantly, we've been able to pull together, in that case, a couple of different providers, someone who's really providing computational horsepower and AI magic, Ginkgo providing a lot of lab infrastructure, Twist at the DNA synthesis side. Okay? And so we're also excited to help Cradle's customers be able to engineer proteins really from the comfort of their browser, as Cradle likes to say. And we can do the wet lab work for them at Ginkgo. I want to say I really love this vision. Okay, so I did a Ph.D. I spent five years holding a pipette. R&D scientists, put down your pipettes. Right? In a model of outsourced infrastructure services, drug development scientists will spend their time designing experiments and workflows, understanding and reading about the biology so they know what they want to try to attack next as they're developing these biotech products, not spend their time manually moving liquids around a lab bench. Right? It is just not the best use of the folks who have all this experience in both experimental design and biology. And in a world of outsourced services, I love Cradle's vision of doing that all from the comfort of your browser, all that lab work. Okay, so another partner I'd like to highlight with sort of an illustrative case study is bit.bio. So bit.bio has created what they call io Cells, which are human iPSC cells, differentiated to represent various wild type and disease models, which are needed to effectively study drug designs and better predict in vivo behaviors. The bit.bio cell lines are a terrific asset that we can incorporate into our programs. They're customized for a particular disease state a customer may be looking at. And we can test a multitude of different drug modalities across these cell lines and also screen our large libraries of optimized promoters and gene editors, like you heard, for example, in the Patch acquisition. So you can integrate some of those technologies in with theirs. The magic here is that Ginkgo gets bit.bio cell lines running at high throughput, which is great for our customers. bit.bio gets a distribution channel in Ginkgo so that they can have more companies licensing their strains and paying them. Right? And I think this is key. Right? So we need to make it easier, both for biopharma companies to access the latest new cell lines like those at bit.bio, but it also needs to be easier for companies like bit.bio to exist and sell cell lines. Right? And as I mentioned, Ginkgo has a 90% commercial team and growing. So we're hopeful this is a real asset to tool companies in the space to get their technology in the hands of these large biopharma R&D customers. And hopefully, by Ginkgo going out and being able to show a multitude of technologies coming together in these programs, it also helps increase that 1%-5% or whatever it is of research spending going out to infrastructure services, make that number go up for the whole industry. So I'll end with this. Biopharma customers repeatedly tell us what they care about is increasing probability of success, reducing the time to results, and reducing R&D costs. Large AI models and big data sets in the hands of the scientists at our customers, we think, can really increase the odds of success in drug development. And it's worth an extra point here. There's a lot of noise about automation and AI, like replacing scientists and robot scientists or whatever. That is not our belief at Ginkgo. We are not trying to take scientists out of the loop with the infrastructure services model for the industry. This is not like CombiChem. It is not like some paper about robotic scientists. This is superpowering your scientists to let them design much larger experiments at enormous scale via automation and then be able to handle all the data that comes out of that using models to give them biological insight back so that they can decide on the next round of experiments to design. Right? Again, think of the change over the last 20 years in product development tools in tech. Right? If you were a software developer 20 years ago and a software developer today, oh my gosh, the software developer today, it is. They're like Tony Stark or something. It's unbelievable the capability difference in developing products. And they're still there. The software developer is still there. In fact, there's more software engineering jobs. Right? Because the market grew so much for software with better product development tools. That's what we're really talking about here. We're not talking about removing scientists. We do see programs completing faster. We want to talk about speed. Now every year as we build bigger scale and as various approaches at Ginkgo get more mature. And one reason to think of how infrastructure services will be better than traditional by hand work on speed is you can try many approaches in parallel rather than guessing one, seeing the result of a small batch of experiments, and then serially moving on to the next approach. As you can increase your scale of experiments, you can try many in parallel. Finally, cost. Another myth I hear a lot of time is, "Oh, biopharma doesn't care about costs in R&D. They just want more success or whatever." If you have talked to an R&D head, you will find out that they do, in fact, think a lot about the research budget at their company. And as we have scaled at Ginkgo, we are seeing a 40%-50% average year-over-year drop in the cost of our campaigns. And a campaign is basically a cycle of designing, building, and then testing genetic designs. The fact of the matter is, in-house, by hand, R&D work at the lab bench does not get cheaper with scale. It really doesn't. But our automation does. And infrastructure services will keep getting cheaper with scale every year if we keep growing, just as they did in tech. And I think that's going to be an important engine for the industry if we want to change how research is done. All right, finally, I want to cover our last strategic topic for the day, which is about how Ginkgo is leading as a systems integrator in global biosecurity infrastructure alongside our work in cell engineering. Now, I've shown you this slide in the past, but I want to reiterate how important it is to plug in global gaps in bio threat data. We put a ton of effort into response measures globally, but they tend to be slower and less effective than they could be because we're so often flying blind or gathering information in a reactive mode. And to give you how I get this in my head, the way that we approach biosecurity today, it would be roughly equivalent to the way we approached hurricane tracking was letting everybody know a hurricane was coming once it had hit the coast of Florida. Right? A gripped Florida. Okay? And that would not be effective. And that is what we do. We wait until, where do we track infectious disease? At hospitals. People arrive in hospitals when they are starting to get sick en masse. Okay? The hurricane has hit the shore. Right? Instead, we should be monitoring for infectious disease at animal husbandry facilities, at airports, at places where people congregate, anywhere we can most cheaply look at a lot of infectious disease genetic data in one shot to get a baseline picture. Again, think like satellites monitoring all the time. That's the vision. And within the past two months and really in the past few days, we made major announcements surrounding our partnerships in this space that will boost our data and intelligence capabilities. Our partnership with Illumina will allow us to more rapidly scale pathogen monitoring programs to new countries. The agreement leverages Illumina's leading next-gen sequencing tools and Ginkgo's end-to-end, we call these BioRadar, per my example, services. And together, we aim to increase the scale and scope of pathogen genomic surveillance globally. So in other words, remember things like variants in COVID, monitoring the genetic sequence of different diseases as they are spreading around the globe so we have more visibility and we empower, in particular, countries with local capacity. And that's what I want to talk about next. Just this week, we announced that we'll be launching the first Center for Unified Biosecurity Excellence in Doha. We call it CUBE-D in partnership with Doha Venture Capital and the Qatar Free Zone. And when complete, we expect CUBE-D to support major BioRadar programs both in Qatar and across the broader region, serving as a key hub in Ginkgo's global network. CUBE-D will be a foundational piece of biosecurity infrastructure to advance pathogen monitoring and biosecurity analytics, enable the development of biological intelligence, and support the development of next-generation biosecurity leaders globally. That's also very important. We're excited and gratified to see our international partners leaning into the long-term growth of regional biosecurity ecosystems. It is a great complement to the work we're doing in the US here with the US CDC. So we do have a program in airports here that's similar to what we do in Doha. These initiatives demonstrate the growing momentum in the new market for global biosecurity and the huge potential for rapid progress in building resilience to biological threats. And this is going to be important. If you want your kids in high school designing flowers, we want that world to happen safely. We need to also build out biosecurity. That's why these two parts of Ginkgo's business are so complementary. So, okay, in summary, I'm really excited about the great work we've done in 2023 and what we've already accomplished in 2024 thus far, especially the work that was done to enable Ginkgo to be a one-stop shop for all types of biotech infrastructure services. I couldn't be more excited for the year to come as we continue to expand our offerings and growth of the business. All right, now I'll hand it back to Megan for Q&A. Great. Thanks, Jason. As usual, I'll start with a question from the public and remind the analysts on the line that if they'd like to ask a question, to please raise their hands on Zoom, and I'll call on you and open up your line. Thanks, all. All right, welcome back, everyone. As usual, we'll start with a retail question, and then I'm just going to go down the line here of folks who raised their hands first. So, Steve Mah, you'll be first after our retail question. But the first one, this goes to both Jason and Mark, is there a measuring stick we can use to evaluate your success as earnings are not yet there, i.e., such as deal announced versus expected? Yeah, I can take that one. So I actually think there's a few good measuring sticks if I look at sort of what our strategy is in 2024. So first, I think you can watch our cash burn. I think it is a tough market in general for biotech companies, and we don't want to have to be raising if we don't want to be raising. We're really focused on why you see us ending 2023 in such a great cash position, more than $950 million in cash and equivalents in the bank. And you want us to see us raise our revenues while reducing our OpEx. So that's a big goal for us in 2024. Towards that end, also watching how our sort of campaign costs move. Right? So in the last year, you saw us drop those campaign costs 40%-50%. This is an example of us seeing these scale economics that we've been talking about for such a long time at Ginkgo, where our infrastructure does get less expensive with scale. So as we take on more business, our per kind of program, per campaign costs can fall. And then finally, I'd watch for blue-chip biopharma companies being added onto our customer list. Each one of those partnerships, we get that first deal with Merck or that first deal with Novo Nordisk, you'll see us adding more business behind those. Mark talked about this, but the research budgets of those biopharma companies are huge. So there's actually a lot more business to come behind a first deal. Each one of those has a lot of lifetime value to us. Those are some of the things I would watch for in 2024 since those are some of the areas the team's going to be focusing on. Thanks, Jason. Like I said, Steve Mah, you're up first. Your line is now open. Oh, great. Thanks for the questions. I have a three-parter on the technology network. First, how can we begin conceptualizing the technology network platform? How is Ginkgo going to monetize that? And then two, what's the incentive structure for the 25+ parties involved and also for customers? Are customers asking for these multifunctional platforms? And then three, aren't you competing with your network partners to some degree? Just try to help us understand the whole dynamics of this technology network. Thanks. I'll get back on the queue. Yeah. Love it. Yeah. All very good questions. So yeah, so I'll give you a little bit of sort of so first, you asked how to conceptualize it and in particular, how do you monetize it. So sort of the easiest way to think about it is it makes it easier for us to bring to bear new technologies and tools as part of customer projects where the customer's interested in it without us necessarily having to, for example, acquire that technology. Right? So I'll give you an example. We acquired Circularis a few years ago. That was circular RNA technology. Our technology in the RNA space helped us get a deal with Pfizer in RNA. There's a world where with a more mature partner network, we're able to draw in a few pieces of technology from smaller RNA tools companies, put them together on Ginkgo's automation, and land a deal that we otherwise wouldn't have been able to get. Right? And so that's new business for us. Right? By us being able to have more tools to bring to bear and Ginkgo, again, I mentioned this before, but selling outsourced infrastructure services is not a thing that is commonly being done in the biotech market today. So our sales team is very specialized. And so I think our ability to go out and land those customers is something that's valuable to the other tool companies in the space. And so that's why they're excited about it. From the customer half of the equation, it's hard. If you're a big biopharma company and there's tens of new small AI companies, tens of new small RNA companies. Right? People do search and evaluation, but they're usually doing search and evaluation around a drug asset, doing search and evaluation to go look at all these small tool players. Frankly, the big biopharma companies, I think, have a hard time doing it. It's just a lot. And it's hard to compare them. They're early. So Ginkgo can also be this sort of clearinghouse, interacting with many different players and again, making that easy. Right? Making it easy for biopharma companies to tap into that innovation because there is more innovative tech happening at these startups than it's happening in-house in many cases, especially for emerging modalities. And so that's the other side of it. And I think the incentive in that case can be pretty straightforward. Ginkgo gets more business that we wouldn't have otherwise had. These small companies get a distribution channel by tacking onto Ginkgo programs with customers. All right? When it comes to competing with the network, I think over time, frankly, I think that's something that could happen. Right? I think you see this with many platforms where you'll have a large platform infrastructure. You might see that platform company initially launch certain products. Apple would have seeded the App Store with certain apps that they were running. That's sort of you can think of it like we've done that with our cell engineering services. But if it turns out other companies want to build cell engineering service platforms and it's going to use Ginkgo's automation and we're going to get lots of fees and maybe pieces of a rev share in the future and so on, what's wrong with that? Right? It works fine in iOS. And so it's a model we're experimenting with. I want to say today, what we have with these partners is the right to go out and sell, kind of co-market to customers and see how it goes. And I think we're going to learn a lot. You're already seeing some of those things we're talking about structuring, for example, with some of the early examples I gave in the talk today. But how that ends up shaking out will be a function of when we get a customer excited about it and we're negotiating that deal and we work out the economics. So it is today more of an experiment to see how it goes. But we had a ton of interest in it on really both sides of the market, both from tool providers to get that distribution and large biopharma and other R&D heads to get access. Does that make sense? Yep, that makes sense. Thanks for the color. Yep. Thanks, Steve. All right. Our next set of questions comes from Tejas at Morgan Stanley. Tejas, your line is now open. Hey, Jason. Mark, Megan, hope you can hear me okay. Yeah, it's great. Awesome. Good evening. So I want to start with one on the global tech network and then one for you, Mark, on the financials. So starting on the tech network side, Jason, in your mind, you've added 25 partners. Are there any key gaps that still remain to be filled there? And then more importantly, I think this is a theme you touched upon sort of in your prepared remarks there. But can you just talk to us about the efforts underway towards convincing customers to outsource what they may view as proprietary capabilities for competitive reasons? One of the analogies that I was thinking about was if you look at sort of outsourcing penetration rates for preclinical discovery work, right, and that versus what you see in stage two, I mean, phase two or three clinical trials, does it function different there? So talk to us about why you think this time and in this context, it's truly different. Yeah, super good question, especially that second one. So on the gaps in the technology, yeah, yeah, tons. I mean, you can see. For example, if you look at just a number of modalities, we don't even have any technology partner really focused there as an example. And then inside a particular modality, again, take an RNA or something, you could have a bunch of different approaches for delivery, a bunch of different approaches for persistence of RNA and decreased persistence of RNA in a cell as examples. And I think one thing that biopharma companies would be excited to do is try multiple approaches. Right? I think one of the challenges you see with internal R&D is you often kind of get a bunch of things tried. Instead of actually getting a chance to compare them all evenly, whichever one happens to get to a sort of clinically something that looks like it's qualified to go into animal studies and clinic, whatever gets there first goes as opposed to having tried all the different approaches you possibly could have and found the best. And I think trying them all and finding the best would be a much would help the industry on the probability of success axes. And it's something that could be enabled with really a network of different tool providers rather than a one-off deal here and there and people just making a bet on their favorite horse. And then secondly, when it comes to convincing customers to outsource, I think this is the most important question for the entire, again, what I would call infrastructure services industry in biotech. Right? How do we convince, again, research leaders to choose to outsource parts of that work rather than do it in-house? And you brought up one of the challenges, which is people can think of it as something proprietary, a secret sauce, and so on. And phase two and phase three isn't looked at that way, although you could sort of debate. I think certain companies do focus on how their trial design and things like that. I actually think it's less about that, to be honest with you. I think it's like it is more that it is perceived that the work is so specialized that an outsourced provider couldn't do it as well as you could internally. I think that's actually been the much bigger source of resistance because you can get the proprietary with patents. Right? You can still file on all this stuff. You can still own the IP. And so you're still going to have all those same very strong proprietary protections, whether you're using an outsourced service or doing it in-house. I think it is the perception that the services aren't specialized enough to do preclinical research. And I think that based on certainly what we've started to see here at Ginkgo and the deals we've been doing, I think as the technology is scaling, that's starting to fall away. And you're going to see just aggregate data assets going into AI models, large-scale robotics that is just bigger at the service providers than it would ever be at any one company. And I'm hopeful that that will start to change that dynamic. But that, to me, is the bigger barrier. Got it. Super helpful. And then one for you, Mark. I mean, I was looking at the program ads here in the guide and also the cell engineering contribution. I think it's $175 million at the midpoint. Can you just share some color in the embedded ramp versus the $27 million you did in the fourth quarter? I think you pointed to sort of large pharma and government as two key drivers there. But just some color around sort of the phasing of that revenue through the year. And then what are the macro recovery assumptions that you've baked into your back half expectations? Yeah. So a couple of things. First of all, we do expect the revenue to ramp during the course of the year. Second point was that when you look at Q4, it's not a great sort of comp because we did have a specific contract amendment on a single customer that impacted revenue negatively in Q4. And so it isn't a good baseline for you to think about sort of where the launching point is for 2024. And then just in terms of the sort of catalysts or opportunities, it is the things that we've been talking about. So we have been adding significantly to the BD Salesforce in biopharma over the course of the past few months. And so that will start to have more of an impact later in the year, for example. Got it. Super helpful. Appreciate the time, guys. And I'll mention Mark didn't touch on it, but you mentioned the government. And that's been sort of a surprise area of growth for us, I think. And I've been trying to kind of chew on why. And this is not government associated with our biosecurity, but on cell engineering. And I think part of the reason is there's RFPs. There's a clear the government has already decided they're going to outsource research. As an infrastructure service provider, you can just compete in a very clean RFP process. So again, if you were to think back to your second question, we sort of want to enter a world where you see more RFPs being emitted from the large biopharma research organizations, almost like the government does today to get certain types of research done. I think that would be quite interesting. But that's one of the reasons I'm pretty excited about government is it's a little more straightforward to sell into . Got it. Thanks, Jason. Appreciate it. Yep. Thanks, Tejas. Our next question will come from Mark Massaro at BTIG. Mark, your line is now open. Hey, guys. Can you hear me okay? Yeah. All right. Excellent. Well, thanks so much for taking the question. So it's nice to see $1.5 billion of revenue potential coming in from downstream milestone payments this year. Would love your maybe any feedback on the $1 billion that came out of the pool, obviously, the $1.5 billion going into the pool. That is a net positive. Can you maybe just talk about some of the puts and takes of what went in and what went out? And then as we think about potentially monetizing the $2.4 billion over time, can you give us a sense for when you think you can realize this? I recognize this will not happen overnight, but just maybe a sense for how much maybe in 2024, 2025, 2026, just how do you guys think about that opportunity? Yeah, I'd be happy to take both of those. So on the Downstream Value Share that fell out, a good chunk of that did relate to a single customer. And I think the important point is that the new Downstream Value Share milestone potential that we added, the $1.5, it was more diversified and, largely speaking, coming from large pharma companies, which are more resourced ultimately to commercialize an opportunity. On the $2.4 billion of milestone potential, so first of all, remember that doesn't include royalty potential or equity potential. So that's just the milestone category only. In terms of the pattern of potentially realizing that, so you can see in the chart that was in the deck, a good portion of that potential is tied to the ultimate commercialization of a product. And in the biopharma space, the commercialization can take time. It's got to get through, for example, clinical trials, etc., and those kinds of approvals. So it is sort of a longer time horizon. But what I can tell you just to get sort of very specific and tactical about 2024, we do have a significant number of Downstream Value Share opportunities in play for 2024. In fact, really almost an order of magnitude higher than what we've had in prior years as potential opportunities. They are binary in nature. And so that is just the fact. They are also, for the most part, I would say, the impact for in-year revenue in 2024 still remains small on any given opportunity. Some of them are more significant than others. But it's really sort of a small potential impact. So higher number of opportunities, relatively small potential impact for 2024. But certainly, there's a lot of opportunity for us to achieve Downstream Value Share in 2024, much higher than the $4 million that was achieved in 2023. Okay. Great. And then congrats on the acquisitions, notably two in AI and machine learning. You guys have $944 million of cash in the balance sheet. Looks like you used $370 million this year or last year. Can you give us a sense for what the priority is with respect to cash in the balance sheet? How much do you think might be used for further acquisitions? It seems like AI is a pretty significant focus area this year. And then as I think about the cash, it appears to me that you have at least two years of cash. But can you just give us a sense for what you think the cash utilization will be in 2024? And again, just help us frame where the cash balance for M&A is as a priority this year. I'm happy to take both of those, Jason. So on the M&A front, the cash generally speaking, I think, as you know, we're structuring deals where the consideration is stock and not cash. And so those are generally not uses of cash. Secondly, we structure the deals with relatively low upfront consideration, with higher consideration in the future in the form of kind of earnouts. And so again, the sort of key point there is our M&A activity is not a significant use of cash right now and wouldn't be expected to be in 2024. Of course, there's always the chance that a particular attractive opportunity comes up, and we would evaluate that. But that's been the kind of recent history. On cash burn, I think, Mark, the best way to think about it is, as you can see from the financial statements, about a $370 million sort of overall burn in 2023. So you should expect us to improve upon that meaningfully in 2024. I think more important, and that's a combination of revenue growth as well as some OpEx reduction. I talked a lot about what we're doing sort of on OpEx and spend management on the earnings call. So think about cash burn improving in 2024. I think good execution on that puts us on a trajectory to further improve on that number in 2025. Okay. Thanks, guys. I'll hop back in the queue. Yeah. Mark, the only thing I would add is I do think this just touching on the topics of sort of DVS and that cash position. From my standpoint, I like how our numbers are looking as we're growing demand on the platform. You're not seeing us actually spend; this wasn't huge amounts of new capacity build in the last year. This was really efficiency gains from more volume coming through improved automation. So I like my scale economics. I like that I'm adding a lot of new business in the market with the biggest research budgets in the world. And I like that we barely penetrated that market. So I have a lot of room to run. The thing that screws me up is if I get over my skis on cash. And so we're watching that, right? And I'm not counting on DVS, downstream value share, to do it for me. I love DVS. I'm here for it. I hope some of our equity positions get sold for a bunch. I hope we get more milestones. It's all great. But when you think about how I'm running the company strategically, I'm going to make sure that the fees that are coming in from cash are there to or the cash coming in from fees are there to support our efforts as we bring our kind of revenue up and our OpEx contained relative to it, that we can get there without needing to do a fundraise I don't want to do. So I want you to know that I'm really trying to do that absent of DVS. And I think it's great to have it, but it's not something I want to count on. Okay. Thanks, guys. Thanks, Mark. Our next question will come from Matt Sykes at Goldman Sachs Group, Inc.. Matt, your line is now open. Great. Thanks for taking my questions. Maybe the first one, Jason. I want to go back to the government and market. I mean, you saw an increase from 6 to 14 and really haven't talked about it much in the past. And I think the RFP structure simplifies the sales process, certainly relative to large pharma, which is a different ballgame. But I would also love to know, how are those contracts structured? Because it would seem to me there might be some opportunities for more upfront relative to downstream value. We'd just love to get more details. As this business grows, how can that have an impact on sort of the financials longer term? And what do you expect that end market to grow at over the course of maybe the next longer term? So I'll take sort of a part of that to just sort of kick it off. So those are structured like a fee-for-services agreement. So as we perform a service, we get paid. And so in the sort of context of upfront versus DVS, it is basically all upfront as we perform the services. And I don't mean sort of cash prepaid. I don't mean upfront in that sense. I mean, just in the context of fee-for-service. There is no DVS on government contracts. So those are structured in a way that they make sense for us economically to do based on the service fees. Yeah. And the thing I would add is I think frequently what we're seeing with the government customers is they're looking for early-stage research. And so it is a way although we're not getting DVS, we're often creating assets internally that we're able to reuse for other things. And so it is a way to kind of generate some of that. It drives demand that is economically good for us overall in terms of the fees. And it's a reliable kind of RFP process to do the sale. So I am really excited about it in that regard, especially in these near years where we're trying to make sure, again, that the cash coming in from non-DVS earlier research funding and fees helps us not need to raise. Got it. That's very helpful. And then, Mark, just following up on some of the comments you made on the OpEx side, looking at the reduction you did Q4 to Q4 year-over-year, how should we be thinking about OpEx for 2024? I know you said that expect it to moderate, but any additional color on that would be very helpful. So in the aggregate, we're targeting we expect total OpEx in 2024 to be less than total OpEx in 2023. You can see that we're sort of on the right trajectory from a just Q4 over Q4 perspective in 2023. So there's a lot going on there. So we would expect to see sort of just a net reduction in G&A expense in your sort of classical support functions like finance and legal and people. And that's because we're moving from a place where we've been spending a lot of money on professional services and consulting to bringing more capabilities in-house at a better cost. So that's sort of one example. But in the G&A line, you've also got selling expenses. And we are expanding, as we've said, the pharma business development team. So you're going to have offsets against some of that G&A reduction with some areas of targeted investment. On the R&D side, sort of the same kind of thing. So there are areas where we're either constraining, holding the line flat, or reducing expenses. And that'll be partially offset or offset by investments, for example, in mammalian capabilities. So the net sort of effect I think, Matt, the right way to think about it is think about Q4 as a good sort of baseline because the earlier quarters in 2023, we were still integrating acquisitions. We were still working through sort of operational improvements. So think about Q4 as a good baseline. From there, you would see R&D expenses increase a bit quarterly because of, like I said, investments in the mammalian capabilities. And then you should see G&A decrease. But it does get a little bit. There are puts and takes sort of across the board in terms of those line items. Great. Thank you very much. And again, I'll be a broken record on this, but just to state it again, from a strategic standpoint, what I'm hoping to do is you see us with a large cash pile right now, $944 million. We want to squeeze down that cash burn by basically growing our customer base and increasing the amount of cash revenue we're getting while containing our spending and getting more efficient, right? And if I do that, by the end of the year, I still have a good cash pile, but I have a lower burn. And then you're once again computing how much runway I have. And the next year, I squeeze it again. And we eventually get to the point where you have no fear that I'm going to get to profitability, again, without needing to raise in an unfortunate environment if that's the one we're in. And so that's my strategic goal because otherwise, I like my I just like my market footing, right? We are getting the scale economics we want. We're the leader. We're getting the customers we want. So as long as I keep that train running, I'm pretty happy. Great. Thanks. Very helpful. Appreciate it. Thanks, Matt. Our next set of questions comes from Derik de Bruin at Bank of America Corporation. Derik, your line is now open. Derik. Hey. How are you? We got you again. You got me again. Yeah. So I've been bouncing around. But did you explain why the program number and basically, you put out a press release on the 10th of January saying you were going to meet your targets. And the numbers actually came in below on the programs and this and all this. If I missed it at the beginning, I'm sorry, my apologies for why it came below those primary results. But can you elaborate? So on the revenue point, we came in a little bit below. Yeah. Yeah. We did or I did make a comment to the effect that we did have a contract amendment late that had a negative accounting impact to revenue. And so that's really what happened with revenue. Without that, we would have been sort of well in the revenue range. And then with program count, it was really, so we certainly had the base of programs that we had started in the quarter to feel good about the guidance range. But there were just a few programs that ultimately didn't progress far enough in the contract discussion for us to count them. And that's what happened there, Derik. Got it. Okay. So I had about 100 programs coming in in 2024, but the revenue numbers are way down. I guess the revenue per active program. Why is that falling so sharply relative to what it is? I mean, is it just the nature of the fact that you're moving to more biopharma and they want to pay more downstream? What's the delta? Because I said your program numbers are coming in essentially where I thought they were. But the revenues are below. Where's the miss? Yeah. A couple of things. So first of all, some of the newer programs we're doing on the industrial biotech side are coming in at a lower revenue per program or booking per program than what we have seen historically. And you can think of that as a function of macroeconomic conditions. In some cases, it's because we're doing success-based pricing. So you do have a component of the new program mix is industrial biotech that's lower than average. The new stuff that we're doing with larger biopharma companies is not at all. In fact, that's the opposite. Those are generally higher program revenue on average or booking per program on average. And so that is not at all the case that somehow those are sort of skewed to Downstream Value Share. So that moves in our favor. The one thing, Derek, that you need to look at is I would average a few quarters. Rather than taking Q4 divided by average active program, I would take the last three quarters, let's say, or the full year of 2023 and divide by average active because there is the quarterly lumpiness that I talked about for a whole variety of reasons that can make a particular quarter that can throw off a revenue per quarter per program metric. So I would do that. I would do some averaging, and then things would make a little bit more sense there. Got it. How should we think about discontinuations this year? I mean, you ended at, I think, 162 active programs. Yeah. I think the sources of growth for us right now, which are government programs and biopharma and then within biopharma, large biopharma, are all better than sort of average revenue per programs. But there's still a very meaningful number of industrial biotech programs that we're signing, which are often being signed at lower than average. So it's going to be a little bit and then, as you know, Derek, it takes a while for a program to ramp up, for example, so the revenue that you actually see. So there are going to be sort of puts and takes. It'll be a little bit of when do programs land in the year? How quickly do they ramp up? But that's generally how to think about it. The government and large biopharma stuff is higher than average. The other stuff is lower. So Jason, not to because I'm going to go I'll ask a bigger picture question so not to focus on the financials. So, I mean, when you look at the outsourcing market, I mean, obviously, there's been a lot of CROs. There's been a lot of outsourcing for a number of years. I mean, how do you sort of see Ginkgo as it fits into that sort of larger ecosystem? I mean, are you trying to take business from some of the other CROs that are out there? Some of the other sort of companies are doing discovery. Do you think you're targeting a new market niche that's there? Sort of, how do you fit in with the bigger, just sort of, how do you see yourself slotting into the bigger biopharma services complex, shall we say? Yeah. Yeah. That's a very good question. So I spent a lot of time looking at that. I would say the major thing is that the bulk of sort of things that get outsourced into CROs, into contract research today is sort of a relatively small slice of work that often pharma companies know they can do well but don't want to do, right? So, "Hey, WuXi, I want you to make this chemical library for me." "Hey, Charles River, I want you to run this animal study for me." Evotec is probably the one that's got the most when it comes to doing any kind of more advanced or discovery-based type contracts. But selling into these research areas, again, that are principally done in-house, which, again, our niche, just to remind you, Derek, is engineering of cells, so changing the DNA of cells to make them do new things. That is really our niche. And that work, by and large, is still done in-house. So we do see that as, and by the way, it's a large part of the research budget at the, for at least the biologics half of pharma, not the small molecule. But for the biologics half of biopharma and also in target discovery, increasingly, there's a good amount of that kind of work. And we just don't see it being substantially outsourced right no w. Got it. Thanks. Yep. Good to hear from you. Thanks, Derik. Our next question comes from Matt Larew at William Blair. Matt, your line is now open. Okay. Good evening. Thanks. So Jason, the comparison to the sort of software tech ecosystem, I think sort of what strikes me is that perhaps the pace of innovation there is so quick because the ultimate customer adoption curve is so fast. There's zero impediment to adoption. The UI is so easy. So that middle layer of companies have race to adopt the underlying technologies. And obviously, something you've found bringing this to biopharma but other end markets is that there are far more barriers to sort of adoption. And something you've launched CDKs. You've done a variety of different things. You've brought capabilities in-house. You have the technology partnership here. What sort of do you think have been the learnings of the last couple of years of what actually can move the needle to reduce those barriers to adoption? Are there regulatory dynamics that you're attacking in government? Is it technology? Is it first mover? I don't know what it is, but maybe share just what you've learned and how that's informing kind of the strategic investments you're making. Yeah. So I'll break it into two parts. I do think one of the again, if I just compare biotech to tech, I would say the overall product development cycle in biotech is probably 2-3x, at least, timelines relative to a substantial tech product. Obviously, you can launch any small thing quickly, but a tech product that would deserve being compared to a drug or something in terms of the opportunity. And so it's not like these things happen instantaneously in tech, but they do happen, I'd say, 2-3x faster. And so that path to market, a lot of what makes it slow is regulatory. And so I do think thinking about regulatory again, not about research tools or anything else, but just about getting biotech products in the market, I think, is important. Now, I think in, for example, pharmaceuticals, that is a well-thought battle, right? In other words, people want to go faster. FDA has got a hard job, right? I don't know if there's secret there's magic wands to wave, although I do think there's an interesting lesson in Operation Warp Speed and what we did with COVID. And so I don't want to rule out that there's a lot faster ways to do it, but it's a harder fight. In some of these other markets, though, like in agriculture, for example, I think regulatory has gotten a bit out of whack. So you've got regulatory that looks like pharmaceuticals, but the market doesn't support that kind of timeline. And I would say the sort of safety history in agriculture, in other words, almost at least, I'm not sure I even know of adverse events when it comes to genetically modified crops and things like that. That feels like it's gotten a little bit out of whack. So I do think there might be opportunities to change that and U.S. competitiveness and so on, where maybe that would help. So I do think that is just a net dampener on biotech versus tech. It doesn't mean biotech is not a big industry. It is. But if you wanted to really unlock it, I do think speed of products to market is one of the big ones. And a lot of that is regulatory. Now, given that, it's still huge. Still, Lilly's still $500 billion. We have big biopharma companies, and they have big research budgets. So there's still a question of getting infrastructure services adopted into the billions and billions of dollars being spent on research every year that are not going to infrastructure services. I think in the near years, particularly with Ginkgo's revenues where they are now, where they're still early and small, we have enormous growth by just tapping into that. That is why you see me continually experimenting with this, Matt, right? Like, "Yep. We're going to try this. We'll try success-based pricing. We'll try a technology network." Because we need that same shift that, for example, the cloud service providers had to figure out when everyone was resistant to outsourcing their IT back in the mid-2000s, mid- to late 2000s, right? You got to try things. You got to figure out what are the right microservices, what's the right sales process, so on. We're happy to be running those experiments. We think we're the leader. Like I said, I like our position overall. But it is still a puzzle to get worked out. The faster we unlock it, especially in biopharma, it's the gating rate on all this technology. Right. Okay. Okay. Thanks. And then we expect us to keep doing it, right? We're not—I'm not going to on the fundamentals. I think this was really true when it came to cloud infrastructure. Back in 2008 or whatever, everyone's like, "I don't want to put my data on Amazon Web Services in Seattle. Servers in Seattle. I trust my IT people. I don't trust their IT people. Their uptime is bad." And AWS is just sitting there knowing the scale economics means that centralized compute will win. And that is when we see this 40%-50% drop in campaign costs. The scale economics means that centralized, automated labs are going to win in the long run. And so we just got to be there for it and ultimately clear the path for adoption. Right. Okay. Thanks for those thoughts. And then speaking of improvement of campaign costs, and you referenced the impact of the RACs from Zymergen. At the analyst day last fall, I think, Mark, you might have laid out a capacity comment that maybe 2x-3x the number of programs you had today, active programs, could be run on existing machinery. Today, you talked about the launch of BioFab 1 in 2025. Maybe just speak to what that investment will get you, why it's the right time to make that investment, and how to think about that in the context of the other capacity you've built. Yeah. I can actually speak to that a little bit. So there's a pretty interesting phenomenon from the semiconductor industry where you would have generational improvements in semiconductor tech, right? So you would have an X nanometer fab. And then 2 years later, 2 or 3 years later, you'd have a smaller nanometer, a Y nanometer fab, right? And in the intervening years, what would happen is you basically build a fab with that generation's technology. The next year, you'd expand capacity. You'd make it bigger. And then you would move on to, in the third year or approximately, a new generation of the tech, that Y nanometer fab. And I think what's really exciting is what we've been doing over the last year and a half in particular is moving to a new generation of the infrastructure. We're moving to this RAC automation. We think it's massively flexible. It's more scalable. And that's been us sort of proving it out in our current facility, getting it working, putting more customer programs through it, migrating more of our stuff from old automation to that. If that keeps going, which I'm bullish it will, then 2025 should be about the right time to build out scale of that technology. And so that's what we're really excited about is we do think it's a chance to expand, again, purpose-built facility around that sort of RAC hardware, largely to do the automation. So we'll see if it plays out exactly like that. But that is what we're going for with that facility. And I'm hopeful, again, with the expansion of work in biopharma and other places, that it'll be good timing to do it. Okay. Thank you for the questions. Thanks, Matt. And our last set of questions come from Michael Freeman at Raymond James. Michael, your line is now open. Great. Hey, Jason, Mark, and Megan. Can you hear me all right? Yeah. It's good to hear you, Michael. Terrific. Good to see you. Thanks for having me on. First, congratulations on bringing this technology network together. These are some of the coolest companies we're aware of in the space. It's cool that you're teaming up. Curious, what are some of the qualifying characteristics for companies to join that technology network? And seeing that on the same day, you announced several acquisitions and the technology network, I wonder what governs your decision-making around which technologies to acquire and bring in-house and which companies and technologies to partner with? Yeah. That's a good question. I can take that. First off, I think that's going to be a moving front, right? What should we bring in-house versus what can we partner? It depends a little bit on how this technology network goes, right? So today, we're acquiring when we see a good opportunity. It's a mix of sort of breakthrough technology. So sometimes it's a piece of in the case of Proof, it's a key piece of molecular technology that we think is quite interesting and a complement to other gene editors and things like that. But sometimes it's just teams, right? We're really, really excited to bring on the teams, particularly as we're trying to grow our strength in AI and modeling and ML in these areas. We can sort of speed up the development of that by bringing in a mix of some of the software and data and the people who are just excellent and have been thinking that way for years. And so that's some stuff that we know we need to have core, right? We know we need to be core in AI as we go into this sort of large data and large neural net model world of the future that I think is absolutely working in biotechnology. So that we know we need to have. But in the future, some of these an editor technology or things like that probably comes in better through the partner network, right? Through the technology network, if I'm being honest. But we got to make sure that we figure out how that all is going to work, right? And one of the things I'm hopeful on you asked about bringing people on network. Today, it was people we thought were you mentioned a lot of the cool technology you've seen is out there. It's people we knew, people we really respected what they were doing. And so we are doing that. Over time, I'd also just like to, as we get some experience with this, I want to standardize those interfaces so that, "Hey, listen, there might be somebody else who's got a brilliant idea. I've never met them. They're a new startup. They're a tool developer." And in a perfect world, it's almost like they can drop into a distribution channel, right? They can go get business from the biopharma industry, even when they're small and a new upstart, via some sort of standardization of how that works at Ginkgo. If you play the tape out a year or two in the future, maybe we get to somewhere like that. But a lot's going to depend on just our experience bringing this kind of combined effort to some of our customers and seeing how it goes. All right. That's helpful. Thank you. And now, next question. Last quarter, you provided some metrics around fully commercialized programs and commercialization in progress. These are 6 and 15, respectively. I wonder if you could provide an update at the end of this year. Yeah. I don't have an update for you, Michael, but I could take that offline, potentially. Sounds good. Thanks very much. That's all from me. I did love the slide where we showed all the programs and how they were progressing to us. My favorite slide. All the guys. All the guys. the guys. Yeah. Swear to God, scale. I think, Michael, my section got too long for today to begin. Yeah. Yeah. Didn't fit anything else in. All right. Thanks, Michael. Thanks, Andy. Thanks, guys. All right. Thanks, Michael. That wraps up all of our questions. Thanks for sticking with us. I know we went a little long today. Jason, do you have any closing thoughts for us? No. I think we covered a lot of it. Again, I like our position going into 2024, our strategy for the year. I think it sets us up well. I appreciate all of your confidence in us. Thanks a lot.