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’re 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 #Ginkgo’s – #GinkgoResults or email [email protected]. 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 drive change across the culture of the industry, fostering collaboration over competition, especially among tool developers. And 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 you know, 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, 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. And I want to really take a minute and focus on our progress in biopharma in 2023. So 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’re 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. And that breadth is a huge vote of confidence for Ginkgo’s sort of core thesis of being a platform business model. So 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. And 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 code base, 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 means we’re going to be able to – 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. And you’re going to hear a lot from me about that a bit later. But 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, the 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 inter-quarter lumpiness in services revenue, even though the underlying foundry platform output is more level, and that is because of the timing of revenue recognition, the achievement of certain technical milestones, contract-specific factors and related accounting adjustments. And 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 in our expanded CDC multi-pathogen 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 spent 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 four acquisitions that we had closed back in the fourth quarter of 2022 has resulted in cost synergies at this point, particularly as relates to G&A 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 G&A 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 rationalize CapEx in 2023. The majority of the spend in 2023 related to various smaller projects and in 2024, the majority of the CapEx relates to the build out 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 and successful execution here would also put us on a trajectory for further improvements to cash burn in 2025. And 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. G&A 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 of 2022 to $432 million in the full year 2023, while G&A 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 4th quarter and in the full year 2023. As a reminder, this is because of 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. And 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 build out of Biofab1. And as discussed, this is one of our targeted areas of investment. Before I get into our guidance update for the year, I would 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. And we expect this kind of growth rate to continue based on the larger deals we signed in the past two 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 would 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 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 would expect, most of the milestone potential is coming from biopharma customers and in 2023 from large pharma in particular. On the right side of the 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 and 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 would like to provide some commentary on our outlook for the full year 2024. We expect to add in the range of 100 to 120 programs in 2024, which represents a growth rate of 41% at the midpoint over 2023. Our Cell Engineering revenue guidance is in a range of $165 million to $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 are seeing the beginning stages of real penetration into the large pharma customer segment. We are expecting expansion 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 them. And finally, we expect total revenue for the full year 2024 to be in a range of $215 million to $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 are focused 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 and 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. And 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. And 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 outsourced 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, it’s less about what we can scale into. 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 outsourced 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 this new to make that happen? And so in the first section, Ginkgo has invested close to $1 billion in software and automation, 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. And our view is that a rising tide will lift all boats in the industry, including Ginkgo. So we’re working to make our automation, our foundry, I’ll talk about the first section available to other service companies. So they don’t need to repeat our investments as they build the 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? So on the left side of this chart, you can see the magical world of the tech industry, okay? So 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. And to give you an example, new $1 trillion companies like NVIDIA are built entirely on top of other companies infrastructure like TSMC. So I want to emphasize, NVIDIA, $1 trillion 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. And every app on your phone is enabled by the iPhone or Android OS ecosystem depending on which ecosystem you’re in. So this is very powerful. It’s very enabling, and in my opinion, is why we see $1 trillion 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? Like 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? Like you’re able to tell someone exactly what you want to 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 a human. And I’m hopeful that means that we could structure our industry, our biotech industry similar to tech and see similar gains. And my hope is that perhaps one of our customers could be the first $1 trillion 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 2000, huge platform story, amazing leadership, great technology. And here’s our 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, it’s fine, Ginkgo, like the story, but inevitably, you’re going to develop your own drug 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 tend to compete with them. 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? Like why is that important in biotech? And why is it important to biopharma R&D leaders? And 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. And 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 10x the number of genes relative to the large public data sets. And importantly, on the right, we’ve run millions of assays on genes from that library and tested their performance. And that chart on the top is actually each row in that is an EC number. This is like a class, different enzyme class. And we have hundreds of thousands of data points for each of these different EC classes. And that gives us an enormous set of labeled data, as we’ll talk about later when it comes to AI as well as the raw of 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? And 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 I’ll just point out. Now, it’s 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 I’ll talk about in 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. 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. And 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’re so excited about, because they had this automation software team that had been supporting Zymergen’s efforts, and so they could drop right in. And since then, we’ve actually evolved. We call these racks reconfigurable automation carts. And 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. And 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 rack 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 racks much more expensively or 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 racks 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 you 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 racks 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 rack automation, hopefully generating the data that powers infrastructure services across the biotech industry. Onto 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. And 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. And Mark showed some numbers of like the total R&D spending at biopharma companies compared to, for example, our revenues today. And 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. And 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 like big contract for Twist when they were a very small startup back in the day, and 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. And 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, omegas for short, with RNA programmable nucleases and nickases that will enhance our code base 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. But the thing I want to highlight today is our technology network. And 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. And 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. And I’ll give you a couple of examples so you get a sense of what might be possible. So Cradle, this is an exciting company, developing AI models for DNA design in the area of enzyme engineering. So let’s say a customer comes to Ginkgo to improve the activity and titer of an enzyme, and then after they find it, they want to express it in high titers in a cell, for example. So that would be a big end-to-end project at Ginkgo take a little bit. And so I’ll give you one part of that where we’d be integrating in Cradle. So we’d first go through our normal steps of designing and screening a metagenomic library to find an ideal candidate for our customer. So, 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 and their 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. 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 lab work for them at Ginkgo. I want to say, I really love this vision. Okay, so I did a PhD. I have – 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. 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 ioCells, 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 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 could 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. And bit.bio gets a distribution channel in Ginkgo, so that they could 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 person 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% to 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 like an extra point here. There’s a lot of noise about automation 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 Combi-Cam. It is not like some paper about robotic scientists. This is super powering 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? It is – 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, 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, 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 we have – as we have scaled at Ginkgo, we are seeing a 40% to 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. I grew up in Florida, okay? And that would not be effective. And that is what we do. We wait until 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’ve 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. And 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 U.S. here with the U.S. 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. And we want that world to happen safely. We need to also build out biosecurity. And 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?

Yes, 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. And so we’re really focused on why you see us ending 2023 with 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% to 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. So 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.

Thanks, Jason. As I mentioned, Steve Mah from Cowen, you're next. Your line is now open.

Oh, great. Thanks for the questions. I have a three-parter on the technology network. So 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 plus 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 in the queue.

Love it. Yeah. All very good questions. So I’ll give you a little bit of sort of – first you asked how to conceptualize it and in particular, like 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 is interested in it without us necessarily having to, for example, acquire that technology, right? So I’d 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 and 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. 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? Like 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 is happening in-house in many cases, especially for emerging modalities. 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 on to 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, like Apple would have seeded the App Store with certain apps that they were running. 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 or 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.

Yes, that makes sense. Thanks for the color.

All right. Our next set of questions comes from Tejas at Morgan Stanley. Tejas, your line is now open.