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. 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 SEC to learn more about these risks and uncertainties. Today, in addition to updating you on the quarter, we are going to provide updates on our path towards adjusted EBITDA breakeven, including a deeper dive on how we're executing against our cost reduction targets as well as what we're doing to drive revenue. 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 X at #GinkgoResults results or e-mail investors at ginkgobioworks.com. All right. Over to you, Jason.

Thanks, Megan, and thanks, everyone, for joining us. We always start with our mission of making biology easier to engineer. And in order to do so, particularly in this quarter and the quarters to come, we're focused on three objectives. First, reaching adjusted EBITDA breakeven while maintaining a cash margin of safety. We ended this quarter with $730 million in cash and no bank debt. We also made aggressive moves in headcount reduction and other reductions that will be reflected in reducing our cash OpEx spending in the coming quarters. Second, while we're cutting these costs, we need to keep serving our current customers well. I'm happy with our revenue number this quarter, which is indicative of continuing to serve our current customers. This is a big lift for the team alongside a shift and a change in how we are organized, but it's an early indication that these changes were effective in improving the efficacy of our delivery. Finally, we want to grow our revenue in solutions and expand into selling tools. So I'm going to cover this more in the strategic session, but we're excited to open our platform directly to our customer scientists. Previously, it's been something we just had available to ourselves here at Ginkgo, and we're getting that out there in a more democratized way. Our technology assets in bioengineering are world-leading. So I'm excited to find more ways to sell them and drive growth. As a reminder, in our Q1 call, we noted our annualized OpEx of about $500 million was simply too high relative to near-term revenues. To address this, we announced a plan to cut this back by $200 million on an annualized basis by mid-'25, including consolidation of our footprint and reduction of our labor expenses across both G&A and R&D. I think, again, with our strong cash position, we're well-positioned to continue executing on these restructuring efforts. I will be providing a detailed update on the cost reduction plan later in this presentation. But for now, I'd like to give you a summary of the actions we took in the second quarter relating to headcount reduction. At present, we have notified approximately 450 employees or roughly 35% of the business that they will be impacted by our reduction in force. Approximately 300 positions were impacted as of the end of Q2, and an additional 100 positions are anticipated to be impacted by the end of this year and the remaining 50% by mid-'25. These cuts, although difficult to make, are estimated to save Ginkgo over $85 million in annualized cost savings once they're fully implemented. Because of these reductions, we're very much on track to hit our goal of reducing our annualized cost by $100 million by the end of the year. I know there's not much consolation, but I do want to take a minute and again, thank our employees who were let go as part of this reduction in force. They contributed enormously to building Ginkgo, and we're deeply grateful for it. Much of what we're doing now, I think with these changes, is to establish a firm base for the company that will allow us to then grow and meet the mission that they all helped us build. Now I'm going to get into cost-cutting details in the strategic section. But before I do, let me hand it over to Mark to go over the financials.

Thanks, Jason. I'll start with the cell engineering business. Cell engineering revenue was $36 million in the quarter, down 20% compared to the second quarter of 2023. Similar to Q1 of this year, this decline was driven primarily by a decrease in revenue from early-stage customers, partially offset by growth in revenue from larger customers. We continue to believe the shift to larger cash-based customers to be an overall positive shift. In the quarter, we supported a total of 140 active programs across 82 customers on the cell engineering platform. This represents a 33% increase in active programs year-over-year with solid growth across most verticals. As we discussed on our prior earnings call, we anticipate the nature of programs that we take on with our customers to evolve in the future following our recent adjustments to commercial terms and offering. While still very early days, this slide gives you some detail on how the nature of programs is changing. We added a total of 18 new programs and contracts in Q2 2024, of which 10 were generally comparable in size and scope to historically reported new programs. Importantly, you'll note that of those 10 deals, 5 included downstream value share potential. In addition, we commenced 8 other customer contracts in the quarter that represent a variety of small deal archetypes. These are generally much smaller in scope and shorter in duration and included 2 lab data-as-a-service deals in the protein characterization space that we signed with a large cap tech company, which itself is an entirely new customer segment. The current sales pipeline for both categories of deals is solid. And while it's still in its early days, we're encouraged that during the course of a major restructuring, we have been able to execute on existing customer programs while converting new opportunities. Now turning to biosecurity. Our biosecurity business generated $20 million of revenue in the second quarter of 2024 at a gross margin of 41%. Revenue and gross margin were up significantly in Q2 due to the timing of a customer contract. 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. We are also breaking out M&A and restructuring-related expenses to provide you with additional comparability. Starting with OpEx. R&D expense, excluding stock-based comp and M&A and restructuring costs, increased from $99 million in the second quarter of 2023 to $110 million in the second quarter of 2024. This increase was mainly driven by an increase in rent expense and AI-related spending. G&A expense, excluding stock-based comp and M&A and restructuring costs, decreased from $59 million in the second quarter of 2023 to $45 million in the second quarter of 2024, which reflects cost reductions we completed in 2023. Importantly, Q2 does not reflect the benefits of our headcount reduction since those only commenced at the end of June. So we would expect both R&D and G&A expenses to decrease meaningfully by Q4 of this year. The M&A and restructuring-related costs this quarter include a goodwill impairment charge of $48 million; other costs relating to restructuring, such as severance and costs relating to smaller M&A transactions that we closed early in the quarter. A full reconciliation of this line item can be found in the appendix to this presentation. Stock-based comp. You'll again notice a significant drop in stock-based comp this quarter, similar to what we have seen over the past year as we complete the roll-off of the original catch-up accounting adjustment related to the modification of restricted stock units when we went public. Additional details are provided in the appendix. Net loss, it is important to note that our net loss includes a number of noncash income and/or expenses as detailed more fully in our financial statements. Because of these noncash and other nonrecurring items, we believe adjusted EBITDA is a more indicative measure of our profitability. We've also included a reconciliation of adjusted EBITDA to net loss in the appendix. Adjusted EBITDA in the quarter was negative $99 million, which was down from negative $80 million in Q2 2023. This decline was driven by a decrease in total revenue, partially offset by a decrease in certain operating expenses. In addition, I would like to note that we are now reporting adjusted EBITDA inclusive of non-cash, in-process R&D charges relating to acquisitions. And so we have separately itemized that amount for you here. Finally, CapEx in the second quarter of 2024 was $13 million, net of tenant improvement allowance as we continue to build out the Biofab1 facility. In terms of outlook for the full year, we are reaffirming our guidance for 2024 with cell engineering revenue expected to be $120 million to $140 million and biosecurity revenue expected to be at least $50 million, totaling $170 million to $190 million. In conclusion, we're pleased with our overall execution of the restructuring thus far as we navigate substantial cost reductions and commercial changes, which we see as foundational to our path to adjusted EBITDA breakeven. Back over to you, Jason.

Thanks, Mark. I'm going to use the first strategic section to focus on Ginkgo's efforts to reduce costs as that's currently my primary focus. Ginkgo is a unique player in the life science tools industry. We have more than 100 active cell engineering programs running on our platform across biopharma, industrial and agricultural biotechnology, and we have a unique scale and breadth in both automation and software and cell engineering. We can deliver on that services business profitably, and we're focused on demonstrating that as quickly as we can. I'll cover how we're taking out costs while maintaining delivery for our customers. Second, I want to talk about how we see the opportunity for growth at Ginkgo by opening our platform up directly to our customers' scientists while focusing our existing service offerings around our areas of strength. Finally, I'd like to spend some time highlighting a growth opportunity within biosecurity as it tackles emerging threats and modalities, specifically H5N1 or bird flu. Okay. Let's get started. During our Q1 call, we announced our plans to cut spending back by a run rate of $100 million by Q4 2024, with an additional $100 million expected to come out by mid-'25. Earlier on this call, I mentioned that we expect to see over $85 million in annualized cost savings from our reduction in force. As you can see from this chart, of the approximate $85 million we expect to save by mid-'25, $75 million of that is expected to be achieved on an annualized run rate basis in '24 based on actions we've already taken. In addition to the people cost savings, we've taken actions expected to result in an additional $25 million in annualized cost savings by the end of the year, putting us on track to hit our goal of reducing costs by $100 million by end of '24 on a run rate basis. Because we are still in progress with the majority of the non-people cost cutting initiatives, I'd like to take a minute and dive deeper into what we're planning to reduce cost there. We've established a number of internal work streams, I think 19 or something focusing on major spending areas. A few examples. Streamlining third-party costs. We're focusing on realizing efficiency with our vendors through strategic sourcing and renegotiations. Additionally, we're reducing our dependence on third-party technical work and consulting, as well as external legal services. We're reducing, as I mentioned previously, our real estate footprint actively and looking for sublease opportunities there as well. Equipment cost alignment. We're adjusting our equipment expenses and related service contracts to match the current utilization and then we'll scale into demand as it comes in. We're undertaking a significant effort to rationalize our software portfolio, a lot of enterprise software, by reducing licenses and consolidating applications. Overall, on the technical side, both in the lab and with software, we have a lot of historical data now on what infrastructure really pays off across these hundreds of programs we've done at Ginkgo. So we've been able to make good decisions, decisive decisions quickly in this area. I'm excited to see that play out and save us money. Okay. Importantly, alongside our cost-cutting initiatives, we're continuing to deliver for our customers. This is key. So recently, we delivered on a major technical milestone for a previously announced large biopharma customer we have. In the midst of all these restructuring efforts, Mark mentioned, we were able to sign four new agriculture deals, the largest of which was with Syngenta, where we're optimizing a microbial strain from their biologics pipeline. This is a molecule that they've earmarked as a pioneering biological solution at Syngenta. Successful, cost-effective and large-scale production of this metabolite would expedite their go-to-market timeline for these biological solutions. Next, as Mark mentioned, we signed our first two. We call them LDaaS, Lab Data as a Service deals with a large tech company, and we're excited to execute on those in the near term. Lastly, I'd like to reiterate we're reaffirming guidance and are confident in our abilities to continue executing for our customers while we act on these cost-cutting initiatives. I want to take a minute and thank the team at Ginkgo, who handled really just a crazy left to deliver so well on customer programs in the same quarter where we had such a large amount of organizational change. That's going to get easier going into the future, but that was no small feat and it's a testament to the strong culture at the company and our focus on delivering for customers. Okay. That's an update on where we expect our cost takeout to come from. And next, I want to talk about how we're building the tools and solutions that are going to grow Ginkgo's revenue going forward. So like these two images. If you look at how the work of engineering cells is done today, it looks like this picture on the left, all right? Lab bench. And first, I want to say that it's a little frustrating because this is exactly what my lab bench looked like in graduate school at MIT 20 years ago. I promise you if you walked into the computer science department at MIT, the tools available to researchers would be wildly different from 20 years ago. But in biotech, the tools have changed a lot less than you would expect. Honestly, I thought about this, and I think it's because this set of tools is actually pretty great for what it's intended to do. It allows bench researchers to explore hypotheses quickly and adopt new protocols that they read in papers in a matter of days, right? They can go into that Thermo catalog and order whatever reagent they just read about in a paper, pick up a pipette and get to work. For many problems, this set of tools and by-hand approach to doing lab work is the best solution, right? The big downside to this approach, though, is that since it's manual, you're doing it by hand, there are no advantages to scale. In other words, it doesn't get cheaper or higher quality as you do more of this genetic engineering research work at the lab bench. We have a very different model at Ginkgo that relies on automation. You can see part of our Boston installation of our proprietary rack robotics hardware in the photo on the right is an automated approach that allows for much more data per research dollar, and it gets better as you do more of it with scale. But it comes at the cost of less flexibility than you get when you work by hand. We've been using this approach at Ginkgo now for hundreds of what I'll say, it's like high-end R&D projects. People tend to send us some of the hardest R&D challenges they have. That's why they're outsourcing it from our customers, and we've learned what works and what doesn't work when you apply this large data approach to genetic engineering. I'm not going to say it's useful for every single problem in biotechnology. I don't think benches are going to go away. I think scientists will still be using those in the Thermo catalog. But there is a large set of problems we see could be better solved with automation and large data generation. In particular, you've seen a surge of interest recently from AI biotech companies that really want to generate large data assets for model training. A big question for Ginkgo is, if we're right about that platform and let's say we are, it is applicable to a big set of problems, what's the best way to sell it, okay, to customers who do need that large-scale data generation? I'll walk through two ways. The way we've been selling it to date is the approach on the left. All right? So we're really primarily selling to the decision-maker at our customer, the Head of R&D, or if it's a smaller company, it's the CEO, and they're deciding to outsource a whole research program. So hey, Ginkgo, we want you to go off and deliver us back to scientific result. A small team of Ginkgo scientists in the middle there is going to use our platform in automation, but ultimately, meet with that customer quarterly at a joint steering committee meeting and return scientific results to them. But our scientists are the ones using our platform. We might return a better manufacturing process to Novo Nordisk or fertilizer-producing microbes to Bayer or an improved enzyme for Merck and the other 100-plus cell programs. That's the kind of work we've been doing for our customers. We're going to keep doing that business, and we see it growing. We're very excited, however, to open our platform in the way you see on the right, by making it directly available to scientists at our customer sites. I'm going to call these two approaches, solutions on the left, the customer is coming to us for us to kind of solve their problem completely or tools on the right. We're providing a set of tools to our customer scientists, and they're going to solve their problems for themselves, all right? For folks that are new to the life science tool space, I want to lay out sort of a spectrum of how I see the industry from solutions to tools. On the y-axis, the top of it represents how customized an offering is for what a customer wants. Is it something we build just specific for the problem you have? It also represents how much technical risk Ginkgo is bearing with the offering. In other words, are we taking all the risk? Or are we sharing some of it with the customer? As these two things go up, you get the most extreme form of a custom, high-technical risk B2B solution in the market up at the top left, which is where a small biotech company develops a drug asset with no intention of ultimately becoming a stand-alone drug company but rather intending to ultimately sell, license that drug asset to a major biotech or biopharma. That's the most extreme form of customization and high technical risk. You see many platform biotech companies in the industry taking this approach. Companies like Absci, Abcellera, Recursion, they all have internal drug pipelines and will profit handsomely if they get a good result in a clinical trial. It can be a functional, good business model. It works. We have not taken this approach at Ginkgo for a variety of reasons. We believe we can bring more of a direct platform services business model into the industry. So we went down that Y-axis. You can find our cell engineering solutions offering there, that service business where Ginkgo is taking on less technical risk than a small biotech that's doing their own drug. For example, our customers pay us fees. That's where you see our revenue in cell engineering is largely fees today that support the technical work. So they're paying us to do a lot of that research service for them. We're making a very customized solution for them. The key point here is the more customized the solution is, in other words, on the left-hand side of this chart, the more likely Ginkgo is to be able to negotiate downstream value share, in other words, to get royalties or milestones, and most of our cell engineering solution deals today have royalties and milestones. If you see us keep signing them up, like Mark mentioned, assigning some of those large programs, they will continue to have royalties and milestones in them in the future. The dotted line in the middle shows that at some point, you're offering something more off the shelf, in other words, less customized. Customers aren't going to share royalties with you, right? This is roughly where I draw the line between, what I'll call, solutions and tools. As you move to the right side of the screen, you'll see things like our lab data as a service, where we're producing data at scale for customers, but we aren't bearing a ton of technical risk. In other words, the customers come to us with a design, and we're generating data. If the design is bad, that's their fault, okay? When we do those deals, we don't expect to see downstream value share. Towards the end of this tail, you'll see that we have AI and automation listed on this chart. At Ginkgo, these two tool pieces are in their early stages, but the idea behind these is that Ginkgo can develop modular tools, robotics, software tools that scientists and developers at our customers can put together to make their own infrastructure in-house for use. We'll talk more about that in the future. Okay, so what I wanted to dig in today first on this chart is your cell engineering solutions business. We love this business, okay? We think we are very differentiated at Ginkgo in this area, both in technology, having a wide enough array to build a custom solution for a customer effectively, and even our sales, our sales team and our approach. These are really complex deals to sell. They're really complex deals to negotiate, and we do a lot of them every quarter, I think more than anyone else. The change we made with the restructuring, though, is that we will no longer be taking any cell engineering work a customer comes and asks for. We're going to limit that work to a more narrow set of offerings in each market that Ginkgo can deliver efficiently. Let's dig in and look at ag and then the biopharma industrial. In agriculture, the first product we'll be offering is strain optimization for existing products. Agrivalle is a customer of ours. They're currently leveraging our strain optimization service to improve the efficacy of one of their existing biocontrol products. So things they already have out there, just improve them, give them back. Another product we developed is based on some of the work we've done with Bayer, where we take an early development lead, something that's still in the lab and take it to field trials. The assets we acquired from AgBiome earlier this year fit well into this. We're excited to continue to expand in that area. Third, we have bioproduction, okay? This is, we believe, a major growth opportunity where customers are looking to either develop or improve the production of a bioactive by fermentation. So you're putting cells in a tank and producing often a small molecule. This is very similar to the work we do in industrial biotechnology. We get a lot of efficiency on the technical side; the same back-end infrastructure we use here will be reused in the next section when I talk about our industrial work. Finally, our last offering is supporting the discovery of plant traits. This is typically a customer looking for novel modes of action or protein optimization. It's a large area of research spending in ag. An important one for us in the future. Okay, now I want to talk about Pharma Industrial Solutions. These businesses are focused on helping customers discover, optimize, and manufacture biologically-derived products in three key areas here. Our first offering is protein engineering services. Our job there is to build better proteins and enzymes for both pharma and industrial process enzymes, as well as therapeutic and diagnostic biosensors. You can see on the bottom, customer projects we have in these areas. These are all areas we currently do work in. Next, we have protein production, which is focused on building and optimizing production strains, including creating better ingredients for foods, things like these milk proteins and so on; we're finding better ways to manufacture vaccines. Lastly, we have a strong offering in small molecule bioproduction. That's what I was just mentioning when I was talking about the ag where we're looking to build and optimize small molecule production strains, including pharma APIs, chemicals, food, flavor ingredients, and so on. We're creating new strains to create products with a wide range of applications. You can see Ginkgo customers where we're currently delivering programs. Like I said, this span of what we're willing to sell is actually much more narrow than it would have been before, but the areas we're doing in are our strongest areas and the areas we can deliver most efficiently. Again, I think this is how we move on this path to profitability in the cell engineering solutions is with this more tight focus. Okay, I want to move on to our newer offerings. But before that, to clear up some confusion I heard after our last call, we will keep signing those solution deals, and we will often be getting milestones and royalties on those deals, okay? So we're not getting out of getting any milestones and royalties. It just depends on the type of work we're doing for a customer. Now as we move to the tool side of this chart, we have our new lab data as a service offering that I announced at Ginkgo Ferment back in April. We believe we have a major opportunity for LDAS with the drug discovery market. In particular, AI and ML are increasingly being used in drug discovery, and the need for large data sets to train models is growing. People have mined a lot of what's out there in these public data sets; things like AlphaFold and so on were trained on the public structure database, the public genome database. Ginkgo's proprietary automation ability to deploy it allows customers to generate large new data sets that they can use to train proprietary models or create data in areas that aren't structure, which is really what the big existing data sets in the public are, so like things like protein activities and so forth. In this case, our customers are designing the experiments themselves, taking on the majority of the biological risk. For example, they design a ton of antibody sequences, send them to Ginkgo, and we synthesize, express, and test those sequences for binding developability assays. Since we're mainly providing data and not providing custom solutions, again, we don't expect IP rights, so our customers own all the IP nor royalties or milestones. Really, clean straightforward interaction. These deals could sign a lot faster. It's very straightforward. In fact, for larger biopharma companies, I think this could just run through procurement rather than needing to be really a sort of a BD negotiation like our solutions. So I know we end up with a lot of customers tuning into these calls. I want to give a little more detail because this is really a new offering at Ginkgo of what our LDAS offerings look like for drug discovery in particular. Our customers start; they're going to give us a scope for a specific data set, what they're trying to accomplish, and they'll either give us a genetic library or ask Ginkgo to build it. We're world-class at building DNA constructs and so forth. This is where our proprietary platform comes into play. We'll use the foundry to generate large multimodal, in other words, different types of data, all assayed on the same cell line. We have software, proprietary software that can curate and annotate that data and make sure it goes back to their AI/ML team in a form they can use for model training. That's a real, I think, unique strength we have, coupled to the lab data generation. The first areas we're offering these services are functional genomics and antibody developability. In functional genomics, we can provide lots of data for AI model development and target discovery. Common use cases would be target discovery and validation and then in antibody developability, robust data packages with key developability metrics for lead optimization or AI/ML training that can predict biophysical performance of antibodies based on their amino acid sequences. Again, a lot more coming here. We have our first sort of customers running now. We're just at the beginning of this, but we are seeing good traction. Please reach out to us if you have a big data set you're planning to generate. Again, this is a new way to access our platform. We hadn't made it available like this directly to customers before. So far, people have been really excited to get access to it. Maybe we should have done this sooner, but we're doing it now. Okay, so that illustrates the shifts we're making with our cell engineering business. I'm now going to turn to what we're seeing within our biosecurity business, especially with the recent emergence of bird flu, H5N1. On the left here, you can see some recent articles about the federal funding as well as state and countrywide plans to help curtail the spread of H5N1. I want to focus more on the right and the timeline about why this is coming to light today. H5N1 is not new; it was first identified in 1996. There have been various bouts of it over the years, but the first major step change occurred in 2020 when HPAI or highly pathogenic avian influenza was detected in Europe, which then traveled over to North America in '21. Since January '22, 48 out of the 50 states have seen outbreaks of H5N1 among poultry impacting over 100 million birds. You might have heard about there's a big deal in the poultry industry. Another step change occurred at the beginning of this year when the virus, unfortunately, mutated again and became transmissible to mammals, specifically cows; obviously, this is not great. Mammals are closer to us. It's not the end of the world for humans because the virus can be pasteurized out of milk, and there's been a lot of news about that lately; don't drink raw milk, and it can be cooked out of beef. There's nothing stopping this virus from mutating yet again into something that could be transmissible to humans. I'm not trying to scare you with this, but this is another example of how important persistent, pervasive monitoring is. We want to catch and crush something like this before hundreds of people show up in a hospital with symptoms. Ideally, we detect it much closer to the animal source that it could jump out of. Fast forward a few months to April of this year when the USDA announced an action plan to protect livestock from this particular variant of H5N1. They announced over $800 million in new funding to combat this virus and mandatory testing of dairy cattle that are moved across state lines. Based on our previous work surrounding biothreat monitoring, we know that there are three keys to a successful plan to detect and combat biological threats, particularly around livestock. The first is, we need to gather information pervasively. Second, we need to collect genomic information regarding the virus without adding much cost or time to those information-gathering plans already there. The third, we need to find a way to work with the communities that are impacted while respecting their privacy and concerns. We learned a lot when we did millions of COVID monitoring tests during the COVID outbreak in K-12 schools. The privacy and parental concerns during that time were huge, and we have a ton of learnings from scaling that gigantic business at the peak of it. In response to these needs, I'm excited to announce Ginkgo's proposed genomic analysis program, GAP for H5N1. Ginkgo will use the existing practice of pooling and sampling milk for food safety to generate genomic analysis of the H5N1 virus. This provides critical data for scientists needed to respond to the virus without adding any extra burden to farmers or the systems they depend on. The process is non-invasive, requires no additional time or logistics from the farm. Importantly, the program does not record or transmit the source of the milk. In the GAP program, the only information captured is the genomic data of the H5N1 virus itself when it's detected. This can be done in a way that's not disruptive to the existing industry. If Ginkgo's pilot plan is successful, we will begin sequencing H5N1. If we are successful at sequencing the virus, our sequences could potentially be used by pharma companies to develop drugs or vaccines to combat the spread, giving you extra time to get started on those things. Lastly, through our sequencing efforts, we're also looking to detect harmful variants, specifically ones that could be transmissible to humans. If this does occur, we're working on developing partnerships to enable rapid scale of testing, similar to what we did during the COVID pandemic to help get resources to the communities that need them most. You'd like to again, test in those areas where things are happening. The spread of H5N1 may never evolve into a human transmissible disease, and let's hope so. But H5N1 shows us how vulnerable we still are as people, as a society. We want to detect anomalies, in other words, where things differ from the norm of the sequence that you hadn't been seeing. We want to do this as soon as we can so the industry can protect their herds and way of life. If H5N1 does become a risk to humans, Ginkgo and its partners stand ready to monitor, detect, and intervene if that time comes. I've said this before, but we should monitor for viruses like we monitor the weather, like we watch for hurricanes, right? We're watching all the time. We have a system for evaluating the risk of a storm when it's brewing, what category is it? H5N1 is a small storm at the moment, but it has the potential to be a Category 5, and we should have our radar running all the time. Hopefully, this pilot work is the start of that. In conclusion, although the second quarter was a difficult one here at Ginkgo, as we had to say goodbye to hundreds of friends and coworkers, I'm proud of what the team has accomplished truly, continuing delivery for our customers and opening new avenues for growth in both our tools offerings and H5N1 offerings. We remain laser-focused on our goal to reach profitability while leading the development of the technology that makes biology easier to engineer. All right. Now I'll hand it back to Megan for the Q&A.

Great. Thanks, Jason. As usual, I'll start with a question from the public. Welcome back, everyone. We'll begin with a retail question, and then we will move on to our list of analysts. Tejas, you'll be up first after the retail question. The first inquiry comes from our investor inbox and is directed to you, Jason. What is the current outlook on some of Ginkgo tech? Are there any signs of breakthroughs we can expect in the next one to two years?

Yes. We've discussed opening our platform directly to customer scientists to democratize access to our infrastructure. The great aspect is that it doesn't require a significant technological breakthrough. Ginkgo scientists have been successfully utilizing this infrastructure across customer programs, and we have the evidence to support that. However, it hasn't been made directly available to our customers' scientists yet. We have several exciting developments, such as the effectiveness of rack automation in-house, which outperforms traditional automation and manual work. Making this accessible to customers would be fantastic. We are seeing traction with Lab Data as a Service, which is tapping into various Ginkgo infrastructure, including flow-through assay and build technologies, packaged together. Customer scientists previously couldn't just send off designs and get this kind of data quickly. I'm particularly enthusiastic about offering a large piece of DNA and computational design tools, as we have extensive in-house infrastructure that represents real technological advancements, and we just need to make them accessible to people. This is what excites me most regarding our tools.

Thanks, Jason. Like I said, Tejas, you're up first. Your line is now open.

Great. Can you guys hear me okay?

Yeah, hi, Tejas.

So Jason, maybe I'll kick it off here with one on the simplified tech and the automated system stuff that you've been working on. How has the progress been with accelerating the start-up time for new projects using the racks you currently have in your Boston facility? And are there any metrics you can share to help us sort of benchmark progress? And similar sort of question on Lab Data as a Service. Just any color on traction there relative to expectations? What type of customers do you see showing the strongest demand in terms of the order funnel for the Lab data offering?

Yeah. I'll go in reverse order. So on the Lab data as a service, yes, we are seeing traction. I think the biggest category for us, I think, is going to be companies that you can call them like tech bio companies or AI in bio companies, but you have a whole bunch of either new startups or occasionally like a group within a large biopharma that's really trying to build in an AI model that's sort of proprietary to them to help them discover drugs. They're often starting with some of the public malls that are out there, maybe some of the protein design models, the ESMs or the AlphaFolds or things like that. But then they want to do is fine-tune those models with a bunch of additional proprietary data. And so they want to generate large amounts of this data. That drives them quickly. I showed that chart of sort of the lab bench and the automation. There's no way to generate that amount of data without deploying automation. Our usual fight with our whole approach, even if we're selling a solutions deal, is sort of to say, hey, for this type of biotech problem, we think generating a huge amount of data is really the way to solve the problem. What's great about the AI bio folks is they believe that implicitly, right? Like that is foundational to model training is having large amounts of data. So that's a real boon for us. I would say that's really going to be the big demand side. Mark mentioned, we have even got a deal with a large tech company, right? There's even new entrants into this space that are kind of interesting. But in general, that category will be first. Then I think you'll see the AI efforts enter the biopharma companies as sort of the next layer of demand for Lab Data as a Service. Does that make sense?

Yeah, that makes sense.

On the automation side, particularly with onboarding and racks, I'm excited about the advancements we've made with the rack system. Our team in Emeryville, California, previously with Zymergen, has been enhancing the rack hardware since our acquisition a few years ago. They've made it more manufacturable, scalable, and cost-effective. Ginkgo has essentially acted as an external customer to this team, allowing us to gain valuable experience in quickly deploying racks. What excites me the most is Ginkgo's capability to add new equipment to our rack setup rapidly, assemble it into a rack, ship it to Boston, and seamlessly integrate it into our existing automation setup. This approach is a significant departure from traditional automation methods, where expanding existing infrastructure is nearly impossible without going through an extensive planning process. We've successfully added new systems and expanded in Boston twice with ease. While the impact of racks on our solutions business varies by project, they play a crucial role in our automation efforts. Ginkgo demonstrates how swiftly new equipment can be integrated into an automation setup, and I'm optimistic about sharing this approach with other customers who wish to use our racks in their labs.

Got it. Super helpful. And then one maybe Mark can chime in here as well. But curious to get your perspective on a decent quarter on both sides of the house, biosecurity as well as the cell engineering piece that the guide sort of intact despite that, is that just you basically derisking the back half of the year? The context of the question is, earlier to speak, one of the largest sort of preclinical CROs talked about a very rapid deterioration in the demand environment on the global larger pharma side of things, and the biotech improvement that's coming through is coming through slower than anticipated as well. So I'm just curious as to what you guys are seeing from those two customer constituencies. As you sort of contemplated the back half, would that really play in the back of your minds as you thought about the guide?

Yes, I'd be happy, Jason, to start on the question. Tejas, I'll start with biosecurity first, just because I think that's a little bit easier than we can get into cell engineering. So in the case of biosecurity, the revenue really popped in the quarter, but you should not interpret that as sort of a new run rate for biosecurity right now. What happened there was we, in effect, had some revenue booked in Q2 that we thought originally would have been scheduled or was originally scheduled for Q1. You had a little bit of, sort of, I would just call it, lumpiness tied to a customer contract in Q2. You sort of want to average Q1 and Q2 on the biosecurity side and not think of Q2 as a run rate. That's why we kept the biosecurity guide intact. On the cell engineering side, first, we are going through a restructuring. We consider that when we brought the guidance down back in May, and we are pleased with the revenue number this quarter, but there's still a lot of work for us to do in terms of both the restructuring as well as the changes we've made in terms of the commercial terms, etc. We just felt it was appropriate to keep it where it was right now.

Got it. And then last one for me. Jason, a bit of a fuzzy question for you, but organizationally, in light of all the headcount cuts, I'm sure there's roles or people in the organization whose roles have evolved, expanded, changed over the last few months and probably more to come in the next few months as well. Where are you in terms of that process? Are you already in steady state? If not, Mark, I mean, to your comments there, can you just help us think through the degree of disruption from that, that you anticipate or have already contemplated in the guide? Thank you.

Yeah, I can speak to that. Yes, we made a lot of those changes right out of the gate. We sort of restructured a little bit, created some business units within the company that are smaller scale than the entire organization to drive a little more P&L accountability and have leaders in charge of those. All those changes were made alongside like during the restructuring process that those leaders could make choices about who would be on their teams and how to do that work. This was really important to us because we felt if we were going to deliver well for our customers while spending less doing it; it wasn't just as simple as keeping things the same and just having fewer people. We were going to have to do it in a better way. Again, I'm along with Mark, I'm pleased with our revenue this quarter and how that process has been going. I think those major changes have occurred. There will be more bits and pieces here. It's not like you're going to get it exactly right on the first shot, but the major changes have been made.

All right. Thanks, guys. Appreciate the color.

Yeah, thanks.

Thanks, Tejas. Next up, we have Matt Sykes at Goldman Sachs. Matt, your line is now open.

Great. Can you hear me?

Yeah, hey Matt.

Hey, thank you for taking the question. Maybe first question for me. Just how do you make sure your commercial capabilities? So I'm talking about like footprint, knowledge-based skill set match with your shift into the tools market, particularly in light of some of the cost reductions you're making?

Yeah. So I may speak to that a little bit. One of the things I like about us entering the tool space is I think we can kind of ramp into it nicely, right? I mentioned this with regard to automation, but I think this is as effectively true when it comes to lab data generation. The scientific team, if you looked on that, I showed that slide where I said, hey, we're selling our customer with the Head of R&D, and we were an outsourced research team delivering them a scientific outcome. The way that was structured at Ginkgo was we had Ginkgo scientists who were basically internal customers of our platform. That interaction, it was a little unfair because they're all under the same roof. We had so many of those teams. We have 100 active R&D projects, all with a leader for each one, all ordering lab work from the foundry at Ginkgo. That's not too different from receiving lab data as a service request from an external scientist. So the nature of how can Ginkgo manage to do our work, the way we're able to scale to so many projects across diverse markets, ag, industrial, biopharma, all with the same infrastructure, I know we were spending too much, but it's still, I think, a quite impressive amount of lab research to be doing all in one place efficiently. We were already operating with a certain tools like model internally. Again, we'll see how it goes, but I'm optimistic that by pointing that externally, there will be some changes, but less than you would think, just the nature of how Ginkgo did our work.

Got it. Thanks for that. And then maybe just kind of conceptualizing the Tools business? And just in the context of the kind of environment we're in, particularly with biopharma customers, it seems like a lot of what you're offering would come out of like an OpEx budget for a customer, libraries, software, AI models, things like that. Then there's the CapEx side, which I can think of rack systems, but I'm curious about others. As you think about sort of maybe in a longer term at a high level, sort of the split between an OpEx purchase and a CapEx purchase from your customers? What do you think the tools business will look like? What would you like it to look like going forward?

This is a great question. Regarding capital expenditures, we mainly focus on rack systems and don't have other initiatives planned. We're not positioning ourselves as specialized equipment developers. At Ginkgo, and similarly at Zymergen, there was a need for flexible automation, which is the unique aspect we can contribute in terms of equipment that would align with CapEx budgets. Much of our sales approach involves our cloud software, which enables users to integrate additional equipment and download new protocols for various functionalities. There's also potential for a software-as-a-service model. Before our acquisition, Zymergen sold systems like this, and in recent years, we have had external customers who pay for a SaaS license for both software and services. This is how we envision the model. Other offerings will primarily be funded through operating expenses. When we provide lab data as a service, we consider it a services business without royalties; payment comes directly when customers use our services. This contrasts with a solutions model that typically has longer timelines for profitability, as we aim to achieve margins directly from service sales.

Got it. Thank you very much.

Thanks, Matt. Next up, we have Steve Mah at TD Cowen. Steve, your line is now open.

Great. Hey, thanks for taking the question. I've got a two-part question on the reduction in force. Can you talk about your confidence in the ability to service your existing customers, new partners, and then also be able to onboard the new cell engineering tools business if it takes off? The second part of the question, can you provide some color on the scope of the RIF? Did it impact all sites equally? Or are there any concentrations to note or are any facilities closed or going to be consolidated? They specifically asked that because of the Syngenta collaboration announced in July. Just wondering if that's being done at Sacramento and if there was any impact to the Sacramento facility.

Okay. I'll address the first question, and then Mark Dmytruk can provide some insights about the facilities. This was our top priority. As a high-touch, white-glove services business, it was crucial for us to protect our existing customer relationships, which is reflected in our revenue. Behind the scenes, everything went smoothly, and our customer programs were not significantly affected during the restructuring process. We did change some operational structures, affecting different groups variably. Those directly involved with customer programs experienced less impact compared to those in indirect roles. This was done to ensure we could maintain consistent delivery for current projects and continue selling new solutions. The commercial team responsible for these product areas remains active, and they've been successful in adding to our business, even in a challenging last quarter. Regarding the expansion of tools, we will pursue selective growth opportunities, encouraging teams to function like startups within Ginkgo. If they show successful traction, particularly with purchases related to racks, AI, and Lab Data as a Service, we will scale up our resources accordingly. We've seen this approach work before, such as with the K-12 COVID testing, which rapidly grew into a significant revenue stream. If we encounter similar demand in any of these tool areas, I believe we can scale effectively. We are proactive in that regard.

I will address the second part of your question. First, West Sacramento has not been affected by site rationalization and continues to support our agriculture business effectively. We have developed strong capabilities there. While there is some excess space that we might consider subleasing opportunistically, the site remains intact. Regarding the recent reductions in force, they were fairly widespread and influenced various departments across the company, including operational, foundry, and administrative functions, as well as different locations. It was a broad-based decision rather than concentrated on a specific site.

Okay, thanks for the color.

I would like to emphasize that we are looking for ways to exit spaces that will lower our overhead costs, including AHS and facility expenses, which have already resulted in significant savings. Once we vacate these spaces, our aim is to sublease them. We are also open to strategic opportunities; for instance, if someone is interested in renting the entire Biofab1, we would consider that. We are focused on finding ways to quickly generate cash to offset our cash burn and ensure we have the stability needed to grow our business. You will see us being opportunistic in identifying subleasing opportunities in the real estate market.

Okay, thanks for the color.

Yeah.

Thanks, Steve. Next up, we have Mike Ryskin at Bank of America. Mike, your line is now open.

Hey, can you guys hear me?

Yeah.

Okay, awesome. Yeah. A couple of quick follow-up questions. First, on the LDaaS workflow, some of the things you highlighted. Obviously, you guys have a lot of capabilities here to sort of take it down, break it down component by component. There are a lot of more traditional pharma that could probably benefit from improved automation, incorporating AI, and just the sort of high-throughput screening for some of these programs. But you're not the only ones doing this, right? Now there are a number of companies that have come up over the last five years that are doing some sort of high-throughput functional genomics, antibody development. If you really look at each of these capabilities, this is a technology that already exists. I'm just wondering, you have the capabilities here, but you're sort of coming out a little bit later than some of the existing players. How do you view catching up in that space in what I think actually is a relatively competitive space when it comes to some of these new large multimodal data sets?

Yeah. I think it depends, and it kind of feathers out pretty quick, right? You do see a couple of players who sort of invested early in the AI space, built up lab infrastructure like Oregon or something, or I do think they have really proprietary good tech. But again, are largely using it for their own pipeline and not really trying to do like a broad-based services thing. Maybe they change that in the future, but I think that's the gist. Among traditional CROs that are out there, like take antibody developability, okay? I think on the antibody binding side, you have a lot of players. But when it comes to developability, those assays are kind of tough to do at high throughput. Honestly, with the customers that are coming to us, they are coming to us because they can't get hundreds or thousands of data points in developability, so I don't actually think we're behind there.

Okay. Yeah. I don't want to name any names, but I think we all know who the existing players are. So we're talking about the same ones. And then on the H5N1, that was an interesting overview and something we've been following for a couple of other different reasons. But just curious, that proposal, the genomic analysis program and all of that makes sense. Any timelines we should look to? Is this just sort of like how do you get this from theoretical to actually implemented?

Yeah. So where we are today is we're currently entering into agreements with dairy farms for sort of H5N1 testing under that pilot. I think if those go well and we start to be able to show data coming out of that, then I think that's what opens the door to ultimately tapping either government or private sector money there. We'll have to just see how the kind of data gen goes in the coming months.

Awesome. All right. Thank you.

Yeah.

Thanks, Mike. Next up, we have Mark Massaro at BTIG. Mark, your line is now open.

Great. Can you hear me okay?

Yeah, hey, Mark.

Great. Hey guys. So my first question is on the lab data as a service transition. How should we think about the potential or expected value or maybe the economics of these types of deals relative to some of the existing cell programs? You have 140 active cell programs. What I'm trying to determine is like is one of these LDAS projects comparably similar to your existing cell engineering program? Just any intel on expected value or economics, I think, would be helpful.

They are significantly smaller in size compared to a typical end-to-end cell engineering program and have a much shorter duration. The revenue from these projects would also come in much faster. However, the concept is that a customer is not merely purchasing one LDAS project from Ginkgo. After acquiring it and obtaining data, they are likely to buy additional data, leading to an aggregate amount purchased that could resemble a larger cell engineering program. Jason, would you like to add anything?

I believe that summarizes it. It’s still a bit early for us to know for sure. The main point is that these projects have quicker closes, involve less total money, and are completed in a shorter timeframe. Each one shouldn’t be viewed similarly to our cell engineering solutions as they are still in the early stages. I am actually optimistic that we will secure many more of these LDAS than we would with cell engineering solutions deals. It’s essential for this business model to function that way. They won’t be directly comparable. The solutions deals are valuable, but they do require a lengthy closing process, almost like forming research partnerships. In contrast, LDaaS is more transactional. As I mentioned earlier, we are likely to connect through procurement when working with larger companies. This will be more like a straightforward purchase.

Okay. Great. My other question is about recognizing that any time there is a reduction in force, it’s a challenging situation. I fully understand how difficult that can be. I believe I heard you mention that 300 individuals were notified or let go by the end of Q2. I understand there might be another 150 who could be notified by mid-2025. How are you communicating this internally to ensure that the team remains motivated and continues to perform well for Ginkgo?

Yeah. And just for clarity, the additional 150 have already been notified. It's just that folks have periods of time where there's a program that's concluding or things like that; they're there for a period. The biggest thing we want to try to do as much of that at once as we could. I think people believe in the mission at Ginkgo; I think we're trying to do a hard thing. There’s a lot of motivation like a pool of motivation. Ultimately, I think what will drive people is to see continued success, right? As we see ourselves building success in Lab Data as a Service, building successful robotics as we are actually delivering on our cell engineering solutions business, getting closer and closer to profitability. Those are the things that are going to drive kind of momentum and good cultural energy at Ginkgo. It's that simple; the only way out is through that for sure.

Understood. Thank you for the clarification.

Yeah.

Thanks, Mark. Our last set of questions will come from Matt Larew at William Blair. Matt, your line is now open.

Hey, good morning. Can you hear me?

Hey, Matt.

Hello, okay. So you're now a quarter since you've announced sort of the plan to achieve adjusted EBITDA breakeven here by the end of 2026. You've talked certainly about the RIF; that's going to be a piece of it. But also the composition of revenue that you're expecting over time is different than obviously, when you initially went public in terms of now contemplating tools. Obviously, LDAS, I think, being a newer opportunity. How are you thinking about the sort of what the revenue mix will look like at that level? Maybe to that point, are you expecting LDaaS at scale and your tools entry to be things that can be margin accretive to perhaps the legacy cell engineering business at scale?

Yes. Very good questions. I think we will see on the tool side. Like I said earlier, during the second half of this year, we're going to be turning over a bunch of cards there to know how well that's going, right? If we see pull, you’ll see us march behind that. I would expect to have that obviously be a big contributor. We don't want to have that be a requirement for us to get to profitability, right? If those weren't hitting at all, then I think you'd make different decisions about what you're investing in, how much you're investing in those things. Ultimately, the solutions business, we believe we could scale up ultimately to a profitable Ginkgo on that. It's just a matter of cutting and rightsizing and making sure we do it right. The tool stuff is neat, right? It's a proven platform; we have been used for hundreds of projects, frankly. I'm pretty excited to get that democratized and out in more people's hands, right? I'm optimistic we'll see that, but we don't want to have to have that work.

Yeah, you mentioned wanting to see how the update looks there. Just in terms of

Another thing I think Ginkgo has been good at over the years, and restructuring is another example of this, is that our mission stays fixed; we want to make biology easier to engineer. That is the goal and the mission of my life, as well as for many of the early and long-time committed folks at Ginkgo. How we approach this depends on what we learn in the market and on the technology side, both of which are continually changing. We are still in an excellent position in the market to pursue that. We have a strong cash position and remarkable technical talent; over the last couple of years, we have integrated much of the core technology in the industry, so I believe we have the best technology base. If you believe that we can fundamentally change how genetic engineering is done, I think Ginkgo is still the right choice. We will remain adaptable to find the best way to achieve that.

Good transition to my next question, which focuses on emphasizing scale and the format of programs. Reflecting on a few years ago, one of the initial barriers was educating customers on both the technical offerings and the legal structure, along with potential downstream value. As you look ahead, you've introduced programs this quarter with DBS and plan to continue this trend, but with a focus on scale. How do you choose your battles with customers, balancing what is crucial to you against simply expanding scale and establishing new customer relationships?

Yes, I have a few comments on that. As I mentioned in the last call, there are two main points of friction with customers. One is the reuse of intellectual property related to the work we are doing for them, and Ginkgo retains some rights to that. The second area of concern involves royalties and milestones based on the commercial success of the product. From our discussions with customers, the issue of intellectual property has been significant. In complex projects where we invest considerable scientific effort and take technical risks, the downstream value-sharing aspect tends to matter less because customers are also focused on the research's success. Biotech research can be unpredictable, and when it does succeed, customers are generally happy to share the rewards. I believe including downstream value sharing is the right pricing strategy for these solutions. We have secured more deals incorporating this approach, and we intend to continue pursuing it. While we do report on the overall amount of potential milestone payments, which is exciting, it can take time to realize in biotech. However, the potential for upside is advantageous for our solutions business, and we should persist in capturing these opportunities. The real challenge is not about value-sharing but rather the willingness of companies to outsource research projects, as many prefer to handle them in-house. This presents us with a significant opportunity. We don't need to re-educate the market; instead, we can focus on biotech customers who appreciate big data. We provide them with robust tools because Ginkgo has been invested in big data generation for ten years. We have developed impressive tools for our scientists in DNA construct design, enabling us to build and analyze large quantities of data for genetic engineering. If a company does not see the value in data generation, they can certainly find their solutions elsewhere. My focus will be on engaging those who believe in big data but currently lack adequate tools for large-scale data generation. I aim to dominate that niche and grow alongside it.

Okay, thanks.

Matt, I was just going to chime in on your points about balance and scale. The other thing I would add is that we do have more focus on the upfront sort of cash economics on a deal just relatively speaking. Obviously, you can grow here by toggling that, and we did that in the past, but we got misaligned with our cost structure and revenue. You will see us thinking a lot about the nearer-term cash kind of service fee economics before entering a new contract with a customer.

Understood. Thanks very much.

Thanks, Matt. I'm not seeing any other questions in the queue, so I'll hand it over to Jason for some closing thoughts.

Yes. Again, I'll say again, it was tough work for Ginkgo. I want to thank our departed employees that we had to let go as part of the RIF. I think we are doing the right things to create a solid base here at Ginkgo and build on it. We appreciate all the support of those listening in on our mission to make biology easier to engineer. Thanks again.