Ginkgo Bioworks Holdings, Inc. (DNA) Earnings Call Transcript & Summary

Okay. Thanks, everyone, for joining us this morning, of the last day, Ginkgo Bioworks here and SVP of Corporate Development, Anna Marie Wagner. Thanks so much for joining us.

My pleasure.

Before I let Anna Marie get into the presentation, just 2 quick things. First, for a complete list of disclosures, you can go to williamblair.com. Second, our breakout session is upstairs in the Richardson room. So if we do have time at the end, we can take a couple of questions. But obviously, the formal Q&A will be up in the Richardson's room. So Anna Marie, take it away.

Great. Thanks so much. And thanks for having me. I noticed I have a clicker up here, which I was never told how to use, but I'm going to do my best...

Green button to forward.

Green button. There we go. All right. So great to be here with you all today. One of the things I love about conferences like this is that I get to talk to investors that some of you know biotech, some of you know Ginkgo, but most folks here actually have exposure to a much broader array of industries. And we've borrowed concepts from many different places. And so I wanted to give -- take this opportunity to share a little bit more of Ginkgo's history and some of our philosophy for folks that may be newer to the story and then certainly happy to dive into specifics in Q&A. But our mission is to make biology easier to engineer. I know it can be somewhat trite to start with a mission statement, but I want to give you a little bit of a background on why this is our mission and what that actually means. The first piece of that is just who cares about biology. And I think it can be actually pretty easy to underestimate biology's potential. Most people, when they think about biology, think about the pharmaceutical industry. That has certainly been a major application of biology to date. Certainly, one of the most valuable end markets for biology today. But we would argue that biology is actually much more than that. And we just take that part for granted. Biology makes the wood that makes these tables. Biology makes our food, biology makes us, right? And so by limiting the scope of biology and biotechnology to one market, which is medicine, we are really under -- we're just not taking advantage of its full potential. Ginkgo is focused on enabling biology across many different applications. And in order to do that, we need to treat biology like an engineering discipline, not just like an arch or a science for specific applications. That mindset came from the guy on the top left here, guy named Tom Knight, he's one of our co-founders. That's in 1972 with his master's thesis at MIT, but it's not a biology lab. You might be able to tell. That was a mini computer. He was an electrical engineering professor at MIT. He taught the semiconductor design class. And in the early 1990s, through some combination of getting bored with semiconductors, he at one point told me that all the hard problems were solved in computers in the early '90s, which I'm not sure I believe, but for him at least he was getting bored. And in his mid-40s, he did the equivalent of putting himself through a biology PhD at MIT. And part of his observation was biology is programmable, just like a computer. You can read the code, you can write the code and you have a machine that can run it, you can program it. Biology is A, C, T, and G is sort of 0s and 1s, but it fundamentally runs on code. And so he put himself through a biology PhD. He's one of the best programmers in the world, I should figure out how to program biology. So he brings this band of misfits together. The folks on the top right are our other 4 co-founders. And they're from various different disciplines. So Reshma on the left, she was a biologist. But Barry, next to her, is a mechanical engineer, Jason was a chemical engineer and Austin was a computer scientist. And so he's bringing together all of these different scientific disciplines to try to figure out how to apply the learnings from these engineering disciplines to biology, which candidly, we know nothing about. The explored and understood world of biology is like infinitesimally small. We are very, very, very bad at this. You can have 2 scientists do exactly the same experiment right next to each other on the same day and get different results, and you will have no idea why. So it's incredibly frustrating for an engineer, for a computer scientists because, again, we created computers. We created those programming languages, they run consistently. Biology created us and we don't know how it works. We're trying to figure it out. But it's worthwhile trying to figure that out because of the potential of biology to do amazing things. Biology is the only technology that can both operate at nanoscale and at continental scale. Biology can grow on sunshine and water, biology is free. Like biology is the only solution that is likely to solve some of the biggest challenges we have in the world right now, food scarcity, climate change, human health. And so we have to figure out how to engineer it. There are 2 parts of our business. I'm going to tell you about both of them. The first is cell engineering, which is the sort of byproduct of what Tom's vision was. The second was biosecurity. How do we protect this platform that we just created. And so I'll spend a little bit of time going through both elements of our business and then give you an update on where we are today. Okay. So the question then becomes like how do we engineer biology. There are 2 components to our platform. And one thing that's important to understand is we're not in the business of making products with biology. You're not going to see Ginkgo selling a biological product anytime soon, selling a drug. We're in the business of making it easier for other people to make their products because they're the best experts in the world that their industry, like I don't know how to run a clinical trial, but we do know how to help our customer in the pharmaceutical sector engineer a cell to make their life-saving drug. And that's what we do. So the input to Ginkgo is always a spec from a customer. The customer is saying, this is what I want. I want to be able to make this chemical at this cost in this type of a manufacturing environment. I want to be able to make a protein that catalyzes this chemical reaction. I want to be able to make yeast that helps bread rise faster. They come to us with a spec. It's typically a product and a cost and then we run that program. We designed that program at Ginkgo. You can think about it as riding the code, we're the code writers. And we do that with 2 core assets on our platform. The first is what we call our Foundry. Basically just a wet lab with a lot of robots. And the thesis there is that biology, because it is physical, it benefits from a scale economics. Unlike digital technologies, which are distributed, anyone can do it is largely free, biology is very expensive to run. It costs -- I think Emily Leproust from Twist is here. We buy a lot of DNA from her. It costs multiple cents per base pair of DNA. That's like a bit in a computer. Imagine if you're computer, every 0 to 1 bit flip charged you a few pennies, you would be broke before you could write anything. Our Foundry is all about bringing down the cost per design, per program we try to write. And we do that by investing in automation, by investing in miniaturization, by driving down the variable cost of doing this work. And we do that by -- again, it's building fixed costs. We get to make the fixed cost investments so that our clients don't have to, and our clients can benefit from that marginal cost. Then the second core asset is IP. It's data. So what we call our code base. And it's a combination of the learnings from the foundry, every time we run an experiment in the foundry, we're generating data. Did that design work? Did it do the thing we wanted it to or not? Whether it did or not, it generates useful insight to us that we should be able to leverage over and over again when we're tackling a similar problem in the future. But it also has in there things that do work, right? Cells that we now know are really good at producing proteins or enzymes that we know catalyze a certain type of reaction more efficiently that we can then pull from over and over again when we see similar problems in the future. One of the things we like to say a lot is that biology did not evolve by end market. And so we are often surprised by the fact that we can reuse learnings across very different programs that you wouldn't think have anything to do with each other but happen to share similar biological properties in the process of making that product. And so we bring that to bear with our programs. What we then deliver at the end of the day to the customer is what we call a cell program. It is a cell that meets their spec, a cell that makes the products that they're looking for, a recipe to manufacture and a recipe to extract whatever their end product is. You name it, like we hand them a package so that they can then go off and manufacture, distribute it, commercialize it. This is just an example of a program. This is for one of our customers, Motif and the protein production space they're making, myoglobin, to make meaty and bloody. And so this just gives you a sense of how we approach that. Again, scale is kind of the name of the game for us. And so we are looking across the range of different proteins to find which proteins give them the best properties. We are engineering cells to try to make that protein cheaply. And then we're iterating on that over and over and over again to optimize that as well as the manufacturing of it so that ultimately, we can hand them then at the cell that makes in this case, hemoglobin, which they are now putting into, I think they're called it's MoBeef, MoPork and MoChicken. So if you want to check it out, Motif's now got a few products with that in it. But what's important is that, again, where biology do not evolve by end market, neither did we. And so we apply this technology across a range of different industries and a range of different types of products. So the customer can come to us with many different requests. Make us a protein, make this chemical, make me a cell that does something, right? Biology responds to its environment. Some of our programs are actually engineering living cells that will then respond, whether it's a cell therapy for a human or a microbe that could be applied to a plant to help that plant get fertilized. And so again, it's a common infrastructure that is enabling all of these different applications. Here are a few of my favorite ones. These are some completed programs. Aldevron is a manufacturer of many different pharmaceutical inputs, plasma DNA, enzymes, et cetera. We did a project for them that dramatically increased to the production efficiency of a key enzyme for mRNA vaccines. Motif, I mentioned to you, that's the plant-based meat and a protein in there that helps that meat get made. And even cannabinoids, the CVG that is in those gummies are from a bacterial strain that we engineered for a company in Canada called Cronos. And all 3 of those projects and the products that resulted from them were built from the same platform, the same common infrastructure, which again is somewhat counterintuitive to people who think about biotechnology. This gives you a different view of it, again, peach flavor, coconut flavor, the cannabinoids, the proteins, all of that on a common infrastructure. One thing I'll mention just because it's recent news in our cell programming platform is that one of the things that we've learned over time as we've kind of been investing in this space is that we do need to help the customer translate research from kind of the lab scale, which is what we do to commercial scale. And then in the industrial application, that meant helping them learn how to manufacture, helping them with downstream processing. And so we're very good at that, and that's part of our package. In agriculture, it looks much more like applying a microbe to a plant and seeing how the plant does, it's that translational research from the lab to a greenhouse or to a field. Something we're really excited about is that Bayer has entrusted us with their ag biologicals research going forward. And they are selling to us their R&D facility in this space. And so seeing both, we're very excited to expand our capabilities in agriculture, but also seeing a very large company in this space, one of the real incumbents making the proactive decision to outsource this element of their R&D process is a huge step for the industry. Our biggest competition is a customer doing it themselves, right? Historically, there are tens of billions of dollars being spent on R&D in-house of these companies today. What we have to convince our customers of is that they have a higher probability of success developing their products with us than doing it in-house. That's our sales pitch. And so seeing a customer like Bayer has been working with us now for several years, decided to kind of go all in, in this space of ag biologicals with us is a huge point of pride for us, candidly. Agriculture also matters. So this is the transaction, they're basically 3 transactions rolled into one, but just to give you a sense of this, we're acquiring that facility on the top right there in West Sacramento, which does that translational research from the lab to the field. It has greenhouses, it's got formulation, et cetera, but then renewing our partnership with Bayer to go after a suite of different products in ag biologicals, which is a rapidly growing field as we try to phase out chemistry in agriculture, excuse me, while not sacrificing food production in a world that needs more. All right. So the biosecurity business feel totally unrelated to most people to our cell engineering business, but it's actually quite core. We've been investing in biosecurity for many years with the view that if our mission is to make biology easier to engineer, we also need to be responsible for how people are engineering biology on our platform. We need -- we cannot take the path of the tech industry, which candidly relegated a lot of that responsibility to its consumers saying, we're just the pipes, we're just the platform. We're not responsible for what goes through our pipes. And we're sort of seeing that unravel today as customers -- sorry, as companies are being forced to take more responsibility for the actions of their users in the technology industry. Our view is in biotechnology, it's too powerful. We can't take that mindset. When somebody uses our platform to do something bad, it kills people, it doesn't just give misinformation, right? And so we've always focused on investing and ensuring we have platform security. But we also recognized in that process that protecting our platform wasn't enough. Biology will get easier to engineer in many different ways, whether or not it's built at Ginkgo. We are going to develop better tools. Those tools will get broadly distributed. And so biosecurity also needs to be broadly distributed. If we compare it to cybersecurity, cybersecurity has endpoint detection, right? All of your computers are running virus scans, all of your inboxes are running virus scans all the time. And then there's a rapid response and kind of quarantining process that then happens when something is found. We need the same thing in biosecurity. We need to be able to have surveillance over biology to be able to identify when novel threats emerge and then we need to have a rapid response system that then responds to that and mitigate spread and contamination across more people. And that's what we've been building in our biosecurity business recently. So we're the first kind of generation of Ginkgo's biosecurity efforts were really focused on our own platform. As COVID emerged, we recognized that we had an obligation to step in and help start figuring out what biosecurity might look like in a pandemic, in a modern pandemic. What became clear was that one of the big needs was for schools. It was the one place where a lot of unvaccinated people had to gather together in person. Virtual school sucks. I've got a 5- and a 7-year old, and I can tell you, I don't ever want to go back to that again. And so we created technology that could then be deployed easily within classrooms, basically pooled testing. I don't know if any of your kids had that in their schools, but we basically went nationwide to school districts, providing this pool testing service, which has become a real business over the last few years -- or sorry, a few quarters. The question I always been get is, well, what's next? COVID is over, we're all at this conference, none of us are wearing masks. Shouldn't this business go away? And our view is, well, this is just the beginning of biosecurity. But this doesn't work if we don't deploy this until we have a pandemic in our minds. Like it was way too late to deploy this. This was kind of a -- this is a reactionary business that got created. We need to create this in a proactive manner. We need to be able to start identifying these threats before they become pandemics. And that's really what we're working on now with our biosecurity business focusing on wastewater testing, passive air monitoring, travel, testing, schools, hospitals, you name it. We need to be monitoring the biology that's around us because you should know, before you go out in the world, what's circulating like we know what the weather is, why don't we know if there's a nasty pathogen spreading around our office or around our kids' school. And so I do believe that this is going to be an important part of our platform and our business going forward at Ginkgo, not just for our platform security, but also as a real emerging player in biosecurity in the future. So this is just a snapshot of where we are today. On the top left, you can see diversified programs. These are the active programs we are running in the first quarter. You can see that, that's quite distributed across industries. So again, biology did not evolve by end market, we're applying that to pharma, yes, but also to food agriculture, industrial products, consumer products, you name it, and we're quite diversified and all of those different segments are important growth vectors for us. Biosecurity business, obviously, I showed you some statistics there earlier. But I'll point you to the bottom because this is really our lifeblood internally at Ginkgo and what we focus the most on, which is the continued scaling and growth of the technology platform itself. One of the kind of key features that we drive internally is something called Knight's Law after Tom, who's our -- who is one of our co-founders and that is the scale economics that we are driving in our foundry. I mentioned that scale economic is the real value driver there. On the bottom left here, you see that what we measure, which is the number of strain tests, basically the number of experiments that we run in our Foundry, and this is on a daily basis. And what we've seen is that, that's grown about threefold every year. So it's growing exponentially and associated with that has been a roughly 50% decline in the unit cost. And so that is the #1 focus for Ginkgo on the cell engineering side of our business, is continuing to drive that forward over time. One of the ways that we do that, and this is near and dear to my heart, is through M&A. And this type of a market environment candidly lends itself to finding really interesting technologies that we can integrate on our platform and help drive continued scaling and make those acquisitions much more affordable. So we've acquired a couple of companies in this space thus far this year in addition to our major acquisition of Bayer's facility. And so I'll end with this. We're in a pretty unique position right now. We raised about $1.5 billion last year and are sitting on about $1.5 billion right now. And I'd say what we've seen recently is the market really shift in a way where a couple of years ago, no one knew what synthetic biology was, no one knew who Ginkgo was to a world now where folks are recognizing that biology is probably the only technology that is going to solve some of these really critical challenges. So we're seeing really strong demand coming from that across industries. We're seeing more openness to working with a third party rather than building these capabilities in-house. And so that's driving really strong demand. Our focus then has to be on operations. How do we scale to meet that demand? How do we continue to grow our capacity? How do we manage the complexity that comes with the diversity of programs that we're working on? But believe with our capital position and focus, we're well positioned to do that. We have a deep M&A pipeline as I just mentioned. We've closed a few deals thus far this year and the market continues to be ripe for that. And as I mentioned, our biosecurity business is opening up at much kind of larger, longer-term opportunities that we believe will last well beyond COVID. So that's a quick introduction to Ginkgo. I know that we have a breakout next door somewhere for Q&A, but I think we have 9.5 minutes left. And so I'm very happy to take questions from folks here.

Okay. I'll leave with one and then we can open it. So again, customers ranging from Sumitomo to Bayer to [indiscernible] or across a wide range of spectrum here. But you mentioned at the end, but give us a sense for what these customers were doing before? What their internal capabilities look like? When they think about outsourcing, who else they consider relative to you? And then ultimately, what they believe about Ginkgo and what you can do for them that others cannot do that ultimately leads them to working with you?

Yes, great question. So one piece of our history that I didn't mention today is that we tackled industries over time, effectively in reverse order of technical sophistication. So 7 years ago, when we were first starting to commercialize our platform, we would have been left out of the room at Biogen or Novo Nordisk, 2 of our customers that we've gotten in the last couple of years because who were we, a bunch of grad students from MIT, puttering around with robots, like we didn't know how real biotechnology worked. And so we went into industries that didn't really have biotech expertise, flavors, fragrances, other consumer products. And they were at least like, hey, there's some smart MIT kids who are willing to work on these projects for free. And so we might as well let them tinker around. Over time then through that type of work, we built enough technical credibility to be able to go after more and more sophisticated customers. So all those customers that you just mentioned, they're very sophisticated. They have internal R&D teams. They've got biotech capabilities in-house. And what they're deciding is they're making decisions on particular areas where they want to focus versus where they want to leverage our capabilities. And the key question is how do we maximize the probability of success. You should not underestimate how hard this is. Most projects fail. Biology is hard. Your project will probably fail, you won't have any idea why, like that is still the status quo. And so our customers are not coming to us because they could save a $1 million here or there. Our customers are coming to us because for the same budget, they think they've got a higher probability of getting the products that they're looking for at the other end. And I'd say customers are at different places in the migration. Some customers are giving us their hardest problems. They're like, "All right, we'll see, hey this is hard. But if we can figure it out, it's really valuable. Let's see if these crazy kids at Ginkgo can do that." Some customers are giving us kind of the bread and butter that's going to be the core for the future and they're retaining the secret sauce discovery stuff in-house. And so we have a little bit of a mix, but what's interesting is that we have the opportunity to kind of grow from both sides of that spectrum towards the whole. And then in some cases, like we've seen with Bayer, they're giving it to us soup to nuts. And I think that is what we will start to see across the industry. The parallel that I typically give is in data processing. It used to be that companies all built their own servers. They had IT teams that manage their own servers. They wrote their own software. It was all local. The first step in changing that were companies like Dell saying, "Hey, we're going to host it for you. We'll bring your IT team on staff, you'll get some cost savings. And now no one would think about building their own server, it's all going to run on the cloud with AWS." That is the same migration that we are trying to drive in biotechnology, where today, it seems crazy to work with somebody else for your biotech R&D, that's your core. You've got your own R&D lab, you've got your own scientists. That is the status quo in the industry. And we are slowly working on those partnerships to get our customers more and more comfortable outsourcing that capability. And what happens is the basis of competition changes a bit where as we can enable more and more customers to have advanced R&D capabilities, that no longer becomes the secret special sauce of the incumbent. The incumbent now needs to differentiate on some other metric. And folks have to work with us, we believe, because otherwise, they'll fall behind because we can enable faster innovation, we can increase the probability of success. That's the bet.

You deliver self [indiscernible]. Is the current commercial production or infrastructure exists for that new cell program? Or is there a parallel base cost that needs to lead what you're building.

It's a great question.

Can you share the...

Yes. The question was around manufacturing capacity. Is there effectively another industry that needs to grow alongside us to enable the manufacturing of these products once we deliver them to customers. The answer is absolutely yes. It's not an area that we've felt the need to invest in our own. It's a reasonably commoditized space, but we are definitely seeing specific areas where bottlenecks are emerging in manufacturing capacity, and we're seeing a lot of companies also come in to invest in that space and fill that bottleneck. So it's one that I think, yes, we'll have some fits and starts as different industries grow. I'd say there's a lot of industrial manufacturing capacity right now where we're seeing small bottlenecks is in areas like food, for example, where you need grass certification, like or there's a lot of pharma manufacturing, which needs GMP. But depending on the requirements for the specific products and then the speed with which those categories grow, you do run into kind of point bottlenecks until capacity can either be converted or built.

The downstream revenue, the royalties, when do you start -- when do you think that will start kicking in?

Yes. So maybe just to summarize because I realized I didn't talk about this much during the presentation. So the way we make money is customers pay us upfront for the services that we do. So the process of engineering a cell, think about it like AWS, you're paying per unit of worked on effectively. But that we also negotiate with our customers, some form of downstream value sharing. It could be royalties. It could be milestones, it could be equity and that really depends on the program success and that's where we see the most value. Today, our business, just given the amount of time it takes to develop a project in biology and then commercialize it, today, our revenue is predominantly that first category. It's the services fees. We do have some royalties that are being paid right now for some of our earlier programs like peach and coconut, and things like that, some of the small flavors and fragrances. But we, today, again the vast majority of our revenue is from the services. And then as programs complete and are commercialized, we would expect to see more downstream value flowing through.

So I'll ask another. When I first started meeting with the team a few years ago -- a couple of years ago, a lot of the sales process started with education -- outreach, education, what you can do and then maybe convincing a customer where they might want to do. You referenced a broader understanding of the platform. How have things changed over time in terms of the way you are interacting with customers, outbound or inbound, sales cycle time, maybe how that's changed and how that maybe connects to the longer-term targets you raised out, which is from 60 new programs this year to a couple of hundred relatively soon?

Yes. So we do get some inbound sales now, which is a big -- the first time somebody actually called us. That was a big moment because again, several years ago no one knew what synthetic biology was. It is still predominantly a boots on the ground enterprise sale to get a customer to make the decision that they either want to not invest in their own capabilities or candidly divest or deprioritize their internal capabilities, is typically a very large strategic decision for a customer. And so it not only requires buy-in from the R&D team that is technically capable of evaluating our capabilities, but it also requires buy-in from the CEO, maybe the Board. And so for our large enterprise clients, it is still very much -- we do get inbound interest, but it is still a very involved, very long sales process. And typically, when we sign those deals, it's for multiple programs because of that because it tends to be a much larger strategic decision. We get a lot of inbound interest from younger companies now. In many ways, our value proposition to young companies is much higher because it's not just about driving that kind of variable cost efficiency. It's about forgoing the need to invest in the fixed cost in the first place. A young startup may not even need to build a lab anymore in the same way that companies don't need to build their own servers anymore. Whereas for a large company, it's much more about shifting how the work is getting done. And so we do see a lot of inbound demand from start-up companies that want to build natively on our platform. And what's really valuable to us is that when a company is built natively on Ginkgo, that's about the stickiest software you can ever imagine, right, because to stop working with Ginkgo then require a company to go build its own lab to develop its own IP and they've gotten accustomed to the scale and the cost advantages that we have provided to them. And so it becomes then very hard to think about doing it themselves.

Okay. Thanks, Anna Marie. And we'll take all the rest of the questions upstairs in the breakout, Richardson room.