Good morning. Welcome to the 43rd Annual J.P. Morgan Healthcare Conference. My name is Michelle Yap. I'm an associate in the J.P. Morgan Healthcare Investment Banking team. It is my great pleasure to introduce our next presenting company, Ginkgo Bioworks. With that, please welcome co-founder and CEO Jason Kelly.
Thanks, Michelle. Everybody, welcome to the last day of the J.P. Morgan Conference. You've almost made it. I'm Jason Kelly. I'm the co-founder and CEO here at Ginkgo. I just want to start by saying I'll be making forward-looking statements. I encourage you to read this before my talk and think about it. OK, so Ginkgo's mission is to make biology easier to engineer. There's been a lot of change at the company in 2024. This doesn't change. So this is why we exist as an organization. I think there's lots of ways to get at this, lots of different business models and technologies that can serve this mission. So I'll tell you a little bit today about what we've changed in the last year as we pursue this. Three big topics for today. First, I want to talk about how Ginkgo continues to grow our business in biopharma.
So this is the fastest area of business for us to grow in. In general, in this market that's really tightened up on biotech, we're seeing a lot less business in the industrial and ag sectors. So this is really important for Ginkgo. Second, we did a big restructuring in 2024. So I want to talk about the progress we've made there, and then finally, alongside that sort of tactical changes in the business, we've also changed our strategy a little bit, and in particular, expanding into direct sales of our platform via life sciences tools and services channels, so I'll talk about that. All right, so first, pharma, so I'm really excited about this. We had an amazing year expanding and adding new logos to our pharma partnerships. To give you a sense, we have about 130 ongoing R&D partnerships at Ginkgo, mostly with mid to large biotech companies.
So about 90 of those are outside of pharma, so companies like Bayer in agriculture, Sumitomo Chemical, and so on in the industrial space. But now more than 40 in the biopharma sector, including Merck, Novo Nordisk. You can see Marcus Schindler, the head of R&D, CSO for Novo Nordisk, on stage at Ginkgo's event in Boston last year, Ginkgo Ferment. Biogen, Pfizer, Boehringer Ingelheim, and also places like the Gates Foundation, all customers now for us on the biopharma side. To give you an example of some progress in one of those partnerships, just in November, we announced meeting one of the middle technical milestones for our relationship with Merck. We have two big projects with them. This is from one of them. We hit that technical goal. We got a $9 million cash milestone payment.
With that complete, we get to actually expand into stage two of this partnership. So this is pretty typical for these types of, we call these solutions deals, where we have a multi-year partnership with a large biopharma. We're meant to deliver technical results. If we hit them, we get paid additional sort of success fees and things like that along the way. And then in the future, God willing, if the products go to market, we get royalties and milestones and things like that, also on the commercialization. OK, but this is really a research milestone along the way. Novo Nordisk, just another thing I wanted to highlight. We started working with Novo back in June 2022 to work on improving expression systems for some of their pharmaceutical products. We've now expanded that relationship four times since.
And so, one of the things to keep in mind with these R&D partnerships, they're kind of like co-development. They're really close relationship. It's a lot of trust building. And so as we grow, as we grow in that relationship with a biopharma customer, it is actually, I think, easier for us to keep adding if we show them results along the way. So I'm really happy to see things like that Merck milestone. That means it's easier to add more deals with them in the future. It's really a trusted relationship. And you see that in some of the comments. I won't belabor this, but great quotes from our customers publicly about the work we're delivering for them at the very high end of R&D partnerships. OK, I want to now talk for a few slides about our restructuring in 2024. This was really my main focus last year.
And so the big goal here was quite simply to get down our OPEX, our cash OPEX. So we started the year in quarter one with an annualized about $500 million of cash operating expenses. Our goal was to get that down $100 million in 2024, an additional $100 million in 2025. We actually are ahead of that. This is a slide from my Q3 earnings call. We are ahead of that $100 million goal, down to $375 million cash OPEX in Q3, annualized. The way we're doing that, we did about a 30%-35% layoff. So that big change mid-year for the team. And then alongside that, we've also consolidated our laboratory space. So we closed two sites in Europe. We closed our site in New York City, our offices in Cambridge.
We've basically consolidated down to our large laboratory facility in the Boston area to do the majority of our lab work. We have about 200,000 sq ft of space there. We also have sites in Emeryville that are specialized for building robotics, which I'll talk about in a minute, and our site up in Davis, where we do our agricultural work for customers like Bayer. We have greenhouses and things like that. Those sites we'll keep, along with the big site in Boston. I do have a bunch of extra space in Boston. I mentioned this on my last earnings call for the people tuning in online. If you're interested in 60% lab, 40% office, beautiful lab space in the Seaport and Cambridge, I am happy to be your landlord. Give me a call.
OK, we're also, I think the team's done an absolutely amazing job. So when we announced that we were going to be doing the RIF, we updated our guide for the year on revenue. I was very happy at the November 12 earnings call to say that we were reiterating that. So you can see on our services side in cell engineering that $120 million-$140 million in revenue. Additionally, we had a one-time, just so you know, non-cash revenue release of $45 million. So we did update our revenue target for that, but that's non-cash. We did want to highlight that. And then finally, biosecurity, $50 million plus as well. So that level of change in the team, while still being able to hit the targets we said we would, I felt very good about that in November and still do.
OK, all right, so I want to talk last about sort of how we're now making strategic changes alongside those tactical changes. All right, so the point of the tactical changes is real simple. I want to get my cash operating expense down. I want to get my cash revenue up. Oh, I should mention we ended Q3 with $616 million in cash and cash equivalents in the bank and no bank debt. OK, so I actually have a very strong balance sheet, particularly in this biotech market, so long as I don't spend it off too fast. And so that was the real big tactical change we made in 2024.
It was just to squeeze that down so we get our cash burn to where I want it to be in 2025, and we don't go through this money, and I don't have to fundraise if I don't want to. And I think I feel very, very good about our ability to do that and get ourselves in a position where we're always raising money from a position of strength so that we're not unnecessarily diluting ourselves as shareholders, as employees of the company, or our investors. So that really was the focus on the OpEx. But it was a good opportunity for looking at the strategic direction of Ginkgo as well. And we did make a pretty big change there that I think I would love for folks to understand this better, because I think you're going to see a lot of progress on this this year for Ginkgo.
So the way we've sold our platform, and just so you understand what's in the Ginkgo platform, we make lab automation directly. We design and build hardware. We build custom software, custom LIMS, custom data informatics systems that are handling the huge amount of data that's coming out of our labs. We have custom intellectual property around biological code base. We have scientific workflows we've developed over the last decade. That big stack of things I'll call the Ginkgo platform. We sold it via the business model of those 130 R&D partnerships I mentioned at the beginning, where I basically try to get to know the head of R&D or someone senior at a biopharma or bio-ag company. I say, listen, this technology is amazing. You can't get it anywhere else. We should help you co-develop your products. They say, great.
Here's my business development team. Talk to them about a partnership. And thus begins a six-month to 18-month dance where we meet people here at JPMorgan and so on and work out IP sharing rights, royalty rights, milestones, the types of things you would see in a partnership between, say, like a CRISPR Therapeutics and a Vertex or something, like a small biotech working with a large pharma, those types of deals. That's roughly like what those 130 deals look like for us. It takes a lot of time. And so the thing I'm most excited about is we're now taking that same platform and selling it in a new way, additionally, through traditional tools and services. So think more like a CRO or like an equipment vendor where you sell directly to the scientists at your customer, not to their boss's boss's boss. OK, does that make sense?
Just to help you get in your head, because I've spent a lot of time thinking about this, and I think for folks in the room that are life science tools folks, I think this is at least my way of thinking about it. That big dotted line in the middle, on the left-hand side of that, are offerings where you get a piece of the value of the customer's product. Then to the right, you are basically selling to the customer's research budget. In other words, you don't get any of the value of your drug or anything like that. You're just being paid fees or capital equipment purchases. All right. The y-axis is the technical risk and the customization level that you are doing for that customer. In other words, are you taking on the technical risk as the vendor?
And how customized is it to what the customer wants? So the extreme form of this is a small biotech developing an asset, hoping to license it to Vertex. That is extremely custom, and they're taking a huge amount of risk. They're spending all the money themselves. And if it doesn't work out, they get nothing. All right, but if you come down a little, you get my solutions business, those research solutions, where a company like us or a company like, for example, Adimab in the antibody space, they share the risk with the customer. The customer pays some fees, but then the service provider ends up getting royalties and milestones on the back end. Does that make sense? All right, so that's what we've got. I like it. But over here on the right, there's three new products we're launching and have launched this year.
The first, Datapoints, is our CRO service, effectively, where we generate large data assets for customers, typically building AI models. So I'll talk about that in a second. The second is we are building our own AI models. And I'm not going to spend too much time on this today, but we have them up on our website via an API where you can pay for them with a credit card, effectively. And then finally, we're starting to sell that in-house automation we built directly with an equipment vendor service model. And so I'll talk about Datapoints and automation now in a little more detail. But again, just to remind you, still doing solutions. These are larger deals. They have tons of upside with the drugs. We've had billions of dollars in bio-bucks and milestones specified through those projects.
They're great because they get to that head of R&D, but they are slow. Still going to do them, but adding in now near-term fees, faster sales cycle tools and services sales of that same platform at Ginkgo. What's great is I was able to launch Datapoints and automation in a matter of months because we didn't have to build anything new. We were just selling the platform we already had in a new way. OK, so let me tell you about Datapoints. This one I am very, very excited about. I had about 40 conversations at this JPMorgan about this particular product. OK, so the basic idea here is to generate large data assets for customers. They own the IP. There's no royalties. There's no milestones. They just pay us a fee. We leverage our automation to generate that data.
OK, so why are people asking for this? So I think you saw real leadership over the last three or four years from folks like Aviv Regev at Genentech, the team at Recursion, basically showing that you could generate big data assets and train models that would be relevant in the area of drug discovery. And so now lots of people have paid attention to that. And they're asking, OK, what does that mean? How do I internalize that? And you probably, if you've been at this meeting, been hearing about foundation models and world models and virtual cell models and all these sort of AI terms in the biotech space. So here's how I get it in my head, all right, because I do spend a lot of time talking with these sort of R&D leaders in the industry.
So on the left-hand side here, I want to just talk about how we discover drugs today and how we advance the science of biotechnology and sort of like disease biology. And the basic way you do it is a scientist reads a bunch of papers, gets a sense of sort of the limits of knowledge, say, in a therapeutic area. And they say, OK, I have a hypothesis that I want to test to ask a question to push this field forward. And then they say, great, to ask and answer that hypothesis, this is the precise experiment that I want to do. I want these reagents. I want this exact piece of equipment. And I'm going to do it myself because I don't trust anybody else. And I'm going to do it on a lab bench.
There's a whole tools and services ecosystem that supports the ability to ask and answer that hypothesis, collect the data back. The scientist then gets to interpret it, think about it, and do that process again. That's why, for example, like the Thermo or NEB catalog is this thick, all right, so that those scientists have the ability to go get whatever exact tool they need for the job. It's why when you look around a lab, there's 400 pieces of equipment from 250 different equipment vendors. It's because they want that very specific assay to ask and answer their hypothesis. The first point is, I don't think that's going away. I actually think that works kind of great. It's amazing the breakthroughs we've made in understanding biology. It's amazing the drugs that we develop with that system.
So, for all the scientists that are hearing about AI, I don't think it's going to just swallow and replace the bench or replace them. However, I do think you have to look at, in particular, I think what we saw with, for example, AlphaFold from Google DeepMind now four years ago, where people have been approaching it with that kind of classic approach on the left in the area of protein structure design. And there was just this breakthrough where it turned out if you applied a big enough data set and enough compute and a big enough neural net, it was actually a better way to move the field forward and have scientific discoveries. It was a new way, a new, different way to either understand therapeutic areas better or to develop drug assets. And that's what's got people excited. It's not that it's a replacement.
It's just different, and so I do think, to give you some examples of the differences, you are no longer using that bench to do your one perfect experiment. The point of the lab is to generate as much data as you can per dollar. The lab is really what we would call a data foundry. That is the point. The point is to make a lot of data. Second, the data can't all be different. If you want to train the model, the data needs to be comparable. The PDB is a beautiful thing because it's 200,000 structures that are all basically against the same standard. That's why that works so well.
So as you're thinking about building up big data assets at your biopharma company or biotech company, the protocol you choose to generate all that data needs to be pretty similar and standardized against the same controls if you want to use it for a large training set for a model. So not infinite variability, much more specified. And then finally, the amount of data you generate means it doesn't land in an ELN, an electronic lab notebook that can handle small amounts of data. It means it's got to go through sort of modern data informatics pipelines, because you're just moving literally terabytes of data. And so that is a different game on the right when it comes to data, when it comes to the fact that it's not being done by hand. It's being done with automation to generate those large data sets.
And it's being done in a standard way. Does that make sense? OK, all right, so let me tell you about what that means for us. So first two products we've launched in DataPoints, this is where we say, all right, maybe you'll someday build your own lab. We'll talk to that in a minute. But right now, I've already got a lab that can run the right-hand side here that can generate large data sets in a standard way for you. So what might you use it for? So the first offering we have is what we call functional genomics. So this is used in the area of target discovery. So you're a pharma company. You want to know more about a certain cell type that's relevant to your disease.
We've worked in more than 100 human cell types at Ginkgo now: lung, cervical, kidney, skin, skeletal, aorta, so on and so forth, all these different cell types. And you say, all right, I've got that cell type. And I now want to perturb it so that I can better understand the biology. I'm going to give you two ways you can perturb it. You can perturb it with a chemical compound library. So you could send us 30,000 chemical compounds. And we'll grow those cells of interest to you on plates. We'll hit them with the compounds. Or you could tell us, I want to make these 5,000 CRISPR knockouts. And we'll go design the guides. We'll get the CRISPR in. We'll do the knockout. And we'll make those 5,000 cell lines with the CRISPR perturbations.
Now that you've got those plates with the perturbed cells, we're going to run assay outputs on that, either transcriptomics so we can do DRUG-seq , so a high-content transcriptomic output, or we can do imaging. We can do cell painting or bright field imaging, high-content imaging on that. And what's really cool is if you're an ML person, people are really liking this. We've released now two data sets. And the GDP 2 one is actually really excellent. It's about a terabyte of data. It's four different cell types: human melanocyte cells, aortic smooth muscle cells, dermal fibroblasts, and skeletal muscle myoblasts. We hit them with 85 different compounds, six concentrations, so you get sort of a dose look there and four replicates. And then we do two million reads of sequencing depth on transcriptomics. And we give you that data dump. And it's all ML ready.
Your AI scientist can play around with it, and you can get a sense for what it would be like to generate a similar data set for your exact cell type of interest, and we're happy to onboard your cells. We've had nearly basically 100% success rate bringing on customer cell types, so we're happy to do that, so QR codes right here. Please download away. People really like this. The other thing, in addition to target discovery that we're seeing people build models for, is to develop their drug assets, so asset development, asset optimization, drug optimization, and the big area, at least the first one that we're pushing, is in antibody developability, and so the idea here is you could send Ginkgo, say, 1,000 sequences, 5,000 sequences of antibodies that have been designed by, say, your model or that you think are interesting to explore.
We synthesize the DNA. We express the antibody, and then we run a battery of 10 different sort of industrially relevant developability assays on this: titer, target binding, aggregation, heparin binding, thermostability, hydrophobicity, colloidal stability. These are very difficult assays to run at high throughput, and so this is where the Ginkgo sort of secret sauce of being able to integrate complex biochemistry onto liquid handling automation and so on and bring these costs down allow us to do this at huge scale. This is something you can get at small scale from a place like Genentech at sort of great expense. But to be able to get large amounts of data in this area, I think we're the best product on the market by far.
So this is something that, again, if you want to now have an antibody model that's not just trained to bind, but rather has the data that also helps you have the antibodies be designed to be developable, in other words, to be ultimately good drug candidates on the other end. This is the kind of data we think would feed that sort of model very well. And I think I didn't mention it, but we have, I think, a top 25 biopharma company on this one, a couple of TechB io companies, and top 10 biopharmas already signed up for the transcriptomic one as well. And I want to just give two examples of projects so you have a sense of, at least for the transcriptomics sort of timelines. So we have example one, data on an oncology target ID, 2,000 chemical perturbations, five different cancer cell lines.
You get a DRUG-seq readout, and then you can get that in about 10 to 11 weeks. If you're example two, data for an Alzheimer's target. In this case, we're doing CRISPR knockouts times 1,000 genes, three cell types, and then giving you back that DRUG-seq and also high-content imaging data back. That's 9 to 10 weeks, so again, this is something we're running all the time. The antibody stuff, I think, is even faster, and so we're very happy to sign customers up for this, and just from a sales cycle business standpoint, we've gotten proof of concept deals done here in like 30 days or less, which back for our more traditional solutions business that we've been running before and are still selling, I will highlight, is just unheard of, so it's really exciting for us to be able to bring online these deals so fast.
The other thing we've been adding recently, because we're seeing a lot of requests for folks building models on the RNA side, is stability, translation, protein expression, immune response for your RNA constructs, and so we can do 20,000 designs in an assay here over a three-month period as an example on the stability, and so if you're building RNA models, please do call us. I think, don't bother building out a lab. We're very happy to do this. Like I said, it's fee-for-service. And if you're a tech person, this is a business model very similar to a company like Scale AI in the tech AI industry, so this is like who OpenAI paid to create data for their models, fee-for-service basis, just generate a lot of data, so that's as much as I want to say about DataPoints, but really excited about that.
I think it's a great direction for Ginkgo. It's a proven business model in the tool space. But we're offering a differentiated product there. We're not just trying to offer a CRO service that someone else offers. Ginkgo Automation. So I'm very excited about this one as well. So we acquired a company called Zymergen now, I think, about three years ago. And the reason, well, not the only reason, but probably the biggest reason we acquired them was for this automation technology that they had been developing now over an eight-year period. And so I think there's probably, I don't know, call it close to $500 million invested into this equipment and software stack here. And the idea is to create modular integrated robotics in the lab, lab automation.
This is the kind of units that you need to understand if you want to understand what we're doing with our system. This is a reconfigurable automation cart. We call it a RAC. These are going to launch. There's like the Society for Laboratory Automation meeting in San Diego at the end of this month. It'll be kind of officially launched. This is the newest generation of these that we just did in this last year. That cart has a single piece of equipment, that orange centrifuge, for example, in this picture, a robotic arm, and a piece of MagneMotion track. What MagneMotion track is, is basically like a maglev track that allows you to move plates along a rail.
And what's cool about this is I can take that one cart and one piece of equipment and connect it to many other carts that also have pieces of equipment in them just through standard connection interfaces. Think like Legos a little bit. And this has a few advantages. For starters, it means if you change your mind about the piece of equipment you want in your integrated automation setup, you just pull out a cart and pop in a new cart. That's sort of unheard of, again, in the world of if you've ever been familiar with work cell automation systems. It's like an arm in the middle of like 10 pieces of equipment. And it's reaching everywhere. And it bangs into stuff. And you have to really be careful. It's very finicky.
Your ability to change what's in there, to take out a piece of equipment and swap a new one out, is a several-month automation engineering and software project. Whereas here, you can make that change in a few hours. And in fact, we sort of proved that last night. So Recursion had a pretty fun event last night at the San Francisco Mint. And so you can see people having cocktails next to our five-rack automation setup that we put together during that day, so over a period of a few hours during the day. And that was operating. Plates were moving around. The arms were picking them up. They were putting them on the equipment. Being able to stand up an integrated automation system in a few hours, again, if you're not in the industry, that's a real breakthrough.
And so, this system—what I really want to achieve with it—is I want to, instead of having an automation work cell, which is an integrated automation system designed to do one thing, say a diagnostic assay or a high-throughput screen, invest in an automation core. So this is like a big room full of lots of these racks. And it's flexible. Your scientists are ultimately submitting many different automation protocols all to the same infrastructure. And that's in part because of the hardware. We can stick together new equipment as you need it in your core in the future. It's future-proof. But it's also because of the software.
So we have cloud-based software where we have an amazing scheduler where that system you saw in Boston, where I had those 25-ish racks all put together, we've had up to 12 workflows submitted by our scientists at the same time run on that system. And so they can be interleaved. So when you submit your, hey, I want to run this thing, and you add your plates to the carousel, the software checks could it fit in with the other things that are running. And if it can, then you can add your workflow to the system and run it at the same time. So this helps you amortize the cost of a large integrated automation system across many workflows instead of just one, as is done with traditional lab automation. And I was also at that Recursion event last night.
Tony Kim from BlackRock, he's a tech investor at BlackRock, was in the panel, and he's like, "I really would love to see horizontal platforms in biotech. You guys just like to make therapeutics, but really, in the tech industry, all the money has been in the horizontal platforms." And I was like, "OK, well, I could talk about business models for horizontal platforms and biotech for about five hours, but the point of this system, what we're trying to achieve with it, is to establish a horizontal platform for the physical infrastructure associated with laboratory automation in biotech." In other words, this system should work for everything because the rack is a standard object. They're all the same. What's inside it can be all kinds of different equipment, and then all that equipment, we connect to our software.
So you have one software that lets you basically push samples among any piece of equipment in your lab in a high-throughput manner by betting on our infrastructure as a horizontal standard. And that's what we're trying to make happen. I think we're the first ones to really do that in a serious way in laboratory automation. So I'm really, really, really excited about this. And we're excited to launch it later this month at SLAS. I'm going to end on this. I will say biotech is a little beat up at the moment. There's a lot of sadness about biotechnology. Meanwhile, out in the wider world, I would say everyone's getting pretty excited about landing rockets and self-driving cars and defense tech. I mean, I think even crypto has attention again. It's an absurdity.
I do want to remind us in the biotech space that biotechnology is very captivating to the general public. It is as captivating a technology as space or autonomous driving or cryptocurrency. Just to highlight a few things, for example, there's a company called Colossal Biosciences that yesterday raised $200 million at a $10.2 billion valuation. They are trying to, I think, basically the business model is to make Jurassic Park. They're trying to bring back woolly mammoths. I think on this crowd, there'll be a lot of scoffing at that sort of thing. It's a highlight of consumer attention to biotech. That is a captivating story. The whole world would be excited to see a woolly mammoth come back. That's a thing you can do with biotech that you don't get that same captivation, say, robotics.
At the Golden Globes, Nikki Glaser gave the intro. She said, "Good evening. Welcome to the 82nd Golden Globes, Ozempic's biggest night." These GLP-1 drugs, I think the right way to think about them is they're really a consumer product. These are long-term, longevity, health span products. They are not just treating a small set of patients. They're ultimately going to go to hundreds of millions, maybe billions of people. People want that. Customers want that. They're excited to see biotechnology come to them as a consumer product. I think we should lean into that. I will also direct you to this Netflix documentary that came out a few days ago. This fellow, Bryan Johnson, you probably read about him. He's trying to live forever, spending like $2 million measuring all his biomarkers.
But there's like an amazing part of the, and by the way, I'm a friend of Bryan. I like Bryan. But there's an amazing part of this documentary where he goes to Roatán, which is an island off the coast of Honduras, to a company called Minicircle to get a gene therapy for follistatin expression for muscle health, effectively. Not approved in the U.S. That's why you're going to a special economic zone off the coast of Honduras. And it's not just him. There's a lot of people doing this now. So there's an interesting thing where I think with the new administration and just how people are approaching this stuff, we could think about how do you do this sort of thing safely. I think consumers are going to want to adopt biotech as a consumer product. What does it look like to get those types of things approved?
How do we make sure this is happening in the United States, in Cambridge, Massachusetts, or California, not Roatán in an unsafe way? This is really an opportunity, and so I think we're coming up on a year where the general public is going to be hungry and excited about new technology. I think biology is the most captivating of new technologies, so I think it's going to be a good 2025 for all of us, so that's it for me. We should grow the world we want to see, and thank you very much for your time, and I appreciate your questions.
Thank you, Jason. I would like to open up the floor to any other questions that you may have. Also, I would like to remind the audience that there are two options for you to ask a question.
One, by raising your hand, and we'll hand you a microphone. Or second is submitting a question through the portal online. Questions from the audience. OK. There is a question that came in online for you, Jason. What are the typical types of customers for your new data point service? And are you seeing interest from large biopharmas, or tech bio startups, or tech companies?
Yeah. So it's actually been interesting to me. I sort of assumed it would be like out of the gate all the AI modeling companies that you've heard about that are sort of like AI experts building better versions of some of the public models and things like that. But I think a lot of those companies view that they're so good at the modeling that they can make a better version of the model trained on the same public data that everybody else has. So it's actually been less from them. Large biopharma, a lot. Yeah. So most major biopharma and mid-size biopharma companies are thinking about AI. They're trying to understand it for their area of interest.
There's a sense that they're going to need the right kind of data sets because they've gone and looked at their internal data and realized it wasn't done in a standard way. It wasn't really comparable. You can't just throw it all together. Their AI teams have finally come back to them and sort of read them the riot act on that. There's a sense of needing to generate new data sets there. Interestingly, we are also seeing it from the tech companies. There is like a weird, yeah, it'll be really interesting to see. I mean, I think there's like a natural intuition among the tech companies about there's sort of two things driving it. One is, I think, this sense that DNA is a coded language. That's sort of the game they play in.
And then second, I think for a lot of the really AI folks, like the Demises of the world at Google and so on, part of the reason they got into AI was to drive science. And biotech, we're really the big spend on science if you look across, out of all of science. And so if you were going to try to do it, that's where you'd do it. So I think for those two reasons, you're going to see it from the tech companies, too.
Perfect.
With the biopharm, we shouldn't, for the companies in the industry that live here and just do biotech all day, I think they should lean in to make sure that it's not something that just gets grabbed up.
Thank you. Questions from the audience. OK. Perhaps one last question that I have for you. What are you worried about as you launch the automation business? This is Ginkgo's first foray into selling equipment. How do you know it's going to go well?
Yeah, good question. So I think the big thing I want to track is the number of racks that we're getting into the world. Because I think back to my point I made at the end there, my view is this has the opportunity to really be a horizontal platform. I'll give you some cool examples of what you could do with racks that you just can't do with other automation systems. So let's say you have a rack setup, or let's say you have a new protocol that you want to put onto the racks. I can use my setup in Boston as long as I have the, let's say, the eight pieces of equipment you want for your protocol there. I can bio-validate, in other words, get that protocol running in Boston first, show you it works, and then sell you the system.
You'll know it's going to work because it's Legos. You're going to stick the same eight pieces of equipment together on the exact same system and run it at your site. It's going to do the same thing. It's not a custom build for you. And that really does change that dynamic. Another example, let's say you're an academic lab and you have an automation core. And you have your grad students coming up with cool new protocols to run at high throughput. And they invent something and they publish it. Any other user of racks that has the same equipment on their racks setup could download your high throughput protocol from your paper and just start running it the next day. No need to build another system. That type of transferability is only possible if you adopt a common physical hardware standard for doing the automation.
And those types of things would really, I think, fundamentally change how automation is done. So my big driver is just getting rack numbers in the world, get them out there so we start to see some of that standardization.
Great. Any further questions? Sure.
Can you think about some of the AI business?
Yeah. Sure. Yeah. So the question was the AI business. Yeah. So it's pretty interesting. So we have this ability. I'll give you some of the examples of the models we've made. So Ginkgo, over the years, has accumulated a large genetic database. So we acquired Warp Drive Bio, Radiant Genomics, Lodo Therapeutics, Zymergen. Those are all places that had done sequencing of both microbes, AgBiome microbes and metagenomic sequence libraries. So we have a huge, bigger than GenBank of deduplicated, not human, but microbial genes and non-microbial genes. And so we have all that. We use it to mine for interesting enzymes. But one of the things we did was we trained basically a version like ESM, like similar style model, protein foundation model on the public data plus our data. And we released that as AA-2, our first protein foundation model.
And what we're doing is, it's literally up on our website, like OpenAI style. You can interact with it through an API as a software developer. And you pay with tokens. So there's no royalty. There's no milestone. There's no SaaS fee. You just put in a credit card. You could spend $10 and use the model. So that's what we're trying. And we've now, I think, also put some of the public models up, ESMs up there. We have, I think, six or seven other models. We have an RNA model. Now we have either up or up soon a diffusion model for antibodies. So yeah. So people should check it out at ai.ginkgobioworks.com or ai.ginkgobioworks.com.
Any further questions? OK. If there are no further questions, thank you so much, Jason.
Yeah. Thanks, everybody.