Hi, everyone. Thanks for coming. I'm from the Life Sciences Tools and Diagnostics team at JP Morgan. Welcome to the 2024 JP Morgan Healthcare Conference. So we're about to do about 20 minutes of questions and 20 minutes of Q&A. And with that, I'd like to welcome CEO Jason Kelly from Ginkgo Bioworks.
Thank you. Sure, why not? Yeah, clap it in. Appreciate that. So I'm Jason Kelly. I'm the Co-founder and CEO at Ginkgo Bioworks. I'm super excited to talk to you today about the progress Ginkgo's made in 2023, especially the work we've been doing in biopharma. I really consider this to be kind of a breakout year for us in that market, so excited to talk about that. And also, I want to spend a little time on why I see Ginkgo as sort of general purpose infrastructure for biopharma R&D in the second half of the talk. Before I get to that, I will be making forward-looking statements. We're a public company. When you have a chance, feel free to read our disclaimer here, and let's jump into it.
Okay, so, Ginkgo's mission is to make biology easier to engineer. I really like this photo here. This is a picture from our facility in Boston. So one of the bays, we have about 300,000 sq ft of highly automated labs, and this particular infrastructure, these robotic carts, we actually build these in-house. So we have, you know, about a 1,200-person company, about a 300-person software and automation team, where we're vertically integrated into hardware manufacturing to support automation. And the reason I give this example is this facility is somewhat akin to, like, an Amazon Web Services data center, right?
So think of us or think of Amazon as investing a large amount of capital into a large data center, and then Amazon Web Services customers accessing it on a service basis, right? That's very much what Ginkgo's trying to do in the area of cell engineering. So designing DNA to engineer cells for different applications, and we're investing, you know, in this case, hundreds of millions of dollars into a facility like this so that our customers can access it as a service. And I think that's particularly important. I know this is a sort of investor-heavy crowd. You know, it's been a tough couple of years in the biotech capital markets, and so there's a lot of pressure and a lot of questions around, you know, when do companies need to raise money?
Do they have enough funding to get to the next milestone? What's the path to profitability? And I want to highlight that there is a pretty substantial distinction between a product-oriented small, mid-cap biotech and a services-oriented one like Ginkgo. And in particular, we have a much smoother path to profitability than a sort of asset-based biotech, where you're waiting to see how is that clinical trial going to pan out? Eventually, I'm going to get to market and get to recurring revenue far into the future. Ginkgo's service fees come in today, and so we'll talk a little bit about how we structure those deals, but I really like our position going into 2024. We're ending 2023 with more than $950 million of cash and cash equivalents in the bank.
We are reiterating our full year 2023 revenue guidance expectations of $250 million-$260 million, $145 million-$150 million of that's coming from cell engineering, which you're going to hear about today, and $110 million from biosecurity. We're expecting to also be within our previously disclosed guidance of 80-85 new cell programs. These are partnerships we have with external partners. We're adding 80-85 new ones last year to the platform.
If you think ahead, and we'll talk more about this at our next earnings call in 2024, a big part of my goal is going to be keep adding new programs to the platform while keeping a lid on our spending and driving more efficiency through that automation, through that sort of data center-like environment we have. And I think we're in a really nice spot to sort of tighten that cash burn at Ginkgo and sit safely within our cash balance here, so that we don't have to raise money if we don't want to. So, that, that's a again, I want to highlight that as a distinction between sort of a services and an asset-based company. That said, 2023 was an absolutely breakthrough year for us in the biopharma space.
Just some highlights, we announced a deal with Pfizer in the area of RNA drug discovery. We do bring in near-term service fees, and I'll show that on the next slide. But our programs also have milestones, kind of similar to what you would see in an asset-based company, where down the road, we have the opportunity, if there is successful technical and clinical results in the hands of our customer, in this case, Pfizer, we could get up to $331 million of milestones. Boehringer Ingelheim, we signed a deal with $406 million in downstream milestones, in this case, for small molecule natural product drug discovery against undruggable targets.
And then with Merck, we signed a deal with $490 million of downstream milestones for optimizing enzymes in service of biologics manufacturing. And then what's really cool is some of our deals that, you know, I spoke, I think, probably, two years ago now and talked about a new deal we had just done with Biogen at the time. Well, just a couple of weeks ago, we announced the successful completion of that collaboration, and this is in the area of AAV manufacturing optimization. And again, we don't do the manufacturing. We're essentially outsourced research services here, okay? And what's great about a successful announcement, this is another distinction between Ginkgo and sort of an asset-based company.
This isn't like, oh, we had an asset and Biogen bought it, they're taking it forward, and it's off, there it goes. There isn't another chance to do it. This is a signal, and if there are biopharma companies in the room, that Ginkgo is good at doing gene therapy, AAV, manufacturing optimization, and they should call us, if they want to use that service. So when we complete these successful programs, it's actually wind in the sails of us doing more business of that type, rather than an end, like you might imagine, with more of an asset-based, business model. And then finally, we announced that we had successful achievement of a first milestone, with our partnership with Novo Nordisk against a opportunity in, manufacturing optimization that we're very excited about.
So, that was great to see. The numbers back this up as well. If you look at the active R&D programs at Ginkgo, we have about 100 of them right now. The percent that are biopharma is now 36%. That's twice what it was two years ago. And our cell engineering revenues from year-over-year have grown 50% in the biopharma space. That's a little bit blocked off there, but it's $48 million, so about a third of our total cell engineering revenue in 2023 coming from biopharma. And that's up from basically nonexistent in 2020. All right, so I do wanna highlight that. And this is a key point I'll again make about Ginkgo as a platform, right?
I'm gonna spend most of our time today talking about biopharma, but Ginkgo also works in the industrial biotech industry. So things like industrial enzymes for laundry detergents and chemical manufacturing. We work in ag biotech, okay? As you know, again, tough capital markets years, I'd say certain sectors have actually gotten impacted even worse than biopharma. So, like, industrial biotech got really squeezed by capital markets. These are people pursuing things like animal-free meats or new applications of biotech. As a broad platform, Ginkgo could pivot our sales effort, our marketing, in the direction of where we still saw biotech research happening, in this case, into biopharma, and that transition happened very nicely.
So I think this is also a thing to keep in the back of your head if you're trying to understand what makes for a company that can move with changing markets and in tough conditions. Sort of a platform and services business model offers us the opportunity to do that. And you can imagine a very similar thing when it came to, for example, modalities, right? So you could imagine a particular modality having a bad clinical result, and, you know, some safety data tanking further investment in that area. If you're an asset-based company in that modality, that's a tough day for you. But as a platform company, we can just move again, our efforts into other modality areas.
This is a nice slide again, for the sort of the biopharma potential customers in the room of Ginkgo. You know, we have now programs in a wide range of different modalities. I mentioned at the top, you can see our discovery programs at the bottom, manufacturing related, but in RNA and therapeutics, talk about Pfizer. Gene therapy, we just announced a partnership with Arbor, more on the discovery side. Obviously, Biogen on manufacturing, small molecule, biologics. And in microbiome, you know, a company like Synlogic is going into phase II, phase III trials with engineered microbes that we helped them develop in the microbiome space. So we're you know, Ginkgo is a 1,200-person company, right? There's no way, again, an asset-based company could be in all these different modalities.
As a service company, the common thread between all these programs is behind all those products is a design piece of DNA, okay? So if you wanna pursue, you know, you're Pfizer, you wanna get a better RNA therapeutic, you are designing nucleic acids, and you wanna test a lot of different ones to see their performance. If you're Boehringer and you're trying to discover a natural product, you wanna go explore microbial genomic collections, synthesize DNA, test it in microbes, express new natural products, but you have to go through thousands or hundreds of thousands of genetic designs to do that project. So the common thread across all these modalities is Ginkgo is able to help our customers essentially program their DNA better than they could themselves. And that last point is very important.
If I'm developing an asset, I can just decide that my technology is good at RNA drug discovery, right? And you have to try, you gotta believe me or not, right? But as a service company, I don't get to decide that. I actually have to get Pfizer to sign a contract and pay me money and say that I'm good at doing RNA drug discovery. So what, what gets me excited about this chart is we've been filling in all these different modalities with customer validation that the technology we have is relevant and additive, to what these companies have in-house. So that's very exciting, and that, I think, behooves us more growth in that industry.
Which is why I was excited to announce, just earlier this week, that we've added, and this is just the inaugural crop of folks, joining us on our biopharma advisory board. But Norbert Bischofberger, longtime technical leader at Gilead, Jeff Legos at Novartis, John Maraganore, founder of Alnylam, Paolo at Moderna, Mark and Christi from Kite. So we have this group who is able to help us make sure we are building technologies that are relevant to what senior R&D leaders are looking for in the biopharma industry. That is really the job of this board, and make sure that Ginkgo keeps investing in things that are breakthroughs for folks developing new drugs.
Also in 2023, if we scope out beyond the biopharma industry, there was a new tech company added to the trillion-dollar tech company club and market cap, and that was NVIDIA. And I was really excited to see NVIDIA speaking here on Monday and hear from Kimberly Powell. And I'll just say her quote: "You know, generative AI presents a new class of tools that will get codified into applications and new methods of discovery. In fact, it will go beyond drug discovery, evolve into design, helping create the conditions to no longer be a hit-or-miss industry, and that will be what helps build the world's first trillion-dollar drug company." Which is a good question, actually, right? Why aren't there trillion-dollar companies in biopharma, right? So we have five trillion-dollar tech companies. This is the biopharma company.
We have quite a number above $100 billion and quite a spread, okay? And what is preventing, you know, this sort of scale, not achieving this scale on the biopharma side? It's not the market, actually. You know, there's a $2 trillion market for medicines today, and this is data from the NIH here on the left. 95% of known human diseases with no treatment. So enormous opportunity for new application development, giant existing market, great margins, right? So it's not a market problem, it's this problem, and this is sort of a well-known problem in the industry. This is the number of new drugs per $1 billion of research spending, over since the 1950s to 2020, going down. We're getting less effective at the development of new applications over time, okay?
This is sort of evidence that the development of new drugs does not benefit from economies of scale. Like, a bigger company pursuing twice as many drug candidates doesn't just magically do it better than a smaller one. All right, that's not a problem exclusive to therapeutics. I mentioned, Ginkgo works across biotech in ag and industrial biotech, too. So if you look in the ag industry, this is the cost to bring a new genetic trait to market. It's not exactly analogous to a drug if you put a few traits together to basically make a new crop, but, you know, $100 million plus, and it's dropped, I don't know, 5%-10% in the last 20 years.
Okay, so similarly stalled out, not having these sort of breakthroughs like we've seen in the tech industry, also takes 10+ years, and Ginkgo works with the largest ag biotechs today, Syngenta, Corteva, and Bayer. Okay, so we're familiar with this. And I think this point at the bottom is the heart of why, why this is, and I wanna spend a minute on it. So each product in biotechnology is, is really developed in a one-off process with a goal of improving the odds of success. In other words, we need a scientific breakthrough to develop that new drug. And you want the scientist to try any crazy new variable idea to try to have that breakthrough that makes the drug happen, and that variability is the enemy of scale.
And by the way, industrializing R&D is not an idea that wasn't tried in pharma, right? In the nineties, there was a big push around this in combinatorial chemistry and da da da, and treating it more like a funnel and making the R&D more standardized to just, you know, run more through it and produce more drugs. Didn't work, okay? And the reason is, you need the scientific breakthroughs. You actually need the flexibility and diversity of thought of scientists to come up with these new drugs, okay? And so I think the industry looks at that and they say, "Well, we need the unique one-off approach to get a drug, and unique one-off approaches aren't scalable, so I guess we won't have trillion-dollar companies.
We won't have economies of scale in drug development." That there's a fundamental tension that we just can't get through. Ginkgo believes strongly that that's a false choice between scale and flexibility. We do agree that you need flexibility, that the scientific leaders and our customers and at Ginkgo need to have the ability to pursue the different trails they wanna pursue to develop new drugs, but we believe that can be done in a way that gets better with scale. And the reason, philosophically, we believe that's possible, and this is true, really, I'm speaking to the biotech side. We're not a chemistry shop here at Ginkgo. But biology has a common origin, right? So four billion years ago, life evolved, and all, everything, all the biology you see around you is derivative from that.
So that turns out that something in what you can learn from an RNA project can read on an antibody project, and something you learn in protein design for agriculture can read on protein design in biologics. And so having a common tool that's aggregating biological learnings across all programs in this industry would be valuable to every scientist, not just a tool specialized to their one area. Secondly, the lab work. A huge amount of the research cost is the lab personnel and the reagents and the equipment we all use. If you walk into a RNA discovery lab or an antibody discovery lab or an even an ag biotech lab, you know, in St. Louis or whatever, the equipment's all the same. Same centrifuge, same pipette, same aspects, you know, same GCs, the kits, half the kits are the same.
What's different is the protocols that are being run, the scientific training of the person doing all of it, but the hardware, the low-level infrastructure, is actually common. Why is that? Because it's all working with biology, right? You know, the fundamentals, we're all working with the same substrate as we're programming these cells to do all these different things in drug development. And so that means that if you could solve the problem of having your samples move between the various equipment in a custom way that scales, that's automated, it should work, right? You should be able to use the same automated infrastructure for all of those different projects across all of those different markets, because at the end of the day, the fundamental hardware is all the same across those different programs.
So those are the two reasons that we're confident, fundamentally, you could get flexibility and scale if you just invested sufficiently in core platform tools. It is not a small amount of investment. It is billions of dollars in investment needed to get to that type of common infrastructure. Why has no one done it? Look at the motivations of the various players, right? Academic community, very early stage, trying to do scientific discovery, don't have the kind of budgets to put billions of dollars into robotics, right? Small biotech. They are rightly very focused on their asset and getting it as far along as they can, as cheaply as they can. They are certainly not interested in spending a bunch of money developing fundamental tools.
Traditional CROs, I think there is an opportunity for them to do something here, but these companies were really built out on outsourcing standardized things, right? "Let me take that animal study from you. Let me take that synthetic chemistry from you." Sort of predictable research. They weren't really driven out of breakthrough technologies. Large pharma, I think, has taken a few attempts at this. You know, Vertex acquired Aurora in the early 2000s, an automation company in San Diego, and built up-- at various times, people built automation, but the major constraint is that therapeutics company really just cares about their pipeline. All right? And it's either some, you know, one to three disease areas or one to two modalities, right?
They're not. They don't have the motivation to build something general across the whole industry, which is what you need to bend down that cost curve on the R&D per project. Okay, so I think the current players really aren't gonna do it, which is why I'm happy we've been investing in it. I think you do need a new player who's really focusing on this, as what they exist to do. And Ginkgo's invested over $1 billion in a decade building a common technology stack to work on this problem. And so I wanna give you a little bit of insight under the hood of what I think are critical assets in that technology stack to make it work. So the first is flexible automation. I showed you that automation infrastructure at the beginning. I'll talk about it in a second.
But again, this has solved that problem of doing general lab work all on the same platform, even if scientists are asking for different work to be done. Second, data assets. Automation will generate a lot of data. You need to actually keep it and store it in a form where it can be reused. It's not a trivial data science problem. And then finally, thank goodness, for our friends in tech who are really pushing the field forward on large neural nets and so on, we're gonna benefit from all of the investment in compute, better chips, lower cost, lower cost, larger neural nets, algorithm development. That's all gonna feed back into making use of that data. And these three pillars are what's going to allow us to have a general purpose platform across all biotech. So let me go through each one.
So if you're not an automation nerd, you might not know this, but this on the left-hand side, this is sort of the general automation spectrum in the average biotech lab. On the left, you have a person with a pipette, completely, infinitely flexible. They can do anything they want with that thing. They can come up with a new protocol tomorrow and try it, super flexible, extremely low throughput and expensive. In the middle, you have walk-up automation or task-targeted automation, like a box that you put some plates in, and then it does a very specific thing, and then when that thing is done, you take the plate, and you move it to either another box or back to your lab bench, and so it's like half flexibility, a little bit of scale.
The third one is what you might buy from, like, an automation vendor like HighRes, an integrated work cell. There's an arm, and it reaches these different equipment, and it moves the plates through the equipment. So like a high-throughput screening workflow in a large biopharma would run on a work cell. Okay? So boom, boom, boom, we're gonna move the plate, and it just does the same thing over and over again, but does it a lot. So very inflexible. It's hard to make it do something new, but it does it at a high throughput. So what Ginkgo's been investing in is essentially a reconfigurable automation system, where we have each piece of equipment in a cart.
It can get plugged into a magnetic track, and samples can be sent to—and you can see a little sample running on the track there—can be sent to any one of the pieces of equipment, and you can swap in and out equipment and also change protocols in software. Okay, this is again, these are built in-house, a big operation. You only really need this if you're trying to do general-purpose lab work. Okay, if you have only one thing you need to automate, just build a work cell. Okay, so this type of stuff is what I'm saying, if you're not trying to solve the large, general problem of flexibility and scale across the whole industry, you don't make these investments, which is why we're the ones who've been making it.
Next, on the data side, so two types of data I want to talk about. One is raw genomic data. So this is the type of data that is present in GenBank, for example. And so I want to show you here is non-human genomes. So basically, how many de-duplicated genes are present in, in this case, UniProt? You know, it's 246 million. That's in the purple bar. Over two billion de-duplicated genes in Ginkgo's internal database. So substantially larger, and you can see the growth rate of our, our database has been quite a lot faster than UniProt's over the last five years. That's half the battle.
This is available genetic variability from nature that you could draw on to learn how to design promoters better, how to make a, a better enzyme, whatever it might be, okay? But importantly, the complement to that is then characterizing all of those different genes. And so we've been doing that at high throughput. Just this chart up here on the right, each one of those lines, you can't see it clearly, but is a EC class, an enzyme class. And we've tested, and this is, you know, across many, many years and lots of experiments, more than 5 million characterized data points like this that we can then use to feed the models I'm gonna talk about in a second. Okay. This summer, we announced a partnership with Google Cloud. Ginkgo's gonna spend $250 million on compute training.
Google is doing about $50 million in milestones back to Ginkgo. Really nice comment from Thomas Kurian here, the CEO at Google Cloud: "This strategic partnership with Ginkgo is first of its kind, underscoring our confidence that Ginkgo will play a critical and pioneering role in the life science industry." So this is a type of thing that we want to invest in. We want to be able to invest in what we think are common, valuable, reusable assets across the industry.
We've started. The first round of tests that we're doing on these models is basically to take some of that data I showed you on the last slide, where we have proprietary assets that are bigger than the public sets, and just push it into models that are very analogous to what are currently out there in the state-of-the-art and see how much does new data help. And the answer is it helps, right? So I, I think there is an opportunity, and we, you know, we've seen significant outperformance, especially when it comes to classes of biology that aren't well represented in the public data sets. So I think there'll be an opportunity both for more data generation and for further algorithm innovation when it comes to improving the quality of these neural nets in the biology space.
And so we're—we've got that data, so we wanna kind of push the bar and help the field understand, are we anywhere near diminishing returns on the improvements to these models from large-scale data? I'm optimistic we're not, that there's a lot more room to go. Okay, so those are the three pillars. They're sort of the automation, the data, and this AI pillar. I want to give a few specific examples of using these in customer partnerships. So, this first one's with sort of large biopharma in the area of enzyme design. In this case, there's a catalytic activity that this customer wants to have. It was not known in the literature to enzyme to do it.
And so what we first did was we started with a prediction from our enzyme engineers of some possible sequences from metagenomic search that could do it. Those are seed sequences. Computationally, we say, "Okay, based on those, here's 183,000 sequences that are kind of similar from that set of 2 billion that we had." Okay, we don't want to make all those, so we computationally predict 1,000 that we think are particularly good, and we synthesize them. We're Twist's biggest customer. We put them in, we automate, clone them in, test them, and see their performance, okay? And we find a few that work. Those few that work become the basis for protein engineering.
And now I want to spend a minute on the next couple boxes here, because you're going to hear this again and again, I think, from us and other people in the future as they are applying AI in bioengineering. Okay, so what you're looking at there is us running our protein model to try to optimize these sequences, on the axis of specificity on the Y-axis and activity on the X-axis. Okay? And we specifically told the model, "Hey, make some variants that are more active and make some variants that are more specific." Okay? It made some choices. We built them, and we tested them. And we found, "Oh, okay, here's one that's 5 times better activity and 14 times better specificity." And that's the first dot, or that, that's the gen one dot there on the far right chart.
And now, here's the important part. You take that data, the middle plot, you go back to your ML AI model, and you do what's basically called reinforcement learning on the tech side or closed loop has become sort of term of art in biotech, biopharma. You give that data back to the model, and you tell the model to get better based on it. You say, "Hey, now, now that you know that, why don't you recommend a new set of designs for me to test?" That's gen two. You can see in gen two, now we're at 10 times better and 25 times better on those two axes. You take that data, and you give it back to the model, and you say, tell the model, "Improve yourself based on this data." That's the loop.
You tell it to recommend again, and that's Gen 3. And in Gen 3, we get a 110-fold improvement in activity and 21-fold improvement in selectivity. And we also now are sitting on a model that is real good at designing this class of enzymes. So if a new customer needed this class of enzyme, we wouldn't need to go all the way back to gen 0. Okay, that's point number 1. But this general loop of start with a generic protein engineering model, understand what you want it to do. Remember, this is my point, like, you have the flexibility as a scientist. You—this is a very specific thing they're asking. Generate some data in the direction of your particular interest, tune up the general asset, and it will get better at your particular thing.
That is flexibility with scale, not flexibility against scale, okay? Right. That, that is, this is the, this is the point I really want to make. We have done similar in a totally different area. So this is now, you know, and this is data before our Pfizer's, not our work for Pfizer, but we have this partnership with Pfizer in RNA. And one of the things we've been developing in the RNA space is Circular RNA. So this is a way to make RNA hang around longer in the cell, so that if you want to express things, you know, vaccines are great because you don't need a lot of protein to trigger the immune system, but if you wanted to express more protein biologics using RNA, you got to have it stick around. So Circular RNA lasts longer.
The way you make it, you have an intron in there that's basically doing some fancy stuff to circularize the RNA. Different designs of the introns, different levels of circularization. So we ran our process, and we've been able to find, you know, introns with about 90% circularization efficiency. Okay, so that's better than state-of-the-art, and we think we can keep driving that loop to make it better. The other thing people are asking for is expression in different cell types, and you see that on the right. And again, think of the loop, right? You get some data on how things perform in these cell types.
That goes back to feed into a model that understands, starts to learn what it is about the design, in this case, it's the design of the HighRes, that affects their expressibility, on a differential basis in different cell types. Okay? And I'm gonna use the next, one more example of this. This is in the area of CAR- T. This is some work we've been doing internally. So we generated, in this case, a library of 10,000 intracellular domains for a CAR construct. That's over on the left. A year ago, I showed us putting that into T cells and showed some nice data on in vitro improvements in the lab for exhaustion. Okay, so making these cells last longer.
We're now taking that work into animals in partnership with WARF and hopeful to see that data soon. But that was sort of the first line of work. Now, what's great is we could take that same library, put it into NK cells, and screen for NK cells that had a strong immune activation, okay? And you can see there's three different in the, that lower right corner, three different metrics that we're using, I'm not going to get into the details, to measure that immune activation, and we found eight out of the 10,000 that had high activity. But what's really exciting is we found differences, right? So you can look at the heat map of all the differences, all the different designs and how they affected activity, and that's the exact kind of data.
It's not just the data about the eight winners, it's the data about the 9,992 losers that you also want to feed into the model so it learns what designs affect good and bad on this particular access you're trying to achieve, okay? And that's, again, not a custom model. It's a general model that's being tuned up with custom data, so it is flexibility on top of scale. Does that make sense? So this is, I think, an important. This general closed loop is, I think, something you're going to see again and again as you, as we have platforms that apply automation, large scale data, and general purpose AI.
One last area I want to mention, Ginkgo does not have, I don't have programs here yet, but I want to highlight that I think you should also, if you're following AI in the biotech space, expect to see this. So there's a pretty cool announcement, Recursion and Tempus talked about in licensing a bunch of human genomic data around basically patient genomes associated with different diseases. That's sort of the top left box. So you're gonna have people use these large models, parse more and more human genome data, have predictions of potentially new targets. So target discovery, okay? That's this category. So new target prediction. Now, the question is, is it real? Is it a real target?
And this would really, you know, traditionally enter the world of systems biology, where we'd say: Okay, great, well, let's go look at it and start trying to understand it as systems biologists. What the hell does this gene do that keeps showing up in these, you know, patient samples? Well, I think the new way you're gonna see people do that is they're gonna take a human cell. They're gonna, in this case, you know, we, we can do, you know, greater than 20,000 perturbations this way in a high throughput. But they're gonna make a bunch of changes to the genome of that cell with high-throughput CRISPR. They're gonna have this big array of variants around that, that target they're trying to explore, and then they're going to test all of those with high-throughput omics all over the place.
So you're gonna have imaging, but you're also gonna have, you know, transcriptomics, metabolomics, and so on. And you're gonna get this giant data dump across 20,000 different variants across that particular target you're interested in, and you're gonna feed all that data into a model, and it's going to learn what the heck is going on, right? And that is gonna go back to the scientist who's trying to identify that target, and they're gonna work with it and say, "You know, I could use a little more information here," and they're gonna go around that loop again. Ginkgo's particularly good at those two green boxes on the right. So if this becomes more of the status quo for how people do drug discovery, I think we're gonna end up, or sorry, not, target discovery.
I think you will see us playing more and more in that space, so I'm pretty excited about this. I think it's a real opportunity. Okay, so I wanna end on this. You know, I think one of the other reasons the tech industry has been so successful is that these trillion-dollar companies build on top of each other. So NVIDIA, you know, the largest chip company by market cap today, does not make its own chips, okay? It manufactures the chips at Taiwan Semiconductor. Like, think about that level of dependency, all right, in this industry.
Netflix, Salesforce, Twitch, you know, these big kind of SaaS companies that exploded, you know, in the 2000s and 2010s, built on top of Amazon Web Services, Google Cloud, the cloud providers that were gonna do all that compute so that they didn't have to build that infrastructure. All your apps that you get in the App Store on either Android or iPhone, all that distribution handled by Apple and Android for those developers, hugely enabling, lets them grow way faster. And I'll give the specific example of OpenAI. I think people are familiar with OpenAI's relationship with Microsoft, which was, "I'm gonna take all the data from the internet, you know, Common Crawl, 10 TB, feed it through a ton of compute, and build this foundation model," which they did. That was not ChatGPT.
ChatGPT needed additional work, so they called a company called Scale AI, and they told Scale AI, "Hey, I need a ton of people to put queries into my language model and train it. Say if it's a good answer, a bad answer, why it's a good answer, why it's a bad answer, and do this human, you know, reinforcement learning," kind of that same closed loop I mentioned earlier that we're doing in the lab. They paid Scale to generate that data from humans talking to the thing, and that made ChatGPT. That's what made it so conversational and so good, all right? It was necessary. They built that on top of Scale. So Scale is basically like...
Tesla works with them to on image analysis to circle, "Hey, that's a cone, that's a dog, that's a stop sign," to help train their self-driving car algorithms. It's a data broker, and it's growing into a quite big company. Ginkgo could very much play that role in the biotech space, right? If you're building an AI algorithm, you need more data, call us, right? Like, we could provide that data scale. If you're a large pharma, you wanna put your toe in the water, and you're trying to build out a data set you don't have to get that closed loop, why build it? Why build it, okay? It would be better to leverage infrastructure that's already in place, let us grow and invest at bigger scale to bring down the cost of that data generation.
I think this type of betting on each other and leveraging each other's investments across the industry is missing today. I think there's a cultural change needed to do it. Ginkgo's very happy to lead the way. I think if we lean into this as a sector, biotech should be bigger than tech. It really should. And Ginkgo is hoping to lead the way there. Very happy to do it with you all. There's my email up there if you wanna talk more about it, and happy to take questions. Thank you very much.
Awesome. And so, just as a reminder here, feel free to submit questions on our-
Yeah, and I'll put my email up also.
Sounds great. On our website here, as with any presentation. Great. Yeah, so it seems like, you know, you. Yeah, great. You flagged some really good updates this morning, including a, you know, an inline quarter, as well as some great partnership momentum here. So, you know, we've seen some of these chunkier partnerships, for example, a multi-target RNA drug discovery collaboration with Pfizer, which could earn, you know, potentially up to $331 million in fees and milestones. But it's kind of hard for investors to sort of understand these partnerships from a financial level.
You know, so how should for investors think about the chunkier partnerships like this, you know, heading into this year, next year, and, you know, how, how do you, how do you think about, you know, your partnerships progressing through your pipeline in general?
Yeah. So I think one thing that that's, again, people don't fully appreciate about a deal like that, Pfizer deal. So you often see these kind of deals announced between, like, a small or mid-sized biotech, where they've got some assets in their own pipeline, and they're partnering with a large pharma on a two or three additional asset deal with $400-$500 million in milestones or more. Okay. That small or mid-sized company would do one deal like that a year. It would be negotiated directly by the CEO. It's a big decision, right? It's about out-licensing in a certain area, a lot of, you know, exclusive tie-up, all these things.
These deals we're getting at Ginkgo, it's a decent amount of milestones, but not quite as much as if you're doing a whole asset, but it's not bad. I'm not doing the deals, right? They're being done by, like, our enterprise sales organization, right? So one of the things I'd like to point out is, like, these shouldn't be like chunky one-off deals. This should, this, in my view, if we start to move to a model where companies are leveraging large-scale, multi-billion-dollar platform investments of other companies in generating data, these sorts of deals should just be coming off the pipe, right? Like, this is just how we should be doing business as an industry, and there are thousands of drug development projects happening, and I'm announcing three deals, right?
So there is a lot of room here. And because I'm not—this is my point again about being an asset company—it's not like I've got one narrow asset that I can only—that the only thing I can partner is geography or something, right? I've got automation, large-scale data pile, and AI models. And so what I wanna do is make this into more of a straightforward thing for people to do, but while still having some. And it doesn't need to be huge, but some amount of participation in the success so that we're aligned with our customers and also it kind of makes the economics of the whole industry work, I think, correctly. Yeah.
That's great. Yeah, and, you know, you recently announced, you know, just speaking of, making the economics of success work, the completion of your program with Biogen. You know, you noted in 2021 this could be a $100 million-$120 million dollar partnership. You know, how should we think about the financial impact of a partnership like that completing? And, you know, how do you think that Ginkgo, as it closes out some of these bigger programs, is able to replace those large programs with additional good ones? You know, we've noted from other companies at this conference that there's been some biotech RFP pressure. So is it, is sort of focusing on key partners, or how do you think about it?
Yeah, so I'll answer the second question, then the first question. Well, I'll go in order, actually. So when it comes to milestones, we'll often have like, the bigger numbers are gonna be tied into, like. And I'll just speak generally because I think we haven't disclosed anything specifically about Biogen. But often you'll have milestones tied into commercial progress and things like that, that take longer. I think that's just gonna be the reality. I think and I think that's healthy, right? Because this is an industry that, again, there is still fundamental risk in developing drugs.
A lot of them are gonna pan out to be zero, and it's hard to get. You know, you can create a ton of value, put a lot of the technology into a drug, but if the thing ends up being worth nothing, the customer feels pretty damn bad about paying you a lot of money for that. So I think it is quite aligning to have big checks when there's big commercial progress. I, I really like that model. It just takes time, right? Like, it's just a thing you gotta wait for. And so I think the reality is we'll keep stacking those up, and Ginkgo will also focus on, again, compressing cash burn via increasing the service fee aspect of our, our, deal so that we make it to all those royalties, right? So just from a strategic standpoint, does that make sense?
Yeah.
And then the second question was about the-
Refilling the pipe.
Refilling the pipe. Yeah, and also, should we, like, double down on our current big customers? The other thing that I'm excited about with Ginkgo today is we have basically not penetrated the biopharma industry in any serious way, right? You know, like, we were talking, we go out still today, like, we were at JP Morgan the last three days, like, having nonstop meetings. We will meet people, and they're like: "I thought, yeah, I thought Ginkgo did like, you know, ginkgo biloba or something." You know, right? Like, like, they're still just like we're early in the transition. And like, you saw my pharma revenue going up from basically nothing in 2020. You know, this is the most recent market for us to apply this general platform.
We started in industrial and agriculture in part because the bar was lower for their capabilities in R&D. You guys are better, okay? And so I'm selling a platform. I still remember, I told you I got to convince a company to use me. I can't just say I'm in gene therapy or RNA. I gotta convince someone that I'm better than their infrastructure in-house. That took longer for me to get to that scale. Took me about eight years of really having funding and growing to get there for biopharma. But now that we're here, we're here, right? Like, we're gonna be here, you know, for the next few years, it's gonna be the focus. So I...
So hopefully, for me anyway, no, there's no reason to double down on the few we have today because there could be someone out there who's a much better customer that we just haven't even met.
Awesome. I think we're out of time, but really appreciate you guys coming.
Thanks a lot. Appreciate everyone's time.