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

Good morning, everyone. My name is [ Michael Sontag ] I am the Life sciences [ diagnostic ] Associate working under [indiscernible] Partners. It's my pleasure to be hosting Ena Cratsenburg today, the Chief Business Officer at Ginkgo Bioworks. Ena, glad to have you here.

Thank you. Great to be here.

So to start a conversation, I was hoping we could begin with the first quarter. So Gingko has seen some continued momentum in their program add, rather than necessarily translate into revenue growth in the top line. So I was wondering if you could work through with investors like -- what is driving that disconnect? And how is Ginkgo working to make sure that program growth is translating to revenue growth?

Yes. You're right. In the latest quarter, we saw an increase in active programs year-over-year but a decrease in cell programming revenue. And it's been frustrating, frankly, for all of us because we actually have a large amount of fees, bookings across many deals. But part of the problem is we're just not converting those bookings to revenues fast enough. And the core challenge to that is the rate by which we're bringing these programs onto the platform to full automation at Ginkgo. Generally speaking, the faster we can get programs to full automation we can generate a large amount of data, that's when the revenue really starts to flow in because of the time lag associated with bringing these programs on, that's what's been translating into the lower-than-expected revenues. We are fixing that through using RACs, and I'll talk more about it later in the discussion. But the acquisition of Zymergen is really kind of a key asset for us right now to be able to use the automation system that they have developed called RACs that would help us to automate a lot more of our processes and be able to reduce the cycle time from when a deal is signed to when we can actually recognize the revenues.

Okay. Yes. And then you've also talked about maybe a bit of a switch in the types of programs you're working to do. So not getting bogged down in licensing deals, but we're doing fee-for-service type work. I think lab data, the surface is kind of one of the key elements of that. So I was wondering if you could talk about how this change affect the sales cycle and what does your expectations for program growth and revenue growth in the year ahead?

Yes. So traditionally, Ginkgo's business model have been to sell essentially a solution to our customers. And that solution is where Ginkgo scientists are exercising scientific control to provide sort of an end-to-end solution to a customer. They have a problem -- a technical problem to solve, and we use our scientists to figure out how to use our platform to generate the solution that they want. And typically, in those models, we own the IP that's generated and we also take downstream value share. Now Lab Data as a Service is a different type of offering where it is putting the scientific control with the customer. They have a scientific problem to solve, they have a path towards solving that solution, and they need specific data that's generated to help them understand and validate their hypotheses. And so these Lab Data as a Service programs tend to be smaller modules of services. And in those deals, we're essentially giving our customers ownership of the data that is generated -- so there's no like IP discussions. They own that IP, which is essentially the data set, and there's no downstream value share. So it's a very simple typical CRO type arrangement. We're offering both and we're seeing a lot of traction in the market for Lab Data as a Service as a complement to the existing solutions services that we've been offering.

Okay. That's interesting. And then for Lab Data as a service, how is Ginkgo really differentiating compared to maybe some of the other CROs offering their lab space and is there any way -- I know obviously, it's -- there's no downturn economics associated with it, but it's a way for Ginkgo to get some sort of long-term value from that work. I don't know if it's either informing your models or stuff like that?

Yes, absolutely. So Lab Data as a Service is I'm sorry, can you repeat the first part of your question?

So I'm sayin,g like what differentiates Lab Data as a Service at Ginkgo versus other CROs.

Got it. So I think one of the key things about Ginkgo that's unique relative to other CROs. The CROs generally do work that is in a similar manner as what the companies contracting them to do. They're very FTE intensive. The business model tends to be I scale by people, not I scale with automation. That's the unique aspect that Ginkgo brings. Just because we're doing more work, doesn't mean we have to add a lot more people. We add more robots. We are optimizing our automation processes to be able to provide those services. So the key difference between Ginkgo and a typical CRO is the extensive automation and high throughput capabilities that we offer. Customers cannot do what we do versus for many CROs, customers can probably do what typical CROs do. And that's also an ability for us to achieve economies of scale as we bring more and more of these customers on board, we don't have to make additional significant investments in people to be able to take on more customers. We actually have the ability to do that with much lower variable costs.

Okay. Yes. I think expanding on that point of automation. I know the first quarter really featured the RAC systems that I guess you acquired via the Zymergen acquisition. So I'm curious what does RAC do for, I guess, [ encode ] P&L? And how do these systems differentiate from some of the other lab automation system people might be familiar with?

Yes. That's a great question. So let me answer the second part first. So with respect to automation, there's automation happening across the board of many different companies, right? So what makes Ginkgo unique? I tend to think about automation in sort of different levels. First level is by hand. So no automation. Second level is walk-up automation, which is what you see in most labs today. You see that in a lot of academic labs where there is a high-throughput instrument that can do a lot of different experiments, and the scientists have a plate that they're walking up to a machine to an instrument for the number of designs for it to be tested or constructed. The third level of automation is work cells, where there are a number of these instruments tied together in a way that performs a certain set of activities in a more efficient fashion. But Level 4 automation is really what we're looking at developing which is RACs. And the way to think about it is flexible automation. So it's not just about developing these work cells that once you put it in place, you're doing a lot of the same thing over and over again. But if you want to do something else, you have to spend a lot of time reconfiguring the Level 3 automation. And Level 4, what it offers you is essentially instruments on carts. So think of it like Lygo blocks, connected through like a magnetic sort of transportation system that would allow you to move things from one system to another and you can change out the cards in a very simple manner. So it gives you the ability to do the automation like you would with a work cell, but the flexibility to handle a lot of different types of projects or experiments.

Yes, that's the build through.

Yes.

And I'm hoping to talk to me a bit more about the profitability outlook for Ginkgo. So at the first quarter call, we talked about reaching EBITDA profitability by 2026. I was wondering if you could walk us through how you get from how you get to there from here. First, on like a top line perspective, but also thinking about maybe the investment in automation and managing the OpEx?

Yes. So there are essentially 3 key levers that we're looking at on the growth side to get to the EBITDA breakeven by the end of 2026. The first is, we already touched on it, is using RAC automation to help improve the rate at which we can bring customer programs on board, and for us to improve efficiencies and how we can deliver on those programs. The second is, and you mentioned a little bit about this earlier, is changing our commercial terms. So even with the solutions that go in today, by moving our IP terms to customers on for an IP and no downstream value share. It removes a lot of friction in the deal negotiation process. So it speeds up the rate at which we can get some of these deals done and it improves our conversion rate. We have gone back to some of our customers, where in the past, they have said, "Hey, Gingko, you own for ground IP. I don't want to work with you anymore. There's -- this is a nonstarter for us. We've gone back to them and have been able to get them back to the negotiating table because now we have this new IP schema. So that the new terms that we're offering is both increasing the rate of conversion as well as the speed of which deals get done. And third is you touched on earlier is [ LDaaS ] Lab Data as a Service, which is another service offering that will allow us to access new customer types. For example, with some of the AI companies, they are interested in large data sets for the model training and we can offer the generation of large data sets to these AI companies that typically would not have been the type of customers that Gingko would go after.

Okay. That's interesting. Yes. And then I guess, we can further about program. I think Ginkgo talked about looking to get a little over 100 services contract in 2024. I'm just kind of curious if you have any views on what type of customers do you think will be driving that growth? And how much will be [ LDaaS ] or some of the more typical programs you've been doing in the path?

I think it's going to be a combination of both. I mean, looking at the pipeline today, still a lot of our typical solutions type customers simply because [ LDaaS ] was just introduced in April. So we're letting some of that work through the system. But from our early read of customer interest, we think it's offering us a lot more opportunities to go to the market with something that people are interested in buying.

Okay. Now I think going, I guess, a little deeper. So starting with pharma. I think that as of the last quarter update, it was 41 of the 140 program to have. I was kind of curious if you could speak to which parts of the platform are driving interest from pharma. And if you have any ideas about what the mix between large pharma and maybe some of the smaller biotech.

So when you think of the Ginkgo platform, you can think of it in terms of the microbial platform and the mammalian platform. And we also have an ag platform. But for the purposes of this pharma question, the ag platform doesn't apply. We really have deals that cover both the microbial platform and the mammalian platform. In the microbial platform for pharma customers, we're doing work like small molecule natural products discovery. We're doing microbial expression systems that helps to drive down cost of manufacturing. We're doing work like enzyme discovery and optimization for biocatalysis. So there are a number of programs that fit within our microbial platform offerings. We also have a number of programs in the mammalian platform offerings, stuff like the deal we signed with Pfizer, that's on RNA therapeutics. The work we've done with Biogen on gene therapy, the work that we're doing with Arbor on gene editing, those are mammalian platform type projects. So we really do have projects covering both of those platforms. And I'd say there's probably an equal amount to both right now. In terms of small biotechs versus large pharmas, it really does cover both as well. I think what's interesting with LDaaS is that it actually provides an ability for us to access even for large pharma, some of what we're calling the R&D budget. So a lot of the solutions selling because they tend to be higher budget projects are not within the R&D heads budgets right now. So those solutions type projects tend to go through a much longer deal cycle and would require a lot more approvals from different parts of the organization to access a strategic budget. But when you're talking about LDaaS it is a smaller module of work. It is in support of perhaps some R&D efforts that's ongoing with a customer's R&D department today. So it fits very naturally within the R&D budget or large pharma. And certainly, because small biotechs have smaller budget, LDaaS is also very attractive offer for them too. So we're really seeing a lot of pipeline activities across Gingko.