So a note on this for the team that’s tuning in, there are many things we are doing at Ginkgo right now that are good things to do in the long run, but aren’t good investments today given the opportunity we have to get to a breakeven business built on technology that keeps making biology easier to engineer as it scales up. No other company in the world, in my opinion, has pulled that off yet. So we need to sacrifice activities on that path. So I think we have a good shot at hitting it at this point, and it’s critical. We’re going through the detailed planning process now and input across our team is essential to get this right, but I can share some of the major places we expect to see savings. So first, facilities are a significant cost for us, both in terms of rent, but also in terms of facilities maintenance and tracking.
We have eight sites today. And with BioFab-1 coming online in mid-2025, expect we could reduce our footprint by up to 60%. These simplified operations require less ops, G&A, HR, finance, facilities, management and other overhead support, which will allow us to significantly reduce G&A costs and overall headcount. And with our movement to a more rack centric foundry, our technical teams will be adjusted to suit highly leveraged automated and pooled workflows. We expect that these combined initiatives will result in 25%-plus reduction in labor expenses, which is inclusive of a reduction in force. We are also taking on other cost-cutting measures to reduce non-strategic overhead expenses through a thorough review of existing internal and external programs while also pausing reviewing professional services at.
We know that many bio workers will be impacted by these changes, and we’re excited that we have to see many of you go. But are thankful for your patience at input throughout this restructuring and dedication to our mission of making biology easier to engineer. It sounds like this when that mission dedication is tested the most. The last piece I will get into today is our updated guidance for 2024. You’ll notice that we are no longer — we no longer have new programs listed on this page, and that’s because we’re not sure as currently defined, it’s the right metric for program growth going forward with the simplifications and changes to our deal structure I’ve described today, and particularly, we’re moving downstream value share on many deals.
Our prior guidance were programs dependent on things like downstream value share to be counted as a program is no longer applicable. Ginkgo does expect to add at least 100 new customer projects, comprising both traditional cell programs as we thought of them as well as new offerings, including lab data as a service. Due to the changes in our deal structure and focus on cost savings, into now expects total revenue of $170 million to $190 million in 2024. Ginkgo revised its expectation for Cell Engineering Services revenue to $120 million to $140 million in 2024. This guidance reflects a weaker-than-expected revenue ramp during the year, uncertainty relating to the timing of technical milestones and the potential near-term impact of the restructuring actions I just described above.
This guidance excludes the impact of any potential downstream value share as well as potential upside from new service offerings. Ginkgo continues to expect biosecurity revenue in 2024 of at least $50 million, representing approximately current contracted backlog with potential upside from additional opportunities in the pipeline. Okay. In conclusion, these are difficult changes, acting decisively now while we’re in a position of strength in terms of cash in the business. It’s critical. This will not be an easy period for our team, and we’re grateful to them for their help and partnership as we make this transition. All right. Now I’ll hand it back to Megan for Q&A.
A – Megan LeDuc: Great. Thanks, Jason. As usual, I’ll start with questions from the public and remind the analysts on the line that if they’d like to ask your questions so please raise your hand on zoom, and I’ll call on you and open up your line. Thanks all. All right. Welcome back, everyone. As usual, we’ll start with a retail question, and then we’ll go down on our list of analysts. So Rahul, you’ll be first after a retail question. First question comes from our IR inbox, and it’s for you, Jason. Can investors get some color on how data as a service is being received? A big part of the original investment thesis was downstream value revenue, but now that is gone. Can you explain why data as a service is the right pivot and how it’s being received?
Jason Kelly: Yes. I touched on some of this on the call. [indiscernible] month by month. I think it’s being received really well. We have right now tens of customers in our sales pipeline, just pretty quick for the type of stuff we sell here. And I made this point in the call and it’s a subtlety, we are able to sell this just to a really different pool of budget at our customers, right? Like we get to walk into the R&D department and basically say, we are an alternative to generating a collection of data yourself. So you can save that money on the range, you can say you can take that team that would have to do it and instead have them get to do something different. If you’re a small biotech, maybe you never build that lab or hire that team in the first place, right?
So we have this kind of new thing we’re able to take to people. The second thing is I get to say, hey, it’s your IP, there’s no royalties. So let me tell you, having sold infrastructure for the last decade, that makes my life a lot easier. So I do think this is the right time. [indiscernible] ton has been something that I think — has been part of our thinking about the company. I think in the long run, it could still be part of our thinking. But in this window of time, there’s just an enormous amount of research budget for us to get after. And I think we are able to tap that budget a lot faster with these terms that are a lot more customer-friendly. So I’m really excited about I think it could be a big part of the business going forward. And I should just mention one last thing.
The point about the sort of AI company, it’s starting, right? Like a lot of these companies are really software first, right? They’re experts. They’re building incredible models. And they’re all leveraging like existing public data sets to do that, right? They’re leveraging the protein data bank, the leverage gene bank to have access to genes and protein structures and so on. And eventually, that’s going to get mined out. Right? In fact, I’d argue it’s probably pretty close to having already been mined out. And so what you’re going to need is new large data sets, right? And I think the way we’ve structured lab data as a service, where these companies can own that data. What’s the point? Why build your own lab? It’s just going to be faster to use Ginkgo infrastructure and our conversations with companies are reflecting that.
So I’m pretty excited about that.
Megan LeDuc: Great. Thanks, Jason. I got Rahul from Raymond James. You are up first. Your line is now open.
Rahul Sarugaser: Thanks, Megan. Can you hear me, right?
Jason Kelly: Yes.
Rahul Sarugaser: Perfect. Jason, Mark, thanks so much for taking my questions and congrats on taking a bit of a reset quarter here. So I guess maybe — first of all, maybe I’ll ask a big global question, right? So Jason you started by talking about how you’ve been doing this a long time? And I think most of attendees on this call are believers in by manufacturing synthetic biology. So my question is, what — given the attrition that we’ve seen, given the fitting in the revenue, fitting in projects, what are the threats out there that you’re pulling on that makes you believe that it’s not too early? How is Ginkgo at the right time? And then maybe a more granular question will be then as you evolve your business model assume you are at the right time, how is Ginkgo not going to be categorized effectively as a big CDMO. That’s it from me.
Jason Kelly: Yes. Okay, great. Let me speak to that. Yes. So, the first point you made around biomanufacturing. So, I share your concerns on this. I think what we’ve seen — if you look at it like taken we look to the company public, majority of our customer base was in the industrial biotechnology sector. And I think people often treat that as like a synonym to synthetic biology. It’s actually not, right? The way to think about synthetic biology is it’s a tools infrastructure, right, as people that are working on new ways to make the process of designing and engineering cells faster and easier. It happened to be that a lot of the demand for that was an industrial biotech because of the complexity of the genetic engineering there.
So, that was sort of why there was a common like equivalency. Industrial biotech has a hit very, very hard with higher interest rates. I think that’s just the reality, like that at your capital ecosystem completely dried up for those companies. Many of those companies have gone out of business. I love this space. It’s been tough. And so I think one of the things that the last few years have shown is even in the face of that, which we weren’t expecting, we took the company public, we were able to show that, hey, we’re a tools platform and actually, we’re adding all these new programs in biopharma, which was a space that we were likely in when we took the company public. And so I think that does speak to like the flexibility of Ginkgo as a platform.
We’re not really hooked exclusively on biomanufacturing or industrial biotechnology. In fact, the story in the last two and a half years have been pretty impressive, in my opinion, shift of our customer base from industrial biotech start-up company that we’re growing on a lot of venture capital to increasingly large biopharma bio ag companies that have big existing research, right? And so I will that be my point, I mean — I think it’s tough. I think we need some breakthroughs in that area. I think some of the consumer biotech stuff that’s happening is pretty interesting. There’s six new GMO like house plants on the market somehow, right? So, I think that kind of stuff is exciting, but you’re — I think that sector has been hit really hard.
You asked about what we thought of — when we focus on the pharma space as a large CDMO. I don’t think that’s the end of the world, right? So, here’s the basic problem with like the CROs today. They don’t really improve as they scale, right? If you look at Controls River right, like you’re essentially outsourcing, doing that same thing, you’re choosing to outsource data generation to an external lab, but they’re just going to do exactly what your lab was going to do. They’re going to hire a bunch of scientists, put them in a lab of benches and do that work by hand. So, the fundamental philosophy of how they do the work is exactly the same as how you do it in-house. So, you’re really just choosing there to hand off some of the things that you don’t want to do.
That’s different than the distinction with Ginkgo as the CRO. People are coming to us because they want a large data asset, okay? They either want it with automation or they want it with pools. They can’t get that from the traditional CROs, okay? And they can’t get it in-house. So, the real question is, do they want it, right? If they start to want it, the advantage of my approach is I have scale economics. I get cheaper on the infrastructure as I do more work, right? And so that’s where we will be different. But like in my view, if you compare like the way lab work gets done, the overwhelming majority of research spending is going to kits and equipment and real estate and labs all the it’s so much a giant pilot expense. And the CROs have tapped almost none of that.
And that’s because they have not offered like really a compelling differentiation from what the customer can do themselves. And I’m hopeful, as we can make this flexible and scalable, which is the big unlock then you heat up more and more and more of that by hand research budget, you really do. So I’m going to have a problem being that way at all. As long as you think of me as one that could ultimately take half of the research budget someday if we were right about this philosophy for doing the lab work.
Rahul Sarugaser: That’s really helpful. Thank you so much and good luck with the big set.
Jason Kelly: Thanks Rahul.
Operator: Thank you. Next up, we have Matt Sykes at Goldman Sachs. Matt, your line is now open.
Matt Sykes: Great. Thanks for taking my questions. I guess kind of a high-level question for Jason or Mark. Just as you kind of look at sort of the proof points of this restructuring and shift in what you’re doing, particularly from how you’re approaching customers. We have moved our model away from new program growth a while ago as we felt the correlation wasn’t there, focusing on active programs. Revenue is obviously going to be key KPI, but as you kind of give advice to the sell side in terms of how to measure success of this shift, what are some of the KPIs that we should really be focusing on at this point?
Jason Kelly: Do you want to take, Mark?
Mark Dmytruk: So I think, Matt, what you heard is we’re going to be focused on cash flow, first and foremost. And so that cash flow is a function, of course, of cash revenue and cash OpEx. And so that’s where a lot of the energy of the company is going to be. But we want to get to a place, as you heard on the call today, we’re moving towards profitability, where we’re adjusted EBITDA breakeven. And that does what stuff can go up for success. We have to prove that we’ve been operate the programs economically. So I think that’s pretty important. On the sort of volume side, which is what you’re getting at, is there going to be like a substitute for the program metric or something like that. We’re going to need a little bit of time to figure that out.
