Q3 2023 Ginkgo Bioworks Holdings Inc Earnings Call

D and will be muted throughout the meeting.

Good evening.

Manager of Investor Relations I think of Eyeworks I'm joined by Jason.

Co founder and CEO and Mark <unk>, our CFO.

As always for joining us we're looking forward to updating you on our progress.

During the presentation today, we will be making forward looking statements, which involve risks and uncertainties. Please refer to our filings with the securities and Exchange Commission to learn more about these risks and uncertainties.

Today. In addition to updating you on the quarter, we're going to dive deeper into a few case studies of how we are seeing our mission to make biology easier to engineered come to life as well as provide further details on our diverse program pipeline and the growth opportunities. We see in Biosecurity business as usual will end with a Q&A session and I will take questions from analysts investors and the public.

Can submit those questions to us in advance via Twitter, a hashtag ginkgo result, or email us at investors at <unk> Dot Com, Alright, 30, Jason I'm Super excited to be chatting with you all today I always start with a reminder, that our mission to make biology easier to engineer.

As we dig into the strategic sections, you'll see the progress, we're making on that mission, particularly with our AI efforts and our strong pipeline of active programs.

We pursue that mission on behalf of a diverse group of customers at one of my favorite slides, having a customer list that ranges from agriculture to consumer goods. The chemicals. The therapeutics is common for our horizontal platform, but it's pretty unique and biotech it makes sense because all these diverse programs benefit from the scale.

Billing of the same underlying technology of Jingo, we've added programs with several new customers this quarter, including smaller companies like nosh bio foods in the industrial biotech field and exact the biosciences in the AG space as well as large companies like Pfizer and pharma. In addition to new programs with many of our existing customers.

We took a view early on it can go that scale would be needed to drive our mission and you see that reflected in our business model as a platform service provider. We had 116 active programs on the platform this quarter, representing 36% growth over last year, and our highest active program count ever as our foundry scaled our data.

Generation capabilities scale and turn you can see this on the slide comparing some of our internal assets to public data assets, our ability to generate data at scale for customers paired with our existing code base is a big part of the reason customers choose to work with ginkgo, particularly at leveraging generative AI becomes a bigger priority for our customers.

For those of you that turned into are tuned into our Investor day, and I encourage you to watch the Youtube regarding if you didn't you know that we are using this data to build AI foundation modeled in buying two and applications for biological engineering. Our recent partnership with Google is helping fuel this in the last quarter and I'm proud of the progress the team is making on track with our plan.

In fact, we've already achieved the first milestone in our partnership with Google The reality of the biology is getting easier to engineer. We're really excited about that what that opens up for our customers better medicines more resilient fluid systems cleaner industry, but it is not lost on us that the advancement of biological engineering tools, particularly when coupled to advancements in AI.

I creates risk we are sitting at the intersection of several exponentially improving tax and the world is grappling with how to keep up with the pace of change and limit. The risks. These technologies create our bio security business works hand in hand, with our cell programming business to address this challenge, helping build early warning systems and decision support for National Security and public health.

<unk> is going to be critical to protecting against the potential misuses of these technologies, along with anything mother nature throws at us.

Can't emphasize enough the synergies between these businesses. It's clear that there is an unmet need for bio security, but the question is who is best positioned to grow into this large addressable market. We think ginkgo is well positioned to do it as the tools. We're building in our cell engineering business help provide the foundation for bio security Likewise are close connection to our Biosecurity platform.

That is highlighting emerging drafts allows us to be a better partner to our vaccine and therapeutic partners ideally, enabling us to make more effective countermeasures earlier in the risk cycle and you're going to hear from me a bit the strategic session about how we're building all of that in bio security Alright, now, let me hand, it over to Mark to give a little more color on our financial performance this quarter.

Thanks, Jason I'll start with the cell engineering business, we added 21, new cell programs and supported a total of 116 active programs across 76 customers on the cell engineering platform in the third quarter of 2023.

This represents a 36% increase in active programs year over year with significant growth in the Biopharma and the food and agriculture verticals.

Notably we added 10, new biopharma programs in the quarter a record number of new programs for any particular market segments in one quarter.

Cell engineering revenue was $37 million in the quarter up 51% compared to the third quarter of 2022, driven by our significantly expanded customer base.

Yes.

Now turning to bio security.

Our bio security business generated $18 million of revenue in the third quarter of 2023 at a gross margin of 62% both revenue and gross margin benefited in the quarter as we closed out our last remaining K to 12 Covid testing contracts.

We're continuing to gain traction on an international scale now totaling 14 countries with either active programs pilots or Mou use.

And concentric is also progressing as by a radar offering with multi pathogen detection and new efforts Enzo and Arctic disease monitoring while also building a suite of next generation biological intelligence capabilities, including AI based epidemic forecasting.

And now I will provide more commentary on the rest of the P&L, where noted these figures exclude stock based compensation expense, which is shown separately.

Starting with Opex R&D expense, excluding stock based comp increased from $74 million in the third quarter of 2000 $22 million to $123 million in the third quarter of 2023, representing growth and capabilities, particularly from our acquisitions in the fourth quarter of last year.

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G&A expense, excluding stock based comp increased slightly from $59 million in the third quarter of 2020 to $2 $62 million in the third quarter 2023, supporting the growth of cell engineering revenue and the integration of prior year acquisitions.

You will also see that we recorded a $96 million noncash impairment charge on Zheimer Gen lease facility with zymogen exited in the third quarter.

While our full accounting for the zymogen bankruptcy is not yet complete we expect to deconsolidation zymogen financial statements effective October <unk> 2023, and so in the fourth quarter. Our accounting is expected to result in removing all zymogen multiyear lease liabilities along with other financial.

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Okay.

Stock based comp you'll notice a significant drop in stock based comp this quarter similar to what we saw in Q1 and Q2 of this year. As a reminder, this was because of the catch up accounting adjustment relating to the modification of restricted stock units. When we went public is mostly rolled off at this point, while the bulk of that adjustment is done.

About half of the total $54 million to stock comp expense in the quarter is still related to RSC was issued prior to us going public.

Additional details are provided in the appendix to this presentation.

Net loss it is important to note that our net loss includes a number of noncash income <unk> expenses as detailed more fully in our financial statements because of these noncash and other nonrecurring items. We believe adjusted EBITDA is a more indicative measure of our profitability. We've also included a reconciliation.

<unk> of adjusted EBITDA to net loss in the appendix.

Adjusted EBITDA in the quarter was negative $84 million compared to negative $72 million in the comparable prior year period the.

The decline in adjusted EBITDA was attributable to both the higher run rate of expenses in cell engineering and the as expected decline in Biosecurity revenue.

And finally capex in the third quarter of 2023 was $4 million.

Moving on to our outlook for the full year in terms of Big picture. We are expecting total revenue of $250 million to $260 million in 2023 in line with previous guidance.

Now looking at the details and starting with new programs.

Based on third quarter results and pacing of new opportunities. We are now targeting 80 to 85 you sell programs in 2023, we continue to see growth in pipeline opportunities. However, the pacing of pipeline conversion has been impacted by both macroeconomic conditions as well as the fact that our.

Cell programs are a complex enterprise sale, while we have undertaken several initiatives in the past year to address these two challenges including process improvements through our contracting cycle success based pricing and a focus on the Biopharma segment.

All of which have helped drive our metrics in the right direction overall, we have not yet fully solve the pacing issue, particularly as deals get into the final stages.

I'll also note that in addition to our formal new program targets Ginkgo signed several tax licensing evaluation agreements in the third quarter, which allow customers to evaluate more advanced assets.

Such as the cap says we acquired from stride bio these customers might then execute a license agreements to continue using the assets. We did not include these deals in our program count as they do not involve foundry work. However, they do represent a new potential source of revenue for ginkgo.

Moving on to revenue, we are updating our cell engineering revenue outlook to be in the range of $145 million to $150 million. This is inclusive of $4 million in downstream value share. We have recognized year to date through September 30.

As for bio security based on year to date results, we are increasing our revenue guidance to land in a range of up to $110 million.

Fourth quarter revenue will be driven by federal and international partnerships supporting pathogen monitoring and bio security infrastructure development as the K to 12 Covid testing business ended in the third quarter.

In summary, we are pleased with the overall direction of progress and continue to focus on scaling the business as we finish out the year, we remain focused on driving new programs to the platform in this challenging macro environment. We are excited about the significant traction we have made in the Biopharma segment.

And we continue to manage our balance sheet and cash flows to maintain a long runway runway, while maintaining flexibility to capitalize on near term strategic opportunities with over $1 billion of liquidity at quarter end.

And now Jason back to you.

Thanks, Mark this was a solid quarter for ginkgo, our deal with Google sets us up well to lead in the application of AI to design DNA and proteins, while our deal with Pfizer is a real signal of commercial progress I don't Wanna be digging in and on a second.

However, I want to address why we're taking down sell engineering guidance. We're building a relationship with you all as a young public company and so while we want to have ambitious but achievable goals. We also want to update them as the year progresses and tightened ranges as we get closer to year end. So we're revising our guidance on the sell engineering services components of our revenue to 140 to 100.

$45 million down from 145 to 160 million generally this is for the reasons I provided on the last call around industrial biotech venture capital drying up and also and reducing the size of programs that we're seeing in that sector as well as our new program counts being lower than hoped for in Q3, which impacts Q4 revenue now.

I do want to spend some time on the program counts being lower because this is a critical metric for us that demonstrates our flywheel spinning up at ginkgo, we get better with scale and it's one that I pay a lot of attention to internally here. So we had 21 new programs this quarter, which was less than I hoped to get but at the same time, our enterprise sales infrastructure is stronger than it's ever been again.

And in particular I want to call out our new program with Pfizer and explain why it is an important demonstration of our commercial capabilities. Here. So this is a drug discovery deal in mrna therapeutics and Thats important first.

Drug discovery is a harder sell than manufacturing R&D deals, which is a number of the previous deals we've done in biopharma and the reason for that is discovery work is more closely held by our customers remember we have to convince customers to outsource work to kinko's platform that they would otherwise do themselves. This is the kind of work that they tend to think they should do themselves.

Alright, and then secondly, mrna as a new modality, it's a new type of drug right and so it's emerging it's high tech and ginkgo is proving that we can lead it right because customers have to choose to work with us in an area like mrna as a general platform in other words the same platform. That's doing mrna biotech is doing agricultural biotech.

And all this is to say that this is not an easy deal to close, especially with several hundred millions of dollars of potential downstream value attached to it which is why it's worth pointing out that I wasn't involved heavily in closing this deal nor was genworth, who heads up our commercial team. This really came out of a normal sales process from our commercial and deal teams here at <unk>.

And this is a big deal because as much as I like to think myself as good at sales I am not scalable, okay. Janice <unk>, our enterprise sales team is scalable and the types of deals ginkgo was doing involving fees during technical work plus hundreds of millions of dollars downstream milestones of royalties are.

<unk> negotiated by the CEO and leadership team of a small biotech company. If they were partnering with a large biopharma lifesize or like that type of deal you see popping up all the time in industry press and so on in the pharma industry ginkgo being able to do a deal like that in a routine manner is a huge strategic advantage for the company and the result of <unk>.

Great work and team building led by Jan who heads up our commercial team over the last two years to really build an enterprise sales engine here at ginkgo, So I am thrilled to see that and if we hit the high end of our updated guidance of 80 by programs that would work out to 30, New program starts in Q4, which would be a great further signal of how we're scaling this enterprise.

<unk> infrastructure, and that's something I'll be watching with the commercial team coming up.

Finally, with the updated guidance, we're still looking at 36% to 44% growth in new programs and 32% to 37% growth in sell engineering services revenue over the last year scale helps dingo, and so I'm happy to see that rate of growth.

Okay. So let's dive in on our three strategic topics.

First I want to share some recent customer case studies, where we apply our AI technologies, you got a little more sense of what we're doing there second we often get asked actually got asked a lot about what programs. We're most excited about and what programs are most advanced sort of our program pipeline I am not going to pick favorites, but I'm going to share a lot more data around that pipeline. So you get a better understanding.

Ending of where all those active programs are and just how much diversity there is in those programs.

And then finally I want to share a little more about how we're thinking about the future of our Biosecurity business as a defense technology business as part of National security infrastructure, and how importantly, it relates back to sell engineering.

Let's jump in.

So first I want to talk about how AI fits into the other assets of Ginkgo and show some case studies of its application.

So we've talked a lot previously about our foundry and codebase that Ginkgo reminder, our foundry is our automated laboratories here in Boston that generate data at lower costs as they grow and scale, Okay think like a factory.

We're testing genetic designs. This data is organized into what we call our code base, which is reused across many different customer programs. In other words, we can use data from one project to help speed the development of a second project from a different customer and this is again another important asset that gets better at scale. So what's exciting is that these data asset.

<unk> can also be used to train large AI models that then inform the sort of experiments, we should do to better train those very models, okay, and thats, a very exciting feedback loop and it's making our models better every day, it's something we've been making good use of that ginkgo.

We did just announce this quarter.

Partnership with Google Cloud that is going to enhance our development efforts here at Gingko and as a reminder, this partnership with Google gives ginkgo scalable compute capacity and attractive prices to train large foundation models, but it also represents a commitment by Google to fund our model development efforts upon completion of certain milestones we are already.

Well on our way to building out those models as we have already achieved our first cash milestone in this deal unexpected earned the second in relatively short order.

One way to measure, making <unk> easier to engineer at ginkgo is by reducing the cost to get to a successful result for our customers and that cost is a function of three things first the cost per unit operation brightness like the various operations happening in our foundry and we drive that down through investments and increased scale automation and <unk>.

<unk> of liquid handling so we can use less reagents and so on.

Second the number of unit operations that we need per design cycle in other words each round of engineering, we do how many how much work do we need to do in the factory and this one is tricky.

It requires judgment, sometimes you want to run a giant campaign or retry tens of thousand designs.

And that's the right thing to do and it's something that ginkgo can do uniquely because of our scale and those early large campaigns can really increase how fast you learn but.

For our project again, the ability to do reinforcement learning from prior results in other words take what we learned from something and feed it back in.

Back into the model is a key part of why customers are working with us but in certain areas. We've developed so much depth in other words, we've done enough projects like that that we can exceed our customers back in just the first design cycle and this is really critical to the customer because the number of cycles.

Using that significantly speeds up programs and often customer, especially in biopharma care much more about speed than they do about budget alright, So I want to share a couple of case studies that highlight how we are seeing those variables moves alright. So the first case study as really call. This is an enzyme engineering program that we started earlier this year customer came to us with an enzyme that had been produced for.

<unk> by another service provider it wasn't sufficient to meet their need in the market on the left you can see the various enzyme designs. We tested so that black dot in the middle is the starting sequence represents a starting sequence our customer gave us dots closer to that our protein sequences that are closer than a more similar and the further away you get the less similar.

Sequences to the original and so this is where the combination of AI in our foundry I become really powerful first we can afford to get to the foundry to test much more broadly than is typical we see over and over again that minor tweaks to an enzyme is not sufficient to get the kind of big step change improvements customers want.

Adding but adding all of that diversity all of that change in the protein is risky right. Many of you've changed sequences a lot they tend to have less success.

So because we can screen enzymes, so much more efficiently, we can search that much wider space and find that kind of needle in a haystack.

Second our AI ml models are getting extremely predictive. So ultimately here, we tested 500 member library, comprising both known enzymes and our code base as well as custom engineered novel enzymes. Each member is represented represented by dots on the left and in the first cycle. We discovered an enzyme that was 21 times better than the.

Original off from the customer and the big win here is speed. Yes. We were also able to use our highly efficient workflow with a relatively small library.

500 members versus again, sometimes we do tens of thousands in a cycle, but we're really unlocked. It was improved accuracy of our <unk> models predicting sequences that would work.

Far exceeding our customers expectation in just our first cycle of design. So that's really exciting.

Second customer case study is around production rather than optimizing that enzymes are a customer that wanted to figure out how to produce a small molecule compound at higher tight space like how much of it you get out of the fermentation putting in those big tank and they had a goal here to do this over the next three to five years, which will be important in a second.

Oh here were here, we were able to deliver a better outcome than what the customer asks for originally Theres wanted us to try a couple of different host drains and we did it faster and cheaper than they want it. So on our first experiments we were able to prove the tighter by 12 five fold.

This is what the customer wanted us to get done by the end of the first year and we did that in the first experiment and it was building on knowledge that we already had in our codebase. So this is less about the AI and more of that we had genetic elements and we knew which ones worked well in this organism. If we could just take them off the shelf.

That's very powerful.

Again strength of scale, because we had that big code base, we can take something off the shelf that another company that didn't have that wouldn't have on the shelf.

And then by the end of the program, which only took US 10 months is that a three to five years, we were able to deliver 50 fold tighter improvements, which is almost double what the customer was originally working towards the second round of improvement was driven by machine learning and driven enzyme improvements similar to the last case study and again. This is something that makes it unique we draw on a wide range of tools.

So in this case genetic elements off the shelf as well as protein design enabled by AI, putting those together gave that outsized outcome for the customer. So these are a couple of examples of how we're integrating our AI tools into customer programs I cannot emphasize we are just getting started with this I'm super excited about aig's ability like I said in that equation to improve efficiency of all of those different stack.

Yes.

In other words, we had to negotiate and sign up a customer and get them to outsource. This work for us to us to get on this program list in Q3, we had our highest number of active programs on our platform of all time with pharma, making up the largest percentage of those programs, but an additional piece of color I want to give you today is where those programs stand in terms of their mature.

<unk> in other words, how are they progressing through the technical work Alright, and again I am happy about that farmer ship I don't want to undersell. It I do think thats really important, especially as industrial biotech has gotten tighter it's really great to see that that is again, a real strength of being a platform right if a certain area.

Get better we can move to areas that have more demand as long as biotech in general is moving we've got something to do.

Okay, So theres, a bunch or to me as a traditional product based biotech company would often show that a pipeline like this for maybe five or 10 drug assets that are sort of moving through preclinical and clinical trials here. We have so many of these programs that we can't put them on one slide. This page is just the programs that are over 50% complete.

And we'll show the rest of the next slide. This is the point I was making earlier of why I'm still excited to see 21 programs being added in a quarter. Even if it was less than we were hoping it's just a really great amount of scale on a relative basis in the biotech industry and to give you an overview of the chart each horizontal bar here represents a program and the dark portion of the bar on the right hand side.

<unk> represents the progress made on that program year to date as a portion of the total program and I cannot tell you. The number of times, we get asked for this ASO I'm very happy to be sharing it with all of you and we will try to do this again if people ask for things, we try to clean it up and get it out there is a good example, so as you can see at the top there are a number of programs that are at 100%, okay. So that mean.

That <unk> program were concluded on that program in Q3 that we did this again for Q4, we began alright and on the next slide you can start to see some of the shift in program mix. So if you again the colors are tricky, but if you look at the colors here.

Given more of our recent efforts into Biopharma youll see that a lot just under half of our newer programs that are earlier in development or in Biopharma. So again I'm happy to see that alright.

After those programs hit 100%, if they do and some failed before they do but that they had 100% then the customer and the customer chooses to move forward with them. They enter commercialization and so you can see we have on the bottom 15 programs that are being actively commercialize now meaning the customer is moving forward, we're taking them through regulatory likes and logic in phase II trials are there going into <unk>.

Gail up like century, and many customers don't announce this again this is like product development. So it can be held closer the vast but we've shared a couple where they have and eventually that commercialization process finishes and we have six programs that are kind of fully commercial in other words, they're giving us royalties or its equity on a program that the customer has put into the market.

So we're very excited to see the pipeline that you saw on the last couple of slides hopefully move into these buckets as the programs get to a 100% complete and were actually excited to add many more programs to the pipeline in the coming quarters. So we need many more slides.

And the scale of all of this is what makes kimco special as a platform rather than a product company and biotech I'm really really proud of this scale, it's pretty awesome to see it all in one place.

Okay I'd like to cover our last strategic topic for the day, which is about the national security priority, that's emerging around Biosecurity and ginkgo is position in this emerging space.

So just the past few weeks have seen a ton of discussion around the convergence of AI in biology, I have linked a bunch here.

Here, which are good reading for those interested in the space last week, Anna Murray, our new head of AI, which is why you got to see Megan had started this presentation that are Anne Marie and Matt Mcknight, The general manager of our bio security business. We're in London during the AI safety summit to discuss this and we've been spending quite a bit of time down in Capitol Hill discussing both how to accelerate.

Leadership in AI in biotech, but also how to advance these technologies responsibly, so while bio security needs to exist irrespective of its potential for misuse by humans and the reason for this is whether we do it or not mother nature is throwing off.

<unk> and pandemic on our own and so we do need biosecurity, regardless for public health. We are seeing that biology is becoming a more clear national security priority with the advancement of AI tools, which is driving more glover global government focus on what needs to be done to ensure the technologies deployed responsibly we've.

We've seen real momentum in defense technologies recently, I would say as a category.

And with particular leadership from our Board Chair, Sean Soccer at voluntary didn't read a really a nice blog posts of charms link there there is not yet a defense tech business for biology, but it is increasingly clear that the defense community believes we have a critical gaps when it comes to biology Ginkgo has been building biodefense tools for years now to protect our platform.

To respond to Covid in a big way and more recently mapping out what a more scalable biodefense ecosystem might look language I'll talk about here. Okay. So ginkgo plays across the tech stack for Biodefense, but we see a significant investment in product gap in the area of monitoring and analytics as most of the investment to date has been.

On this third box at the bottom response, so things like vaccines and therapeutics and Thats in part because kind of our approach of vectors disease has been wait till it gets out of control and then do something about it.

Work hard if we can put a damper on it but hey things are going to happen and we've got to be able to respond. After the fact, that's been the overwhelming investment as opposed to prevention and early detection I think the impact of Covid has changed that calculus, you have countries looking and saying hey, the national security effects of this mean, we can't just let it happen and clean.

It up afterwards, we need to be able to detect and respond and you can think about this a bit like cybersecurity right. Your computer is constantly monitoring for something dangerous characterizing it addressing the threat right as it comes about.

Our security we wanted the exact same thing we want to build that infrastructure to be constantly monitoring leverage AI to reduce the time to threat detection, and then mitigation and it could be even a different kind of navigate mitigation than vaccines. If you detected early enough you snapping out okay.

Our bio radar product, where we collect samples from wastewater on on planes and anonymously from voluntary swabs from passengers on airlines. It is exactly the type of infrastructure were building to do this type of monitoring this bio radar product enables continuous data collection, so not just on COVID-19, but we.

Just announced we're expanding to over 30 pathogen targets.

<unk> announced the expansion of our partnership with the CDC. Just this week. Some of these pathogens have actually little to no genetic data publicly available in recent years. So we're really tackling some big blind spots with this expansion into more disease, we're up and running and nine International airport, so far and that means we're already getting visibility into flights originating from over 100 countries.

In addition to airports, we're working in complex zones and this quarter. We've made a lot of progress with new efforts to monitor agricultural and animal samples from zoonotic spillover, including partnering on two new USDA funded projects and.

And we are now progressing to deepen our analytical insights by integrating AI based tools with our bio radar data and we're examining historical epidemic data and routinely used common AI methods for bioinformatics genetic engineering detection I've talked about before our work with IR, but there and modeling and these will continue to get better as our AI platform.

That's better but right now we're really excited to build new prediction capabilities and we're working with a consortium of partners funded by the CDC center for forecasting and analytics to find new ways to tell when is the disease about the spike and what measures should you be taken which should be taken against it and again I would highlight we already do this sort of thing for weather right.

When's that hurricane looking like it's going to come in right where is it going to land and all that sort of stuff. This is exactly the kind of infrastructure, which you have for infectious disease. So all of these data and analytic capabilities are at the foundation of our novel Bio and so things like bio intelligence product for National security as biological risk accelerates and intersects with global conflict and geopolitics.

<unk> bio and will represent a critical component of intelligence capabilities. So this is now think like satellites. Okay. Yeah, theyre looking for hurricanes, but they're also looking for missile launches alright. So we also want to see if someone doing something that some type of mischief is going to be monitoring to detect that we're working to build out a <unk> platform that can support attribution scenario based response planning and medical counter measures and.

Through this work <unk> will be able to address critical questions for security decision makers, such as what threats and outbreaks or on the horizon, how dangerous as a new threat, where did emerge and how what can I do about it and the key here is that last part of what can you do about it we will need bio and if we want to effectively neutralize bio threats before they cause a lot of damage, it's a lot easier to.

Put out that buyer when it's small then when it's too late alright, finally, I want to touch on why I think it's so valuable to have ginkgo bio security business alongside our cell engineering business. These things makes sense together with our by our radar product and buyer. We provide we can provide meta genomic data to feed our cell engineering platform as more data for training and the tools, we build or understand.

Biology, and our cell engineering platform can be reused to make analytics better on the bio security side, Hey, I want to understand what this protein is a useful thing for <unk>. It's also useful for an emerging pathogen.

Finally, our bio security platform could also provide early warning information to help develop new countermeasures vaccines and therapeutics that are self engineering platform could help build right our customers develop vaccines and therapeutics right.

So both our bio security intelligence offerings enable one another and we believe they'll continue to grow especially through the use of AI.

In summary, I'm really excited about the great work, we've done this quarter, especially the demonstration of our commercial sales engine with the Pfizer program that I mentioned and our strategic relationship with Google and AI and I'm looking forward to continuing our growth in this space Alright, now hand, it back to Megan for Q&A, great. Thanks, Jason as usual I'll start with a question from the public and remind the analysts.

Line, then if they'd like to ask a question to please raise their hands on them and I'll call on you and open up your line. Thanks al.

Welcome back everyone. Our first question comes from at Clifford and long on Twitter, formerly known as <unk>.

What milestones will be tracked measure ginkgo success in building DNA is AI.

What metrics can you share that will track the accuracy improvements when building AI overtime.

Yeah.

Sure I can take that one.

Yes, so I think one of the thing Thats very interesting about ginkgo is we have a lot of ongoing programs today and so.

I think the first place we will see the application of AI is in driving efficiency of all of that ongoing work. So you saw some examples of that.

In the slides I showed but.

A big part of what we're trying to do next year is add lots of new programs, while keeping a lid on our operational expenses.

You will see that in part driven by the efficiencies, we're going to gain NII.

And then secondly, I think in the longer term.

Ah represents a interesting interface to our platforms I think we will ultimately be able to open it up more directly to customers through AI tools and so that's something we're excited about and part of the model building, we're doing with Google.

Great. Thanks, Jason will start opening it up to analysts now pages from Morgan Stanley. Your line is now open.

Hey, guys. Good evening can you hear me okay.

Perfect.

So Jason one quick question for you just in terms of the the lighter program at the R&D implied cell Engineering guide for what our.

How should we be thinking about 2024.

Consensus has you doing about $300 million and cell engineering revenue, but you guys are sort of in that $40 million to $50 million quarterly run rate at the moment. So are there any sort of like missing pieces. There that we should be thinking about as we think about the year over year progression.

Yeah. So.

So obviously not sharing guidance yet on 2024.

I would say mainly the we have an aggressive push around expanding the scale of our enterprise sales efforts and our ability to add new programs to the platform I'm really happy to see like if you look at the number of.

Active programs going up on the platform is our ability to handle more work.

Gone up a lot and so I think that's part of <unk>.

What gets us excited for next year, but we will be sharing obviously guidance.

At the next at the next call.

Fair enough and then one on just the tech licensing evaluation deals that you mentioned, obviously early days still.

And youre not including them in the program count, but can you just give us some context around how meaningful a contribution this could be and over what timeframe do you expect sort of some early wins based upon your conversations so far.

Yes, Mark you I talked a little bit about how we think about those tech licensing.

Yes, so I would think sort of single digit millions in terms of potential licenses or kind of lower double digit millions and.

Potentially some.

Some wins certainly within the next 12 months.

Got it fair enough. Thanks, guys I appreciate it and maybe the only thing I would add to that.

It'll be interesting thing for us to think about the long term obviously, we have certain definitions for what makes for a major program at Genco that gets added to our program count.

I kind of hope over time, we have more assets in our code base that.

Can more easily be directly licensed into customers. That's obviously great.

Revenue back to us without a bunch of work.

So I think those are nice things to see but.

As we get more of them I think we want to figure out how to communicate that to you all better.

Fair enough thanks, guys.

Next up we have Steve Marc Frahm Cowen Ethernet line is now open.

Great can you hear me.

Yes, yes.

Great. Thanks for taking the questions.

With regards to the new program ads.

Can you give us a sense if there's any particular partner class, which is harder to get over the deal signing <unk> line.

And what specifically are you guys going to be doing to improve the deal closing timeline, if I heard correctly.

It looks like you said your enterprise.

Sales team is bright size. So if it's right size what exactly are you doing to kind of improve the deal closing timing.

Yes.

I'll speak to you generally mark if you want to add anything can go for it and I know the timing.

Timing is something you think a lot about.

So the first thing I would tell you that I don't know that I don't think we are right sized on the total size of the enterprise sales team I think what is exciting to me is if you look at the new programs 10 out of 21 and we're in Biopharma this quarter and so Steve we've.

We've talked about this previously about like.

If you look across different industries for biotechnology, the largest R&D budget spend is in biopharma. So in terms of our ability to expand into a market is the one I'm. The most excited about now.

Now we started in industrial biotech, even before we got into <unk> and that's in part because that industry had less in house infrastructure right at the end of the day Ginkgo is convincing a customer to outsource to our platform something they might otherwise do in house and that was an easier argument to a startup industrial biotech company versus Pfizer.

Okay.

Five years ago now what's happened in the interim is the venture capital ecosystem around industrial biotech has gotten really tight.

That has been headwinds for us in terms of adding new programs there, but we built out more of our enterprise sales team like I mentioned on clothing that Pfizer deal in Biopharma.

Expect us to make that team bigger.

We see a lot more opportunity there I think we are fundamentally limited by the number of people we have out talking to customers right now and so I think that's one of the ways. We're going to grow program counts next year is growing that sales team, but now I'm confident we have the right thing to grow right. If you round the clock two years and I just throw on a ton of folks to try to sell into Biopharma, we didn't have.

The reps.

We do now in terms of knowing what it takes to get deals what are the right kind of people to hire and all that sort of stopped.

We're much more confident about that now so it's the right time to scale that team.

Okay got it and then with regards to any particular partner class being harder or easier to get over the goal line.

Stark startup industrial biotech.

One I think that used to be like a real strength for us just because we had good reputation a lot of good examples out there. It's just a market that is kind of in shell shock right now because a lot of the venture funding is right up there. So that's one I think that has been tough.

Now I like it in the long term, it's one of the more interesting markets remember our mission here is make it easier to engineered biology, and so what's exciting about industrial biotech is unlike a therapeutic that ends up in a human.

Industrial biotech is going to get the engineering a lot faster. So that's exciting to me, but it's still like we still have to deal with the ebbs and flows of capital market interest.

Okay cool.

Maybe a quick one on bio security.

Mark on the gross margins, yes noticeably.

Up as you're exiting and the mix shift goes away from the K 12 testing, but.

How should we think about the go forward run rate X K 12 testing.

The go forward run rate on margin or on revenue.

Gross margin, yes, so so I would more or less ignore what happened in Q3 as you think about go forward. So we benefited.

On both revenue and gross margin from the closeout of some legacy K to 12 contracts and so there was some.

I would just call like onetime revenue and some of that came through a good gross margin that.

That hit in the first half of the quarter and so that's why you saw the pop in Q3, it's not because.

The newer business, the new federal and international business as sort of a higher portion of the mix and as somehow higher gross margin. It is and so just to kind.

To reiterate what I've said in the past, we don't know how the gross margin will evolve, but we're certainly targeting something around that 40% range. Once we get to kind of an appropriate scale, but as you saw when we were building the business to begin with the gross margin did fluctuate quite a bit until we got to.

The right sort of scale.

We certainly think about our target margins in that 40% plus or minus range, we'll see sort of how it evolves over time.

Got it thank you.

Thanks, Steve next up we have Derik de Bruin from Bank of America Derek.

Eric Your line is now open.

Hi, how are you.

Eric Good to hear from you yeah.

Because I just feel better I'm still here.

I'm not dead yet.

We're all happy.

<unk>.

So Jason.

Jason you've added a number of new programs.

It considerably I guess, how should we think about two points like one what's your related party revenues exiting this year and I'm sure it's down quite a bit I'm sure. It is a little bit clarity on that end.

Any preliminary color on sort of like cash burn, particularly as you sort of like get rid of the legacy Xyrem changes, how should we sort of thinking about cash burn metrics from here.

Yeah.

I kicked out to mark those to market and then pick it up at the Ana can give a little extra color on it because I think my number.

So there is the exit rate on related party revenues as it's going to be like a substantial decrease from anything that <unk> seen in prior years. It fluctuates a bit if you were to look at it this year quarter to quarter and we disclose those figures. So it so you've got the right youll see it moves around a little bit but in the aggregate. It's certainly much less than it was last.

Year.

And I would expect.

Yes, I mean that mix shifts.

Largely at this place.

At this time taken place.

Present, this quarter, but it was I don't want to.

Yeah, we'll pull it up or down, but its alright, but keep the coupon mark and we'll get it.

The appendix to the earnings Duck Okay.

The related party mix I mean, it was still sort of in the.

Yeah, It was about 25% of revenues.

At third quarter, So you can see compared to the past.

70% or something else, it's gone down quite a bit I would expect it to even be less than that.

<unk>.

So yes, so and then sorry, the second question on cash burn if you could maybe just restate the question.

Just sort of wondering if you've added a lot of programs just wondering what.

Yeah.

How are you sort of thinking about cash burn I mean, you've got in your cash balance and just sort of thinking about.

R&D expenses and things evolving next year, yes, yes. So if you think about how we think we will finish this year.

We don't guide to cash burn, but I think if you take the Q3 year to date cash flow statements and just extrapolate it.

Then there is going to be some stuff that happens with the deconsolidation of zymogen cash right. So youre going to get the extrapolation add a little bit more burn on top of that.

That'll get you to a number in the range of 400. This year. If you just do that extrapolation plus.

And we would expect to improve on that next year.

Got it and then one final one if I can.

Significant and expectations for more downstream value in 2024.

Sorry, I missed the beginning of the question.

When we have more yes.

Yet about $4 million, including in terms like Dr. Jasmine value. This year does that number go up next year than where biosensor, it's sort of a going back to <unk> question on trying to get at a revenue number which I know you're not going to answer, but I got to drive that.

Yeah, we're not guiding on <unk>.

Even in year ECS, we didn't say, where we're at right now, but we're not even guiding for the rest of this year and that's in part because it's really not one thing thats under our control in a very direct way it basically depends on those.

Those commercializing programs when they had certain points for customers that.

Triggered downstream value share for us or in the longer term things like royalties. So I think we're going to stick with that model.

I know, it's not ideal, but we are sharing more things like the.

Program pipeline, we shared today and I think over time as we get bigger numbers on stuff hopefully we can we can give you a little more to work with our dark, but but I understand that that's something people want to say.

Thank you very much.

Maybe the only thing I would add.

Just a couple of things on that.

And part of that rate related party. If you look back in time, a lot of that was like again, new companies getting started on the platform things like that.

And so I would I would highlight as venture capital has gotten tighter in other words higher interest rates.

That whole line of customers like New company starts we had on person residents I can't go that we're launching companies.

Just isn't there right now.

In the market. We're in today, which is why I am even though I know Rob on a program counts the ability for ginkgo to have pivoted into selling from <unk> that are launching a company on the platform being a lot of our demand three years ago to Pfizer and Merck and Novo Nordisk and boehringer are customers like that.

That's a pretty different scale.

And I think it also reflects the flexibility of having a platform business models is one of the reasons I like us.

Our ability.

To survive and changing markets, especially in this earlier stage of the company, where we're still spending upscale I like strategically I like that flexibility I think that was borne out this year.

I do want to just highlight that and then on the cash point.

Well I would say were in the quarter of a billion dollars plus.

We're very sensitive to cash and we appreciate that cant go get better with scale and we also have all this downward value share that we want to get too but to get there we have not run out of money and so that is internally are really one of the big things that we do all our planning around so that's not something we won't pay attention to.

How are you.

Great. Thanks, Yeah, that's good to hear from you Derrick.

Thanks for calling in.

Thanks, Eric next up we have Michael Freeman at Raymond James Michael Your line is now open.

Alright, Hey, Jason Martin Megan Thanks, very much for taking the question.

And thanks very much for for I'd like you to Michael I'll, just Derek never call them. So I'm just trying to encourage them to.

Do it in the future.

Sorry go ahead.

I appreciate I appreciate that.

Really appreciate also the you guys, putting it that swimmers plot on project maturity I think cat's shipped a lot of light on what's going on inside you can go platform now one one blind spot in the data visualization data visualization and I Trust for Ginkgo is what happens between.

100% completion and commercial yes.

So I wonder.

I wonder.

What sort of are thinking we can do.

To help its partners undertake whatever work needs to be done between 100% and commercialization.

Good question.

I mean the.

Sort of flipping answer is have enough programs that it kind of comes out in the wash you know in other words like we can be responsible for.

Yeah animal free meat go to market to cannabinoid, two new pharmaceuticals to agricultural trades.

The range of products, just makes it tough for us to really be.

A major player in ensuring that those steps downstream of the cell engineering are successful for our customers.

So now.

That said I mean, I think you will get bigger and more of the world is running on our platform investments that generally help biotech products make it through that will pay off in big ways for us, but I would say today.

I'm more focused on just getting more people on the platform and just kind of.

It's up to them to do that part I think realistically in terms of we're going to need to put our resources, it's making the platform more efficient so that I can get it.

Do better on fees versus our spending and make it through the downstream value share. So so right now today, we're not spending a lot on on that Michael Gotcha.

Got you got you now.

Other key feature of a biotech sure Biopharma a sore spot is how many patients how many programs die.

You mentioned of course, some programs don't get your 100.

Curious.

How can we get a sense of of how programs fail and what proportions, what proportion of programs might fail.

Yes, I guess I guess like what does it take for for you an apartment to agree that a program is done.

Yes, so I would actually kind of like to share that over time right now.

I want to get like a little more out of the pipeline so that I have like a better.

Kind of a better set of data there I would say is like the major thing holding me back on that but I think that is something ultimately will be able to share with you.

And what in terms of what hits. It I mean, we set technical milestones negotiated with each customer because they're going to pay us on.

Hitting those typically and so that that is what sets is at a 100% right ultimately the some agreed upon technical target with the customer.

Other obvious challenges like it changes right like in other words like cut like program to program their different. So so it is also a little bit like.

You know.

Certain programs are going to be harder than others right.

Not making widgets here. So I think that's another thing that we're probably just stuck with.

In terms of you know.

Making it tougher to model.

Gotcha.

Our goal here is like be a utility right like we really want as much of the world running on our platform as possible.

It'll be fine right, but I appreciate that in this era of people are trying to handicap.

Programs, but I hope I have the numbers go up.

It gets a little easier.

Okay, alright, thanks, very much I'll pass it on.

Thanks, Michael next up we have <unk> from Goldman Sachs. Your line is now open.

Hi.

Hello, Okay.

Hi, Thanks for taking my questions just filling in for Matt Tonight.

Following up on new programs could you maybe just give an update on what youre seeing in the sales funnel from customers I think in the past you said theres a lot of potential of new programs in the funnel are you seeing some of these conversations are being pushed out data capital conservation as we've seen many headlines from pharma R&D cuts or is there anything else you're hearing from customers on program cancellation.

Make sure you bring question there as sort of you guys program cancellation at the end, but I'll I'll, so I'll defer that for them and that's like a slightly different topic, but in terms of like sales pipeline.

Yeah, again, it's very strong and and Mark touched on this a little bit in his comments that like one of the challenges. We have is like the timing of the close so we do we end up starting certain programs like in advance of closing assignment to customers that we got started a little faster and things like that we do that as we get very close to being <unk>.

Cross the line with the customer from a deal standpoint, we have we have lots of those right now so I like where we're at going into.

And so the upcoming quarters I like our sales infrastructure I like our pipeline like those things are good.

We will see quarter to quarter variability on what gets across the line, sometimes that'll break in our favor and sometimes it won't I think thats kind of marks not about words your amount Mark General point about the timing challenges of complex enterprise sales.

I overall I get.

And then on and then is that does that answer your question I just want to make sure that yeah. That's helpful. And then on program cancellations I mean more on like the biotech programs or projects like you are programs, if that makes sense kind of a difference there and.

In other words like once I hand, something off to a customer and then they fail in <unk>.

Trial and shut down the commercialization is that what you mean.

Yeah, or if we're seeing less discovery work being done more prioritization of like later stage projects I think that is true across the industry. Yes. The good thing about Biopharma is ginkgo is penetration into that industry today is like laughably small.

Even though that is true.

Okay.

So first I'll talk in I like companies that I've never even talked to us before us. So it sounds like it's not as if Oh Wow. We've got this level of penetration and Theyre backing off and it's also not like industrial biotech, where it's kind of gone to us like it's really tightened up a lot. There's still a good amount of funding certainly at the large biopharma, but even at the small ones people are still pursuing research.

Most of them have not given gigawatt serious look yet so that all bodes well for.

Our enterprise sales team to go around and talk to people and show what we got.

Great. That's Super helpful. And then one more you gave a lot of detail in bio security at the Investor Day Nice to see the guidance raised there could you talk to the competitive environment in that market. Obviously, it's it's very new and emerging has anything come up in talking to customers with other players players you're seeing in that space.

Actually those three boxes of like kind of like monitor decision, making and then response obviously in response Theres responses, just the whole biopharma industry vaccine developer.

But by jingles focus has been on.

The monitor and decision, making and in that area. There is some you might've seen theres like people doing like.

Darryl is doing like a wastewater in the U at like like for like.

Municipal wastewater.

So that's all that's happened and that was with another smaller company biomarker. So those are folks that are kind of like at least doing some monitoring on the airport side right now theres not really a lot of that going on we're more fighting like convincing people that this is a good thing to have out in the world and I think we're making good progress on that but it's a little more fighting like getting the infrastructure built in the first place and getting it funded.

Versus like a being a blood red competition. So so I'd say overall, that's the bigger thing is just increasing the.

The profile of bio security as a category, it's not as competitive at the moment.

Okay, Great Super helpful. Thank you.

And I think we have time for one more question from Republic. This one comes from the Investor Inbox. Please provide more details on the recent Pfizer deal specifically what is going to need to do to earn the $330 million mentioned in the recent press release.

Yes, so I can give extra color on this so I mean, what we had in the press release is basically what we can actually say about the.

The deal, but it does highlight that we get.

Research.

Payments as well as milestones.

Or like once we've handed off the assets at our customer and Theyre going to move it through things like clinical trials and so on and then there is also potential for royalties.

I will say in general do you think you'll see there's a lot of across a lot of biopharma deals. We will typically have some type of milestone payments as a if we're doing drug development R&D. It's different if we're doing say manufacturing R&D, which we've done with partners like Biogen and Novo Nordisk, but for drug development, you will see things like okay. If it <unk>.

Get through.

Phase one phase two phase III clinical trial that those are the types of things that would.

Typically provide milestones that are doing this type of research that sort of stuff and then obviously if there is a royalty that's on once it's gone commercial.

Great. Thanks, Jason that about places that aren't getting any closing thoughts for us today.

No.

Other than to say like like I mentioned, I'm really quite proud of the infrastructure that's been being built up on the on the enterprise sales side I think that.

Is unappreciated.

How difficult that is because it is we're selling a different thing right. There are <unk> out in the world that are selling what I call like straightforward research services like give me something that you pretty much know you could do or anybody else could do and I can do it cheaper or whatever ginkgo selling high.

High end drug discovery, it's a much more complicated sale so to be able to do that at scale. I think is it's going to really be valuable for us in the long run so I'm happy to see that.

Alright. Thanks, so much that concludes this quarter's earnings call talk to you all next quarter.

Everybody. Thank you.

Goodbye.