Q4 2025 Ginkgo Bioworks Holdings Inc Earnings Call

February 26, 2026

Youre up communications and ownership.

I'm joined by Jason Kelley, our co founder and CEO and Steve Cohen, our CFO. Thanks as always for joining US we're looking forward to updating you on our progress.

As a reminder, during the presentation today, we will be making forward looking statements, which involve risks and uncertainties. Please refer to our filings with the SEC to learn more about these risks and uncertainties, including our most recent 10-K.

Today. In addition to updating you on our quarter results, we're going to provide insight into the autonomous lab, how we believe it will transform biotechnology and how we plan to commercialize autonomous last going forward.

As usual, we will end with a Q&A session and I will take questions from analysts investors and the public you can submit those questions to us in advance via apps hashtag <unk> results or through E Mail investors I think our borrowers dot com.

Our REO Jason.

Alright, Thanks Daniel.

Q4 was really a breakout quarter for us in sort of defining and really leading in the category of autonomous labs, and so you can hear a lot for me about that in the future, but I want to start by saying our mission remains to make biology easier to engineer, but in 2026, the technology technological focus for the company and really.

Our business focus is going to drill down on investing to win in this category of autonomous labs and this is really a part of what I see as an emerging movement around robotics, and AI and autonomy, that's coming to a lot of sectors in the economy and I think we happen to be in a sweet spot in bringing that into really a high value area around laboratory research.

An increasing amount of excitement about and I intend to win that alright. So how are we going to do it in 26. So first we want to focus.

Our investments in our platform into that area, primarily I'll talk in a minute, but we mentioned.

In our recent earnings announcement, just now that will be divesting our bio security business that allows me to focus <unk> investment in our dollars really into autonomous labs and bring in other new investors to invest alongside us into bio security. So that focused in investment second internal to the company, we want to demonstrate the capabilities.

Jason Kelly: Really. I'll talk in a minute, but you know, we mentioned in our recent earnings announcement just now that we'll be divesting our biosecurity business. That allows me to focus Ginkgo's investment and our dollars really into autonomous labs and bring in other new investors to invest alongside us into biosecurity. That's that focus in investment. Second, internal to the company, we wanna demonstrate the capabilities of our large autonomous lab here in Boston. And the way we're gonna do that is we're gonna start to systematically decommission our lab benches, our walk-up automation, our work cells, the way that we've traditionally done our R&D services over the last 10 years, and move more and more of that work onto a single large autonomous lab that's software controlled here in Boston.

Of our large autonomous lab here in Boston and the.

The way, we're going to do that is we're going to start to systematically decommission, our lab benches or walk up automation our work cells. The way that we've traditionally done our R&D services over the last 10 years and move more and more of that work onto a single large autonomous lab.

Software controlled here in Boston and the reason I want to do that is that serves as a demonstration to the merc's and the Takeda is in the Pfizer's and all the folks who have huge investments in traditional manual laboratories that it is possible to take open ended research and run it through a large autonomous laboratory system and so I think thats really fundamentally the most.

Important work, we're doing this year and then finally I want to book sales of autonomous labs, you're going to hear and one of our big announcements from last quarter that we did a $47 million deal with Pacific Northwest National Labs, So I want to sell it to office labs National labs like that deal, but I also want to sell into Biopharma I want to sell in the research universities and so.

Jason Kelly: The reason I wanna do that is that serves as a demonstration to the Mercks and the Takeda's and the Pfizer's and all the folks who have huge investments in traditional manual laboratories, that it is possible to take open-ended research and run it through a large autonomous laboratory system. I think that's really fundamentally the most important work we're doing this year. Finally, I wanna book sales of autonomous labs. You're gonna hear in one of our big announcements from last quarter that we did a $47 million deal with Pacific Northwest National Laboratory. I wanna sell autonomous labs to National Labs like that DOE deal, but I also wanna sell them to biopharma. I wanna sell them to research universities.

Sort of bookings and landing new deals that the other thing we want to do in 2026 in this direction.

I do want to take a minute and talk about that bio security divestiture.

So you might remember over the last five years, we've invested a ton of energy into this space. There's really came about starting during COVID-19 because we honestly just saw need COVID-19 was sort of a global scale biological disaster, and we felt we should lean in and help where we could.

Jason Kelly: That sort of bookings and landing new deals is the other thing we wanna do in 2026 in this direction. I do wanna take a minute and talk about that biosecurity divestiture. You might remember, over the last 5 years, we've invested a ton of energy into this space. This really came about starting during COVID, because we, you know, honestly just saw a need. You know, COVID was sort of a global scale biological disaster, and we felt we should lean in and help where we could. The niche that we found in that moment was doing really monitoring, so not diagnostic testing, but rather monitoring testing, in order to reopen congregate areas and in particular, reopening schools here in the US.

Niche that we found in that moment was doing really monitoring for it so not diagnostic testing, but rather monitoring testing in order to reopen congregate areas and in particular reopening schools here in the United States. So I'm really proud of this as a decent sized business for us, but really importantly, we helped open.

1000 schools nationwide and this is one of the like really political topics and I think what's neat about technologies you can sometimes find a third way between one and at the time, which was.

Hey, we really should be closing the school's ranking for teachers and we care about spreading disease.

And then on the other side Hey, this is hurting kids and we'd open the school's everyone should just go back and whenever it comes comes and there was a third way, which was why don't we open the schools and have persistent monitoring so that if an outbreak started to happen in a school you can send two or three kids home and stop it and that's exactly what we want to build in a nationwide level and what's continued app.

Jason Kelly: You know, I'm really proud of this. This is a decent sized business for us, but really importantly, we helped open 5,000 schools nationwide. This is one of these like really political topics, and I think what's neat about technology is you can sometimes find a third way, between, you know, one end, at the time, which was, Hey, you know, we really should be closing the schools, dangerous for teachers, you know, we care about spreading disease. Then on the other side, Hey, this is hurting kids, and we need to open the schools. Everyone should just go back and, you know.

After COVID-19 and are monitoring and airports that we do in partnership with the CDC looking for viruses in the wastewater of planes and other inputs both here and internationally in places like Joe Hahn, Qatar at the airport, there and so that sort of identify it put it out put that fire out before it spreads is something that's needed.

Jason Kelly: Well, whatever comes." There was a third way, which was why don't we open the schools and have persistent monitoring so that if an outbreak starts to happen in a school, you can send two or three kids home and stop it. That's exactly what we wanna build at a nationwide level and what's continued after COVID in our monitoring at airports, that we do in partnership with the CDC, looking for viruses in the wastewater of planes, and other inputs, both here and internationally in places like Doha and Qatar at the airport there. So, that sort of identify it, put it out, put that fire out before it spreads, is something that's needed nationally and globally for the US to be secure.

5,000 schools Nationwide. And this is 1 of these, like, really political topics. And I, I think what's neat about technology is you can sometimes find a third way, uh, between, you know, 1 end, uh, at the time which was, uh, hey, you know, we really should be closing the schools dangerous for teachers. Um, you know, we care about spreading disease. Uh, and then on the other side, hey, this is hurting kids and we had to open the schools and everyone should just go back and, you know, whatever it becomes comes. Uh, and there was a third way, which was why don't we open the schools and have persistent monitoring? So that if an outbreak starts to happen,

In a school you can send 2 or 3 kids home and stop it.

Nationally and globally for the U S to be secure the other thing that's been <unk>.

Happened in that period of time, you might have noticed our companies like anderle talent here Shawn soccer, our board chair as the CTO at volunteer this sort of defense tech sector really exploded over the last five years and so there has been increasing interest from pure play investors in the defense space, who want to see next generation sort of biotech.

Fence primes. So again these are companies that would be focused on serving the government and others.

Jason Kelly: The other thing that's then happened in that period of time, you might have noticed companies like Anduril, Palantir. Shyam Sankar, our board chair, is the CTO at Palantir. This sort of defense tech sector really exploded over the last 5 years. There's been increasing interest from pure play investors in the defense space, who wanna see next generation sort of biodefense primes. Again, these are companies that would be focused on serving the government and others on biodefense needs directly. That's very exciting because it means there's lots of new capital interested in that. To my point earlier, where I want Ginkgo to focus very clearly, in 2026, is on autonomous labs.

On biodefense needs directly.

Very exciting because it means there's lots of new capital interested in that but to my point earlier, where I want gingko to focus very clearly in 2026 is an autonomous labs and so one of the great things that happened was we got a lot of inbound from these types of investors and we saw an opportunity to say all right why don't we share in the upside of bio security by taking that.

This unit in the company spinning it off taking it private bringing in investment from some of these great investors Ginkgo will still hold a minority position in that so we got to get a piece of the upside of what we built but the investment needed to build that bio security prime doesn't need to come from the floor.

And that's exactly what we want to build at a nationwide level. And what's continued, uh, after Co in our monitoring at airports, uh, that we do in partnership with the CDC, uh, looking for viruses in the Wastewater of planes, um, and and other, uh, inputs both here and internationally in places like, uh, Doha and Qatar at the airport there. And so, uh, that's sort of identify it. Put it out, put that fire out before it spreads. Uh, it's something that's needed. Uh, uh, nationally and globally for the US to be secure. Uh, the other thing, uh, that's then happened in that period of time. You might have noticed, uh, companies like anderl, pallante, or Shams soccer, or board chairs, the CTO at palantir, uh, this sort of Defense Tech sector really exploded over the last 5 years. And so there's been increasing interest from Pure Play investors in the defense space, uh, who wanted to see Next Generation sort of Bio defense primes. So, um, again, these are companies that would be focused on serving the government and others uh, on buyid defense needs directly. Um,

Jason Kelly: One of the great things that happened was we got a lot of inbound from these types of investors, and we saw an opportunity to say, All right, why don't we share in the upside of biosecurity by taking that business unit in the company, spinning it off, taking it private, bringing in investment from some of these great investors. Ginkgo will still hold a minority position in that, so we get to get a piece of the upside of what we've built, but the investment needed to build that biosecurity prime doesn't need to come from the $430 million, as I'll mention in a second, that we had on our books at the end of the year. We can focus that into autonomous labs. I think this is a win-win all around.

The $30 million as I mentioned in a second that we had on our books at the end of the year, we can focus that into autonomous labs. So I think this is a win win all around and I also think bringing in these types of great folks that we have coming into the private entity or is it really going to open doors with the defense sector, and so on and having a beer.

That's very exciting because it means there's lots of new capital interested in that. Uh, but to my point earlier, uh, where I want to go to focus very clearly, uh, in 2026—Is it on autonomous labs? And so one of the great, uh, things that happened was we got a lot of inbound from these types of investors. Uh, and we saw an opportunity to say, all right, why don't we share in the upside, uh, of biosecurity by taking that business unit in the company, spinning it off, taking it private, bringing in investment from some of these great investors. Ginkgo will—

Our sole branded Biodefense company, it's the right time, so I'm Super excited about this I think it's I don't want to give again credit to the vascular team again go who did absolutely amazing work through ginkgo oxide through Covid and now has a real opportunity here I think to build a generational business coming up in the defense sector.

Jason Kelly: I also think bringing in these types of great folks that we have coming into the private entity is really gonna open doors with the defense sector and so on, and having it be a sole branded biodefense company, it's the right time. I'm super excited about this. I think I wanna give again credit to the biosecurity team at Ginkgo, who did absolutely amazing work through Ginkgo, or sorry, through COVID, and now has a real opportunity here, I think, to build a generational built business coming up in the defense sector. Okay. The last point I wanna make before I hand it to Steve. Again, I think tremendous work over the last two years. We've sort of did two things at the same time.

Okay.

One I want to make before I hand, it to Steve. So again I think tremendous work over the last two years. We've started two things at the same time.

We dramatically cut back spending as we saw sort of a downturn in the biotech sector and a lot of our customers pull back on outsourced large R&D projects, which is really our bread and butter here at the company over the years because of that we drew down on our spending.

Here, I think to build a generational business coming up in the Defence sector.

Pretty pretty substantially so in fiscal year 'twenty four.

We're at $383 million and just last year 171, so 55% reduction in our annual cash burn.

Jason Kelly: We dramatically cut back spending as we saw sort of a downturn in the biotech sector, and a lot of our customers pull back on outsourced large R&D projects, which was really our bread and butter here at the company over the years. Because of that, we drew down on our spending and pretty substantially. In fiscal year 2024, we were at $383 million, and just last year, $171 million. A 55% reduction in our annual cash burn. That sets us up very nicely. You're gonna hear from Steve on our targets for cash burn for this year, even with the investment, our focused investment in autonomous labs, and moving that investment in biosecurity into a separate private entity.

So that's up very nicely youre going to hear from Steve.

Our targets for cash burn for this year, even with the investment our focused investment in autonomous labs, and moving that an investment adviser security into the into separate private entity, we're actually able to do better than what we were spending 25, but.

I think for investors is important to understand where we're at from a cash position and how we've done a really nice job getting cash spending under control as we continue to make investments and get in the right place at the right time with autonomous labs, alright, so I'm going to hand, it to Steve to dive in a little more on the financials.

Thanks Jayson.

Jason Kelly: We're actually able to do better than what we were spent in 2025. That, you know, I think for investors, it's important to understand where we're at from a cash position and how we've done a really nice job getting cash spending under control. As we continue to make investments and get in the right place at the right time with Autonomous Labs. All right, I'm gonna hand it to Steve to dive in a little more on the financials.

I'll start with the cell engineering business.

So engineering revenue was $26 million in the fourth quarter of 2025 down 26% compared to the fourth quarter of 2024.

Uh, okay, uh, last point, I want to make before I hand it to Steve. Uh, so again, uh, I think tremendous work over the last 2 years. We sort of did 2 things at the same time. Uh, we dramatically cut back spending as we saw sort of a downturn in the biotech sector, and a lot of our customers pull back on outsourced, large R&D projects, which was really our bread and butter, uh, here at the company over the years, uh, because of that, we drew down on our spending uh, and pretty, pretty substantially. So in fiscal year 24, uh, we are at 383 million uh and just last year 171 so 55% reduction in our annual cash burn. Uh, that sets us up very nicely. You're going to uh, hear from Steve uh, on our targets for cash burn for this year, even with the investment, our focused investment in autonomous Labs, uh, and moving that investment into bioc into the into, uh, separate private entity. We're actually able to do better, uh, than what we were, uh, spent in 25. But uh, that, you know, I think for investors is important to understand where we're at from a cash position and

In the fourth quarter of 2025, we supported a total of 100 and non revenue generating programs. This represents a 4% decrease year over year.

How we've done a really nice job. Getting cash, spending under control uh as we continue to make investments and get in the right place at the right time uh with autonomous Labs. All right, so I'm going to hand it to Steve to dive in a little more on the financials.

Steve: Thanks, Jason. I'll start with the Cell Engineering business. Cell Engineering revenue was $26 million in Q4 2025, down 26% compared to Q4 2024. In Q4 2025, we supported a total of 109 revenue-generating programs. This represents a 4% decrease year-over-year, primarily attributed to ongoing program rationalization as part of our restructuring activities. Turning to the next slide. On a full year basis, Cell Engineering revenue was $133 million in 2025, as compared to $174 million in 2024. As previously disclosed, revenue in Q1 2025 included $7.5 million of non-cash revenue from a release of deferred revenue relating to the mutual termination of the BiomEdit agreement.

Steve Coen: Thanks, Jason. I'll start with the Cell Engineering business. Cell Engineering revenue was $26 million in Q4 2025, down 26% compared to Q4 2024. In Q4 2025, we supported a total of 109 revenue-generating programs. This represents a 4% decrease year-over-year, primarily attributed to ongoing program rationalization as part of our restructuring activities. Turning to the next slide. On a full year basis, Cell Engineering revenue was $133 million in 2025, as compared to $174 million in 2024. As previously disclosed, revenue in Q1 2025 included $7.5 million of non-cash revenue from a release of deferred revenue relating to the mutual termination of the BiomEdit agreement.

Romero attributed to ongoing program rationalization as part of our restructuring activities.

Thanks Jason.

I'll start with the cell engineering business.

Turning to the next slide.

On a full year basis, So engineering revenue was $133 million in 2025 as compared to $174 million in 2024.

Sell engineering Revenue was 26 million in the fourth quarter of 2025 down 26% compared to the fourth quarter of 2024.

As previously disclosed.

Revenue in the first quarter of 2025 included $7 5 million goals of noncash revenue from a release of deferred revenue relating to the mutual termination agreement.

In the fourth quarter of 2025, we supported a total of 109 revenue-generating programs. This represents a 4% decrease year-over-year, primarily attributed to ongoing program rationalization as part of our restructuring activities.

Agreement.

Trying to the next slide.

In the third quarter of 2024 cell engineering revenue included $45 million of.

Noncash revenue from a release of deferred revenue relating to the mutual termination automotive food works agreement.

On a full year basis. Cell engineering Revenue was 133 million in 2025 as compared to 174 million dollars in 2024.

Excluding these impacts sell engineering revenue was $125 million in 2025 and $129 million in 2024.

This decrease was primarily driven by customer program rationalization related to the restructuring.

Steve: In Q3 of 2024, Cell Engineering revenue included $45 million of non-cash revenue from a release of deferred revenue relating to the mutual termination of the Motif FoodWorks agreement. Excluding these impacts, Cell Engineering revenue was $125 million in 2025 and $129 million in 2024. This decrease was primarily driven by customer program rationalization related to the restructuring, as all discussed previously. The biosecurity business generated $7 million of revenue in Q4 of 2025, and $37 million of revenue in the full year 2025. It is important to note that our net loss includes a number of non-cash and other non-recurring items, as detailed more fully in our financial statements. Because of these non-cash and other non-recurring items, we believe Adjusted EBITDA is a more indicative measure of our profitability.

Steve Coen: In Q3 of 2024, Cell Engineering revenue included $45 million of non-cash revenue from a release of deferred revenue relating to the mutual termination of the Motif FoodWorks agreement. Excluding these impacts, Cell Engineering revenue was $125 million in 2025 and $129 million in 2024. This decrease was primarily driven by customer program rationalization related to the restructuring, as all discussed previously. The biosecurity business generated $7 million of revenue in Q4 of 2025, and $37 million of revenue in the full year 2025. It is important to note that our net loss includes a number of non-cash and other non-recurring items, as detailed more fully in our financial statements. Because of these non-cash and other non-recurring items, we believe Adjusted EBITDA is a more indicative measure of our profitability.

As previously disclosed Revenue in the first quarter of 2025, includes the 7.5 million of non-cash revenue from a release of deferred revenue relating to the mutual, termination of the biome edit agreement.

It was all discussed previously.

So bio security business generated $7 million of revenue in the fourth quarter of 2025 and.

And the third quarter of 2024 cell, engineering Revenue included, 45 million of non-cash revenue from a release of deferred revenue, relating to the mutual termination of the motif foodworks agreement.

$37 million of revenue in the full year 2025.

It is important to note.

Net loss includes a number of noncash and other nonrecurring items as detailed more fully in our financial statements.

Excluding these impacts, cell engineering revenue was $125 million in Q4 2025 and $1,290 million in Q4 2024.

Because of these noncash and other nonrecurring items, we believe adjusted EBITDA is.

This decrease was primarily driven by customer program rationalization related to the restructuring.

As all discussed previously.

More indicative measure of our profitability.

A full reconciliation between segment operating loss adjusted EBITDA and GAAP net loss can be found in the appendix.

The baiyoke business generated 7 million dollars of Revenue in the fourth quarter of 2025.

And $37 million of revenue in the full year 2025.

So engineering R&D expense decreased 44% from $50 million in the fourth quarter of 2000 $24 million to $28 million in the fourth quarter of 2025.

It is important to note that our net loss includes a number of non-cash and other non-recurring items, as detailed more fully in our financial statements.

For the full year of 2025 cell engineering R&D expense decreased 42% from $272 million in 2000 $24 million to $159 million in 2024.

Because of these non-cash and other non-recurring items, we believe adjusted EBIT is a more indicative measure of our profitability.

Steve: A full reconciliation between segment operating loss, Adjusted EBITDA, and GAAP net loss can be found in the appendix. Cell Engineering R&D expense decreased 44% from $50 million in Q4 2024 to $28 million in Q4 2025. For the full year 2025, Cell Engineering R&D expense decreased 42% from $272 million in 2024 to $159 million in 2025. As reported last quarter, the full year 2025 period, R&D expenses included a $21 million shortfall obligation related to our multi-year strategic cloud and AI partnership with Google Cloud. In October 2025, we amended and reset the annual commitments for future years and settled the shortfall obligation for $14 million.

Steve Coen: A full reconciliation between segment operating loss, Adjusted EBITDA, and GAAP net loss can be found in the appendix. Cell Engineering R&D expense decreased 44% from $50 million in Q4 2024 to $28 million in Q4 2025. For the full year 2025, Cell Engineering R&D expense decreased 42% from $272 million in 2024 to $159 million in 2025. As reported last quarter, the full year 2025 period, R&D expenses included a $21 million shortfall obligation related to our multi-year strategic cloud and AI partnership with Google Cloud. In October 2025, we amended and reset the annual commitments for future years and settled the shortfall obligation for $14 million.

A full reconciliation between segments, operating loss, adjusted EBITDA, and GAAP net loss can be found in the appendix.

As reported last quarter, the full year 2025 period R&D expenses included a $21 million.

Shortfall obligation related to our multi year strategic cloud and AI partnership with Google Cloud.

Cell engineering R&D expense decreased 44% from 50 million in the fourth quarter of 2024 to 28 million in the fourth quarter of 2025.

In October 2025, we amended and reset the annual commitments for future years and settled the shortfall obligation for $14 million.

For the full year. 2025 cell engineering R&D expense decreased 42% from 272 million in 2024 to 159 million in 2025.

Resetting the commitment reduced our future minimum commitments by more than $100 million compared to the original terms and extended the commitment term from three to six years.

So engineering G&A expense decreased 40% from $20 million in the fourth quarter of 2000 $24 million to $12 million in the fourth quarter of 2024.

As reported last quarter, the 4 year 2025 period R&D expenses included, a 21 million shortfall obligation related to our multi-year, strategic cloud, and AI partnership with Google Club.

For the full year selling generic R&D I'm, sorry, G&A expense decreased 51% from $115 million and $24 million to $56 million in 2024.

Steve: Resetting the commitment reduced our future minimum commitments by more than $100 million compared to the original terms, and extended the commitment term from 3 to 6 years. Cell Engineering G&A expense decreased 40% from $20 million in the Q4 of 2024 to $12 million in the Q4 of 2025. For the full year, Cell Engineering R&D, I'm sorry, G&A expense decreased 51% from $115 million in 2024 to $56 million in 2025. These decreases were all driven by our restructuring efforts. Cell Engineering segment operating loss was $17 million in the Q4 of 2025, compared to a loss of $38 million in the 2024 period.

Steve Coen: Resetting the commitment reduced our future minimum commitments by more than $100 million compared to the original terms, and extended the commitment term from 3 to 6 years. Cell Engineering G&A expense decreased 40% from $20 million in the Q4 of 2024 to $12 million in the Q4 of 2025. For the full year, Cell Engineering R&D, I'm sorry, G&A expense decreased 51% from $115 million in 2024 to $56 million in 2025. These decreases were all driven by our restructuring efforts. Cell Engineering segment operating loss was $17 million in the Q4 of 2025, compared to a loss of $38 million in the 2024 period.

in October 2025, we amended and reset the annual commitments for future years and settled the shortfall obligation for 14 million

Resetting the commitment, reduced our future minimum commitments by more than 100 million dollars compared to the original terms and extended the commitment term from 3 to 6 years.

These decreases were all driven by our restructuring efforts.

So engineering segment operating loss was $17 million in the fourth quarter of 2020 compared to a loss of $38 million in the 2024 period.

Cell engineering DNA expense decreased 40% from 20 million in the fourth quarter of 2024 to 12 million in the fourth quarter of 2025.

For the full year of 2025, So engineering segment operating loss was $96 million compared to a loss of $219 million.

For the full year, selling, engineering, and R&D. I'm sorry. DNA expense decreased 51% from $115 million in 2024 to $56 million in 2025.

In 2024.

The lower loss was directly related to our restructuring efforts will partially impacted by the matters previously mentioned.

The bio security segment operating loss improved 60% in the fourth quarter of 2025 compared to the 2024 period.

Cell engineering segment. Operating loss was 17 million in the fourth quarter of 2025 compared to a loss of 38 million in the 2024 period.

Steve: For the full year 2025, Cell Engineering segment operating loss was $96 million, compared to a loss of $219 million in 2024. The lower loss was directly related to our restructuring efforts, while partially impacted by the matters previously mentioned. The biosecurity segment operating loss improved 60% in Q4 2025 compared to the 2024 period. The biosecurity segment operating loss improved 38% in the full year 2025 compared to 2024. Moving further down the page, you'll note that total Adjusted EBITDA in Q4 2025 was negative $36 million, which was down from negative $57 million in Q4 2024.

Steve Coen: For the full year 2025, Cell Engineering segment operating loss was $96 million, compared to a loss of $219 million in 2024. The lower loss was directly related to our restructuring efforts, while partially impacted by the matters previously mentioned. The biosecurity segment operating loss improved 60% in Q4 2025 compared to the 2024 period. The biosecurity segment operating loss improved 38% in the full year 2025 compared to 2024. Moving further down the page, you'll note that total Adjusted EBITDA in Q4 2025 was negative $36 million, which was down from negative $57 million in Q4 2024.

And the bio security segment operating loss improved 38% and the full year of 2025 compared to 2024.

For the full year 2025, the Cell Engineering segment had an operating loss of $96 million, compared to a loss of $219 million in 2024.

Moving further down the page you'll note that total adjusted EBITDA in the fourth quarter of 2025 was negative $36 million.

The lower loss was directly related to our restructuring efforts, while partially impacted by the matters previously mentioned.

Which was down from negative $57 million in the fourth quarter of 2024.

Total adjusted EBITDA for the full year 2025 was negative $167 million, which was down from negative $293 million in 2024.

The biosecurity segment offering loss improved 60% in the fourth quarter of 2025 compared to the 2024 period.

and the biosecurity segment, operating loss improved 38% and the full year 2025 compared to 2024

Again, the period over period declines can be attributed to our restructuring efforts more partially impacted by the amount as previously mentioned.

Moving further down the page, you'll note that total adjusted evidence in the fourth quarter of 2025 was negative $36 million.

Turning to the next one.

We show adjusted EBITDA at the segment level to show the relative profitability of the.

Which was down from negative $57 million in the fourth quarter of 2024.

Steve: Total Adjusted EBITDA for the full year 2025 was negative $167 million, which was down from negative $293 million in 2024. Again, the period-over-period declines can be attributed to our restructuring efforts, while partially impacted by the matters previously mentioned. Turning to the next slide. We show Adjusted EBITDA at the segment level to show the relative profitability of our segments. The principal differences between segment operating loss and total Adjusted EBITDA relates to the carrying cost of excess leased space, which was $54 million in 2025. This carrying cost was $15 million in Q4. The cost represents the base rent and other charges related to leased space, which we are not occupying, net of sublease income.

Steve Coen: Total Adjusted EBITDA for the full year 2025 was negative $167 million, which was down from negative $293 million in 2024. Again, the period-over-period declines can be attributed to our restructuring efforts, while partially impacted by the matters previously mentioned. Turning to the next slide. We show Adjusted EBITDA at the segment level to show the relative profitability of our segments. The principal differences between segment operating loss and total Adjusted EBITDA relates to the carrying cost of excess leased space, which was $54 million in 2025. This carrying cost was $15 million in Q4. The cost represents the base rent and other charges related to leased space, which we are not occupying, net of sublease income.

The principle differences between segment operating loss in total adjusted EBITDA relates to the carrying cost of excess lease space.

Total adjusted Eva for the full year. 2025 was negative 167 million which was down from -293 million in 2024.

Which was $54 million in 2025.

This carrying cost was $15 million in Q4.

again the period over period declines can be attributed to our restructuring efforts while partially impacted by the matters previously mentioned

The cost represents the base rent and other charges related to lease space, which we are not occupying net of sublease income.

turning to the next slide.

We show adjusted, Evo at the segment level to show the relative profitability of our segments.

This is a cash operating cost that is not related to drive revenue right now and can be potentially mitigated through subleasing.

And finally, turning to cash burn.

The principal differences between segments, operating loss and total adjusted e-mail relates to the carrying cost of excess lease space.

Cash burn in the fourth quarter of 2025 was $47 million down from $55 million in the fourth quarter of 2024% to 15% decrease.

Which was $504 million in 2025.

And this carrying cost was 50,000 in Q4.

Cash burn for the full year of 2025 was $171 million down from $383 million in 2020 for a 55% decrease.

The clock represents the base rent and other charges related to leased space which we are not occupying, net of sublease income.

Steve: This is a cash operating cost that is not related to driving revenue right now and can be potentially mitigated through subleasing. Finally, turning to cash burn. Cash burn in Q4 2025 was $47 million, down from $55 million in Q4 2024, a 15% decrease. Cash burn for the full year 2025 was $171 million, down from $383 million in 2024, a 55% decrease. Cash burn does not include the proceeds from the ATM issuances or certain cash restrictions. The significant decrease in cash burn was a direct result of the restructuring. Turning to guidance.

Steve Coen: This is a cash operating cost that is not related to driving revenue right now and can be potentially mitigated through subleasing. Finally, turning to cash burn. Cash burn in Q4 2025 was $47 million, down from $55 million in Q4 2024, a 15% decrease. Cash burn for the full year 2025 was $171 million, down from $383 million in 2024, a 55% decrease. Cash burn does not include the proceeds from the ATM issuances or certain cash restrictions. The significant decrease in cash burn was a direct result of the restructuring. Turning to guidance.

This is a cash operating cost that is not related to driving Revenue right now and can be potentially mitigated through sub. Leasing

Cash burn does not include the proceeds from the ATM issuances.

And finally turning into Cash burn.

Certain cash constructions.

Restrictions.

The significant decrease in cash burn was a direct result of the restructuring.

Cash burn in the fourth quarter of 2025 was $47 million, down from $55 million in the fourth quarter of 2024, a 15% decrease.

Turning to guidance.

In terms of the outlook for 2026 as Jason has mentioned and will go into further 26 is about continuing to be cost efficient while investing in our AI robotics and software to bring a ton of Ms labs to our bioscience customers, including the build out of our frontier Autonomous lab in Boston.

Cash burn for the full year. 2025 was 171 million down from 383 million. In 2024 a 55% decrease

Cash burn does not include the proceeds from the ATM issuances for a certain cache, restructures.

Restrictions.

We have turned the page on a pure focus on restructuring actions for the last few years to focus this year not only on cost efficiency, but on investing in what we see as our opportunities while continuing to provide our customers. The advanced services they have come to expect.

The significant decrease in cash burn was a direct result of the restructuring.

Steve: In terms of the outlook for 2026, as Jason has mentioned and will go into further, 2026 is about continuing to be cost-efficient while investing in our AI robotics and software to bring autonomous labs to our bioscience customers, including the build-out of our frontier autonomous lab in Boston. We have turned the page on our pure focus on restructuring actions for the last 2 years to focus this year not only on cost efficiency, but on investing in what we see as our opportunities while continuing to provide our customers the advanced services they have come to expect. We will also close our transaction for the biosecurity business as announced and disclosed. For these reasons, in 2026, we will not be providing revenue guidance as we believe cash burn best reflects our continuing services and tools and further investments in autonomous labs.

Steve Coen: In terms of the outlook for 2026, as Jason has mentioned and will go into further, 2026 is about continuing to be cost-efficient while investing in our AI robotics and software to bring autonomous labs to our bioscience customers, including the build-out of our frontier autonomous lab in Boston. We have turned the page on our pure focus on restructuring actions for the last 2 years to focus this year not only on cost efficiency, but on investing in what we see as our opportunities while continuing to provide our customers the advanced services they have come to expect. We will also close our transaction for the biosecurity business as announced and disclosed. For these reasons, in 2026, we will not be providing revenue guidance as we believe cash burn best reflects our continuing services and tools and further investments in autonomous labs.

Turning to guidance.

We will also close our transaction for the bio security business as announced and disclosed.

In terms of the outlook for 2026 as Jason has mentioned and will go into further. 2026 is about continuing to be cost efficient while investing in our AI Robotics and software to bring autonomous labs to our bioscience customers, including the buildout of our Frontier autonomous lab in Boston.

For these reasons in 2026, we will not be providing revenue guidance.

We believe cash burn best reflects our continuing services and tools and further investments in autonomous labs.

Between 2006, our overall expected cash burn guidance is to be in the range of $125 million to $150 million.

We have turned the page on our Pure focus on restructuring actions for the last 2 years to focus this year. Not only on cost efficiency but on investing in what we see as our opportunities while continuing to provide our customers, the Advanced Services, they have come to expect.

This range reflects a firm balance amongst cost efficiency, continuing services and tools and the further investments we are making.

we will also close our transaction for the bio security business as announced in disclosed

In conclusion, we are pleased with the continued improvements in cash burn and cost reductions in 2025 and are excited for what will come in 2026.

Steve: For 2026, our overall expected cash burn guidance is to be in the range of $125 to $150 million. This range reflects a firm balance amongst cost efficiency, continuing services and tools, and the further investments we are making. In conclusion, we are pleased with the continued improvements in cash burn and cost reductions in 2025 and are excited for what will come in 2026. With that, I'll hand it back over to you, Jason.

Steve Coen: For 2026, our overall expected cash burn guidance is to be in the range of $125 to $150 million. This range reflects a firm balance amongst cost efficiency, continuing services and tools, and the further investments we are making. In conclusion, we are pleased with the continued improvements in cash burn and cost reductions in 2025 and are excited for what will come in 2026. With that, I'll hand it back over to you, Jason.

for these reasons in 2026, we will not be providing Revenue guidance. As we believe CAC Burns best reflects our continuing services in tools and further investments, in autonomous labs,

And with that I'll hand, it back over to you Jason.

Thanks, Steve.

Okay. So before I jump into my section I want to spend a minute talking a little bit more about what Steve was talking about there at the end in terms of our guidance for the year, how we're going to be guiding on cash burn rather than on revenues and sort of why we're doing that so this is in line with my theme for this earnings call, which is getting US focus. So one thing is we want to be focusing on investing in the right things and so on.

For 2026. Our overall expected cash burn, guidance is to be in the range of 125 to 150 million.

This range reflects a firm balance amongst cost efficiency, continuing services and tools, and the further Investments. We are making

In conclusion, we are pleased with the continued improvements in cash burn and cost reductions in 2025 and are excited for what will come in 2026.

I believe again, it's important for our investors and you can go to understand what we're doing with our our cash supply how fast that's being spent down what were spending it on and again. The highlight here is we are spending it very deliberately on autonomous labs, and we're doing it in a controlled way, where hopefully spending substantially less than we spent in the last year and our relative position there to our cash.

Jason Kelly: Thanks, Steve. Okay. Before I jump into my section, I want to spend a minute talking a little bit more about what Steve was talking about there at the end in terms of our guidance for the year, how we're gonna be guiding on cash burn rather than on revenues and sort of why we're doing that. This is in line with my theme for this earnings call, which is Ginkgo's focus. One thing is we wanna be focusing on investing in the right things. I believe, again, it's important for our investors in Ginkgo to understand what we're doing with our cash supply, how fast that's being spent down, what we're spending it on.

And with that, I'll hand it back over to you Jason.

File it looks pretty good and so from my standpoint, we have a solid margin of safety as we are investing to lead in this area of autonomous labs going forward, but.

But the second thing we need to keep focus as our attention within the company.

Jason Kelly: Again, the highlight here is we are spending it very deliberately on autonomous labs, and we're doing it in a controlled way. We're hopefully spending substantially less than we spent in the last year, and our relative position there to our cash pile, it looks pretty good. From my standpoint, we have a solid margin of safety as we're investing to lead in this area of autonomous labs going forward. The second thing we need to keep focus is our attention within the company. The majority of our revenue today does come from our R&D services. We love serving those customers, and hopefully, we grow those services.

And so the majority of our revenue today. It does come from our R&D services, we love serving those customers and hopefully grow those services, but as I mentioned earlier the focus of the team in 2026 is not on hitting a short term revenue target around a service right on top of our autonomous labs to make sure we had a target or trying to predict exactly what that revenue is.

To be over the next 12 months, what I wanted to focus to be on is decommissioning all of the different labs are again go and moving that work onto our autonomous lab. So that we can show all of our customers.

Jason Kelly: As I mentioned earlier, the focus of the team in 2026 is not on hitting a short-term revenue target around a service run on top of our autonomous lab to make sure we hit a target or trying to predict exactly what that revenue is gonna be over the next 12 months. What I want their focus to be on is decommissioning all of the different labs here at Ginkgo and moving that work onto our autonomous lab so that we can show all of our customers that this works, that autonomous labs can be a true replacement for the humongous spending they have across their manual laboratories in both biotech and academic science. That's the main event. I felt that, again, continuing a focus on revenue targets and things like that was gonna take people's eye off the ball.

This works that autonomous labs can be a true replacement for the humongous spending they have across their manual laboratories in both biotech and academic science. That's the main event and I felt that again, continuing a focus on revenue targets and things like that was going to take People's eye off the ball and I also think it sort of takes away from our long term orientation.

I think it is going to be critical for ginkgo. So that's why we made that decision happy to talk more about that in questions or otherwise, but just so you know where it's coming from alright. Okay. So as I said, our mission is to make biology easier to engineer, we had three really amazing things happened last quarter's who's going to run through them. So first we had announcement of a project we've been working on for the last six months with.

Relative position, uh, there to our cache file. It looks pretty good. So, for my standpoint, we have a solid margin of safety as we're investing to lead in this area of autonomous Labs going forward. Uh but the second thing we need to keep focus is our attention within the company uh and so the majority of our Revenue today does come from our R&D Services. We love serving those customers. I'm hoping we grow those Services. Uh but as I mentioned earlier, the focus of the team in 2026 is not on hitting a short-term Revenue Target around a service, run on top of our autonomous lab, to make sure we hit a Target or trying to predict exactly what that revenue is going to be over the next 12 months. What I want their focus to be on is decommissioning, all of of the different Labs here at Ginkgo and moving that work onto our autonomous lab so that we can show all of our customers. Uh, that this works that autonomous Labs can be a true replacement for the humongous spending. They have across their uh manual Laboratories in both biotech and academic science. Uh that's the main event.

<unk> AI. This is their blog post about it where we had talked about connecting GPT five as sort of an AI scientists are doing the work of our scientists designing experiments.

Jason Kelly: I also think it sort of takes away from a long-term orientation, which I think is gonna be critical for Ginkgo. That's why we made that decision. Happy to talk more about that in questions or otherwise, but just so you know where it's coming from. All right. Okay. As I said, our mission is to make biology easier to engineer. We had three really amazing things happen last quarter, so I'm just gonna run through them. First, we had an announcement of a project we've been working on for the last six months with OpenAI.

Except they would submit those experiments to our autonomous lab here in Boston The lab would conduct the work send that data back to GBP five and then over the course of six rounds of doing that we were able to beat state of the art on a pretty complicated.

Jason Kelly: This is their blog post about it, where we talked about connecting GPT-5 as sort of an AI scientist, so doing the work of a scientist designing experiments, except they would submit those experiments to our autonomous lab here in Boston. The lab would conduct the work, send that data back to GPT-5, and then over the course of six rounds of doing that, we were able to beat state-of-the-art on a pretty complicated experimental scientific challenge in cell-free protein synthesis by 40%. What I think is cool about this is, number one, the sort of views on this X post where they announced Codex on the same day, about equal to what they saw with Codex, right?

And I felt that again, continuing a focus on Revenue targets, and things like that, was going to take people's Eye Off the Ball. Uh, and I also think it it sort of takes away from a long term orientation which I think is going to be critical uh, for Geno. So that's why we made that decision. Happy to talk more about that in questions or otherwise but just so, you know where it's coming from. All right. Okay, so as I said, our mission is to make biology easier to engineer. Uh, we had 3, really, uh, amazing things happen last quarter, so it's going to run through them. Uh, so first, uh, we had an announcement of a project, we've been working on for the last 6 months, uh, with open.

Sort of scientific experimental scientific challenge and celebrate protein synthesis by 40% what do think it is all about this is number one that the sort of views on this ex post where they announced a codec on the same day about equal to what they saw it right. So I think there is really a lot of excitement right now and how reasoning models can enter the physical.

World.

I'm going to talk in a minute about that in the area of transportation were like way modes that brought them into the physical world.

We really stand to be the ones to bring AI into the physical world of the lab, we are absolutely in the pole position on that so I'll talk more about that.

We were either absolute privilege to do a press conference with.

Jason Kelly: I think there is really a lot of excitement right now in how reasoning models can enter the physical world. All right? I'm gonna talk in a minute about that in the area of transportation, where like Waymos have brought them into the physical world. I think we really stand to be the ones to bring AI into the physical world of the lab. We are absolutely in the pole position on that. I'll talk more about that in a sec. I had the absolute privilege to do a press conference with Department of Energy Secretary Wright up at Pacific Northwest National Labs in Washington, where we announced in December that the first 18 robots that we were installing for PNNL as part of the Genesis project.

Apartment of energy Secretary right.

At Pacific Northwest National Labs in Washington.

We announced in December that the first 18 robots that we were installing for P&L as part of the Genesis project is a new project out of the White house to bring AI into science and AI into the National Labs in particular, but alongside that ribbon cutting and the secretary I got to sign on.

I this is their blog post about it where we talked about connecting GPT 5 uh as sort of an AI scientist. So doing the work of a scientist designing experiments uh except it would submit those experiments to our autonomous lab here in Boston. The lab would conduct the work, send that data, back to GPT 5. Uh, and then over the course of 6 rounds of doing that, we were able to beat state-of-the-art on a pretty complicated, uh, sort of scientific experimental scientific challenge in self-reporting synthesis by 40%. Uh, what I think is cool about this is number 1, the the sort of views on this, uh, xpost where they announced, uh, codec on the same day about equal to what they saw with codec, right? So I think there is really a lot of excitement right now in how reasoning models can enter the physical world, all right. I'm going to talk in a minute about that in the area of Transportation where like whoos have brought them into the physical world. Uh, but I think we really stand to be the ones to bring, uh, AI into the physical world of the lab. We are absolutely in the pole position on that. So I'll talk more about that in a sec.

Our system you can see them signing it there we also announced a new $47 million contract with the department of energy to build a 97 robot 97 rack.

Thomas Lab at that same site in P&L in the future. So a really exciting and I think this showcases that.

Jason Kelly: This is a new project out of the White House to bring AI into science and AI into the national labs in particular. Alongside that ribbon-cutting, and the Secretary got to sign our system, you can see him signing it there. We also announced a new $47 million contract with the Department of Energy to build a 97 robot, 97 rack autonomous lab at that same site in PNNL in the future. Really exciting, and I think this showcases that autonomous labs are of interest to the federal government, which is the other big pool of research spending. A place like the NIH is spending $40 billion a year on lab work. Frankly, that's pretty close to what you're seeing in the pharma companies as well.

Autonomous labs are of interest to the federal government, which is the other big pool of research spending so a place like the NIH spending $40 billion a year.

Second, uh, we were, I had the absolute privilege, uh, to do a press conference with, uh, depart Department of energy secretary Wright. Um, up at Pacific Northwest National Labs in Washington, uh, where we announced in December, uh, that the first 18 robots that we were installing, uh, for pnnl, as part of The Genesis Project. This is a new uh, project out of uh, the White House to bring AI into science and AI into the National Labs in particular. Uh, but a lot

On lab work frankly, and that's that's pretty close to what Youre seeing in the pharma companies as well. So those are sort of big pools of spending and so I think it's important to see it coming from the federal government as well as from pharma companies.

But at least we.

Had SLA assets as the society for laboratory automation and screening conference that was just at the conference Center, it's about five minutes away from here very Fortunately for us that can go.

Alongside that ribbon cutting. And the secretary got to sign 1, our our system. You can see him signing it there. We also announced a new 47 million contract with the Department of energy to build a 97, robot 97 rack, uh, autonomous Lab at that same site in PA now in the future. So uh, really exciting and I think this showcases that

We hosted tours of Nebula are now more than 50 Iraq.

Jason Kelly: Those are your sort of big pools of spending, and so I think it's important to see it coming from the federal government as well as from pharma companies. Last but not least, we had SLAS. This is the Society for Laboratory Automation and Screening conference that was just at the conference center. It's about 5 minutes away from here, very fortunately for us at Ginkgo. We hosted tours of Nebula, our now more than 50 rack autonomous lab set up here in Boston. We had 590 people come through, and it was very eye-opening to see what a difference it made for people to see a lab like this actually doing real science during the day, right? Like, people coming in and just seeing what our scientists were doing with it.

Thomas Lab set up here in Boston, We had 590 people come through and it was very eye opening to see what a difference it made for people to see a lab like this actually doing real science.

During the day people coming in and just seeing what our scientists were doing with it.

It was eye opening for them and so I think this makes it more and more clear to me that we're making the right choice with this focus in 2006 on really driving the further expansion on the system. We're going to go from 50 racks to a 100 racks.

H one that's the sort of stuff I want you to be following how quickly are we able to expand that how quickly are we able to add more of our work onto that system, because that's exactly what our pharma and national lab and.

autonomous labs are of interest to the federal government which is the other big pool of research spending so a place like the NIH is spending forty billion dollars a year uh, on lab work, right frankly. Uh and that's that's pretty close to what you're seeing in the Pharma companies as well. So those are your sort of big pools of spending and so I think it's important to see it coming from the federal government as well as from Pharma companies. Uh last but not least. Uh we had slas. This is the society for laboratory Automation and screening conference. That was just at the conference center is about 5 minutes away from here. Uh very fortunately for us at genko. And so we hosted tours of nebulae are now more than 50 rack. Uh autonomous Labs set up here in Boston. We had 590 people come through and it was very eye-opening to see what a difference. It made for people to see a lab like this actually doing real science.

Jason Kelly: It was eye-opening for them. I think this makes it more and more clear to me that we're making the right choice with this focus in 2026 on really driving the further expansion of this system. We're gonna go from 50 racks to 100 racks by H1. That's the sort of stuff I want you to be following. How quickly are we able to expand that? How quickly are we able to add more of our work onto that system? Because that's exactly what our pharma and national lab and university research leaders are gonna be looking at to see if they wanna buy a system like this. Okay. All right.

And University research leaders are going to be looking at to see if they want to buy a system like this.

Alright, so now going to do a deep dive into autonomous labs, because again I think this is really our focus certainly in 'twenty six and I think the technological foundation for the company over the next decade.

So I've been talking about then is it what is it autonomous lab why is it going to transform biotechnology secondly, what does it look like very specifically like what do you need to have the lab to be able to do in order to deliver biotech R&D and then finally, how are we going to bring it to market and the two ways. We're doing that is one we'll build one for you like we did a P&L to that beautiful lab I think you just saw pictures of.

During the day, right? Like people coming in and just seeing what our scientists were doing with it. Uh, it was eye-opening for them. And so, I think this makes it more and more clear to me that we're making the right choice with this focus in '26, on really driving the further expansion of the system. We're going to go from 50 racks to 100 racks, uh, by H1. That's the sort of stuff I want you to be following—how quickly are we able to expand that? How quickly are we able to add more of our work onto that system? Because that's exactly what our Pharma and National Lab, um, and University Research leaders are going to be looking at, uh,

To see if they want to buy a system like this.

Jason Kelly: Now I'm gonna do a deep dive into autonomous labs, because again, I think this is really our focus, certainly in 2026, and I think the technological foundation for the company over the next decade. I'm gonna talk about. What is an autonomous lab? Why is it gonna transform biotechnology? Secondly, what does it look like very specifically? Like, what do you need to have the lab be able to do in order to deliver biotech R&D? Finally, how are we gonna bring it to market? The two ways we're doing that is, one, we'll build one for you, like we did at PNNL. Two, that beautiful lab that you just saw pictures of, we're able to run that sort of in a cloud service model through our R&D services and new services we're adding coming up.

We're able to run that sort of in a cloud service model through our R&D services and new services, we are adding coming up alright.

Alright.

Okay. So here's the analogy I like to give and I talked to you.

Talking flash can talk to that.

But a lot of people I think it's a good one alright, so I'm going to start in the transportation industry to help explain what I consider autonomy to be so if you look at this chart on the Y axis you have the amount of automation.

Alright, and then on the X axis you have the flexibility of the request from a user to that automation that it's willing to tolerate so in transportation. If you are a low amount of request flexibility in a high amount of automation that the subway right you sit down the back of a subway and it just takes you away right there youre not having to do any.

Jason Kelly: All right. Okay, here's the analogy I like to give, and I give a talk at SLAS, and I've talked to this with a lot of people. I think it's a good one. All right. I'm gonna start in the transportation industry to help explain what I consider autonomy to be. If you look at this chart on the Y-axis, you have the amount of automation. All right? On the X-axis, you have the flexibility of the request from a user to that automation that it's willing to tolerate. In transportation, if you have a low amount of request flexibility and a high amount of automation, that's a subway. Right? You sit down in the back of a subway, and it just takes you away, right? There you're not having to do anything.

Guess certainly in 26. And I think the technological foundations for the company over the next decade. Uh so I'm going to talk about then, is it? What is the Thomas lab? Why is it going to transform biotechnology? Secondly, what does it look like very specifically, like what do you need to have the lab be able to do uh in order to deliver biotech R&D? Uh and then finally, how are we going to bring it to Market? And the 2 ways we're doing? That is 1. We'll Build 1 for you. Like we did at pnnl 2 that beautiful lab that you just saw pictures of, we're able to run that sort of in a cloud service model through our our R&D services and new Services. We're adding coming up

All right.

It is fully automated but you better want to go to one of the stops on that subway line, because it's not going to take you to your house or the grocery store just wherever you want to go at it on rails alright, so its very inflexible.

Okay so here's the analogy I like to give and I I talked to I I talked at slash and talk to this with a lot of people. I think it's a good 1. All right. So uh I'm going to start in the transportation industry to help. Explain what I consider autonomy to be. So if you look at this chart on the y axis, you have the amount of automation.

Now low automation high amount of requests flexibility that's a car right you put your hands on the wheel your feet on the pedals and you can drive it straight to your front door or to that grocery store right and that those two poles is basically what the transportation industry is look like for the last 100 years.

Jason Kelly: It is fully automated. You better wanna go to one of the stops on that subway line because it's not gonna take you to your house or the grocery store or just wherever you wanna go. It's on rails. All right? It's very inflexible. Now, low automation, high amount of request flexibility, that's a car. Right? You put your hands on the wheel, your feet on the pedals, and you can drive it straight to your front door or to that grocery store. Right? Those two poles is basically what the transportation industry has looked like for the last 100 years. Forward a slide. Unless you've been in San Francisco over the last four or five years, and you've seen these driving around. This is a Waymo. You sit in the back seat, just like sitting on a subway seat.

All right, and then on the x axis, you have the flexibility of the request from a user. To that automation, that it's willing to tolerate. So, in transportation, if you have a low amount of requests flexibility and a high amount of automation, that's a Subway, right? You sit down the back of a Subway and it just takes you away right there. There you're not having to do anything. It is fully automated

For a slide unless you Ben.

San Francisco over the last four or five years and you're seeing these driving around so this is a way Mo you sit in the back seat just like sitting on a subway seat you do absolutely nothing it takes you away except unlike the subway it'll take you right to your house.

To that grocery stores. So it has the flexibility of a car, but the automation of the subway.

And that's such a surprising thing that we're giving it a new word we're calling it autonomy.

But you better want to go to one of the stops on that subway line, because it's not going to take you to your house or the grocery store or just wherever you want to go. It's on rails. All right? So it's very inflexible. Now, low automation, high amount of requests, flexibility—that's a car, right? You put your hands on the wheel, your feet on the pedals, and you can drive it straight to your front door or to that grocery store, right? And those two poles are basically what the transportation industry has looked like for the last 100 years.

Alright, and I think you will see this replicated when you're seeing all this interest in like humanoid robotics and all of this is a huge amount of investment going into it right now what we're trying what is happening on a broad investor level is the industrial Revolution was essentially the application of automation and systematization too all of the tasks that we're like low flexibility.

Jason Kelly: You do absolutely nothing. It takes you away. Except unlike the subway, it'll take you right to your house, right to that grocery store. It has the flexibility of a car but the automation of a subway. That's such a surprising thing that we're giving it a new word. We're calling it autonomy. All right? I think you will see this replicated when you're seeing all this interest in, like, humanoid robotics and all this. There's a huge amount of investment going into it right now. What's happening on a broad investor level is the Industrial Revolution was essentially the application of automation and systematization to all of the tasks that were, like, low flexibility required, like back to the loom, right? Everything that wasn't, that required a lot of flexibility, we kept manual.

Ford slide, and unless you've been, uh, in San Francisco over the last four or five years, and you've seen these driving around. So, this is a Waymo. You sit in the back seat, just like sitting on a subway seat. You do absolutely nothing. It takes you away, except unlike the subway, it'll take you right to your house.

right to that grocery store so it has the flexibility of a car, but the automation of a Subway

Required like back to the loom right.

Everything that wasn't that required a lot of flexibility we kept manual and what's happening now is the AI models are getting good enough. The software is getting good enough to allow automation to be applied to flexible things and we're going to see how far we can push that and the more you can push into flexibility the bigger the opportunity there is for robotics and so we're going to drive that change.

And that's such a surprising thing that we're giving it a new word, we're calling it autonomy.

And lapsed now.

Last point. This is the kicker if you were to look at the split between miles traveled on trains and subways versus cars and trucks in the United States anyway, it's 99% cars and trucks.

All right. And I think you will see this replicated when you're seeing all this interest in like humanoid Robotics and all this like there's a huge amount of investment going into it right now. What we're trying, what's happening at a broad, investor level is the Industrial Revolution. Was essentially the application of Automation and systematization to all of the tasks that were like low flexibility. Required. Like back to the loom, right.

Jason Kelly: What's happening now is the AI models are getting good enough, the software is getting good enough to allow automation to be applied to flexible things, and we're gonna see how far we can push that. The more you can push into flexibility, the bigger the opportunity there is for robotics. We're gonna drive that change in labs. Now, last point, this is the kicker. If you were to look at the split between miles traveled on trains and subways versus cars and trucks in the United States anyway, it's 99% cars and trucks. Because you need that flexibility to go places, right? It's a requirement. It's not like we didn't know about rails. They just did not tolerate the flexibility needed. All right. Let's look in the lab. Low amount of flexibility, high amount of automation.

Because you need that flexibility to go places right. It's a requirement it's not like we didn't know about rail. They just did not tolerate the flexibility needed alright, so let's look in the lab.

Low amount of flexibility high amount of automation software that subway was on the last slide we do have that actually we call. It automation work sell and you can buy this from companies like Haier, <unk> and <unk> and Thermo Fisher and basically you tell them what protocol you want and they build you work cell that will run that protocol for you and it's great. It's totally end to end.

Everything that wasn't that required, a lot of flexibility, we kept manual. And what's happening now is the AI models are getting good enough. The software is getting good enough to allow automation, to be applied to flexible things. And we're going to see how far we can push that and the more you can push into flexibility the bigger the opportunity, there is for Robotics and so we're going to drive that change in Labs now.

Last point, this is the kicker. If you were to look at the splits between miles, traveled on trains and Subways versus cars and trucks in the United States. Anyway, it's 99% cars and trucks.

There's not a person in the middle it's fully automated the top of that chart, but you better be asking for the same protocol that you asked it to do yesterday, because it cannot handle variety in the request from the scientists that are using it.

Because you need that flexibility to go places, right? It's a requirement. It's not like we didn't know about rails—they just did not tolerate the flexibility needed. All right, so let's look at the lab.

Jason Kelly: Up where that subway was on the last slide, we do have that actually. We call it a automation work cell, and you can buy this from companies like HighRes Biosolutions and Biosero and Thermo Fisher Scientific. Basically, you tell them what protocol you want, and they build you a work cell that will run that protocol for you. It's great. It's totally end-to-end. There's not a person in the middle. It's fully automated. It's the top of that chart. You better be asking for the same protocol that you asked it to do yesterday because it cannot handle variety in the request from the scientists that are using it. Drop down that automation line and go over on request flexibility. Not automated, but very, very flexible. That is the lab bench, and we've had it for 100 plus years.

Dropped down that automation line and go over on request flexibility, so not automated but very very flexible that is the lab bench and we've had it for 100 plus years. It lets you do whatever experiment you want and the scientists the human scientists in the middle is whats providing the flexibility.

And that's what this system has looked like we've had works out of automation for 40 plus years now.

That we've been kind of those two poles for the last four years.

Low amount of flexibility high amount of automation so up where that Subway was on the last slide. We we do have that actually we call it an automation work cell and you can buy this from companies like higher-res bio and biosero and thermofisher and basically you tell them what protocol you want and they build you a work cell that will run that protocol for you and it's great. It's totally end to end. There's not a person in the middle. It's fully automated. It's the top of that chart, but you better be asking for the same protocol that you asked it to do yesterday because it cannot handle Variety in the request from the scientists that are using it.

And much like research sorry, much like <unk>.

Transportation I add a couple of heads of R&D to pharma companies over my house during its all I ask for dinner and I ask the question.

Drop down that automation uh line and go over on request flexibility, so not automated but very very flexible. That is the lab bench.

Jason Kelly: It lets you do whatever experiment you want, and the scientist, the human scientist in the middle is what's providing the flexibility. All right. That's what the system has looked like. You know, we've had work cell automation for, you know, 40-plus years now, that we've been kind of those two poles for the last 40 years. Much like research or, sorry, much like transportation, I had a couple of heads of R&D's at 2 pharma companies over my house during SLAS for dinner, and I asked the question, What's your spend between work cells and lab benches? They said actually 99% on the lab benches, but let's call it more than 95% of the research budgets is going to the lab bench. It's for the same reason that 99% goes to the cars and trucks.

What's your spend between work cells and lab benches.

They said actually 99% on the lab benches, but let's call it more than 95% of the research budget is going to the lab bench and its for the same reason that 99% goes to the cars and trucks you need the flexibility to do science and if you can tell this if you walk around at Merck or Pfizer Takeda and you walk the hallways you will.

And we've had it for 100-plus years. It lets you do whatever experiment you want. And the scientists, the human scientists in the middle, are what's providing the flexibility.

<unk> see robots.

You will see lab bench after lab bench with bench top equipment on top of it and scientists basically being the human glue that connects all of that different equipment and manages to do liquid handling by hand, with ipads and all the things that they do.

Jason Kelly: You need the flexibility to do science. If you can tell this, if you walk around at Merck or Pfizer, Takeda, and you walk the hallways, you will not see robots. You will see lab bench after lab bench with benchtop equipment on top of it, and scientists basically being the human glue that connects all that different equipment and manages to do liquid handling by hand with pipettes and all the things that they do. That is the overwhelming majority of research spending and pharma's doing, again, $40 to $60 billion of not development, but research spending every year through that platform. All right. What are we trying to build? We're trying to build that Waymo.

That is the overwhelming majority of research spending in pharma is doing again $40 billion to $60 billion of not development, but research spending every year through that platform alright.

You will not see robots.

What are we trying to build or try to build that way Mo what ginkgo believes we have when it comes to our rack hardware and very importantly, the software that run it runs it isn't.

As an autonomous lab. It is the flexibility of the lab bench, but the automation of the work so and that's that is we believe fundamentally different it's a much bigger market than the workstyle market. It works out market again, just like the subway is very limited in terms of the amount of research dollars flowing to it and so we want to go right.

Jason Kelly: What Ginkgo believes we have when it comes to our rack hardware, and very importantly, the software that runs it, is an autonomous lab. It is the flexibility of the lab bench, but the automation of the work cell. That is, we believe, fundamentally different. It's a much bigger market than the work cell market. The work cell market, again, just like the subway, is very limited in terms of the amount of research dollars flowing to it. We wanna go right at that autonomous lab market. The key technical question, next slide, is how do you get both high automation and high flexibility without having those human hands in the lab? All right. That's the next thing I wanna talk about. What do we actually have to pull off technically to make this a reality?

That autonomous lab market. The key technical question next slide is how do you get both high automation and high flexibility without having those human hands in the lab alright. So that's the next thing I want to talk about what do we actually have to pull off technically to make this a reality what are people's so impressed with when they come visit our lab here in Boston and see what we built.

You will see lab bench after lab bench with bench top equipment on top of it and scientists basically being the human glue that connects all that different equipment and manages to do liquid handling, by hand with pipe hats and all the things that they do, uh, they that that is the overwhelming majority of research spending and Farmers doing again, 40 to 60 billion dollars of not development, but research spending every year through that platform. All right, what are we trying to build? We're trying to build that way. Mo, uh, what go believes we have when it comes to our our rack hardware and a very importantly, the software that run. It runs. It uh, is an autonomous lab. It is the flexibility of the lab bench, but the automation of the work cell

Alright.

I don't know if you noticed we follow me on Linkedin, you've seen I have become a bit of a influencer lately. So this is what it looks like if you're standing out a lab bench doing your work by hand, and the real major activity is number one youre serving as.

Manual liquid handler in other words, you are moving small volumes very precisely between different liquid containers to set up an experiment with the right materials in it.

Jason Kelly: What are people so impressed with when they come visit our lab here in Boston and see what we've built? All right. Oh, also, I don't know if you've noticed. If you follow me on LinkedIn, you've seen I've become a bit of an influencer lately. This is what it looks like if you're standing at a lab bench doing your work by hand. The real major activities is, number 1, you're serving as a manual liquid handler. In other words, you are moving small volumes very precisely between different liquid containers to set up an experiment with the right materials in it. Second, you're moving samples, in other words, that liquid you just set up in a plastic tube or whatever it might be in, to different devices across the lab.

Youre moving samples and other words that liquids you just set up in a plastic tube or whatever it might be in two different devices across the lab.

And that that is we believe uh, fundamentally different. It's a much bigger Market than the work cell Market. The work cell Market again just like the subway uh, is very limited in terms of the amount of research dollars flowing to it and so we want to go right at that autonomous lab Market. The key technical question. Next slide is, how do you get both High Automation and high flexibility without having those human hands in the lab, all right? And so, that's the next thing I want to talk about. What do we actually have to pull off technically to make this a reality? What are people so impressed with, uh, when they come visit, our lab here in Boston and see see what we've built. All right. Uh, oh also I don't know if you've noticed. We've Fallen me on LinkedIn. You've seen. I've become a bit of a influencer lately. Uh, so this is what it looks like if you're standing at a lab bench, uh, doing your work by hand,

You are moving samples as the protocol demands across maybe three maybe 10 different devices, depending on the complexity of the protocol Youre doing and then finally every time that sample ends up on a device.

All of those devices. These are all like complicated long tail scientific devices. They have some settings that you need to set in order to have it do the thing you wanted to do so you as a scientist or the one.

Jason Kelly: You are moving samples as the protocol demands across maybe 3, maybe 10 different devices, depending on the complexity of the protocol you're doing. Finally, every time that sample ends up on a device, all those devices, these are all like complicated, long tail scientific devices. They have some settings that you need to set in order to have it do the thing you want it to do. You, as a scientist, are the one putting those settings in, and you're either doing that with a touch screen or with sort of third-party software. Okay. All right. To replace traditional labs, an autonomous lab has to do those same things I just said. That's 1, 2, and 3, reliable liquid handling, material transport, and parameterized control of the device.

Putting those settings, and you're either doing that with a touch screen or what sort of third party software.

And the real major activities is number 1. You're you're serving as um uh manual liquid Handler. In other words, you are moving small volumes very precisely uh between different liquid containers to set up an experiment with the right. Uh materials in it second you're moving samples. In other words, that liquid you just set up in a plastic tube or whatever, it might be in to different devices across the lab.

Alright, so to replace traditional labs and autonomous lab has to.

Do those same things I'd, just add that's one two and three reliable liquid handling material transport and parameterized control of the device.

So you are moving samples as the protocol demands, across maybe three, maybe ten different devices, depending on the complexity of the protocol you're doing. And then finally, every time that sample ends up on a device,

But very importantly, if you think about one of those labs that Takeda, our Merck looks like in one floor with a bunch of benches in maybe 20 or 30 scientists using it you're going to have more than 50 devices around that lab that those scientists are making use of different ones different days different ones as part of different protocols. So you got to be able to put at least 50.

Devices into one big setup.

The other thing that those scientists are doing when they first scientist gets in the lab in the morning, They do not close the door behind them lock it and put up a sign that says lab and use no.

Jason Kelly: Very importantly, if you think about what one of those labs at Takeda or Merck looks like, in one floor with a bunch of benches and maybe, you know, 20 or 30 scientists using it, you're gonna have more than 50 devices around that lab that those scientists are making use of, different ones, different days, different ones as part of different protocols. You got to be able to put at least 50 devices into one big setup. The other thing that those scientists are doing, You know, the first scientist gets in the lab in the morning, they do not close the door behind them, lock it, and put up a sign that says, Lab in use. No one else can come in. Right? It's busy. Lab is busy.

No one else can come in right. It's busy lab is busy.

But on a work cell like one of those subway system automation that we have in the lab that's exactly how it works once it's been used you cannot and interject yourself into that process and submit a new job, but in the manual lab. Absolutely 10, 2030 scientists are all walking around that lab, basically sharing the equipment and avoiding each others.

All those devices, these are all like, complicated, long-tail scientific devices. They have some settings that you need to set in order to have it do the thing you want it to do. So you, as a scientist, are the one putting those settings in, and you're either doing that with a touchscreen or with sort of third-party software, okay? All right. So to replace traditional labs, an autonomous lab has to do those same things I just said. That's 1, 2, and 3: reliable liquid handling, material transport, and parameterized control of the device. But very importantly, if you think about what one of those labs at Tada or Merck looks like—with one of the benches and maybe, you know, 20 or 30 scientists using it—you're going to have more than 50 devices around that lab that those scientists are making use of: different ones on different days, different ones as part of different protocols. So, you've got to be able to put at least 50 devices...

Usage of the equipment, so if I'm using something in the morning Youll use it in the afternoon, but other than that constraints. They have access to all of that equipment and they can use it in parallel.

Into 1 big setup. Uh, the other thing that those scientists are doing uh, when they, you know, the first scientist gets in the lab in the morning, they do not close the door behind them, lock it and put up a sign that says lab in use

Jason Kelly: On a work cell, like one of those subway system automations that we have in the lab, that's exactly how it works. Once it's in use, you cannot interject yourself into that process and submit a new job. In the manual lab, absolutely, 10, 20, 30 scientists are all walking around that lab, basically sharing the equipment, and avoiding each other's usage of the equipment. If I'm using something in the morning, you'll use it in the afternoon. Other than that constraint, they have access to all that equipment, and they can use it in parallel. Finally, it's very easy to use the lab bench. You don't have to write software programs and things like that.

And then finally, it's very easy to use the lab that you don't have to write software programs and things like that I won't have as much time to talk about it today, but in the coming earnings call I'll do a little bit of a deeper dive on our software, but one of the things. We're really benefiting from is all of this investment in coding agents things like codecs.

From open AI and Claude code are now, allowing human language to turn into a pretty complicated software.

I want to turn scientific intent into work that runs on automation without scientists meeting to code I think that is going to be very doable thankfully.

Jason Kelly: I won't have as much time to talk about it today, but in a coming earnings call, I'll do a little bit of a deeper dive on our software. One of the things we're really benefiting from is all this investment in coding agents. Things like Codex from OpenAI and Claude Code are now allowing human language to turn into pretty complicated software. We wanna turn scientific intent into work that runs on automation without scientists needing to code. I think that is gonna be very doable, thankfully, and that's number six. It needs to feel like when I go in the lab every day to do my work, I don't have to sit down and write code. You shouldn't have to do that for the autonomous lab. This is really a difficult set of challenges.

That's number six it needs to feel like when I go into lab everyday to do my work I don't have to sit down and write code you Shouldnt have to do that for the autonomous lab. This is really a difficult set of challenges work cells. Today do do those first three they deliver liquids, we have liquid handler automation companies like Hamilton and <unk> been around for 25 years or more they're great.

No 1 else can come in right? It's busy lab is busy but on a work cell like 1 of those subway system automations that we have in the lab. That's exactly how it works. Once it's in use, you cannot interject yourself into that process and submit a new job, but in the manual lab absolutely 10, 20, 30 scientists are all walking around, that lab, basically sharing the equipment, uh, and avoiding each other's, uh, usage of the equipment. So, if I'm using something in the morning, you'll use it in the afternoon. But other than that, constraints, they have access to all that equipment and they can use it in parallel. Uh, and then finally, it's very easy to use the laptops. You don't have the right software programs and things like that. I won't have as much time to talk about it today. But in a coming earnings call, I'll do a little bit of a deeper dive on our our software, but 1 of the things we're really benefiting from is all this investment in coding agents, things like codecs. Um uh from open Ai and Cloud code are. Now allowing human language to turn into pretty complicated software. Uh, we want to turn scientific intent into work.

Second reliable material transport can be done with arms and third parameterized control is doable for five and six are not delivered well by traditional lab automation today, but we do have it working again alright.

Jason Kelly: Work cells today do those first three. They deliver liquids. We have, you know, liquid handler automation. Companies like Hamilton and Tecan have been around for 25 years or more. They're great. Second, reliable material transport can be done with arms, and third, parameterized control is doable. Four, five, and six are not delivered well by traditional lab automation today, but we do have it working at Ginkgo. All right. The first thing to understand about how to deliver four, five, and six is that a work cell, in other words, that subway, is designed around a protocol. The first thing one of those companies will ask if you're gonna build an automation system for you is, What's your protocol? Are you doing high-throughput screening? That's one of the most common ones. Antibody developability, protein production.

Thing to understand about how to deliver four five and six.

Workstyle in other words that subway is designed around a protocol. So the first thing one of those companies will ask if you're going to build it out of it they're going to an automation system for you as well.

What's your protocol are you doing high throughput screening thats one of the most common ones antibody develop ability protein production what is it you're doing right and you say Oh all doing. This these are established the equipment I need and this is the throughput and then they'll design a subway that delivers you to that stop.

Autonomous labs are not designed around your workflow, but theyre rather designed around the equipment. Because this is exactly what happens when you are setting up a new manual lab at Takeda, our Merck if you're the person in charge of that lab, you have that kind of group leader you ask your scientists what equipment will they need to do their work over the next five years in that.

How to deliver 4 or 5 and 6? Uh is that a work cell? In other words that Subway is designed around a protocol. So the first thing 1 of those companies will ask. If you're going to build an automate, they're going to build an automation system for you is

Jason Kelly: What is it you're doing, right? Oh, I'm doing this. These are the steps. This is the equipment I need, and this is the throughput. They'll design a subway that delivers you to that stop. Autonomous labs are not designed around your workflow, but they're rather designed around the equipment because this is exactly what happens when you're setting up a new manual lab at Takeda or Merck. If you're the person in charge of that lab, you're that kind of group leader, you ask your scientists what equipment will they need to do their work over the next five years in that lab... They don't know for sure what protocols they're gonna do. Depending on the type of work they're doing, mammalian work, bacterial work, cancers, whatever, they're gonna use different types of equipment.

They don't know for sure what protocols theyre going to do but depending on the type of work, they're doing mammalian work bacterial where cancers, whatever theyre going to use different types of equipment. So we oriented the design of our hardware our robotics hardware not around a protocol, but around a device and so this here you can see our rack.

What's your protocol? Are you doing High throughput screening? That's 1 of the most common ones antibiotic availability. Protein production. What is it? You're doing right? And you say oh I'm doing this. These are the steps. This is the equipment I need. And this is the throughput and then they'll design a Subway that delivers you to that stuff.

autonomous labs are not designed around your workflow, but they're rather designed around the equipment because this is exactly what happens when you're setting up a new manual Lab at to

Automation carts inside each card is a device in this case that the centrifuge, a six axis industrial robotic arm and a piece of Magnum Ocean track in that track allows you to deliver a sample between connected rack. So each one of those racks there little tracks connect to each other and you can send samples around.

Jason Kelly: we oriented the design of our robotics hardware, not around a protocol, but around a device. This here, you can see, our rack automation carts. Inside each cart is a device. In this case, that's a centrifuge, a six-axis industrial robotic arm, and a piece of MagneMover track. That track allows you to deliver a sample between connected racks. Each one of those racks, their little tracks connect to each other, and you can send samples around, and deliver them. If you go to the next slide, we can show a video here, of samples moving through our autonomous lab, here in Boston. This is actually, interestingly, this is one of the protocols from OpenAI, right?

And deliver them and if you go to the next slide we can show a video here.

Samples moving through.

Our autonomous lab here in Boston This is actually.

Interestingly. This is one of the protocols from open AI right and so what you can see as this runs.

We have the sample getting put onto the track that the 384 well plates in each well have that plate is a set of conditions that were designed by GBP five the plates travel on that Magnum motion tracking in this case. They are delivered to that center views right and so the centrifuge is going to spend down that that samples as though it just happens to be the <unk>.

Or Merc if you're the person in charge of that lab you're that kind of group leader. You ask your scientists, what equipment will they need to do their work over the next 5 years in that lab? They don't know for sure what protocols they're going to do, but depending on the type of work, they're doing mammalian work, bacterial workers, whatever that the they're going to use different types of equipment. So, we oriented the design of our Hardware, our robotics Hardware, not around a protocol, but around a device. And so this, here you can see our rack automation cards. Uh, inside each cart is a device. In this case, that's a centrifuge, a 6 axis, industrial robotic arm, and a piece of Magnum motion track, and that track allows you to deliver a sample between connected racks. So, each 1 of those racks, they're little tracks connect to each other and you can send samples around

Uh and and deliver them. And if you go to the next slide, we can show a video here uh of samples moving through.

First step in this protocol now it's going to one of those liquid handling devices. So this is what's called an acoustic liquid handler. It moves liquids with sounds so one of the things Thats great. About this is it's actually can handle smaller volumes at a greater precision than a scientist could do by hand. So we can move nanometers of liquids around as a scientist using our pipette by half.

Jason Kelly: What you can see as this runs, you know, is we have the sample getting put onto the track. That's a 384-well plate. In each well of that plate is a set of conditions that were designed by GPT-5. The plates travel on that MagneMover track, and in this case, they're delivered to that centrifuge, right? The centrifuge is gonna spin down that sample. This just happens to be the first step in this protocol. Now, it's going to one of those liquid handling devices. This is what's called an acoustic liquid handler. It moves liquids with sounds. One of the things that's great about this is this actually can handle smaller volumes at a greater precision than a scientist could do by hand.

And you're kind of limited microliters in terms of your ability to be accurate.

Now, we're going to be adding in this case DNA to each one of those wells. So the project. We did with open AI a piece of DNA was being added to what's called a cell free mix of reagents.

And the idea is that self remix turns that piece of DNA code into a protein and the protein level as what we were trying to optimize with open AI. We're trying to see could you change the conditions such that you've got higher protein production than any scientist had shown before in the literature. So once that DNA got added we know.

Jason Kelly: It can move nanoliters of liquids around as a scientist using a pipette by hand, you're kind of limited to microliters in terms of your ability to be accurate. Now we're gonna be adding, in this case, DNA to each one of those wells. The project we did with OpenAI, a piece of DNA was being added to a what's called a cell-free mix of reagents. The idea is that cell-free mix turns that piece of DNA code into a protein, and the protein level is what we were trying to optimize with OpenAI. We're trying to see, could you change the conditions such that you got higher protein production than any scientist had shown before in the literature?

Our autonomous lab, uh, here in Boston. This is actually, uh, interestingly, this is 1 of the protocols from openai, right? And so what you can see, as this runs, uh, is we have, uh, you know, the sample getting put onto the track, that's a 384. Well plate in each. Well of that plate is a set of conditions that were designed by gb5. The plates travel on that Magnum motion track. And in this case they're delivered to that Center fuse, right? And so, the centrifuge is going to spin down that that sample. So this just happens to be the first step in this protocol. Now it's going to 1 of those liquid handling devices. So this is is what's called an acoustic liquid Handler. It moves liquids with sounds. So 1 of the things that's great about this is it's actually can handle smaller volumes at a greater Precision than a scientist could do by hand. So we can move Nano liters of liquids around as a as a scientist, using a pipette by hand, you're kind of limited to microliters uh in terms of your ability to be accurate. Uh now we're going to be adding in this case, DNA to

It up make sure it's well mixed.

And then it's going to end up onto an analytical device in order to basically run the reaction and then measure the levels of protein that are coming out of that particular of each well in that 384 wall plate and so to give you a sense for the open AI project. Each time, we did around with the model. We ran 100 of these three.

Each 1 of those Wells. So the project we did with openai a piece of DNA was being added to a what's called a cell-free mix of reagents. Uh and the idea is that cell-free mixed turns that piece of DNA code into a protein and the protein level is what we were trying to optimize with openai. We're trying to see. Could you change the conditions such that you got higher protein production?

Jason Kelly: Once that DNA got added, we now shake it up, make sure it's well mixed, and then it's gonna end up onto an analytical device in order to basically run the reaction and then measure the levels of protein that are coming out of each well in that 384-well plate. To give you a sense for the OpenAI project, each time we did a round with the model, we ran 100 of these 384-well plates, collected all that data, gave it back to the model, and then the model was able to design the next round of experiments. That's what it looks like for a sample to move through the system.

84, well plates collected all of that data gave it back to the model and the model was able to design. The next round of experiments. Okay. So that's that's what it looks like for a sample to move through the system at.

At the beginning of that video you would've seen a quick picture of sort of.

The data coming in like the particular designs from open AI and then the scheduler that schedule on that one was just running the one protocol. This is what the schedule. It looks like when people who are scientists again, you'll have submitted 30 protocols to the system and so what you are looking at is each row. In this is a different device on the system and then the X axis.

Jason Kelly: At the beginning of that video, you would have seen a quick picture of sort of the data coming in, like the particular designs from OpenAI and then the scheduler. That schedule on that one was just running the one protocol. This is what the scheduler looks like when our scientists at Ginkgo have submitted 30 protocols to the system. What you're looking at is each row in this is a different device on the system, and the X-axis is time. That orange bar, in this case, is like now. All right? What is great is we can basically predict the future, right? We know that the system knows exactly what piece of equipment is gonna be used for what protocol, and each protocol is a different color on this chart.

Is time.

So that Orange bar in this case is like now alright, and what is great is we can basically predict the future right. We know the system knows exactly what piece of equipment is going to be used for what protocol in each protocol has a different color on this chart what piece of equipment is going to use for each protocol in the future and what the schedule or does is if you.

Than any scientists had shown before in the literature. So once that DNA got added, we now, Shake it up, make sure it's well mixed and then it's going to end up, um, onto an analytical device in order to basically run the reaction and then measure the levels of protein uh, that are coming out of that particular of each well in that 384, well plate. And so, to give you a sense for the openai project, each time we uh, did a round with the model. Uh, we ran 100 of these 384. Well, plates collected. All that data, gave it back to the model and the model was able to design the next round of experiments, okay? So that's that's what it looks like for a sample to move through the system. Uh, at the beginning of that video, you would have seen a, a quick picture of sort of the, uh, the data coming in like the particular designs from openai. And then the scheduler that schedule on that 1 was just running the 1 protocol. Uh this is what the scheduler looks like when people when our scientists at go have submitted 30, protocols to the system. And so, what you're looking at is each row,

Load up it can go as a scientist and use admitted a new job.

In this is a different device on the system and then the x-axis is time.

Into this into the autonomous lab, you would say, okay I'm using the centrifuge for for five minutes and then I need can wait any up to two hours before I need to end up on the Echo and thought about how you would you would specify with time Windows. Your protocol. The scheduler will check could you fit in and this is very analogous to one of those.

Jason Kelly: What piece of equipment is gonna be used for each protocol in the future. What the scheduler does is if you showed up at Ginkgo as a scientist, and you submitted a new job into our autonomous lab, you would say, Okay, I'm using the centrifuge for 5 minutes, and then I can wait up to 2 hours before I need to end up on the Echo, and da, da. You would specify with time windows, your protocol. The scheduler will check, could you fit in? This is very analogous to one of those scientists walking around the manual lab, asking their bench mates, Hey, when are you gonna be done with the PCR machine? How long? Is it okay if I run the HPLC overnight or something? Do you need to get on it? Right?

Scientists walking around the manual lab asking their bench mates Hey, when are you going to be done with the PCR machine. How long is it okay. If I run the HPLC overnight or something do you need to get on it right like having conversations about the availability of equipment, except in this case, it's all computer controlled and computer scheduled so we can essentially schedule it perfectly.

So that orange bar, in this case is like now, all right, and what is great is we can basically predict the future, right? We know that the system knows exactly what piece of equipment is going to be used for what protocol and each protocol is a different color on this chart. Uh, what piece of equipment is going to be used for each protocol in the future. And what the scheduler does is if you showed up at gingo as a scientist and you submitted a new job,

Job.

And so as you add more more protocols and Theres a complicated algorithm to handle all of this we are the only people in the world as far as I know that are doing anything close to this scale of variable protocols on a single automated system and that was pretty well confirmed at vial.

Jason Kelly: Like having conversations about the availability of equipment, except in this case, it's all computer-controlled and computer-scheduled, so we can essentially schedule it perfectly. As you add more protocols in, there's a complicated algorithm to handle all this. We are the only people in the world, as far as I know, that are doing anything close to this scale of variable protocols on a single automated system. That was pretty well confirmed at by, you know, wide-open eyes during the SLAS tour when I was able to show this off to people. Okay, go to the next slide. This is just a different color. Yeah, each of those protocols, you can see being submitted by a different user at Ginkgo as well.

Why don't I as during the <unk> when I was able to show. This after people go to the next slide.

This is just a different color, yes. So each of those protocol as you can see being submitted by a different user at kimco as well. So that's I think we're actually really interesting where we have.

Not just.

Not just like a large number of protocols, but also a large number of unique users submitting those protocols. That's also very.

Unique in the World. When you have those works out there's an automation engineer or two who are sort of in charge of it and everything funnels through them in.

Hey, when are you going to be done with the PCR machine? How long uh, is it okay if I run the hplc overnight or something? Do you need to get on it? Right? Like having conversations about the availability of equipment, except in this case it's all computer controlled and computer. Scheduled. So we can essentially schedule it perfectly. Uh, and so as you add more more uh protocols in there's a complicated algorithm to handle all this. Uh we are the only people in the world as far as I know that are doing anything close to this scale of variable, protocols, on a single automated system, and that was pretty well confirmed at, by, you know, wide open eyes during the slis tour when I was able to show this off to people, okay? It's got the next slide.

In the case of our autonomous lab here in Boston, we have tens of scientists submitting protocols everyday different protocols from yesterday that are all scheduled simultaneously okay.

Jason Kelly: That's, I think, actually really interesting, where we have not just like a large number of protocols, but also a large number of unique users submitting those protocols. That's also very unique in the world. When you have those work cells, there's an automation engineer or two who are sort of in charge of it, and everything funnels through them. In the case of our autonomous lab here in Boston, we have tens of scientists submitting protocols every day, different protocols from yesterday, that are all scheduled simultaneously. Hopefully that. Next slide.

Uh, this is just the different color, you know. So each of those, protocols, you can see being submitted by a different user at genko as well. So that's, I think actually really interesting where we have, uh, not just, um, uh, uh,

So hopefully that next slide gives you a picture of how we've checked off those sort of four five and six on that list in terms of many pieces of equipment. All on one set up being run simultaneously in parallel easy enough to use by scientists who arent automation engineers.

Not, not just like a large number of protocols, but also a large number of unique users submitting those protocols. That's also very, um,

Unique in the world. When you have those work cells, there's an automation engineer or 2 who are sort of in charge of it and everything funnels through them.

That system is now 50, plus racks again go started off as I think seven or eight it's very expandable in fact on the next slide after we finished up at SLA as we were able to.

In the case of our autonomous lab here in Boston. Uh we have tens of scientists submitting protocols every day. Different protocols from yesterday uh that are all scheduled simultaneously. Okay.

Jason Kelly: Hopefully, that gives you a picture of how we've checked off those sort of 4, 5, and 6 on that list in terms of many pieces of equipment, all on one setup, being run simultaneously in parallel, easy enough to use just by scientists who aren't automation engineers. Just note that system that's now 50-plus racks, at Ginkgo started off as, I think, 7 or 8. It's very expandable. In fact, on the next slide, after we finished up at SLAS, we were able to bring over the rack carts that we had at the conference. There was, I think, 7 or 8, and install them all in a day on the system.

Bring over the rack Hearts that we had at the conference. There was I think seven or eight and install them all in a day on the system. So the ability to really grow this system.

I think again unique when compared to traditional sort of subway style automation alright.

Alright, so what's the value prop the customers. There's three things I think that the lack of large biopharma or national lab would get excited about first save overhead costs by closing your traditional out of this as one of the things I'm. Most excited to do this year with our CRO are kind of research services that we run on across all of our labs that can go as I move more and more.

Jason Kelly: The ability to really grow this system is, I think, again, unique when compared to traditional sort of subway-style automation. All right, what's the value prop to customers? There's three things I think, that the, like, a large biopharma or a national lab would get excited about. First, save overhead costs by closing your traditional labs. This is one of the things I'm most excited to do this year with our CRO, our kind of research services that we run on across all our labs at Ginkgo. As I move more and more of that work onto the autonomous lab, I can shrink the footprint of my labs, which saves me in EHS costs, saves me in rent, saves me in all these different things that you have to carry when you're running these labs.

That work onto the autonomous lab I can shrink the footprint of my labs, which saves me.

Jeff's cost saves me and ran saves me and all of these different things.

That you have to carry when you're running these labs second it increases the research productivity of your researchers so right now a lot of their ideas are ultimately bottleneck by the amount of time they have to spend in the lab, we want to really open that up and get much more data per research dollar out of your scientists and then five.

So, hopefully that next slide that hopefully that gives you a picture of how we've checked off the sort of 4 5 and 6 on that list, in terms of many pieces of equipment. All on 1 setup being run simultaneously in parallel easy enough to use by scientists, who aren't automation Engineers, uh, just note that system. That's Now 50 plus racks. Again, go started off as I think 7 or 8, uh, it's very expandable. Um, in fact, on the next slide after we, uh, finished up at slas, uh, we were able to, um, bring over the rack carts, uh, that we had at the conference. There was, I think 7 or 8 and install them all in a day on the system. So the ability to really grow this system, uh, is I think again unique when compared to traditional sort of Subway style automation. All right, so what's the value prop to customers? Uh, there's 3 things, I think, uh, that the like a large biofarma or a National Lab would get excited about first save overhead costs by closing, your traditional allows, this is 1 of the things. I'm most excited to do this year with our cro, uh, our kind of research.

Finally, like we did with open AI you can have AI scientist run what are kind of in the industry called lab in a loop experiments, where the AI model is designing experiments theyre running on the autonomous lab data going back and so we're seeing a lot more interest in that from pharma companies as well alright. Okay. So last section I want to talk about is how are we going to sell these.

Jason Kelly: Second, it increases the research productivity of your researchers. Right now, a lot of their ideas are ultimately bottlenecked by the amount of time they have to spend in the lab. We want to really open that up and get much more data per research dollar out of your scientists. Finally, like we did with OpenAI, you can have AI scientists run what are kind of in the industry called lab-in-a-loop experiments, where the AI model is designing experiments, they're running on the autonomous lab, and data is going back. We're seeing a lot more interest in that from pharma companies as well. All right. Okay, the last section I want to talk about is, you know, how are we gonna sell these autonomous labs? There's sort of two ways we're gonna do it.

Services that we run on across all our Labs at go as I move more and more of that work onto the autonomous lab. I can shrink the footprint of my labs, which saves me in EHS, costs saves me in rent, saves me in all these different things uh that that you have to carry. Um, when you're running these labs.

<unk> labs, and Theres sort of two ways were going to do it one we will place a system like we did Pacific northwest National ads, we will place it out of customer sites Lasalle Capex will sell.

Basically service feasible for the software and for our maintenance of the equipment and then the future I could see us evens selling things like.

Reagents, and consumables and things like that to the users of our system that are sort of automation specific.

Second, uh, it increases, the research productivity of your researchers. So right now, a lot of their ideas are ultimately bottlenecked by the amount of time. They have to spend in the lab. We want to really open that up and get much more data per research dollar out of your scientists. Uh, and then finally, like we did with openai, you can have ai scientists run. Uh, what are kind of in the industry called lab and a loop experiments, where the AI model is designing experiments, they're running on the autonomous lab and data is going back. And so we're seeing a lot more interest in that from Pharma companies as well. All right.

Additionally, we have this big autumn autonomous lab in Boston that we can offer services on top of alright, and so what's like the overall market potential this is back to that.

Jason Kelly: One, we will place a system like we did Pacific Northwest National Labs. We'll place it at a customer site. We'll sell CapEx. We'll sell basically service fees, both for the software and for our maintenance of the equipment. In the future, I could see us even selling things like reagents and consumables and things like that to the users of our system that are sort of automation-specific. Additionally, we have this big auto-autonomous lab in Boston that we can offer services on top of. All right. What's, like, the overall market potential? This is back to that, you know, 1% on the subways, 99% in the cars. The overwhelming majority of research spending, that $40 to 60 billion in pharma, the $40 billion-plus from the government and so on, that's all funneling through benches today.

<unk>, 1% on the subway is 99% in the cars. The overwhelming majority of research spending that $40 billion to $60 billion in pharma, the $40 billion plus and from the government and so on that's all funneling through ventures today and that's before we also there is the other big industry, we haven't talked about it all as sort of diagnostics.

Okay. So the last section I want to talk about is you know how are we going to sell these autonomous labs and there's sort of 2 ways we're going to do it 1. We will place a system like we did specific Northwest National Labs, we will place it at a customer site, we'll sell capex, we'll sell. Um, uh, basically service fees, both for the software and for our maintenance of the equipment and then the future, I could see us even selling things like, um, uh, reagents and consumables and things like that, to the users of our system that are sort of automation specific.

And I also see opportunities there as well so all of that bench labs spending I think ultimately have the opportunity to funnel through our platform. If our autonomous lab is able to replace the bench the way we're going to get there is we're going to start by commercializing in two ways.

First build those are Thomas last for customers second run the cloud Lob Alright, So cloud lab services two of them are ones you have already heard about so our solution services. This is where our ginkgo scientists use our autonomous lab to deliver a research outcome to a customer so.

Jason Kelly: That's before we also, the other big industry we haven't talked about at all is sort of diagnostics, and I also see opportunities there as well. All of that bench lab spending, I think ultimately has the opportunity to funnel through our platform if our autonomous lab is able to replace the bench. The way we're gonna get there is we're gonna start by commercializing in two ways. First, build those autonomous labs for customers. Second, run the cloud lab. All right, cloud lab services. Two of them are ones you've already heard about. Our solution services, this is where Ginkgo scientists use our autonomous lab to deliver a research outcome to a customer.

Additionally we have this big Auto autonomous lab in Boston that we can offer services on top of all right. And so what's like the overall Market potential, this is back to that you know 1% on the Subways 99 uh percent in the cars. The overwhelming majority of research spending that 40 to 60 billion dollars in Pharma the 40 billion plus in from the government and so on that's all funneling through Ventures today. And that's before we also have this

Our deals with Novo Nordisk, and bear crop science, and Merck and Pfizer and all of these people over the years, where it ginkgo scientist use kinko's labs to deliver a research outcome. We've got a royalty we get milestones infrastructure. These in different ways. We do a lot of work with the governments in R&D grants and things like that through our solutions business.

Second in data points customer scientists use our autonomous lab they design, what they want to run on it. This was run like a traditional CRO theres no royalty is theres no milestones, we send them a huge amount of data back usually to their ml team and they use that to train bio AI models for protein design or.

Jason Kelly: You know, our deals with Novo Nordisk and Bayer CropScience and Merck and Pfizer and all these people over the years, where Ginkgo scientists use Ginkgo's labs to deliver a research outcome. We get a royalty, we get milestones. We can structure these in different ways. We do a lot of work with the government and R&D grants and things like that through our solutions business. Second, in Datapoints, customer scientists use our autonomous lab. They design what they want to run on it. This is run like a traditional CRO. There's no royalties, there's no milestones. We send them a huge amount of data back, usually to their ML team, and they use that to train bio AI models for protein design or RNA design or whatever they might be doing.

RNA design or wherever they might be doing.

The third and a lot more about this in future earnings calls, but we'll be announcing as soon as our cloud lab offering and so what we're gonna have here is customer scientists outsourcing small amounts of lab work directly to our autonomous lab, so think like a $50 order or a $200 or where.

We do a lot of work with the government and and R&D grants and things like that through our Solutions business.

Where the actual experiment will get run on the cloud lab and data we will go back to the scientist and I think this is a great way for Cytosorb.

Jason Kelly: The third, and I'll have more about this in future earnings calls, but we'll be announcing this soon, is our cloud lab offering. What we're gonna have here is customer scientists outsourcing small amounts of lab work directly to our autonomous lab. Think like a $50 order or a $200 order, where the actual experiment will get run on the cloud lab and data will go back to the scientist. I think this is a great way for scientists who are curious about what it's like to engage with autonomous labs to sort of try it before they buy. There's lots of things to try there, different ways to bring it to market. You'll hear it from us, coming up on that. I'm real excited about it.

Scientists, who are curious about what it's like to engage with autonomous labs to sort of try I try it before they buy and there's lots of things that drive there are different ways to bring it to market you'll hear from us coming up on that I'm really excited about it.

Just to say, we're not new to the solutions business. We got 250 partnerships in the last 10 years, we're continuing to sign these every quarter, where youre doing a lot of business with the government and large pharma are really the two areas, where we can see the most of this but also agriculture industrial biotech has been a lot harder since joining trying to basically, but but AG pharma and.

Government still will sign up for solutions deals.

The other area, that's growing really well for us over the last year and I want to give a shout out to the data points team at ginkgo.

Jason Kelly: You know, just to say, we're not new to the solutions business. We've done 250 partnerships in the last 10 years. We're continuing to sign these every quarter. We're doing a lot of business with the government and large pharma are really the two areas where we see the most of this, but also agriculture. Industrial biotech has been a lot harder since 2022, basically. Ag, pharma, and government still will sign up for solutions deals. The other area that's grown really well for us over the last year and, you know, I want to give a shout-out to the Datapoints team at Ginkgo, is we've been growing this business where we run our robotics to generate big data sets against the designs of customers.

Uh, second in data points, uh customer scientists, use our autonomous lab. They design what they want to run on it. This is run like a traditional cro. There's no royalties, there's no milestones. We send them a huge amount of data back, uh, usually to their ml team. And they use that to train bio AI models, for protein design or RNA design or whatever. They might be doing, uh, the third and a lot more about this, in future earnings calls. But we'll be announcing this soon, uh, is our Cloud lab offering. And so what we're going to have here is customer scientists, Outsourcing small amounts of lab work directly to our autonomous lab. So think like a $50 order or a $200 order. Uh, where the actual experiment will get run on the cloud lab and data will go back to the scientists. And I think this is a great way for, uh, scientists who are curious about what it's like to engage with autonomous labs to sort of try. Try it before they buy, uh, and there's lots of things to try. There are different ways to bring it to Market. You'll, you'll hear it from us, uh, coming up on that. I'm really excited about it.

As we've been growing this business, where we run our robotics to generate big datasets against the designs of customers.

And.

So as a business I got started a year and a half ago. We now work with 10 of the top.

Yeah.

'twenty, I think or 30 top pharma customers.

Um you know just to say uh we're not new to the solutions business. We've done 250 Partnerships in the last 10 years. We're continuing to sign these uh every quarter. We're doing a lot of business with the government and large Pharma are really the 2 areas where we see the most of this. But also agriculture uh industrial biotech has been a lot harder uh since 2022 basically. But but a Pharma and uh government still uh will sign up for Solutions deals.

In the first year, we launched this thing so people are really excited about it. It's a good fit we've actually released a bunch of public datasets. If you go to the data points website, you can download some of the largest datasets for drug seek and for antibody develop ability and things like that so next slide I think that we've also done a really nice job being a community builder here.

Jason Kelly: This, you know, this is a business that got started a year and a half ago. We've now worked with 10 of the top 20, I think, or 30 top pharma customers, just in the 1st year we launched this thing. People are really excited about it. It's a good fit. We've actually released a bunch of public data sets. If you go to the Datapoints website, you can download some of the largest data sets for DRUG-seq and for antibody developability and things like that. Go next slide. I think that we've also done a really nice job being a community builder here. We've launched a developability competition.

Uh, the other area. That's grown really well for us over the last year. And, you know, want to give a shout out to the data points team at Ginkgo, uh, is we've been growing this business where we run our robotics to generate, big data sets against the designs of customers. Uh, and

We've launched relaunched develop ability competition, we have a virtual sell pharmacology initiative, where we do free data generation as part of building up a big public datasets. So really I think if you're interested in this area if youre doing by the way I definitely checkout data points come to some of our events.

Yes, you know, this is a business that got started a year and a half ago. We've now worked with 10 of the top uh, uh,

Last point I'll mention about us running the <unk> labs is that our scientists using our big Autonomous lab in Boston is a little bit like.

20, I think or 30 uh top Pharma customers uh just in the in the first year, we launched this thing. So people are really excited about it. It's a good fit. We've actually released a bunch of public data sets so if you go to the data points website, you can download some of the largest data sets for uh drug seek and for um antibody developability and things like that.

Jason Kelly: We have a virtual cell pharmacology initiative where we do free data generation as part of building up a big public data set. Really, I think if you're interested in this area, if you're doing bio AI, definitely check out Datapoints, come to some of our events. The last point I'll mention about us running these CRO labs is that our scientists using our big autonomous lab in Boston is a little bit like the Waymo engineer five years ago going through Palo Alto, sitting in the driver's seat with their fingers like this right next to the steering wheel, like, ready to grab it if the car turns into a mailbox or something. They are the first ones to push lab autonomy to the frontier. Right?

The way Moe engineer five years ago going through Palo Alto sitting in the driver's seat with their fingers like this right next to the steering wheel like ready to grab it if the car turns into a mailbox or something they are the first ones to push lab autonomy to the frontier right. When you saw those 30 protocols running on our system.

Go next, slide. Uh, I think that we've also done a really nice job being a community Builder here. Uh, We've launched, we launched a developability competition, we have a virtual Cellar pharmacology initiative, where we do free data generation uh as part of building up a big uh public data set. Uh so really, I think if you're interested in this area, if you're doing bio AI, definitely check out, uh, data points come to some of our events.

The first people to do that right and so things break and that allows us to very quickly speed our development cycle cycle on the autonomous lab compared to companies that really just focus on robotics are on software or things like that because we are doing wet lab research on our own infrastructure, we're learning really fast about.

Jason Kelly: When you saw those 30 protocols running on a system, we're the first people to do that, right? Things break, and that allows us to very quickly speed our development cycle on the autonomous lab compared to companies that really just focus on robotics or on software, things like that. Because we are doing wet lab research on our own infrastructure, we are learning really fast about what works and doesn't, and very importantly, about how to onboard scientists into autonomous labs. Like, that is a cultural change, right? It involves technical tools to make that easier and faster so that they can still get their very important work done, but they can run it overnight.

Works and doesn't and very importantly, about how to onboard scientists into autonomous labs.

Uh, the last point I'll mention, uh, about us running the cro Labs, if you, uh, is that our scientists using our big autonomous lab in Boston is a little bit like, uh, the way Mo engineer 5 years ago, going through Palo Alto sitting in the driver's seat with their fingers, like this, right next to the steering wheel like ready to grab it. If the car turns into a mailbox or something, they are the first ones to push lab autonomy to the frontier. Right. When

Like that is a cultural change right and so it involves technical tools to make that easier and faster. So that they can still get their very important work done, but they can run it overnight one of the things that our scientists have really enjoyed doing if you watch like the ramp of protocols getting onto nebula are big Autonomous lab here at spikes in the afternoon and then.

People, who experiments run overnight and they come in the morning to data.

It's been awhile since I've been at the lab, but that sort of the dream is to show up in the morning with a coffee to fresh datasets. So I do think scientists get really excited about this as we bring the barriers down but again ginkgo team gets to be the Guinea pigs, so that our customers of the autonomous lab end up being able to see what's possible and also have.

You saw those 30 protocols running on a system there that were the first people to do that, right? And so things break, and that allows us to very quickly speed our development cycle cycle on the autonomous lab compared to companies that really just focus on robotics or on software things like that, because we're doing wet lab research on our own infrastructure. Uh, we are learning really fast about what works and doesn't and very importantly about how to onboard scientists into autonomous labs,

Jason Kelly: One of the things that our scientists have really enjoyed doing, if you watch, like, the ramp of protocols getting onto Nebula, our big autonomous lab here, it spikes in the afternoon, then people's experiments run overnight, they come in the morning to data. Which it's been a while since I've been at the lab, that's sort of the dream, is to show up in the morning with a coffee to a fresh data set. I do think scientists get really excited about this as we bring the barriers down. Again, Ginkgo's team gets to be the guinea pigs that our customers of the autonomous lab end up being able to see what's possible also have a lot of that debugged in advance. I'll talk a minute about our OpenAI project.

Out of that de bugged in advance.

I'll talk a minute about our opening I project.

If you're sitting in the back of Alaimo and autonomous car, you tell them where to go.

Once you close the door and get out or get out and close the door.

<unk> AI takes over and it tells the autonomous car where to go so.

So what the autonomous car is solving is the problem of replacing the manual driving not the directing.

Same idea here when we're solving the autonomous lab problem. The ginkgo, what we're solving is the manual lab work.

Like that is a cultural change, right? And so it involves technical tools to make that easier and faster so that they can still get their very important work done. But, uh, they can run it overnight. 1 of the things that our scientists have really enjoyed doing. If you watch like the ramp of protocols getting onto nebula or big autonomous lab, here it spikes in the afternoon and then people whose experiments run overnight and they come in the morning to data, which it's been a while since I've been at the lab. But that's sort of the dream is to show up in the morning with a coffee to a fresh data set. Uh, so I do think science get really excited about this as we bring the barriers down, but again, goes, uh, team gets to be the guinea pigs so that our customers of the autonomous lab, uh, end up being able to see what's possible. And also have a lot of that debugged in advance,

Jason Kelly: You know, if you're sitting in the back of a Waymo, an autonomous car, you tell it where to go. Once you close the door and get out or get out and close the door, Waymo's AI takes over, and it tells the autonomous car where to go. What the autonomous car is solving is the problem of replacing the manual driving, not the directing. Same idea here. When we're solving the autonomous lab problem at Ginkgo, what we're solving is the manual lab work, not the directing of what lab work to do. That could be done by scientists, as is done every day at Ginkgo, as you saw all those protocols on the scheduler. You could also try to have those experiments run by AI scientists.

Uh, I'll talk a minute about our open AI project.

Not the directing of what lab work to do and that could be done by scientists as is done every day I can go as you saw all those protocols on the scheduler, but you could also try to have those experiments run by AI scientists and so our project with GBT five like I was mentioning we're doing 100 of these trade it before while Plaids, we're giving that data.

you know, if you're sitting in the back of a, who an autonomous car, you tell it where to go,

Once you close the door and get out, or get out and close the door, uh, whose AI takes over and tells the autonomous car where to go?

so what the autonomous car is solving is the problem of replacing the manual driving, not the directing

Back to the model it was interpreting the data and then sending in new designs. We are a great archive paper on this if you Google if you look at the <unk>.

same idea here, when we're solving the autonomous lab problem at Ginkgo, what we're solving is, the manual lab work.

Opening I bought post you can find it.

We learned a number of things about this I think we did some really smart stuff with opening I here I'll just give you one quick vignettes.

Jason Kelly: Our project with GPT-5, like I was mentioning, we were doing 100 of these 384-well plates. We were giving that data back to the model. It was interpreting the data and then sending in new designs. We have a great archive paper on this. If you Google it, if you look at the OpenAI blog post, you can find it. We learned a number of things about this. I think we did some really smart stuff with OpenAI here. I'll just give you one quick vignette. The model is designing the parameters of the experiment, but we don't let it just run anything.

So the model is designing the parameters of the experiment, but we don't let it just run anything.

Have what's called up Identic model, which was basically a software defined set of rules and we have open source that you can download it were GBP five submitted designs into that and it had to pass a series of task for us to be willing to run it and if it didn't we would tell it failed and it would redesign until it met the test so simple things.

384 wall plate submit 384 wells the volume of the well is this much like wed do not exceed that amount of liquid or else, it's going to spill everywhere right.

Jason Kelly: We have what's called a Pydantic model, which was basically a software-defined set of rules, and we have open sourced this, you can download it, where GPT-5 submitted designs into that, and it had to pass a series of tests for us to be willing to run it. If it didn't, we would tell it it failed, and it would redesign until it met the test. Simple things. 384-well plate, submit 384 wells. The volume of the well is this much liquid. Do not exceed that amount of liquid, or else it's gonna spill everywhere, right? More complicated things. Do your experiment in quadruplicate because we wanna publish a paper about this, and scientists are gonna wanna see replication.

But more complicated things do your experiment and Quadruplicate, because we want to publish a paper about this and scientists are going to want to see replication.

<unk> set a standard controls experiment to experiment. So we can fairly compare how youre doing over time. So we put those rules in but then within the experimental wells like the rest of the plate as long as it put the right amount of volume they could do whatever it wants and so across 500 plates in the experiment. We had only two that we thought were just total nonsensical.

All those protocols on the scheduler, but you can also try to have those experiments run by AI scientists. And so our project with gb5, like I was mentioning we were doing a 100 of these 384 while plates, we were giving that data back to the model. It was interpreting the data. And then sending in New Designs, uh we have a great uh, archive paper on this. If you Google, if you look at the, the openai blog post, you can you can find it. Um, we learned a number of things about this. I think we did some really smart stuff with openai here. I'll just give you 1 quick venue yet. Uh, so the model is designing, the parameters of the experiment, but we don't let it just run anything. Uh, we had what's called a pantic model, which is basically a software-defined set of rules and we have open source this, you can download it where gb5, submitted designs into that and it had the pass a series of tests for us to be willing to run it. And if it didn't, we would tell it it failed and it would redesign until it met the test. So simple things, uh, 384 well, plate. Submit 384 Wells, the volume of the well is

This much liquid. Do not exceed that amount of liquid, or else it's going to spill everywhere, right? Um.

<unk>.

<unk> designs and one of them was a problem with our pedantic model where we're.

Jason Kelly: Include a set of standard controls experiment to experiment, we can fairly compare how you're doing over time. We put those rules in within the experimental wells, like the rest of the plate, as long as it put the right amount of volume, it could do whatever it wanted. Across 500 plates in the experiment, we had only two that we thought were just total nonsensical designs. One of them was a problem with our Pydantic model where GPT-5 designed negative volumes of certain reagents to try to squeeze more reagents in under the volume limits. Obviously, you can't do negative volumes, we added that to the model, it learned not to do that. Really, I think this is a first demonstration of really more open-ended experimental work beating state-of-the-art.

<unk> designed negative volumes of certain reagents to try to squeeze more reagents in under the volume limits. Obviously, you can't do negative volumes as we added that to the model and it learned not to do that so really I think this is the first demonstration of really more open ended experimental work, beating state of the yards there is definitely really.

But more complicated things, uh, do your experiment in quadruplicate because we want to publish a paper about this and scientists are going to want to see replication. Uh include a set of standard controls experiment to experiment. So we can fairly compare how you're doing over time. So we put those rules in but then within the experimental Wells, like the rest of the plate, as long as it put the right amount of volume, it could do whatever it wanted. And so across 500 plates and

Great ways to take this work in the future that we're going to continue following up on open I basically used us as the cloud lab right. They paid us to do do the data generation and their model was able to send and receive commands and data back from our autonomous lab in Boston, Alright, I'm going to sort of end on this next one.

Nearly I think it was the right company to bring autonomous labs to market at scale I deeply believe this this is now apparent to me in particular over the last quarter, we have our cash burn under control and that's why we wanted to guide to that and keep the team and have investors understand how much we plan to invest in this we have extensive practical experience automating.

Jason Kelly: Uh, there's definitely really great ways to take this work in the future that we're gonna continue following up on. OpenAI basically used us as a cloud lab, right? They paid us to do, do the data generation, and their model was able to send and receive commands and data back, uh, from our autonomous lab in Boston. All right, uh, I'm gonna sort of end on this next one, you know, uh, or nearly. Uh, Ginkgo is the right company, uh, to bring autonomous labs to market at scale. I, I deeply believe this. This is now apparent to me, in particular over the last quarter. Uh, we have our, our cash burn under control. That's why we wanted to, to guide to that and keep the team and have investors understand how much we plan to invest in this. Uh, we have extensive practical experience automating lab work.

<unk>. This is what we have been doing the last decade, plus at Ginkgo. We know what is hard we know what it takes to move bench work onto liquid handlers to two what are the little tips and tricks associated with each bench top device. When you run it at high throughput and high capacity all of that information is getting embedded into models and our software.

Experiment. We had only two that we thought were just total nonsensical, uh, designs. And one of them was a problem with our pediatric model where, uh, GB D5 designed negative volumes of certain reagents to try to squeeze more reagents in, under the volume limits. Obviously, you can't do negative volumes. Uh, so we added that to the model and it learned not to do that. So, uh, really, I think this is a first demonstration of really more open-ended experimental work beating state-of-the-art. Uh, there's definitely really great ways to take this work in the future that we're going to continue following up on open. I basically use us as a cloud lab, right? They paid us to do the data generation and their model was able to send and receive commands and data back from our autonomous lab in Boston. All right, uh, I'm going to sort of end on this next one, you know, uh, or nearly, uh, Ginkgo is the right company, uh, to bring autonomous labs to market at scale. I deeply believe this. This is now apparent to me, in particular, over the last, uh, quarter. Uh, we have our cash burn under control. That's why we wanted to...

To make this really just work magically or scientist as they move to the autonomous lab I think we're the only wants to do it and it is a dead mission fit for making biology easier to engineer I am convinced that the number one problem in that space right. Now is the lab work, we're just not able to try enough genetic designs to get good at genetic engineering.

Jason Kelly: This is what we have been doing the last decade plus at Ginkgo. We know what is hard. We know what it takes to move bench work onto liquid handlers, what are the little tips and tricks associated with each benchtop device when you run it at high throughput and high capacity. All of that information is getting embedded into models and our software to make this really just work magically for scientists as they move to the autonomous lab. I think we're the only ones to do it, and it is a dead mission fit for making biology easier to engineer. I'm convinced that the number one problem in that space right now is the lab work. We are just not able to try enough genetic designs to get good at genetic engineering. That's not Ginkgo, that's the whole industry. Next slide.

So I think that's the whole industry next slide.

Do you want to mention because I'm sure some folks aren't doing it are scientists or type of customers and so on.

To guide to that and keep the team and have investors understand how much we plan to invest in this. Uh, we have extensive practical experience automating, lab work. This is what we have been doing the last decade Plus at Ginkgo. We know what is hard. We know what it takes to move bench, work on to liquid, handlers to to what are the the little tips and tricks associated with each benchtop device. When you run it at high throughput and high capacity, all of that information is getting into models and our software to make this really

A lot of times, we also hear from side to say should I be worried about this obviously part of my job is working at the lab bench generating this data I really like this.

Old advertisement from $19 51, IBM and it talks about how.

The mechanical calculator or electronic calculator I should say.

Jason Kelly: I do wanna mention, 'cause I'm sure some folks tuning in are scientists or, you know, potential customers and so on. A lot of times we also hear from scientists, "Hey, should I be worried about this?" Obviously, part of my job is working at the lab bench generating this data. I really like this old advertisement from 1951, IBM, and it talks about how the mechanical calculator, or electronic calculator I should say, is gonna do the work of 150 extra engineers at your company. What I love about this is if you're not familiar with that device, the younger folks or whatever, on this call, that is a slide rule. This is back when computation was done manually. This device predates general purpose computers.

Just work magically for scientists as they move to the autonomous lab. I think we're the only ones to do it and it is a dead Mission fit for making biology easier to engineer. I am convinced that the number 1 problem in that space right now is the lab work. We are just not able to try enough genetic designs to get good at genetic engineering. Uh, that's not go. That's the whole industry next slide.

Going to do the work of a 150 extra engineers at your company and what I Love about this is if youre not familiar with that device to the younger folks or whatever on this call and that is a slide rule. So this is back when computation was done manually.

And this devices. This predates general purpose computers. This was literally just a device like added and subtracted and divided basic arithmetic was going to do the work of 150 engineers and you might say that device will replace 150 engineers.

Uh, I do want to mention because I'm sure some folks don't tuning in or scientists or, you know, potential customers and so on. Um uh, a lot of times we also hear from scientists, hey, should I be worried about this? Obviously part of my job is working at the lab bench, generating this data, and I really like this, um, uh, old advertisement from 1951 IBM. And it talks about how

Now of course, you fast forward 70 years and there are.

100 times more engineers than there were back in $19 51, and that's because the return on investments on what is in the head of people, who understand engineering increased dramatically on the other side of the automation of the manual work of computation.

The uh, mechanical calculator, uh our electronic calculator. I should say uh was is going to have do the work of 150x extra engineers at your company. And what I love about this is if you're not familiar with that device, this is the younger folks or whatever. Uh, on this call, uh, that is a slide rule. So, this is back when computations was done manually.

Jason Kelly: This was literally just a device that like added and subtracted and divided basic arithmetic, was going to do the work of 150 engineers. You might say, that device will replace 150 engineers. Now, of course, you fast-forward 70 years and there are 100 times more engineers than there were back in 1951, and that's because the return on investment on what is in the head of people who understand engineering increased dramatically on the other side of the automation of the manual work of computation. If you go to the next slide, I very much believe that will be the case for the manual work of laboratories. It is insanity that we take people who are PhD caliber, understand all the biology, like, all the ins and outs of these ridiculously complicated biological systems.

And if you go to the next slide I very much believe that will be the case for the manual work of laboratories. What is it is insanity that we take people who are Phd caliber understand all the biology like all the ins and outs of these ridiculously complicated biological system, they understand human biology, <unk> and other things.

And this device is this predates general purpose computers. This was literally just a device that like added and subtracted and divided and basic arithmetic was going to do the work of 150 engineers and you might say that device will replace 150 engineers

Now, of course, you fast forward 70 years and there are

And while they're at it they have to be extremely careful laboratory technicians in order to to move liquids and do this work with great fidelity to EBIT reality to try and test and hypothesize their experiments we need to divide those two things just like computation did back in the 19 fifties and if you do that I assure you you.

Hundred times more Engineers than there, were back in 1951 and that's because the return on investment on what is in the head of people who understand engineering increased dramatically on the other side of the automation of the manual work of computation.

We'll get many many more genetic engineers many many more scientists than we have today when our ROI is limited by the manual work at the bench and we just got to do it and get it was going to do it. So please put down your pipettes and join US if youre interested alright, So next slide.

Jason Kelly: They have to understand human biology, 1,800 things, and while they're at it, they have to be extremely careful laboratory technicians in order to move liquids and do this work with great fidelity to even be able to try and test and hypothesize their experiments. We need to divide those two things just like computation did back in the 1950s. If you do that, I assure you will get many, many more genetic engineers, many, many more scientists than we have today when our ROI is limited by the manual work at the bench. We just gotta do it, and Ginkgo's gonna do it. Please put down your pipettes and join us if you're interested. All right, next slide. That's my email up there.

There is always feel free to email if you're excited about this stuff.

I appreciate the time today and happy to take questions.

Thanks, Jason.

As usual I'll start with a question from the public and remind the analysts on the line if you'd like to ask a question. Please raise your hands and I'll call on you and open up your lines.

Biology like all the ins and outs of these ridiculously complicated biological systems. They have to understand human biology 18, other things. And and while they're at it, they have to be extremely care. Careful, laboratory technicians, in order to, to move liquids and do this work, um, with great Fidelity to even be able to, to try and test and hypothesize, their experiments. We need to divide those 2 things just like, uh, computation did back in the 1950s and if you do that, I assure you, you will get many many more genetic Engineers many, many more scientists, uh, than we have today. When our Roi is limited by the manual, work at the bench,

Thanks, everyone.

Jason Kelly: As always, feel free to email if you're excited about this stuff. Appreciate the time today and happy to take questions.

Right.

Let's get started.

The first question that we have is from back in <unk>.

and we, we just got to do it and get those going to do it. Uh, so, uh, please put down your pipe pets and join us if you're interested. Yeah. All right. So, next slide, uh, that's my email up there. As always, feel free to email if you're excited about this stuff. Uh, appreciate the time today and happy to take questions.

Operator: Thanks, Jason. As usual, I'll start with a question from the public and remind the analysts on the line that if you'd like to ask a question, to please raise your hands on Zoom, and I'll call on you and open up your line. Thanks, everybody. All right, let's get started. First question that we have is from Bag, and this is on X. With the planned expansion of Rack's capacity from roughly 50 to 100 units at the Boston facility, could you help us understand how this increased capacity is expected to translate into 2026 revenue? Specifically, what portion of that contribution do you anticipate will be recurring in nature, for example, software, operations, consumables versus project-based services?

Thanks Jason.

With a planned expansion of racks capacity from roughly 50 to 100 units in the Boston facility could you help us understand how this increased capacity is expected to translate into 2026 revenue.

As usual, I'll start with a question from the public and remind the analysts on the line that if you'd like to ask a question, please raise your hands on Zoom and I'll call on you and open up your line.

Thanks everybody.

Specifically what portion of that contribution do you anticipate will be recurring in nature. For example software operations consumables versus project based services.

All right.

Yeah, I can take that so.

The rack expansion and part is to again be able to move we have extensive labs here, where there is a variety of different levels of automation that sort of walk up automation, where a person is going up to like a liquid handler that liquid handler does some automated work then you take that sample somewhere else in the lab work sell automation, which is that subway I was talking about and then benches, we see now for some of our lab.

We're still at about right and so we wanted to take all of that work and basically shifted onto nebula onto that big 100 rack system in the coming year. So so that's sort of the point of it now on top of that system will do those services right. So we'll have our our data point service, our upcoming cloud lab service and that our solution service. So I can speak to sort of the repeatable.

Jason Kelly: Yeah, I can take that. For starters, the rack expansion in part is to again, be able to move. You know, we have extensive labs here where there's a variety of different levels of automation. There's sort of walk-up automation, where a person is going up to like a liquid handler. That liquid handler does some automated work, then you take that sample somewhere else in the lab. Work cell automation, which is that subway I was talking about, and then benches. We, you know, for some of our lab work, we're still at the bench, right? We wanna take all that work and basically shift it on to Nebula, onto that big 100-rack system, in the coming year. That's sort of the point of it. Now, on top of that system, we'll do those services, right?

Well, let's get started. Um, so first question that we have is from bag and this is on uh, on X uh with a planned expansion of racks capacity from roughly 50 to 100 units. At the Boston facility, could you help us? Understand how this increase capacity is expected to translate into 2026 Revenue? Uh, specifically, what portion of that contribution? Do you anticipate will be recurring in nature, for example, software operations, consumables versus project Based Services.

Yeah, I can take that. So

City of the services.

The way solutions deals work those are usually multi year R&D deals. So theres some reproducibility like across a deal like we do a big program for ARPA age our long standing partnership with Bayer.

Bayer crop science, that's been going on for five or six years. So you have some repeat ability in inside a contract, but each time, we do a new contract that is hunting for a new project.

Jason Kelly: We'll have our data point service, our upcoming cloud lab service, and then our solution service. I can speak to sort of the repeatability of those services. The way solutions deals work, those are usually multi-year R&D deals. There's some reproducibility like across a deal. Like, we do a big program for ARPA-H. Our long-standing partnership with Bayer Crop Science that's been going on for 5 or 6 years. You have some repeatability inside a contract. Each time we do a new contract, that is hunting for a new project. Data point's a bit different.

Data points a bit different we are starting to see now as I mentioned in the call are in kind of the top.

20, or 30 pharma companies, where we have.

That is becoming more repeat business as we're able to basically build trust with those partners that we can serve as that outsource data generation for their teams the cloud blah blah blah, and we will see I mean, that's a new experiment, where I want to go after that smaller batch work from scientists at the bench I think if you look at more narrow band CRO.

Jason Kelly: We are starting to see now, as I mentioned in the call, we're in 10 of the, you know, top, again, I forget it's 20 or 30, pharma companies where we have, that is becoming more repeat business as we're able to basically build trust with those partners that we can serve as that outsourced data generation for their teams. The cloud lab one, we'll see. I mean, that's a new experiment where I wanna go after that smaller batch work from scientists at the bench. I think if you look at more narrow band CROs, you know, the folks that like build DNA, express proteins, these sorts of things, I think you do see a lot of repeat business once someone has confidence with a vendor.

The folks at like built DNA Express proteins. These sorts of things I think you do see a lot of repeat business. Once someone has a has confidence with our vendor. We're obviously trying to do more flexible work and that'll be a new a new experience, we'll see how it goes but I'm hopeful that would also look like repeat business on the platform. So of the three I think solutions is the one where they are.

All that work and basically shifted on to nebula onto that big 100 rack system um in the coming year so so that that's sort of the the point of it. Now, on top of that system we'll do those Services, right? So we'll have our our data point service, our upcoming Cloud lab service and then our solution service. So I can speak to sort of the repeatability of those Services, um, the way Solutions deals work, those usually multi-year R&D deals. So there's some reproducibility, like, across a deal. Like, we do a big program for arpa age our, our long-standing partnership with, um, Fair crop science. Has been going on for 5 or 6 years. So, you have some repeatability in inside a contract, but each time we do a new contract. That is hunting for a new project, um, data points a bit different. We are starting to see now, as I mentioned in the call or in 10 of the, you know, top again I forget it's 20 or 30, uh, Pharma companies where we have um uh that is becoming more repeat business as we're able to basically build trust with those partners that we can serve as that Outsource data generation.

Hunting each time to add new research partnerships, but the other two are a bit more repeat business and then just to mention it that's all on the side of using our system in Boston went to what you asked about but of course, we're also selling our system. So like we sell that system to a P&L or or a pharma company or whomever.

Jason Kelly: We're obviously trying to do more flexible work, and that'll be a new experience. We'll see how it goes. I'm hopeful that would also look like repeat business on the platform. Of the three, I think solutions is the one where you're really off hunting each time to add new research partnerships. The other two are a bit more repeat business. Just to mention it, that's all on the side of using our system in Boston, which is what you asked about. Of course, we're also selling our system. Like we sell that system to a PNNL or a pharma company or whomever.

Our robotics go in there is a initial initial spend on Capex, but then we have a service and software license. That's ongoing over time that is also repeat more like a SaaS business.

Uh, for their teams, uh, the cloud Lot 1. I'll, we'll see, I mean, that's a new experiment where I want to go after, uh, that smaller batch work from scientists at the bench. I think if you look at more narrow band crows, um, you know, the folks that like build DNA, uh, Express proteins, these sorts of things, I think you do see a lot of repeat business. Once someone has a has confidence with a vendor, we're obviously trying to do uh, more flexible work and that'll be a new uh, a new experience, we'll see how it goes. But, um, I'm hopeful that would also look like uh, repeat business on the platform. So of the 3, I think.

And then if you were to if we add like sort of specialized reagents and high throughput things as.

As the system was used that would also be.

Repeat business as well, but the initial capex would be one time.

Jason Kelly: When our robotics go in, there's an initial spend on CapEx, but then we have a service and software license, that's ongoing over time that is also repeat, more like a SaaS business. If we had like sort of specialized reagents, some high throughput things, as the system was used, that would also be, repeat business as well. The initial CapEx would be one time.

Thanks, Jason.

Our next question is from Brendan at TD.

Two questions. So I'll start with the first one first.

One is how should we think about U S onshoring of manufacturing as a potential tailwind to revenue growth and what do you think ginkgo will need to do to maximize our share of this trend over the next couple of years.

Yes, so on the manufacturing side, we have been seeing interest.

Solutions is the 1 where you're really off Hunting each time to add research Partnerships. Um, but the other 2 are a bit more repeat business and then just to mention it, that's all on the side of using our system in Boston, which is what you asked about. But of course, we're also selling our systems. So like we sell that system to a, A pnnl or, or a Pharma company or whomever, uh, when our robotics go in, there's an initial initial spend on capex, but then we have a service and software license, uh, that's ongoing over time, that is also repeat more like a SAS business. Uh, and then if you were to, if we had like sort of specialized reagents some high, throughput things. Uh as the system was used, that would also be um repeat business as well. But the initial capex would be 1 time.

Operator: All right. Thanks, Jason.

On the sort of manufacturing QC right. So again when our systems are doing.

Jason Kelly: Yeah.

Operator: Our next question is from Brendan at TD. He actually has 2 questions, and so I'll start with the first one. The first one is, how should we think about US onshoring of manufacturing as potential tailwind to Rack's revenue growth? What do you think Ginkgo will need to do to maximize its share of this trend over the next couple of years?

The typical manufacturing environment youre going to be doing production in larger tanks. The racks are really about integrating laboratory bench top equipment. Now you do have a bunch of laboratory bench top equipment in manufacturing plants, and it's used to do quality control across batches.

Jason Kelly: Yeah. On the manufacturing side, we have been seeing interest on the sort of manufacturing QC, right? Again, what our systems are doing, you know, in a typical manufacturing environment, you're going to be doing production in larger tanks. The racks are really about integrating laboratory benchtop equipment. Now, you do have a bunch of laboratory benchtop equipment in manufacturing plants, and it's used to do quality control across batches, sometimes associated with even kind of like semi-diagnostics work, associated with following up on patients over time in a lot of these drugs. There is actually a decent amount of lab work tied to post-clinical, you know, sort of like once a drug is on the market, that does keep going in a repeated way.

Okay. Thanks Jason. Uh, our next question is from Brendan uh, at TV. Um, he actually has 2 questions and so I'll start with the first 1. The first 1 is, how should we think about us onshoring, but manufacturing, as potential Tailwind to Rack Revenue growth? And what do you think Google will need to do to maximize uh, a share of this trend over the next couple of years?

So associated with even kind of like semi diagnostics work associated with following up on patients over time and a lot of these drugs. So there is actually a decent amount of lab work tied to post.

Clinical sort of like once the drug is on the market that does keep going in a repeated way I think our sweet spot there would be being able to handle many different QC protocols on one big system right. So again the strength is.

Yeah, so on the manufacturing side, we have been seeing interest from, uh, on the sort of manufacturing QC, right? So again, with what our systems are doing—you know, in the typical goal manufacturing environment, you're going to be doing production in larger tanks. Uh, the racks are really about integrating laboratory benchtop equipment. Now, you do have a bunch of laboratory benchtop equipment in manufacturing plants, and it's used to do quality control across batches. Um,

Complicated protocols multi step, but today you may be doing at the lab bench, you often have folks at those research centers.

Our part of the kind of manufacturing team and so on that not sort of open ended research Phd scientists and so being able to do sort of like the latest type of assay or something like that.

Jason Kelly: I think our sweet spot there would be being able to handle many different QC protocols on one big system, right? Again, the strength is complicated protocols, multi-step. Today you may be doing at the lab bench, you often have folks at those research centers, that are part of the kind of manufacturing team and so on, not sort of open-ended research PhD scientists. Being able to do sort of like the latest type of assay or something that, you know, being able to deploy that out as a QC step, I think the racks open up the door for that. We are talking to some customers about putting our automation into manufacturing sites. I think there'll maybe a little bit of tailwind there. We'll see. Yeah.

<unk> that out as a QC step.

<unk> opened up the door for that so we are talking to some customers about putting our automation into manufacturing sites I think that maybe a little bit of tailwind there we'll see.

Yep.

Cool.

Question is how do we get those data points offering is being received among customers are there any material tailwind that you expect for this part of the business over the next 12 months.

Yes, it really well I think we're kind of finding the sweet spot in providing so like whats going I guess I'll just make a quick point on the AI side right. So there's sort of two halves to how AI is impacting the biotech industry. One is what I just spent a lot of time on the call talking about which is reasoning models and coding model at the same sort of model that everybody is using in.

Sometimes associated with even kind of like semi Diagnostics, work associated with following up on patients over time and a lot of these drugs. So there's actually a decent amount of lab work tied to post. Uh clinical, you know, sort of like once a drug is on the market that does keep going in a repeated way, I think our sweet spot there would be being able to handle many different QC. Protocols on 1 big system, right? So again the strength is, uh, complicated protocols, multi-step. Today you may be doing at the lab bench, uh, you often have Folks at those research centers, uh, that are part of the kind of manufacturing team and so on not, not sort of open-ended research, PhD scientists. Um, and so being able to do sort of like, the latest type of assay or something, that was that, you know, being able to deploy that out, it's a QC step. Um, I think thorax opened up the door for that, so, we were talking to some customers about putting, uh, our automation into manufacturing sites. I think they'll maybe a little bit of Tailwind there. Uh, we'll see ya.

Operator: Cool. Next question is, how is Ginkgo's Datapoints offering being received among customers? Are there any material tailwinds that you expect for this part of the business over the next 12 months?

Basically any information technology space are going to impact the ability for scientists to use autonomous labs in robotics in the lab, that's all being made easier by reasoning models.

Cool. Uh, next question is, how is Geno's data points? Offering being received among customers. Uh, are there any material? Tailwind that? You expect for this part of the business over the next 12 months?

Jason Kelly: Yeah, really well. I think we're kind of finding the sweet spot in providing. I guess I'll just make this quick point on the AI side, right? There's sort of two halves to how AI is impacting the biotech industry. One is what I just spent a lot of time on the call talking about, which is reasoning models and coding models. The same sort of models that everybody's using in, you know, basically, any information technology space are gonna impact the ability for scientists to use autonomous labs and robotics in the lab. That's all being made easier by reasoning models. Separate from that, there are bio AI models, and the most famous one of these is AlphaFold that Google came out with, which was a model trained on not human language, not human reasoning, but rather biological language.

Separate from that there are bio AI models and the most famous one of these is alpha fold that Google came out with which was a model trained on non human language not human reasoning, but rather biological language and in particular in that case like amino acid sequences from proteins and the structure of those proteins. So theres a lot of work going on in that area.

Yeah, really well. Uh, I think we're kind of finding the sweet spot in providing. So, like, what's going— I guess I'll just make this quick point on the AI side, right? So there's sort of two halves to how AI—

And in order to build those bio AI models, you need to generate very large datasets with sort of a variety of in that case, they are proteins and their structures, but there's many other things and functional genomics antibody develop ability other areas, where pharma companies are asking us to make those big datasets for their ml teams that side of the house has gotten.

Is impacting the Biotech Industry. 1 is what I just spent a lot of time on the call talking about, which is reasoning models and coding models, the same sort of models that everybody's using in. You know, basically any information technology space are going to impact the ability for scientists to use, autonomous, labs and Robotics in the lab. That's all being made easier by reasoning models.

Jason Kelly: In particular in that case, like amino acid sequences from proteins and the structure of those proteins. There's a lot of work going on in that area, and in order to build those bio AI models, you need to generate very large datasets with sort of a variety, in that case, say, of proteins and their structures, but there's many other things in functional genomics, antibody developability, other areas where pharma companies are asking us to make those big datasets for their ML teams. That side of the house has gotten tailwinds. There was a lot of, if you're at the JP Morgan conference this year, folks like Chai Discovery announced their partnership with Lilly and Noetik.

Tailwind there was a lot of you at the JP Morgan.

Conferences, your folks like <unk> announced their partnership with Lilly and <unk>, because there's a whole bunch of companies in the startup side, who are starting to partner with the large farmers because they have great bio AI models and the reason they are great bio AI models, they have proprietary data.

Not just how smart they are at the modeling is that they've been generating these large datasets. So thats sort of opening People's eyes up and I think creating a little bit of a wave there and we're definitely the rights I'd say that the leader in providing datasets to again to the large pharma large biotech.

Jason Kelly: There's a whole bunch of companies in the startup side who are starting to partner with the large pharmas because they have great bio AI models, and the reason they have great bio AI models is they have proprietary data. It's not just how smart they are at the modeling, it's that they've been generating these large datasets. That's sort of opening people's eyes up and I think creating a little bit of a wave there. We're definitely the right, I'd say the, you know, the leader in providing datasets to, again, to the large pharma, large biotechs, ML teams that are all ready to go for training and everything else. We can do the robot half and the data cleanup half, and they can focus on the biological modeling. It's been good, I would say. Yeah.

<unk> teams that are all ready to go for training and everything else. We can use the work we can do the robot have and the data cleanup app and they can focus on the biological modeling.

So it's been good I would say, yes I'm.

And in particular, in that case like amino acid sequences from proteins and the structure of those proteins. So there's a lot of work going on in that area and in order to build those bio AI models, you need to generate very large data sets uh with sort of a variety in that case, say of proteins and their structures, but there's many other things and functional genomics antibody, developability other areas. Where, uh, Pharma companies are asking us to make those big data sets for their ml teams. That side of the house has gotten Tailwind. There was a lot of uh, if you're at the JP Morgan conferences this year, um, folks like chai bio announced their partnership with Lily and noetic, there's a whole bunch of companies in the startup side who are starting to partner with the large Farmers because they have great bio AI models. And the reason they have great bio AI models is they have proprietary data

I'm excited about it for this year.

Alright.

Next question is from at <unk> Big Road, there's not ask the question is about basically not just the utilization of fracs, but the manufacturing and deployment Fracs and so the question is similar to our teflon it used to get compressed to dramatically improve manufacturing efficiency is king of exploring any specific strategies technologies or.

It's not just how smart they are at the modeling. It's that they've been generating these large data sets so that's sort of opening people's eyes up and I think creating a little bit of a wave there and we're definitely the right. I'd say that, you know, the leader in providing data, sets to again, to the large Pharma, large biotechs. It's, uh, ml teams that are all ready to go for training and everything else. We can use the work, we can do the robot half and the data cleanup half and they can focus on the biological modeling.

Jason Kelly: I'm excited about it for this year.

Methods to significantly enhance the production efficiency and scalability of racks.

So, is that good? I would say, yeah, I'm excited about it for this year.

Operator: All right. next question is from @TTBigRoad. This is on X. The question is about basically not just the utilization of racks, but the manufacturing and deployment of racks. The question is, similar to how Tesla used the Giga Press to dramatically improve manufacturing efficiency, is Ginkgo exploring any specific strategies, technologies, or methods to significantly enhance the production efficiency and scalability of racks?

Yes, that's something we are starting to think about I mean, mainly because those things take time to put in place. We did make some good decisions. So over the last four four years or so I guess since we acquired XI imaging, where this technology started we actually did do a like a generational upgrade to the hardware of the racks and made them in that design change was not.

Really about back to our old <unk> that we got back in the day as I imagine these are the new ones are compatible.

Jason Kelly: Yes. This is something we are starting to think about. I mean, mainly because those things take time to put in place. We did make some good decisions. Over the last four years or so, I guess, since we acquired Zymergen where this technology started, we actually did do a like a generational upgrade to the hardware of the racks. That design change was not really about, in fact, our old racks that we had back in the day at Zymergen. The new ones are compatible. The system has many less components, and that was all done for design for manufacturability for exactly this reason. We make them today in San Jose.

All right. Uh, next question is from, uh, at TT big road. This is on X. Uh, the question is about basically not just the utilization of racks but the the manufacturing and deployment of racks. And so the question is, uh, similar to how Tesla used the gigapress to dramatically improve manufacturing. Efficiency, is Geo exploring any specific strategies Technologies or methods to significantly enhance the production efficiency and scalability of racks?

The system has many less components and that was all done for design for manufacture ability for exactly. This reason, we make them today in San Jose We do final Assembly.

An hour through a partner and then we do final Assembly and integration with third party equipment at our site in Emeryville, California.

As we were to scale up or selling more and more of these I think you will see us.

Best in.

Like basically larger partners to repeat that manufacturing process, but even as we have and now we can actually scale pretty decently on this so it's something I think we want to plan ahead for but it's not the immediate problem.

Jason Kelly: We do final assembly through a partner, then we do final assembly and integration with the third-party equipment at our site in Emeryville, California. As we were to scale up and selling more and more of these, I think you will see us invest in, you know, like basically larger partners to repeat that manufacturing process. Even as we have it now, we can actually scale pretty decently on this. It's something I think we wanna plan ahead for, but it's not the immediate problem we have. Yeah.

Okay.

Sure.

Alright. Thanks.

Thanks, that's all that we have for Tonight. So of course, if anyone has any questions in general they can always E mail us and investors I think a virus dot com. Jason also his personal email up there earlier, so you can message.

And yes, thank you and have a great night and now everyone has a great quarter.

Thanks, everybody.

Yes.

Yes, this is something. We are starting to think about I mean, because mainly because those things take time to put in place, um, we did make some good decisions so over the last 4 4, 4 years or so, I guess since we acquired Zen where this technology started, we actually did do a um, like a generational upgrade to the hardware of the racks and made them and that design change was not really about. In fact, our old racks that we had back in the day and these are the new ones are compatible, um, but the system has many less components and that was all done for design for manufacturability. For exactly this reason, uh, we make them today, uh, in San Jose, we do final assembly in our through a partner. And then we do final assembly, and integration with the third party equipment, at our site and emmeryville California. Uh, as we were to scale up at selling more and more of these, I think you will see us uh invest in, you know like basically larger Partners to repeat that manufacturing process. But even as we have it now we can actually scale pretty decently on this. Um so it's something I think we want to plan ahead for but it's not the immediate problem.

Operator: Cool. All right. I think that that's all that we have for tonight. Of course, if anyone has any questions in general, they can always email us at investors at Ginkgo Bioworks dot com. Jason also put his personal email up there earlier, so you can message him. Yeah, thank you and have a great night, and hope everyone has a great quarter.

Jason Kelly: Thanks, everybody.

All right. Uh, I think that that's all that we have for tonight. So, uh, of course, if anyone has any questions in general, they can always email us at investors. Uh, I can go buy works.com. Jason also put his personal email up there earlier so you can message him. Uh, and yeah, thank you, and have a great night and hope everyone has a great quarter.

Operator: All right.

Thanks everybody. All right.

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