Q3 2025 Ginkgo Bioworks Holdings Inc Earnings Call

November 6, 2025

Brian Jason colleague cofounder, and CEO and Steve Cohen, our CFO. Thanks, as always for joining US we're looking forward to updating you on our progress.

Daniel Waid Marshall: Communications and Ownership at Ginkgo. I'm joined by Jason Kelly, our co-founder and CEO, and Steven Coen, 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'll 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 the quarter results, we're going to be providing insight into how we believe AI models will impact biotechnology, how our tools are positioned to support those impacts, and how those tools are winning us new deals with customers. As usual, we'll end with a Q&A session, and I'll take questions from analysts, investors, and the public.

Daniel Marshall: Communications and Ownership at Ginkgo. I'm joined by Jason Kelly, our co-founder and CEO, and Steven Coen, 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'll 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 the quarter results, we're going to be providing insight into how we believe AI models will impact biotechnology, how our tools are positioned to support those impacts, and how those tools are winning us new deals with customers. As usual, we'll end with a Q&A session, and I'll take questions from analysts, investors, and the public.

Speaker #2: of 2025 .

Speaker #2: 2025 period R&D

Speaker #2: expense

Speaker #2: included

Speaker #2: a $21 million shortfall

Speaker #2: obligation .

Speaker #2: related to our

As a reminder.

Speaker #2: multi-year October 2025 ,

Speaker #2: strategic

Speaker #1: And so I think if we're going to turn that around , both for biotechnology and for for science at large , we need to do it by investing in robotic infrastructure .

Presentation today, we'll 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.

Speaker #2: cloud

Speaker #2: and

Speaker #2: AI annual partnership with

Speaker #2: Google Cloud

Speaker #2: .

Speaker #2: In October 2025 ,

Speaker #2: we amended and reset the

Speaker #2: annual

Speaker #2: commitments

Speaker #2: commitments in Cell

Speaker #1: And I think that's not lost on the US government . And I think Ginkgo , if you go to the next slide , has exactly the right technology for that .

Speaker #2: future engineering

Speaker #2: years and settled the

Speaker #2: shortfall

Speaker #2: obligation

Speaker #2: for

Today. In addition to updating you on our quarter results, we're gonna be providing insight into how we believe AI models will impact biotechnology, how our tools are positioned to support those impacts and how those tools are winning us new deals with customers.

Speaker #2: G&A expense decreased

Speaker #1: And so I've shown these before , but these are reconfigurable automation carts or rack carts . And this is the first big area where I think AI is coming into biotechnology .

Speaker #2: from

Speaker #2: $23 million in the related third quarter of 2024 to $12 million in the third quarter of 2025 . These

Speaker #2: decreases to our were all driven by our restructuring efforts . Cell engineering segment

As usual I'll 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 Ax hashtag Giga results or email investors I can't go buy works Dot com alright over to you Jason.

Speaker #2: operating loss Google was $37 million in the third

Speaker #2: I'm joined by Jason Kelly , our co-founder and CEO , and Steve Cohen , our CFO . Thanks , as always , for joining us .

Speaker #2: quarter of Cloud 2025 , compared to a loss of $5 million in the comparable prior year period . The increase loss year over year was due to two factors .

Daniel Waid Marshall: You can submit those questions to us in advance via X, hashtag GinkgoResults, or email investors@ginkgobioworks.com. All right. Over to you, Jason.

Daniel Marshall: You can submit those questions to us in advance via X, hashtag GinkgoResults, or email investors@ginkgobioworks.com. All right. Over to you, Jason.

Speaker #2: We're looking forward to updating you on our progress . As a reminder , during the presentation today , we'll be making forward looking statements which involve risks and uncertainties .

Okay. Thanks Daniel.

Speaker #2: First , as

<unk> mission is to make biology easier to engineer, we always start with that.

Speaker #2: previously contract that mentioned ,

Jason Kelly: All right. Thanks, Daniel. Ginkgo's mission is to make biology easier to engineer. We always start with that. I want to highlight the three big objectives for us going into 2026, and I'm going to give you a little more detail on these today. The first is to deliver the robotics and software that bring autonomous labs on-prem, in other words, at our customer site so that they can run them themselves, through our tools business. We really grew into that sort of tools business model last year. But this robotics, automation, and AI controlling it, I think, is having a big moment right now, and I think we've got the right tool stack to bring that to customers. Second, we want to expand sort of our frontier autonomous lab here in Boston. We have the largest rack install in the world.

Speaker #2: the third quarter of 2025 expense

Speaker #2: 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 the quarter results , we're going to

Speaker #2: included

I want to highlight the three big objectives for us going into 2026, and I could give you a little more detail on these today. The first is to deliver the robotics and software that bring autonomous labs on Prem in other words at our customer sites. So that they can run them themselves through our tools business and we really grew into that sort of tools business model.

Speaker #2: a

Speaker #2: $21 million shortfall related to

Speaker #2: our Google Cloud

Speaker #2: contract that was

Speaker #2: subsequently settled

Speaker #2: . included

Speaker #2: Second ,

Speaker #2: providing insight into how will be we believe AI models will impact biotechnology , how our tools are positioned to support those impacts , and how those tools are winning us .

Speaker #2: previously mentioned

Speaker #2: ,

Speaker #2: the third

Speaker #2: quarter

Speaker #2: of termination 2024 included

Speaker #2: $45 million of

Speaker #2: non-cash

Speaker #2: revenue from the

Speaker #2: motif contract .

Speaker #2: New deals with customers . As usual , we'll end with a Q&A session and I'll take questions from analysts , investors and the public .

Speaker #2: Termination

Speaker #2: segment operating

Speaker #2: loss

Speaker #2: improved

Last year, but this robotics and automation and AI controlling and I think it's having a big moment right now and I think we've got the right tools stack to bring that to customers second we want to expand sort of our frontier Autonomous lab here in Boston, we have the largest rack install in the world I want to keep it that way it will be continuing to expand that even as our <unk>.

Speaker #2: 21% in the third

Speaker #2: quarter

Speaker #2: of

Speaker #2: 2025 ,

Speaker #2: You can submit those questions to us in advance via X . Ginkgo results or email . Investors at Ginkgo Bioworks . Com . All right .

Speaker #2: compared

Speaker #2: to the prior year

Speaker #2: comparable

Speaker #2: period

Speaker #2: . Moving further down the page , you'll

Speaker #2: note that

Speaker #2: total which was

Speaker #2: adjusted down from EBITDA in the third

Speaker #2: quarter -$20 million in of

Speaker #2: Over to you , Jason . Thanks , Daniel .

Speaker #2: was -$56 million , which

Speaker #2: was

Speaker #2: -$20 million

Speaker #3: Ginkgo's mission is to make biology easier to engineer . We always start with that . I want to highlight the three big objectives for us going into 2026 .

Speaker #2: in the

Speaker #2: third

Speaker #2: quarter previously

Speaker #2: of 2020 .

Speaker #2: For

Speaker #2: again

Jason Kelly: I want to keep it that way. We'll be continuing to expand that even as our customers build larger systems as well. And we want to use that to be able to show just the art of the possible to customers, what you can do when you have ultimately hundreds of pieces of equipment all connected in a single robotic setup that can be controlled by AI. And so I'll show a few photos and what we're doing there coming up. And then finally, our two big services are CRO services, solutions, and data points. We want to offer best-in-class services, best-on-the-market services to customers thereby leveraging that in-house robotic infrastructure. And that helps us kind of, again, demonstrate what's possible with those robotics and also offer great services to customers. So you're going to get to hear about all three of those things later from me.

Build larger systems as well.

Speaker #2: , this

Speaker #2: year the third

Speaker #2: over year quarter

And we want to use that to be able to show just the art of the possible to customers. What you can do when you have ultimately hundreds of pieces of equipment all connected in a single.

Speaker #2: previously mentioned Google

Speaker #3: And I'm going to give you a

Speaker #2: Cloud shortfall of expense recorded in the third quarter of 2025 , as well as

Speaker #3: detail on these

Speaker #3: The

Speaker #3: first is to deliver the

Speaker #3: robotics and

Speaker #3: software that

Speaker #2: the motif related excess non-cash revenue and the comparable prior year period

Speaker #3: bring

Speaker #3: autonomous

Speaker #3: labs

Speaker #3: on

Robotics setup that can be controlled by AI and so I'll show a few photos in what we're doing there coming up and then finally, our two big services, our CRO services solutions of data points, we want to offer best in class service. The best on the market services to customers there by leveraging that in house robotic infrastructure and that helps us kind of again it demonstrates.

Speaker #3: prem . In

Speaker #3: other words , at

Speaker #2: .

Speaker #3: our customer

Speaker #3: sites so that they can run them

Speaker #2: So turning to the space next slide . We show adjusted EBITDA at the segment level to show the relative profitability of our segments .

Speaker #3: themselves . Through

Speaker #3: our tools business . And we

Speaker #3: really on

Speaker #3: grew

Speaker #3: into that

Speaker #3: sort of tools

Speaker #3: business model last

Speaker #3: year . But this

Speaker #3: robotics and

Speaker #3: automation and

Speaker #2: The

Speaker #2: principal differences , which you can segment operating loss and total

Speaker #3: AI controlling

Speaker #3: it , I think ,

Speaker #3: a big moment right

Speaker #3: now .

Speaker #3: And I

Speaker #2: adjusted

Speaker #3: think we've got the

Speaker #2: EBITDA related to

Speaker #3: right tool

Speaker #3: stack to bring that to

Speaker #2: the $14 million in

Speaker #2: carrying cost of

Speaker #2: excess lease

Speaker #3: Second ,

Speaker #2: space cost

Speaker #3: we

Speaker #3: want to having a

Speaker #3: expand sort

Speaker #2: , which you can see

Speaker #2: was

Speaker #2: $14 million in the third

Speaker #3: frontier autonomous lab

It's possible with those robotics and also offer great services to customers, so you're going to get to hear about all three of those things layer for me what.

Speaker #2: quarter

Speaker #2: of 2025 . This

Speaker #3: here in

Speaker #3: Boston .

Speaker #3: We have

Speaker #2: cost

Speaker #3: the Second , largest

Speaker #2: represents

Speaker #3: rack install in the world . I

Speaker #3: want to keep it that

Speaker #2: rent and other charges related to

Speaker #3: We'll be continuing to

Speaker #3: expand

Speaker #2: lease

Speaker #2: space , is a which we

But youre not going to hear as much about in 'twenty, six, but I'm very proud of us pulling off in 'twenty five as this chart.

Speaker #3: that even as our

Speaker #2: are not

Speaker #2: occupying .

Speaker #3: larger systems as

Speaker #2: Net of related to

Speaker #3: well .

Speaker #2: sublease

Jason Kelly: What you're not going to hear as much about in 2026, but I'm very proud of us pulling off in 2025, is this chart: dramatic reduction in our quarterly cash burn over the last year, doing all that while still maintaining a strong margin of safety in our cash position. So after Q3, we have $462 million in cash and cash equivalents and no bank debt. So I think this is really, again, particularly in what's been a tough biotech market over the last few years, puts us in a very, very strong spot as a growing tools company. And so again, very proud of the team for doing that. You're going to hear less about cost takeouts in 2026 and a lot more about our investments for growth and what we're doing for customers as we expand in AI and automation. All right.

Speaker #2: income

Speaker #3: And we

Speaker #3: that to be

Speaker #3: show just the art of the possible to customers .

Speaker #2: , this is a

Speaker #2: cash

Speaker #2: operating

Dramatic reduction in our quarter.

Speaker #2: cost that is not

Speaker #3: What you build larger can do when

Speaker #2: related to

Speaker #2: driving

Speaker #3: ultimately

Speaker #2: revenue . Right

Quarterly cash burn over the last year doing all of that while still maintaining a strong margin of safety in our cash position. So after Q3, we have $462 million in cash and cash equivalents.

Speaker #3: hundreds And of pieces of

Speaker #2: now .

Speaker #2: And can

Speaker #2: potentially be

Speaker #3: in a

Speaker #3: single

Speaker #2: mitigated through

Speaker #2: subleasing . And

Speaker #3: robotic

Speaker #3: setup that can you can

Speaker #2: finally ,

Speaker #3: be do

Speaker #3: controlled by

Speaker #2: cash $114 million in the

Speaker #2: burn

Speaker #3: so I'll show a

Speaker #2: in the third

Speaker #2: quarter of For 2025

Speaker #3: few photos

Speaker #2: was $28 million , down from

Speaker #3: and what

Speaker #3: there . Coming up . And then

Speaker #2: $114 million

Speaker #2: in the not

Speaker #2: third quarter of 2020 .

Speaker #3: , our

Speaker #3: CRO

Speaker #3: services

And no bank debt.

Speaker #3: solutions and

Speaker #2: For proceeds

Speaker #3: data

Speaker #2: a

Speaker #3: points .

This is really again, particularly in what.

Speaker #3: We want

Speaker #3: to offer best

Speaker #3: in class

Speaker #3: services ,

Speaker #3: best on the

Speaker #2: cash

Speaker #3: market

Speaker #2: burn

Speaker #3: services to

Biotech market over the last few years puts us in a very very strong spot as a growing tools company and so again very proud of the team for doing that you're going hear less about cost take outs in 26 at a lot more about our investments for growth and what we're doing for customers as we expand in AI and automation.

Speaker #2: include the

Speaker #3: customers .

Speaker #2: proceeds from result of

Speaker #3: There by

Speaker #2: ATM

Speaker #2: sales during the quarter

Speaker #3: that

Speaker #3: in-house robotic

Speaker #2: . .

Speaker #3: infrastructure . And

Speaker #2: The

Speaker #2: significant decrease

Speaker #3: that helps us kind of again

Speaker #2: in cash burn was a

Speaker #3: demonstrate

Speaker #2: direct in result of the restructuring . Now , turning to guidance

Speaker #3: what's

Speaker #3: possible with those robotics . And also offer great

Speaker #3: services to

Speaker #3: So you're going

Speaker #3: to get to

Speaker #3: hear

Speaker #3: three of those things later from

Speaker #2: in terms of outlook for the full year , we are reaffirming our overall revenue guidance for 2025 , totaling 167 to $187 million , with cell engineering revenue to be 117 to $137 million , and biosecurity revenue

Speaker #3: me . What you're

Speaker #3: not going to

Speaker #3: hear as much

Speaker #3: about in to 26 . But I'm

Speaker #3: very

Speaker #3: proud

Speaker #3: of us

Speaker #3: pulling off in

With that I'm going to pass it to Steve.

Speaker #3: 25 . Is this

Speaker #3: chart

Speaker #3: dramatic

Looking forward to giving you more detail in a moment.

Speaker #3: reduction in

Jason Kelly: With that, I'm going to pass it to Steve, but looking forward to giving you more detail in a moment.

Speaker #3: our

Thanks, Jason.

Speaker #3: cash

Speaker #3: burn over the last

Speaker #3: year , doing

I'll start with the cell engineering business.

Speaker #3: all that

Steven Coen: Thanks, Jason. I'll start with the cell engineering business. Cell engineering revenue was $29 million in Q3 2025, down 61% compared to Q3 2024. As previously disclosed, cell engineering revenue in Q3 2024 included $45 million of non-cash revenue from a release of deferred revenue relating to the mutual termination of a customer agreement with Motif FoodWorks, one of our platform ventures. Excluding this, revenue in Q3 2025 was down 11% from the prior year period. In Q3 2025, we supported a total of 102 revenue-generating cell engineering programs. This represents a decrease of 5% in revenue-generating programs year-over-year. This decrease can be primarily attributed to the ongoing program rationalization as part of our restructuring activities. Turning to biosecurity.

Speaker #3: while still

Speaker #3: maintaining a

Speaker #3: strong reduction margin

Selling engineering revenue was $29 million in the third quarter of 2025 down 61% compared to the third quarter of 2024.

Speaker #2: expected to be at least $40 million . In terms of outlook for

Speaker #3: safety in our cash

Speaker #3: position . So after

Speaker #3: Q3 , we while

Speaker #3: cash and

Speaker #3: cash

Speaker #3: bank debt .

Speaker #3: So I have think this is really ,

As previously disclosed so afternoon revenue in the third quarter of 'twenty 'twenty four included $45 million of noncash revenue from a release of deferred revenue relating to the mutual termination of a customer agreement with multi food works one of our platform ventures.

Speaker #3: again , $462 million in cash

Speaker #3: particularly

Speaker #3: what's been a tough biotech

Speaker #3: market over the

Speaker #3: few

Speaker #3: years , puts us in a very , very strong

Speaker #3: spot as as a growing tools company .

Speaker #3: And so , a growing again , very proud of the team

Speaker #3: for doing that . You're going to hear tools less about cost Takeouts in 26 and a lot more

Speaker #3: about company .

Speaker #3: our

Speaker #3: investments for

Speaker #3: we're

Speaker #3: doing doing

Speaker #3: for customers as

Excluding this revenue in the third quarter of 2025 was down 11% from the prior year period.

Speaker #3: we

Speaker #3: expand in

Speaker #3: automation .

Speaker #3: All right .

Speaker #3: With that , I'm

Speaker #3: it to Steve . Looking forward to giving you more detail in a moment . Thanks ,

In the third quarter of 2025, we support a total of 102 revenue generating cell engineering programs. This represents a decrease of 5% and revenue generating programs year over year.

Speaker #3: Jason

Speaker #3: .

Speaker #4: I'll

Speaker #4: start

Speaker #4: with the

Speaker #4: cell

Speaker #4: engineering

Speaker #4: business

Speaker #4: Cell engineering

Speaker #4: was

Speaker #4: $29 million in

Speaker #4: the third

Speaker #4: of 2025 ,

Speaker #4: down

Speaker #4: 61% compared to the

Speaker #4: quarter of 2024 .

Decrease can be primarily attributed to the ongoing program rationalization as part of our restructuring activities.

Speaker #4: As previously disclosed , cell of engineering revenue in the third quarter of 2024 included

Speaker #4: $45 million of non-cash 2024 revenue from a release of deferred revenue relating to the mutual termination of a

Turning to Biosecurity.

Biosecurity business generated $9 million of revenue in the third quarter of 2025 at a segment gross margin of 19% as a reminder segment gross margin excludes stock based compensation.

Steven Coen: Our biosecurity business generated $9 million of revenue in Q3 2025 at a segment gross margin of 19%. As a reminder, segment gross margin excludes stock-based compensation. Turning to the next slide. 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. A full reconciliation between segment operating loss, Adjusted EBITDA, and GAAP net loss can be found in the appendix. In Q3 2025, Cell Engineering R&D expense decreased 8% from $55 million in Q3 2024 to $51 million in Q3 2025.

Speaker #4: customer

Speaker #4: agreement with

Speaker #4: motif

Speaker #4: platform

Speaker #4: ventures

Speaker #4: . Excluding

Speaker #4: this

Speaker #4: revenue , in

Speaker #4: the third

Speaker #4: quarter of

Speaker #4: 2025 was

Speaker #4: down

Speaker #4: 11% from ventures the

Turning to the next slide.

Speaker #4: prior year .

Speaker #4: period Excluding

It is important to note that our net loss includes a number of noncash and other nonrecurring items as detailed more fully in our financial statements because of these noncash and other nonrecurring items. We believe adjusted EBITDA is a more indicative measure of our profitability.

Speaker #4: in the third

Speaker #4: quarter

Speaker #4: of 2025 , we

Speaker #4: supported a

Speaker #4: total of

Speaker #4: 102 revenue generating

Speaker #4: cell engineering

Speaker #4: programs .

Speaker #4: This

Speaker #4: represents

Speaker #4: a

Speaker #4: decrease

Speaker #4: of

Speaker #4: revenue generating

Speaker #4: programs year

Speaker #4: over

Speaker #4: year . This

Speaker #4: decrease can be

Speaker #4: primarily attributed

Speaker #4: ongoing program

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

Speaker #4: rationalization . As

Speaker #4: part of our

Speaker #4: restructuring This

Speaker #4: activities decrease

Speaker #4: .

Speaker #4: Turning to attributed to bio

Speaker #4: security

Speaker #4: , our

Speaker #4: biosecurity

In the third quarter of 2025 cell engineering, R&D expense decreased 8% from $55 million in the third quarter of 2000 $24 million to $51 million in the third quarter of 2025.

Speaker #4: business

Speaker #4: generated

Speaker #4: $9 million of

Speaker #4: revenue in the third quarter

Speaker #4: of

Speaker #4: 2025 at a Our

Speaker #4: segment gross margin

Speaker #4: of 19% .

Speaker #4: As a

Speaker #4: segment

Speaker #4: gross

Speaker #4: margin excludes stock based

Speaker #4: . Turning to the

The 20 to 25 period R&D expense included a $21 million shortfall obligation related to our multi year strategic cloud and AI partnership with Google Cloud.

Speaker #4: next

Speaker #4: slide a

Speaker #4: . It

Speaker #4: note that our net

Steven Coen: The 2025 period R&D expense 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 in future years, and settled this shortfall obligation for $14 million. Cell Engineering DNA expense decreased 47% from $23 million in Q3 2024 to $12 million in Q3 2025. These decreases were all driven by our restructuring efforts. Cell Engineering segment operating loss was $37 million in Q3 2025 compared to a loss of $5 million in the comparable prior year period. The increased loss year-over-year was due to two factors. First, as previously mentioned, the Q3 2025 expense included a $21 million shortfall related to our Google Cloud contract that was subsequently settled.

Speaker #4: loss includes a number of

Speaker #4: non-cash and other

Speaker #4: non-recurring

Speaker #4: items .

Speaker #4: detailed , more

Speaker #4: fully in note

Speaker #4: our

Speaker #4: financial

Speaker #4: statements .

Speaker #4: Because of these

In October 2025, we amended reset the annual commitment in future years and settle this shortfall obligation of $14 million.

Speaker #4: non-cash and

Speaker #4: other

Speaker #4: non-recurring

Speaker #4: items ,

Speaker #4: believe

Speaker #4: adjusted financial EBITDA is

Speaker #4: a more

Speaker #4: indicative .

Speaker #4: measure

Speaker #4: of our

Speaker #4: . A full

Speaker #4: reconciliation

Speaker #4: between

So engineering G&A expense decreased 47% from $23 million in the third quarter of 2000 $24 million to $12 million in the third quarter of 2025.

Speaker #4: segment

Speaker #4: operating EBITDA is a loss

Speaker #4: , adjusted EBITDA

Speaker #4: and

Speaker #4: GAAP net

Speaker #4: loss can profitability be found in the

Speaker #4: appendix

Speaker #4: . In

Speaker #4: quarter

Speaker #4: of 2025 . Cell

Speaker #4: engineering

Speaker #4: R&D loss

Speaker #4: expense

Speaker #4: decreased

These decreases were all driven by our restructuring efforts.

Speaker #4: 8%

Speaker #4: from

Speaker #4: $55 million in the third quarter of

Speaker #4: 2024 to $51 million in the

So engineering segment operating loss was $37 million in the third quarter of 2025 compared to a loss of $5 million in the comparable prior year period.

Speaker #4: third quarter

Speaker #4: of 2025 .

Speaker #4: 2025 period R&D expense included

Speaker #4: a

Speaker #4: $21 million shortfall obligation

The increased loss year over year was due to two factors.

Speaker #4: related The to our

Speaker #4: multi-year strategic

Speaker #4: cloud and AI

Speaker #4: partnership with Google Cloud

As previously mentioned the third quarter of 2025 expense included a $21 million shortfall related to our Google cloud contract that was subsequently settled.

Speaker #4: In

Speaker #4: we amended

Speaker #4: and

Speaker #4: reset the

Speaker #4: commitments in

Speaker #4: future years and

Speaker #4: settled the shortfall

Speaker #4: obligation

Speaker #4: for

Speaker #4: $14 million .

Speaker #4: G&A

Second as previously mentioned the third quarter of 'twenty 'twenty four included $45 million of noncash revenue from the motif contract termination.

Speaker #4: expense

Speaker #4: decreased

Speaker #4: 47% from

Speaker #4: $23 million in the third quarter of 2024 to $12 million in the third quarter of 2025 . These decreases were all driven by our restructuring efforts .

Steven Coen: Second, as previously mentioned, the third quarter of 2024 included $45 million of non-cash revenue from the Motif contract termination. Biosecurity segment operating loss improved 21% in the third quarter of 2025 compared to the prior year comparable period. Moving further down the page, you'll note that total Adjusted EBITDA in the third quarter of 2025 was negative $56 million, which was down from negative $20 million in the third quarter of 2024. Again, this year-over-year decline can be attributed to the previously mentioned Google Cloud shortfall expense recorded in the third quarter of 2025, as well as the Motif-related non-cash revenue in the comparable prior year period. So turning to the next slide. We show Adjusted EBITDA at the segment level to show the relative profitability of our segments.

Our securities segment operating loss improved 21% in the third quarter of 2025 compared to the prior year comparable period.

Speaker #4: Cell engineering segment operating loss was $37 million in the third quarter

Moving further down the page you'll note that total adjusted EBITDA in the third quarter of 2025 was negative $56 million, which was down from negative $20 million in the third quarter of 2024.

Speaker #4: year over year

Speaker #4: was due to two factors . First , as previously mentioned , the third quarter of 2025 expense included $14 million . a engineering

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Steven Coen: The principal differences between segment operating loss and total adjusted EBITDA related to the carrying cost of excess leased space, which you can see was $14 million in Q3 2025. This cost represents the base rent and other charges related to leased space, which we are not occupying, net of sublease income. This is a cash operating cost that is not related to driving revenue right now and can potentially be mitigated through subleasing. And finally, cash burn in Q3 2025 was $28 million, down from $114 million in Q3 2024, a 75% decrease. Cash burn does not include the proceeds from ATM sales during the quarter. The significant decrease in cash burn was a direct result of the restructuring. Now, turning to guidance.

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Steven Coen: In terms of outlook for the full year, we are reaffirming our overall revenue guidance for 2025, totaling $167 to 187 million, with cell engineering revenue to be $117 to 137 million and biosecurity revenue expected to be at least $40 million. In conclusion, we're pleased with the continued improvements in cash burn and cost reduction. In Q4, we will continue to execute against our core objectives while navigating continued uncertainty and the macro environment. With that, I'll hand it back over to you, Jason.

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Thanks, Steve Alright, So we'll start that a strategic review of the three topics we want to cover today. The first believe AI models are going to impact biotechnology fundamentally the two big ways and I think ginkgo is well positioned to sell tools into both of those I'm going to talk about that.

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Jason Kelly: Thanks, Steve. All right. So we'll start the strategic review. There's 3 topics we want to cover today. The first, I believe AI models are going to impact biotechnology fundamentally in 2 big ways, and I think Ginkgo is well positioned to sell tools into both of those. So I'm going to talk about that. Second, we are continuing to offer that research solutions business on top of our in-house robotics platform at Ginkgo. And we had 2 big wins in the last quarter. I want to touch on that briefly. And then finally, we are expanding our sort of Frontier Autonomous Lab here in Boston, the big rack setup. So I'll show you some photos and a little bit of background on what we're doing there. And please do come visit. I'll mention that when we get to that section.

Second we are continuing to offer that research solutions business on top of our in House Robotics platform again go and we had two big wins in the last quarter I want to touch on that briefly and then finally, we are expanding our sort of frontier autonomous lab here in Boston, the Big rack set up as I'll show you some photos and a little bit of background on what we're doing there and please do come visit I'll mention that when we get to that.

Section, but if you want to come see it yeah Youre very welcome alright, so let's dig in on on really how AI is impacting biology, but before I do that I do want to remind you know we made again over 25 in the second half of 'twenty 'twenty four we made a big shift in the business, where we went from just offering research solutions, which is the left hand side of this chart here.

Jason Kelly: But if you want to come see it, yeah, you're very welcome. All right. So let's dig in on really how AI is impacting biology. Before I do that, I do want to remind, we made, again, over 2025 and the second half of 2024, we made a big shift in the business where we went from just offering research solutions, which is the left-hand side of this chart here. These are these types of research partnerships. We get fees, and we get downstream value share. We get royalties or milestones in the sort of ultimate end products that our customers are developing, leveraging our platform. It's a very close partnership with a customer. There's a lot of our scientists involved, as well as our robotics. We've done about 250 of those R&D partnerships over the last eight to 10 years. That is a business we will be continuing.

Operator: Vacations and ownership at Ginkgo. I'm joined by Jason Kelly, 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'll 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 the quarter results, we're going to be providing insight into how we believe AI models will impact biotechnology, how our tools are positioned to support those impacts, and how those tools are winning us new deals with customers. As usual, we'll end with a Q&A session, and I'll take questions from analysts, investors, and the public.

These are these types of research partnerships, we get fees and we got downstream value share, we get royalties or milestones in the sort of ultimate end products that our customers are developing leveraging our platform. It's a very close partnership with our customer there is a lot of our scientists involved as well as our robotics, we got about 250 of those R&D partnerships over the.

Eight eight to 10 years.

That is a business, we will be continuing but in the last year and a half we expanded into the tools space with our data points automation and reagents businesses and so I want to spend a minute talking about how AI and what's really been coming down the pipeline I think offers us a nice niche and entry point into the tools market, where we really have I think that sort of.

Jason Kelly: But in the last year and a half, we expanded into the tool space with our Datapoints, Automation, and reagents businesses. So I want to spend a minute talking about how AI and what's really been coming down the pipeline, I think, offers us a nice niche and entry point into the tools market where we really have, I think, the sort of category-defining technology. So first, why is AI important right now in sort of sciences in general and bioscience in particular? So this was America's AI Action Plan, came out of the White House in the last few months. There's one specific section I draw your attention to, which was investing in AI-enabled science. And the general idea here is to have AI reasoning models leveraging, and they highlight automated cloud-enabled labs.

Operator: You can submit those questions to us in advance via X, hashtag Ginkgo Results, or email investors@ginkgobioworks.com. All right, over to you, Jason.

Category defining technology. So first why is why is AI important by now in sort of.

Jason Kelly: Thanks, Daniel. Ginkgo's mission is to make biology easier to engineer. We always start with that. I want to highlight the three big objectives for us going into 2026. I'm going to give you a little more detail on these today. The first is to deliver the robotics and software that bring autonomous labs on-prem, in other words, at our customer site, so that they can run them themselves through our tools business. We really grew into that sort of tools business model last year. This robotics and automation and AI controlling it, I think, is having a big moment right now. I think we've got the right tool stack to bring that to customers. Second, we want to expand sort of our frontier autonomous lab here in Boston. We have the largest rack install in the world. I want to keep it that way.

Sciences in general in Bioscience in particular, so this was the Americas AI action plan came out of the White House and the last few months.

If there's one specific section I draw your attention to which was investing in AI enabled science and the general idea here is to have AI reasoning models, leveraging and they highlight automated cloud enabled labs and that's why I'm excited to share more on what we've been building here in Boston, which I think is a great example of one of these cloud enabled labs.

Jason Kelly: And that's why I'm excited to share more on what we've been building here in Boston, which I think is a great example of one of these cloud-enabled labs. That if you connect those two things together, you could potentially change how science is done. And the idea is the reasoning models could be thinking and the labs could be doing that lab work. And I'll talk about that more in a second. And the reason this is important is shown here. I think we're, particularly in the biosciences, are going to be the first sort of battleground for AI-enabled science if you look at what's happening between the US and China. So there was a New York Times editorial just a few months ago saying China's biotech is cheaper and faster.

They have you connect those two things together you could potentially change how science is done and the idea that the reasoning models could be thinking in the labs can be doing that lab work and I'll talk about that more in a second.

And the reason this is important as shown here I think we're a particularly in the biosciences are going to be the first sort of.

Jason Kelly: We'll be continuing to expand that even as our customers build larger systems as well. We want to use that to be able to show just the art of the possible to customers, what you can do when you have ultimately hundreds of pieces of equipment all connected in a single robotic setup that can be controlled by AI. I'll show a few photos and what we're doing there coming up. Finally, our two big services, our CRO services, solutions, and Data Points. We want to offer best-in-class services, best on the market services to customers there by leveraging that in-house robotic infrastructure. That helps us kind of, again, demonstrate what's possible with those robotics and also offer great services to customers. You're going to get to hear about all three of those things later from me.

Ground for AI enabled science, if you look at what's happening between U S and China. So there was a new York times editorial just a few months ago, saying China's biotech is cheaper and faster I think that's largely true. If you think about the traditional way we're doing biotech today, which is you're basically have well trained scientist working by hand in.

Jason Kelly: I think that's largely true if you think about the traditional way we're doing biotech today, which is you basically have well-trained scientists working by hand in laboratories here in Boston. It's in the Kendall Square area here down the street. It's also in South San Francisco, California, San Diego, California, Research Triangle, North Carolina, a few hubs in the United States where you have sort of scientists working by hand doing biotechnology research. For a long time, if you go back and stay back a slide, for a long time, that was we had an advantage over China just in the sense that our people were better trained, and we had access to sort of better facilities and things like that. That advantage has largely evaporated over the last 10 to 15 years. There are just as good academic institutions, just as good startup ecosystem, and so on in China.

Corey herein and Boston It yes in the Kendall square area here down the Street and also in South San Francisco, and California, San Diego Research Triangle of North Carolina, a few hubs in the United States, where you have sort of scientists working by hand doing biotechnology research for a long time, if you go back and stay back a slide <unk> for a long time that was.

Jason Kelly: What you're not going to hear as much about in 2026, but I'm very proud of us pulling off in 2025, is this chart: dramatic reduction in our quarterly cash burn over the last year, doing all that while still maintaining a strong margin of safety in our cash position. After Q3, we have $462 million in cash and cash equivalents and no bank debt. I think this is really, again, particularly in what's been a tough biotech market over the last few years, puts us in a very, very strong spot as a growing tools company. Again, very proud of the team for doing that. You're going to hear less about cost takeouts in 2026 and a lot more about our investments for growth and what we're doing for customers as we expand in AI and automation.

We had an advantage over China just in the sense that our people were better trained and we had access to and sort of like better facilities and things like that that advantage has largely evaporated over the last 10 to 15 years. There are just as good academic institutions, just as good startup ecosystem and so on in China.

And there are more scientists trained and Theyre paid last frankly on so I don't really see where we have an advantage on physical labor anymore versus China. So I was really excited that these saturday on who's sort of heading up that Natura Security Commission on emerging biotechnology put in a number of bills around this topic NSF launched a $100 million AI program programs.

Jason Kelly: There are more scientists trained, and they're paid less, frankly. So I don't really see where we have an advantage on physical labor anymore versus China. So I was really excited to see Senator Yang, who's sort of heading up that National Security Commission on Emerging Biotechnology, put in a number of bills around this topic. NSF launched a $100 million AI Programmable Cloud Labs initiative. The big theory behind these things is, if we're going to compete with China in biotechnology, we need to do it with robotics rather than hands at the bench. If we don't do it, I think you're going to see what we've seen over the last 2 or 3 quarters, where an increasing number of the early-stage biotech startups that are being acquired by large pharma or invested in by USDCs are based in China.

Jason Kelly: All right, with that, I'm going to pass it to Steve, looking forward to giving you more detail in a moment.

Both cloud Labs initiative and the Big theory behind these things is if we're going to compete with China and biotechnology, we need to do it with robotics rather than hands at the bench.

Steve Cohen: Thanks, Jason. I'll start with the cell engineering business. Cell engineering revenue was $29 million in the third quarter of 2025, down 61% compared to the third quarter of 2024. As previously disclosed, cell engineering revenue in the third quarter of 2024 included $45 million of non-cash revenue from a release of deferred revenue relating to the mutual termination of our customer agreement with Motif FoodWorks, one of our platform ventures. Excluding this, revenue in the third quarter of 2025 was down 11% from the prior year period. In the third quarter of 2025, we supported a total of 102 revenue-generating cell engineering programs. This represents a decrease of 5% in revenue-generating programs year over year. This decrease can be primarily attributed to the ongoing program rationalization as part of our restructuring activities.

And if we don't do it I think youre going to see what we've seen over the last.

Two or three quarters, where an increasing number of the early stage biotech startups that are being acquired by large pharma or invested in by USB C is are based in China.

And so I think if we're going to turn that around both in it for biotechnology and for science at large we need to do it by investing in robotic infrastructure and I think that's not lost on the U S. Government I think gave it up you go to the next slide has exactly the right technology for that.

Jason Kelly: And so I think if we're going to turn that around, both for biotechnology and for science at large, we need to do it by investing in robotic infrastructure. And I think that's not lost on the US government. And I think Ginkgo, if you go to the next slide, has exactly the right technology for that. And so I've shown these before, but these are our Reconfigurable Automation Carts, our rack carts. And this is the first big area where I think AI is coming into biotechnology. And so this is around reasoning models. So again, think like GPT-5 from OpenAI and so on. These are in Gemini from Google. These are these models that are able to think over a period of time, come to sort of a conclusion based on what you've asked them to do.

As shown on these before but these are reconfigurable automation cards, Iraq Hearts and this is the first big area, where I think AI is coming into biotechnology and so this is around reasoning models. So again I think like GPT five from opening AI and so on these aren't Gemini from Google. These are these models that are able to think over a period of time.

<unk> come to sort of a conclusion based on what you've asked them to do any other they could write code and they can do other things that can kind of use browser and tools to go off and do sort of a multi step operation and come back and bring our result to you I think the first big frontier here is gonna be connecting those reasoning models to physical automation in the lab.

Steve Cohen: Turning to biosecurity, our biosecurity business generated $9 million of revenue in the third quarter of 2025 at a segment growth margin of 19%. As a reminder, segment growth margin excludes stock-based compensation. Turning to the next slide. 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. A full reconciliation between segment operating loss, adjusted EBITDA, and GAAP net loss can be found in the appendix. In the third quarter of 2025, cell engineering R&D expense decreased 8% from $55 million in the third quarter of 2024 to $51 million in the third quarter of 2025.

Jason Kelly: And either they could write code, they can do other things, they can kind of use browsers and tools to go off and do sort of a multi-step operation and come back and bring a result to you. I think the first big frontier here is going to be connecting those reasoning models to physical automation in the lab. And the reason this is necessary is if you think about how science gets done outside of areas like math or theoretical physics that are purely kind of people thinking about stuff; it's purely intellectual. The majority of science, experimental physics, experimental chemistry, experimental biology, and so on, is moved forward by lab work, right? We have a hypothesis. Scientist has a hypothesis about how some disease works or whatever. But the only way they really know the answer is to go off and run carefully constructed laboratory experiments.

And the reason this is necessary is if you think about how science get done outside of areas like math or theoretical physics that are purely kind of people thinking about stop it's purely intellectual the majority of science experimental phase of experimental chemistry experimental biology, and so on is moved forward by lab work right like we have a hypothesis that this is a hypothesis.

This is about how some disease works or whatever but they only way to really know the answer is to go off and run carefully constructed laboratory experiments.

And so if you want these models to really be AI scientists and Youre seeing your future houses had a great new model coming out yesterday are now called Edison scientific Super excited about that those models need to be able to do experiments and if you go. The next slide the way Theyre going to do experiments is using this technology like what we've built at kimco.

Jason Kelly: So if you want these models to really be AI scientists, and you're seeing FutureHouse just had a great new model come out yesterday or is now called Edison Scientific, super excited about that, those models need to be able to do experiments. And if you go to the next slide, the way they're going to do experiments is using the technology like what we've built at Ginkgo. This is our reconfigurable automation carts. Each cart has a piece of lab equipment, a robotic arm, and a plate transport track. And I'm going to spend a minute later showing you these in action. But basically, what it allows you to do is sort of Lego block together. If you go to the next slide, 5 of these in a linear setup, 20 of these in a circular setup, or here's a setup.

Steve Cohen: The 2025 period R&D expense included a $21 million shock pull obligation related to our multi-year strategic cloud and AI partnership with Google Cloud. In October 2025, we amended and reset the annual commitments in future years and settled the shock pull obligation for $14 million. Cell engineering DNA expense decreased 47% from $23 million in the third quarter of 2024 to $12 million in the third quarter of 2025. These decreases were all driven by our restructuring efforts. Cell engineering segment operating loss was $37 million in the third quarter of 2025 compared to a loss of $5 million in the comparable prior year period. The increased loss year over year was due to two factors. First, as previously mentioned, the third quarter of 2025 expense included a $21 million shock pull related to our Google Cloud contract that was subsequently settled.

Reconfigurable automation cards, each cart has a piece of lab equipment, a robotic arm and a plate transport tracking them to spend a minute later showing you the sudden action, but basically what it allows you to do is sort of Lego block together. If you go to the next slide five it isn't a linear setup 20th either the circular setup or here's a setup.

Just.

Sold one of these systems with 97 cards on it in one giant setup and so the idea here is to be able to connect ultimately hundreds of pieces of lab equipment Lego block style into a huge setup, where the whole thing is software controlled and the reason it's important that it's software controlled is just like these reasoning models can right.

Jason Kelly: We actually just sold one of these systems with 97 carts on it in one giant setup. And so the idea here is to be able to connect ultimately hundreds of pieces of lab equipment, Lego block style, into a huge setup where the whole thing is software controlled. And the reason it's important that it's software controlled is just like these reasoning models can write code for Python or whatever, right, for a website, they're also able to write code to run this automation and design and execute experiments and interpret data. And so if we want to have these sort of AI-controlled science, these cloud-enabled labs, this is what they look like. And you really need a new hardware technology like what we've built with the racks to do that.

Either they could write code, they can do other things. They can kind of use browsers and tools to go off and do sort of a multi-step operation uh and come back and bring a result to you. Uh I think the first big Frontier here is going to be connecting those reasoning models to physical Automation in the lab. And the reason this is necessary is if you think about how science gets done outside of areas like math, or theoretical physics, that are purely kind of people thinking about stuff. It's purely intellectual, the majority of science experimental, physics, experimental, chemistry, experimental biology. And so, on is moved forward by lab work, right? Like we have a hypothesis. I just has a hypothesis about how some disease works or whatever. But they only way they really know the answer is to go off and run. Uh carefully constructed laboratory experiments.

Code for.

Python or whatever right for a website. They are also able to write code to run this automation in design and execute experiments and interpret data and so if we want to have these sort of AI controlled science. These cloud enabled labs. This is what they look like and you really need a new hardware technology like what we've built with the racks to do that so I think we're extremely.

Steve Cohen: Second, as previously mentioned, the third quarter of 2024 included $45 million of non-cash revenue from the Motif contract termination. Biosecurity segment operating loss improved 21% in the third quarter of 2025 compared to the prior year comparable period. Moving further down the page, you'll note that total adjusted EBITDA in the third quarter of 2025 was negative $56 million, which was down from negative $20 million in the third quarter of 2024. Again, this year-over-year decline can be attributed to the previously mentioned Google Cloud shock pull expense recorded in the third quarter of 2025, as well as the Motif-related non-cash revenue in the comparable prior year period. Turning to the next slide, we show adjusted EBITDA at the segment level to show the relative profitability of our segments.

We are well positioned for this and you'll see us leaning in heavily here in 2026, the second area, where we're seeing AI applied to biotechnology is in using the same kind of like math and compute that was used for the reasoning model. So large neural networks gpus that whole infrastructure, except instead of two.

Jason Kelly: So I think we're extremely well positioned for this, and you'll see us leaning in heavily here in 2026. The second area where we're seeing AI applied to biotechnology is in using the same kind of math and compute that was used for the reasoning models. So large neural networks, GPUs, that whole infrastructure, except instead of training those neural nets on human language, human reasoning, code, and programming, things that humans kind of read, understand, and interpret, you train them on biological language. So DNA, amino acid sequences from proteins, the language of life, the language of living organisms. And you do the same type of training, the same infrastructure, but these things learn to speak biology. And so this is a more nascent area compared to the reasoning models when it comes to AI and biotech.

And so if you want these models to really be AI scientists and you're seeing you know future house just had a great new model come out yesterday. Um or now called Edison scientific, super excited about that. Those models need to be able to do experiments and if you go to the next Slide, the way they're going to do, experiments is using the technology, like what we've built at go, uh, this is our reconfigurable automation cards. Each cart has a piece of lab equipment, a robotic arm and a plate transport track. And I'm going to spend a a minute later showing you these in action. Uh, but basically what it allows you to do is sort of Lego block together. If you go to the next slide, you know, 5 of these in a linear setup, 20 of these at a circular setup or, you know, here's a setup. Um, we actually just

Training those neural nets on human language in human reasoning and coded programming things that humans kind of read and understand and interpret you train them on biological language. So DNA amino acid sequences from proteins. The language of life the language of living organisms and you do the same type of.

Training the same infrastructure, but these things learn to speak biology.

Steve Cohen: The principal differences between segment operating loss and total adjusted EBITDA related to the carrying cost of excess lease space, which you can see was $14 million in the third quarter of 2025. This cost represents the base rent and other charges related to lease space, which we are not occupying, net of sub-lease income. This is a cash operating cost that is not related to driving revenue right now and can potentially be mitigated through sub-leasing. Finally, cash burn in the third quarter of 2025 was $28 million, down from $114 million in the third quarter of 2024, a 75% decrease. Cash burn does not include the proceeds from ATM sales during the quarter. The significant decrease in cash burn was a direct result of the restructuring. Now, turning to guidance.

And so this is a more nascent area compared to the reasoning models when it comes to AI in biotech, but I think it's also going to be extremely important and with our ginkgo data point service, we really want to build the community in that area. So we highlight here are our antibody developed ability competition. This is just I think at the end of November going to wrap up. So you should if you go to that slide should check.

Jason Kelly: But I think it's also going to be extremely important. With our Ginkgo Datapoints service, we really want to build the community in that area. So we highlight here our antibody developability competition. This is just, I think, at the end of November going to wrap up. So if you go to the next slide, you should check it out. You can go to datapoints.ginkgo.bio. You can sign up. We have more than 200 teams now competing in that competition. And the idea there is build a model like the one I just mentioned, like train a model on data for the developability of antibodies. In other words, is this antibody sequence going to work well as a drug? Will it be soluble and so forth? Is it not immunogenic? That is a very valuable feature set for biopharma companies.

Sold 1 of these systems with 97 carts on it in 1, giant setup. Uh, and so uh, the idea here is to be able to connect ultimately hundreds of pieces of lab equipment, Lego block style into a huge setup where the whole thing is software controlled. And the reason it's important that its software, controlled is just like these reasoning models can write code for, you know, python or whatever, right? For a website. Uh, they're also able to write code to run this Automation and design and execute experiments and interpret data, uh, and so if we want to have these sort of AI controlled science, these cloud enabled labs, this is what they look like. And you really need a new hardware technology, like what we've built with the racks to do that. So I think we're extremely well positioned for this uh and you'll see us leaning in heavily here in 2026. The second area, uh, where we're seeing AI applied to biotechnology is in using the same kind of like math and compute that was used for the reasoning model. So large, neural networks GP.

It out you've got a gig data points I'd go to a biologic and sign up we have more than 200 teams now competing in that competition and the idea. There is build a model like the one I just mentioned like trainer model on data for the develop ability of antibodies in other words is this antibody sequence going to work well as a drug will it be soluble.

And so forth.

Not immunogenic.

That is a very valuable feature set for Biopharma companies. So if you're a violent from addition, or you're a startup that has a great new AI model I encourage you to compete in our competition here, we basically generate a large amount of develop ability data we shared some of that with the community. We kept some of that back as the competition set in your job is.

To that whole infrastructure except instead of training, those neural Nets on human language and human reasoning and code and programming things that that humans kind of read and understand and and interpret you train them on by a logical language. So DNA amino acid sequences from proteins, the language of Life, the language of of living organisms, and you do the same type of training, the same infrastructure, but these things learn to speak biology. Uh, and so uh, this is this is

Jason Kelly: So if you're a bioinformatician or you're a startup that has a great new AI model, I encourage you to compete in our competition here. We basically generate a large amount of developability data. We shared some of that with the community. We kept some of it back as the competition set. Your job is to predict the held-back data, and we'll rank who does the best. The other thing we're doing to help build the community is we're releasing data sets for free. Again, you go to our website there and download these sort of AI ML-ready data sets. They're an example of the sort of data that we generate on a fee-for-service basis for customers through our Datapoints service. So go download those, play around. If you wanted to buy data from us, we're very happy to do that.

Steve Cohen: In terms of our look for the full year, we are reaffirming our overall revenue guidance for 2025, totaling $167 to $187 million, with cell engineering revenue to be $117 to $137 million, and biosecurity revenue expected to be at least $40 million. In conclusion, we're pleased with the continued improvements in cash burn, and cost reduction. In the fourth quarter, we will continue to execute against our core objectives while navigating continued uncertainty in the macro environment. With that, I'll hand it back over to you, Jason.

To predict the held back data and will and will rank who does the best.

The other thing we're doing to help build the community is we're releasing datasets for free again, you've got our website there and download these sort of AI ml already data sets. They are an example of the sort of data that we generate on a fee for service basis for customers through our data point service. So go download those play around if you wanted to buy data from us where I'm very happy to do that and we're really here to build a can.

Munity of folks who are trying to train AI models using biological data and so really excited about this.

Jason Kelly: Thanks, Steve. All right, we'll start the strategic review. There are three topics we want to cover today. First, I believe AI models are going to impact biotechnology fundamentally in two big ways, and I think Ginkgo's well positioned to sell tools into both of those. I'm going to talk about that. Second, we are continuing to offer that research solutions business on top of our in-house robotics platform at Ginkgo, and we had two big wins in the last quarter. I want to touch on that briefly. Finally, we are expanding our sort of frontier autonomous lab here in Boston, the big rack setup. I'll show you some photos and a little bit of background on what we're doing there. Please do come visit. I'll mention that when we get to that section.

Jason Kelly: We're really here to build a community of folks who are trying to train AI models using biological data. So really excited about this as a sort of a nascent area for AI applied to biology. All right. Second thing I wanted to talk about. Those are the two big buckets for AI. Again, reasoning models, controlling robotics in the lab, and then basically neural nets trained on biological data. And they're both involving AI, but they are different. Ginkgo will play there through our automation in the first one and our data points for the second one. All right. Next category. This is now going back to that left-hand side of this chart. The business that Ginkgo sort of primarily focused on over the last 10 years are research solutions business. We are still doing these.

Is it sort of a nascent area for AI applied to biology alright.

First thing I wanted to talk about so those are the two big buckets for AI again read the model's controlling robotics in the lab and then.

More Nathan area compared to the reasoning models when it comes to Ai and biotech. But I think it's also going to be extremely important and with our genko data, point service we really want to build the community in that area. So we highlight here our our antibody, developability competition. This is just I think at the end of November going to wrap up so you should uh if you go to the next slide you should check it out. You can go to G data points gingo bio. You can sign up we have more than 200 teams now competing in that competition and the idea there is build a model. Like the 1, I just mentioned, like, train a model on, uh, data for the developability of antibodies. In other words, is this antibody sequence going to work? Well, as a drug, will it be soluble and and so forth. These other, uh, is it not immunogenic? Uh, that, uh, is a very valuable feature set for Bio Pharma companies. So if you're a bioinformatician or you're a startup that has a great new AI model, I encourage you to compete in our competition here, we basically uh generate a large amount of developability data, we shared some of that with the community.

Basically neural nets trained on biological data and they're both involving AI, but they are different and so I think it will play there through our automation and the first one in our data points for the second one alright. So next category this without going back to the left hand side of this chart they've business I can go sort of like primarily focused on over the last 10.

Jason Kelly: If you want to come see it, yeah, you're very welcome. All right, let's dig in on really how AI is impacting biology. Before I do that, I do want to remind, you know, we made, again, over 2025 and the second half of 2024, we made a big shift in the business where we went from just offering research solutions, which is the left-hand side of this chart here. These are these types of research partnerships. We get fees, and we get downstream value share. We get royalties or milestones in the sort of ultimate end product that our customers are developing, leveraging our platform. It's a very close partnership with a customer. There's a lot of our scientists involved, as well as our robotics. We've done about 250 of those R&D partnerships over the last eight to 10 years.

Here's our research solutions business. We are still doing is if you are looking for sort of breakthrough research and any of the areas.

Jason Kelly: If you are looking for sort of breakthrough research in any of the areas that could basically leverage high-throughput biotechnology, I think Ginkgo is still a very good call. If you go to the next slide, we won a couple of great deals in the last quarter. BARDA awarded us and our partners $22 million around the manufacturing of monoclonal antibodies, bringing that back in the US, making that cheaper, particularly around producing key medical countermeasures. So I think this is both important for national security and also important for reducing the cost of manufacturing drugs, particularly biologics drugs. And you heard the administration talking about this recently on the regulatory side to try to lower the cost of biologics. This is a technical approach to dropping the cost of biologics. If you go to the next slide, in the agricultural sector, very happy to extend our partnership.

We kept some of it back as the like competition set. And your job is to predict uh the the held back data and we'll and we'll rank who does the best. Um, the other thing we're doing to help build the community is we're releasing data sets for free. Again. You can go to our website there and download these sort of AI ml ready data sets. They're an example of the sort of data that we generate on a fee for service basis for customers through our data point service. So go download those play around. If you wanted to buy data from us, we're very happy to do that. Uh and we're really here to build a community of folks.

That could basically leverage like high throughput biotechnology I think it goes still a very good call.

Go to the next slide we wanted a couple of great deals in the last quarter BARDA worried us and our partners $22 million around the manufacturing of monoclonal antibodies, bringing up.

Back in the U S, making that cheaper, particularly around producing key medical countermeasures. So I think this is both important for national security and also important for reducing the cost of manufacturing drugs, particularly biologics drugs and you heard.

Jason Kelly: That is a business we will be continuing. In the last year and a half, we expanded into the tool space with our Data Points, automation, and reagents businesses. I want to spend a minute talking about how AI, and what's really been coming down the pipeline, I think, offers us a nice niche and entry point into the tools market where we really have, I think, the sort of category-defining technology. First, why is AI important right now in sciences in general and bioscience in particular? This was America's AI Action Plan. It came out of the White House in the last few months. There's one specific section I'd draw your attention to, which was investing in AI-enabled science. The general idea here is to have AI reasoning models leveraging, and they highlight automated cloud-enabled labs.

The administration talking about this recently on the regulatory side to try to lower the cost of biologics that is our technical approach to dropping the cost of biologics to go. The next slide in the agricultural sector very happy to have extended our partnership that's the part that's been going on for five years Bear we're really working on engineering microbes. If you go to the next slide four.

Jason Kelly: It's a partnership that's been going on for 5 years with Bayer. We're really working on engineering microbes, if you go to the next slide, for the production of fertilizers. If you remember, this is actually a pretty amazing story. If you think about elementary school biology, you learned about crop rotation, right? You would rotate in a legume, like soybeans, peanuts, or things like that, and they would refertilize the soil. Then you'd plant something like corn, and corn largely takes fertilizer out of the soil. That's sort of how we used to do it. Then in the early 1900s, we invented the Haber-Bosch process where you take nitrogen out of the atmosphere by burning natural gas and combining the nitrogen with that and producing synthetic ammonia.

The production of fertilizers and if you remember that this is actually a pretty amazing story. So if you think about like elementary school biology, you learned about crop rotation right. So you would rotate in a lagoon like soybeans, peanuts or things like that and they would re fertilize the soil and then you plant something like core.

And corn largely takes fertilizer out of the soil.

So that's sort of how we used to do it and then in the early 19, hundreds we invented a haber Bosch process, where you take nitrogen out of the atmosphere by burning natural gas and combining the nitrogen.

Jason Kelly: That's why I'm excited to share more on what we've been building here in Boston, which I think is a great example of one of these cloud-enabled labs. If you connect those two things together, you could potentially change how science is done. The idea that the reasoning models could be thinking and the labs could be doing that lab work. I'll talk about that more in a second. The reason this is important is shown here. I think particularly in the biosciences, we are going to be the first sort of battleground for AI-enabled science if you look at what's happening between the US and China. There was a New York Times editorial just a few months ago saying China's biotech is cheaper and faster.

Uh in the first 1 and our data points for the second 1. All right. Uh so next category uh this is now going back to that left-hand side of this chart the business that can go sort of like uh primarily focused on over the last 10 years, our research Solutions business. Uh, we are still doing these. Uh, if you are looking for sort of breakthrough research in, um, any of the areas, uh, that could basically leverage like high throughput biotechnology. I think inko is still a very good call. Uh, if you go to the next slide, we want a couple, great deals in the last quarter, uh, Barta worded us and our partners 222 million, uh, around the manufacturing of, monoclonal antibodies bringing that, uh, uh, back in the US making that cheaper particularly around producing, uh, key medical countermeasures. So, I think this is both important for National Security and also important for uh, reducing the cost of manufacturing drugs, particularly biologic, drugs and you you heard um uh the administration talking about this recently, on the regulatory side, to try to lower the cost of biologics. This is a technical approach.

With that and producing synthetic ammonia.

And that goes out to the tune of many billions of dollars a year and about a.

Jason Kelly: And then that goes out to the tune of many billions of dollars a year and about 4% of global greenhouse gas and so on. So it's a big, big chemistry industry, and it's largely based in China. That's a huge input into things like corn farming. Well, those crops that you rotate in, like soybeans and legumes, they're able to re-fertilize the soil because they have microbes on their roots running that Haber-Bosch Process, taking nitrogen out of the air, fertilizing the crop. So I'm really happy to see this project continuing. I think it's the kind of world-changing stuff that only biotechnology can do in the physical world. And so really excited to keep that going. All right.

4% of global greenhouse gas and so on so it's a big big chemistry industry and its largely based in China.

That's a huge input into things like corn farming, well those crops that you rotate in like soybeans and lagoons theyre able to re fertilize the soil because they have microbes on their routes running that haver box process, taking nitrogen out of the air fertilizing their crop so really happy to see this project continuing I think it's the kind of world changing.

Jason Kelly: I think that's largely true if you think about the traditional way we're doing biotech today, which is you basically have well-trained scientists working by hand in laboratories here in Boston. It's in the Kendall Square area here down the street. It's also in South San Francisco, in California, San Diego, Research Triangle, North Carolina, a few hubs in the United States where you have sort of scientists working by hand doing biotechnology research. For a long time, if you go back and stay back a slide, for a long time, that was. We had an advantage over China just in the sense that our people were better trained, and we had access to sort of like better facilities and things like that. That advantage has largely evaporated over the last 10 to 15 years.

Uh, to dropping the cost of biologics. Uh, if you go to the next slide, uh, in the agricultural sector, we're very happy to extend our partnership. It's a partnership that's been going on for five years. We're really working on engineering microbes. If you go to the next slide for the production of fertilizers and, uh, if you remember, like, this is actually, I think, a pretty amazing story. So if you think about, like, elementary school biology, you learned about crop rotation, right? So you would rotate in a legume like soybeans or peanuts or things like that.

That only biotechnology can do in the physical world and so really excited.

Keep that going.

Right.

And if you're in agriculture, industrial biotech Biopharma you Wanna try large scale biotech on your problem I encourage you to call us up and we're happy to have our scientist work with yours to leverage the infrastructure here at gingko to deliver that I really like this photo to micro routers that ratio in Austin in the labs at a few weeks ago. The reason I bring this up is very pronounced.

Jason Kelly: Again, if you're in agriculture, industrial biotech, biopharma, you want to try large-scale biotech on your problem, I encourage you to call us up, and we're happy to have our scientists work with yours to leverage the infrastructure here at Ginkgo to deliver that. I really like this photo. This is two of my co-founders, Rachel and Austin, in the lab just a few weeks ago. The reason I bring this up is Rachel and Austin had not been in the lab prior to a few months ago for the last, I don't know, 10 or 15 years since we started the company. The reason they're back in the lab is because what we've been doing on the automation side at Ginkgo, building out our racks that are here in Boston, has gotten sort of ridiculously exciting over the last six months or so.

And not been in the lab prior to a few months ago for like the last I don't know 10 or 15 years, when we started the company.

And the reason they are back in the lab is because what we've been doing on the automation side and it didn't go billing out of Iraq set up here in Boston has gotten sort of a ridiculously exciting over the last six months or so so if you go to the next slide I want to talk about what we're building with our frontier Autonomous lab, we're getting a ton of interest in this right now both for customers and just internally.

Jason Kelly: There are just as good academic institutions, just as good startup ecosystem, and so on in China. There are more scientists trained and they're paid less, frankly. I don't really see where we have an advantage on physical labor anymore versus China. I was really excited to see Senator Young, who's sort of heading up that National Security Commission on Emerging Biotechnology, put in a number of bills around this topic. NSF launched a $100 million AI programmable cloud labs initiative. The big theory behind these things is if we're going to compete with China in biotechnology, we need to do it with robotics rather than hands at the bench. If we don't do it, I think you're going to see what we've seen over the last.

Jason Kelly: So if you go to the next slide, I want to talk about what we're building with our Frontier Autonomous Lab. We're getting a ton of interest in this right now, both from customers and even just internally. So we've been expanding our setup here in Boston. So you can see our rack carts there in the photo inside of one of our kind of big foundry bays here in Boston. If you go to the next slide, we're going to have about 45 instruments, 46 instruments on this setup. A lot like 10 carts are getting installed right now to bring it up to 36 racks. Ultimately, I'd like to get it in that room to about 100 racks. You can see a photo on the left of one of the racks going in. That's pretty exciting, right? So this is us putting a new piece of equipment on.

And they would referral the soil and then you'd plant something like corn and corn, largely takes, uh, fertilizer out of the soil, uh, so that that sort of how we used to do it. And then in the early 1900s, we invented the Haber Bosch process where you take, nitrogen out of the atmosphere by burning natural gas and combining uh, the nitrogen uh, with that and producing synthetic ammonia. Uh, and then that goes out, you know, to the tune of many billions of dollars a year and about 4% of global greenhouse gas and so on. So it's a big big chemistry industry and it's largely based in China. Uh, that's a huge input into things like corn farming. Well, those crops that you rotate in like soybeans and legumes, they're able to referral the soil because they have microbes on their routes running that Haber, Bosch process taking nitrogen out of the air fertilizing, the crop. So, I'm really happy to see this project continuing. I think it's the kind of world changing stuff. That only biotechnology can do in the physical world. Um, and so, really excited to, to,

Keep that going.

So we've been expanding our setup here in Boston. So you can see our rack carts there.

And the photo inside of one of our kind of big foundry based here in Boston.

Next slide we're going to have about 45 instrument Boise instruments on this set up a lot like 10 cars are getting installed right now to bring it up to 36 racks ultimate I'd like to get it in that room to about 100 racks.

Jason Kelly: Two or three quarters where an increasing number of the early-stage biotech startups that are being acquired by large pharma or invested in by USBCs are based in China. I think if we're going to turn that around, both for biotechnology and for science at large, we need to do it by investing in robotic infrastructure. I think that's not lost on the US government. I think Ginkgo, if you go to the next slide, has exactly the right technology for that. I've shown these before, but these are reconfigurable automation carts or rack carts. This is the first big area where I think AI is coming into biotechnology. This is around reasoning models. Again, think like GPT-5 from OpenAI and so on. These are in Gemini from Google.

Can see a photo on the left a lot of the racks going in that's pretty exciting right. So it's not putting a new piece of equipment on it that video sped up but it takes us a couple of hours really to get that device on the setup. This is because we have invested in product tithing. The cart hardware. So that we have greatly simplified and if you're not in the laboratory automation.

All right. Uh again if you're in agriculture, industrial biotech biofarma you want to try uh large scale biotech on your problem. I encourage you to call us up and, and we're happy to have our scientists work with yours to leverage the infrastructure here at go to deliver that. Uh, I really like this photo. This is uh, 2 of my co-founders, uh, Raymond Austin, uh, in the lab just a few weeks ago. Uh, the reason I bring this up is very awesome, had not been in the lab, uh, prior to a few months ago for like, the last, I don't know, 10 or 15 years. So, we started the company, uh, and

Jason Kelly: That video is sped up, but it takes just a couple of hours, really, to get that device on the setup. This is because we have invested in productizing the cart hardware so that we have greatly simplified. And if you're not in the laboratory automation business, you may not know this, but integrating equipment into laboratory setups right now is done as a custom job. You basically pay an engineering firm, and they spend months making CAD designs, and they build you this kind of Rube Goldberg machine device. We've taken all that and standardized it with carts, turned it into a product that you can just buy off the rack and install in these big setups. And so we're really excited to be building this out. The picture in the middle there that's running is actually a rack inside of an anaerobic chamber.

Business you may not know this but.

Integrating equipment into laboratory setups right now as dawn is a custom job you basically pay an engineering firm and they spend months, making pad designs and the ability to just kind of root Goldberg machine device, we've taken all of that and standardized it with parts turned it into a product that you can just buy off Iraq and install in these big setups and so we're really excited to be building. This.

Jason Kelly: These are these models that are able to think over a period of time, come to sort of a conclusion based on what you've asked them to do. They can write code, they can do other things, they can kind of use browsers and tools to go off and do sort of a multi-step operation and come back and bring a result to you. I think the first big frontier here is going to be connecting those reasoning models to physical automation in the lab. The reason this is necessary is if you think about how science gets done outside of areas like math or theoretical physics that are purely kind of people thinking about stuff, it's purely intellectual. The majority of science, experimental physics, experimental chemistry, experimental biology, and so on, is moved forward by lab work. Right?

The picture in the Middle there that's running is actually a rack inside of an anaerobic chamber. We built this for Pacific Northwest National Lab P&L like.

Jason Kelly: We built this for Pacific Northwest National Laboratory, PNNL. It's like, I think, 14 or 18 of our robotic arms and rack setups inside of an anaerobic chamber where people can't go in because there's no air. And so very exciting, big setup. We're excited to see more customers bringing those in-house. If you go to the next slide, I just wanted to kind of show what it looks like. So each row in that is a different piece of equipment. Those red bars are when a sample is interacting with that piece of equipment. So that's sort of like the timeline of a protocol being submitted. So a plate, and in this case, this is a standard piece of labware, that little plastic rectangle you see moving on our track system, is a 384-well plate. So there's 384 samples in there.

Like I think 14 or 18 of our robotic arms and rack setups inside of an anaerobic chamber where people can't go in because there's no air.

So very exciting big setup, we're excited to see more customers, bringing those in house.

The reason they're back in the lab is because what we've been doing, uh, on the automation side, at Ginkgo building out our rack set up here in Boston, has gotten sort of ridiculously exciting over the last, uh, 6 months, or so. So, if you go to the next slide, uh, I want to talk about what we're building, uh, with our Frontier autonomous lab. Uh, we're getting like a ton of interest in this right now both from customers and even just internally. Uh, so we've been expanding, uh, our setup here in Boston so you can see our rack carts there. Um, and, and the photo, uh, inside of 1 of our kind of big uh Foundry Bays here in Boston uh to go to the next slide. Uh, we're going to have about uh, 45 instrument, 46 instruments on this, uh, setup the last like 10 cards are getting installed right now to bring it up to 36 racks. Uh, ultimately, I'd like to get it in that room to about 100 racks. Uh, you can see a photo on the left of 1 of the racks going in, uh, that's pretty exciting, right? So, This Is Us putting a new piece of equipment on that. That video is sped up, but it takes just a couple hours really to get that device on the setup. Uh, this is because we have invested in productizing the car.

Next slide I, just wanted to kind of show what it looks like so each ROE and that is a different piece of equipment. Those red bars are when a sample is interacting with that piece of equipment. So that's sort of like the timeline of our protocol being submitted so our plate and in this case. This is a standard piece of lab, where that little plastic rectangle you see moving on.

Hardware, so that we have greatly simplified. I mean, if you're not in the laboratory automation, business, you may not know this but

Jason Kelly: Like we have a hypothesis, scientists have a hypothesis about how some disease works or whatever. The only way they really know the answer is to go off and run carefully constructed laboratory experiments. If you want these models to really be AI scientists, you're seeing Future House just had a great new model come out yesterday, or now called Edison Scientific, super excited about that. Those models need to be able to do experiments. If you go to the next slide, the way they're going to do experiments is using the technology like what we've built at Ginkgo. This is our reconfigurable automation carts. Each cart has a piece of lab equipment, a robotic arm, and a plate transport track. I'm going to spend a minute later showing you these in action.

Our track system is at 384, well place. So there is 384 samples in there it's being put onto a centrifuge.

Integrating equipment into laboratory setups right now is done as a custom job, you basically pay an engineering firm and they spend months making CAD designs. And they build you this kind of rude Goldberg machine device. Uh, we've taken all that and standardized it with cards. Turned it into a product that you can just buy off the rack, uh, and install, um, in in these big setups. And so, we're really excited to be building this out. The picture in the middle there.

This video here so that play it goes in and then that center for you just going to spin.

Jason Kelly: It's being put onto a centrifuge in this video here. So that plate goes in, and then that centrifuge is going to spin. This plate now is then, after the centrifuge step, being delivered to an ECHO liquid handler. This is an acoustic liquid handler that's able to move liquids with sound. And what it's going to do is it's going to set up the reaction conditions on each of those 384-well plates as programmed by the software that is telling the system what to do. And importantly, again, to nerd out a little bit, each piece of equipment, this is like a Bravo liquid handler. That was an ECHO. Each one has its own piece of sort of proprietary third-party software that's kind of a pain to deal with, honestly.

This played out and then after the center if you stop being delivered to an echo liquid handler. This is an acoustic liquid handler, that's able to move liquids with sound and when it's going to do is it's going to set up a reaction conditions on each of those 384, well plates as programmed by.

That's running is actually a rack inside of an Anor. We we built this for uh, Pacific Northwest National Lab pnnl. It's like, I think, 14 or 18 of our, our robotic arms and rack setups inside of an Anor where people can't go in because there's no are, uh, and so, uh, very exciting big setup. We're excited to see more customers, uh, bringing those in-house.

The software that is telling the system what to do and importantly began to nerd out a little bit each piece of equipment. This is like a bravo lipid handle or that was the echo. Each one has its own piece of sort of proprietary third party software, that's kind of a pain to deal with honestly and so what we've done.

Jason Kelly: What it allows you to do is sort of Lego block together. If you go to the next slide, you know, five of these in a linear setup, 20 of these in a circular setup, or you know, here's a setup. We actually just sold one of these systems with 97 carts on it in one giant setup. The idea here is to be able to connect ultimately hundreds of pieces of lab equipment, Lego block style, into a huge setup where the whole thing is software controlled. The reason it's important that it's software controlled is just like these reasoning models can write code for Python or whatever, right, for a website, they're also able to write code to run this automation and design and execute experiments and interpret data.

As part of the rack system.

Jason Kelly: And so what we've done as part of the rack system on the software side is we have connected into each piece of hardware with our software. So you're able to write a multi-step protocol. It's like what you're watching here. This particular protocol is cell-free protein expression. What you're able to do is connect many different pieces of equipment in a single protocol where you're controlling in a parameterized way each piece of equipment. This is a shaker. And then it's going to go on finally to a piece of assay equipment, a thermocycler, to go kind of complete this reaction. And so all of those steps are encoded in the Ginkgo software. And then the scheduler and larger system goes and talks to all the equipment in a seamless way. So your scientists aren't dealing with 18 different types of software to do an 18-equipment run.

The software side is we are connected into the each piece of hardware with our software. So you are able to write a multistate protocols like what you're watching here. This is this particular protocol is protein cell free protein expression.

We're able to do is connect many different pieces of equipment in a single protocol, where you're controlling and a parameterized way each piece of equipment is a shaker and then it's going to go on and finally to a piece of assay equipment.

Jason Kelly: If we want to have these sort of AI-controlled science, these cloud-enabled labs, this is what they look like. You really need a new hardware technology like what we've built with the racks to do that. I think we're extremely well positioned for this, and you'll see us leaning in heavily here in 2026. The second area where we're seeing AI applied to biotechnology is in using the same kind of like math and compute that was used for the reasoning models. Large neural networks, GPUs, that whole infrastructure, except instead of training those neural nets on human language and human reasoning and code and programming, things that humans kind of read, understand, and interpret, you train them on biological language. DNA, amino acid sequences from proteins, the language of life, the language of living organisms.

Thermal cycle or to go kind of complete this reaction and so so all all of those steps are encoded in the ginkgo software and then the scheduler and larger system goes and talks to all the equipment in a seamless way. So your scientists aren't dealing with 18 different types of software to do an 18 equipment run that's a really big deal and.

Looks like so each row in that is a different piece of equipment. Those red bars are when a sample is interacting with that piece of equipment. So, that's sort of like the timeline of a protocol being submitted so a plate. And in this case, this is a standard piece of labware that little plastic rectangle. You see, moving on our track system is a 384, well plate. So there's 384 samples in there, it's being, put onto a center fuse uh in this video here so that plate goes in and then that Center fuse is going to spin uh this plate. Now is then then after the center fuse step being delivered to an echo liquid Handler. This is an acoustic liquid Handler, that's able to move liquids with sound. And what it's going to do is it's going to set up the reaction conditions on each of those. 384, well plates as programmed by, uh, the software that is telling the system what to do. Uh, and importantly, again to nerd out a little bit. Each piece of equipment, this is like a Bravo liquid Handler, that was the echo. Uh each 1 has its own piece of sort of proprietary third-party software that's kind of a pain to deal with

It also means it can be connected back to reasoning models to do that type of.

Jason Kelly: That's a really big deal. It also means it can be connected back to reasoning models to do that type of design of experiments as well. If you go to the next slide, we are able, like I mentioned, to set these up quickly. So this is these 10 cards that have been coming in. This is literally from last week. If we've already had the equipment that's relevant, and again, we're at 45 pieces of equipment now on this setup for the protocol you want to do. If you go to the next slide, we are able to then demo it for you in pretty short order. So if your group has been thinking about just automation in general, you can try our system.

Design of experiments as well.

If you go to the next slide.

We are able like I mentioned to set these up quickly. So this is the <unk>.

10 cards that have been coming in and this is like literally from last week.

And so if we've already have the equipment.

That's relevant and again were at 45 pieces of equipment now on the setup for the protocol you want to do if you go to the next slide.

Honestly. Uh, and so what we've done, um, as part of the rack system, for, on the software side is we have connected, uh, into the each piece of Hardware with our software. So, you're able to write a multi-step protocol. So like what you're watching here? This is, this particular protocol is, uh, protein, uh, self-reporting expression. Uh, what you're able to do is connect many different pieces of equipment, and a single protocol, where you're controlling in a parameterized way, each piece of equipment, this is a Shaker, uh, and then it's going to go.

We are able to then demo it for you in pretty short order. So if your group has been thinking about just automation in general you can try our system. If you want to see what it's like as a scientist to interact with our system through our language model that we have human language interface now to that setup. So you can play around with that and then finally, if you wanted to have an AI reason.

Jason Kelly: You do the same type of training, the same infrastructure, but these things learn to speak biology. This is a more nascent area compared to the reasoning models when it comes to AI and biotech. I think it's also going to be extremely important. With our Ginkgo Data Points service, we really want to build the community in that area. We highlight here our antibody developability competition. I think at the end of November, we're going to wrap up. If you go to the next slide, you should check it out. You can go to datapoints.ginkgo.bio. You can sign up. We have more than 200 teams now competing in that competition. The idea there is build a model like the one I just mentioned, like train a model on data for the developability of antibodies.

Jason Kelly: If you want to see what it's like as a scientist to interact with a system through a language model, we have a human language interface now to that setup, so you can play around with that. And then finally, if you wanted to have an AI reasoning model controlling this setup to work on a problem of interest to you, we can do that too. And what's exciting is we do all that just on our setup here in Boston. It's very inexpensive for you. You're not buying a bunch of equipment or anything else. And you can see if it works. Try it before you buy it, right? If it works, then we're very happy to install this in your lab so that your labs could have the same sort of just very latest scale in terms of automation and AI that we're running here at Ginkgo.

The model controlling this setup to work on our problem of interest to you. We can do that too and what's exciting is we do all of that just on our setup here in Boston. It is very inexpensive or you are not buying a bunch of equipment or anything else and you can see if it works.

To a piece of assay equipment um a thermostat to go kind of complete this reaction. And so so all all of those steps are encoded in the ginkgo software and then the uh, scheduler and larger system goes and talks to all the equipment in a seamless way. So your scientists aren't dealing with 18 different types of software to do, uh, an 18 equipment run. Uh, that's a really big deal and it also means it can be connected back to, uh, reasoning models to do that, um, type of, um, uh, design of experiments as well. Uh, if you go to the next slide, uh,

Try before you buy it right.

If it works then we're very happy to install them in your lab. So that your labs could have the same sort of just very latest scale in terms of automation and.

Jason Kelly: In other words, is this antibody sequence going to work well as a drug? Will it be soluble, and so forth? Is it not immunogenic? That is a very valuable feature set for biopharma companies. If you're a bioinformatician or you're a startup that has a great new AI model, I encourage you to compete in our competition here. We basically generate a large amount of developability data. We shared some of that with the community, and we kept some of it back as the competition set. Your job is to predict the held back data, and we'll rank who does the best. The other thing we're doing to help build the community is we're releasing data sets for free. Again, you go to our website there and download these sort of AI/ML-ready data sets.

AI that we're running here at <unk> and I'm, telling you. It is very very excited against working really well so I do think.

Jason Kelly: I'm telling you, it is very, very exciting. It's working really well. I do think folks should come and try it. If you just want to come visit, please do just shoot me a note, and we're happy to do that and have you come by. All right. That's what I had today. Happy to answer questions about all that, but super excited. I think we've done the team, again, a big round of thanks for 2025. It's a very difficult year, bringing down our costs in a huge way while maintaining that sort of large margin of safety. That's what's allowing us to really now invest for growth in the future, particularly in this area of building out basically the automation and AI tooling for biosciences.

Books should come and try it and he has a lot to come visit.

Please do just shoot me a note and we're happy to do that and have you come by Alright. That's all I had today I'm happy to answer questions about all of that but Super excited I think we've done the team again, a big round of banks for 2025 is a very difficult year, bringing down our cost in a huge way, while maintaining that sort of large margin of safety and that's what's allowing us to really know.

Invest for growth in the future, particularly in this area of applying.

Apply building up basically the automation and AI tooling for Biosciences, and I think that's going to be the niche that we grow into the coming five to 10 years in a big way. So excited for your questions and thanks again.

We are able like I, I mentioned, uh, to set these up quickly. So uh, you know, this is these 10 cards that have been coming in. This is like literally from last week. Um, and so if we've already have the equipment um that's relevant. And again we're we're at 45 pieces of equipment. Now, on this setup for the protocol you want to do um, if you go to the next slide um we are able to then demo it for you in pretty short order. So if your group has been thinking about just Automation in general, you can try our system. Uh if you want to see what it's like as a scientist to interact with a system through a language model, like we have uh human language interface now to that setup so you can play around with that and then finally if you wanted to have an AI reasoning model controlling, this setup to work on a problem of interest to you, we can do that too. And what's exciting is we do all that just on our setup here in Boston. It's very inexpensive for you, you're not buying a bunch of equipment or anything else and you can see if it works, you know, like try it before you buy it.

Jason Kelly: They're an example of the sort of data that we generate on a fee-for-service basis for customers through our Data Points service. Go download those, play around. If you wanted to buy data from us, we're very happy to do that. We're really here to build a community of folks who are trying to train AI models using biological data. We're really excited about this as a sort of nascent area for AI applied to biology. All right. Second thing I wanted to talk about. Those are the two big buckets for AI: reasoning models controlling robotics in the lab, and then basically neural nets trained on biological data. They're both involving AI, but they are different. Ginkgo will play there through our automation in the first one and our Data Points for the second one. All right.

Jason Kelly: I think that's going to be the niche that we grow into in the coming 5 to 10 years in a big way. So excited for your questions, and thanks again.

Great. Thanks, Jason.

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

Daniel Waid Marshall: Great. Thanks, Jason. As usual, I'll start with a question from the public and remind the analysts on the line that if they'd like to ask a question, to please raise their hands on Zoom, and I'll call on you and open up your line. Thanks, everyone. All right. Let's get started. So the first question was one that we got on Twitter from an account @DavidZhuTweets. And this question is, "Can you comment on the extent of Ginkgo's exposure to US government business and how that has been impacted by the shutdown?

Daniel Marshall: Great. Thanks, Jason. As usual, I'll start with a question from the public and remind the analysts on the line that if they'd like to ask a question, to please raise their hands on Zoom, and I'll call on you and open up your line. Thanks, everyone. All right. Let's get started. So the first question was one that we got on Twitter from an account @DavidZhuTweets. And this question is, "Can you comment on the extent of Ginkgo's exposure to US government business and how that has been impacted by the shutdown?

Alright.

Let's get started so.

The first question was one that we got on Twitter.

From an account.

David you tweets.

This question is can you comment on the extent of getting who has exposure to U S government business and how that has been impacted by the shutdown.

Jason Kelly: Next category, this is now going back to that left-hand side of this chart. The business that Ginkgo sort of like primarily focused on over the last 10 years are research solutions business. We are still doing these. If you are looking for sort of breakthrough research in any of the areas that could basically leverage like high throughput biotechnology, I think Ginkgo is still a very good call. If you go to the next slide, we won a couple of great deals in the last quarter. BARDA awarded us and our partners $22 million around the manufacturing of monoclonal antibodies, bringing that back in the US, making that cheaper, particularly around producing key medical countermeasures. I think this is both important for national security and also important for reducing the cost of manufacturing drugs, particularly biologics drugs. You heard.

Yeah, I can put it on that.

Shortly after the shutdown has not had a big impact on us so sort of the areas.

Jason Kelly: Yeah, I can touch on that. So short answer on the shutdown has not had a big impact on us. So sort of the areas, the grants and funding there keeps flowing during the shutdown. I would say, in general, though, we have a good amount of exposure to the government overall. So between our biosecurity business and then things like the new BARDA awards, you'll see us announcing some recently also ARPA-H awards. We've been doing very well, I guess I would say, with bringing in research partnerships with the government. So overall, I think hopefully we're even doing more in the future with some of this sort of cloud labs work and investments I hope to see from sort of government labs around automation. But the shutdown doesn't impact us.

Grants and funding their keeps flowing during the shutdown.

Right, if it works then we're very happy to install this in your lab so that your Labs could have uh the same sort of just very latest scale in terms of uh Automation and uh, uh AI that we're running here at gigo and I'm telling you. It is very, very exciting. It's working really well. Uh, so I I do think, uh, folks should come and try it. Uh, and if you just want to come visit, uh, you know, please do uh, just shoot me a note and we're we're happy to to do that. Have you come by? All right. Uh, that's what I had today. Uh, have a good answer your questions about all that but super excited. Uh, I think we've done the team again. Uh, a big round of thanks for 2025. It's very difficult year. Bringing down our costs. Uh, in a huge way while maintaining that sort of large margin of safety. And that's what's allowing us to really. Now, invest for growth in the future, particularly in this area of, uh, applying, you know, building up basically the Automation and AI tooling for biosciences and I think that's going to be the niche that we grow into, uh, in the coming, you know, 5 to 10 years in a big way. So uh, excited for your questions and thanks again.

Great. Thanks. Jason.

I would say in general, though we have a good amount of exposure to the government overall so between our.

Our security business, and then things like the New BARDA Award Youll see us announcing.

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

Recently also our age awards, we've been doing very well I guess I would say with bringing in research partnerships with the government. So overall I think hopefully we're even doing more in the future with some of the sort of cloud labs work and investments I hope to see.

From sort of a government labs around automation.

But the shut down doesn't impact us.

Okay.

Alright, and our first question from Brendan from TD Securities.

Daniel Waid Marshall: Cool. All right. And our first question from Brendan from TD Securities, he writes, "How do you see the broader development or rollout path ahead for the rack system over the next 18 months? Are there any additional validation steps or accounts to land that you expect could really unlock this opportunity and widen the commercial funnel for this over the near term?

Daniel Marshall: Cool. All right. And our first question from Brendan from TD Securities, he writes, "How do you see the broader development or rollout path ahead for the rack system over the next 18 months? Are there any additional validation steps or accounts to land that you expect could really unlock this opportunity and widen the commercial funnel for this over the near term?

All right, uh, let's get started. So um, the first question was 1 that we got on Twitter, uh, from an account at davidu tweets. Uh, and this question is, can you comment on the extent of Geno's exposure to US, Government business and how that has been impacted by the shutdown?

Jason Kelly: The administration talking about this recently on the regulatory side to try to lower the cost of biologics. This is a technical approach to dropping the cost of biologics. If you go to the next slide, in the agricultural sector, very happy to extend our partnership. This partnership has been going on for five years. It's fair. We're really working on engineering microbes, if you go to the next slide, for the production of fertilizers. If you remember, this is actually a pretty amazing story. If you think about elementary school biology, you learned about crop rotation, right? You would rotate in a legume like soybeans or peanuts or things like that, and they would refertilize the soil. Then you'd plant something like corn, and corn largely takes fertilizer out of the soil. That's sort of how we used to do it.

He writes how do you see the broader development rollout path ahead for the rack system over the next 18 months are there any additional validation steps or accounts to land that you expect could really unlocks opportunity and widen the commercial funnel for this over the near term.

Yeah, I can touch on that too.

So first of all that I think what's super exciting about the racks and again I tried to mention this but.

Jason Kelly: Yeah, I can touch on that too. So first off, I think what's super exciting about the racks, and again, I tried to mention this, but there's sort of walkup automation, companies like Hamilton and so on, where you're getting a liquid handling deck. That is a very productized offering. But then there's integrated automation, which basically means there's a robotic arm in the middle of a bunch of equipment. The key there is one piece of equipment maybe does the liquid handling, but then you got to take your samples to the next piece of equipment. You saw in the video the plates moving on that track and getting delivered to six or seven different pieces of equipment in that single protocol. You might have protocols that interact with 15 different pieces of equipment.

They're sort of like walk up automation like companies like Hamilton, and so on where youre getting like a liquid handling deck and that is very product offering, but then there's integrated automation, which basically means.

Theres, a robotic arm in the middle of a bunch of equipment and the key there is one piece of equipment, maybe does the liquid handling, but then you've got to take your samples to the next piece of equipment and you saw in the video there.

New barter Awards, you'll see us announcing, uh, some recently. Also, our page Awards, uh, we've been doing very well. I guess I would say with, um, bringing in research Partnerships with the government. So, um, so overall, I think hopefully we're even doing more in the future with some of this sort of cloud Labs work and Investments. I hope to see, uh, from sort of government labs around automation, um, but the shutdown doesn't impact us.

Cool.

Jason Kelly: In the early 1900s, we invented the Haber-Bosch process where you take nitrogen out of the atmosphere by burning natural gas and combining the nitrogen with that, producing synthetic ammonia. That goes out to the tune of many billions of dollars a year and about 4% of global greenhouse gas and so on. It's a big, big chemistry industry, and it's largely based in China. That's a huge input into things like corn farming. Well, those crops that you rotate in, like soybeans and legumes, they're able to refertilize the soil because they have microbes on their roots running that Haber-Bosch process, taking nitrogen out of the air, fertilizing the crop. I'm really happy to see this project continuing. I think it's the kind of world-changing stuff that only biotechnology can do in the physical world.

Moving on that track and getting delivered to six or seven different pieces of equipment in that single protocol you might have protocols that interact with 15 different piece of equipment and a human by and large is doing that in 99% of the labs that are out there. There is a small niche industry around integrated automation for things like hydro, but screening where do you put an arm in the middle of 15.

Jason Kelly: A human, by and large, is doing that in 99% of the labs that are out there. There is a small niche industry around integrated automation for things like high-throughput screening, where you put an arm in the middle of 15 pieces of equipment that is built basically application-specific. In other words, it's a design of a setup just for the one thing you want to do. Our carts are not like that. They are productized. They're coming off the line the same, and then we're just connecting them so that you have whatever equipment you want initially, and then actually able to expand that equipment over time into bigger and bigger setups. That's something you just cannot get with the traditional integrated automation.

All right, and our first question uh from Brendan from T Securities. Uh, he writes, how do you see the broader development or rollout path ahead for the rack system over the next 18 months? Are there any additional validation steps or accounts to land that? You expect could really unlock this opportunity and why didn't the commercial funnel for this over the near term?

Yeah, I can.

Pieces of equipment that is built basically application specific in other words, it's a design of a set up just for the one thing you want to do our cards are not like that they are they're prototypes.

We're coming off the line the same and then we're just connecting them. So that you have whatever equipment you want initially and then actually able to expand that equipment over time, it's a bigger and bigger setup. So that's something you just cannot get with the traditional integrated automation. So so what I'm excited about on a rollout basis is continuing to scale up our manufacturing of these cards being the costs.

Jason Kelly: Really excited to keep that going. All right, if you're in agriculture, industrial biotech, or biopharma, you want to try large-scale biotech on your problem, I encourage you to call us up. We're happy to have our scientists work with yours to leverage the infrastructure here at Ginkgo to deliver that. I really like this photo. This is two of my co-founders, Rachel and Austin, in the lab just a few weeks ago. The reason I bring this up is Rachel and Austin had not been in the lab prior to a few months ago for the last, I don't know, 10 or 15 years since we started the company.

Jason Kelly: So what I'm excited about on a rollout basis is continuing to scale up our manufacturing of these carts, bring the cost down, turn that again into a more and more productized offering. But then on the sale side, it's basically getting folks to see this distinction between application-specific work cells that they buy today and general-purpose autonomous labs, like what I was showing you there with our Frontier Lab here in Boston. It's that adoption, this idea that automation isn't the thing you build for one application and then literally decommission and throw away three or four years later. That's what happens with these systems. But something that just keeps expanding over years and then ultimately replaces, hopefully, tens of thousands, hundreds of thousands of sq ft of laboratory benches because we're just going to move off that system.

Down like turn that into more and more product ties operating but then on the on the sales side, it's basically getting folks to see this distinction between.

That too. Um, so first off, I think what's super exciting about the racks? And, and again, I tried to mention this but, um, there's sort of like walk up automation like companies like Hamilton and so on where you're, you're getting like, a liquid handling deck. And that, that is a very productized offering. Uh, but then there's integrated automation, which basically means, uh, there's a robotic arm in the middle of a bunch of equipment. And the key, there is 1 piece of equipment. Maybe does the liquid handling, but then you got to take your samples to the next piece of equipment. And you saw on the video. The, the plates, moving on that track and getting delivered to 6 or 7 different pieces of equipment in that single protocol. You might have protocols that interact with 15 different pieces of equipment and a human by and large is doing that in 99% of the labs that are out there uh there.

Application specific work cells that they buy today and general purpose.

Autonomous labs like what I was showing you there with our frontier lab here in Boston.

That adoption this idea that that automation isn't the thing you build for one application and then literally decommission and throw away three or four years later, that's what happens with these systems, but something that just keeps expanding over years and then ultimately replaces hopefully.

Jason Kelly: The reason they're back in the lab is because what we've been doing on the automation side at Ginkgo, building out our racks setup here in Boston, has gotten sort of ridiculously exciting over the last six months or so. If you go to the next slide, I want to talk about what we're building with our frontier autonomous lab. We're getting a ton of interest in this right now, both from customers and even just internally. We've been expanding our setup here in Boston. You can see our rack carts there in the photo inside one of our kind of big foundry bays here in Boston. If you go to the next slide, we're going to have about 45 instruments, 46 instruments on this setup. Like 10 carts are getting installed right now to bring it up to 36 racks.

Tens of thousands hundreds of thousands of square feet of laboratory benches, because we're just going to move off that system, we have to move away from the bench as the general purpose laboratory infrastructure.

Jason Kelly: We have to move away from the bench as the general-purpose laboratory infrastructure to the automated bench, to the autonomous lab. And that's the transition that I want to drive. So if you're looking for milestones, I want internal milestones at Ginkgo. So one of the things I want to see is 50-plus scientists internally at Ginkgo ordering simultaneously from our automation system in a single day. That's the thing I think I can have happening in 2026. That's something that's never been seen with an automated lab previously. So there's internal milestones. And then what I would love to see, we're starting to see this on the government side, but I'd also like to see it in the private sector, ideally with a large biopharma, a purpose of a very large system with an intent for a general-purpose autonomous lab.

The automated bench to the autonomous lab and that's the transition that I want to drive so if youre looking for milestones I want internal milestones that ginkgo. So like one of the things I want to see is 50, plus scientists internally I can't go ordering simultaneously from our automation system in a single day. That's the thing that I think I can get have happening in 2026, that's up.

Jason Kelly: Ultimately, I'd like to get it in that room to about 100 racks. You can see a photo on the left of one of the racks going in. That's pretty exciting, right? This is us putting a new piece of equipment on. That video is sped up, but it takes just a couple of hours, really, to get that device on the setup. This is because we have invested in productizing the cart hardware, so that we have greatly simplified. If you're not in the laboratory automation business, you may not know this, but integrating equipment into laboratory setups right now is done as a custom job. You basically pay an engineering firm, and they spend months making CAD designs, and they build you this kind of Rube Goldberg machine device.

That's never been seen with <unk>.

Is a small Niche industry around integrated automation for things like high throughput, screening, where you put an arm in the middle of 15 pieces of equipment. That is built. Basically application specific. In other words, it's a design of a setup, just for the 1 thing, you want to do our carts, are not like that. They are, they are productized. They're you know, they're coming off the line, the same and then we're just connecting them so that you have whatever equipment you want initially. And then actually able to expand that equipment over time into bigger and bigger setups. So, that's something you just cannot get with the traditional integrated automation. So, so what I'm excited about on a roll out, basis is, you know, continuing to scale up our manufacturing of these cards. Bring the cost down like turn that again into more and more productized offering. But then, on the, on the sales side, it's it's basically getting folks to see this distinction between, uh, application specific work cells, that they buy today, and general purpose, autonomous Labs. Like, what I was showing you there with our Frontier lab here in Boston, it's that adoption.

And automated lab previously so there's internal milestones and then what I would love to see we're starting to see this on the government side, but I'd also like to see it in the private sector ideally with large biopharma are similar like a purchase of a very large system with an intent.

That this idea that, that automation isn't the thing, you build for 1 application. And then literally decommission and throw away, 3 or 4 years later. That's what happens with these systems. Uh, but something that just keeps expanding over years and then ultimately replaces.

For a general purpose autonomous lab and so that those are the those are kind of my two big things I'd love to see it.

Jason Kelly: Those are kind of my two big things I'd love to see in 2026: us demonstrating just what you can do with already having one of these kind of autonomous labs, and then a large biopharma leaning in and making a purchase for one. We'll still sell opposite the work cell. That's what we're selling today. But I would love to see someone kind of lean in on the dream of the big general-purpose autonomous lab. I think it's the time for it. And we're going to prove it either way at Ginkgo. But I think our customers will be sort of adopting that mindset soon too, is my view. I don't know. It's gotten so much easier to use automation with the AI stuff. And so I do think that's going to just bring the barrier down massively for this in the industry.

In 'twenty rates ex us demonstrating just what you can do with already having one of these.

Kind of autonomous labs, and then a large biopharma leaning in and making a purchase for one.

We will still sell opposite the works out well, that's what we're selling today.

Jason Kelly: We've taken all that and standardized it with parts, turned it into a product that you can just buy off the rack and install in these big setups. We're really excited to be building this out. The picture in the middle there that's running is actually a rack inside of an anaerobic chamber. We built this for Pacific Northwest National Laboratory, PNNL. Like, I think, 14 or 18 of our robotic arms and rack setups inside of an anaerobic chamber where people can't go in because there's no air. Very exciting, big setup. We're excited to see more customers bringing those in-house. If you go to the next slide, I just wanted to kind of show what it looks like. Each row in that is a different piece of equipment. Those red bars are when a sample is interacting with that piece of equipment.

But I would love to see some on kind of lean in on the dream of the Big General purpose Autonomous lab I think it's the time for it and we're going to prove it either way. It can go but I think our customers will be.

Sort of adopting that mindset soon to my view.

I don't know, it's got so much easier to use automation with the AI stuff and so I do think that's going to just bring the bear down massively for this in the industry.

Cool Alright, and Brendan had one more question, which was as you look at the current revenue mix between sell price.

Daniel Waid Marshall: Cool. All right. And then Brendan had one more question, which was, "As you look at the current revenue mix between cell processing," as he said, "cell engineering and biosecurity, and then consider your internal assumptions about the AI tools and racks rollouts, what do you see as the ideal revenue mix for Ginkgo by 2030? What has to happen to get there?

Daniel Marshall: Cool. All right. And then Brendan had one more question, which was, "As you look at the current revenue mix between cell processing," as he said, "cell engineering and biosecurity, and then consider your internal assumptions about the AI tools and racks rollouts, what do you see as the ideal revenue mix for Ginkgo by 2030? What has to happen to get there?

Cell processing and he said, so engineering and bio security and then consider your internal assumptions about the air cools and racks Rollouts, what do you see as the ideal revenue mix for ginkgo by 2030, what has to happen to get there.

Hopefully, you know, tens of thousands, hundreds of thousands of square feet of laboratory benches, because we're just going to move off that system. We have to move away from the bench as the general purpose laboratory infrastructure. Uh, to the automated bench to the autonomous lab and and that's the transition that I want to drive. So if you're looking for Milestones, I want internal milestones at go. It's like 1 of the things I want to see is 50 plus scientists internally at go, ordering simultaneously from our automation system in a single day. That's the thing. I think I can get have happening in 2026. That's something that you that's never been seen uh with a uh, an automated lab previously. So there's internal milestones and then I, what I would love to see, we're starting to see this on the government side, but also like to see it in the private sector. Ideally, with a large biofarma, a similar like a purchase of a very large system with an intent, uh, for a general purpose, autonomous lab. Uh, and so that those are the, those are kind of my 2 big things. I'd love to see, uh, in 2026, us demonstrating, just what you can do with already having 1 of these autonomous.

Uh, kind of autonomous labs. And then uh a large biofarma leaning in and and making a purchase for 1.

Jason Kelly: That's sort of like the timeline of a protocol being submitted. A plate, and in this case, this is a standard piece of labware. That little plastic rectangle you see moving on our track system is a 384 well plate. There are 384 samples in there. It's being put onto a centrifuge in this video here. That plate goes in, and that centrifuge is going to spin. This plate now is then, after the centrifuge step, being delivered to an Echo liquid handler. This is an acoustic liquid handler that's able to move liquids with sound. What it's going to do is set up the reaction conditions on each of those 384 well plates as programmed by the software that is telling the system what to do.

30.

Okay. Yeah, so is interesting.

Jason Kelly: 2030? Okay. Yeah, so that's interesting. I mean, so my dream by 2030 is we're starting to put a bunch of benches to bed. And so my expectation, if I think about the balance between, we'll leave biosecurity. I'll come back to that in a second. But between the sort of tools business, in other words, robotics, software on the robotics, reagents going into all that infrastructure, devices, that whole ecosystem of our tools business versus the services offerings that we offer on top of our setup, like data points and solutions, that tools versus services, I would say, is 80/20 in the tools side of the house in terms of our revenue mix in 2030. My hope would be we're largely taking over the general-purpose R&D infrastructure and being that provider of the tools into the whole industry. So that should be dominant.

So my Dream by 2030, as we're starting to put it onto a benches to bed.

And so my expectation like if I think about the balance between.

We've got a security or come back to that in a second but between like the sort of tools business in other words like robotics software on the robotics reagents going into all of that infrastructure devices that whole ecosystem of like our tools business versus the services offerings that we offer on top of our setup like data points and solutions that tools versus services I would say.

We'll still sell opposite, the work cells. That's what we're selling today into the. But uh, I would love to see someone kind of lean in, on the dream of the big, general purpose, autonomous lab. I I think it's the time for it and we're, we're going to prove it either way. I can go, but I think our customers will will be sort of adopting that mindset soon, too. As my view. The, the, just the

I don't know. It's gotten so much easier to use automation with the AI stuff and so I I do think that's going to just bring the barrier down massively for this in the industry.

Like 80 20 in the tools side of the house in terms of our revenue mix in 2030 like my hope would be we are largely taking over.

Jason Kelly: Importantly, again, to nerd out a little bit, each piece of equipment, this is like a Bravo liquid handler. That was an Echo. Each one has its own piece of sort of proprietary third-party software that's kind of a pain to deal with, honestly. What we've done as part of the rack system on the software side is we have connected into each piece of hardware with our software. You're able to write a multi-step protocol. Like what you're watching here, this particular protocol is cell-free protein expression. What you're able to do is connect many different pieces of equipment in a single protocol where you're controlling, in a parameterized way, each piece of equipment. This is a shaker, and then it's going to go on finally to a piece of assay equipment, a thermocycler, to go kind of complete this reaction.

Yeah.

Ed.

General purpose, R&D infrastructure and being that provider of the tools into the whole industry. So that should be dominant when it comes to bio security there it's very dependent on.

Cool. All right, and then Brendan had 1 more question, which was, uh, as you look at the current Revenue mix between sell process, uh, sell processing, as we said, sell engineering and bio security, uh, and then consider your internal assumptions about the AI tools and racks rollouts. What do you see as the ideal Revenue mix for go by 2030? What has to

Happen to get there.

Jason Kelly: When it comes to biosecurity, there, it's very dependent on how things play out. It's a very interesting time right now. So CDC is getting rebuilt. There was a great post from Matt McKnight, who heads up our biosecurity business today. I encourage folks to read about sort of what a rebuilt CDC looks like. I think, fundamentally, you need persistent, pervasive monitoring of viruses as a foundational layer for biosecurity in the future, whether you're in an outbreak or not, just all the time. And so if that type of infrastructure gets built here in the US and worldwide, then who knows? Biosecurity could be 50/50 with the rest of the business. But it does depend on whether we see that adoption of sort of monitoring technology as one of the core pillars of biosecurity that works, a CDC that could stop the next COVID.

Play out like a very interesting time right now so you know the CDC is getting rebuilt.

Great posts from Matt Mcknight, who heads up our bio security business today, I encourage folks to read about and sort of like what a rebuild CEC looks like.

I think fundamentally you need persistent pervasive monitoring of viruses.

Foundational layer for bio security in the future, whether youre, an outbreak or not just all the time.

And so at that type of infrastructure gets built out here in the U S and worldwide than you know who knows it could be 50 50 with the rest of the business, but it does depend on whether we see that adoption of sort of like monitoring technology at the core one of the core pillars of.

Jason Kelly: All of those steps are encoded in the Ginkgo software, and then the scheduler and larger system goes and talks to all the equipment in a seamless way. Your scientists aren't dealing with 18 different types of software to do an 18-equipment run. That's a really big deal, and it also means it can be connected back to reasoning models to do that type of design of experiments as well. If you go to the next slide, we are able, like I mentioned, to set these up quickly. This is the 10 carts that have been coming in. This is literally from last week. If we've already had the equipment.

Sort of tools business. In other words like robotics software on the robotics reagents going into all that infrastructure devices that whole ecosystem of like our tools business versus the Services offering that we offer on top of our setup like data points and solutions. That tools for services, I would say is like, 80/20 in the tools side of the house, in terms of our Revenue mix in 2030 like my hope would be. We are largely taking over the uh

Our bio security that works CDC that could stop ex COVID-19.

Cool So we got a question for Steve.

Daniel Waid Marshall: Cool. So we got a question for Steve. So Steve, you mentioned in October 2025, Ginkgo reset the annual commitments and its contract with Google. Can you provide a little more color on that?

Daniel Marshall: Cool. So we got a question for Steve. So Steve, you mentioned in October 2025, Ginkgo reset the annual commitments and its contract with Google. Can you provide a little more color on that?

So Steve you mentioned in October 2025 gig or reset the annual commitments in its contract with Google can you provide a little more color on that.

Sure.

When we were negotiating.

Steven Coen: Sure. When we were negotiating the Google Cloud contract, obviously, we had a shortfall to solve for in Q3. We talked about that. We reset going forward, in my view, very favorable terms for Ginkgo. We were able to reduce our go-forward commitment by over $100 million and extended out the period by 2x. So going out over 6 years over the prior 3 years. From that standpoint, I think that puts us right where we want to be.

The Google cloud.

Contract, obviously, we had a shortfall to solve for in Q3, we talked about that we reset going forward in mind My view very favorable terms for ginkgo.

Jason Kelly: That's relevant, and again, we're at 45 pieces of equipment now on this setup for the protocol you want to do. If you go to the next slide, we are able to then demo it for you in pretty short order. If your group has been thinking about just automation in general, you can try our system. If you want to see what it's like as a scientist to interact with a system through a language model, we have a human language interface now to that setup, so you can play around with that. Finally, if you wanted to have an AI reasoning model controlling this setup to work on a problem of interest to you, we can do that too. What's exciting is we do all that just on our setup here in Boston. It's very inexpensive for you.

We were able to reduce our go forward commitment by over $100 million and extended healthy period.

Alright.

So going out over six years over the prior three years from that standpoint, I think that puts us right, where we want to be.

The general purpose, R&D and infrastructure and and being that provider of the tools into the whole industry. Uh, so that should be dominant. Um when it comes to bio security there, it's very dependent on how things play out. It's like a very interesting time right now, so you know, CDC is getting rebuilt. Um, there's a great post from Matt mcnight, who heads up our bio security business today. I encourage folks to read about sort of like, what a rebuild CDC looks like, uh, you know, I I think fundamentally, uh, you need, uh, persistent pervasive monitoring of viruses to, as the as like, foundational layer for biosecurity in the future, whether you're in a in a outbreak or not, just all the time. Uh, and so, if that type of infrastructure gets built, uh, here in the US and worldwide and then, you know, who knows about security could be 50/50 with the rest of the business. Um, but it does depend on whether we see that adoption of sort of like monitoring technology as the core 1 of the core pillars of uh a bio security Aid. That works a CDC that could stop the next Co

Yeah, just a little extra color on that because we haven't made that.

Jason Kelly: Yeah, just a little extra color on this. We had made that investment on sort of the Google Cloud side around remember I mentioned the two areas of AI, the sort of reasoning model-based AI and the bio model-based AI? It was originally made with a mindset of that bio-based AI was going to grow quickly. And I think what we've seen in the industry is it's being adopted, but it has not grown at anywhere near the rate that the reasoning models have. And so this is more a reflection of kind of how we see the deployment of and really training needs internal to Ginkgo in the future to much more smooth ramp over a longer period of time compared to if you were seeing massive investment across bio AI models. And that just hasn't been at the rate we were expecting back then.

That's one of the sort of Google cloud side around the revenue the two areas of AI that sort of breathing model based AI and the bio model based AI I was originally made.

Cool. So uh we got a question for Steve. Um so Steve uh you mentioned in October 2025 uh gko reset, the annual commitments and its contracted Google. Can you provide a little more color on that?

Jason Kelly: You're not buying a bunch of equipment or anything else. You can see if it works. Try it before you buy it, right? If it works, then we're very happy to install this in your lab so that your labs could have the same sort of just very latest scale in terms of automation and AI that we're running here at Ginkgo. I'm telling you, it is very, very exciting. It's working really well. I do think folks should come and try it. If you just want to come visit, please do just shoot me a note, and we're happy to do that and have you come by. All right. That's what I had today. Happy to answer questions about all that, but super excited. I think we've done the team, again, a big round of thanks for 2025.

A mindset of that bio based AI was going to grow quickly and I think what we've seen in the industry as it's being adopted but it has not grown at anywhere near the rate that the reasoning modeled out and so this is more a reflection of kind of how we see the deployment of <unk>.

Really like training needs internals that can go in the future is much more smooth ramp over a longer period of time comparator. If you were seeing massive investment across bio AI models and that just hasn't been at the rate. We were expecting back then so I'm very happy that this is putting up very nicely as I said, given the team and our great partners at Google I've worked with us on this really.

Jason Kelly: So I'm very happy that this was cleaned up very nicely by Steve and the team, and our great partners at Google have worked with us on this. So really happy about where it landed.

Uh, sure I uh, uh, when we, uh, were negotiating, uh, you know, the Google Cloud, uh, contract. Uh, obviously, we had a shortfall to solve for in. Q3 we talked about that, we reset, uh, going forward, uh, in my my view very favorable terms for Ginkgo. We were able to reduce our go forward commitment. Uh, by over 100 million and extended out the uh period uh by 2x. So going out over 6 years over the prior 3 years, from that standpoint, I think that puts us right where we want to be.

It'd be about where Atlanta.

Jason Kelly: It's a very difficult year bringing down our costs in a huge way while maintaining that sort of large margin of safety. That's what's allowing us to really now invest for growth in the future, particularly in this area of building out basically the automation and AI tooling for biosciences. I think that's going to be the niche that we grow into in the coming five to 10 years in a big way. Excited for your questions, and thanks again. Great. Thanks, Jason. As usual, I'll start with a question from the public and remind the analysts on the line that if they'd like to ask a question, to please raise their hands on Zoom, and I'll call on you and open up your line. Thanks, everyone. All right. Let's get started.

Alright, the next one for Jason.

Jason you mentioned future labs, new announcement of its nexgen AI scientists cosmo's can you say more about how your experience you can't go kind of informs your viewpoint on AI not just analyzing data, but also designing experiments et cetera.

Daniel Waid Marshall: All right. The next one's for Jason. Jason, you mentioned FutureHouse's new announcement of its next-gen AI scientist, Kosmos. Can you say more about how your experience at Ginkgo kind of informs your viewpoint on AI, not just analyzing data, but also designing experiments, et cetera?

Daniel Marshall: All right. The next one's for Jason. Jason, you mentioned FutureHouse's new announcement of its next-gen AI scientist, Kosmos. Can you say more about how your experience at Ginkgo kind of informs your viewpoint on AI, not just analyzing data, but also designing experiments, et cetera?

Yes.

It's worth checking this thing out I mean, it's a future allowed us now about Edison scientific side of it used to be a non-profit sort of.

Jason Kelly: Yeah. I mean, it's worth both checking this thing out. I mean. So FutureHouse's now called Edison Scientific. So it used to be a nonprofit sort of doing the OpenAI thing, becoming a for-profit. But what they're doing is they basically built up a model that's read all the scientific literature. You can kind of ask it a scientific question. It'll run for several hours and then kind of come back with either kind of hypotheses, predictions, learnings, or conclusions. And they were able to show this model making several, frankly, new scientific discoveries just from reading the literature. So that's already very exciting. And it's sort of this indicator that we're on this inevitable path where I think the logic of the models, their ability to just do complex reasoning, is going to work. It already works, frankly.

Okay, I think becoming a for profit.

So.

But what they're doing is they basically built up a model for that spread all the scientific literature, you can kind of ask it like a scientific question it'll run for several hours.

Yeah, just a little extra color on this. We had made that um, investment on the sort of Google Cloud side around. Remember I mentioned the 2 areas of AI, the sort of reasoning model. They say Ai and the bio model they say, I uh, it was originally made with a a, a mindset of that. Bio-based AI was going to grow quickly. And I think what we've seen in the industry is it's being adopted but it has not grown it anywhere near the rate that that the reasoning models have. Uh and so this is more a reflection of kind of how we see uh, the deployment of and like really like training needs internal to go in the future. It's a much more smooth ramp over a longer period of time. Compared to if you were seeing massive investment across

Then kind of come back with.

Yes.

I kind of hypotheses are predictions or learnings or conclusions and they were able to show this model, making several like frankly, new scientific discoveries just from reading the literature. So that's already very Excitingly I think it's sort of this indicator of that.

Jason Kelly: The first question was one that we got on Twitter from an account at David Zhu Tweets. This question is, can you comment on the extent of Ginkgo's exposure to US government business and how that has been impacted by the shutdown? Yeah, I can touch on that. Short answer on the shutdown has not had a big impact on us. Sort of the areas that grants and funding there keeps flowing during the shutdown. I would say in general, though, we have a good amount of exposure to the government overall. Between our biosecurity business and then things like the new BARDA awards, you'll see us announcing some recently, also ARPA-H awards. We've been doing very well, I guess I would say, with bringing in research partnerships with the government.

Models and that just hasn't been at the rate. We were expecting back then, so I'm very happy to. This is cleaned up very nicely by Steve and the team and and the our great Partners at Google have worked with us on this so really happy about where it landed.

Brian it's like inevitable path or I think like the logic of the models like their ability to just do complex reasoning is going to work it already works frankly.

All right, the next 1 for Jason. Uh Jason. You mentioned future Labs, new announcement of its nextg AI scientist Cosmos. Uh can you say more about how you're experiencing? Go kind of informs your viewpoint on AI. Not just analyzing data but also designing experiments. Etc.

I think the limitation will then move to what tools can you give access to these models.

Jason Kelly: I think the limitation will then move to what tools can you give access to these models? And the big one, we believe, is important in the realm of science, like I mentioned earlier, is hands in the lab. That's just it. It's hands in the lab. And so that type of a model with the ability to then say, "Well, what I actually believe I should do to really answer your question based on everything I read in the literature is run these 10 experiments or these 100 experiments, see what I learn, and then run another 100 and do that a few more times, and then I'll come back to you with the answer." I mean, that's what a PhD does. I mean, that's what I did for 5 years at MIT in my PhD. It's like, "Yep, I got this question I'm trying to answer.

And the Big one we believe is important in the realm of science like I mentioned earlier his hands in the lab.

It's a hands on lab and so that type of a model with the ability to then say well what I actually believe I should do to really answer your question based on everything I read in the literature is run. These experiments are these hundred experiments see what I learn and then write another hundred I do that a few more times and then I'll come back to you with the answer I mean thats.

Jason Kelly: Overall, I think hopefully we're even doing more in the future with some of this sort of cloud labs work and investments I hope to see from sort of government labs around automation. The shutdown doesn't impact us. Cool. All right. Our first question from Brendan from TD Securities, he writes, how do you see the broader development or rollout path ahead for the rack system over the next 18 months? Are there any additional validation steps or accounts to land that you expect could really unlock this opportunity and widen the commercial funnel for this over the near term? Yeah, I can touch on that too. First off, I think what's super exciting about the racks, and again, I tried to mention this, but there's sort of like walk-up automation, like companies like Hamilton and so on, where you're getting like a liquid handling deck.

That's what a Phd to us I mean, that's what I did for five years that I might it might be a stated like I got this question Ive tried to answer I'm going to run some experiments I'm going to look at the result of an interpret them and I'm going to go around that loop.

Yeah, I mean, so there it's worth checking it. This thing out, I mean uh, it's a future Labs. It's now called Edison scientific sort of a used to be a nonprofit sort of doing the OPA. Open AI thing becoming a for-profit. Uh, and so, um, but uh, what they're doing is they basically built up a model for that's read. All the read, all the scientific literature, you can kind of ask it like a scientific question, it'll run for several hours, uh, and then kind of come back with um, either like kind of hypotheses or predictions, or learnings or conclusions and they were able to show uh this model. Making several like frankly new scientific discoveries just from reading the literature. Uh, so that's already very exciting. Like I think it and it's sort of this indicator that

Jason Kelly: I'm going to run some experiments. I'm going to look at the results. I'm going to interpret them, and I'm going to go around that loop." And a lot of it is understanding what other people have done in the literature. I think that's what this model does from FutureHouse, Edison. And then the other half is kind of just not basic logic, but not the world's most complex analysis of what you're seeing in the lab. It's really your ability to conduct and design the experiments and then interpret the results. Just the craft of that is what keeps a lot of people out of science. And I think that can just be replaced now, I think, with programming and a robotic interface to the lab. And I don't know what that does.

And there are a lot of it is understanding what other people have done in the literature I think that's what the model does for future House Edison.

And then the other half is.

Kind of just not basic logic, but not the world's most complex analysis of what Youre seeing in the lab is really your ability to conduct and design of experiments and then interpreted results just the craft of that is why it keeps a lot of people out of science and I think.

Ron, it's like inevitable path or I think like the logic of the, of the models like their ability to just do complex. Reasoning is going to work. It already Works, frankly. Uh, I think the limitation will then move to what tools can you give access to these models?

And the big 1, we believe is important in the realm of science. Like I mentioned earlier is hands in the lab.

That can just be replace now I think with programming and robotic interface that allowed but I don't know what that does I mean that might blow open access to asking hard scientific questions NOI number of areas, which would be very exciting.

Jason Kelly: That is a very productized offering. Then there's integrated automation, which basically means there's a robotic arm in the middle of a bunch of equipment. The key there is one piece of equipment maybe does the liquid handling, but then you got to take your samples to the next piece of equipment. You saw on the video the plates moving on that track and getting delivered to six or seven different pieces of equipment in that single protocol. You might have protocols that interact with 15 different pieces of equipment. A human, by and large, is doing that in 99% of the labs that are out there. There is a small niche industry around integrated automation for things like high-throughput screening, where you put an arm in the middle of 15 pieces of equipment. That is built basically application-specific.

Jason Kelly: I mean, that might blow open access to asking hard scientific questions in a wide number of areas, which would be very exciting. So we'll see. But we want to provide the hands. That's our role in that. And we're very happy to have other places build those genius models.

That's just it, it's hands on the lab and so that type of a model with the ability to then say well what I actually believe I should do to really answer your question based on everything I read in the literature is run these 10 experiments or these 100 experiments. See what I learned and then run another 100 and do that a few more times and then I'll come back to you with the answer. I mean that's

So, let's say, but that where we want to provide the hands. That's our role in that and we're very happy to have other places build those genius models.

So the next question is kind of a follow up to that one actually and so the question is how do you see this AI plus robotics platform changing the R&D landscape sort of writ large.

Daniel Waid Marshall: So the next question is kind of a follow-up to that one, actually. And so the question is, "How do you see this AI plus robotics platform changing the R&D landscape sort of at large? And what is the initial feedback then from potential tools customers?

Daniel Marshall: So the next question is kind of a follow-up to that one, actually. And so the question is, "How do you see this AI plus robotics platform changing the R&D landscape sort of at large? And what is the initial feedback then from potential tools customers?

Experiments. I'm going to look at the results, I'm going to interpret them and I'm going to go around that Loop and you know, a lot of it is understanding what other people have done in the literature. I think that's what this model does from future house Edison.

And then the other half is.

And what is the initial feedback then from potential tools customers.

Yes, I think what like if you think commercially how does it can make a big difference right. So.

Jason Kelly: Yeah. So I think if you think commercially, how this can make a big difference, right? So the way, like, say, drug discovery, for example, right? You have an idea. You've read about and you've read the literature. You're an expert in this area. You have a hypothesis about a certain disease and how it works. And you're looking for an interesting drug target around your hypothesis. So you would sort of plan a line of experiments. You and a team of researchers would go conduct that over a period of six months or a year or a year and a half and then try to get to an answer on your hypothesis. I think what's exciting is that first, maybe those original hypotheses, maybe stuff like FutureHouse can just come up with those. Who cares?

The way I'd like take drug discovery for example, right like your you have an idea you've read about.

Jason Kelly: In other words, it's a design of a setup just for the one thing you want to do. Our carts are not like that. They're productized, they're coming off the line the same, and then we're just connecting them so that you have whatever equipment you want initially, and then actually able to expand that equipment over time into bigger and bigger setups. That's something you just cannot get with the traditional integrated automation. What I'm excited about on a rollout basis is continuing to scale up our manufacturing of these carts, bring the cost down, like turn that again into a more and more productized offering. On the sales side, it's basically getting folks to see this distinction between application-specific work cells that they buy today and general-purpose autonomous labs, like what I was showing you there with our frontier lab here in Boston.

Kind of just not basic logic, but not the world's most complex analysis of what you're seeing in the lab. It's really your ability to conduct and design the experiments and then interpret the results. Just the craft of that is why it keeps a lot of people out of Science. And I think

You read the literature you are an expert in this area you have a hypothesis about a certain disease and how it works and you're looking for an interesting drug target around your hypothesis. So you would sort of plan a line of experiments you and a team of researchers will go conduct that over a period of six months or a year year and a half and then try to get to an answer on your hypothesis I think.

That can just be replaced. Now I think with programming and robotic interface to the lab and I I don't know what that does. I mean that might blow open access to asking hard scientific questions in a in a wide number of areas, which would be very exciting.

So we'll see but that where we want to provide the hands that's uh our role in that and we're very happy to have other places build. Those uh, genius models.

What's exciting is that first maybe those original hypothesis, maybe start looking at your house can just come up with those who cares even if they can't you always have a longer list of hypotheses. Then you have the resources to go out and test in the lab based on the number of Sciences.

Jason Kelly: Even if they can't, you always have a longer list of hypotheses than you have the resources to go out and test in the lab based on the number of scientists you have, fundamentally. That is the limit. And so if instead you could basically spider these models out and say, "Hey, I want you to pursue my top 100 hypotheses instead of my top 3." And for each one, again, it's not just one experiment. It's got to do some lab work, interpret the results, and then plan some more lab work and keep going down that trail. You could be running that across 100 or 1,000 hypotheses in parallel as a single researcher, potentially, with access to robotics to go spider and then have it just come back and tell you when it gets interesting results. And that is just I mean, I don't even know.

So the next question is kind of a follow-up to that 1 actually. Um, and so the question is, how do you see this AI plus robotics platform changing the R&D landscape, sort of at large, uh and what is the initial feedback then from potential tools, customers.

Like fundamentally like that that is the limit and so if instead you could basically spider these modeled out and say Hey, I want you to pursue my top 100 hypotheses that are my top three and for each one again, it's not just one experiment, it's gotta do some lab work.

Jason Kelly: It's that adoption, this idea that automation isn't a thing you build for one application and then literally decommission and throw away three or four years later. That's what happens with these systems. Something that just keeps expanding over years and then ultimately replaces, hopefully, tens of thousands, hundreds of thousands of square feet of laboratory benches because we're just going to move off that system. We have to move away from the bench as the general-purpose laboratory infrastructure to the automated bench, to the autonomous lab. That's the transition that I want to drive. If you're looking for milestones, I want internal milestones at Ginkgo. One of the things I want to see is 50-plus scientists internally at Ginkgo ordering simultaneously from our automation system in a single day. That's the thing I think I can have happening in 2026.

Interpret the results and then plants are more lab work and keep going down that trail.

Yeah. So I think what, like, if you think commercial how this could make a big difference, right? So what the way like, take drug Discovery for example, right? Like you're, uh, you have an idea you've read about, um, again, you've read the literature, you're an expert in this area. You have a hypothesis about a certain disease and how it works. And you're looking for an interesting drug Target around your hypothesis. So you would sort of plan a line of experiments you

You could be running that across 100 or 1000 hypotheses in parallel as a single research and potentially with access to robotics to go Spider and then having just come back and tell you. When it gets interesting results and that is just I mean, I don't even know that that's a fundamentally different way to.

As we pursue our goal around and say how does this disease work.

Jason Kelly: That's a fundamentally different way to pursue a goal around, say, how does this disease work? Fundamentally, what is limited is reasoning and experimental hands. If we can take both those off the table, then I think all the cost just turns into reagent costs. It's literally the consumables you're going through, which is just crazy. That is not at all the cost right now. The costs now are 100% dominated by basically human time in all these areas, really, and laboratory space, just literally square footage. Both of those could compress massively with automation plus AI. It's really exciting.

It just it.

You and a team of researchers would go conduct that over a period of 6 months, or a year or a year and a half and then try to get to an answer on your hypothesis. I think what's exciting is that first, you know, maybe those original hypothesis, maybe stuff like future house can just come up with those who cares? Even if they can't, you always have a longer list of hypotheses. Then you have the resources to go out and test in the lab based on the number of scientists you have

Fundamentally what is limited his reasoning and experimental hands and if we can take both of those off the table.

I think all of the costs just turns into like reagent costs. It's like literally the consumables are going through which is just crazy like that is not at all the cost right now the costs in our 100% dominated by.

Jason Kelly: That's something that's never been seen with an automated lab previously. There's internal milestones. What I would love to see, we're starting to see this on the government side, but I'd also like to see it in the private sector, ideally with a large biopharma, a similar purpose of a very large system with an intent for a general-purpose autonomous lab. Those are kind of my two big things I'd love to see in 2026. Us demonstrating just what you can do with already having one of these kind of autonomous labs, and then a large biopharma leaning in and making a purchase for one. We'll still sell opposite the work cell. That's what we're selling today. I would love to see someone kind of lean in on the dream of the big general-purpose autonomous lab. I think it's the time for it.

Basically human time.

Like fundamentally like that. That is the limit. And so if instead you could basically spider these models out and say Hey I want you to pursue my top 100 hypotheses instead of my top 3 and for each 1 again, it's not just 1 experiment, it's got to do some lab work.

In all these areas really and like laboratory space like just like literally square footage and both of those could compress massively.

Interpret the results and then plan some more lab work and keep going down that trail.

With automation plus AI, it's really exciting.

Alright, that's all the questions that we have for Tonight.

Daniel Waid Marshall: All right. That's all the questions that we have for tonight. A reminder, you can always ask questions by emailing us at investors@ginkgobioworks.com. And also, as Jason said earlier, if you're interested in coming by and seeing some of this equipment, reach out, and we'll make it happen.

Daniel Marshall: All right. That's all the questions that we have for tonight. A reminder, you can always ask questions by emailing us at investors@ginkgobioworks.com. And also, as Jason said earlier, if you're interested in coming by and seeing some of this equipment, reach out, and we'll make it happen.

A reminder, you can always ask questions by E mailing us at investors that ginkgo fireworks Dot com and also as Jason said earlier, if you're interested in coming by and seeing some of this equipment reach out and we'll make it happen.

Great. Thanks, everybody I appreciate the question.

Jason Kelly: Great. Thanks, everybody. Appreciate the questions.

Daniel Waid Marshall: Good night.

Daniel Marshall: Good night.

You know, you could be running that across a 100 or a thousand hypotheses in parallel as a single researcher potentially with access to robotics to go spider and then have it just come back and tell you when it gets interesting results. And and that is just, I mean, I don't even know that that's a fundamentally different way to, to pursue a goal around say, how does this disease work? Um, it it just it, you know, fundamentally what is limited is reasoning and experimental hands and if we can take both those off the table,

Jason Kelly: We're going to prove it either way at Ginkgo. I think our customers will be sort of adopting that mindset soon too, is my view. I don't know, it's gotten so much easier to use automation with the AI stuff. I do think that's going to just bring the barrier down massively for this in the industry. Cool. All right. Brendan had one more question, which was, as you look at the current revenue mix between cell processing, as I said, cell engineering and biosecurity, and then consider your internal assumptions about the AI tools and racks rollouts, what do you see as the ideal revenue mix for Ginkgo by 2030? What has to happen to get there? 2030. Okay. Yeah. That's interesting. I mean, my dream by 2030 is we're starting to put a bunch of benches in bed.

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Then then I think all the cost just turns into like reagent costs it's like literally the consumables you're going through, which is just crazy. Like that is not at all the cost right now, the cost now are 100% dominated by

Uh, basically human time.

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In all these areas really and and like laboratory space like just like literally square footage and both of those could compress massively um with automation plus AI it's it's really exciting.

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All right. Uh, that's all the questions that we have for tonight. Um, a reminder you can always ask questions by emailing us at investors. It can go by works.com. Uh and also as Jason said earlier, if you're interested in coming by and seeing some of this equipment, uh, reach out and we'll make it happen. Great. Thanks everybody. Appreciate the questions. Good night.

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Jason Kelly: My expectation, like if I think about the balance between, we'll leave biosecurity, I'll come back to that in a second, but between the sort of tools business, in other words, like robotics, software on the robotics, reagents going into all that infrastructure, devices, that whole ecosystem of our tools business versus the services offerings that we offer on top of our setup, like Data Points and solutions, that tools versus services, I would say is like 80-20 in the tools side of the house in terms of our revenue mix in 2030. My hope would be we are largely taking over the general-purpose R&D infrastructure and being that provider of the tools into the whole industry. That should be dominant. When it comes to biosecurity, there it's very dependent on how things play out. It's like a very interesting time right now.

Jason Kelly: CDC is getting rebuilt. There's a great post from Matt McKnight, who heads up our biosecurity business, today. I encourage folks to read about sort of what a rebuilt CDC looks like. I think fundamentally, you need persistent, pervasive monitoring of viruses as a foundational layer for biosecurity in the future, whether you're in an outbreak or not, just all the time. If that type of infrastructure gets built here in the US and worldwide, then who knows? Biosecurity could be 50-50 with the rest of the business. It does depend on whether we see that adoption of sort of monitoring technology as one of the core pillars of a biosecurity that works, a CDC that could stop the next COVID. Cool. We got a question for Steve. Steve, you mentioned in October 2025, Ginkgo reset the annual commitments and its contract to Google.

Jason Kelly: Can you provide a little more color on that? Sure. When we were negotiating the Google Cloud contract, obviously, we had a shortfall to solve for in Q3. We talked about that. We reset going forward. In my view, very favorable terms for Ginkgo. We were able to reduce our go forward commitment by over $100 million and extended out the period by 2x, so going out over six years over the prior three years. From that standpoint, I think that puts us right where we want to be. Yeah, just a little extra color on this. We had made that investment on the Google Cloud side around, remember I mentioned the two areas of AI, the sort of reasoning model-based AI and the bio model-based AI? It was originally made with a mindset of that bio-based AI was going to grow quickly.

Jason Kelly: I think what we've seen in the industry is it's being adopted, but it has not grown at anywhere near the rate that the reasoning models have. This is more a reflection of kind of how we see the deployment of really training needs internal to Ginkgo in the future to a much more smooth ramp over a longer period of time compared to if you were seeing massive investment across bio AI models. That just hasn't been at the rate we were expecting back then. I'm very happy that this was cleaned up very nicely by Steve and the team, and our great partners at Google have worked with us on this. I'm really happy about where it landed. All right. The next one's for Jason. Jason, you mentioned FutureLab's new announcement of its next-gen AI scientist, Cosmos.

Jason Kelly: Can you say more about how your experience at Ginkgo kind of informs your viewpoint on AI, not just analyzing data, but also designing experiments, etc.? Yeah. I mean, it's worth checking this thing out. I mean, it's a FutureLab. It's now called Edison Scientific. It used to be a nonprofit sort of doing the OpenAI thing, becoming a for-profit. What they're doing is they basically built up a model that's read all the scientific literature. You can kind of ask it like a scientific question. It'll run for several hours and then kind of come back with either kind of hypotheses, or predictions, or learnings, or conclusions. They were able to show this model making several, frankly, new scientific discoveries just from reading the literature. That's already very exciting. It's sort of this indicator that.

Jason Kelly: We're on this inevitable path where I think the logic of the models, like their ability to just do complex reasoning, is going to work. It already works, frankly. I think the limitation will then move to what tools can you give access to these models? The big one we believe is important in the realm of science, like I mentioned earlier, is hands in the lab. That's just it. It's hands in the lab. That type of a model with the ability to then say, well, what I actually believe I should do to really answer your question based on everything I read in the literature is run these 10 experiments or these 100 experiments, see what I learn, and then run another 100 and do that a few more times, and then I'll come back to you with the answer. I mean, that's.

Jason Kelly: What a PhD does. I mean, that's what I did for five years at MIT in my PhD. It's like, yep, I got this question I'm trying to answer. I'm going to run some experiments, I'm going to look at the results, I'm going to interpret them, and I'm going to go around that loop. A lot of it is understanding what other people have done in the literature. I think that's what this model does from FutureHealth, Edison. The other half is kind of just not basic logic, but not the world's most complex analysis of what you're seeing in the lab. It's really your ability to conduct and design the experiments and then interpret the results. Just the craft of that is what keeps a lot of people out of science. I think.

Jason Kelly: That can just be replaced now, I think, with programming and a robotic interface to the lab. I don't know what that does. I mean, that might blow open access to asking hard scientific questions in a wide number of areas, which would be very exciting. We'll see. Where we want to provide the hands, that's our role in that. We're very happy to have other places build those genius models. The next question is kind of a follow-up to that one, actually. The question is, how do you see this AI plus robotics platform changing the R&D landscape at large? What has the initial feedback been from potential tools customers? Yeah. I think if you think commercially, how this can make a big difference, right? The way, like say, drug discovery, for example, right?

Jason Kelly: You have an idea. You've read about. You've read the literature. You're an expert in this area. You have a hypothesis about a certain disease and how it works, and you're looking for an interesting drug target around your hypothesis. You would sort of plan a line of experiments. You and a team of researchers would go conduct that over a period of six months, a year, or a year and a half, and then try to get to an answer on your hypothesis. I think what's exciting is that for maybe those original hypotheses, maybe stuff like FutureHealth can just come up with those. Who cares? Even if they can't, you always have a longer list of hypotheses than you have the resources to go out and test in the lab based on the number of scientists you have. Fundamentally, that is the limit.

Jason Kelly: If instead you could basically spider these models out and say, hey, I want you to pursue my top 100 hypotheses instead of my top three, and for each one, again, it's not just one experiment. It's got to do some lab work, interpret the results, and then plan some more lab work and keep going down that trail. You could be running that across 100 or 1,000 hypotheses in parallel as a single researcher, potentially with access to robotics, to go spider and then have it just come back and tell you when it gets interesting results. That is just, I mean, I don't even know. That's a fundamentally different way to pursue a goal around, say, how does this disease work? Fundamentally, what is limited is reasoning and experimental hands.

Jason Kelly: If we can take both those off the table, then I think all the cost just turns into reagent costs. It's literally the consumables you're going through, which is just crazy. That is not at all the cost right now. The costs now are 100% dominated by basically human time in all these areas, really, and laboratory space, just literally square footage. Both of those could compress massively with automation plus AI. It's really exciting. All right. That's all the questions that we have for tonight. A reminder, you can always ask questions by emailing us at investors@ginkgobioworks.com. Also, as Jason said earlier, if you're interested in coming by and seeing some of this equipment, reach out, and we'll make it happen. Great. Thanks, everybody. Appreciate the questions. Big dreams start small. At GoDaddy, we help you take that first step and every step after.

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