Q1 2026 Ginkgo Bioworks Holdings Inc Earnings and Business Update Call

May 7, 2026

Speaker #1: And CEO and Steve Cohen, our CFO. Thanks as always for joining us. We're looking forward to updating you on our progress. As a reminder, during the presentation today, we will be making forward-looking statements which involve risks and uncertainties.

Speaker #1: 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 provide insight into how and why we see autonomous labs like Nebula, our autonomous lab, replacing the lab bench, which is where nearly all of biological science is done today.

Speaker #1: As usual, we'll end with a Q&A session, and I'll take questions from analysts, investors, and the public. You can submit those questions to us in advance via X, hashtag Ginkgo Results, or email investors@ginkgobioworks.com.

Speaker #1: All right, over to you, Jason.

Speaker #2: Thanks, Daniel. We always start with this. Ginkgo's mission is to make biology easier to engineer, and I mentioned this at the last earnings call, but in 2026, our focus will be on investing to win the category of autonomous labs.

Speaker #2: And I'm really excited, even since we just spoke a few months ago, this category has really been growing in attention, new companies in Silicon Valley pursuing this, a lot of interest from the AI frontier labs about the application of AI models in science via autonomous labs, government talking more about this.

Speaker #2: So I do think we're onto the right track with this focus for the company. The two big ways I'm going to be pursuing that goal in 2026.

Speaker #2: The first is to take our services in solutions, in data points, in cloud lab, and run them on top of our autonomous lab here in Boston that we call Nebula.

Speaker #2: That's a chance to prove out the capabilities of our system with real-world activities. And then the second big area of activity will be getting early adopters of autonomous labs out in the world to buy our systems, like we've done already with Pacific Northwest National Labs that I talked about last time.

Speaker #2: So excited to pursue both of those, and you're going to hear more about it from me and the strategic section. We also in the last quarter were able to close on a deal I talked about extensively last time, which is the spin-off of our biosecurity unit.

Speaker #2: Into a new company called Perimeter, I want to say congratulations to the team at biosecurity at Ginkgo and pulling that off. $60 million and a lot of great new investors coming into that focus really firmly in the area of defense tech and building sort of a biosecurity prime.

Speaker #2: Ginkgo is a shareholder in that company. We're super excited to see it succeed. And I think this is a really nice, as I talked about last time, opportunity both for Ginkgo to keep our focus on the autonomous labs and for the team at Perimeter to grow under their own brand with a new set of defense tech-focused investors.

Speaker #2: Our focus over the last couple of years was very much on getting these numbers where they are today, bringing down our cash burn in the company.

Speaker #2: We guided towards this and Steve will touch on that in his section. But again, happy to have a very strong cash position, $373 million with no bank debt, as of Q1 2026.

Speaker #2: And so you'll hear a little bit more from Steve on this. But this sets us up very nicely where well capitalized to pursue this area of autonomous labs.

Speaker #2: We have these base service businesses to build on top of and the lead in developing the technology. And you put all that together, and I think we're by far the best bet in this sector.

I can't go as a shareholder in that, see, it succeed. I think this is a really nice talked about last time. The opportunity both for GTO to keep your focus on this labs. And for the Team at Perimeter to grow under their own brand, with a new set of Defense Tech, focused investors,

I can go as a shareholder in that, see, it succeed. I think this is a really nice talk about last night, both for GTO to keep your focus on this labs. And for the Team at Perimeter to grow under their own brand, with a new set of Defense Tech, focused investors,

Biocare holder in that company. We're super excited to see it succeed. And I think this is a really nice. As I talked about last time. I'm opportunity both for genko to keep our focus on the autonomous ads. And for the Team at Perimeter have to grow under their own brand, with a new set of Defense Tech focused investors

Security holder in that company. We're super excited to see it succeed and I think it was a really nice as I talked about last time opportunity, both for genko to keep our focus on the autonomous ads and for the Team at Perimeter have to grow under their own brand, with a new set of Defense Tech focused investors

Speaker #2: All right, I'm going to pass it on to Steve to dig into the financials.

Uh, our our focus of last year's was very much on getting these numbers that they are today.

Uh, our our Focus over the last years was very much on getting these numbers where they are today.

Speaker #1: Thanks, Jason. Before I walk through our financials, I want to take a moment to frame an important change in how we are presenting our results beginning in Q1 2026.

In the company, we added towards this, as people will touch on that section and happy to have a very strong cast person, 373 million dollars.

In the company, we added towards this and people will touch on that intersection and happy to have a very strong cast person, 373 million dollars.

Speaker #1: As we announced in February, we entered into a definitive agreement to sell our biosecurity business, which was previously reported as a separate segment. Further, as Jason noted, we closed that transaction on April 3.

Speaker #1: The biosecurity transferred assets met the criteria under US accounting to be classified as health for sale and the financial results reported as discontinued operations as of March 31, 2026.

Q1 so on this, but this set very nicely. We're well capitalized through this area but we have these services to build on top of and the lead in developing the technology and you put all that together and I think we're by far the best uh, best best best sector.

Of q1. So you're a little bit more on this, but this is a very nicely. We're well, capitalized through this area of a top. We have these services to build on top of and the lead in developing the technology. But all that together, and I think we're by far the best uh, best best best sector.

Uh, our our focus is the last couple years was very much on, uh, getting these numbers where they are today on our, our cash burn the company. Uh, we guided Georgia and people will touch on that and section but again happy to have a very strong passion 373 million dollars, we Bank debts as of 2126 and so you'll hear a little bit on this, but this is a very nicely. We're well, capitalized through this area of, on top of glass, we have these Base Service build on top of in the lead. In developing the technology and you put all that together. And I think we're by far the best, uh, step

Uh, our our focus is the last couple years was very much on, uh, getting these numbers where they are today on our, our cash burn the company. Uh we guided towards this and people will touch on that infection but again happy to have a very strong passion 373 billion debts, as of 2126 and so you'll hear a little bit on this but this is a very nicely. We're well capitalized through this area of on top of the last. We have this Base Service build on top of in the lead in developing this technology and you can all that together and I make sure bye.

All right, I'm going to pass you on to Steve, but they can.

all right, I'm going to pass you on to Steve but they can

All right, and pass on to receive credentials.

All right, and pass it on to receive the credentials.

thanks Jason.

Thanks Jason.

thanks Jason.

Speaker #1: This is the first quarter in which biosecurity is reflected as discontinued operations within our financial statements. In accordance with the accounting rules, we have and will retrospectively recast all prior periods presented to conform to this presentation.

Before I walk you off, I want to take a moment to frame an important change and how we are presenting results. Beginning in 2121,

Before I walk you up, I want to take a moment to frame an important change and how we are presenting results. Beginning in 2124,

Before I walk you through our financials, I want to take a moment to frame an opportunity change and how we representing our results beginning in q1 2021.

Before I walk you through our financials, I want to take a moment to frame an appointment change and how we are presenting our results. Beginning in Q1 '20,

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Speaker #1: That means the revenue, operating expenses, and cash flows previously attributed to the biosecurity business are removed from each line item of our continuing operations and cash flows as the prior period information is presented.

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The biosecurity transferred assets, domestically carried to the US.

The biosecurity assets met the criteria on the U.S. accounting.

The bio security assets met the criteria on the US accounting.

Speaker #1: Including for Q1 of last year. The form of biosecurity results are now reported as a single net line loss from discontinued operations. Below loss from continuing operations.

to be classified as discontinued operations, as of month, as far as 2026

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Speaker #1: To be clear, all of the financial commentary I will provide today relates exclusively to continuing operations. We will not be discussing the biosecurity business further in our prepared remarks.

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Recap all prior periods performed to the presentation.

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That means the revenue. Operating cash flows previously attributed to business.

Speaker #1: On April 7, 2026, for your information, we filed a current report on Form 8K that includes pro forma financial information for fiscal years 2023, 2024, and 2025 on a continuing operations basis.

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Speaker #1: Following the biosecurity divestiture, we now operate as a single segment. So with that, I'll now discuss our Q1 results. Revenue was $19 million in the first quarter of 2026, down 49% compared to the first quarter of 2025.

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Speaker #1: As previously disclosed, revenue in the first quarter of 2025 included $7.5 million in non-cash revenue relating to the mutual termination of the Biomedit agreement.

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we filed and form 18.

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We filed a current on Form 83 that includes pro forma financial information for fiscal years.

Speaker #1: Excluding this, revenue in the first quarter of 2026 was down 37% from the prior year period. 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.

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Speaker #1: 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 adjusted EBITDA and gap net loss from continuing operations can be found in the appendix.

Revenue was 19 million, this quarter of 2020 around 49. We had the first quarter of 2025

Revenue, 19 million dollars, first quarter, appointment down 49 compared to the first quarter of 2025.

Revenue was 19 million this quarter of 2020 around 49% of the first quarter of 2025.

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Speaker #1: In the first quarter of 2026, R&D expense decreased 38% from $49 million in the first quarter of 2025 to $30 million in the first quarter of 2026.

excluding from the

Speaker #1: G&A expense decreased 35% from $20 million in the first quarter of 2025 to $13 million in the first quarter of 2026. These decreases were all driven by our restructuring efforts.

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Speaker #1: Net loss from continuing operations was $36 million in the first quarter of 2026 compared to a loss of $83 million in the prior year period.

Speaker #1: The reduction in loss year over year was due to our restructuring efforts. Moving further down the page, you'll note that adjusted EBITDA in the first quarter of 2026 was negative $42 million which was down from negative $44 million in the first quarter of 2025.

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Speaker #1: Since we are now only operating in a single segment, we only present a single measure of adjusted EBITDA. And it is important to note that adjusted EBITDA includes the carrying cost of excess lease space, which you can see was $16 million in the first quarter of 2026.

Steve: Continuing operations was $76 million in Q1 2026, compared to a loss of $83 million in the prior year period. The reduction in loss year-over-year was due to our restructuring efforts. Moving further down the page, you'll note that adjusted EBITDA in Q1 2026 was -$42 million, which was down from -$44 million in Q1 2025. Since we are now only operating in a single segment, we only present a single measure of adjusted EBITDA. It is important to note that adjusted EBITDA includes the carrying cost of excess lease space, which you can see was $16 million in Q1 2026. Previously, this cost would not have been included in the former presentation of segment adjusted EBITDA.

Steve Coen: Continuing operations was $76 million in Q1 2026, compared to a loss of $83 million in the prior year period. The reduction in loss year-over-year was due to our restructuring efforts. Moving further down the page, you'll note that adjusted EBITDA in Q1 2026 was -$42 million, which was down from -$44 million in Q1 2025. Since we are now only operating in a single segment, we only present a single measure of adjusted EBITDA. It is important to note that adjusted EBITDA includes the carrying cost of excess lease space, which you can see was $16 million in Q1 2026. Previously, this cost would not have been included in the former presentation of segment adjusted EBITDA.

Continuing operations was 30 was 76 million in the first quarter of 2026 compared to a loss of 83 million in the prior year period.

The reduction in Los year-over-year was due to our restructuring efforts.

Speaker #1: Previously, this cost would not have been included in the former presentation of segment adjusted EBITDA. This cost represents the base rent and other. Charges relating to lease space, which we are not occupying, net of sublease income.

Moving further down the page, you'll note that adjusted Ava in the first quarter of 2026 was -42 million which was down from negative 44 million in the first quarter of 2025.

Speaker #1: This is a cash operating cost that is not related to driving revenue right now and can be potentially subleasing. And finally, cash burn in the first quarter of 2026 was $48 million down from $58 million in the first quarter of 2025, a 17% decrease.

Since we are now only operating in a single segment.

We only present a single measure of adjusted evidence and it is important to note that adjusted evil to includes the carrying cost of excess lease space, which you can see was 16 million dollars in the first quarter of 2026.

Speaker #1: As previously reported in October 2025, we amended and reset the annual commitments with Google Cloud for $14 million. Resetting the commitment reduced our future minimum commitments by more than $100 million, compared with the original terms, and extended the commitment term from three to six years.

Steve: This cost represents the base rent and other charges relating to lease 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 be potentially mitigated through subleasing. Finally, cash burn in Q1 2026 was $48 million, down from $58 million in Q1 2025, a 17% decrease. As previously reported, in October 2025, we amended and reset the annual commitments with Google Cloud for $14 million. Resetting the commitment reduced our future minimum commitments by more than $100 million compared with the original terms, and extended the commitment term from 3 to 6 years. We paid this $14 million in Q1 2026, which is reflected in our cash burn for the quarter.

Steve Coen: This cost represents the base rent and other charges relating to lease 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 be potentially mitigated through subleasing. Finally, cash burn in Q1 2026 was $48 million, down from $58 million in Q1 2025, a 17% decrease. As previously reported, in October 2025, we amended and reset the annual commitments with Google Cloud for $14 million. Resetting the commitment reduced our future minimum commitments by more than $100 million compared with the original terms, and extended the commitment term from three to six years. We paid this $14 million in Q1 2026, which is reflected in our cash burn for the quarter.

Previously this cost would not have been included in the form of presentation of segments, adjusted e with them.

This cost represents the base rent and other charges relating to lease space which we are not occupying. Net of sublease income.

Speaker #1: We paid this $14 million in Q1 of 2026, which is reflected in our cash burn for the quarter. Excluding the payment to Google Cloud, cash burn reflects a significant decrease in the first quarter of 2026 compared to the first quarter of 2025, which was a direct result of the restructuring.

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

And finally cash burn. In the first quarter of 2026 was 48 million down from 58 million. And the first quarter of 2025, a 17% decrease,

Speaker #1: Now, turning to guidance. As we discussed in February, 2026 is about continuing to be cost-efficient while investing in our AI robotics and software to bring autonomous labs to our bioscience customers.

As previously reported in October 2025, we amended and reset the annual commitments with Google Cloud for $14 million.

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

Speaker #1: Including the build-out of our frontier autonomous lab in Boston. We have turned the page on our pure focus on restructuring actions to focus this year not only on cost efficiency, but on investing in what we see as our opportunities while continuing to provide our customers the advanced services that they've come to expect.

Steve: Excluding the payment to Google Cloud, cash burn reflects a significant decrease in Q1 2026 compared to Q1 2025, which was a direct result of the restructuring. Now, turning to guidance. As we discussed in February, 2026 is about continuing to be cost efficient while investing in our AI robotics and software to bring autonomous labs to our bioscience customers, including the build-out of our frontier autonomous lab in Boston. We have turned the page on our pure focus on restructuring actions to focus this year not only on cost efficiency, but on investing in what we see as our opportunities while continuing to provide our customers the advanced services that they have come to expect. For these reasons, we believe cash burn best reflects our continuing services and tools and further investments in autonomous labs.

Steve Coen: Excluding the payment to Google Cloud, cash burn reflects a significant decrease in Q1 2026 compared to Q1 2025, which was a direct result of the restructuring. Now, turning to guidance. As we discussed in February, 2026 is about continuing to be cost efficient while investing in our AI robotics and software to bring autonomous labs to our bioscience customers, including the build-out of our frontier autonomous lab in Boston. We have turned the page on our pure focus on restructuring actions to focus this year not only on cost efficiency, but on investing in what we see as our opportunities while continuing to provide our customers the advanced services that they have come to expect. For these reasons, we believe cash burn best reflects our continuing services and tools and further investments in autonomous labs.

We paid the 14 million in q1 of 2026, which is reflected in our cash burn for the quarter.

Excluding the payment to Google Cloud, cash burn reflects a significant decrease in the first quarter of 2026 compared to the first quarter of 2025, which was a direct result of the restructuring.

Now turning to guidance.

Speaker #1: For these reasons, we believe cash burn best reflects our continuing services and tools and further investments in autonomous labs. In terms of outlook for the full year, we are reaffirming our overall cash burn guidance for 2026, totaling $125 to $150 million.

As we discussed in February 2026, is about continuing to be cost, efficient while investing in our AI Robotics and software to bring autonomous labs.

To a bioscience customers including the buildout of our Frontier autonomous lab in Boston.

Speaker #1: This range reflects a firm balance amongst cost efficiency continuing services and tools and further investments we are making. In conclusion, we are pleased with our continued improvements in cash burn efficiency and our business pursuits for 2026.

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

Speaker #1: And with that, I'll hand it back over to you, Jason.

Steve: In terms of outlook for the full year, we are reaffirming our overall cash burn guidance for 2026, totaling $125 to 150 million. This range reflects a firm balance among cost efficiency, continuing services and tools, and further investments we are making. In conclusion, we are pleased with our continued improvements in cash burn efficiency and our business pursuits for 2026. With that, I'll hand it back over to you, Jason.

Steve Coen: In terms of outlook for the full year, we are reaffirming our overall cash burn guidance for 2026, totaling $125 to 150 million. This range reflects a firm balance among cost efficiency, continuing services and tools, and further investments we are making. In conclusion, we are pleased with our continued improvements in cash burn efficiency and our business pursuits for 2026. With that, I'll hand it back over to you, Jason.

For these reasons, we believe cash Burns. Best reflects, our continuing services and tools, and further Investments, and autonomous labs,

Speaker #2: Thanks, Steve. So I'm going to get diving on the strategic section. I'm excited to go into this today. Our mission is to make biology easier to engineer.

Speaker #2: And the way we're really aiming to solve that problem, we believe the bottleneck fundamentally is the laboratory work associated with bioengineering and so I'm going to dig deep today and talk about why autonomous labs will be replacing the lab bench, I want to highlight some of what we're doing with Nebula, our system, because we have some news this month in terms of expanding that system.

In terms of outlook for the full year, we are reaffirming our overall cash burn guidance for 2026 toting, 125 to 150 million.

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

In conclusion, we are pleased with our continued improvements in cash, burn efficiency, and our business pursuits for 2026.

Speaker #2: And then finally, the services that we've put on top of Nebula are Cloud Lab, data points, and solutions. These are sort of like we call it like our Starlink, right?

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

Jason Kelly: Thanks, Steve. I'm gonna get dive in on the strategic section. I'm excited to go into this today. Our mission is to make biology easier to engineer, the way we're really aiming to solve that problem, we believe the bottleneck fundamentally is the laboratory work associated with bioengineering. I'm gonna dig deep today and talk about why autonomous labs will be replacing the lab bench. I wanna highlight some of what we're doing with Nebula, our system, 'cause we have some news this month in terms of expanding that system. Finally, the services that we've put on top of Nebula, our cloud lab Datapoints and Solutions. These are sort of like, we call it like our Starlink, right?

Speaker #2: If you think about SpaceX, 70% of the launches last year were actually Starlink, their own internal product. In the coming year, the ability for us to scale up an autonomous lab and showcase that you can make money on services without having people in the middle of a lab doing those laboratory services, I think is a real highlight and will help drive sales of our systems into the world.

Speaker #2: So I'm going to talk about all three and let's dive in. OK. So I gave this analogy. I'm going to do it again because I think this is for new folks listening on the call.

Speaker #2: It's worth understanding what we say when we say autonomous lab as distinct from traditional lab automation. So I'm going to give an analogy from the transportation industry.

Jason Kelly: You think about SpaceX, 70% of the launches last year were actually Starlink, their own internal product. In the coming year, the ability for us to scale up an autonomous lab and showcase that, you can make money on services without having people in the middle of a lab doing those laboratory services, I think is a real highlight and will help drive sales of our systems into the world. I'm gonna talk about all three, let's dive in. Okay. I gave this analogy, I'm gonna do it again 'cause I think this is, for new folks listening on the call, it's worth understanding what we say when we say autonomous lab as distinct from traditional lab automation. I'm gonna give an analogy from the transportation industry.

Speaker #2: On the Y-axis here, we have the amount of automation for a certain type of transport. And on the X-axis, the request flexibility. In other words, the user's asking the transportation system to do something different or not.

Thanks Steve. So, I'm gonna get dive in on the Strategic action. I'm excited to go into this today. Uh, our mission is to make biology easier to engineer. Uh, and the way we're really aiming, uh, to solve that problem, we believe the bottleneck fundamentally is the laboratory work associated with bioengineering. And so, I'm going to dig deep today and talk about why autonomous Labs will be replacing the lab bench. I want to highlight, uh, some of what we're doing with nebula, our system because we have some news this month in terms of expanding that system. And then finally, the services that we've put on top of nebulae are Cloud lab data points, and solutions. Uh, these are sort of like we call it like our starlink, right? If you think about SpaceX 70% of the launches last year, were actually starlink their own internal products. Uh, in the coming year, the ability for us to scale up an autonomous lab and showcase that. Uh, you can make

Speaker #2: And so for a low request flexibility and a high level of automation, that's your subway, right? It's the red line here in Boston. You sit down in the subway and it takes you away.

Money on services without having people uh in the middle of the lab doing those laboratory services. I think is a real highlight and will help Drive sales of our systems into the world. So I'm going to talk about all 3 and let's Dive In.

Speaker #2: You don't have to do anything. It is high level of automation, totally automated transport, but it is very inflexible. You have to want to go to one of the stops on the red line.

Jason Kelly: On the Y-axis here, we have the amount of automation for a certain type of transport. On the X-axis, the request flexibility. In other words, the user's asking the transportation system to do something different, or not. For a low request flexibility and a high level of automation, that's your subway, right? It's the Red Line here in Boston. You sit down in the subway, and it takes you away. You don't have to do anything. It is high level of automation, totally automated transport, but it is very inflexible. You have to wanna go to one of the stops on the Red Line. Low amount of automation, high amount of flexibility, that's a car, right?

Speaker #2: Low amount of automation, high amount of flexibility. That's a car, right? You get your hands on the wheel, foot on the pedals, and it'll take it right, take you right to your house or to the grocery store or anywhere you want to go.

Speaker #2: And that's roughly what the transportation system has looked like for the last 100 years. Unless we go to the next slide, you've been to California in the last four or five years or now LA or Austin or soon in Boston.

Speaker #2: And you sat in the backseat of a Waymo and it is amazing. It is like sitting on a subway. You don't have to do anything, but it'll take you right to your house.

Speaker #2: It has the flexibility of a car. And so it's those two things together that sort of flexibility plus a high level of automation that mean we actually give it a new name.

Jason Kelly: You get your hands on the wheel, your foot on the pedals, and it'll take you right to your house or to the grocery store, anywhere you wanna go. That's roughly what the transportation system has looked like for the last 100 years. Unless, go to the next slide, you've been to California in the last four or five years, or now LA or Austin or soon in Boston, and you sat in the back seat of a Waymo, and it is amazing. It is like sitting on a subway. You don't have to do anything, but it'll take you right to your house. It has the flexibility of a car. It's those two things together that sort of flexibility plus a high level of automation that mean we actually give it a new name.

Speaker #2: We don't call it an automated car. We call it an autonomous car because up till now you've needed a human being with our brain in the loop in order to manage that amount of flexibility into the system.

And the subway and it takes you away, you don't have to do anything, it is high level of automation, totally automated transport, but it is very inflexible. You have to want to go to 1 of the stops on the red line. Uh, low amount of automation, high amount of flexibility, that's a car, right? You get your hands on the wheel. You put on the pedals and it'll take it right, take it right to your house or to the grocery store or anywhere you want to go. And that's roughly what the transportation system. Has looked like for the last 100 years.

Speaker #2: All right. So if you look at the next slide, you'll see the miles traveled by cars and trucks versus subways and trains in the United States.

Speaker #2: It's more than 99% in cars and trucks. And that's not because we don't know about subways. It's that we need that flexibility to do our day-to-day lives.

Jason Kelly: We don't call it an automated car, we call it an autonomous car. Because up till now, you've needed a human being with our brain in the loop in order to manage that amount of flexibility into the system. All right. If you look at the next slide, you'll see the miles traveled by cars and trucks versus subways and trains in the United States. It's more than 99% in cars and trucks. That's not because we don't know about subways. It's that we need that flexibility to do our day-to-day lives, it's required for the transportation that humans need. All right, now let's go into the lab bench and into the lab. Low amount of flexibility, high amount of automation. We have actually automation in the lab. It's called a work cell.

Speaker #2: It's required for the transportation that humans need. All right. So now let's go into the lab bench. And into the lab. So low amount of flexibility, high amount of automation.

Speaker #2: We have actually automation in the lab. It's called a work cell. That's a 3D schematic of a work cell we have here at Ginkgo companies like Highres and Thermo and Biosera make these.

Speaker #2: And they're like a subway, right? They're great. They're fully automated. They'll do an experiment without a scientist in the loop, but it better be the experiment that you ordered yesterday.

Speaker #2: It's not going to do a new experiment for you today. Low amount of automation, high amount of flexibility. We have those two in the lab.

Unless you go to the next slide, uh you've been to California in the last 4 or 5 years or now LA or Austin or soon in Boston. And you sat in the backseat of a whim Mo and it is amazing. It is like sitting on a subway, you don't have to do anything but it'll take you right to your house. It has the flexibility of a car and so it's those 2 things together, that, that sort of flexibility plus a high level of automation. That mean, we actually give it a new name. We don't call it an automated car. We call it an autonomous car because up, until now you've needed a human being with our brain, in the loop, in order to manage that amount of flexibility and uh, uh, into the system. All right, so if you look at the next slide, you'll see the miles traveled, uh, buy cars and trucks versus Subways and trains the United States. Uh it's more than 99% and cars and trucks and and that's not because we don't know about Subways. It's that we need that flexibility to do our day-to-day lives that it's required for the transportation that humans need. All right. So now let's go and

Speaker #2: It's called the lab bench. And you as a scientist are basically the human glue connecting all of these different devices in the lab together to do whatever protocol you want to do.

Jason Kelly: That's a 3D schematic of a work cell we have here at Ginkgo. Companies like HiRes and Thermo and BioCero make these. They're like a subway, right? They're great. They're fully automated. They'll do an experiment without a scientist in the loop, it better be the experiment that you ordered yesterday. It's not gonna do a new experiment for you today. Low amount of automation, high amount of flexibility. We have those two in the lab. It's called the lab bench. You as a scientist are basically the human glue connecting all of these different devices in the lab together to do whatever protocol you wanna do. Again, here's the kicker.

Speaker #2: And so again, here's the kicker. If you look at research budgets between laboratory work cells, which are used in things in pharma companies like hydropoot screening, or combinatorial chemistry, or things like that, versus at the lab bench, it's about 95% plus at the bench.

Speaker #2: And again, it's not like we don't know about work cells. It's that scientists need flexibility to explore all the different hypotheses they have for discovering a new drug or developing a new crop trait or whatever type of biotechnology they're doing.

Into the lab bench and into the lab. Uh, so, low amount of flexibility, high amount of automation. We have actually automation in the lab; it's called a work cell. That's a 3D schematic of a work cell we have here at Ginkgo. Companies like HiRes and Thermo and Biosero make these, uh, and they're—they're like a Subway, right? Uh, they're great, they're fully automated. They'll do an experiment without a scientist in the loop, but it better be the experiment that you ordered yesterday. It's not going to do a new experiment for you today.

Low amount of automation, high amount of flexibility. We have those 2, uh, in the lab. It's called the lab bench and you as a scientist or basically the human glue connecting, all of these different devices in the lab together to do whatever protocol you want to do.

Speaker #2: So this is what we're trying to build at Ginkgo. We're trying to get up to that top right corner and make a Waymo. We're trying to make an autonomous lab that has the flexibility of the bench.

Jason Kelly: If you look at research budgets between laboratory work cells, which are used in things in pharma companies like high-throughput screening or combinatorial chemistry or things like that, versus at the lab bench, it's about 95% plus at the bench. Again, it's not like we don't know about work cells. It's that scientists need flexibility to explore all the different hypotheses they have for discovering a new drug or developing a new crop trait or whatever type of biotechnology they're doing. This is what we're trying to build at Ginkgo. We're trying to get up to that top right corner and make a Waymo. We're trying to make an autonomous lab that has the flexibility of the bench, so scientists can order whatever experiment they want, but the automation of a work cell.

Speaker #2: So scientists can order whatever experiment they want. But the automation of a work cell, in other words, they don't have to be there to do each and every step and move the samples among the different equipment and program the equipment.

Speaker #2: They can just hit go and have that protocol run end to end for them via the automation. But whatever they want to order that day, that's the target.

Speaker #2: And look, if you go to the next slide, the value prop here I think is very clear for getting rid of the lab bench.

Speaker #2: Massive overhead cost savings. We've heard a lot about overhead costs at academic research labs and things like that. That's really paying for ultimately millions of square feet of laboratory space, 50 million square feet of laboratory space just in the Boston area.

Jason Kelly: In other words, they don't have to be there to do each and every step and move the samples among the different equipment and program the equipment. They can just hit go and have that protocol run end to end for them via the automation, but whatever they want to order that day. That's the target. Look, if you go to the next slide, the value prop here I think is very clear for getting rid of the lab bench. Massive overhead cost savings. We've heard a lot about overhead costs at academic research labs and things like that. That's really paying for ultimately millions of square feet of laboratory space. It's 50 million square feet of laboratory space just in the Boston area. You can dramatically reduce that.

Speaker #2: So you can dramatically reduce that. You can increase the research productivity of your human scientists as we need more data from AI. And we'll talk about that in a minute.

And so again, here's the kicker, uh, if you look at research budgets between, uh, laboratory work cells, which are used in things in Pharma companies like Hydro but screening or combinatorial chemistry or things like that versus at the lab bench. Uh it's about 95% plus uh at the bench and again it's not like we don't know about work cells. It's that scientists need flexibility to explore all the different hypotheses they have, uh, for discovering a new drug or developing a new crop trait, or whatever type of biotechnology they're doing. So, this is what we're trying to build at gingo. We're trying to get up to that top right corner and make a weo. We're trying to make an autonomous lab that has the flexibility of the bench so scientists can order whatever experiment they want, but the automation of a work cell, in other words, they don't have to be there to do each and every

Speaker #2: And then we can enable AI scientists to run these lab-in-the-loop experiments. We just announced a project with OpenAI a few months ago where GPT-5 ran our lab.

Speaker #2: That's that kind of lab-in-the-loop. Experiments that we're seeing increasingly also in the biopharma industry. But to put a point on it, like a typical large pharma biopharma biotech spends one to three billion dollars a year on research, not clinical trials, but spending on a million plus square feet of lab benches, and if you look at their spending within that, on automation, it's well below $100 million.

Jason Kelly: You can increase the research productivity of your human scientists as we need more data from AI. We'll talk about that in a minute. Then we can enable AI scientists to run these lab-in-the-loop experiments. We just announced a project with OpenAI a few months ago where GPT-5 ran our lab. That's that kind of lab-in-the-loop experiment that we're seeing increasingly also in the biopharma industry. To put a point on it, like a typical large pharma, biopharma, biotech spends $1 to 3 billion a year on research. Not clinical trials, but spending on, you know, 1 million plus square feet of lab benches. If you look at their spending within that on automation, it's well below $100 million, usually much less. Frankly, I think those numbers should flip.

Speaker #2: Usually much less. And frankly, I think those numbers should flip. I think really the majority of the capital should be going towards automated laboratory work rather than manual benches.

Speaker #2: And the reason for that is I think a relatively straightforward calculation on the next slide. You can see a comparison between a traditional manual lab and an autonomous lab.

Every step and move the samples among the different equipment and program. The equipment, they can just hit go and have that protocol run end to end for them via the automation but whatever they want to order that day that's the target. Um and look if you go to the next Slide, the value prop here I think is very clear for getting rid of the lab bench. Uh massive overhead cost savings. We've heard a lot about overhead costs at academic research labs and things like that. That's really painful. Ultimately, millions of square feet of laboratory, space is 50 million square feet of laboratory space, just in the Boston area. Uh, so you can you can dramatically reduce that, you can increase, the research productivity of your human scientists as we need more data from Ai. And we'll talk about that in a minute and then we can enable AI scientists to run these lab in the loop experiments. We just announced a project with openai a few months ago where gbt 5 ran our lab, that's that kind of lab in the loop um experiments that we're seeing increasingly also in the biofarma industry. But to put a point on it like a typical large, uh, Pharma biofarma biotech.

Speaker #2: It's about a threefold space improvement when you take all these equipment that's often very spread out in a normal human-operated lab because people need to get around it and safety reasons and all these things.

Spends 1 to 3 billion dollars a year on Research not clinical trials, but spending on on, you know, a million plus square feet of lab benches. And if you look at their spending within that, uh, on automation, it's well, below hundred million dollars, um, usually much less.

Speaker #2: But in an autonomous lab, you can jam all that equipment right next to each other about as tight as you can make it for the arms to work and things like that.

Jason Kelly: I think really the majority of the capital should be going towards automated laboratory work, rather than manual benches. The reason for that is, I think, a relatively straightforward calculation. The next slide, you can see a comparison between a traditional manual lab and an autonomous lab. It's about a 3-fold space improvement when you take this equipment that's often very spread out in a normal human-operated lab 'cause people need to get around it and safety reasons and all these things. In an autonomous lab, you can jam all that equipment right next to each other, about as tight as you can make it for the arms to work and things like that. It's about a 3-fold space reduction. The manual labs really are just run 40 hours a week, right?

Speaker #2: So it's about a threefold space reduction. And then the manual labs really are just run 40 hours a week, right? I mean, it's when people come into the lab and humans are there and they got to be there.

Speaker #2: And that's when you can get people to work. There's now almost never multiple shifts in these types of sort of high-end research labs. It's really a 40-hour work week.

Speaker #2: And our system here in Nebula is running 168 hours a week. So 24/7, that's a fourfold improvement in sort of hours available for utilization of your laboratory.

Speaker #2: So I think a real clear threefold, fourfold on two different axes it is a clear value driver. If you go to the next slide.

Uh, and frankly, I think those numbers should flip. I think really the majority of the capital should be going towards automated laboratory work. Um, rather than manual benches and the reason for that is I think a relatively straightforward calculation. Um, the next slide you can see uh comparison between a traditional manual lab and an autonomous lab. Uh, it's about a 3-fold uh, space Improvement. When you take all these this equipment that's often, um, uh, very spread out in a normal human operated lab as people need to get around it, and safety reasons, and all these things. Uh, but in autonomous, lab, you can jam all that equipment right next to each other, uh, about as tight as you can make it for the arms to work, and things like that. So, it's about a 3-fold space reduction. Uh, and then

Jason Kelly: I mean, it's when people come into the lab and humans are there, and they gotta be there, and that's when you can get people to work. There's now almost never multiple shifts in these types of sort of high-end research labs. It's really a 40-hour workweek. Our system here, Nebula, is running 168 hours a week. You know, 24/7, that's a fourfold improvement in sort of hours available for utilization of your laboratory. I think a real clear threefold, fourfold on two different axes. It is a clear value driver. If you go to the next slide. I think there's little question the ROI is there. I think the big question with autonomous labs is the technical one.

Speaker #2: I think there's little question the ROI is there. I think the big question with autonomous labs is the technical one. It's how do you get that high level of automation and the high level of flexibility that top right corner without a human being in the loop, right?

Really are just run 40 hours a week, right? I mean, it's when people come in to the lab and humans are there and they got to be there and that's when you can, you can get people to work and there's now almost never multiple shifts in these types of sort of high-end,

Speaker #2: It's the same question of the Waymo. How do you get it to navigate all these different environments and different roads without a human in the loop?

Speaker #2: If you can do it, it's an obvious win. If we can do this in the lab, it's an obvious win. All right. So let me talk through a little bit the design constraints that we focused on at Ginkgo on the next slide.

Speaker #2: You can see a work cell, one of those subways. The way it's designed is it's designed against a particular protocol. So if you're a biopharma company and you want to build a high-throughput screening work cell and you co-call a traditional automation vendor, they're going to first question they're going to ask you is, tell me about your protocol.

Jason Kelly: It's how do you get that high level of automation and the high level of flexibility, that top right corner, without a human being in the loop, right? It's the same question of the Waymo. How do you get it to navigate all these different environments and different roads without a human in the loop? If you can do it's an obvious win. If we can do this in the lab, it's an obvious win. All right, let me talk through a little bit the design constraints that we focused on at Ginkgo. On the next slide, you can see, you know, a work cell, one of those subways. The way it's designed is it's designed against a particular protocol.

Speaker #2: And tell me what throughput you need it run at. How many samples do you want to get through every week? Because they're building you a subway line.

Speaker #2: It's going to be built to do that protocol for you. But if you're a new facility head who is opening a lab in Central Square or in Kendall Square here in Cambridge, and you are building it for a scientific lead, you don't ask that scientific lead hey, what's the protocol you're going to run in this lab that 30 of your scientists are going to use to do work?

Jason Kelly: If you're a biopharma company and you want to build a high-throughput screening work cell and you call a traditional automation vendor, the first question they're going to ask you is, tell me about your protocol and tell me what throughput you need it run at. How many samples do you want to get through every week? They're building you a subway line. It's going to be built to do that protocol for you. If you're a, you know, a new facility head who is opening a lab in Central Square or, you know, in Kendall Square here in Cambridge, and you are building it for a scientific lead, you don't ask that scientific lead, Hey, what's the protocol you're going to run in this lab that 30 of your scientists are going to use to do work? You say, What kind of science are you doing?

Research Labs. It's really a 40-hour work week, uh, and our system here in nebula is running 168 hours a week. So, uh, you know, 24/7 and that's a 4-fold Improvement in sort of hours available, uh, for utilization of your laboratory. Uh, so I think a real clear 3-fold 4-fold on 2, different axis, uh, it it is a clean. Clear value driver. If you go to the next slide, uh, I think there's little questions. The ROI is there. I think the big question with autonomous Labs is a technical 1. It's how do you get that high level of Automation and the high level of flexibility that top right corner without a human being in the loop, right? It's the same question of the way, but how do you get it to navigate all these different environments and different roads without a human in the loop? If you can do it, it's an obvious win. And if we can do this in the lab, it's an obvious win. All right, so let me talk through a little bit, the design constraints that we focused on at go on the next slide. You can see, you know, a work cell 1 of those Subways. The, the way it's designed is it's designed against a particular.

Protocol.

Speaker #2: You say, what kind of science are you doing? And very specifically, what equipment do you want me to install in that lab so that your scientists can be productive over the next three to five years as they use the lab?

Speaker #2: And so it is oriented around the equipment rather than the protocol. And that's sort of a subtle point, but it has a huge amount of consequences when it comes to how you design the hardware and software that responds to this challenge.

So, if you're a biofarma company and you want to build a high, throughput, screening, work cell, and you co call a traditional automation vendor, they're going to first question, they're going to ask you is tell me about your protocol and tell me what throughput you need. It brought out. How many samples do you want to get through every week because they're building you a subway line? It's going to be built to do that protocol for you.

Speaker #2: And so if you go to the next slide, you can see our hardware solution here is what we call our rack carts, our reconfigurable automation carts.

Speaker #2: And we have it's basically a robot wrapped around each laboratory device. And we have control over that environment. There's a HEPA filter on the top, which is important for a lot of biological work.

Jason Kelly: Very specifically, what equipment do you want me to install in that lab so that your scientists can be productive over the next three to five years as they use the lab? It is oriented around the equipment rather than the protocol. That's a sort of a subtle point, but it has a huge amount of consequences when it comes to how you design the hardware and software that responds to this challenge. If you go to the next slide, you can see our hardware solution here is what we call our RAC carts or Reconfigurable Automation Carts. It's basically a robot wrapped around each laboratory device, and we have control over that environment. There's a HEPA filter on the top, which is important for a lot of biological work, where you have opportunities for contamination and things like that.

Speaker #2: We have opportunities for contamination and things like that. We have a six-axis robotic arm and a piece of magna motion track that allows you to if you go to the next slide, Lego block these together into ultimately very large setups.

But if you're a, you know, a new facility head, who is opening, uh, a lab in Central Square or, you know, in Kendall Square here in Cambridge and you are building it for a scientific lead. You don't ask that scientific lead. Hey what's the protocol? You're going to run in this lab that 30 of your scientists are going to use to do work. You say what kind of science are you doing and very specifically what equipment do you want me to install in that lab so that your scientists can be productive over the next 3 to 5 years as they use the lab?

Speaker #2: And we're at 50 plus right now in the lab. And it's growing quickly. I'll show you some photos in a second. We have 100 and three racks.

Speaker #2: We'll be coming online just in about a week, all in one big setup here in Boston. And so if you go to the next slide, we can I want to show so this is actually a video of the OpenAI protocol.

Speaker #2: So we had this project with OpenAI where GPT-5 controlled the lab. And we made a video of one of the samples just moving through the various racks for that protocol.

Jason Kelly: We have a six-axis robotic arm and a piece of Magnum motion track that allows you to, if you go to the next slide, Lego block these together into ultimately very large setups. We're at 50+ right now in the lab, and it's growing quickly. I'll show you some photos in a second. We have 103 racks will be coming online just in about a week, all in one big setup here in Boston. If you go to the next slide, we can. I wanna show this. This is actually a video of the OpenAI protocol. We did this project with OpenAI, where GPT-5 controlled the lab, and we made a video of one of the samples just moving through the various racks for that protocol.

Speaker #2: And so this just gives you a sense of how does it work, right? So you have these tracks. And we're able to move our one sort of constraint on the system is that we pass things in what's called SBS format.

And so, it is oriented around the equipment, rather than the protocol and that's a sort of a subtle point. But it has a huge amount of consequences when it comes to how you design the hardware and software that responds to this Challenge. And so, if you go to the next slide, you can see our Hardware solution here is what we call our rack carts, our reconfigurable automation cards. And we have it's basically a robot wrapped around each laboratory device and we have control over that environment. There's a HEPA filter on the top, which is important for a lot of biological work. Uh, we have opportunities for contamination and things like that. Uh, we have a 6 axis, robotic arm, and a piece of Magnum motion track that allows you to to go to the next slide Lego block these together into a

Speaker #2: So that little rectangle you saw there is like a three by five-inch square. Rectangle. And it can contain a 96 or this is a 384 well plate or 1586 well plate.

Speaker #2: It can also carry consumables like tips and other things. And the arm picks up that piece of plastic ware or samples or tips or whatever it might be.

Jason Kelly: This just gives you a sense of, like, how does it work, right? You have these tracks, and we're able to move. Our one sort of constraint on the system is that we pass things in what's called SBS format. That little rectangle you saw there is like a 3-by-5 inch square rectangle, and it can contain a 96, or this is a 384 well plate or 1,586 well plate. It can also carry consumables like tips and other things. The arm picks up that piece of plasticware or samples or tips or whatever it might be, and then puts it onto a particular device. In this case, it's going, you know, just went through an acoustic liquid handler. Now it's going onto a Bravo liquid handler.

Speaker #2: And then puts it onto a particular device. So in this case, it's going just went through an acoustic liquid handler. Now it's going onto a Bravo liquid handler.

Speaker #2: You saw, again, that first thing that got put down there was actually the plastic tips that are now getting picked up by the liquid handler.

Speaker #2: And the other two plates are sort of sample and destination plates. So we're in this case for the OpenAI project, we're picking up some synthetic DNA and we're putting it into reaction mixes that were designed by the GPT-5 model at the time, all right?

Ultimately, very large setups and we're at uh 50 plus right now in the lab and it's growing quickly, I'll show you some photos in a second. We have 100 uh and 3 racks, will be coming online just in about a week. All In 1 big setup here in Boston. Uh and so if you go to the next slide, we can I want to show this. So This is actually a video uh of the openai protocol. So we did this project with openai where gb5 controlled the lab and we made a video of 1 of the samples just moving through, the various racks, for that protocol. And so this just gives you a sense of like how does it work, right? So you have these tracks and we're able to move like our our 1 sort of constraint on the system is that we pass things and what's called SBS format. So that little rectangle, you saw there is like a 3 by 5 inch square rectangle and it can contain a 96 or this is a 384 well plate or 1586 well plate. It can also carry consumables like tips and other things and the arm picks up that

Speaker #2: And so again, key feature here is any device that accepts those SBS plates, we can integrate into the racks. It takes us usually like a month to a month and a half if it's a new device.

Jason Kelly: You saw, again, that first thing that got put down there was actually the plastic tips that are now getting picked up by the liquid handler. The other two plates are sort of sample and destination plates. In this case, for the OpenAI project, we're picking up some synthetic DNA, and we're putting it into reaction mixes that were designed by the GPT-5 model at the time, right? Again, key feature here is any device that accepts those SBS plates, we can integrate into the RACs. It takes us usually like a month to month and a half if it's a new device. We've now got 80-plus devices on there. We're adding new devices all the time. If a customer asks us for one, if we wanna add one to Nebula, we just bring it online.

Speaker #2: We've now got 80-plus devices on there. We're adding new devices all the time. If a customer asks us for one, if we want to add one to Nebula, we just bring it online.

Speaker #2: So now that plate's going to get shaken up and put onto ultimately this thermocycle or this final analytical device to run the qPCR reactions and give a readout on the performance of each one of those samples back to, in this case, an AI model.

piece of plastic wear or samples or tips, or whatever it might be, and then puts it onto a particular device. So, in this case, it's going, you know, just went through an acoustic, liquid Handler. Now it's going onto a Bravo, liquid handle, or you saw again that first thing that got put down, there was actually, the plastic tips that are now getting picked up by the liquid Handler and the other 2 plates are sort of sample and destination plates. So we're we're in this case for the open AI project. We're we're picking up some synthetic DNA, and we're putting it into reaction mixes that were designed by the um, GPT 5 model.

Speaker #2: That data for most of the runs on Nebula is going back to a human scientist. As opposed to an AI scientist. But we expect there'll be a mix of both as science goes forward.

Speaker #2: We'll have both scientists and their agents ordering experiments on autonomous labs. Go to the next slide. A great thing about this system is it can expand.

Jason Kelly: Now that plate's gonna get shaken up and put onto ultimately this thermocycler, this final analytical device to run the qPCR reactions and give a readout on the performance of each one of those samples back to, in this case, an AI model. That data for most of the runs on Nebula is going back to a human scientist, as opposed to an AI scientist. We expect there'll be a mix of both, as science goes forward. We'll have both scientists and their agents ordering experiments on autonomous labs. Go to the next slide. A great thing about this system is it can expand.

All right. And so, uh, again, key feature here is any device that accepts those SPS plates, we can integrate into the racks. It takes us usually like a month-to-month and a half. If it's a new device, we've now got 80 plus devices on their. We're adding new devices all the time. If a customer asks us for 1, if we want to add 1 to nebula, we just bring it online.

Speaker #2: We started Nebula, I think, with about eight racks doing NGS, like next-generation sequencing prep for our samples here. At Ginkgo and expanded ultimately now up to over 100 racks on the system.

Speaker #2: So let's dig in a little bit. I want to go in the next section. So that was sort of the theory. How we designed the hardware in order to solve some of the challenges of the autonomous lab and what autonomy means.

Speaker #2: And now I want to dig in a little bit on Nebula specifically. Because I think what's really unique about Ginkgo is we're not just a hardware company.

Both, uh, as science goes forward, we'll have both scientists and their agents ordering experiments on autonomous labs.

Jason Kelly: You know, we started Nebula, I think, with about 8 racks doing NGS, like, next generation sequencing for our samples here at Ginkgo and expanded ultimately, now up to over 100 racks on the system. Let's dig in a little bit. I wanna go to the next section. That, that was sort of the theory, you know, like how we designed the hardware in order to solve some of the challenges of the autonomous lab and what autonomy means. Now I wanna dig in a little bit on Nebula specifically, because I think what's really unique about Ginkgo is we're not just a hardware company. We actually run BSL-2 labs here in Boston and do scientific partnerships with some of the largest biotech, ag biotech, industrial biotech companies in the world.

Speaker #2: We actually run BSL2 labs here in Boston and do scientific partnerships with some of the largest biotech, ag-biotech, industrial biotech companies in the world.

Speaker #2: And so we can actually show what it looks like to do real science on a system like this. And so if you go to the next slide, one of the things I'm quite proud of that we've been able to show in the last quarter is over 100 protocols with more than 30 of them being unique submitted by scientists.

Speaker #2: And I'll mention this, but these are not being submitted by automation engineers or experts in robotics. Being submitted onto our system Nebula here in Boston, which has 50-plus lab devices all integrated together where you can send point-to-point samples from any device to any other device as requested by those scientists.

Jason Kelly: We can actually show what it looks like to do real science on a system like this. If you go to the next slide, one of the things I'm quite proud of that we've been able to show in the last quarter is over 100 protocols with more than 30 of them being unique, submitted by scientists. I'll mention this, but these are not being submitted by automation engineers or experts in robotics, being submitted onto our system, Nebula, here in Boston, which has 50-plus lab devices all integrated together, where you can send point-to-point samples from any device to any other device as requested by those scientists.

Speaker #2: There is nothing else like Nebula in the world today doing sort of open-ended scientists like the science like this at this scale with this number of unique protocols and users.

Go to the next slide. Uh, great thing about this system is it can expand. You know, we started, uh, nebula. I think it was about 8, uh, NGS, like next Generation sequencing prep, uh, for our samples here at Ginkgo and expanded. Ultimately, uh, now up to, uh, over a hundred racks on the system. So, let's dig in a little bit. Uh, I want to go to the next section so that that was sort of the theory, you know, like how we designed the hardware uh, in order to solve some of the challenges of the autonomous lab and what autonomy means. I now want to dig in a little bit, on any, any nebulous specifically. Because I think what's really unique about gingko is we're not just a hardware company. Uh, we actually run bsl2 Labs here in Boston and and do scientific Partnerships with some of the largest biotech, a biotech industrial biotech companies in the world. Uh, and so we can actually show what it looks like to do. Real science on a system like this. And so if you go to the next slide, 1 of the things, I'm quite proud of that. We've been able to show in the last quarter, uh, is over a hundred protocols, with more than

Speaker #2: And it's proof that autonomous labs are feasible. I mean, there's work to do and talk about that. Things break as we are scaling this up, that's for sure.

Speaker #2: But it is evidence in my view that this is going to land. We are going to beat the manually operated lab. And so if you go to the next slide, I want to walk through a few of the key things that you got to show if you're going to take out one of those laboratory floors at Takeda or Merck or Novartis or wherever or bear crop science or any of these companies that do a lot of laboratory work.

Jason Kelly: There is nothing else like Nebula in the world today doing sort of open-ended scientists like science like this at this scale, with this number of unique protocols and users. It's proof that autonomous labs are feasible. I mean, there's work to do and talk about that. Things break as we are scaling this up, that's for sure. It is evidence, in my view, that this is gonna land. Like, we are gonna beat the manually operated lab.

Thirty of them being unique, submitted by scientists, and I'll mention this. But these are not being submitted by automation engineers or experts in robotics, uh, being submitted onto our system Nebula here in Boston, which has 50-plus lab devices all integrated together, where you can send point-to-point samples from any device to any other device as requested by those scientists.

Speaker #2: So first, you want to connect 100-plus devices in a single automation setup, all right? So it can't just be five or 10. A scientist expects to have access to many different devices.

Speaker #2: In order to do whatever protocol they might read about in a scientific paper this week, and I think about 100 is the right number.

Jason Kelly: If you go to the next slide, I wanna walk through a few of the key things that you gotta show if you're gonna take out, you know, one of those laboratory floors at Takeda or Merck or Novartis or wherever or Bayer Crop Science or any of these companies that do a lot of laboratory work. First, you wanna connect 100 plus devices in a single automation setup. All right? It can't just be 5 or 10. You know, a scientist expects to have access to many different devices in order to do whatever protocol they might read about in a scientific paper, you know, this week. That, I think about 100 is the right number. This week, we'll find out. We're turning it on in about 5 days.

Speaker #2: So we've been able to this week, we'll find out. We're turning it on in about five days. 105 or 103 racks all in one big setup.

Speaker #2: And the reason we can do that is because of that rack productized hardware, that cart I showed you. We just rolled in. They came in off a truck, and we rolled another 50 in.

Speaker #2: And those have all gone in the actual install over the last three or four weeks. So it's pretty fast to put that many new devices on an automated setup.

Speaker #2: We'll see if it works. Second, we have run now, like I said, 30-plus unique protocols, 100-plus different 100-plus total protocols. But that's kind of you got to get in that, I think, 50 to 100 to maybe 200 unique protocols all running on the autonomous lab at the same time.

Uh, there is nothing else like nebula in the world today, doing sort of open-ended scientists like the science like this at this scale, um, with this number of unique, protocols and and users. Uh, and it's proof that autonomous labs are feasible. I mean, there's work to do and talk about that, uh, things break as we are scaling this up, that's for sure. Uh, but uh, it is it is evidence. Uh, in my view that this is this is going to land. Like we are going to be the manually operated lab and so if you go to the next slide, I want to walk through a few of the key things that you got to show. If you're going to take out, you know, 1 of those laboratory floors at De or Merc or novardis, or wherever or Bayer crop science or any of these companies that, um, do a lot of laboratory work. So first, you want to connect a 100 plus devices in a single automation setup. All right. So it can't just be 5 or 10, you know, a scientist expects to have access to many different devices in order to do whatever protocol, they might read about in a scientific paper. You know, this week, um, and that I think about

Jason Kelly: 105, all in, or 103 racks, all in one big setup. The reason we can do that is because of that rack productized hardware, that cart I showed you. We just rolled in, you know, they came in off a truck, we rolled another 50 in, those have all gone in the actual install over the last 3 or 4 weeks. It's pretty fast to put that many new devices on an automated setup. We'll see if it works. Second, we have run 10 now, like I said, 30-plus unique protocols, 100-plus total protocols. You gotta get in that, I think 50 to 100 to maybe 200 unique protocols all running on the autonomous lab at the same time.

Speaker #2: And we do that with our catalysts. This is our software. Our scheduler that we built is a very complicated scheduling problem. It's really easy to mess this up.

Speaker #2: Biology is very sensitive to timing. Things break all the time as we keep driving the scale up here. And so we're getting to do that quick cycle of debugging and improving the system.

100 is the right number. So we've been able to this week, we will we'll find out. We're turning it on in about 5 days, 105, all or 103 Rags? All In 1 big setup. And the reason we can do that is because of that rack, productized Hardware, that cart. I showed you, we just rolled in, uh, you know, they came in off a truck and we rolled another 50 in and those have all gone in the actual install, over the last 3 or 4 weeks. So, it's pretty fast to put that many new devices on, on a, on a, um, automated setup. We'll see if it works. Uh, second, uh, we have

Speaker #2: But that scheduler is really the key piece of software driving that. And then finally, scientists, scientists, not automation engineers. And I think on a peak day, on Nebula, we had 439 or so scientists submitting so that's really exciting.

Jason Kelly: We do that with our Catalyst. This is our software, our scheduler that we built. This is a very complicated scheduling problem. It is really easy to mess this up. Biology is very sensitive to timing. Things break all the time as we keep driving the scale up here. We are getting to do that quick cycle of debugging and improving the system. That scheduler is really the key piece of software driving that. Finally, scientists, not automation engineers, and I think on a peak day on Nebula, we had 439 or so scientists submitting. That is really exciting. Like, to have that many different scientists submitting protocols on one automation system, again, I do not know of any automation system in the world that has been able to do that before.

Speaker #2: To have that many different scientists submitting protocols on one automation system, again, I don't know of any automation system in the world that's been able to do that before.

Speaker #2: And we're able to do that in part by leveraging AI coding tools with custom harnesses wrapped around them that basically understand how to transfer a scientist's intent in human language into code to operate the autonomous lab.

Speaker #2: And that is a big unlock. We're very thankful for what's going on with all the coding agents. That's a real help for improving the ease of use because at the end of the day, to make robots do something, you have to program them.

Speaker #2: And to walk up to a lab bench and do your work by hand, you don't. So we have to solve this problem of we can't make it so that scientists have to become coders to do their job.

Jason Kelly: We're able to do that in part by leveraging AI coding tools with custom harnesses wrapped around them that basically understand how to transfer a scientist's intent in human language into code to operate the autonomous lab. That is a big unlock. We're very thankful for what's going on with all the coding agents. That's a real help for improving the ease of use because at the end of the day, to make robots do something, you have to program them. To walk up to a lab bench and do your work by hand, you don't.

Speaker #2: And we've really just been given a gift by these AI coding tools again, like the Codexes and the Claude codes and things like that that can sit inside other tools that are specific to the automation to get this done.

Have run 10. Now like I said, 30 plus unique, protocols, 100 plus different Pro 100, plus total protocols, but that's kind of you got to get in that. I think 50 to 100 to maybe 200 unique, protocols, all running on the autonomous lab. Uh, at the same time and we do that with our catalysts. This is our software, our, uh, scheduler that we built. This is a very complicated scheduling problem. It's really easy to mess. This up, biology is very sensitive to timing things break all the time as we keep driving the scale up here. So we're getting to do that quick cycle of debugging and improving the system. But that's scheduler is really the key uh, piece of software driving that. And then finally scientists scientists not automation engineers and I think on a peak day uh uh on nebula, we had 439 or so, uh, scientists submitting. So that's really exciting like to have that many different scientists. Uh, submitting protocols on 1 automation system again. I don't know of any automation system in the world, that's been able to do that before. Uh, and the and we're able to do that in part by leveraging, AI coding tools.

Speaker #2: So those are the three big ones. And I'm pretty happy with the progress on all of them. So if you go to the next slide, as I mentioned several times now, we're going from 50 to 105 racks by the end of this month.

With custom harnesses wrapped around them that basically understand how to transfer a scientist's intent in human language into code, to operate the autonomous lab. And that is a big unlock. We're very thankful, uh, for what's going on with all the coding agents. That's a real help for, uh, improving the ease of use. Because at the end of the day, to make robots do something, you have to program them.

Speaker #2: It's going to be awesome. It's really cool system. To see people should come out and visit it. If you go to the next slide, that scheduler is not trivial.

Jason Kelly: We have to solve this problem of we can't make it so that scientists have to become coders to do their job, and we've really just been given a gift by these AI coding tools, again, like the Codexes and the Claude Code and things like that can sit inside other tools that are specific to the automation to get this done. Those are the three big ones, and I'm pretty happy with the progress on all of them. If you go to the next slide, as I've mentioned several times now, we're going from 50 to 105 racks by the end of this month. It's gonna be awesome. It's a really cool system to see. People should come out and visit it.

And to walk up to a lab bench and do your work by hand.

Speaker #2: So this is an example of our scheduler, I think, running 17 or 20 different protocols at the same time. Each color is a different protocol.

Speaker #2: Each row is a different device on the system. The X-axis is time. And so you can imagine if you want to add a new protocol to that, and you're like, "Okay, I need to use the device on row three, the device on row seven, and the device on row nine, and I need the device on row three for the first three minutes, then I'm not willing to tolerate up to one-hour gap, then the second device for 15 minutes, then up to a 30-minute gap, then the last device." It'll check.

Jason Kelly: If you go to the next slide, that scheduler is not trivial. This is an example of our scheduler, I think, running 17 or 20 different protocols at the same time. Each color is a different protocol. Each row is a different device on the system.

Speaker #2: Can it fit you in? And if it can fit you in or if it can fit you in by moving a couple other things, it doesn't disrupt them in a way that breaks the protocol.

Jason Kelly: The X-axis is time. You can imagine if you wanna add a new protocol to that, and you're like, Okay, I need to use the device on row three, the device on row seven, and the device on row nine, and I need the device on row three for the first 3 minutes, then I'm willing to tolerate a up to 1-hour gap, then the second device for 15 minutes, then up to a 30-minute gap, then the last device. It'll check, can it fit you in, if it can fit you in, or if it can fit you in by moving a couple other things that doesn't disrupt them in a way that breaks the protocol, it'll fit you in. That's awesome, right? That's very, very much not how the traditional lab automation, the subways work. They're running a batch.

Speaker #2: It'll fit you in. That's awesome, right? That's very, very much not how the traditional lab automation, the subways work. They're running a batch. That subway line is showing up at a certain time.

Speaker #2: You can't just jump and insert yourself in the middle. But you can with our scheduling software here. The next slide, the little green one, it's hard to see, but that column, third column over from the left over there, has the names of all the different scientists submitting.

Speaker #2: So I really love this. I love that we're seeing different people submitting different orders for protocols every day. It's really exciting. And again, I think it's unique.

Speaker #2: We're also seeing a lot of energy on the US government side. If you go to the next slide, a lot of new policy action here.

It's going to be awesome. It's really cool system, uh, to see people should come out and visit it. Um, if you go to the next slide that scheduler is not trivial. Uh, so uh, this is an example of our scheduler, I think running 17 or 20 different protocols at the same time. Uh, each color is a different protocol, each row is a different device on the system. Uh, the x-axis is time and so you can imagine if you want to add a new protocol to that and you're like, okay, I need to use the device on Row. 3, the device on row 7 and the device on row 9 and I need uh the device on Row 3 for the first 3 minutes. Then I'm not willing to tolerate a up to 1 hour gap. Then the second device for 15 minutes, then up to a 30 minute Gap then the last device it'll check can it fit you in and if it can fit you in or if it can fit you in by moving a couple other things that doesn't disrupt them in a way that breaks the protocol, it'll fit you in

Jason Kelly: That subway line is showing up at a certain time. You can't just jump and insert yourself in the middle, but you can with our scheduling software here. The next slide, the little green one, it's hard to see, but that column, third column over from the left over there, has the names of all the different scientists submitting. I really love this. I love that we're seeing different people submitting different orders for protocols every day. It's really exciting. Again, I think it's unique. We're also seeing a lot of energy on the US government side. If you go to the next slide, a lot of new policy action here. There's the Genesis mission, which we're fortunate to be a part of, from the White House to bring AI into the national labs.

Speaker #2: There's the Genesis mission, which we're fortunate to be a part of from the White House to bring AI into the national labs. But there's a big motion right now where we're seeing an increasing amount of drug discovery work moving to China.

Speaker #2: From Kendall Square, I was talking about earlier here in the Boston area. And that's because simply Chinese scientists were paid a third as much, and they're doing equal work to what's happening here in the US.

That's awesome, right? That's very, very much not how the traditional lab automation. The Subways work, they're running a bats. That subway line is showing up at a certain time. You can't just jump and insert yourself in the middle, uh, but you can, uh, with our our scheduling software here, um, the next slide, uh, the little green 1, it's hard to see, but that, that column third column over from the left over there has the names of all the different scientists submitting. So I really love this, I love that you were seeing different people, submitting different orders for for protocols every day.

Speaker #2: They're just as good. They're just as smart. And so I think if we want to remain competitive, we got to think about doing our research in a fundamentally different way in the United States.

Speaker #2: I don't think we can just rest on our laurels of having the only smart scientist in the world in this area or at least versus China.

Jason Kelly: You know, there's a big motion right now where we're seeing an increasing amount of drug discovery work moving to China from Kendall Square, I was talking about earlier, here in the Boston area. That's because simply Chinese scientists are paid a third as much, and they're doing equal work to what's happening here in the US. Like, they're, you know, they're just as good, they're just as smart. I think if we want to remain competitive, we gotta think about doing our research in a fundamentally different way in the United States. I don't think we can just rest on our laurels of having the only smart scientists in the world in this area, or at least versus China, I think that era is over, firmly over at this point.

Speaker #2: And I think that era is over firmly over at this point. And so we got to think about a new way to do it.

Speaker #2: I'm pretty heartened to see activity out of the National Science Foundation is funding $100 million for a network of cloud laboratories and autonomous labs.

Speaker #2: There's a new bill introduced by Senator Young to sort of do more of this cloud labs and autonomous labs. So hopefully, we see more here.

Speaker #2: But I'm encouraged by what we see already. Go to the next slide. We're obviously very fortunate. I had a chance in December to sort of ribbon cut the first 18 of our rack robots going in at Pacific Northwest National Labs and signed a new contract for a $47 million much larger autonomous lab setup nearly 100 racks going in a new building in a couple of years at P&NNL.

It's really exciting. And again I think it's Unique um we're seeing a lot of energy on the US government side. If you go to the next slide, a lot of new policy act action here, there's the Genesis Mission which we're fortunate to be a part of uh from the White House to bring AI into the National Labs. Um but you know there's a big motion right now where we're seeing an increasing amount of drug Discovery work moving to China uh from Kendall Square talking about earlier here in the Boston area and that's because simply Chinese scientists are paid a third as much and they're uh, doing equal work uh, to What's Happening Here in the US. Like they're, you know, they're just as good, they're just as smart. Uh, and so I think if the, if we want to remain competitive, we got to think about doing our research, in a fundamentally different way, in the United States. I don't think we can just rest on our Laurels of having the only smart scientists in the world in this area, or at least, um,

Jason Kelly: We gotta think about a new way to do it. I'm pretty heartened to see, you know, activity out of the National Science Foundation is funding $100 million for a network of cloud laboratories and autonomous labs. There's a new bill introduced by Senator Young to sort of do more of this, cloud labs and autonomous labs. Hopefully we see more here, but I'm encouraged by what we see already. If you go to the next slide, we're obviously very fortunate.

Versus China and I think that era is over uh firmly over at this point. And so we got to think about a new way to

I'm pretty heartened to see uh, activity out of uh, you know.

Speaker #2: So this is really exciting. And I think sort of highlights the direction I believe our national labs will go, our scientific research in the country.

Speaker #2: If you go to the next slide, we're lucky to give ARPA-H a tour of Nebula. We have a great project with them. And the work is accelerated by having these autonomous labs available to our scientists they can go.

Jason Kelly: I had a chance in December to sort of ribbon cut the first 18 of our RAC robots going in at Pacific Northwest National Laboratory and signed a new contract for a $47 million, much larger autonomous lab set up, nearly 100 RACs going in a new building in a couple years at PNNL. This is really exciting, and I think sort of highlights the direction I believe our national labs will go, our scientific research in the country. If you go to the next slide, we were lucky to give ARPA-H a tour of Nebula. We have a great project with them. You know, the work is accelerated by having these autonomous labs available to our scientists that can go.

Speaker #2: I think this is something that makes a lot of sense for a lot of labs at the national institutes of health, for example, or NSF-funded labs or academic research universities.

National Science Foundation is funding a hundred million dollars for a network of cloud, Laboratories, and not Thomas Labs. There's a new bill introduced by Senator young to sort of uh, do more of this, um, Cloud labs and autonomous Labs. So hopefully we see more here but I'm, I'm encouraged by what we see already. Uh, to go to the next slide. We're obviously very fortunate. I had a chance in December, to sort of ribbon cut, uh, the first 18 of our rack robots. Going in at Pacific Northwest National Labs and signed a new contract.

Speaker #2: They would all be accelerated if our scientific talent could get many more of their hypotheses tested than are today due to the limitation of the manual lab.

Speaker #2: Next slide. Listen, Nebula is showcasing what is possible. And that means that early adopters are getting excited about it. So we are also building autonomous labs for that left end of the chart here, the very earliest adopters, the people that are excited to try this out as a different way, as an alternative to their lab benches.

For 47 million, much larger, autonomous Labs set up, um, nearly 100 racks, uh, going in a new building, uh, in a in a couple years at the end. And now so this is really exciting. And I think sort of highlights the direction. I'm uh, I believe our Our National Labs will go our, our scientific research in the country. If you go to the next slide, we're lucky to give RPA age a tour of nebula. We have a great project with them uh, and

Jason Kelly: I think this is something that makes a lot of sense for a lot of labs at the National Institutes of Health, for example, or NSF-funded labs or academic research universities. They would all be accelerated if our scientific talent could get many more of their hypotheses tested than are today due to the limitation of the manual lab. Next slide. Listen, Nebula is showcasing what is possible, and that means that early adopters are getting excited about it. We are also building autonomous labs for that left end of the chart here, the very earliest adopters, the people that are excited to try this out as a different way, as an alternative to their lab benches. We'll keep leaning in there, building those systems as that demand comes in.

Speaker #2: And so we'll keep leaning in there, building those systems as that demand comes in. And we are seeing, if you go to the next slide, a lot of interest.

Speaker #2: So we've had 600-plus visitors in the first quarter. I'll show it at the end. We have a great little sign-up. We do tours weekly.

You know, the work is accelerated by having these autonomous Labs available to our scientists and go. I think this is something that makes a lot of sense for a lot of Labs, at the National Institutes of Health, for example, or NSF funded Labs or academic research universities, uh, they would all be accelerated if our scientific Talent could get many more of their hypotheses tested than our today, due to the limitation of the manual lab,

Speaker #2: If any of you want to sign up or listening in, we're very happy to give you a tour. Okay. So that's Nebula. And that's the dive on that.

Next slide. Uh,

Speaker #2: All right. Now I want to talk a little bit about our service solutions, which I think of a little bit like I said, like our Starlink, right?

Speaker #2: So last year, 70% of the launches at SpaceX were Starlink. If you go to the next slide. That's a huge advantage for SpaceX. That means they get to be creating an asset, a money-making asset in the form of Starlink while also getting to test over and over and over again their launch platform.

Jason Kelly: We are seeing, if you go to the next slide, a lot of interest. We've had, you know, 600+ visitors in Q1. I'll show it at the end. We have a great, like, a little sign-up. We do tours weekly if any of you wanna sign up or are listening in. We're very happy to give you a tour. Okay. That's Nebula, and that's the dive on that. All right, now I wanna talk a little bit about our service businesses, Cloud Labs, Datapoints, and Solutions, which I think of a little bit, like I said, like our Starlink, right? Last year, 70% of the, you know, launches at SpaceX were Starlink. If you go to the next slide. That's a huge advantage for SpaceX.

Speaker #2: And their launch platform ultimately I think in their view is the big product, right, that they can have that sort of transportation layer to space.

Listen nebula is showcasing. What is possible? Uh, and that means that early adopters are getting excited about it. So uh, we are also building autonomous labs for that left. End of the chart here, the very earliest adopters, the people that are excited, uh, to try this out as a different way, as an alternative to their lab benches. And so, we'll keep leaning in their building those systems. Uh, as that demand comes in and we are seeing if you go to the next slide, a lot of interest. So we've had uh you know 600 plus visitors uh in the first quarter uh I'll show it at the end. We have a great like a little sign up. You can sign we do tours weekly. If any of you want to sign up or listening in, we're very happy to give you a tour. Uh so okay so that's nebula and that's the dive on that, all right. Now I want to talk a little bit about

Speaker #2: But today, they're 70% of the demand for that platform, right? I see a similar situation with the autonomous lab. We are able to have a big system here in Boston and basically prove out moving over our work from data points, Ginkgo Cloud Lab solutions, even our reagents business onto that platform.

Uh, our service, businesses, Cloud, Labs, data, points, and solutions, which I think of a little bit, like I said, like our Starlink.

Jason Kelly: That means they get to in creating an asset, a money-making asset in the form of Starlink, while also getting to test over and over and over again their launch platform. Their launch platform ultimately, I think in their view, is the big product, right? That they can have that sort of transportation layer to space. Today, there's 70% of the demand for that platform, right? I see a similar situation with the autonomous lab. We are able to have a big system here in Boston and basically prove out moving over our work from Datapoints, Ginkgo Cloud Lab Solutions, even our reagents business onto that platform. If you go to the next slide, I'm really excited we got our cloud lab off the ground just in the last quarter. It's really been exciting.

Speaker #2: And if you go to the next slide, I'm really excited. We got our cloud lab off the ground just in the last quarter. It's really been exciting.

Speaker #2: This is from the Times in London. Do you want to run an experiment for $39? Robots will do it for you. Go check out cloud.ginkgo.bio.

Speaker #2: You can go in the estimate tab at the top, type in whatever protocol you're interested in. It'll look up and see, do we have the equipment needed to do your protocol?

Speaker #2: And if so, it'll make an estimate of what the price would be to run that protocol in a cloud lab. And people are I think pretty surprised at how inexpensive it can be.

Speaker #2: And that is a reflection of where all the costs lie in doing lab work, which is in manual lab work done 40 hours a week, done at low equipment density, low equipment utilization, in laboratories that cost a fortune to run.

Jason Kelly: This is from The Times in London. Do you want to run an experiment for $39? Robots will do it for you. Go check out cloud.ginkgo.bio. You can go in the estimate tab at the top, type in whatever protocol you're interested in. It'll look up and see do we have the equipment needed to do your protocol, and if so, it'll make an estimate of what the price would be to run that protocol in a cloud lab. People are, I think, pretty surprised at how inexpensive it can be. That is a reflection of where all the costs lie in doing lab work, which is in manual lab work done 40 hours a week, done at low equipment density, low equipment utilization in laboratories that cost a fortune to run.

Speaker #2: That then flows through. And it means all of the CRO services you order and so on are very expensive. We think we can solve that problem through automation.

Speaker #2: And the cloud.ginkgo.bio, our cloud lab service, is really a great way to do that. If you go to the next slide, this is what OpenAI took advantage of when we did this project where GPT-5 ran the lab.

Big product, right? That they can have that sort of Transportation layer to space but today, they're 70% of the demand for that platform, right? Uh, I see a similar situation with the autonomous lab. Uh, we are able to have a big system here in Boston and basically prove out moving over our work from data points, Google Cloud lab Solutions, even our reagents business, uh, onto that platform and to go to the next slide. Uh, really excited. We, we got our Cloud lab off the ground, just in, in the last quarter, it's really been exciting. Wow, this is, this is from the times in London. Uh do you want to run an experiment for $39? Robots will do it for you. Uh, go check out cloud.google.com bio, you can go in uh the estimate tab at the top type in whatever protocol you're interested in. It'll look up and see do we have the equipment needed to do your protocol and if so it'll make an estimate of what the price would be to run that protocol in a cloud lab and people are. I think pretty surprised at how inexpensive it can be and that is a reflection of

Speaker #2: And we had an awesome result back in February. We showed that after six rounds of design, we had improved the cost of self-free protein synthesis by 40% over scientific state-of-the-art.

Jason Kelly: That then flows through, and it means all of the CRO services you order and so on are very expensive. We think we can solve that problem through automation and the cloud.ginkgo.bio, our cloud lab service is really a great way to do that. If you go to the next slide, this is what OpenAI took advantage of when we did this project where GPT-5 ran the lab, and we had an awesome result. Back in February, we showed that after six rounds of design, we had improved the cost of cell-free protein synthesis by 40% over scientific state-of-the-art. That opened a lot of eyes. I think people We didn't know ahead of time whether the models would even be able to design experiments and interpret data at this level of sophistication.

Speaker #2: That opened a lot of eyes. I think people weren't really we didn't know ahead of time whether the models would even be able to design experiments and interpret data at this level of sophistication.

Speaker #2: So really excited about that. Really excited about future work we're going to be doing to keep proving this out with OpenAI. It's a neat line of work.

where all the costs lie in doing lab work, which is in manual. Lab work, done, 40 hours a week done at low equipment density. Low equipment utilization in Laboratories that cost a fortune to run, uh, that then flows through. And it means all of the cro Services you order. And and so on are very expensive. Uh, we think we can, we can solve that problem, through Automation. And the cloud, uh, dot go.io. Our Cloud lab service is really a great way to do that.

Speaker #2: I would say it's distinct from the autonomous lab. I'd call this really an AI scientist. Using the autonomous lab, using a cloud lab to get its work done, but it is also really an important thing to watch if you're following kind of how AI is changing science.

Speaker #2: On the next slide, also excited just in the last quarter, three new channels coming to our deliver business to our cloud lab and data points service.

Jason Kelly: Really excited about that. Really excited about future work we're gonna be doing to keep proving this out with OpenAI. It's a neat line of work. I would say it's distinct from the autonomous lab. I'd call this really an AI scientist using the autonomous lab, you know, using a cloud lab to get its work done. It is also really an important thing to watch if you're following kind of how AI is changing science. On the next slide. Also excited, just in the last quarter, three new channels coming to our deliver business to our cloud lab and Datapoints service. Amazon Bio Discovery got launched by AWS, which is basically a platform to allow you to design antibodies. All three of these are sort of in the antibody space.

Speaker #2: Amazon Biodiscovery got launched by AWS. Basically, a platform to allow you to design antibodies. All three of these are sort of in the antibody space, benching, similarly.

Speaker #2: And then Tamarind Bio. These are Tamarind and Amazon are sort of ways for pharma companies to access these frontier biomodels. So if you think of things like AlphaFold, which got the Nobel Prize for Demis at Google, that was one of the earliest protein design models.

I have to go to the next slide. This is what openai took advantage of when we did this project where GPT 5 ran the lab and we had an awesome result uh back in February. We showed uh that after 6 rounds of design we had improved the cost of uh self- free protein synthesis by 40% over scientific state of the art that that opened a lot of eyes. I think people weren't really, we didn't know ahead of time whether the models would even be able to design experiments and interpret data at this level of sophistication. So, uh, really excited about that. Really excited about future work. We're going to be doing, uh, to keep proving this out with open AI. It's it's, it's a neat line of work. I would say it's distinct from the autonomous lab. I'd call this really an AI scientist using the autonomous lab, you know, using a cloud lab to get its work done. Uh, but it is also really an important thing to watch. Uh, if you're following kind of how

Always changing science.

Speaker #2: There's many more now. They're computationally intensive. They're interesting. And they help drug discovery scientists come up with a design for an antibody or a protein for their drug.

Speaker #2: But then you got to test it, right? We don't know if these things work in biology unless you go into the lab. And so the idea is, could you have these layers where you access the latest models and all the compute to power them?

Jason Kelly: Benchling, similarly, and then Tamarind Bio. Tamarind and Amazon are sort of ways for pharma companies to access these frontier bio models. If you think of things like AlphaFold, which got the Nobel Prize for Demis at Google, that was like one of the earliest protein design models. There's many more now. They're computationally intensive, they're interesting, and they help drug discovery scientists come up with a design for an antibody or a protein for their drug. You got to test it, right? Like, we don't know if these things work in biology unless you go into the lab.

Speaker #2: And then when you're ready to do your experiment, you hit a button. And it kicks the designs to a cloud lab to do it for you.

Speaker #2: And the data flows back very nicely, well packaged right to the model. And you can run that loop as many times as you want.

Speaker #2: So that's sort of what's going on with Amazon and Tamarind and then benching is really the leader in electronic lab notebooks. And it's a similar idea.

Uh, on the next slide. Uh, also excited just in the last quarter, uh, 3, new channels coming to our, uh, delivered business to our Cloud lab and data points Service, uh, Amazon bio Discovery got launched by AWS, uh, which is basically a platform to allow you to design antibodies. All 3 of these are sort of in the antibody space, benching. Uh, similarly and then, uh, Tamarind bio. These are are, uh, Tamarind and Amazon are sort of ways for Pharma companies to access these Frontier biom models. So, if you think of things like Alpha fold, which got the Nobel Prize uh for Demis at Google those that was like 1 of the earliest protein design models. There's many more now. They're computationally intensive. They're interesting and they help drug.

Speaker #2: If you're in your ELN as a scientist and you've designed this experiment, could you ultimately hit go and kick it off to a cloud lab?

Discovery. Um scientists come up with a design for an antibody or a protein for their drug.

Jason Kelly: The idea is, could you have these layers where you access the latest models and all the compute to power them, and then when you're ready to do your experiment, you hit a button, and it kicks the designs to a cloud lab to do it for you, and the data flows back very nicely, well-packaged, right to the model. You can run that loop as many times as you want. That's sort of what's going on with Amazon and Tamarind. Benchling is really the leader in electronic lab notebooks. It's a similar idea. If you're in your ELN as a scientist and you've designed this experiment, could you ultimately hit go and kick it off to a cloud lab? We partnered there with our Datapoints service again around antibodies. Super exciting to see these.

Speaker #2: And we partnered there with our data point service again around antibodies. So super exciting to see these. I think this is like early indications of a way that could become a norm for how scientists do their work in the future and kind of order their laboratory experiments.

Speaker #2: Okay. I'll just say a couple more quick things about data points. Really excited by the progress here, working with 10 of the top biopharma companies in the world just in the first year of running it.

Speaker #2: It's a good mix of pharma and government and even tech companies and tech bio companies. We've done a nice job on the next slide of really being a community leader here.

Jason Kelly: I think this is like early indications of a way that could become a norm for how scientists do their work in the future and kind of order their laboratory experiments. Okay. I'll just say a couple more quick things about Datapoints. Really excited by the progress here, working with, you know, 10 of the top biopharma companies in the world just in the first year of running it. It's a good mix of pharma and government and even tech companies and tech bio companies. We've done a nice job on the next slide of really being a community leader here. We're running competitions. There's the Virtual Cell Pharmacology Initiative where we'll actually test compounds for free. People should definitely check that out if you're in the small molecule drug discovery space.

Speaker #2: We're running competitions. There's the virtual cell pharmacology initiative where we'll actually test compounds for free. People should definitely check that out if you're in the small molecule drug discovery space.

Speaker #2: So really neat opportunities. And we host these summits and things like that. It's been good. I think AI as applied to the design of drugs is a big area.

But then you got to test it, right? Like we don't know if these things work in biology, unless you go into the lab. And so the idea is could you have these layers where you access the latest models and all the compute to power them? And then when you're ready to do your experiment, you hit a button and it kicks the designs to a cloud lab to do it for you. And the data flows back very nicely, well packaged, right to the model and you can run that loop as many times as you want. So that's sort of what's going on with Amazon and Tamarind and then benching is really the leader in electronic lab notebooks and it's a similar idea. If you're in your eln as a scientist and you've designed this experiment, could you ultimately hit go and kick it off um to a cloud lab and we partnered there with our data point service again around. Um antibodies. So super exciting to see these. I think this is this is like early indications of a way that could become a norm for how scientists do their work in the future and kind of order their laboratory experiments.

Speaker #2: And with data points, we're sort of operating almost like a scale AI. Creating those just big data packages that train the models. All right.

Speaker #2: Next slide. We have had a long-standing business in solutions. About more than 250 of these research partnerships over the last 10 years. It's gotten us to work with the R&D groups of some of the largest companies in pharmaceuticals, industrial biotech, and agricultural biotech.

Jason Kelly: Really neat opportunities, and we host these summits and things like that. It's been good. I think AI, as applied to the design of drugs is a big area. With Datapoints, we're sort of operating almost like a Scale AI, like creating those just big data packages that train the models. All right, next slide. We have had a long-standing business in Solutions. We have more than 250 of these research partnerships over the last 10 years. It's gotten us to work with the R&D groups of some of the largest companies in pharmaceuticals, industrial biotech, and agricultural biotech.

Okay, I'll I'll just say a couple more quick things about data points. Uh, really excited by the progress here, working with, you know, 10 on the top biofarma companies in the world, just in the first year of running. It, it's a good mix of Pharma and government and even tech companies and Tech. Bio companies. Uh we've done a nice job on the next slide uh of really being a community leader here, we're running competitions. There's the virtual cell pharmacology initi initiative, where we'll actually test compounds for free. People should definitely check that out. If you're in the in the uh small molecule drug Discovery Space.

Speaker #2: And uniquely at Ginkgo, it is a huge range of different kinds of research. From microbes associated with the roots of corn and trying to engineer them to produce fertilizer to mRNA therapeutics or antibody development in pharmaceuticals to enzymes for industrial biotech.

Uh so really neat opportunities and we host these Summits and things like that. It's been good. I think AI has applied to the design of of drugs, uh is a big area and with data points we're sort of operating almost like a scale AI like creating those just big data packages, that train the models

Speaker #2: Really wide range of different types of genetic engineering and biotech lab work that has happened at Ginkgo. In sort of a not totally automated way.

Speaker #2: In other words, not like no people in the lab, but like semi-automated. So a human interacting with a liquid handling robot and a human interacting with various benchtop devices that can take a lot of samples at once.

Jason Kelly: Uniquely at Ginkgo, it is a huge range of different kinds of research, from, you know, microbes associated with the roots of corn and trying to engineer them to produce fertilizer to mRNA therapeutics or antibody development in pharmaceuticals to enzymes for industrial biotech. Really wide range of different types of genetic engineering and biotech lab work that has happened at Ginkgo in sort of a not totally automated way. In other words, not like no people in the lab, but like semi-automated. A human interacting with a liquid handling robot and a human interacting with various benchtop devices that can, you know, take a lot of samples at once. We're sort of like not all the way to an autonomous lab, but we're doing a lot of variable work over the years in semi-automated setups.

The largest companies in Pharmaceuticals, industrial biotech and agricultural biotech and uniquely. At Ginkgo, it is a huge range of different kinds of research.

From.

Speaker #2: So we were sort of like not all the way to an autonomous lab, but we're doing a lot of variable work over the years in semi-automated setups.

Speaker #2: And so if you go to the next slide, I'm most excited to move this kind of work onto Nebula. It is the hardest work to move, right?

Speaker #2: This is the stuff that really is that car I mentioned earlier, the lab bench. It's totally variable. It's really different. It's not just doing the same experiment over and over again like you would at a traditional CRO.

Speaker #2: But if you go remember my slide, it's worth 95% of the spending is going at all of our customers. They spend a bit with us, but they mostly spend on huge internal research labs.

Jason Kelly: If you go to the next slide, I'm most excited to move this kind of work onto Nebula. It is the hardest work to move, right? This is the stuff that really is that car I mentioned earlier, the lab bench. It's totally variable. It's really different. It's not just doing the same experiment over and over again like you would at a traditional CRO. If you remember my slide, it's where 95% of the spending is going at all of our customers. They spend a bit with us, but they mostly spend on huge internal research labs to do this kind of work. If we want to replace the manual lab bench, migrating the work from our Solutions business onto Nebula is a really critical demonstration. I'm excited about the progress there. We're trying to share that publicly. Venyads, we bring people through.

Speaker #2: To do this kind of work. And so if we want to replace the manual lab bench, migrating the work from our solutions business onto Nebula is a really critical demonstration.

Speaker #2: So I'm excited about the progress there. We're trying to share that publicly, vignettes. We bring people through. If you go to the next slide, one of the best things we do is we bring people through, show them the lab, let them talk to our scientists, see how scientists are submitting new protocols every day.

You know uh microbes associated with the roots of corn and trying to engineer them to produce, fertilizer to mRNA Therapeutics or antibody development and pharmaceuticals to enzymes for industrial biotech. Really wide range of different types of genetic, engineering and biotech. Lab work that has happened at go in sort of a, not totally automated way. In other words, not like no people in the lab but like semi-automated. So a human interacting with a liquid handling, robot and a human interacting with various benchtop devices that can, you know, take a lot of samples at once. So we were sort of like, not all the way to an autonomous lab, but we're doing a lot of variable work over the years in, in semi-automated setups. And so if you go to the next slide, I'm I'm most excited to move this kind of work onto nebula. It is the hardest work to move, right? This is the stuff that really is that that car I mentioned earlier the lab bench, it's totally variable, it's really different. It's not just doing the same experiment over and over again. Like you would at a tradition

Speaker #2: And this has been really exciting to bring research leaders from I've had, I don't know, three heads of pharma or ag R&D come through to visit just this year, right?

Speaker #2: To see the system. And so if you just want to visit, there's the link. You really should come by. But I think Nebula and our services on top of it is a truly unique asset to demonstrate what we think fundamentally is a better way to do biotech R&D.

Jason Kelly: If you go to the next slide, one of the best things we do is we bring people through, show them the lab, let them talk to our scientists, see how scientists are submitting new protocols every day. This has been really exciting to bring research leaders from, you know, I've had, I don't know, three heads of pharma R&D come through to visit just this year, right, to see the system. If you just want to visit, there's the link. You really should come by. I think Nebula and our services on top of it is a truly unique asset to demonstrate what we think fundamentally is a better way to do biotech R&D. We would love to get it in at every company out there and replace their benches. Go to the next slide.

Speaker #2: And we would love to get it in at every company out there and replace their benches. So go to the next slide. That is the world that I want to see.

Speaker #2: And so please if you're interested, you can email me at Jason@GinkgoBioWorks.com. Happy to follow up and happy to take your questions now. Thank you.

Speaker #1: 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, please raise your hands on Zoom.

Speaker #1: And I'll call on you and open up your line. Thanks, everyone. All right. Thanks for joining us, everyone. So just a reminder to the analysts on the line, if you have a question, feel free to raise your hand and I'll call on you.

Cro. But if you go remember my slide, it's worth 95% of the spending is going at all of our customers there. You know, they they spend a bit with us, but they mostly spend on huge internal research Labs uh to do this kind of work. And so if we want to replace the manual lab bench, migrating the work from our Solutions business onto nebula is is a really critical demonstration. So I'm excited about the progress there, we're trying to share that publicly vignettes, we bring people through. If you go to the next slide, 1 of the best things we do is we bring people through, show them the lab. Let them talk to our scientists. See how scientists are submitting new, protocols every day and this has been really exciting to bring uh, research leaders from, you know, I've had, I don't know, 3 heads of uh farmer or a R&D come through to visit that just this year, right? Um, to see the system and and so if you just want to visit, There's the link uh you really should come by but I think nebula and our services on top of it is a truly unique asset to demonstrate what we think fundamentally is a better way.

Jason Kelly: That is the world that I want to see. Please, if you're interested, you can email me at Jason at ginkgobioworks.com. Happy to follow up and happy to take your questions now. Thank you.

Uh, to do biotech R&D and we would love to get it in at every company out there and replace their benches.

So, go to the next slide. Uh, that is the world that I want to see. Uh, and so please, uh, if you're interested, you can email me at Jason, govworks.com happy to follow up.

[Company Representative] (Ginkgo Bioworks): Great. Thanks, Jason. As usual, I'll start with a question from the public. Remind the analysts on the line that if you'd like to ask a question, please raise your hands on Zoom and I'll call on you and open up your line. Thanks, everyone. All right. Thanks for joining us, everyone. Just a reminder to the analysts on the line, if you have a question, feel free to raise your hand and I'll call on you. We have one to start off submitted from Brendan at TD. We got it over email. He has 2 questions. The first one is, how should we think about the potential impact to revenues this year from the AWS and Benchling announcements? How have the launches gone thus far? What is baked into your assumptions for the rest of 2026 for these new platforms?

I'm happy to take your questions now. Thank you.

Speaker #1: We have one to start off submitted from Brendan@td. We got over email. He has two questions. So the first one is, how should we think about the potential impact to revenues this year from the AWS and benching announcements?

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, please raise your hands on zoom and I'll call on you and open up your line.

Thanks everyone.

Speaker #1: How have the launches gone thus far? And what is baked into your assumptions for the rest of 2026 for these new platforms?

Speaker #2: Yeah, I can take that one. So yes, we talked about AWS benching. The other one in that same category is the Tamarind Bio partnership as well.

Speaker #2: I'm super excited about these. I mean, this is the first time I've seen this sort of kind of like cloud layer talking directly to labs as a sales channel.

Speaker #2: So I'm excited to see where it goes. It is definitely new, right? So I was like seeing like a flood of inbound there. We are seeing some people reaching out to us because of the channel.

Speaker #2: So that's exciting. I'm most excited that it's starting around antibodies. Right? Because that's just kind of naturally, there's a number of these AI models associated with antibodies and so on.

Jason Kelly: Yeah, I can take that one. Yes, we talked about AWS Benchling. The other one in that same category is the Tamarind Bio partnership as well. I'm super excited about these. I mean, this is the first time I've seen this sort of kind of like cloud layer talking directly to labs as a sales channel. I'm excited to see where it goes. It is definitely new, right? We're not like seeing like a flood of inbound there. We are seeing some people reaching out to us because of the channel. That's exciting. I'm most excited that, you know, it's starting around antibodies, right? Because that's just kind of naturally there's a number of these AI models associated with antibodies and so on. Because there's a few different providers that'll do these antibody services for you.

All right, uh, thanks for joining us, everyone. So just a reminder to the analysts on the line, uh, if you have a question, feel free to raise your hand and I'll call on you. Uh, we have 1 to start off, submitted from Brendan at TD, uh, we got over email. Um, he has 2 questions. So the first 1 is, how should we think about the potential impact to revenues this year from the AWS? And benching announcements? How have the launch has gone thus far? And what is baked into your assumptions for the rest of 2026 for these new platforms?

Yeah, I can take that 1.

Speaker #2: And because there's a few different providers that'll do these antibody services for you. But what I'm most excited about is with our cloud lab, we're not limited to testing antibody binding.

Um so yeah. So we talked about AWS benching, the other 1 in that same category as The Tamarind bio uh partnership as well. I I'm super excited about these. I mean, this is the first time I've seen this sort of uh,

Speaker #2: Right? If you look at already on the, I don't know, eight or nine, ten protocols we posted, and we're posting a new one every week, it's a pretty wide variety of stuff.

Speaker #2: We're doing mass spec metabolomics. All kinds of things. And so you can come and ask for a protocol on cloud lab. We'll add it.

Kind of like Cloud layer talking directly to Labs as a sales channel. So I'm excited to see where it goes. It is definitely knew, right. Um, sometimes like seeing like a flood of inbound, there we are seeing some people reaching out to us because of the channel, so that's exciting. Um, I'm I'm most excited that, you know, it's starting around antibodies.

Speaker #2: I'd love that to turn into a channel straight from an electronic lab notebook or whatever where a scientist is like, this is the protocol I want.

Speaker #2: Price it. You get a price back from cloud.ginkgo.bio. And then you go run your experiment. I think that's a much that feels a lot closer to AWS and sort of like what we saw as successful with cloud compute than where these are today, which is really much more just in a more narrow lane around antibodies, which I think is an exciting place to start.

Jason Kelly: What I'm most excited about is with our Cloud Lab, we're not limited to testing antibody binding, right? If you look already on the, I don't know, 8 or 9, 10 protocols we've posted, and we're posting a new one every week. It's a pretty wide variety of stuff. We're doing mass spec, metabolomics, all kinds of things. You can come and ask for a protocol in Cloud Lab. We'll add it. I'd love that to turn into a channel straight from an electronic lab notebook or whatever, where a scientist is like, this is the protocol I want. Price it. You get a price back from, you know, cloud.ginkgo.bio, and then you go run your experiment.

Right, because that's just kind of naturally. There's a number of these AI models associated with antibodies, and so on, uh, and because there's a few different providers, that'll do these antibodies services for you.

but what I'm most excited about is with our Cloud lab, we're not limited to

Speaker #2: But I am super excited to fan that out. I think that then it could become really quite an interesting channel. And something that scientists just don't have access to today.

Speaker #2: At the end of the day, you can't get custom stuff done. So I think that's one of the most excited about there.

Jason Kelly: I think that feels a lot closer to AWS and sort of like what we saw as successful with cloud compute than where these are today, which is really much more just in a more narrow lane around antibodies, which I think is an exciting place to start. I am super excited to fan that out. I think that it could become really quite an interesting channel and something that scientists just don't have access to today. You know, at the end of the day, you can't get custom stuff done. I think that that's what I'm most excited about there.

Speaker #1: Cool. All right. Next question from Brendan. What are you hearing on data points and the collective AI-driven offerings with Ginkgo as especially attractive for customers as biotech and pharma companies continue to roll out their own AI capabilities?

Speaker #1: In other words, what kind of demand dynamics are you seeing here? And are there any potential revenue funnel unlocks we should watch for over the coming quarters from this part of the business?

Speaker #2: Yeah. So I've been super I mean, we launched data points a year and a half ago now. And to have 10 of the top pharma companies as customers now is really exciting.

[Company Representative] (Ginkgo Bioworks): Cool. All right. Next question from Brendan. What are you hearing on Datapoints and the collective AI driven offerings with Ginkgo as especially attractive for customers as biotech and pharma companies continue to roll out their own AI capabilities? In other words, what kind of demand dynamics are you seeing here? Are there any potential revenue funnel unlocks we should watch for over the coming quarters from this part of the business?

Exciting place to start. Uh, but I am super excited to fan that out. I think that then it could become really quite an interesting channel, and something that scientists just don't have access to today. You know, at the end of the day, you can't get custom stuff done. So, uh, I think that that's one of the things I'm most excited about there.

Speaker #2: I think the revenue unlock is just repeat. Repeat business from those customers. And so we are starting to see that. And what we saw was sort of like pilot projects, data gen projects, and then now you've got again, because you are seeing people trying to build in-house models.

Speaker #2: Now remember, these are not reasoning models. These are not like in-house versions of Claude or Codex or Opus or whatever or GPT-5.5. They are models trained on biological data.

Jason Kelly: Yeah. I've been super, I mean, we launched Datapoints a year and a half ago now and to have, you know, 10, the top pharma companies as customers now is really exciting. I think the revenue unlock is just repeat business from those customers. We are starting to see that. What we saw was sort of like pilot projects, data gen projects. Now you've got, again, because you are seeing people trying to build in-house models. Now remember, like these are not reasoning models. These are not like in-house versions of Claude or Codex or, you know, Opus or whatever, or GPT-5.5. They are models trained on biological data. They're much more specialized.

Cool. All right. Next question. From Brendan uh what are you hearing on data points? And the collective AI driven offerings with Gino as a specially attractive for customers as biotech and Pharma companies continue to roll out their own AI capabilities. In other words, what kind of demand Dynamics, are you seeing here? Uh and are there any potential Revenue? Funnel unlocks? We should watch for over the coming quarters from this part of the business.

Speaker #2: So they're much more specialized. And so I do think it makes sense actually in the field that you're going to see a lot of people having their own data sets, their own models that are sort of tuned up versions maybe of various protein models.

Yeah, so I've been super, I mean, we launched data points mostly a year and a half ago now and to have, you know, 10 the top Pharma companies as customers Now is really exciting. I think the revenue unlock is just repeat uh, repeat business from those customers. And so we are starting to see that.

Speaker #2: That's not going to be uncommon at all. Much more common than I think you'll see in the reasoning model and coding space. Because these things are very different.

Speaker #2: And people have different data sets. And so I'm sort of hopeful as people are building these models, we'll keep seeing this sort of repeat demand as they're like, OK, I found one.

Speaker #2: I like what I'm seeing in terms of return on data and performance of my internal model. Give me more data. And so that's the revenue unlock.

Jason Kelly: I do think it makes sense actually in the field that you're going to see a lot of people having their own data sets, their own models that are sort of tuned up versions maybe of various protein models. That's not going to be uncommon at all. Much more common than I think you'll see in the reasoning model and coding space because these things are very different and people have different data sets. I'm sort of hopeful as people are building these models, we'll keep seeing this sort of repeat demand as they're like, okay, I found one. I like what I'm seeing in terms of return on data and performance of my internal model. Give me more data. That's the revenue unlock. The more that we see, then I think we become sort of like a default provider.

And and what we saw was sort of like pilot projects data, Jen projects and then now you've got again because you are seeing people trying to build in-house models. Now remember like these are not reasoning models. These are not like in-house versions of clot or codecs or you know or you know Opus or whatever or G55 they they are uh models trained on biological data.

Speaker #2: The more of that we see, then I think we become sort of like a default provider that's certainly what happened with scale and other places in the early days of image models and then language models when people saw, oh, I'm seeing performance increase with more data.

Speaker #2: They turned around and bought more data. That's what we're going to be watching as these protein models and other and it does not just protein, other types of models come out too in the future.

Speaker #2: I think that's the lane for data points.

Speaker #1: Cool. So sort of on the theme of AI, there was someone who was on X who asked us a question. I think this is sort of based on our project with OpenAI.

Jason Kelly: That's certainly what happened with Scale AI and other places in the early days of image models and then language models. When people saw, I'm seeing performance increase with more data, they turned around and bought more data. That's what we're going to be watching as these protein models and other, and it's not just protein, other types of models come out too in the future. I think that's the lane for Datapoints.

Speaker #1: How much efficiency improvements after using GPT-5.5? Any idea for space left for improvement? Will this be a transitional factor?

Speaker #2: Yeah. So we did this project and just to remind, that we announced back in February with OpenAI as our first project with them where we had GPT it was actually not 5.5, it was 5.

So they're much more specialized and so I do think it makes sense, actually in the field that you're going to see a lot of people having their own data sets. Their own models that are sort of tuned up versions maybe of various protein models. That's not going to be uncommon at all much more common than I think you'll see in the reasoning model and coding space. Uh because these things are very different and people have different data sets and so I'm sure hopeful as are people are building these models. We'll keep seeing this sort of repeat demand as they they're like, okay, I found 1, I like what I'm seeing in terms of return on data and performance of my internal model, give me more data. Um and so that that's the revenue, unlock the more that we see then I think we become sort of like a default provider that's certain what happened with scale and other places in the early days of image models. And then language models, when people saw, oh I'm seeing performance increase with more data, they turn around and bought more data, that's what we're going to be watching, um, as these, uh, protein models and other, and it does not just protein other types of models.

[Company Representative] (Ginkgo Bioworks): Cool. Sort of on the theme of AI, there was someone who was on X who asked us a question. I think this is sort of based on our project with OpenAI. How much efficiency improvements after using GPT-5.5? Any idea for space left for improvement? Will this be a transitional factor?

Speaker #2: Because we started much earlier and that was when that was the model that was out. And we kind of kept the same model through the whole thing for like more scientific paper purposes.

Come out too. In the future. Uh I think that's that's the lane for data points.

Speaker #2: And so we were able to show over a series of six rounds of running the model, with 100 384 well plates designed by GPT-5 per round, a 40% improvement over state of the art in the scientific goal we were trying to achieve.

Jason Kelly: We did this project, and just to remind, that we announced back in February with OpenAI as our first project with them, where we had GPT. It was actually not 5.5, it was 5, 'cause we started much earlier, and that was the model that was out, and we kinda kept the same model through the whole thing for, like, more scientific paper purposes. We were able to show over a series of six rounds of running the model with 103 four-well plates designed by GPT 5 per round, a 40% improvement over state-of-the-art in the scientific goal we were trying to achieve. I think there's real interesting questions. A, how much further could you push that?

Cool. The sort of on the the theme of AI there is someone who is on X, who asks us a question. I think this is sort of based on our project with openai um how much efficiency improvements after using GPT 5.5. Any idea uh for space left for improvement. Will this be a transitional Factor?

Speaker #2: I think there's a real interesting questions. A, how much further could you push that? Like sort of what is actually diminishing returns look like in some of these scientific areas?

Speaker #2: Can the model have sort of breakthrough ideas that create really new ways of doing this? TBD. And then as the models have gotten better, yeah, it would 5.5 be better than what we got with 5.

Speaker #2: Right? I think that's all going to be exciting stuff to test. So we're excited to do more with OpenAI. And we're planning to. And so I think this is open terrain in terms of how good the reasoning models can be at basically experimental design and experimental analysis.

Speaker #2: That's the those are the two things that's really doing. It's like, here's an experiment I want to run. Give me back the data, cloud lab.

Jason Kelly: Like, sort of, you know, what does actually diminishing re-returns look like in some of these scientific areas? Can the model have sort of breakthrough ideas that create really new ways of doing this TBD? As the models have gotten better, yeah, would 5.5 be better than what we got with 5, right? I think that's all gonna be exciting stuff to test. We're excited to do more with OpenAI, and we're planning to. I think this is open terrain in terms of how good the reasoning models can be at basically experimental design and experimental analysis. Those are the two things it's really doing. It's like, Here's an experiment I wanna run. Give me back the data, Cloud Lab, you know, autonomous lab.

Speaker #2: Autonomous lab. Give me back what are the results of my experiments I just designed. And then I'm going to analyze them and design more experiments.

Speaker #2: We'll see. I think that's a real exciting to watch what's going to be capable of there. It's a new way to do science. It really is.

Yeah, so so we have this project and just to remind um, that we asked back in February with open AIS. Our first project with them where we had, uh, GPT is actually not, 55 is 5, uh, because we started much earlier and that was when that was the model that was out. And we kind of kept the same model through the whole thing for like more scientific paper purposes. Uh, and so we, um, we were able to show over a series of 6 rounds of running the model with 1384. Well, plates designed by gbt 5 per round, a 40% improvement. Over state of the art in the scientific goal. We were trying to achieve I think it's real interesting questions a how much further could you push that? Like sort of, you know, what is actually diminishing the returns look like and some of these scientific areas. Uh, can the model have sort of breakthrough ideas, that create really new ways of doing this TBD. Um, and then at the models have gotten better.

Speaker #2: And I won't belabor this too much, but I think it roughly can turn individual like the access to a model like that plus an autonomous lab can let individual scientists operate closer to how a principal investigator of an academic lab or a head of a drug discovery group who has a lab of eight people or a lab of 30 people and is sort of assigning hypotheses to different people and kind of pursuing that over time, an individual could push that out for probably close to the same cost as they're currently costing to be themselves at a lab bench in terms of they're just their utility costs and everything else and utilization, low utilization of equipment.

Jason Kelly: Give me back what are the results of my experiments I just designed, and then I'm gonna analyze them and design more experiments. We'll see. You know, I think that's real exciting to watch what's gonna be capable out there.

Yeah, it would 555 be better than what we got with 5, right? I think that that's all going to be exciting stuff to test. So we're excited to do more with openai uh and we're planning to and so I think this is an open Terrain in terms of how good the reasoning models can be at basically experimental design and experimental analysis. That that's the those are the 2 things that's really doing. It's like here's an experiment. I want to run, give me back the data. Cloud lab, you know, a Thomas lab. Give me back. What? What are the results of my experiments? I just designed and then I'm going to analyze them and design more experiments.

[Company Representative] (Ginkgo Bioworks): Cool.

Jason Kelly: It's a new way to do science. It really is. I won't belabor this too much, but I think it roughly, the access to a model like that plus an autonomous lab can let individual scientists operate closer to how a principal investigator of an academic lab or a head of a drug discovery group who has a lab of 8 people or a lab of, you know, 30 people, and is sort of assigning hypotheses to different people and kind of pursuing that over time. An individual could push that out for probably close to the same cost as they're currently costing to be themselves at a lab bench, in terms of their utility costs and everything else, and utilization, low utilization of equipment.

we'll see, you know, I think that's a real exciting to watch what's going to be capable of their

Speaker #2: They could push out five agents on top of an autonomous lab to go pursue a bunch of experiments. That is real exciting if that works.

Speaker #2: I think it really fundamentally changes the rate we can do science. That's why you see the Genesis mission in the US investing in this sort of stuff because their goal is to 2x the output of US science.

Speaker #2: These are the ways that'll do it. And our science-based industries, of which pharma is the biggest, will be completely changed by this. If you can two or 3x the rate, no question about it.

Jason Kelly: They could push out five agents on top of an autonomous lab to go pursue a bunch of experiments. That is real exciting if that works. I think it really fundamentally changes the rate we can do science. That's why you see the Genesis mission in the US investing in this sort of stuff because their goal is to 2x the output of US science. These are the ways that'll do it, and our science-based industries, of which pharma is the biggest, will be completely changed by this, if you can 2x or 3x the rate, no question about it.

Speaker #1: All right. Our next question is really a bundle of questions from DK, who's writing from South Korea. And these questions are all about how the move onto Nebula, our autonomous lab, has sort of changed the science that we're doing.

Speaker #1: So the questions are, how does the use of Ginkgo's automated lab affect overall costs? Are there meaningful differences in speed, for example, turnaround time for experiments?

Experiments, that that is is real exciting, if that works. I think it really fundamentally changes the rate we can do science. Uh, that's why you see the Genesis mission in the US investing in this sort of stuff, because their goal is to 2x the output of us science.

Speaker #1: And have you observed improvements and success rates reproducibility or scalability since moving to the autonomous lab?

These are the ways that that'll do it and our science-based industries of which Farm is the biggest will be completely changed by this. Um, if you can 2 or 3x the rate, no question about it.

[Company Representative] (Ginkgo Bioworks): All right. Our next question is really a bundle of questions from DK, who's writing from South Korea. These questions are all about how the move onto Nebula, our autonomous lab, has sort of changed the science that we're doing. The questions are, how does the use of Ginkgo's automated lab affect overall costs? Are there meaningful differences in speed, for example, turnaround time for experiments? Have you observed improvements in success rates, reproducibility, or scalability since moving to the autonomous lab?

Speaker #2: Yeah. So on cost, I tried to touch on this a little bit in the talk. But I think the clear ROI, not just for us, but for any one of our customers looking at an autonomous lab, is about a threefold reduction in space utilization compared to a manual lab and a fourfold increase in the time.

All right, our next question is really a bundle of questions, uh, from DK who's writing from South Korea. Uh, and these questions are all about how the move onto nebula. Our autonomous lab has sort of changed the science that we're doing.

Speaker #2: In other words, the amount of time the lab is being used to do lab work from that 40 hours to 168, 24/7 week. That's really that those improvements is where it's going to yield the cost reduction.

Speaker #2: But that is a huge amount. Because those are really the two sort of people time and space time are the two big things we spend money on in research.

Jason Kelly: Yeah. On cost, I tried to touch on this a little bit in the talk, but I think, like, the clear ROI, not just for us, but for any one of our customers looking at an autonomous lab, is about a 3-fold reduction in space utilization compared to a manual lab, and a 4-fold increase in the time. In other words, like, the amount of time the lab is being used to do lab work, right? From that 40 hours to 168, 24/7 week. Those improvements is where it's gonna yield the cost reduction. That is a huge amount, like, 'cause those are really the two, like, sort of people time and space time are the two big things we spend money on in research. On the speed front, yeah, it's interesting.

So the questions are, how does the use of Go's automated lab affect overall costs? Are there meaningful differences in speed for example, turnaround time for experiments and have you observed improvements in success rates reproducibility or scalability since moving to the autonomous life.

Speaker #2: On the speed front, yeah, it's interesting. An individual protocol doesn't really get shorter. Then necessarily you would do it at the bench. You can imagine ways to do that in the future, rebuild protocols differently.

Speaker #2: But the first thing scientists are going to do is just take work they're doing at the bench and move it onto the autonomous lab.

Speaker #2: And in that world, it does not need to get faster in terms of end-to-end time for the protocol. However, it in practice can get faster because you can start a protocol at 4:00 p.m.

Yeah, so on cost, I try to talk to you on this a little bit in the talk, but the um, I think like the clear Roi not just for us, but for any 1 of our customers looking at an autonomous lab is about a 3-fold reduction in space utilization compared to a manual lab and a 4-fold increase in the time. In other words, like the amount of time, the lab is being used to do lab work right from that 40 hours to 168 24/7 week. That's really that, that, that those improvements is where it's going to yield, the cost reduction. Um, but that is a huge amount because those are

Speaker #2: in the afternoon where you never would have planned to spend the next seven hours in the lab, kick it off, and have the thing run overnight.

Jason Kelly: An individual protocol doesn't really get shorter, like, than necessarily you would do it at the bench. You can imagine ways to do that in the future, rebuild protocols differently. The first thing scientists are gonna do is just take work they're doing at the bench and move it onto the autonomous lab. In that world, it does not need to get faster in terms of, like, end-to-end time for the protocol. However, it, in practice, can get faster 'cause you can start a protocol at 4:00 PM in the afternoon, where you never would have planned to spend the next 7 hours in the lab, kick it off, and have the thing run overnight.

Speaker #2: So in that world, you took an experiment that you would have started tomorrow at 10:00 a.m. and started at 4:00 p.m. and have the results by tomorrow at 8:00 or 10:00 a.m.

Speaker #2: And so that can shave a whole day off. So I think you will see actually a massive speed up because scientists will start taking advantage of the 4x more time that they have available every week.

Speaker #2: So if they plan it right, in theory, you could see a fourfold improvement in a lot of the times depending on how serialized your experiments need to be.

Speaker #2: So I think that's really exciting. And our scientists at Ginkgo really like that. And then on the just sort of improvement in I would call this like the quality of the experiments.

Jason Kelly: In that world, you took an experiment that you would've started tomorrow at 10:00AM and start it at 4:00PM and have the results by tomorrow at 8:00AM or 10:00AM. That can shave a whole day off. I think you will see actually a massive speed up because scientists will start taking advantage of the 4x more time that they have available every week. If they plan it right, you know, in theory, you could see a four-fold improvement in a lot of the times, depending on how serialized your experiments need to be. I think that's really exciting, and our scientists at Ginkgo really like that. On the just sort of like improvement and like the quality of the experiments.

Really the 2 um like sort of people time and space time or the 2, big things we spend money on and research um, on the speed front. Yeah, it's interesting. An individual protocol doesn't really get shorter like the necessarily you would do it at the bench. You can imagine ways to do that, in the future rebuild protocols differently, but the first thing scientists are going to do is just take work. They're doing at the bench and move it onto the autonomous lab. And in that world, it does not need to get faster in terms of like end to end time for the protocol. However, it in practice can get faster because you can start a protocol at 4 p.m. in the afternoon where you never would have planned to spend the next 7 hours in the lab, kick it off and have the thing run overnight.

Speaker #2: I think reproducibility is inherently advantaged on automation. And that's mainly to do with the audit trail. You kind of if an instrument errors, if a liquid handler makes a mistake, these are all tracked.

So in that world you took an experiment that you would have started tomorrow at 10:00 a.m. and started at 4 pm and have the results by tomorrow at 8 or 10 a.m.

Speaker #2: So you kind of know those experiments that you don't catch at the manual lab bench, you catch if there's such a mistake on the autonomous lab.

and so that that can shave a whole day off. So so I think you will see actually a massive speed up because scientists will start taking advantage of the Forex more time that they have available every week.

Speaker #2: So if you saw a really, wow, that's a surprising result, you might go back, look at your experiment and say like, oh, oops, I see what I did there.

So a big plan it right? You know in theory you can see a 4-fold Improvement in a lot of the times depending on how serialized your experiments need to be. So I think that's really exciting and our scientists and I can't go really like that.

Jason Kelly: I think reproducibility is inherently advantaged on automation. That's mainly to do with, like, the audit trail. If an instrument errors, if a liquid handler makes a mistake, you know, like, these are all tracked. You kinda know, like, those experiments that you don't catch at the manual lab bench, you catch if there's such a mistake on the autonomous lab. If you saw a really, Wow, that's a surprising result, you might go back, look at your experiment, and say like, Oh, oops, I see what I did there. I, like, designed this experiment in a way that was, like, a little silly, and that's actually what's giving me this result, as opposed to assuming you did the experiment you wanted to do, and that was the origin of this, like, amazing result you got.

Speaker #2: I designed this experiment in a way that was like a little silly. And that's actually what's giving me this result as opposed to assuming you did the experiment you wanted to do and that was the origin of this amazing result you got.

Speaker #2: I think, yeah, that's a common thing that can happen for no nefarious reasons for scientists at the bench. And so I think that you will see a big improvement in reproducibility.

Um, and then on the um, just sort of like Improvement in like the, I would say, I would call this like the quality of the experiments I think, uh, reproducibility is inherently advantaged on automation. Uh, and that's, and that's mainly to do with like the audit Trail like you kind of if an instrument errors if a liquid Handler makes a mistake. You know, like what these are all tracked. So you kind of know like those those experiments that you don't catch.

Speaker #2: And then the other thing that got brought up there was throughput. Yeah, the throughput increase is going to be insane. I think people are surprised when they go to cloud.ginkgo.bio, which I encourage people to do, and type in a protocol and see how much it costs.

Speaker #2: Because I'm basically pricing that protocol based on reagent use and equipment time and a markup on that. And it is not the insane costs that you have when you're have a whole team doing this work at the bench.

Jason Kelly: I think, yeah, that's a common thing that can happen for no nefarious reasons for scientists at the bench. I think that you will see a big improvement in reproducibility. The other thing that got brought up there was throughput. Yeah, the throughput increase is going to be insane. I think people are surprised when they go to cloud.ginkgo.bio, which I encourage people to do, and type in a protocol and see how much it costs. I'm basically pricing that protocol based on reagent use and equipment time and a markup on that, and it is not the insane cost that you have when you have a whole team doing this work at the bench. It's just not.

Speaker #2: It's just not. So if scientists really understood just how low cost each sample could be in an experiment and they didn't and in order to do many more, they just hit a button rather than have to slave in the lab for three days doing 1,000 experiments, they're going to just order 1,000 experiments.

At the manual, lab bench. You catch if there's such a mistake on the atoms. So, if you saw a really wow, that's a surprising result. You might go back. Look at your experiments and say, like, oh oops. I see what I did there. I like designed this experiment in a way that was like, a little silly. And that's actually what's giving me this result as opposed to assuming you did the experiment you wanted to do and that was the origin of this, like, amazing result. You got? Uh, I think yeah, that's a common thing. That can happen for no nefarious reasons for scientists at the bench. And so I think um, that that you will see a big Improvement in reproducibility. Uh and then the other thing that got brought up there was throughput

Yeah, the 3-foot increase is going to be insane.

Speaker #2: And so I think you will see an explosion in the amount of data. And this is 100% what happened in every other field that's ever been automated.

I think people are surprised when they go to cloud.google.com IO, which I urge people to do and type in and protocol and see how much it costs. Because I'm basically pricing that protocol based on reagent use and Equipment time and a markup on that and it is not

Speaker #2: It's the beginnings of the automation of computation. When we went from slide rules to automated computation in an explosion in the amount of compute you use and a massive increase in the return on investment from what people who understood how to design computation could do.

Jason Kelly: Like, so if scientists really understood just how low cost each sample could be in an experiment, and in order to do many more, they just hit a button rather than have to slave in the lab for 3 days doing 1,000 experiments, they're gonna just order 1,000 experiments. I think you will see an explosion in the, in the amount of data. This is 100% what happened in every other field that's ever been automated, right? It's the like the beginnings of the automation of computation, right? Like when we went from slide rules to automated computation, an explosion in the amount of compute you use and a massive increase in the return on investment from what people who understood how to design computation could do.

Speaker #2: And that's what I want to do for the scientists, for the drug discovery leads when they have access to an autonomous lab compared to the ROI and the throughput that they can get out of manual labs.

The insane costs that you have. When you're have a whole team doing this work at the bench, it's just not like like so. So if scientists really understood just how low cost each sample could be in an experiment and they didn't and in order to to do many more they just hit a button rather than have to slave in the lab for 3 Days, doing a thousand experiments. They're going to just order a thousand experiments.

Speaker #2: It's just going to be no comparison. So yeah, I think all three, you're going to see big gains on. And the cool thing is we're going to keep showing this on Nebula.

And so I think you will see an explosion in the in the amount of data and this is 100% what happened in every other field that's ever been automated.

Speaker #2: So we just had a again, head of R&D through today, and we went through with his team and showed all the gains and it's yeah, it's really exciting right now.

Speaker #1: So I think we'll end on a note. Can I relate it to that, which is you guys mentioned in the call, you've mentioned other places.

Jason Kelly: That's what I wanna do for the scientists, for the drug discovery leads when they have access to an autonomous lab compared to the, to the ROI and the throughput that they can get out of manual labs. It's just gonna be no comparison. Yeah, I think, I think all three you're gonna see big gains on. The cool thing is we're gonna keep showing this on Nebula. We, you know, just had a, again, head of R&D through today, and we went through with his team and showed all the, all the gains, and it's Yeah, it's really exciting right now.

Speaker #1: You're trying to get to 100 racks. When do you actually expect to get there?

Speaker #2: Yeah. So it's been pretty fun. I had to put some behind-the-scenes videos up. But we have been installing racks for the last three weeks here at Ginkgo.

Speaker #2: They're just showed up on trucks, built by our team in Emeryville. And we just added the additional 50. They are all fully connected now in the lab.

Speaker #2: Just took a tour of it. It's insane. And we can run them now. The original system is running. And now the new 50 is running.

[Company Representative] (Ginkgo Bioworks): I think we'll end on a note kind of related to that, which is, you guys mentioned in the call, you've mentioned other places, that you're trying to get to 100 racks.

Throughput that they can get out of manual Labs it's just no going to be no comparison. Um, so yeah, I think I think all 3, you're going to see big games on and the cool thing is we're going to keep showing this on nebula. So we, you know, just had a again had a R&D for today and we went through with this team and showed all the all the gains and it's it's a yeah, it's really exciting right now.

Speaker #2: And there is a connection between the two. And that connection is going to get turned on, I think, in the 14th. Next week. So it is imminent.

Jason Kelly: Yeah.

[Company Representative] (Ginkgo Bioworks): When do you actually expect to get there?

Jason Kelly: Yeah. It's been pretty fun. We're gonna put the behind-the-scenes videos up. We have been installing racks for the last 3 weeks here at Ginkgo. They just showed up on trucks, built by our team in Emeryville. We just added the additional 50. They are all fully connected now in the lab. Just took a tour of it. It's insane. We can run them now like the original system is running, and now the new 50 is running, and there is a connection between the two, and that connection is gonna get turned on, I think, on the 14th. I don't know. Next week. It is imminent. Really excited to see it all come together, we already have it up now running as two separate loops.

Speaker #2: It's a really excited to see it all come together. But we already have it up now running as two separate loops. So to put in 50 new pieces of equipment in three weeks, again, these are just things that no one's ever done in laboratory automation.

So, I think we'll end on a note, kind of related to that, which is, uh, you guys mentioned in the call, you've mentioned other places, uh, you're trying to get to 100 racks. Um, when when do you actually expect to get there?

Speaker #2: So I do think we are doing a very unique thing here at Ginkgo. That's the bet. That's certainly what I'm leaning in on the company.

Speaker #2: It's what we're investing our capital into. It's where our new customers are coming from. And so if you like that idea, I think now is a really exciting time to get involved with the company in any way.

Speaker #2: And so but yes, we're going to be at 100 next week. 103 or 5, I got to count them up. Yeah.

Jason Kelly: To put in 50 new pieces of equipment in 3 weeks, again, these are just things that no one's ever done in laboratory automation. I do think we are doing a very unique thing here at Ginkgo. That's the bet. That's certainly what I'm leaning in on the company. It's what we're investing our capital into. It's where our new customers are coming from. If you like that idea, I think now is a really exciting time to get involved with the company in any way. Yes, we're gonna be at 100 next week. 103 or 5. I gotta count them up. Yeah.

Speaker #1: Thanks. All right. If you want to follow us on that journey, you can go to X or LinkedIn. Instagram and keep watching. We'll have a lot of content coming about the unveiling of the new full system.

Speaker #1: And as always, if you have questions, you can reach out to us at investors@ginkgobioworks.com. Thanks so much, everyone. Until next time.

Yeah, so it's been pretty fun. Uh, I had to put it behind the scene videos up, uh but we have been installing racks for the last 3 weeks here at go. Just showed up on trucks built by our team at Emeryville, uh, and we just added, uh, the additional 50. They are all fully connected now, and allow you to take a tour of it. It's insane. Uh, and so and we can run them now, like, the original system is running and now, the new 50 is running and there is a connection between the 2 and that connection is going to get turned on I think on the 14th next week. So it is imminent. Uh it's a really excited to see it all come together, but we already have it up now running as 2 separate Loops. So to put in 50 New pieces of equipment in, in 3 weeks, again, these are just things that no 1's ever done in laboratory automation. So, I I do think we are doing a very unique thing here at go. Uh, that's the bat that's currently. What I'm, I'm, uh, leaning in on the company. It's what we're investing. Our Capital into is where our new customers are coming from. Uh, and so if you, if you like that idea, I think now,

[Company Representative] (Ginkgo Bioworks): Thanks. All right. If you want to follow us on that journey, you can go to X or LinkedIn, Instagram and keep watching. We will have a lot of content coming about the unveiling of the new full system. As always, if you have questions, you can reach out to us at investors@ginkgobioworks.com. Thanks so much, everyone. Until next time.

Was really exciting. Uh time to get involved with the company in any way uh and so uh but but yes we're we're going to be at 100 next week, 103 or 5. I got to count them up. Yep.

Thanks.

Jason Kelly: Thanks, everybody.

All right, and if you want to follow us on that Journey, you can go to X or LinkedIn. Uh, Instagram. And and keep watching, we'll have a lot of content coming about the unveiling of the the new full system. Um and as always if you have questions you can reach out to us at investors at GetGo. Buy works.com. Thanks so much everyone. Until next time

Thanks everybody.