Ginkgo Bioworks Holdings Inc (DNA) 2025 Q3 法說會逐字稿

Thanks as always for joining us. We are looking forward to updating you on our progress.

感謝您一如既往的參與。我們期待向您報告我們的進展。

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

再次提醒各位，在今天的演講中，我們將發表一些涉及風險和不確定性的前瞻性聲明。請查閱我們向美國證券交易委員會提交的文件，以了解更多關於這些風險和不確定性的信息，包括我們最新的 10-K 表格。

Today, in addition to updating you on the quarter results, we are going to be providing insight into how we believe AI models will impact biotechnology, how our tools are positioned to support those impacts.

今天，除了向大家報告季度業績外，我們還將深入探討我們認為人工智慧模型將如何影響生物技術，以及我們的工具如何支援這些影響。

Manager of communications and ownership at Genkko. I am joined by Jason Kellady, our co-founder and Chief Executive Officer , and Steve Cohen, our Chief Financial Officer.

Genkko 的傳播與所有權經理。與我一同出席的還有我們的共同創辦人兼執行長傑森凱拉迪，以及我們的財務長史蒂夫科恩。

Thanks as always for joining us. We are looking forward to updating you on our progress.

感謝您一如既往的參與。我們期待向您報告我們的進展。

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

再次提醒各位，在今天的演講中，我們將發表一些涉及風險和不確定性的前瞻性聲明。請查閱我們向美國證券交易委員會提交的文件，以了解更多關於這些風險和不確定性的信息，包括我們最新的 10-K 表格。

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

今天，除了向大家報告季度業績外，我們還將深入探討我們認為人工智慧模型將如何影響生物技術，我們的工具如何支援這些影響，以及這些工具如何幫助我們贏得與客戶的新交易。

As usual, we will end with a Q&A session and I will take questions from analysts, investors and the public. You can submit those questions to us in advance via X, hashtag ginkgo results or email investors at ginkgo Bioworks.com. Alright, over to you, Jason.

照例，最後我們將進行問答環節，我將回答分析師、投資人和公眾的提問。您可以提前透過 X、#ginkgoresults 標籤或發送電子郵件至 ginkgoBioworks.com 向我們提交這些問題。好了，傑森，輪到你了。

Alright thanks Daniel. Ginkgo mission is to make biology easier to engineer. We always start with that. I want to highlight the three big objectives for us going into 2026, and I am going to give you a little more detail on these today.

好的，謝謝你，丹尼爾。銀杏的使命是讓生物學更容易被改造。我們總是從那裡開始。我想重點介紹我們2026年的三大目標，今天我將為大家詳細介紹這些目標。

The first is to deliver the robotics and software that bring autonomous labs on-prem, in other words, at our customer sites so that they can run them themselves through our tools business, and we really grew into that sort of tools business model last year, but this robotics.

首先，我們將提供機器人和軟體，將自主實驗室部署到客戶現場，也就是說，部署到客戶現場，以便他們可以透過我們的工具業務自行運行這些實驗室。去年，我們真正發展出了這種工具業務模式，但機器人技術方面…

Automation and AI controlling it, I think it is having a big moment right now and I think we have got the right tool stack to bring that to customers. Second, we want to expand sort of our frontier Autonomous lab here in Boston.

自動化和人工智慧控制技術目前正處於蓬勃發展的時期，我認為我們已經擁有合適的工具組合，可以將這項技術帶給客戶。其次，我們希望擴大我們在波士頓的前沿自主實驗室。

We have the largest rack install in the world. I want to keep it that way. We will be continuing to expand that even as our customers build larger systems as well, and we want to use that to be able to show just the art of the possible to customers.

我們擁有世界上最大的機架安裝項目。我希望能維持現狀。即使我們的客戶也在建立更大的系統，我們也會繼續擴展這項技術，我們希望利用這項技術向客戶展示各種可能性。

What you can do when you have ultimately hundreds of pieces of equipment all connected in a Single robotic setup that can be controlled by AI and so I will show a few photos of what we are doing there coming up. And then finally our two big services, our CRO services, solutions and data points.

當您擁有數百台設備，全部連接到一個可由人工智慧控制的機器人系統中時，您可以做的事情有很多，接下來我將展示一些我們正在進行的工作的照片。最後，我們還有兩大服務：轉換率最佳化 (CRO) 服務、解決方案和數據點。

We want to offer best in class services, best on the market services to customers there by leveraging that in-house robotic infrastructure and that helps us kind of again demonstrate what is possible with those robotics and also offer great services to customers.

我們希望利用公司內部的機器人基礎設施，為客戶提供一流的、市場上最好的服務，這有助於我們再次展示這些機器人技術的可能性，並為客戶提供優質的服務。

So you are going to get to hear about all three of those things later from me. Which you are not going to hear as much about in 26, but I am very proud of us pulling off in 25 is this chart, dramatic reduction in our quarterly cash burn over the last year, doing all that while still maintaining a strong margin of safety in our cap position.

所以，我稍後會跟你們講這三件事。在第 26 期中，您可能不會聽到太多關於這方面的消息，但我非常自豪的是，我們在第 25 期中取得了這樣的成就：在過去一年中，我們大幅減少了季度現金消耗，同時還保持了資本狀況的強大安全邊際。

So after Q3, we have $462 million in cash and cash equivalents and Bank debt. So this is really again particularly in what has been a tough biotech market over the last few years, puts us in a very strong spot as a growing tools company.

因此，第三季結束後，我們擁有 4.62 億美元的現金及現金等價物和銀行債務。因此，尤其是在過去幾年生物技術市場情況嚴峻的情況下，這確實使我們這家不斷發展的工具公司處於非常有利的地位。

And so again very proud of the team for doing that. You are going to hear less about cost take outs in 2016 and a lot more about our investments for growth and what we are doing for customers as we expand in AI and automation.

因此，我再次為團隊所取得的成就感到非常自豪。2016 年，您將聽到關於削減成本的消息減少，而關於我們為成長進行的投資以及我們在人工智慧和自動化領域擴展時為客戶所做的工作的消息則會增多。

Alright, with that, I am going to pass it to Steve, but looking forward to giving you more detail in a moment.

好了，接下來我要把這個任務交給史蒂夫，我稍後會再提供更多細節給你。

Thanks, Jason.

謝謝你，傑森。

I'll start with the cell engineering business.

我先從細胞工程業務說起。

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

2025 年第三季細胞工程收入為 2,900 萬美元，比 2024 年第三季下降了 61%。

As previously disclosed, cell engineering revenue in the third quarter of 2024 included $45 million of non-cash revenue from a release of deferred revenue relating to the mutual termination of a customer agreement with Motif Food Works, one of our platform ventures.

如先前揭露，2024 年第三季細胞工程收入包括 4,500 萬美元的非現金收入，該收入來自與 Motif Food Works（我們的平台合資企業之一）共同終止客戶協議而釋放的遞延收入。

Excluding this, revenue in the third quarter of 2025 was down 11% from the prior year period. In the third quarter of 2025, we supported a total of 102 revenue generating cell engineering programs. This represents a decrease of 5% in revenue generating programs year over year. This decrease can be primarily attributed to the ongoing program rationalization as part of our restructuring activities.

在剔除此項因素後，2025 年第三季的營收比去年同期下降了 11%。2025 年第三季度，我們共支持了 102 個產生收入的細胞工程項目。這意味著創收項目比去年同期減少了 5%。這一下降主要歸因於我們正在進行的重組活動中的項目合理化。

Turning to biosecurity. Our biosecurity business generated $9 million of revenue in the third quarter of 2025 at a segment gross margin of 19%. As a reminder, segment gross margin excludes stock-based compensation.

轉向生物安全。2025 年第三季度，我們的生物安全業務創造了 900 萬美元的收入，毛利率為 19%。需要提醒的是，分部毛利率不包含股權激勵費用。

Turning to the next slide. It is important to note that our net loss includes a number of non-cash and other non-recurring items as detailed more fully in our financial statements. Because of these non-cash and other non-recurring items, we believe adjusted EBITDA is a more indicative measure of our profitability.

翻到下一張投影片。值得注意的是，我們的淨虧損包括一些非現金項目和其他非經常性項目，詳情請參閱我們的財務報表。由於這些非現金和其他非經常性項目，我們認為調整後的 EBITDA 更能反映我們的獲利能力。

A full reconciliation between segment operating laws, adjusted EBITDA, and GAAP net laws can be found in the appendix. In the third quarter of 2025, shell engineering R&D expense decreased 8% from $55 million in the third quarter of 2024 to $51 million in the third quarter of 2025.

附錄中提供了分部經營法、調整後 EBITDA 和 GAAP 淨額法之間的完整調整表。2025 年第三季度，殼牌工程研發費用較 2024 年第三季的 5,500 萬美元下降 8%，至 2025 年第三季的 5,100 萬美元。

The 2025 period R&D expense included a $21 million shortfall obligation related to our multi-year strategic cloud and AI partnership with Google Cloud. In October 2025, we amended and reset the annual commitments in future years and settled the shortfall obligation for $14 million.

2025 年期間的研發費用包括與我們和 Google Cloud 的多年戰略雲和人工智慧合作夥伴關係相關的 2,100 萬美元的資金缺口義務。2025 年 10 月，我們修改並重新設定了未來幾年的年度承諾，並以 1,400 萬美元的價格解決了資金缺口。

Cell engineering G&A expense decreased 47% from $23 million in the third quarter of 2024 to $12 million in the third quarter of 2025. These decreases were all driven by our restructuring efforts. Cell Engineering's segment operating loss was $37 million in the third quarter of 2025 compared to a loss of $5 million in the comparable prior year period.

細胞工程一般及行政費用從 2024 年第三季的 2,300 萬美元下降 47% 至 2025 年第三季的 1,200 萬美元。這些下降都是由我們的重組措施所推動的。2025 年第三季度，細胞工程部門的營運虧損為 3,700 萬美元，而去年同期虧損為 500 萬美元。

The increased loss year over year was due to two factors. First, as previously mentioned, the 3rd quarter 2025 expense included a $21 million shortfall related to a Google Cloud contract that was subsequently settled. Second, as previously mentioned, the third quarter of 2024 included $45 million of non-cash revenue from the motif contract termination.

虧損逐年增加的原因有兩個。首先，如前所述，2025 年第三季的支出包括與 Google Cloud 合約相關的 2,100 萬美元缺口，該缺口後來已解決。其次，如前所述，2024 年第三季包括因圖案合約終止而獲得的 4,500 萬美元非現金收入。

Biosecurity segment operating loss improved 21% in the third quarter of 2025 compared to the prior year comparable period. Moving further down the page, you'll note that total adjusted EBITDA in the third quarter of 2025 was $56 million which was down from $20 million in the third quarter of 2024.

2025 年第三季生物安全業務部門的營業虧損較上年同期減少了 21%。繼續往下看，你會注意到 2025 年第三季調整後的 EBITDA 總額為 5,600 萬美元，低於 2024 年第三季的 2,000 萬美元。

Again, this year over year decline can be attributed to the previously mentioned Google Cloud shortfall expense recorded in the 3rd quarter of 2025, as well as the motif-related non-cash revenue in the comparable prior year period.

同樣，年減可歸因於前面提到的 2025 年第三季記錄的 Google Cloud 資金缺口支出，以及去年同期與主題相關的非現金收入。

So turning to the next slide. We show adjusted even at the segment level to show the relative profitability of our segments. The principal differences between segment operating loss and total adjusted EBITDA related to the carrying cost of excess lease space, which you can see was $14 million in the third quarter of 2025.

接下來請看下一張投影片。我們甚至在細分市場層面也進行了調整，以顯示我們各個細分市場的相對獲利能力。分部經營虧損與調整後 EBITDA 總額之間的主要差異與超額租賃空間的持有成本有關，您可以看到，2025 年第三季該成本為 1,400 萬美元。

This cost represents the base rent and other charges related to leased space which we are not occupying net of sublease income. This is a cash operating cost that is not related to driving revenue right now and can potentially be mitigated through subleasing.

此成本代表基本租金和與我們未佔用的租賃空間相關的其他費用，扣除轉租收入後。這是一項與目前收入成長無關的現金營運成本，可以透過轉租來降低。

And finally, cash burn in the third quarter of 2025 was $28 million down from $114 million in the third quarter of 2024, a 75% decrease. Cash burned does not include the proceeds from ATM sales during the quarter.

最後，2025 年第三季的現金消耗為 2,800 萬美元，低於 2024 年第三季的 1.14 億美元，降幅達 75%。現金消耗量不包括本季ATM銷售所得收入。

The significant decrease in cash burn was a direct result of the restructuring. Now turning to guidance. In terms of outlook for the full year, we are reaffirming our overall revenue guidance for 2025, totalling $167 to $187 million with cell engineering revenue to be $117 million to $137 million and biosecurity revenue expected to be at least $40 million.

現金消耗量的大幅下降是重組的直接結果。現在進入指導環節。展望全年，我們重申 2025 年整體收入預期，總計 1.67 億至 1.87 億美元，其中細胞工程收入為 1.17 億至 1.37 億美元，生物安全收入預計至少為 4000 萬美元。

In conclusion, we're pleased with the continued improvements in cash burn and cost reduction.

總之，我們對現金消耗和成本降低的持續改善感到滿意。

In the fourth quarter, we will continue to execute against our core objectives while navigating continued uncertainty in the macro environment.

第四季度，我們將繼續朝著核心目標努力，同時應對宏觀環境持續存在的不確定性。

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

那麼，我就把麥克風交還給你了，傑森。

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

謝謝你，史蒂夫。好的，那我們開始進行策略評估。今天我們要討論三個話題。首先，我認為人工智慧模型將從根本上以兩種主要方式影響生物技術，我認為 Ginko 在向這兩個領域銷售工具方面處於有利地位，所以我將談論這方面。

Second, we are continuing to offer that research solutions business on top of our in-house robotics platform at Dingo, and we had two big wins in the last quarter.

其次，我們繼續在 Dingo 的內部機器人平台基礎上提供研究解決方案業務，並且在上個季度取得了兩場重大勝利。

I want to touch on that briefly. And then finally we are expanding our sort of Frontier Autonomous lab here in Boston, the big rack set up, so I'll show you some photos and a little bit of background on what we're doing there.

我想簡單地談談這一點。最後，我們正在波士頓擴大我們的前沿自主實驗室，也就是大型機架裝置，所以我將向你們展示一些照片，並簡要介紹我們在那裡所做的工作。

And please do visit. I'll mention that when we get to that section, but if you want to see it, yeah, you're very welcome.

歡迎您來參觀。等我們講到那部分的時候我會提到，不過如果你想看的話，當然歡迎。

Alright, so let's dig in on really how AI is impacting biology. Before I do that, I do want to remind you know we made again over 25 in the second half of 2024, we made a big shift in the business where we went from just offering research solutions, which is the left hand side of this chart here.

好了，讓我們深入探討一下人工智慧是如何影響生物學的。在此之前，我想提醒大家，我們在 2024 年下半年再次實現了 25% 以上的成長，我們的業務也發生了重大轉變，從僅僅提供研究解決方案（即此圖表左側的內容）發展到提供其他服務。

These are the types of research partnerships, we get fees and we get downstream value share. We get Royalties or milestones in the sort of ultimate end products that our customers are developing leveraging our platform.

這就是研究合作的類型，我們獲得費用，也獲得下游價值分成。我們從客戶利用我們的平台開發的最終產品中獲得版稅或里程碑付款。

It is a very close partnership with the customer. There is a lot of our scientists involved as well as our robotics. We have done about 250 of those R&D partnerships over the last eight years to 10 years. That is a business we will be continuing, but in the last year and a half, we expanded into the tool space with our data points. Automation and reagents businesses.

這是與客戶之間非常緊密的合作關係。我們的許多科學家以及機器人專家都參與其中。在過去的八到十年裡，我們已經開展了大約 250 項這樣的研發合作計畫。我們將繼續開展這項業務，但在過去一年半的時間裡，我們利用數據點拓展到了工具領域。自動化和試劑業務。

And so I want to spend a minute talking about how AI and what has really been coming down the pipeline, I think offers us a nice niche and entry point into the tools market where we really have, I think the sort of category defining technology. So first, why is AI important right now in sort of sciences in general and bioscience in particular? So this was America's AI Action Plan came out of the White House in the last few months.

因此，我想花一分鐘時間談談人工智慧以及真正正在醞釀中的技術，我認為這些技術為我們提供了一個很好的利基市場和進入工具市場的切入點，我們真正擁有我認為是定義該類別的技術。首先，為什麼人工智慧在當今科學領域，尤其是在生物科學領域如此重要？這就是美國白宮在過去幾個月發布的AI行動計畫。

There's one specific section I'd draw your attention to, which was investing in AI enabled science. And the general idea here is to have AI reasoning models leveraging, and they highlight automated cloud enabled labs, and that's why I'm excited to share more on what we've been building here in Boston, which I think is a great example of one of these cloud enabled labs that if you connect those two things together, you could potentially change how science is done, and the idea is the reasoning models could be thinking and the labs could be doing that lab work, and I'll talk about that more in a second.

我想特別提請大家注意其中一個部分，那就是對人工智慧賦能科學的投資。這裡的整體思路是利用人工智慧推理模型，重點是自動化雲端實驗室。因此，我很高興能分享我們在波士頓正在建造的項目，我認為這是一個很好的雲端實驗室範例。如果將這兩者結合起來，就有可能改變科學的運作方式。其理念是，推理模型可以進行思考，而實驗室可以進行實驗工作。稍後我會詳細介紹這一點。

And the reason this is important, is shown here, I think we are particularly in the biosciences are going to be the first sort of battleground for AI enabled science if you look at what is happening between US and China.

而這之所以重要，原因就在於此，我認為，如果我們看看中美之間正在發生的事情，就會發現生物科學領域將成為人工智慧賦能科學的第一個戰場。

So there was a New York Times editorial just a few months ago saying China's biotech is cheaper and faster. I think that is largely true if you think about the traditional way we are doing biotech today, which is you basically have, well trained scientists working by hand and Laboratories here in in Boston and you know it is in the Kendall Square area here down the street, it's also in South San Francisco in California, San Diego, Research Triangle in North Carolina, a few hubs in the United States where you have sort of scientists working by hand, doing biotechnology research.

就在幾個月前，《紐約時報》發表了一篇社論，稱中國的生物技術更便宜、更快。我認為，如果你想想我們今天進行生物技術研究的傳統方式，這在很大程度上是正確的。這種傳統方式基本上是由訓練有素的科學家手工操作實驗室完成的，這些實驗室位於波士頓，你知道，就在這條街的肯德爾廣場附近，加利福尼亞州的南舊金山，聖地亞哥，北卡羅來納州的研究三角區，以及美國的一些中心地帶，這些地方的科學家們都在手工進行生物技術研究。

For a long time, if you go back, you stay back a slide, for a long time that was, we had an advantage over China just in the sense that our people were better trained and we had access to sort of Better facilities and things like that.

很長一段時間以來，如果你回顧過去，你會發現自己落後了一大截。很長一段時間以來，我們相對於中國來說具有優勢，因為我們的人民訓練有素，我們能夠獲得更好的設施等等。

That advantage has largely evaporated over the last 10 to 15 years. There are just as good academic institutions, just as good startup ecosystems and so on in China and there are more scientists trained and they are paid less, frankly, and so I do not really see where we have an advantage on physical labor anymore versus China.

在過去的 10 到 15 年裡，這種優勢基本上已經消失了。中國的學術機構和創業生態系統同樣出色，而且培養的科學家更多，但坦白說，他們的收入卻更低，所以我真的看不出我們在體力勞動方面比中國有什麼優勢了。

So I was really excited to see Senator Yng who is sort of heading up the National Security Commission on Emerging biotechnology, put in a number of bills. On this topic, NSF launched a $100 million AI programmable cloud labs initiative and the big theory behind these things is if we are going to compete with China in biotechnology, we need to do it with robotics rather than hands at the bench.

所以我很高興地看到，作為新興生物技術國家安全委員會的負責人，參議員 Yng 提出了一系列法案。關於這個主題，美國國家科學基金會啟動了一項1億美元的人工智慧可編程雲端實驗室計劃，背後的主要理論是，如果我們要在生物技術領域與中國競爭，我們需要用機器人技術而不是人工進行實驗。

And if we don't do it, I think you are going to see what we have seen over the last, two or three quarters where an increasing number of the early stage biotech startups that are being acquired by large pharma or invested in by USBCs are based in China.

如果我們不這樣做，我認為你們將會看到過去兩三個季度以來我們所看到的現象，即越來越多的早期生物技術創業公司被大型製藥公司收購或被美國生物技術公司投資，而這些公司都位於中國。

And so I think if we are going to turn that around both for biotechnology and for science at large, we need to do it by investing in robotic infrastructure and I think that is not lost on the US government and I think gingko, if you go to the next slide, has exactly the right technology for that.

所以我認為，如果我們想要扭轉生物技術和整個科學領域的局面，就需要投資機器人基礎設施，我認為美國政府並沒有忽視這一點，而且我認為，如果你看下一張幻燈片，就會發現銀杏擁有完全合適的技術。

And so I have shown this before, but these are reconfigurable automation carts or rack carts, and this is the first big area where I think AI is coming into biotechnology and so this is around reasoning models. So again, think GPT 5 from OpenAI and so on.

我之前已經展示過，這些是可重新配置的自動化推車或機架推車，我認為這是人工智慧進入生物技術領域的第一個重要領域，而這主要圍繞著推理模型。所以，可以想想 OpenAI 的 GPT 5 等等。

These are Gemini from Google. These are these models that are able to think over a period of time, come to sort of a conclusion based on what you've asked them to do, and either they can write code, they can do other things, they can kind of use browsers and tools to go off and do sort of a multi-step operation and come back and bring a result to you.

這是谷歌的Gemini。這些模型能夠經過一段時間的思考，根據你要求它們做的事情得出結論，它們可以編寫程式碼，可以做其他事情，可以使用瀏覽器和工具進行多步驟操作，然後返回並向你提供結果。

I think the first big frontier here is going to be connecting those reasoning models to physical automation in the lab, and the reason This is necessary is if you think about how science gets done outside of areas like math or theoretical physics that are purely kind of people thinking about stuff, it's purely intellectual.

我認為第一個重大突破點是將這些推理模型與實驗室中的物理自動化聯繫起來，而這之所以必要，是因為如果你想想數學或理論物理等領域之外的科學是如何進行的，這些領域純粹是人們思考問題，純粹是智力活動。

The majority of science, experimental physics, experimental chemistry, experimental biology, and so on is moved forward by lab work, right? Like we have a hypothesis, scientists have a hypothesis about how some disease works or whatever, but the only way they really know the answer is to go off and run carefully constructed laboratory experiments.

科學，包括實驗物理學、實驗化學、實驗生物學等等，大部分都是透過實驗室工作推動的，對吧？就像我們有一個假設一樣，科學家對於某種疾病的運作方式或其他方面也有一個假設，但他們真正知道答案的唯一方法就是進行精心設計的實驗室實驗。

And so if you want these models to really be AI scientists and you're seeing Future house just had a great new model come out yesterday or now called Edison Scientific, super excited about that. Those models need to be able to do experiments. If you go to the next slide, the way they're going to do experiments is using the technology like what we built at Ginkgo.

所以，如果你希望這些模型真正成為人工智慧科學家，並且你看到 Future House 昨天或現在推出了一個很棒的新模型，名為 Edison Scientific，對此感到非常興奮。這些模型需要能夠進行實驗。如果你看下一張投影片，他們進行實驗的方式是使用類似我們在 Ginkgo 開發的那種技術。

This is our reconfigurable automation carts. Each cart has a piece of lab equipment, a robotic arm, and a plate transport track, and I'm going to spend a minute later showing you this in action.

這是我們的可重構自動化推車。每個推車都配備了一台實驗室設備、一個機械手臂和一個載板運輸軌道，我稍後會花一分鐘時間向你們展示它的實際操作。

But basically what it allows you to do is sort of Lego block together if you go to the next slide, five of these in a linear setup, 20 of these in a circular setup, or, here's a setup we actually just sold one of these systems with 97 carts on it in one giant setup, and so the idea here is to be able to connect ultimately hundreds of pieces of lab equipment Lego block style into a huge setup where the whole thing is software controlled.

但基本上，它允許你像搭樂高積木一樣把設備連接起來。如果你看下一張投影片，你會發現五個這樣的設備可以線性排列，二十個這樣的設備可以環形排列，或者，這裡展示的是我們剛剛售出的一套系統，上面有 97 個推車，組成一個巨大的裝置。所以，這裡的想法是能夠像搭樂高積木一樣，最終將數百台實驗室設備連接成一個巨大的裝置，整個裝置都由軟體控制。

And the reason it's important that it's software controlled is just like these reasoning models can write code for, Python or whatever. For a website, they're also able to write code to run this automation and design and execute experiments and interpret data.

之所以說它由軟體控制很重要，是因為這些推理模型可以編寫程式碼，無論是 Python 還是其他任何語言。對於網站而言，他們還可以編寫程式碼來運行這種自動化流程，設計和執行實驗，並解釋數據。

And so if we want to have these sort of AI controlled science, these cloud enabled labs, this is what they look like, and you really need a new hardware technology like what we've built with the racks to do that. So I think we're extremely well positioned for this, and you'll see us leaning in heavily here in 2026.

因此，如果我們想要擁有這種人工智慧控制的科學、這種雲端實驗室，它們看起來就是這樣的，而要做到這一點，你真的需要像我們用機架建造的這種新的硬體技術。所以我認為我們在這方面已經做好了非常充分的準備，你們將會看到我們在 2026 年大力投入其中。

The second area where we're seeing AI applied to biotechnology is in using the same kind of like math and compute that was used for the reasoning model, so large neural networks, GPUs, that whole infrastructure, except instead of training those neural nets on human language and human reasoning and Code and programming things that humans kind of read and understand and interpret.

我們看到人工智慧應用於生物技術的第二個領域是使用與推理模型相同的數學和計算方法，例如大型神經網路、GPU 以及整個基礎設施，只不過訓練這些神經網路的物件不是人類語言、人類推理以及人類能夠閱讀、理解和解釋的程式碼和程式。

You train them on biological language, so DNA, amino acid sequences from proteins, the language of life, the language of living organisms, and you do the same type of training, the same infrastructure, but these things learn to speak biology.

你訓練它們學習生物語言，也就是 DNA、蛋白質中的胺基酸序列、生命的語言、生物體的語言，你進行同樣的訓練，使用同樣的基礎設施，但這些東西學會了說生物學。

And so this is a more nascent compared to the reasoning models when it comes to AI and biotech, but I think it's also going to be extremely important.

因此，與人工智慧和生物技術領域的推理模型相比，這還處於起步階段，但我認為它也將極為重要。

And with our Ginkgo Data Point Service, we really want to build the community in that area. So we highlight here our antibody developability competition.

我們希望透過銀杏數據點服務，在這個領域建立起真正的社區。因此，我們在此重點介紹我們的抗體開發能力競賽。

This is just, I think, at the end of November going to wrap up. So you should, if you go to the next slide, you should check it out. You can go to ginkgodatapoints.ginkgo.bio.

我認為，這件事到11月底就會結束了。所以，如果你要看下一張投影片，就應該看看。您可以存取 ginkgodatapoints.ginkgo.bio。

You can sign up. We have more than 200 teams now competing in that competition, and the idea there is Build a model like the one I just mentioned, like train a model on data for the developability of antibodies. In other words, is this antibody sequence going to work well as a drug? Will it be soluble and so forth? These other, is it not immunogenic?

您可以註冊。現在有 200 多支隊伍參加這項比賽，比賽的理念是建立一個像我剛才提到的那種模型，例如用數據訓練一個模型來評估抗體的可開發性。換句話說，這種抗體序列作為藥物是否有效？它是否可溶等等？其他的這些，難道不具有免疫原性嗎？

That is a very valuable feature set for biopharma companies. So if you're a bioinformatician or you're a startup that has a great new AI model, I encourage you to compete in our competition here. We basically generate a large amount of developability data.

對於生物製藥公司而言，這是一套非常有價值的功能。所以，如果你是生物資訊學家，或者你是一家擁有優秀人工智慧模型的新創公司，我鼓勵你來參加我們這裡的比賽。我們基本上會產生大量的可開發性數據。

We shared some of that with the community. We kept some of it back as the competition set and your job is to predict the held back data and we will rank who does the best. The other thing we are doing to help build the community is we are releasing data sets for free. Again, you go to our website there and download these sorts of AIL ready data sets.

我們與社區分享了其中的一些內容。我們保留了一些數據作為比賽規則，你的任務是預測保留的數據，我們將對錶現最佳者進行排名。為了幫助建立社區，我們還在免費發布資料集。同樣，您可以訪問我們的網站並下載這些 AIL 就緒資料集。

They are an example of the sort of data that we generate on a fee per service basis for customers through our data point service. So go download those. Around if you wanted to buy data from us, we are very happy to do that and we are really here to build a community of folks who are trying to train AI models using biological data.

這些是我們透過數據點服務按服務收費為客戶產生的數據的範例。所以快去下載吧。如果您想從我們這裡購買數據，我們非常樂意這樣做，我們真心希望建立一個使用生物數據訓練人工智慧模型的社群。

And so we're really excited about this as a sort of a nascent area for AI applied to biology. All right, second thing I wanted to talk about, so those are the two big buckets for AI again, reasoning models, controlling robotics in the lab, and then, basically neural nets trained on biological data and They're both involving AI but they are different, and so Ginkgo will play there through our automation, in the first one and our data points for the second one.

因此，我們對此感到非常興奮，因為這是人工智慧應用於生物學的新興領域。好的，我想談的第二件事是，人工智慧的兩大領域分別是推理模型、實驗室機器人控制，以及基於生物資料訓練的神經網路。它們都涉及人工智慧，但又有所不同。 Ginkgo 將透過自動化在第一個領域發揮作用，並透過數據點在第二個領域發揮作用。

All right, so next category, this is now going back to that left-hand side of this chart, the business that Gingko sort of like, primarily focused on over the last 10 years, our research solutions business. We are still doing these, if you are looking for sort of breakthrough research in, any of the areas that could basically leverage like high throughput, biotech. Technology, it is still a very good call. If you go to the next slide, we won a couple of great deals in the last quarter.

好的，下一個類別，現在回到這張圖表的左側，也就是銀杏在過去 10 年裡主要關注的業務，我們的研究解決方案業務。我們仍在進行這些研究，如果您正在尋找突破性的研究，例如高通量、生物技術等領域，我們很樂意與您合作。就技術而言，這仍然是一個非常明智的選擇。如果您翻到下一張投影片，您會發現我們在上個季度達成了幾筆非常棒的交易。

Barda awarded us and our partners $22 million around the manufacturing of monoclonal antibodies, bringing that back in the US, making that cheaper, particularly around producing key medical countermeasures. So this is both important for national security and also important for reducing the cost of manufacturing drugs, particularly biologics drugs, and you heard.

Barda 向我們和我們的合作夥伴提供了 2,200 萬美元，用於生產單株抗體，將生產帶回美國，從而降低成本，尤其是在生產關鍵醫療對策方面。所以，這對國家安全來說非常重要，對於降低藥品（特別是生物製劑）的生產成本也很重要，你們都聽到了。

The administration talking about this recently on the regulatory side to TRY to lower the cost of biologics. This is a technical approach to dropping the cost of biologics. To go to the next slide, in the agricultural sector, we are very happy to extend our partnership.

政府最近在監管方面談到了這一點，試圖降低生物製劑的成本。這是降低生物製劑成本的一種技術方法。接下來，在農業領域，我們非常高興能夠擴大我們的合作關係。

The partnership has been on for five years. We are really working on engineering microbes if you go to the next slide for the production of fertilizers, and if you remember like this is actually a pretty amazing story. So if you think about like element. School biology, you learned about crop rotation, right?

雙方的合作關係已經持續了五年。如果你看下一張投影片，你會發現我們正在致力於改造微生物以生產肥料，如果你還記得的話，這其實是一個非常了不起的故事。所以，如果你像思考元素一樣思考。你在學校生物課上學過輪作吧？

So you would rotate in a legume like soybeans or peanuts or things like that, and they would fertilize the soil and then you'd plant something like corn and corn largely takes fertilizer out of the soil.

所以你可以輪作種植豆科作物，像是大豆或花生之類的，它們可以給土壤施肥，然後你再種植玉米之類的作物，而玉米會大量吸收土壤中的肥料。

So that's sort of how we used to do it. And then in the early 1900s we invented the Haber-Bosch process where you take nitrogen out of the atmosphere by burning natural gas and combining the nitrogen.

所以，我們以前就是這麼做的。然後在 20 世紀初，我們發明了哈伯-博世法，透過燃燒天然氣並從大氣中分離氮氣，然後將氮氣與其他物質結合。

With that and producing synthetic ammonia, and then that goes out to the tune of many billions of dollars a year and about 4% of global greenhouse gas and so on. So it is a big chemistry industry and it is largely based in China. That is a huge input into things like corn farming. Well, those crops that you rotate in like soybeans.

再加上合成氨的生產，每年產生數十億美元的收入，並排放約佔全球溫室氣體 4% 的氣體等等。所以這是一個龐大的化學產業，而且主要集中在中國。這對玉米種植等領域來說是一筆巨大的投資。嗯，就是那些輪作作物，像是大豆。

And legumes, they're able to fertilize the soil because they have microbes on their roots running that Haber Bosch process, taking nitrogen out of the air, fertilizing the crop. So I'm really happy to see this project continuing. I think it's the kind of world changing stuff that only biotechnology can do in the physical world, and so really excited to keep that going.

豆類植物能夠施肥，因為它們的根部有微生物進行哈伯-博世過程，從空氣中吸收氮，為作物施肥。所以我很高興看到這個項目得以繼續進行。我認為這是只有生物技術才能在現實世界中實現的改變世界的那種事情，所以我非常興奮能夠繼續推進這項工作。

All right. Again, if you're in agriculture, industrial biotech, biopharma, you want to TRY, large scale biotech on your problem, I encourage you to call us up and we're happy to have our scientists work with yours to leverage the infrastructure here at Ginkgo to deliver that.

好的。再說一遍，如果您從事農業、工業生物技術、生物製藥行業，並且想嘗試用大規模生物技術來解決您的問題，我鼓勵您聯繫我們，我們很樂意讓我們的科學家與您的科學家合作，利用銀杏的基礎設施來實現這一目標。

I really like this photo. This is, two of my co-founders, Rachel and Austin, in the lab just a few weeks ago. The reason I bring this up is Rach van Austin had not been in the lab, prior to A few months ago for like the last I don't know 10 or 15 years since we started the company, and the reason they are back in the lab is because what we have been doing on the automation side at ginkgo building out our rack set up here in Boston has gotten sort of ridiculously exciting over the last six months or so.

我非常喜歡這張照片。這是我的兩位共同創辦人瑞秋和奧斯汀，幾週前在實驗室裡的照片。我之所以提起這件事，是因為 Rach van Austin 在幾個月前之前，自從我們公司成立以來，大概有 10 到 15 年的時間沒有來過實驗室了。而她回到實驗室的原因是，在過去的六個月左右的時間裡，我們在 Ginkgo 的自動化方面所做的工作，以及在波士頓搭建機架裝置的工作，變得異常令人興奮。

So if you go to the next slide, I want to talk about what we are building with our frontier Autonomous Lab. We are getting a ton of interest in this right now, both from customers and even just Internally. So we have been expanding our setup here in Boston so you can see our rack carts there, in the photo inside of one of our kind of big foundry bays here in Boston.

所以，如果你翻到下一張投影片，我想談談我們正在用我們的前沿自主實驗室建構什麼。目前，無論是客戶還是公司內部，都對這個主題表現出了極大的興趣。因此，我們一直在擴大我們在波士頓的設備配置，您可以在照片中看到我們的機架推車，這是我們在波士頓的大型鑄造車間之一的內部照片。

If you go to the next slide, we are going to have about 45 instruments, 46 instruments on this setup, a lot like 10 carts are getting installed right now to bring it up to 36 racks. Ultimately, I would like to get it in that room to about 100. Racks. You can see a photo on the left of one of the racks going in. That's pretty exciting, right? So this is us putting a new piece of equipment on.

如果你看下一張投影片，我們在這個裝置上將有大約 45 台、46 台儀器，現在正在安裝大約 10 個推車，使其達到 36 個機架。最終，我希望那個房間的數量能達到 100 左右。架子。左邊的照片展示了其中一個正在安裝的貨架。那真是太令人興奮了，對吧？這是我們正在安裝一台新設備。

That video is sped up, but it takes just a couple of hours really to get that device on the setup. This is because we have invested in productizing the cart hardware so that we have greatly simplified and if you're not in the laboratory automation business, you may not know this, but Integrating equipment into laboratory setups right now is done as a custom job.

影片播放速度加快了，但實際上設定這個設備只需要幾個小時。這是因為我們投資將推車硬體產品化，這大大簡化了操作。如果您不是實驗室自動化行業的從業者，您可能不知道這一點，但目前將設備整合到實驗室設置中是作為定制工作完成的。

You basically pay an engineering firm and they spend months making CAD designs and they build you this kind of Rube Goldberg machine device. We've taken all that and standardized it with cards, turned it into a product that you can just buy off the rack and install in these big setups and so we're really excited to be building this out.

你基本上是付錢給一家工程公司，他們花幾個月時間製作 CAD 設計圖，然後為你建造這種類似魯布戈德堡機械的裝置。我們已經將所有這些標準化為卡片，並將其轉化為可以直接購買並安裝在大型系統中的產品，因此我們非常高興能夠建造這個產品。

The picture in the middle there that's running is actually a rack inside of an anaerobic chamber. We built this for Pacific Northwest. National Lab PNNL, it's like I think 14 or 18 of our robotic arms and rack setups inside of an anaerobic chamber where people can't go in because there's no air. And so very exciting, big setup. We're excited to see more customers bringing those in-house.

中間那張正在運轉的圖片其實是一個厭氧室裡的一個架子。我們為太平洋西北地區建造了它。國家實驗室 PNNL，我想，我們把 14 到 18 個機械手臂和機架裝置放置在一個厭氧室裡，人們進不去，因為裡面沒有空氣。真是太令人興奮了，規模很大。我們很高興看到越來越多的客戶將這些技術引入公司內部。

If you go to the next slide, I just want to kind of show like what it looks like. So each row in that is a different piece of equipment. Those red bars are when a sample is interacting with that piece of equipment. So that's sort of like the timeline. Of a protocol being submitted.

如果你看下一張投影片，我只想給你看它的樣子。所以每一行都代表一件不同的設備。當樣品與該設備相互作用時，這些紅色條形圖就會顯示出來。這就是大致的時間軸。正在提交的協議。

So a plate, and in this case, this is a standard piece of lab where, that little plastic rectangle you see moving on our track system, is a 384 well plate. So there's 384 samples in there. It's being put onto a centrifuge in this video here. So that plate goes in and then that centrifuge is going to spin. This plate now is then after the centrifuge step being delivered to an echo liquid handler.

所以，一塊板，在這個例子中，這是一個標準的實驗室零件，你看到在我們軌道系統上移動的那個小塑膠矩形，就是一塊 384 孔板。裡面共有 384 個樣本。影片中，它正被放入離心機中。所以把那個板子放進去，然後離心機就會旋轉。經過離心步驟後，培養板將送至回波液體處理機。

This is an acoustic liquid handler that's able to move liquids with sound. And what it's going to do is it's going to set up the reaction conditions on each of those 384 well plates as programmed by the software that is telling the system what to do and importantly again to nerd out a little bit, each piece of equipment, this is like a Bravo liquid handler that was the Echo, each one has its own piece of sort of proprietary third-party software that's kind of a pain to deal with, H1stly.

這是一個利用聲波移動液體的聲波液體處理裝置。它將按照軟體的程序，在每個 384 孔板上設置反應條件，該軟體告訴系統該做什麼。更重要的是，再稍微深入一下，每個設備，例如 Bravo 液體處理機（以前是 Echo），每個設備都有自己的專有第三方軟體，處理起來有點麻煩，首先。

And so what we've done as part of the rack system on the software side is We have connected into each piece of hardware with our software, so you're able to write a multi-step protocol like what you're watching here, is this particular protocol is protein, self-free protein expression.

因此，作為機架系統的一部分，我們在軟體方面所做的是，我們將我們的軟體連接到每個硬件，這樣你就可以編寫像你在這裡看到的這樣的多步驟協議，這個特定的協議是蛋白質，無自體蛋白質表達。

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

你可以將許多不同的裝置連接到單一的協定中，並以參數化的方式控制每個裝置。這是一個搖床，然後最終連接到一台檢測設備——熱循環儀上。

Kind of complete this reaction. And so all of those steps are encoded in the ginkko software and then the scheduler and larger system talks to all the equipment in a seamless way, so your scientists aren't dealing with 18 different types of software to do an 18-equipment run. That's a really big deal and it also means it can be connected back to reasoning models to do that type of design of experiments as well.

算是完成了這個反應。因此，所有這些步驟都編碼在 ginkko 軟體中，然後調度程式和更大的系統以無縫的方式與所有設備通信，因此您的科學家不必處理 18 種不同的軟體來完成 18 台設備的運作。這意義重大，也意味著它可以與推理模型連結起來，進行這類實驗設計。

If you go to the next slide, We are able, like I mentioned, to set these up quickly, so, this is the 10 cards that have been coming in. This is like literally from last week, and so if we've already have the equipment, that's relevant, and again we're at 45 pieces of equipment now on the setup for the protocol you want to do, if you go to the next slide, we are able to then demo it for you in pretty short order.

如果你看下一張投影片，正如我剛才提到的，我們可以快速設定這些，所以，這是收到的 10 張卡片。這其實就是上週的情況，所以如果我們已經有了設備，那就很有意義了。現在，為了執行你想要的協議，我們已經準備了 45 件設備。如果你看下一張投影片，我們很快就能為你示範。

So if your group has been thinking about just automation in general, you can TRY. Our system, if you want to see what it's like as a scientist to interact with a system through a language model, we have a human language interface now to that setup so you can play around with that.

所以，如果你的團隊一直在考慮自動化，那麼你可以嘗試一下。如果您想了解科學家如何透過語言模型與系統進行交互，我們的系統現在提供了一個人類語言介面，您可以親自體驗。

And then finally, if you wanted to have an AI reasoning model controlling this setup to work on a problem of interest to you, we can do that too. And what's exciting is we do all that just on our setup here in Boston. It's very inexpensive for you. You're not buying a bunch of equipment or anything else, and you can see if it works. Like TRY it before you buy it, right?

最後，如果您希望使用人工智慧推理模型來控制此設置，以解決您感興趣的問題，我們也可以做到。令人興奮的是，我們僅憑在波士頓的這套設備就能完成所有這些工作。對你來說非常便宜。你不需要購買一大堆設備或其他任何東西，而且你可以看看它是否有效。就像先試用再購買一樣，對吧？

If it works, then we're very happy to install this in your lab so that your labs could have the same sort of just very latest scale in terms of automation and AI that we're running here at Geo, and I'm telling you it is very exciting. It's working really well, so I do think folks should come and TRY it. And if you just want to visit, please do just shoot me a note and we're happy to do that and have you come by.

如果效果好，我們非常樂意在您的實驗室安裝這套系統，這樣您的實驗室就能擁有和我們在 Geo 這裡運行的一樣最新的自動化和人工智慧規模，我告訴您，這非常令人興奮。它的效果非常好，所以我認為大家都應該來試試看。如果您只是想來參觀，請隨時給我留言，我們非常樂意接待您。

All right, that's what I have today. Happy to answer questions about all that, but super excited. I think we've got the team again, a big round of thanks for 2025.

好了，今天就到這裡。我很樂意回答關於這方面的問題，我真的超級興奮。我想我們又組了一支優秀的團隊，在此向2025年表達衷心的感謝。

It's a very difficult year, bringing down our costs in a huge way while maintaining that sort of large margin of safety, and that's what's allowing us to really now Invest for growth in the future, particularly in this area of, applying, building up the automation and AI tooling for biosciences and I think that is going to be the niche that we grow into in the coming, 5 to 10 years in a big way.

今年是非常艱難的一年，我們大幅降低了成本，同時保持了較大的安全邊際，這使得我們現在能夠真正投資於未來的成長，尤其是在生物科學領域應用和建立自動化和人工智慧工具方面。我認為這將是我們未來 5 到 10 年內大力發展的利基市場。

So excited for your questions and thanks again.

非常期待你們的提問，再次感謝。

Great.

偉大的。

Thanks Jason.

謝謝你，傑森。

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

照例，我將首先回答公眾提問，並提醒線上分析師，如果你們想提問，請在 Zoom 上舉手，我會點名並開啟你們的線路。謝謝大家。

Alright, let us get started. So, the first question was one that we got on Twitter, from an account at David Zhu tweets, and this question is, can you comment on the extent of Ginkgo exposure to US government business and how that has been impacted by the shutdown?

好了，我們開始吧。第一個問題是我們從推特上收到的，來自一個名為 David Zhu tweets 的帳號，問題是：您能否評論一下 Ginkgo 與美國政府業務的接觸程度，以及政府停擺對此產生了怎樣的影響？

Yeah, I can touch on that. So short answer on the shutdown has not had a big impact on us, so sort of the areas that grants and funding there keeps flowing during the shutdown. I would say in general though, we have a good amount of exposure to the government overall, so between our biosecurity business and then things like the new barter award, you will see us announce.

是的，我可以談談這方面。簡言之，政府停擺對我們影響不大，撥款和資金在停擺期間仍持續流入相關領域。不過總的來說，我們與政府的聯繫相當緊密，所以從我們的生物安全業務到新的以物易物貿易獎勵等項目，你們將會看到我們宣布這些消息。

Some recently also RH awards. We have been doing very well I guess I would say with bringing in research partnerships with the government. So overall hopefully we are even doing more in the future with some of this sort of cloud lab work and investments I hope to see from sort of government labs around automation, but the shutdown does not impact us.

最近也有一些獲得了RH獎。我認為我們在與政府建立研究夥伴關係方面做得非常好。所以總的來說，希望未來我們能在這方面做更多的事情，例如雲端實驗室的工作和投資，我希望看到政府實驗室在自動化方面進行更多投資，但政府停擺對我們沒有影響。

Go. Right, and our first question from Brendan from TE Securities, he writes, how do you see the broader development or roll out path ahead for the rack system over the next 18 months?

好的，我們的第一個問題來自TE Securities的Brendan，他寫道，您如何看待未來18個月內機架系統的更廣泛發展或推廣路徑？

Are there any additional validation steps or accounts to land that you expect to really unlock this opportunity and widen the commercial funnel for this over the near term?

您是否期望透過其他驗證步驟或獲取客戶來真正抓住這個機會，並在短期內擴大商業管道？

Yeah, I can touch on that too. So first off, I think what's super exciting about the racks, and again I tried to mention this, but there's sort of like walk-up automation like companies like Hamilton and so on where you're getting like a liquid handling deck, and that is a very productized offering.

是的，我也可以談談這方面。首先，我認為貨架最令人興奮的地方在於，我之前也提到過這一點，那就是像 Hamilton 這樣的公司提供的類似步入式自動化設備，例如液體處理平台，這是一種非常產品化的產品。

But then there's integrated automation, which basically means there's a robotic arm in the middle of a bunch of equipment, and the key there is one piece of equipment maybe does the liquid handling, but then you've got to take your samples to the next piece of equipment and you saw on the video the Plates moving on that track and getting delivered to six or seven different pieces of equipment in that single protocol.

但還有整合自動化，這基本上意味著在一堆設備中間有一個機械臂，關鍵在於一台設備可能負責液體處理，但你必須將樣品送到下一台設備，你在視頻中看到，培養板在軌道上移動，並在一個流程中被送到六七台不同的設備。

You might have protocols that interact with 15 different pieces of equipment, and a human by and large is doing that in 99% of the labs that are out there.

你可能有一些需要與 15 種不同設備互動的方案，而 99% 的實驗室裡，這些工作基本上都是由人來完成的。

There is a small niche industry around integrated automation for things like high throughput screening, where you put an arm in the middle of 15 pieces of equipment that is built basically application specific. In other words, it's a design of a setup just for the one thing you want to do.

圍繞著整合自動化，存在著一個小型利基行業，例如用於高通量篩選，其中將一個機械手臂安裝在 15 件設備中間，這些設備基本上是針對特定應用而製造的。換句話說，它是專門為你想做的一件事而設計的設定。

Our cars are not like that. They are, they're productized. They're coming off the line the same, and then we're just connecting them so that you have whatever equipment you want initially and then actually able to expand that equipment over time into bigger and bigger setups. So that's something you just cannot get with the traditional integrated automation.

我們的車不是這樣的。它們是，它們已被商品化。它們從生產線上下來時都是一樣的，然後我們只是把它們連接起來，這樣你一開始就能擁有你想要的任何設備，然後隨著時間的推移，你還可以將這些設備擴展到越來越大的配置中。這是傳統整合自動化無法實現的。

So what I'm excited about on a rollout basis is, continuing to scale up our manufacturing of these cars, bring the cost down, like turn that again into more and more productized offering.

因此，我感到興奮的是，在推廣階段，我們將繼續擴大這些汽車的生產規模，降低成本，並將其轉化為越來越產品化的產品。

But then on the sales side, it's basically getting folks to see this distinction between application specific work cells that they buy today and general purposely autonomous labs like what I was showing you there with our frontier Lab here in Boston.

但就銷售方面而言，基本上就是讓人們看到他們今天購買的特定應用工作單元與像我剛才在波士頓向您展示的那種通用自主實驗室之間的區別，就像我們的前沿實驗室一樣。

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

正是這種接受度，這種理念，即自動化不是為某個應用程式建構的東西，然後在三、四年後就徹底停用和丟棄。這就是這些系統的發展規律，它會隨著時間的推移而不斷擴張，最終取代舊系統。

Hopefully, tens of thousands, hundreds of thousands of square feet of laboratory benches because we're just going to move off that system.

希望能夠獲得數萬、數十萬平方英尺的實驗室工作台，因為我們將徹底告別那個系統。

We have to move away from the bench as the general purpose laboratory infrastructure to the automated bench to the autonomous lab, and that's the transition that I want to drive. So if you're looking for milestones, I want internal milestones at Ginkgo.

我們必須從以實驗台為通用實驗室基礎設施，轉向自動化實驗台，再轉向自主實驗室，而這正是我想要推動的轉變。所以，如果你在尋找里程碑，我希望看到的是 Ginkgo 內部的里程碑。

It's like one of the things I want to see. Is 50 plus scientists internally at ginkgo ordering simultaneously from our automation system in a single day. That is the thing I think I can get have happening in 2026.

這正是我一直想看到的景象之一。一天之內，公司內部有 50 多名科學家同時透過我們的自動化系統訂購銀杏。我覺得這件事在2026年就能實現。

That is something that has never been seen with an automated lab previously, so there are internal milestones and what I would love to see, we are starting to see this on the government side, but I would also like to see it in the private sector ideally.

這是自動化實驗室以前從未出現過的現象，因此內部有一些里程碑，我希望看到的是，我們已經開始在政府方面看到這一點，但理想情況下，我也希望在私營部門看到這一點。

A larger biopharma, a similar like a purchase of a very large system with an intent for a general purpose autonomous lab, and so those are kind of my two big things I'd love to see in 2026, us demonstrating just what you can do with already having one of these kind of autonomous labs and then a large biopharma leaning in and making a purchase for one.

一家大型生物製藥公司，類似於購買一個非常大的系統，目的是建立一個通用的自主實驗室，所以這是我希望在 2026 年看到的兩件大事：我們展示一下擁有這種自主實驗室能做什麼，以及一家大型生物製藥公司積極參與併購買一個。

We'll still sell opposite the work cells. That's what we're selling today, but I would love to see someone kind of lean in on the dream of the big general purpose autonomous lab.

我們仍然會在工作區對面進行銷售。這就是我們今天銷售的產品，但我很希望看到有人能夠認真考慮打造大型通用自主實驗室的夢想。

I think it's the time for it, and we're going to prove it either way, I can go, but I think our customers will be sort of adopting that mindset soon too, is my view, because the I know, it's gotten so much easier to use automation with the AI stuff, and so I do think that's going to just bring the bearer down massively for this in the industry.

我認為現在正是時候，無論如何我們都會證明這一點。我可以繼續前進，但我認為我們的客戶很快也會接受這種想法，因為我知道，使用人工智慧等自動化技術已經變得容易得多，所以我認為這將大大降低行業內自動化的門檻。

Cool. Alright, and then Brennan had one more question, which was, as you look at the current revenue mix between cell processing we said cell engineering and biosecurity, and then consider your internal assumptions about the AI tools and racks rollouts. What do you see as the ideal revenue mix for Genkgko by 2030? What has to happen to get there.

涼爽的。好的，然後布倫南還有一個問題，那就是，當你審視目前細胞處理（我們說的是細胞工程和生物安全）之間的收入組成時，再考慮一下你對人工智慧工具和機架推廣的內部假設。您認為到 2030 年 Genkgko 的理想收入結構是什麼？要達到那個目標需要發生什麼事？

For 2030? Okay, yeah, that is interesting. I mean, so my dream by 2030 is we're starting to put a bunch of benches to bed, and so my expectation, like if I think about the balance between, let's believe biosecurity, I'll come back to that in a second, but between like the sort of tools business, in other words, like robotics, software on the robotics, reagents going into all that infrastructure, devices, that whole.

到2030年？好的，這很有意思。我的意思是，到 2030 年，我的夢想是我們將開始完成許多工作，所以我的期望是，如果我考慮一下平衡，例如生物安全（我稍後會再談到這一點），以及工具業務，換句話說，例如機器人、機器人軟體、用於所有這些基礎設施的試劑、設備等等。

Ecosystem of our tools business versus the services offerings that we offer on top of our setup like data points and solutions, that tools versus services I would say is like 80/20 in the tools side of the house in terms of our revenue mix in 2030.

我們的工具業務生態系統與我們在現有架構之上提供的服務產品（如數據點和解決方案）相比，工具與服務的比例，我認為到 2030 年，工具業務的收入佔比將達到 80/20。

Like my hope would be we are largely taking over the general purpose R&D infrastructure and being that provider of the tools into the whole industry, so that should be dominant. When it comes to biosecurity, there it's very dependent on How things play out. It's like a very interesting time right now.

我希望我們能夠基本上接管通用研發基礎設施，並成為整個產業的工具提供者，從而佔據主導地位。就生物安全而言，這很大程度取決於事態的發展。現在真是一個非常有趣的時代。

So you know CDC is getting rebuilt, there is a great post from Matt McKnight who heads up our biosecurity business today. I encourage folks to read about sort of like what a rebuilt CDC looks like.

既然你們知道美國疾病管制與預防中心（CDC）正在重建，那麼我們生物安全業務負責人 Matt McKnight 就發表了一篇很棒的文章。我鼓勵大家了解重建後的美國疾病管制與預防中心（CDC）大概是什麼樣子。

I think fundamentally, you need persistent pervasive monitoring of viruses as like a foundational layer for biosecurity in the future, whether you are in an outbreak.

我認為從根本上講，無論是否處於疫情爆發期，都需要對病毒進行持續廣泛的監測，作為未來生物安全的基礎層。

Not just all the time. And so if that type of infrastructure gets built here in the US and worldwide, then you know who knows, biosecurity could be 50-50 with the rest of the business, but it does depend on whether we see that adoption of sort of like monitoring technology as the core, one of the core pillars of biosecurity that works, the CDC that could stop the next COVID.

並非一直如此。因此，如果這種基礎設施在美國和世界各地得以建立，那麼誰知道呢，生物安全或許能與其他業務達到五五開的程度，但這取決於我們是否看到監測技術被採納為生物安全的核心支柱之一，成為美國疾病管制與預防中心（CDC）能夠阻止下一次新冠疫情爆發的關鍵。

Cool. So, we got a question for Steve.

涼爽的。我們有個問題想問史蒂夫。

So Steve, you mentioned in October 2025, Ginkgo reset the annual commitments and its contract with Google. Can you provide a little more color on that?

所以史蒂夫，你提到在 2025 年 10 月，Ginkgo 重置了年度承諾及其與谷歌的合約。能再詳細說說嗎？

Sure, I, when we were negotiating, the Google Cloud, contract, obviously we had a shortfall to solve for in Q3. We talked about that. We reset, going forward, in my view very favourable terms for ginkgo. We were able to reduce our go forward commitment by over 100 million.

當然，在談判谷歌雲端合約時，顯然我們在第三季存在資金缺口需要解決。我們談過這件事。在我看來，我們為銀杏的未來重新設定了非常有利的條件。我們得以將未來的支出承諾減少超過1億英鎊。

And extended out the period by 2x, so going out over 6 years over the prior 3 years from that standpoint, I think that puts us right where we want to be.

並將期限延長了 2 倍，也就是從之前的 3 年延長到 6 年，我認為這讓我們達到了我們想要的目標。

Yeah, just a little extra color on this, we had made that investment on the Google Cloud side around, remember I mentioned the two areas of AI, the sort of reasoning model based AI and the bio model based AI.

是的，再補充一點，我們在谷歌雲端方面進行了一些投資，還記得我提到過人工智慧的兩個領域嗎？一個是基於推理模型的人工智慧，另一個是基於生物模型的人工智慧。

It was originally made with a mindset of that bio-based AI was going to grow quickly and I think what we have seen in the industry is it is being adopted but it has not grown anywhere near the rate that the reasoning models have and so this is more reflective.

最初的設想是生物人工智慧將會快速發展，而我認為我們在業界看到的是，它確實被採用了，但其發展速度遠不及推理模型，因此這更具反思性。

Of kind of how we see the deployment of like really like training needs internal to Ginkgo in the future. It is a much more smooth ramp over a longer period of time compared to if you were seeing massive investment across bioAI models and that just has not been at the rate we were expecting back then.

這就是我們未來如何看待 Ginkgo 內部真正需要的培訓部署方式。與當時在生物人工智慧模型領域大規模投資相比，這是一個更為平穩、持續時間更長的發展過程，而生物人工智慧模型領域的投資速度並沒有達到我們當時的預期。

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

所以我很高興史蒂夫和他的團隊把這件事處理得非常好，我們在谷歌的優秀合作夥伴也與我們一起完成了這項工作，所以我對最終的結果非常滿意。

Alright, the next one is for Jason.

好了，下一個是給傑森看的。

Jason, you mentioned Future Lab's new announcement of its next gen AI scientist Cosmos. Can you say more about how your experience at Ginkko kind of informs your viewpoint on AI, not just analysing data but also designing experiments, etc.

Jason，你提到了Future Lab新發布的下一代人工智慧科學家Cosmos。您能否詳細談談您在 Ginkko 的經歷如何影響您對人工智慧的看法，而不僅僅是分析數據，還包括設計實驗等等。

Yeah, I mean, it's for folks checking this thing out. I mean, so Future Labs, is now called Edison Scientific, used to be a nonprofit, sort of doing the open AI thing, becoming a for-profit, and so, but what they're doing is they basically built up a model for that's read all the scientific literature, you can kind of ask it like a scientific question.

是的，我的意思是，這是為了方便那些正在了解這個東西的人。我的意思是，Future Labs 現在叫 Edison Scientific，以前是一個非營利組織，做著開放人工智慧方面的工作，後來變成了營利性公司，等等。但他們所做的，基本上是建立了一個模型，可以讀取所有科學文獻，你可以像問科學問題一樣向它提問。

And it'll run for several hours and then kind of come back with, you know like kind of hypotheses or predictions or learnings or conclusions and they were able to show this model making several like, frankly new scientific discoveries just from reading the literature.

它會運行幾個小時，然後得出一些假設、預測、經驗或結論，他們能夠證明這個模型僅通過閱讀文獻就做出了幾個坦率地說是全新的科學發現。

So that's already very exciting, like I think and it's sort of this indicator that We're on this like inevitable path where I think like the logic of the of the models, like their ability to just do complex reasoning is going to work. It already works, frankly. I think the limitation will then move to what tools can you give access to these models.

所以這已經非常令人興奮了，我覺得這某種程度上表明我們正走在一條不可避免的道路上，我認為模型的邏輯，例如它們進行複雜推理的能力，將會奏效。坦白說，它現在已經奏效了。我認為接下來的限制將轉移到你可以讓哪些工具存取這些模型。

And the big one we believe is important in the realm of science like I mentioned earlier is hands in the lab. That's just it. It's hands in the lab.

而我們認為在科學領域最重要的一點，正如我之前提到的，就是在實驗室裡動手做。就是這樣。這是實驗室裡的雙手。

And so that type of a model with the ability to then say, well, what I actually believe I should do to really answer your question based on everything I read in the literature is run these 10 experiments or these 100 experiments, see what I learn, and then Run another 100 and do that a few more times and then I'll come back to you with the answer.

因此，這種模型能夠說，嗯，根據我在文獻中讀到的所有內容，我認為為了真正回答你的問題，我應該做的是進行這 10 個實驗或這 100 個實驗，看看我能學到什麼，然後再進行 100 個實驗，如此反复幾次，然後我再給你答案。

I mean that's what a PhD does. I mean that's what I did for 5 years at MIT in my PhD. It's like, yes, I got this question I'm trying to answer. I'm going to run some experiments. I'm going to look at the results. I'm going to interpret them and I'm going to go around that loop and a lot of it is understanding what other people have done in the literature. I think that's what this model does from Future House, Edison.

我的意思是，這就是博士學位的作用。我的意思是，我在麻省理工學院攻讀博士學位期間，花了五年時間就是這麼做的。就像，是的，我遇到了這個問題，我正在努力回答。我打算做一些實驗。我要看看結果。我打算解讀它們，然後沿著這個循環往復，其中許多內容是理解其他人在文獻中所做的工作。我認為這就是 Future House 的這款模型所實現的功能，愛迪生。

And then the other half is Kind of just not basic logic, but not the world's most complex analysis of what you're seeing in the lab.

而另一半則有點不像是基本的邏輯，但也不是對你在實驗室裡看到的現象進行世界上最複雜的分析。

It's really your ability to conduct and design the experiments and then interpret the results. Just the craft of that is what keeps a lot of people out of science, and I think that can just be replaced now, I think with programming and a robotic interface to the lab, and I don't know what that does.

真正考驗的是你進行和設計實驗以及解釋實驗結果的能力。正是這門技藝讓很多人遠離了科學，我認為現在可以用程式設計和實驗室的機器人介面來取代它，但我不知道它具體能做什麼。

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

我的意思是，這可能會極大地促進人們在眾多領域提出尖銳的科學問題，這將非常令人興奮。所以我們拭目以待，但我們希望提供人手，這是我們在這方面的作用，我們也非常樂意看到其他地方建造這些精妙的模型。

So the next question is kind of a follow-up to that one actually and so the question is how do you see this AI plus robotics platform changing the R&D landscape sort of large and what is the initial feedback then from potential tools costs?

那麼下一個問題其實是對上一個問題的後續，問題是：您認為人工智慧加機器人平台將如何大規模地改變研發格局？從潛在工具的成本來看，初步回饋是什麼？

Yeah, so I think what like if you think commercially how this can make a big difference, right? So what the way that like say drug discovery, for example, right, like you're, you have an idea, you've read about, again, you've read the literature, you're an expert in this area, you have a hypothesis about a certain disease and how it works and you're looking for an interesting drug target around your hypothesis.

是的，所以我覺得，如果你從商業角度考慮，這會造成很大的不同，對吧？例如，在藥物發現過程中，假設你有一個想法，你已經閱讀過相關文獻，你是這個領域的專家，你對某種疾病及其作用機制有一個假設，並且你正在尋找圍繞你的假設的有趣的藥物靶點。

So you would sort of plan a line of experiments, you and a. A team of researchers will go conduct that over a period of six months or one year or one year and a half and then TRY to get to an answer on your hypothesis.

所以你會計劃一系列實驗，你和 a。一個研究團隊將進行為期六個月、一年或一年半的研究，然後嘗試驗證你的假設。

I think what's exciting is that first, maybe those original hypotheses, maybe stuff like future House can just come up with those. Who cares, even if they can't. You always have a longer list of hypotheses than you have the resources to go out and test in the lab based on the number of scientists you have.

我覺得令人興奮的是，首先，也許那些最初的假設，也許像未來的豪斯醫生那樣的東西，就能提出這些假設。即使他們做不到，又有什麼關係呢？你提出的假設清單總是比你擁有的科學家數量所能提供的實驗室驗證資源要長得多。

Like fundamentally, like that is the limit. And so if instead you could basically spider these models out and say, hey, I want you to pursue my TOP 100 hypotheses instead of my TOP 3, and for each one, again, it's not just one experiment, it's got to do some lab work, interpret the results, and then plan some more lab work and keep going down that trail.

從根本上講，那就是極限了。因此，如果你能將這些模型展開，然後說：「嘿，我希望你研究我的前 100 個假設，而不是我的前 3 個假設。」對於每一個假設，這不僅僅是一個實驗，它還需要進行一些實驗室工作，解釋結果，然後計劃更多的實驗室工作，並繼續沿著這條線索前進。

You could be running that across 100 or 1,000 hypotheses in parallel as a single researcher potentially with access to robotics to go spider and then have it just come back and tell you when it gets interesting results, and that is just, I mean, I don't even know that that's a fundamentally different way to pursue a goal around, say, how does this disease work.

你可以讓一個研究人員同時對 100 或 1000 個假設進行並行運行，他可能可以使用機器人進行搜索，然後讓它返回並告訴你何時獲得了有趣的結果，我的意思是，我什至不知道這是否是一種從根本上不同的方法來追求目標，例如，這種疾病是如何運作的。

It just, it, fundamentally what is limited is reasoning and experimental hands, and if we can take both those off the table. Then then all the cost just turns into like reagent costs. It's like literally the consumables you're going through, which is just crazy. Like that is not at all the cost right now.

從根本上說，受限的是推理和實驗，如果我們能把這兩者都排除在外就好了。這樣一來，所有成本就都變成試劑成本了。這簡直就像你消耗的那些耗材一樣，太瘋狂了。現在這根本不是成本。

The costs now are 100% dominated by basically human time. In all these areas really and like laboratory space, like just like literally square footage and both of those could compress massively with automation plus AI. It's really exciting.

現在的成本100%都取決於人力時間。所有這些領域，例如實驗室空間，例如實際的面積，都可以透過自動化和人工智慧大幅壓縮。真是太令人興奮了。

Alright, that is all the questions that we have for tonight.

好了，今晚的問題就這些了。

A reminder, you can always ask questions by emailing us at investors@go buyerworks.com and also as Jason said earlier, if you are interested in coming by and seeing some of this equipment, reach out and we will make it happen.

再次提醒大家，您可以透過電子郵件至 investors@go Buyerworks.com 向我們提出問題。另外，正如 Jason 之前所說，如果您有興趣過來看看這些設備，請聯絡我們，我們會安排的。

Great. Thanks everybody. Appreciate the question. Good night.

偉大的。謝謝大家。感謝您的提問。晚安。