DigitalOcean Holdings, Inc. (DOCN) 2026 Q1 法說會逐字稿

Thank you for standing by. My name is Jill, and I will be your conference operator today. At this time, I would like to welcome everyone to the DigitalOcean's first quarter 2026 earnings conference call. (Operator Instructions)

感謝您耐心等候。我叫 Jill，今天將擔任本次電話會議的接線員。此刻，我謹代表主辦方歡迎各位參加 DigitalOcean 2026 年第一季財報電話會議。（接線員指示）

I would now like to turn the conference over to Radu Patricho, Head of Investor Relations. You may begin.

現在我想將會議交給投資人關係主管 Radu Patricho。您可以開始了。

Great. Thank you, Jill, and good morning, everyone. Thank you all for joining us today to review DigitalOcean's first quarter 2026 results. Joining me on the call today are Paddy Srinivasan, our Chief Executive Officer; and Matt Steinfort, our Chief Financial Officer. For those of you following along, an accompanying slide presentation is available on the webcast.

很好。謝謝你，Jill，各位早安。感謝各位今天加入我們，一同回顧 DigitalOcean 2026 年第一季業績。今天與我一同出席的還有我們的執行長 Paddy Srinivasan，以及財務長 Matt Steinfort。若各位同步收看，網路直播上提供了配套的簡報投影片。

Before we begin, let me remind you that certain statements made on the call today may be considered forward-looking statements, which reflects management's best judgment based on currently available information. Our actual results may differ materially from those projected in these forward-looking statements, including our financial outlook. I direct your attention to the risk factors contained in our earnings -- in our filings with the SEC as well as those referenced in today's press release that is posted on our website. DigitalOcean expressly disclaims any obligation or undertaking to release publicly any updates or revisions to any forward-looking statements made today.

在開始之前，提醒各位，今天電話會議中的某些陳述可能構成前瞻性陳述，反映管理層基於目前可得資訊所作出的最佳判斷。我們的實際結果可能與這些前瞻性陳述（包括我們的財務展望）中所預測者存在重大差異。請各位留意我們財報——以及我們向美國證券交易委員會（SEC）提交之文件中所載的風險因素，並參考今日新聞稿（已發布於我們網站）中所提及者。DigitalOcean 明確聲明，對於今日所作任何前瞻性陳述，並無義務或承諾公開發布任何更新或修訂。

Additionally, non-GAAP financial measures will be discussed on this conference call and reconciliations to the most directly comparable GAAP financial measures can be found in today's earnings press release as well as our as well as in our earnings presentation that outlines the discussion on today's call. The webcast of today's call is available on the IR section of our website.

此外，本次電話會議將討論非 GAAP 財務衡量指標，與最直接可比的 GAAP 財務衡量指標之調節表，可於今日的財報新聞稿中查閱，也可在我們的財報簡報中找到（其中概述了今日電話會議的討論內容）。今日電話會議的網路直播可於我們網站的投資人關係（IR）專區收看。

And with that, I'll turn it over to Paddy.

接下來，我把時間交給 Paddy。

Thank you, Raju. Good morning, everyone, and thank you for joining us today. We had an outstanding Q1 2026, and I'll start with four headlines. First, our momentum is accelerating. Q1 revenue was $258 million up 22% year-over-year, with million dollar plus customers growing 179% year-over-year to $183 million in ARR. AI customer ARR grew 221% to $170 million, and we beat every financial target we shared in our last call.

謝謝你，Raju。各位早安，感謝各位今天加入我們。我們在 2026 年第一季表現非常出色，我先用四個重點標題開場。第一，我們的動能正在加速。第一季營收為 2.58 億美元，年增 22%；年經常性收入（ARR）達 100 萬美元以上的客戶年增 179%，至 1.83 億美元 ARR。AI 客戶 ARR 年增 221% 至 1.70 億美元，而且我們達成並超越了上次電話會議所分享的每一項財務目標。

Number two, we launched the DigitalOcean AI native cloud last week, the most significant product launch in our history. With more than 15 new product launches across five fully integrated layers built into a modern, open unified stack, purpose built for the inferencing and Agentic Era.

第二，我們上週推出 DigitalOcean AI 原生雲，這是我們公司史上最重要的產品發布。在一個現代化、開放且統一的技術堆疊中，打造五個完全整合的層級，並推出超過 15 項新產品，專為推論與 Agentic 時代量身打造。

Third, we are investing to meet our growing customer demand and to seize the material opportunity in front of us. We raised $888 million in equity during Q1 to strengthen our balance sheet and quickly utilize that flexibility to secure 60 megawatts of incremental capacity that is slated to ramp throughout 2027, bringing our total committed capacity to 135 megawatts. And finally, we are again raising our near- and medium-term guidance on the strength of customer demand and the incrementally committed capacity.

第三，我們正在加大投資，以滿足不斷成長的客戶需求，並把握眼前重大的機會。我們在第一季透過股權融資籌得 8.88 億美元，以強化資產負債表，並迅速運用這項彈性，取得額外 60 兆瓦的增量產能，預計將在 2027 年逐步爬坡，讓我們的總承諾產能達到 135 兆瓦。最後，基於強勁的客戶需求與新增承諾產能，我們再次上調近、中期指引。

For 2026, we are increasing our full year revenue growth projection from 21% and to approximately 26% year-over-year and expect to exit Q4 approaching 30%. And this revised 2026 growth is entirely driven by our previously committed capacity, without any top line benefit in 2026 from the new 60 megawatts. With the projected ramp of the incremental 60 megawatts in 2027, we are now projecting revenue growth of 50% or more in 2027, meaningfully higher than the 30% growth we communicated just last quarter.

就 2026 年而言，我們將全年營收成長預估由年增 21% 上調至約年增 26%，並預期在第四季末接近 30%。而這次修訂後的 2026 年成長，完全由我們先前已承諾的產能所驅動；新增的 60 兆瓦在 2026 年不會對營收帶來任何上行貢獻。隨著 2027 年增量 60 兆瓦的預計爬坡，我們現在預估 2027 年營收成長可達 50% 或以上，顯著高於我們僅在上一季所溝通的 30% 成長。

I'll now spend a few minutes drilling down on each of these four headlines. The momentum we are generating is clear evidence of both our differentiated position and our strong execution across the board. It starts with the accelerating top line growth. Q1 revenue was $258 million, up 22% year-over-year and up over 400 basis points over Q4 2025 already strong 18% exit growth rate. We are delivering this growth by continuing to delight our top cloud and AI native customers.

接下來我會花幾分鐘，逐一深入說明這四個重點。我們所創造的動能，清楚證明了我們的差異化定位，以及我們在各方面強而有力的執行。首先是加速的營收成長。第一季營收為 2.58 億美元，年增 22%，較 2025 年第四季已相當強勁的 18% 期末成長率再提升超過 400 個基點。我們之所以能交出這樣的成長，是因為我們持續讓我們最重要的雲端與 AI 原生客戶感到滿意。

Our AI customer ARR reached $170 million, growing 221% year-over-year. Our $1 million customer ARR rates $183 million, growing 179% year-over-year. These are not just customers experimenting on our platform. These are cloud and AI native companies scaling their businesses on DigitalOcean.

我們的 AI 客戶 ARR 達到 1.70 億美元，年增 221%。我們 100 萬美元客戶的 ARR 達到 1.83 億美元，年增 179%。這些並非只是在我們平台上試驗的客戶。這些是正在 DigitalOcean 上擴大規模、成長其業務的雲端與 AI 原生公司。

Our rate of acceleration is also increasing. We delivered a record $62 million in incremental organic ARR, the highest in the company's history. Customers see our differentiated value and are leaning into our platform. RPO reached $243 million, up an extraordinary 1,700% year-over-year. And we are doing all of this with strong profitability. We delivered 41% adjusted EBITDA margin and 18% trailing 12-month adjusted free cash flow margins.

我們的加速幅度也在提高。我們交出創紀錄的 6,200 萬美元有機新增 ARR，為公司史上最高。客戶看見我們的差異化價值，並更加倚重我們的平台。剩餘履約義務（RPO）達到 2.43 億美元，年增幅高達驚人的 1,700%。同時，我們也維持強勁的獲利能力。我們的調整後 EBITDA 利潤率為 41%，過去 12 個月的調整後自由現金流利潤率為 18%。

Drilling into our growth. Our largest customers continue to be our fastest growing and their growth continues to accelerate. ARR from our $100,000 customers grew 73%, while our $500,000 customer ARR grew 132%. ARR from our $1 million-plus customers reached $183 million, growing at 179% year-over-year versus 123% last quarter.

進一步看我們的成長。我們最大的客戶仍是成長最快的客戶，而且其成長仍在加速。ARR 達 10 萬美元的客戶 ARR 成長 73%，而 ARR 達 50 萬美元的客戶 ARR 成長 132%。ARR 達 100 萬美元以上的客戶 ARR 達到 1.83 億美元，年增 179%，相較上一季的 123% 進一步提升。

Our AI customers are the other key driver of accelerating growth. AI customer ARR reached $170 million, growing 221% year-over-year. And most critically, inference and core cloud pull-through increased to more than 80% of total AI customer ARR, up from 70% in Q4. That number tells you something important. We are not a GPU rental business. We are a full stack cloud platform that AI native companies depend on to build, run and scale their production AI software.

我們的 AI 客戶是加速成長的另一個關鍵驅動因素。AI 客戶 ARR 達到 1.70 億美元，年增 221%。更關鍵的是，推論與核心雲端的帶動效應（pull-through）提升至 AI 客戶 ARR 總額的 80% 以上，高於第四季的 70%。這個數字傳達了一個重要訊息。我們不是 GPU 租賃業者。我們是一個全棧雲端平台，AI 原生公司仰賴我們來建置、運行並擴展其生產環境中的 AI 軟體。

Last week, at our Deploy conference in San Francisco, we launched the DigitalOcean AI native cloud. And let me explain why this is a very significant step. Four forces are fundamentally reshaping AI right now. Inferencing has overtaken training as the dominant AI computing workload. Open source AI is now in production at over half of AI native companies.

上週在舊金山舉行的 Deploy 大會上，我們推出了 DigitalOcean AI 原生雲。我來說明為何這是非常重要的一步。目前有四股力量正在從根本上重塑 AI。推論已超越訓練，成為主導性的 AI 運算工作負載。開源 AI 現在已在超過一半的 AI 原生公司中投入生產環境。

Reasoning models are driving the majority of token consumption. And Agentic systems are rapidly moving from experimentation to production. Together, these forces represents AI evolution from thinking in which AI plays an advisory role to both thinking and doing in which AI delivers outcomes by executing autonomous tasks. The thinking part is powered by AI bottles in inferencing mode and the doing part is delivered by a variety of modern cloud computing modules, all working together to take intelligent, autonomous real-world action.

推理模型正驅動大多數的 token 消耗。而 Agentic 系統正迅速從實驗走向生產。綜合而言，這些力量代表 AI 的演進：從「思考」——AI 扮演顧問角色——走向「思考與行動」——AI 透過執行自主任務來交付成果。「思考」的部分由處於推論模式的 AI 模型所驅動；「行動」的部分則由各式各樣的現代雲端運算模組所實現，彼此協同運作，以在真實世界中採取智慧且自主的行動。

DigitalOcean's AI native cloud is purpose built for AI natives building exactly these types of workloads. It starts at the bottom with foundational layers. We operate a global scale infrastructure with 20 data centers purpose built for AI workloads running a full stack core computing platform with a complete set of computing primitive that Agentic workloads demand. Kubernetes, CPU and GPU droplet, advanced networking stack, including virtual private cloud, object block and file storage and high-performance NFS. This is part of the doing layer, the foundation that vast majority of GPU-centric cloud simply don't have.

DigitalOcean 的 AI 原生雲是專為打造這類工作負載的 AI 原生公司量身打造。它從最底層的基礎層開始。我們營運具全球規模的基礎設施，擁有 20 座專為 AI 工作負載打造的資料中心，運行完整堆疊的核心運算平台，提供 Agentic 工作負載所需的一整套運算基元。Kubernetes、CPU 與 GPU Droplet、進階網路堆疊（包含虛擬私有雲）、物件/區塊/檔案儲存，以及高效能 NFS。這些屬於「執行層」的一部分，是多數以 GPU 為中心的雲端根本不具備的基礎。

Last week, we launched a new inference engine, which we co-invented with our customers to address their most critical inferencing needs, and it delivers a lot more than just serving tokens. It provides serverless and dedicated end points for serving up AI models batch processing for asynchronous token generation, an intelligent policy of our inference router that automatically selects the best model for cost and performance a catalog of over 70 open source and close source frontier models with day zero access, multimodal capabilities and guardrails.

上週，我們推出了全新的推論引擎，這是我們與客戶共同發明，用以滿足他們最關鍵的推論需求，而且它提供的遠不只是提供 token 服務。它提供無伺服器與專用端點來提供 AI 模型服務、用於非同步 token 生成的批次處理、我們推論路由器的智慧策略（可依成本與效能自動選擇最佳模型）、超過 70 個開源與閉源前沿模型的目錄並提供 Day 0 存取、多模態能力與防護欄（guardrails）。

For customers who want to run their own models, we support BYOM, or Bring Your Own Model. This is the thinking layer, and it is far more than just serving tokens. It is about serving tokens efficiently with best-in-class performance, tightly integrated with other parts of the cloud.

對於想要運行自有模型的客戶，我們支援 BYOM（Bring Your Own Model，自帶模型）。這是「思考層」，而且遠不只是提供 token 服務。重點在於以同級最佳效能高效率地提供 token，並與雲端的其他部分緊密整合。

Augmenting this new inference engine is our data and learning layer for which we announced an enterprise version of our managed MySQL and [PaaS CRIs] databases for advanced workloads. We also announced new vector database support for building Agentic workloads. We also launched a brand-new managed agents platform to give AI native everything they need to build, execute and operate autonomous agents at scale with open harnesses, sandbox, state management, agent observability, toolbox for external integration and Plano based orchestration on an open platform without getting boxed into a single LLM or platform provider.

為了強化這個全新的推論引擎，我們推出資料與學習層，並宣布針對進階工作負載的企業版託管 MySQL 與 [PaaS CRIs] 資料庫。我們也宣布新增向量資料庫支援，用於建置 Agentic 工作負載。我們同時推出全新託管代理（agents）平台，為 AI 原生公司提供建置、執行與大規模營運自主代理所需的一切：開放式 harness、sandbox、狀態管理、代理可觀測性、外部整合工具箱，以及基於 Plano 的協調編排；在開放平台上運作，不會被鎖定在單一 LLM 或平台供應商。

This is the DigitalOcean AI native cloud, five fully integrated layers from silicon to agents with zero lock-in because we offer open source options at every single layer. This is absolutely essential as our target customers are AI native companies who are creating and monetizing software. AI infrastructure is a material cost of revenue line item for these AI natives, especially when they scale, maintaining flexibility across models and platforms and leveraging the most efficient model capabilities for every specific task is an existential requirement for them. AI natives are increasingly adopting open source at every level, including multiple open source models to open agent harnesses, open source vector databases and so on, to a wide lock in and deliver compelling unit economics for their customers as they go into hyper growth mode themselves.

這就是 DigitalOcean 的 AI 原生雲：從矽到代理的五個完全整合層，且零鎖定（zero lock-in），因為我們在每一層都提供開源選項。這點至關重要，因為我們的目標客戶是正在創造並變現軟體的 AI 原生公司。AI 基礎設施是這些 AI 原生公司的重要營收成本項目，尤其在規模化時；在模型與平台之間維持彈性，並針對每個特定任務運用最高效率的模型能力，對他們而言是攸關存亡的需求。AI 原生公司正日益在各層採用開源，包含多個開源模型、開放式代理 harness、開源向量資料庫等，以避免鎖定，並在自身進入高速成長模式時，為其客戶帶來具吸引力的單位經濟效益。

Building a truly open, fully integrated platform is hard, and that difficulty is precisely what makes our platform durable. The market is validating what we have long believed that infrastructure without intelligence, without orchestration and a full cloud platform is insufficient for what AI native workloads actually demand. Agentic applications require intelligence CPU-based execution, stateful memory, manage high-performance storage and databases and orchestration, all working together natively not assembled after the fact. Our integrated stack is built for exactly this architecture, and that's what enables us to deliver differentiated performance with compelling unit economics that matter to our AI native customers.

打造真正開放、完全整合的平台並不容易，而這種難度正是讓我們的平台具備持久性的原因。市場正在驗證我們長期以來的信念：沒有智慧、沒有協調編排、也沒有完整雲端平台的基礎設施，無法滿足 AI 原生工作負載的實際需求。Agentic 應用需要智慧、以 CPU 為基礎的執行、具狀態的記憶、受管的高效能儲存與資料庫，以及協調編排；所有這些都必須原生協同運作，而不是事後拼裝。我們的整合式堆疊正是為此架構而建，這也使我們能提供差異化效能與具吸引力的單位經濟效益，這些正是 AI 原生客戶所重視的。

Leading independent benchmarking company, artificial analysis recently reported that DigitalOcean delivers the number one output speed for leading open source model like DeepSeek version 3.2, Qwen version 3.5, the $397 billion parameter model across all cloud providers. Our 230 output tokens per second on DeepSeek V3.2 is 3.9x faster than one of the leading hyperscalers. This wasn't just a hardware story. It required co-designing every layer of the stack from NVIDIA's Blackwell ultra GPUs to custom VLLM optimizations, including speculative decoding and kernel fusion, which is exactly the kind of deep engineering that differentiates the modern AI native platform from GPU farms and inference wrapper providers.

領先的獨立基準測試公司 Artificial Analysis 近期報告指出，DigitalOcean 在 DeepSeek 3.2、Qwen 3.5 等領先開源模型（以及參數規模達 3,970 億的模型）上，於所有雲端供應商中提供了第一名的輸出速度。我們在 DeepSeek V3.2 上達到每秒 230 個輸出 token，速度比某家領先的超大規模雲供應商快 3.9 倍。這不只是硬體的故事。它需要從 NVIDIA Blackwell Ultra GPU 到自訂 VLLM 最佳化的全堆疊逐層共同設計，包含推測式解碼（speculative decoding）與 kernel fusion；這正是讓現代 AI 原生平台有別於 GPU 農場與推論封裝供應商的深度工程能力。

The clearest validation of our strategy is the caliber of customers choosing to build and scale on us. We recently onboarded Cursor one of the fastest-growing AI applications ever built, for production inference, model fine-tuning and core cloud services. Ideogram, a leading text-to-image foundation model company migrated production inference from a hyperscaler to our AI infrastructure running their own model weight at scale. And Higgsfield AI, serving over 20 million creators with cinematic video generation run its full multi-model workflow on our integrated stack. Three different AI native companies in hyper-growth mode, running their production AI on our AI native cloud. And our pipeline continues to grow in both volume and strategic scale.

對我們策略最清楚的驗證，是選擇在我們平台上建置並擴展的客戶品質。我們近期導入 Cursor——史上成長最快的 AI 應用之一——用於生產推論、模型微調與核心雲端服務。領先的文字轉圖像基礎模型公司 Ideogram，已將生產推論從某超大規模雲遷移到我們的 AI 基礎設施上，並以規模化方式運行其自有模型權重。而 Higgsfield AI 為超過 2,000 萬名創作者提供電影級影片生成，並在我們的整合式堆疊上運行其完整的多模型工作流程。三家處於高速成長模式的不同 AI 原生公司，都在我們的 AI 原生雲上運行其生產級 AI。而我們的商機管線在數量與策略規模上都持續成長。

Let me spend a couple of minutes on our competitive positioning with our new platform announcement. At a high level, unlike the hyperscalers, we are more open, purpose built for modern software without the legacy complexity of enterprise workloads designed for the previous era. Compared to the GPU Neoclouds, which are optimized for large training clusters, we are a full stack inferencing and Agentic platform. And finally, while the inference wrapper providers offer tokens, we offer the breadth AI-native builders need to build complete modern software without forcing them to stitch a platform together themselves.

接下來我用幾分鐘談談我們在這次新平台發布後的競爭定位。整體而言，與超大規模雲供應商不同，我們更開放，且專為現代軟體打造，不受上一個時代為企業工作負載設計所遺留的複雜性所拖累。相較於針對大型訓練叢集最佳化的 GPU Neocloud，我們提供的是全堆疊推論與 Agentic 平台。最後，當推論封裝供應商只提供 token 時，我們提供 AI 原生建置者所需的廣度，讓他們能建置完整的現代軟體，而不必被迫自行把平台拼接起來。

What makes our position genuinely durable is three compounding layers. Number one, our AI middleware. The Plano data plane and inference router built on technology from our recent Cataneo acquisition completed last quarter, sits between the agents and the underlying infrastructure, intelligently steering workloads across models, regions and accelerator types based on cost, latency and availability trade-offs at real time.

讓我們的定位真正具備持久性的，是三個相互疊加的層次。第一，我們的 AI 中介軟體。Plano 資料平面與推論路由器，建立在我們於上季完成的 Cataneo 收購所帶來的技術之上，位於代理與底層基礎設施之間，能依成本、延遲與可用性的即時權衡，在不同模型、區域與加速器類型之間智慧地導引工作負載。

Second, our managed agents platform extends computing primitives up the stack with secure run times, execution sandboxes, background workers, observability, orchestration and much more. All purpose-built for Agentic applications to be built and scaled on this platform. And the third is data gravity through managed databases, vector stores, cashing and object storage, production data lives inside our DigitalOcean AI native platform. Models and GPUs are not sticky, data is.

第二，我們的託管代理平台將運算基元向上延伸，提供安全執行環境、執行 sandbox、背景工作者、可觀測性、協調編排等更多能力。全部都是為了讓 Agentic 應用能在此平台上被建置並擴展而量身打造。第三是透過託管資料庫、向量儲存、快取（caching）與物件儲存所形成的資料重力：生產資料存放在我們的 DigitalOcean AI 原生平台內。模型與 GPU 並不黏著，資料才是。

For AI native, the decision of where to build is rarely about a single feature. It is about platform breadth quality of abstractions, openness of the platform and the absence of friction. Delivering that requires deliberate integrated engineering across every layer from silicon to agents. It needs an AI native cloud, which is what digital ocean has been building towards with millions of R&D hours over the last dozen-plus years.

對 AI 原生公司而言，選擇在哪裡建置很少只取決於單一功能。而是取決於平台廣度、抽象層品質、平台的開放性，以及是否沒有摩擦。要做到這點，需要從矽到代理的每一層都進行有意識的整合式工程。這需要一個 AI 原生雲——而這正是 DigitalOcean 在過去十多年投入數百萬小時研發所持續打造的方向。

The market opportunity is generational and we are poised to earn more than our fair share. Global inference traffic will grow 10 times by 2030, and Agentic workloads consumed 15 times more tokens than human users, a multiplier that compounds as AI matures.

市場機會是世代級的，而我們已蓄勢待發，將取得超過我們應得份額的成果。到 2030 年，全球推論流量將成長 10 倍，而 Agentic 工作負載消耗的 token 是人類使用者的 15 倍；隨著 AI 成熟，這個乘數效應還會持續疊加。

And we're already seeing it in our numbers. Our AI customer ARR is growing 221%, and over 80% of that is coming from infant services and core cloud, not Bare Metal, these are companies running full stack production AI on digitation and they're accelerating. We are investing to meet this growing customer demand and to seize the opportunity in the massive inferencing and Agentic markets. In Q1, we raised $888 million in equity proceeds that enable us to expand our data center and GPU capacity to meet our growing customer demand while strengthening our balance sheet.

而我們已經在數據中看到了這一點。我們的 AI 客戶 ARR 成長 221%，其中超過 80% 來自入門級服務與核心雲端，而非裸機（Bare Metal）；這些公司正在 Digitation 上運行全棧、可投入生產的 AI，並且正在加速。我們正在加大投資，以滿足不斷成長的客戶需求，並把握龐大的推理（inferencing）與代理式（Agentic）市場機會。在第一季，我們透過股權融資募集了 8.88 億美元，使我們得以擴充資料中心與 GPU 產能，以滿足日益成長的客戶需求，同時強化資產負債表。

Matt will provide more details on the equity raise and our capital strategy later in our comments. But let me give you a brief highlight on our expansion plans. Starting with our existing committed capacity. We remain on track to deliver our previously communicated 31 megawatts as planned in 2026. With our Richmond facility beginning to ramp revenue in March.

Matt 稍後會在我們的評論中，提供更多關於此次股權募資與資本策略的細節。不過先讓我簡要說明我們的擴張計畫重點。先從我們既有的已承諾產能談起。我們仍按計畫在 2026 年交付先前已對外溝通的 31 兆瓦。里奇蒙（Richmond）設施自 3 月開始逐步放量並帶動營收。

On top of this, we have now secured approximately 60 megawatts of incremental data center capacity across four locations. Capacity that will ramp revenue throughout 2027. This brings our total committed data center capacity to approximately 135 megawatts. And given growing customer demand, we continue to actively pursue additional capacity beyond this new 60 megawatts capacity that will be targeted to come online in 2027 and 2028. The opportunity in front of us is enormous genuinely once in a generation.

此外，我們目前已在四個地點額外取得約 60 兆瓦的新增資料中心產能。該產能將在 2027 年全年逐步放量並帶動營收。這使我們已承諾的資料中心總產能提升至約 135 兆瓦。鑑於客戶需求持續成長，我們也正積極尋求在這新增 60 兆瓦之外的更多產能，目標於 2027 年與 2028 年上線。我們眼前的機會極其龐大，確實是世代難逢。

Every data point we see from our growing customer pipeline to the demand signals we are seeing and hearing from our largest customers to the reactions and interest in our AI native cloud reinforces that conviction. As we scale our business to meet this opportunity, we will continue to make the right long-term business decisions to seize this moment while building a durable and profitable growth engine.

我們看到的每一個數據點——從不斷成長的客戶管線、我們從最大客戶看到與聽到的需求訊號，到市場對我們 AI 原生雲的反應與興趣——都強化了這份信念。在我們擴大業務規模以把握此一機會的同時，我們將持續做出正確的長期商業決策，抓住當下契機，並打造具韌性且可獲利的成長引擎。

With momentum continuing to grow, we are further raising our near- and medium-term outlook for the full year 2026. We now expect revenue growth of approximately 25% to 27% year-over-year with an exit growth rate approaching 30%, a full year ahead of the guidance we provided just last quarter. This accelerated 2026 growth is based solely on the performance of our previously committed capacity and doesn't include any projected revenue uplift from the newly committed 60 megawatts. We expect to deliver this 2026 growth with high 30s adjusted EBITDA margins and 9% to 12% adjusted free cash flow margins, which does include some start-up costs for the new 60 megawatts.

隨著動能持續增強，我們進一步上調 2026 全年的近期與中期展望。我們目前預期營收年增約 25% 至 27%，期末（exit）成長率接近 30%，比我們僅在上一季提供的指引提前整整一年達成。這項加速的 2026 年成長，完全是基於我們先前已承諾的產能表現，並未納入新承諾的 60 兆瓦所帶來的任何預估營收上修。我們預期在達成 2026 年成長的同時，維持接近 40%（high 30s）的調整後 EBITDA 利潤率，以及 9% 至 12% 的調整後自由現金流利潤率；其中已包含新 60 兆瓦的一些啟動成本。

Looking further out, we now expect 2027 revenue growth of 50% or more, up from our 30% guidance last quarter with approximately 40% adjusted EBITDA margins and high teens adjusted free cash flow margins. This combination of rapid revenue growth and true durable profitability puts us in a ratified company. DigitalOcean is one of just a handful of names across a broad set of software and AI infrastructure players, delivering both attractive GAAP operating margins and material revenue growth. As I shared on our last call, growth and discipline are not trade-offs for us. They're both operating principles. And our execution of these principles is clear in our results.

再往後看，我們目前預期 2027 年營收成長 50% 或以上，高於上一季所給的 30% 指引；同時調整後 EBITDA 利潤率約 40%，調整後自由現金流利潤率為十幾個百分點的高段（high teens）。這種快速的營收成長與真正具韌性的獲利能力相結合，使我們成為一家經驗證的公司。DigitalOcean 是少數能在廣泛的軟體與 AI 基礎設施同業中，同時交出具吸引力的 GAAP 營業利潤率與實質營收成長的公司之一。如同我在上次電話會議中分享的，成長與紀律對我們而言並非取捨。它們都是營運原則。而我們對這些原則的執行，在成果中清楚可見。

With that, I will turn it over to Matt to walk through our Q1 results and our updated guidance in more detail. Matt, over to you.

接下來，我把時間交給 Matt，請他更詳細說明我們第一季的業績以及更新後的指引。Matt，交給你。

Thanks, Paddy. Good morning, everyone, and thanks for joining us. As Paddy just shared, we had a very good quarter. In my comments, I will review the financial results in detail, walk through our recent balance sheet and capital allocation actions and then provide an update to our near-term and medium-term outlooks.

謝謝你，Paddy。各位早安，感謝大家加入。如同 Paddy 剛才分享的，我們這一季表現非常不錯。在我的評論中，我將詳細回顧財務結果，說明我們近期在資產負債表與資本配置上的行動，並更新我們的近期與中期展望。

Starting with Q1, our results were very strong, and we exceeded the guidance we last provided on all key metrics. Q1 revenue was $258 million, up 22% year-over-year. above the top end of our recent guide. The vast majority of this Q1 revenue beat came from strong retention in our top [D&E] cohorts and from expansion in our top cloud and AI native customers.

先從第一季開始，我們的結果非常強勁，並在所有關鍵指標上都超越了我們上次提供的指引。第一季營收為 2.58 億美元，年增 22%，高於我們近期指引區間的上緣。第一季營收超預期的絕大部分，來自我們頂級 [D&E] 客群的強勁留存，以及我們頂級雲端與 AI 原生客戶的擴張。

The Richmond data center, which began ramping revenue in March, contributed less than $500,000 of revenue and less than 20 basis points of year-over-year growth in Q1. Our top customers continue to drive our growth. Our $1 million customer ARR reached $183 million, growing 179% year-over-year. AI customer ARR reached $170 million, growing 221% year-over-year.

里奇蒙資料中心自 3 月開始逐步放量，第一季貢獻的營收不到 50 萬美元，對第一季年增率的貢獻也不到 20 個基點。我們的頂級客戶仍持續驅動成長。我們的「年經常性收入（ARR）達 100 萬美元」客戶 ARR 達到 1.83 億美元，年增 179%。AI 客戶 ARR 達到 1.70 億美元，年增 221%。

And we continue to deliver both durable and profitable growth. First quarter adjusted EBITDA was $105 million, up 21% year-over-year with an adjusted EBITDA margin of 41%. GAAP operating income was $37 million, with an operating income margin of 14%. Adjusted operating income was $64 million, with an adjusted operating income margin of 25%.

同時，我們也持續交出兼具韌性與獲利性的成長。第一季調整後 EBITDA 為 1.05 億美元，年增 21%，調整後 EBITDA 利潤率為 41%。GAAP 營業利益為 3,700 萬美元，營業利益率為 14%。調整後營業利益為 6,400 萬美元，調整後營業利益率為 25%。

Trailing 12-month adjusted free cash flow was $171 million or 18% of revenue. Trailing 12-month adjusted free cash flow less lease principal payments was $154 million or 16% of revenue after including $17 million in financed equipment principal payments over the last 12 months.

過去 12 個月的調整後自由現金流為 1.71 億美元，約占營收的 18%。過去 12 個月的調整後自由現金流（扣除租賃本金支付）為 1.54 億美元，約占營收的 16%；其中已包含過去 12 個月 1,700 萬美元的融資設備本金償付。

Next, I'll spend a few minutes on the recent equity raise and what it means for our financial profile and for our capacity plans. In Q1, we raised $888 million in equity, and we have already put the proceeds to work across two important priorities.

接下來，我將花幾分鐘談談近期的股權募資，以及這對我們的財務結構與產能計畫意味著什麼。在第一季，我們透過股權融資募集了 8.88 億美元，而我們已經將這些資金投入兩項重要優先事項。

The first priority was strengthening the balance sheet. We repaid our full $500 million Term Loan A, saving roughly $50 million per year in cash interest and mandatory prepayments. We intend to use a portion of the remaining cash to retire the outstanding $312 million 2026 convertible notes when they mature. Collectively, these actions result in a flexible balance sheet with no material maturities until 2030.

第一項優先事項是強化資產負債表。我們已全額償還 5 億美元的 A 期定期貸款（Term Loan A），每年可節省約 5,000 萬美元的現金利息與強制性提前償還。我們計畫使用剩餘現金的一部分，在 2026 年到期時償還尚未償付的 3.12 億美元 2026 年可轉換公司債。綜合而言，這些行動使我們擁有更具彈性的資產負債表，在 2030 年之前沒有重大到期債務。

The second priority was expanding capacity to meet demand. As Paddy shared, we have secured approximately 60 megawatts across four new locations, an 80% increase in our committed capacity. This capacity is projected to begin ramping revenue over the course of 2027. While there won't be any 2026 revenue impact, the build-out of some of this capacity is likely to start in late 2026, which will impact 2026 cash flow and margins.

第二項優先事項是擴充產能以滿足需求。如 Paddy 所分享，我們已在四個新地點取得約 60 兆瓦產能，使我們的已承諾產能增加 80%。該產能預計將在 2027 年期間開始逐步放量並帶動營收。雖然 2026 年不會有任何營收影響，但其中部分產能的建置很可能在 2026 年下半年開始，這將影響 2026 年的現金流與利潤率。

We expect the CapEx per megawatt in this new capacity to be higher than for the equipment ordered last year, for the 31 megawatts. The increase is driven both by the rising component cuts the entire market is seeing and higher cost and higher token capacity equipment that we plan to install. We expect the incremental ARR per megawatt to be higher as well. And importantly, we expect to generate the same or higher return on investment in these new data centers. We are likely to continue to align the timing of our investments with revenue by financing a material portion of the equipment for these facilities.

我們預期這批新增產能的每兆瓦資本支出（CapEx）將高於去年為 31 兆瓦所訂購的設備。這項增加同時受到整體市場都在面臨的零組件成本上升，以及我們計畫安裝的更高成本、且具更高 token 產能的設備所驅動。我們也預期每兆瓦所帶來的新增 ARR 會更高。更重要的是，我們預期在這些新資料中心中，仍能創造相同或更高的投資報酬率。我們很可能會持續透過為這些設施的設備融資相當一部分，來讓投資時點與營收更好地匹配。

With all of this, we expect to exit 2026 at approximately 3 times net leverage with no material debt maturities until 2030. Looking forward, we are again raising our near-term and medium-term outlook. The strong Q1 retention and growth in our top cloud and AI native cohorts has continued in Q2.

綜合以上，我們預期在 2026 年底時淨槓桿約為 3 倍，且在 2030 年之前不會有任何重大債務到期。展望未來，我們再次上調短期與中期展望。我們在第一季強勁的留存表現，以及在頂尖雲端與 AI 原生客群的成長動能，在第二季持續延續。

For the second quarter of 2026, we expect revenue of $272 million to $274 million, representing 24% to 25% year-over-year growth. We expect second quarter adjusted EBITDA margins in the range of 37% to 38%. And which is $102 million at the midpoint, up 14% year-over-year.

針對 2026 年第二季，我們預期營收為 2.72 億至 2.74 億美元，年增 24% 至 25%。我們預期第二季調整後 EBITDA 利潤率介於 37% 至 38%。以中位數計算約為 1.02 億美元，年增 14%。

We expect non-GAAP diluted net income per share of $0.20 to $0.23. And based on approximately 121 million to 122 million weighted average fully diluted shares outstanding. Note that our shares outstanding projection includes a benefit from the projected anti-dilutive impact of the cap call that we purchased along with the issuance of our 2030 notes.

我們預期非 GAAP 稀釋後每股淨利為 0.20 至 0.23 美元，係以約 1.21 億至 1.22 億股加權平均完全稀釋流通股數為基礎。請注意，我們對流通股數的預估包含一項效益，來自我們在發行 2030 年到期票據時一併購買之上限買權（cap call）所帶來的預期反稀釋影響。

For the full year 2026, we are again meaningfully raising our outlook. We now expect full year 2026 revenue of $1.13 billion to $1.145 billion, representing 25% to 27% year-over-year growth, with a negative growth rate approaching 30% in Q4. Again, this does not include any projected revenue from the newly committed 60 megawatts.

針對 2026 年全年，我們再次大幅上調展望。我們目前預期 2026 年全年營收為 11.3 億至 11.45 億美元，年增 25% 至 27%，且第四季的成長率接近 30%。同樣地，以上不包含任何來自新近承諾之 60 兆瓦容量的預估營收。

We expect strong full year adjusted EBITDA margins of 37% to 39%, which is $432 million at the midpoint. Projected adjusted free cash flow margin will be in the range of 9% to 12%. And which includes roughly $100 million cash flow impact in 2026, a projected nonrecurring start-up costs for some of our newly committed capacity.

我們預期全年調整後 EBITDA 利潤率強勁，介於 37% 至 39%，以中位數計算約為 4.32 億美元。預估調整後自由現金流利潤率將介於 9% 至 12%。其中包含 2026 年約 1 億美元的現金流影響，為部分新近承諾容量的預估一次性啟動成本。

Without these costs, adjusted free cash flow margin would be roughly 18% to 21% for the year, above prior guidance. We expect adjusted free cash flow margin less equipment finance principal payments to be slightly positive for 2026, including the impact of the $100 million in cost for 2027 capacity.

若不計入上述成本，全年調整後自由現金流利潤率約為 18% 至 21%，高於先前指引。我們預期 2026 年在扣除設備融資本金償付後的調整後自由現金流利潤率將略為正值，並已包含為 2027 年容量所支出的 1 億美元成本影響。

We expect full year non-GAAP diluted net income per share of $1.10 to $1.20 on $118 million to 119 million weighted average fully diluted shares outstanding. This is an increase to our prior guidance despite the equity raise as the interest savings from retiring our Term Loan A more than offset the impact of the higher share count.

我們預期全年非 GAAP 稀釋後每股淨利為 1.10 至 1.20 美元，係以 1.18 億至 1.19 億股加權平均完全稀釋流通股數為基礎。儘管進行了股權募資，該預期仍較先前指引上調，因為償還 Term Loan A 所節省的利息支出，足以抵銷股數增加的影響。

We are also increasing our medium- to long-term outlook, the 30% 2027 revenue growth outlook we provided last call was based solely on the 75 megawatts of capacity that we had active or under contract at that time. With approximately 60 megawatts of additional committed capacity, projected to begin generating revenue over the course of 2027, we now expect 2027 revenue to exceed $1.7 billion, full year growth of 50% or more year-over-year. We will deliver this growth while working to make smart investments generate attractive returns and maintain a strong and flexible balance sheet.

我們也上調中長期展望；上次電話會議所提供的 2027 年營收成長 30% 展望，僅以當時已啟用或已簽約的 75 兆瓦容量為基礎。隨著額外約 60 兆瓦的承諾容量，預計將在 2027 年期間逐步開始貢獻營收，我們目前預期 2027 年營收將超過 17 億美元，全年年增 50% 或以上。我們將在推動此成長的同時，致力於進行明智投資以創造具吸引力的報酬，並維持強健且具彈性的資產負債表。

Our margin outlook for 2027 is healthy. We project approximately 40% adjusted EBITDA margins and high teens adjusted free cash flow margins. While we are excited by our progress and the increased growth outlook, we're not stopping there. We continue to actively look for opportunities to further accelerate durable and profitable growth.

我們對 2027 年的利潤率展望健康。我們預估調整後 EBITDA 利潤率約 40%，調整後自由現金流利潤率約為十幾個百分點的高段（high teens）。儘管我們對進展與上調的成長展望感到振奮，我們不會就此止步。我們仍持續積極尋找機會，以進一步加速可持續且具獲利性的成長。

With that, I'd like to turn it back over to Paddy.

接下來，我想把時間交還給 Paddy。

Thank you, Matt. Before we move to Q&A, let me recap what we shared today. First, our momentum has never been stronger. Our $1 million customer ARR reached $183 million, growing 179% year-over-year. Our AI customer ARR reached $170 million, growing 221%, and over 80% of that is coming from infant services and core cloud, not Bare Metal. We are an AI-native inference cloud, not a GPU landlord.

謝謝你，Matt。在進入問答之前，我先回顧一下我們今天分享的重點。第一，我們的動能前所未有地強勁。我們 100 萬美元級客戶的 ARR 達到 1.83 億美元，年增 179%。我們的 AI 客戶 ARR 達到 1.70 億美元，成長 221%，其中超過 80% 來自推論服務與核心雲端，而非裸機（Bare Metal）。我們是 AI 原生推論雲，而不是 GPU 房東。

Second, we launched the DigitalOcean AI native cloud. We unveiled our full platform last week at Deploy conference. We acquired Cataneo to accelerate our open source AI stack. We landed multiple marquee AI-native customers, including Cursor. Our differentiation is clear. The pipeline is deep and the wins are real. We are the AI native cloud.

第二，我們推出了 DigitalOcean AI 原生雲。我們上週在 Deploy 大會上揭示了完整平台。我們收購 Cataneo，以加速我們的開源 AI 技術堆疊。我們拿下多家指標性的 AI 原生客戶，包括 Cursor。我們的差異化非常清楚。商機管線深厚，勝出案例也是真實的。我們就是 AI 原生雲。

Third, we are investing to meet our customer demand. $888 million raised 60 megawatts of incremental capacity committed. We are building for 2027 and beyond with disciplined capital allocation and a strengthened balance sheet.

第三，我們正投資以滿足客戶需求。我們募集了 8.88 億美元，並承諾新增 60 兆瓦的增量容量。我們以嚴謹的資本配置與更強健的資產負債表，為 2027 年及更長遠的未來建置。

Finally, we again raised our near- and medium-term outlook. Projected exit 2026 revenue growth approaching 30%, accelerating to 50% or more revenue growth in 2027, attractive margins and a flexible balance sheet. We continue to build a durable and profitable growth engine. The inference and Agentic economy is real. The demand is real. And DigitalOcean with its AI native cloud is purpose-built for this opportunity.

最後，我們再次上調短期與中期展望。預期 2026 年底的營收成長率接近 30%，並在 2027 年加速至 50% 或以上的營收成長，同時具備具吸引力的利潤率與具彈性的資產負債表。我們持續打造可持續且具獲利性的成長引擎。推論與 Agentic 經濟是真實存在的。需求是真實存在的。而 DigitalOcean 及其 AI 原生雲正是為此機會量身打造。

With that, let's open it up for questions.

接下來，我們開放提問。

(Operator Instructions)

（接線員指示）

Kingsley Crane, Canaccord Genuity.

Kingsley Crane，Canaccord Genuity。

Needless to say, congrats on the momentum you've earned it, you continue to earn it. It's great to see One of the ideas over the past couple of weeks is that the mix of CPU and GPU should be closer to 1:1 with the Agentic workloads compared to pure LLM calls. And you talk about that new arrow thinking and doing in your deck, which was really well prepared. Just curious how relevant is that CPU renaissance for your business given your large core cloud and CPU footprint? Just trying to think about the quantitative benefit that could create.

不用多說，恭喜你們取得這樣的動能，你們當之無愧，而且還在持續創造。很高興看到，過去幾週有個觀點是：相較於純 LLM 呼叫，Agentic 工作負載下 CPU 與 GPU 的組合應該更接近 1:1。你們在簡報中也談到那支「新箭」——思考與執行——而且準備得非常好。我想請教，考量你們龐大的核心雲與 CPU 佈建規模，這波 CPU 復興對你們的業務有多大相關性？我在思考它可能帶來的量化效益。

Yes. Thank you, Kingsley. Appreciate your question. Yes, I think it is unmistakable that we are moving more and more towards an agent ear where more software is going to be rearchitected and there will be a heavy dose of autonomous agents performing tasks that were previously handled by humans. So in that era, the doing part, as I mentioned, will also require intelligence, but it is going to require a tremendous amount of computing that until about 12 months ago or more precisely until open [plot] really showed us the blueprint. We were in really as an industry contemplating how compute intensive it is going to be.

是的。謝謝你，Kingsley。感謝你的提問。是的，我認為非常明顯的是，我們正愈來愈走向一個代理（agent）時代，更多軟體將被重新架構，並會有大量自主代理去執行過去由人類處理的任務。因此在那個時代，我提到的「執行」部分同樣需要智慧，但也將需要龐大的運算資源；直到大約 12 個月前，或更精確地說，直到 open [plot] 真正向我們展示藍圖之前，整個產業其實都在思考這會有多麼吃重的運算需求。

When I say compute intensive, it is just not CPUs, right? It is high bandwidth memory. It is advanced databases like the ones that we just announced last week, it is safe agent execution, it is orchestration between these agents. There is a tremendous amount of modern computing primitives that are required to orchestrate all of this. So I don't know whether the ratios that have propped up with say, CPUs to GPUs will go from 1:12, as we were previously thinking to 1:1, I don't know exactly what that ratio will end up being.

當我說運算密集時，不只是 CPU，對吧？還包括高頻寬記憶體。也包括我們上週剛宣布的那類先進資料庫、安全的代理執行，以及這些代理之間的協同編排（orchestration）。要把這一切協調起來，需要大量現代運算基元（computing primitives）。所以我不確定外界提出的 CPU 對 GPU 比例，是否會從我們先前認為的 1:12 變成 1:1；我也不確定最終會落在什麼比例。

But what I can tell you is that we are going to need a hell a lot of more compute to do all of these things as more software gets rearchitected over the next handful of years to be more Agentic, which requires both inferencing for the thinking part and a lot of computing for the doing part. So we are preparing for that, the new capacity that we have just took on.

但我能告訴你的是，隨著未來幾年有更多軟體被重新架構、變得更具代理式（Agentic），要做完這些事情我們將需要多得驚人的算力；這同時需要用於「思考」部分的推理（inferencing），以及用於「執行」部分的大量運算。因此我們正在為此做準備，也就是我們剛剛新增承擔的那批新產能。

All of our new data centers are deploying our full stack AI native cloud. So it is just not inferencing services. It is the full stack AI native cloud that is getting deployed in these data centers. And we are getting ready for a compute-heavy future, and we are starting to see that in a very pronounced way from some of our advanced AI native customers as they themselves move into an Agentic Era.

我們所有新的資料中心都在部署我們的全棧 AI 原生雲。所以不只是推理服務。而是完整的全棧 AI 原生雲正在這些資料中心落地部署。我們正在為一個算力密集的未來做準備，而且我們已經從一些先進的 AI 原生客戶身上非常明顯地看到這一點，因為他們自己也正在邁入代理式時代（Agentic Era）。

It's really helpful. And then for either Paddy or Matt, we've been thinking about low to mid-teens revenue per megawatt for AI. You mentioned that the incremental capacity you're bringing on could be higher. And then just in addition to that, like to what extent can software capabilities like inference engine and French router, open source model adoption, agent framework, push that revenue per megawatt higher. I think we're all doing that megawatt math, but just curious to what extent that figure can become untethered from the peers there?

這真的很有幫助。接著想請教 Paddy 或 Matt，我們一直在思考 AI 每兆瓦的營收大概在低到中十幾%的水準。你提到你們新增帶進來的增量產能可能會更高。另外再延伸一下，像推理引擎與 French router 這類軟體能力、開源模型採用、代理框架（agent framework），在多大程度上能把每兆瓦營收推得更高。我想大家都在做那個「兆瓦數學」，但也好奇這個數字在多大程度上可以不再被同業的對比所綁定？

That's a great question. We definitely expect that we can increase that $13 million per ARR per megawatt over time. I mean you're already seeing that non Bare Metal over 80% of our AI customer ARR, and that should increase the ARR by itself.

這是個很好的問題。我們確實預期，隨著時間推進，我們可以把每兆瓦對應的 ARR 由 1,300 萬美元再往上提高。我的意思是，你已經看到非 Bare Metal 已占我們 AI 客戶 ARR 的 80% 以上，而這本身就會推升 ARR。

We're also expecting, as you just pointed out, there's going to be a lot of core cloud and a lot of compute that gets pulled through with that. Right now, it's still -- it's a modest amount of core cloud pull-through, and we think there's upside there. And then to your point, all of the capabilities that we announced to deploy, the serverless inferencing and a lot of these other capabilities, they detach the pricing and the value creation from a dollars per GPU hour and enable us to capture both higher revenue and higher margins with stickier services. So we're very optimistic about our ability to drive the ARR per revenue up over time. And certainly, that's part of our investment thesis as we've taken on this incremental capacity.

另外如你所指出的，我們也預期會帶動大量核心雲（core cloud）以及大量算力的連帶拉動（pull-through）。目前核心雲的 pull-through 仍然——仍是相對溫和的量，我們認為這裡有上行空間。再者，正如你所說，我們宣布要部署的所有能力——無伺服器推理（serverless inferencing）以及許多其他能力——它們會把定價與價值創造從「每 GPU 小時多少美元」中解綁，讓我們能以更黏著的服務同時取得更高營收與更高毛利。因此我們對於隨時間推進提升每兆瓦 ARR 的能力非常樂觀。而且這也確實是我們在承擔這些增量產能時投資論點的一部分。

Gabriela Borges, Goldman Sachs.

Gabriela Borges，高盛。

Paddy, you start up this conversation talking about how the beat in the quarter was not driven by new capacity coming online, but rather previously committed capacity. So I defer your thoughts on that. Talk to us a little bit about how we should think about the beat on rate cans. You're already giving us visibility into 2027 based on capacity coming online. But in any given quarter, what levers do you have to be in raise? And maybe if you could comment on the pricing dynamics and believe as you can pull on pricing within that.

Paddy，你在一開始談到本季的超預期（beat）並不是由新產能上線所驅動，而是來自先前已承諾的產能。所以我想先聽聽你對此的看法。也請你談談我們應該如何看待 rate cans 的超預期。你們已經基於即將上線的產能，讓我們對 2027 年有一定能見度。但在任何一個季度，你們有哪些槓桿可以用來做到 beat and raise（超預期並上調指引）？也許你也可以評論一下定價動態，以及你們在其中能在多大程度上拉動定價。

That's a great question. I think when we guided to 2026, and we outlined the pace at which capacity was going to come online this year, there's a number of assumptions that we had to make in that, that gave us the ability to have very strong confidence in the guidance that we were providing. One was the timing of the facilities coming online. The second was the -- our ability to sell into that capacity as it came on and the third, the pricing at which we're selling into that capacity.

這是個很好的問題。我認為當我們給出 2026 年指引、並勾勒今年產能上線的節奏時，我們必須做出若干假設，這些假設讓我們能對所提供的指引有非常強的信心。第一是設施上線的時間點。第二是——我們在產能上線時將其銷售出去的能力；第三則是我們以什麼價格把這些產能賣出去。

And if you think about all of those dimensions, again, when we provided that guidance, which was late last year, early or early this year, we had to make sure that we had enough cushion. And what we're finding is we're doing pretty well on all three of those dimensions. The Richmond data center came online. We had said second quarter, it came online in March. It didn't contribute much the first quarter, but it's online and ready to go ahead of what we had said.

如果你從這些面向來看，同樣地，當我們在去年底、今年初或今年更早提供該指引時，我們必須確保有足夠的緩衝。而我們發現，在這三個面向上我們都做得相當不錯。里士滿（Richmond）資料中心已經上線。我們原先說是第二季上線，但它在三月就上線了。它在第一季貢獻不大，但它已經上線並準備就緒，時間點比我們先前所說的更早。

We're able to sell into it. Much, I'd say, on a very appropriate and aggressive time line, which is really good. And then as you're seeing in the market, the pricing for GPO hour, even services right now is not seeing any kind of price compression. In fact, we're seeing increases in the prices for [H100s and H200s] and some of the legacy gear.

我們也能把產能賣進去。我會說，以非常合適且積極的時間表在推進，這非常好。再者，如你在市場上所看到的，目前不論是 GPU 小時的定價，甚至是服務，都沒有出現任何價格壓縮。事實上，我們看到 [H100 與 H200] 以及部分舊世代設備的價格正在上升。

So I'd say we have sufficient ability to continue to beat and raise we just outlined the incremental 60 megawatts for next year. And we're taking a very similar approach, which is we'll be cautious about our expectations around timing of delivery, we'll be cautious about expectations of how long it takes to sell into it, and we'll be cautious about the pricing that we get and then we'll work to exceed that.

所以我會說，我們有足夠的能力持續做到 beat and raise；我們剛剛也概述了明年額外增加的 60 兆瓦。而我們採取非常類似的方法：我們會對交付時程的預期保持保守，會對銷售進入所需時間的預期保持保守，也會對我們能取得的定價保持保守，然後再努力超越這些預期。

Matt, maybe I'll pick it up just some of those comments on being cautious. So I think we can all agree that we're pretty early in what is going to be an incredible product cycle. At some point, the product cycle will peak. So I guess the question is for the both of you. What are the demand signals that you're watching to be able to figure out whether it's 2027 growing north of 50%. Is that the peak growth rate? Does it accelerate from there, does it normalize and come down? What are some of the metrics that we could potentially be tracking from the outset? And what do you track internally?

Matt，也許我接著你剛才關於「保持保守」的一些評論來問。我想大家都同意，我們仍處於一個將會非常驚人的產品週期的早期階段。在某個時間點，產品週期會達到高峰。所以我想對你們兩位的問題是：你們在觀察哪些需求訊號，來判斷 2027 年是否會成長超過 50%？那會是成長率的峰值嗎？之後是再加速、還是回歸常態並下滑？有哪些指標是我們從一開始就可能追蹤的？你們內部又在追蹤什麼？

Yes, I can start at a high level, and then I'll let Matt comment on your specific 2027 question. So we all agree, Gabriela that this is such a tectonic shift in how software is built and delivered. And one thing that I also want to highlight here is that inferencing and Agentic workloads will scale very differently compared to training. Training is a onetime, almost episodic turn on, the entire cluster comes online and just stays static from a workload perspective. While inferencing and Agentic workloads have more of a cloud kind of characteristics in terms of how the workload ramps, although the gradient of the ramp has been significantly steeper than we have ever seen with traditional cloud software.

是的，我可以先從高層次開始，然後再讓 Matt 回應你關於 2027 的具體問題。Gabriela，我們都同意，這是在軟體如何被建構與交付方面的一次板塊式轉變（tectonic shift）。我也想在這裡強調一點：推理（inferencing）與代理式（Agentic）工作負載的擴張方式，會與訓練（training）非常不同。訓練是一次性的、幾乎是事件式（episodic）的啟動：整個叢集上線後，從工作負載角度看就基本保持靜態。而推理與代理式工作負載則更具雲端特性，體現在工作負載如何爬坡（ramp）；不過其爬坡斜率明顯比我們在傳統雲端軟體中見過的任何情況都更陡。

So a lot of our confidence is coming from observing our big marquee AI native customers and seeing their workload growth and hence, the inferencing demand that they translate on to us and our platform. So in terms of the product cycle peaking, I think that is -- we are still a few revisions of our products, certainly and also as an industry to get to that peak cycle.

因此，我們很大一部分信心來自於觀察我們那些大型、具代表性的 AI 原生客戶，看到他們的工作負載成長，進而看到他們轉化到我們與我們平台上的推理需求。至於產品週期何時見頂，我認為——無論是我們自身產品，還是整個產業，要到達那個週期高峰，肯定還需要再經過幾次產品迭代（revisions）。

[Openly], I have to remind everyone is barely 100 days old. And since then, there have been a few other personal productivity agents like Hermes agent and a few others that have come and the whole industry is now figuring out what agent harnesses should look like. It is still a very, very early days of the Agentic architecture. So I expect the product cycle refresh to continue for quite a bit into the next several quarters before we can say, okay, we now have a blueprint for how these modern autonomous systems are going to be built and operated in scale. So I think we still have a lot of innovation ahead of us.

[坦白說]，我必須提醒大家，is barely 100 days old。而自那之後，還出現了幾個其他的個人生產力代理（agent），例如 Hermes agent 以及其他一些，整個產業現在都在摸索 agent harness 應該長什麼樣子。Agentic 架構仍然非常、非常早期。因此我預期產品週期的更新換代，還會在接下來好幾個季度持續一段時間，直到我們能說：好，我們現在已經有一份藍圖，知道這些現代自主系統要如何建置並以規模化方式運營。所以我認為我們前方仍有大量創新。

And what gives us a lot of confidence is having this front-row seat working with these marquee AI-native customers gives us a tremendous opportunity to learn about their application patterns. And this luxury is available to us because we are not just a Bare Metal provider. These customers want us to be in the room where they are solving these problems, and that's how we were able to build a lot of these things that we saw last week in terms of innovation, like the intelligent routing, the -- many of the cashing techniques that made us the number one in DeepSeek and Qwen token throughput and time to first token and things like that, it gives us a front-row seat and a co-invention opportunity to do this alongside our customers. So I definitely feel like the product cycle is not going to peak anytime soon.

而讓我們非常有信心的一點是：能以第一排視角與這些指標性的 AI 原生客戶合作，讓我們有極大的機會去學習他們的應用模式。而我們之所以能享有這種優勢，是因為我們不只是裸機（Bare Metal）供應商。這些客戶希望我們能在他們解題的現場一起參與；也正因如此，我們才能打造出上週看到的許多創新成果，例如智慧路由、以及——多種快取（caching）技術，讓我們在 DeepSeek 與 Qwen 的 token 吞吐量、首 token 時間（time to first token）等指標上做到第一。這讓我們能坐在第一排，並與客戶共同發明、並肩完成這些事情。所以我確實覺得產品週期短期內不會見頂。

And I think the best metric to watch, which we're watching is ARR per megawatt. I mean if you think of token efficiency being one of the primary differentiators in terms of your ability to provide value to your customers is how much revenue can you get for those tokens and how efficiently can you provide them? And how sticky are those services that you're providing, that should all translate into higher ARR per megawatt, which is why we've introduced that metric, we track it internally, and it's all about optimization for us, and that's where we're focused that's what we would point the market to watch as well.

我認為最值得關注的指標——我們也正在關注——是每兆瓦的 ARR（ARR per megawatt）。如果你把 token 效率視為能否為客戶提供價值的主要差異化因素之一，那麼關鍵就在於：你能從這些 tokens 取得多少營收，以及你能多有效率地提供它們？以及你所提供的服務有多黏著（sticky）。這些都應該轉化為更高的每兆瓦 ARR，這也是我們引入這個指標的原因；我們在內部追蹤它，對我們而言核心就是最佳化，而這也是我們的聚焦點——同時也是我們會建議市場一起關注的指標。

Mark Zhang, Citi.

Mark Zhang，花旗（Citi）。

So very nice to see the growingness of the non Bare Metal ARR this quarter. Just want to dig into some of the dynamics there in the input. So I wanted to get a sense of contributions from just new land versus existing conversions of the existing Bare Metal customers? And then how should we sort of like think of the pace of the mix shift going forward? And can you give us a sense of the ASP upfront, when you convert from Bare Metal?

本季看到非裸機（non Bare Metal）ARR 的成長很不錯。我想深入了解一下其中的一些動態。我想了解：新增客戶（new land）與既有裸機客戶轉換（existing conversions）的貢獻各是多少？另外，往前看，我們應該如何思考組合（mix）轉移的速度？以及當你們從裸機轉換時，能否給我們一個前端 ASP（平均售價）的概念？

Thank you, Mark. Your line was a little choppy, but I think I got the essence of your question. So in terms of the mix of the customers, it's a healthy mix of AI native customers that are new to our platform, that are not just consuming core AI services, but also by the nature of their inferencing workloads, they use storage systems and database systems and also increasingly core computing primitive, but we also have some of our existing digital native enterprise customers also starting to ramp up their AI innovation and AI workloads. So it goes both ways, and we are super happy to see that.

謝謝你，Mark。你的線路有點斷斷續續，但我想我抓到問題的重點。就客戶組合而言，這是一個健康的組合：有新加入我們平台的 AI 原生客戶，他們不只使用核心 AI 服務，也因其推論（inferencing）工作負載的特性而使用儲存系統與資料庫系統，並且也愈來愈多地使用核心運算基元（computing primitive）；同時，我們也看到一些既有的數位原生（digital native）企業客戶開始加速他們的 AI 創新與 AI 工作負載。所以兩邊都有，我們也非常高興看到這點。

And in terms of the Bare Metal consumption, pretty much most of the customers that come to us now are coming to us because they see this rich set of inferencing entry points. So last week, we announced serverless inferencing, dedicated inferencing, batch inferencing and things like that. Increasingly, customers are realizing, especially the AI natives that they were forced to deal with all this complexity over the last couple of years, not because they wanted to, but they have to because there were very few vendors who were able to provide this kind of kernel optimization and performance enhancement using software and hardware codesign.

至於裸機（Bare Metal）的使用情況，現在幾乎大多數來找我們的客戶，都是因為他們看到了這一整套豐富的推論入口。所以上週我們宣布了無伺服器推論（serverless inferencing）、專用推論（dedicated inferencing）、批次推論（batch inferencing）等等。客戶——尤其是 AI 原生客戶——愈來愈意識到，過去幾年他們被迫處理這些複雜度，不是因為他們想要，而是因為他們不得不做；因為能透過軟硬體協同設計（software and hardware codesign）提供這種核心（kernel）最佳化與效能提升的供應商非常少。

But now that these kinds of capabilities are available out of the box from our AI native cloud. We are seeing a lot more appetite from our customers to come in at a higher altitude in our platform and we are not having to sell Bare Metal at all. In fact, we don't even have that as part of our standard pitch.

但現在，這類能力已經能在我們的 AI 原生雲上開箱即用（out of the box）。我們看到客戶更有意願以更高層級（higher altitude）的方式使用我們的平台，而我們根本不需要去賣裸機。事實上，裸機甚至不在我們的標準銷售話術裡。

And from a timing standpoint, this is one of the benefits of our consumption-based model with but where we're not locking in bare metal prices for four and five years. As these Bare Metal customers, if you notice in the materials we provided the Bare Metal not only decreased as a percentage, but it actually decreased in absolute dollars of the AI customer ARR.

從時間點來看，這也是我們以用量計費（consumption-based）模式的好處之一：我們不會把裸機價格鎖定四、五年。如果你注意我們提供的資料，裸機不僅占比下降，而且在 AI 客戶 ARR 中的絕對金額也下降了。

That's because as these customers come up for contract renewal, we have the opportunity to resize and reconfigure that capacity. If we want to make that available to serverless inferencing, where we know we'll earn a higher return than Bare Metal, that's what we do. And so we have the ability to steer that percentage down by not consuming our scarce capacity for Bare Metal services.

這是因為當這些客戶合約到期續約時，我們有機會重新調整（resize）並重新配置（reconfigure）那部分容量。如果我們希望把容量釋出給無伺服器推論，因為我們知道那會比裸機帶來更高的回報，我們就會這麼做。因此，我們能透過不把稀缺容量用在裸機服務上，來引導那個占比往下走。

So not only are new customers not asking for it, but the customers that are on it right now, we can rotate them off into the new services or we can repurpose the capacity for higher-margin services, and we control that.

所以不只是新客戶不再要求裸機，對於目前仍在使用裸機的客戶，我們也可以把他們轉移到新服務上，或把容量重新用於更高毛利的服務——而這一切都在我們的掌控之中。

No, that's terrific. And then just maybe a follow on. It's terrific to see the new five layers also referencing a new platform that you guys had provided last week at the pot. How should we sort of think of the maybe like changes to the gold market from here? Obviously, there's a lot to sell.

不，這太棒了。接著可能再追問一下。也很高興看到新的五層（five layers）同時也提到你們上週在 pot 上提供的新平台。接下來我們應該如何思考對 go-to-market 的可能變化？顯然有很多東西可以賣。

There's much more products to for customers to consume. How do you -- how are you thinking about just in terms of the go-to-market partnerships and how you really like officially land new customers won this new module?

客戶可消費的產品也多了很多。你們如何思考——就 go-to-market 夥伴關係而言，以及你們如何真正、正式地透過這個新模組去切入並拿下新客戶？

So our go-to-market over the last several quarters has been aimed at getting marquee AI-native logos. And that's how we have landed some of the customers that I was so proud to announce today. And we just have to scale up in doing what we are already doing.

我們在過去幾個季度的 go-to-market，目標一直是拿下指標性的 AI 原生客戶標誌（marquee AI-native logos）。也正因如此，我們才得以拿下我今天非常自豪宣布的部分客戶。接下來我們只需要把我們已經在做的事情規模化。

So just as a reminder, we have a very small but mighty team of AI native focused sellers that are quite capable of selling our AI native cloud stack. On top of it, we also have a very focused start-up ecosystem team that nurtures high-quality AI native companies in Silicon Valley and nurture them through their growth phases. We also have a tremendous luxury of having perhaps the best product-led growth machine, which keeps growing in strength. So we get a tremendous amount of traffic and volume through our product-led growth flywheel, which includes a heavy dose of AI native customers that absolutely just love the simplicity and the absence of friction in our platform that enables them to just come and try our platform and do it without any human intervention.

提醒一下，我們有一支人數不多但戰力很強、專注 AI 原生的銷售團隊，他們非常有能力銷售我們的 AI 原生雲端堆疊（cloud stack）。此外，我們也有一支非常聚焦的新創生態系團隊，在矽谷培育高品質的 AI 原生公司，並在其成長階段一路扶植。我們也擁有一個巨大的優勢：或許是最好的產品導向成長（product-led growth）機器，而且它的動能持續增強。因此我們透過產品導向成長的飛輪獲得大量流量與規模，其中包含很大比例的 AI 原生客戶；他們非常喜歡我們平台的簡潔與低摩擦，讓他們可以直接進來試用平台，且不需要任何人工介入。

So we have multiple front doors as a way to solicit customer entry into our platform. So we'll be fortifying some of those things, and we have a very strong partnership team that enables us to build relationships with various frontier model and open source model companies in the rest of the ecosystem.

所以我們有多個前門（front doors）來吸引客戶進入我們的平台。我們會強化其中一些作法；同時我們也有一支非常強的合作夥伴團隊，讓我們能與生態系中各種前沿模型（frontier model）與開源模型公司建立關係。

Jason Ader, William Blair.

Jason Ader，William Blair。

Paddy, you guys are exploiting a gap in the market right now, especially with the Neoclouds, but the Neoclouds are all messaging shifting to a full stack approach and a focus on inferencing. So I guess my question is, how sustainable is your differentiation relative to the Neoclouds and what drives that?

Paddy，你們現在正在利用市場上的一個缺口，特別是在 Neoclouds 方面；但 Neoclouds 都在對外傳達要轉向全堆疊（full stack）的方法並聚焦於推論（inferencing）。所以我想問的是，相較於 Neoclouds，你們的差異化有多可持續？其驅動因素是什麼？

Yes. Great. Thank you, Jason. I think the market opportunity is just huge and tremendous, right? We feel that the Neoclouds adding software capabilities is a great validation of our strategy and we've been saying that for a long time.

是的。很好。謝謝你，Jason。我認為市場機會非常巨大、非常可觀，對吧？我們覺得 Neoclouds 增加軟體能力，對我們的策略是很好的驗證；而且我們很久以前就一直這麼說。

But we are in fundamentally different businesses than the Neocloud. They're training first, and that's a great model. And they have a small number of highly concentrated customers with take-or-pay agreements and their needs, that type of contract needs a tremendous amount of infrastructure and discipline and execution to pull that off. So it is a significant heavy lift to deliver on these massive hyperscaler offtake contracts.

但我們與 Neocloud 從根本上是在不同的業務中。他們以訓練（training）為先，這是一個很好的模式。而且他們的客戶數量不多、集中度很高，並且有 take-or-pay（要麼提貨要麼付款）協議；這類合約需求要成功落地，需要極其大量的基礎設施、紀律與執行力。因此，要交付這些面向超大規模雲服務商（hyperscaler）的巨量承購（offtake）合約，是一項相當艱鉅的工程。

So I like our chances of continuing to innovate on the software stack, as I said, it takes a lot of hard work to build a well-integrated stack like the one that we announced last week. It is just not a stack that lives on a PowerPoint slide. You can log into cloud.digitalocean.com and see how these layers work together. We are also incredibly proud of the fact that we have made the stack completely open with open source options at every single layer.

所以我看好我們持續在軟體堆疊上創新的機會；如我所說，要打造像我們上週宣布的那種高度整合的堆疊，需要非常多的苦功。這絕不是只存在於 PowerPoint 投影片上的堆疊。你可以登入 cloud.digitalocean.com，看看這些層如何協同運作。我們也非常自豪的一點是：我們讓整個堆疊完全開放，在每一層都提供開源（open source）選項。

That is a pretty big deal that I want everyone to appreciate because our target customers are AI-native customers. and they feel very uncomfortable boxing themselves into a single LLM provider. That is just not how their businesses will scale. And for them, having open source work as well as close source as part of the native stack is very, very important.

這是一件相當重要的事，我希望大家能理解，因為我們的目標客戶是 AI 原生（AI-native）客戶，而他們對把自己鎖定在單一 LLM 供應商上會感到非常不安。那不是他們的業務能夠擴張的方式。對他們而言，在原生堆疊中同時具備開源與閉源（closed source）能力，是非常、非常重要的。

So driving this kind of integrated open source enabled stack is really hard. And I like our focus. I like our discipline in terms of doing this. And the market opportunity is going to be so big that I feel very, very convinced that if we focus on learning and understanding our customers better than anyone else and translate that to product innovation, everything else is going to take care of itself.

因此，要推動這種整合式、由開源賦能的堆疊真的很難。我喜歡我們的聚焦。我也喜歡我們在做這件事上的紀律。而市場機會將會非常大，所以我非常、非常確信：只要我們專注於比任何人都更好地學習並理解客戶，並把這轉化為產品創新，其他一切都會水到渠成。

I keep telling my teams be extraordinarily customer-obsessed and competitive aware, not the other way around. We should obsess over our customers first so that we can build the best product for them while being aware of competition, and not the other way around. So I feel we have a lot of room to run with this strategy.

我一直告訴我的團隊：要極度以客戶為中心（customer-obsessed），同時保持對競爭的敏銳（competitive aware），而不是反過來。我們應該先痴迷於客戶，這樣才能在了解競爭的同時，為他們打造最好的產品，而不是反過來。所以我覺得我們用這個策略還有很大的發揮空間。

Okay. Great. And then one for Matt. Matt, for 2027, you talked about adjusted free cash flow margin in the mid- to high teens, I believe. Could you give us a sense of what it would be, including lease payments?

好的。很好。接著有一題問 Matt。Matt，關於 2027 年，你提到調整後自由現金流利潤率（adjusted free cash flow margin）大約在十幾個百分點的中段到高段，我記得是這樣。你能否讓我們了解一下，如果把租賃付款（lease payments）也算進去，會是多少？

That's a great question, Jason. It's hard to answer, though, because it will depend entirely on the lease terms that we have. So whether we lease over four years or five years or a longer period, and it will also depend on the mix of what we lease versus what we pay for upfront. That's why we're not guiding to that at this point.

這是個很好的問題，Jason。不過很難回答，因為這完全取決於我們的租賃條款。例如我們是租四年、五年或更長期間，也取決於我們租賃的比例相對於一次性預付（upfront）支付的比例。這就是為什麼我們目前不對此提供指引。

What I can tell you is that we continue to make very disciplined investments, we've created a lot of balance sheet flexibility for ourselves with the equity raise. We've got a lot of options at our disposal. And we're very excited by the return on investment that we're underwriting for these new facilities. So we'll continue to operate with discipline, but we can't provide specificity on the -- what the lease payments are going to look like in 2027 because we don't know yet.

我能告訴你的是，我們持續進行非常有紀律的投資；透過這次增資（equity raise），我們也為自己創造了很大的資產負債表彈性。我們手上有很多可用的選項。而且我們對這些新設施所承作（underwriting）的投資報酬率感到非常振奮。所以我們會持續以紀律來營運，但我們無法就 2027 年的租賃付款會長什麼樣子提供具體數字，因為我們現在還不知道。

Wamsi Mohan, Bank of America.

Wamsi Mohan，美國銀行（Bank of America）。

Paddy, for -- when you look across your customer cohorts, how much penetration are you seeing of AI-driven workloads, as you look at sort of $1 million plus in the $500,000 plus customer cohort? And are you actually seeing because of AI, do you expect over the next two years to have an even higher chunk of customers graduating from this $500,000 to $1 million-plus cohort as you look through the next few years? And I have a follow-up for Matt.

Paddy，當你檢視不同客戶群（customer cohorts）時，在 AI 驅動工作負載（AI-driven workloads）方面，你看到的滲透率有多少？例如在年支出 100 萬美元以上、以及 50 萬美元以上的客戶群。另外，因為 AI 的因素，你是否預期未來兩年會有更高比例的客戶，從 50 萬美元級距「畢業」到 100 萬美元以上的級距，放眼未來幾年也是如此嗎？我還有一個問題要問 Matt。

Yes. sees, you're absolutely right. I think the short answer is yes to both. We have a good mix of AI as well as cloud native customers in the $500,000 and $1 million customers. And yes, it is a very important motion that we drive internally to look at every 100,000 customer and drive our teams to find out what is blocking our customers from being a $500,000 customer.

是的，Wamsi，你說得完全正確。我想簡短的答案是兩個都是「是」。在 50 萬美元與 100 萬美元級距的客戶中，我們同時有相當不錯的 AI 客戶與雲原生（cloud native）客戶組合。而且是的，這是一個我們在內部非常重要的推動機制：我們會檢視每一位 10 萬美元級距的客戶，並推動團隊找出是什麼阻礙客戶成為 50 萬美元級距的客戶。

And similarly, we look at every 500,000 customer and find out how we can make them $1 million customer and so forth. So with the increased adoption of AI in these customer cohorts, we fully expect those numbers to keep going up to the right for sure.

同樣地，我們也會檢視每一位 50 萬美元級距的客戶，找出我們如何能讓他們成為 100 萬美元級距的客戶，依此類推。因此，隨著這些客戶群對 AI 的採用增加，我們完全預期這些數字會持續向上成長，毫無疑問。

Tom Blakey, Cantor.

Tom Blakey，Cantor。

Congratulations on the great results here. Maybe a couple of questions on my side. Paddy, we've talked prior about 3 to 4x demand in terms of your 75-megawatt capacity was really impressive to see you announce Cursor here, a great win. Congratulations. Just wondering if you could just maybe update us on the framework of what you're seeing there in terms of your customer selectivity and maybe even turning some customers away in this type of market?

恭喜你們交出很棒的成績。我這邊可能有幾個問題。Paddy，我們之前談過，就你們 75 兆瓦容量而言，需求大約是 3 到 4 倍；看到你們在這裡宣布 Cursor，令人印象深刻，是個很大的勝利。恭喜。想請問你能否更新一下你們目前看到的框架：在這種市場環境下，你們如何做客戶篩選（customer selectivity），甚至是否會拒絕一些客戶？

And then secondly, for Matt and maybe the team just CapEx per megawatt, I think investors would love a little bit more color in terms of how much higher this can go for the 60 megawatts. And would it be difficult to just upgrade the prior capacity from a software upgrade perspective to the AI native cloud capacity to maybe kind of pull some of that in, that would be helpful.

第二個問題是給 Matt，或許也給團隊：每兆瓦的資本支出（CapEx per megawatt）。我想投資人會希望多一些細節，了解在這 60 兆瓦上，這個數字可能還會提高多少。另外，從軟體升級的角度，把既有容量升級到 AI 原生雲（AI native cloud）容量是否會很困難？如果能把其中一部分提前拉進來，會很有幫助。

Yes. I think on the last thing, we -- it is hard to have a non-AI data center deployed with AI hardware because of the limitations, especially all of the new ones that we're deploying are all direct liquid cooled and the hardware specs are just different, Thomas. So that's that.

是的。我想就最後那點而言，我們——要在非 AI 的資料中心部署 AI 硬體是很困難的，因為有各種限制；尤其我們正在部署的所有新機房都是直接液冷（direct liquid cooled），硬體規格就是不同，Thomas。所以就是這樣。

And going back to your first question around the pipeline coverage and how we allocate capacity. I mean, that is some -- a new muscle that everyone in the industry is learning, right? Our pipeline, as I mentioned several times, is 3 to 4x, if not more, in terms of the actual capacity that we have. Which is a great problem to have, but it is a problem that we are very and very thoughtful about resolving because we have to make some bets just like our customers are making bets on us. We have to make bets on how we want to allocate the capacity.

回到你第一個關於管線覆蓋（pipeline coverage）以及我們如何分配容量的問題。我的意思是，這是——產業裡每個人都在學的一項新能力（new muscle），對吧？如我多次提到，我們的管線大約是 3 到 4 倍，甚至更多，相對於我們實際擁有的容量。這是個很棒的問題，但同時也是我們必須非常、非常審慎去解決的問題，因為我們必須做一些押注，就像客戶也在押注我們一樣。我們必須押注我們想要如何分配這些容量。

Because, as I said in the last call, if we decide to just sell the capacity to the first or the biggest or the loudest customer we'll be all done. We can go home and the capacity will all be taken. But we have an intention to run this like a cloud, right, where we want as many customers as possible so that we can learn, we can build a better product and build a bigger competitive moat that customers that only have -- or platforms that only have a few concentrated customers simply don't have the luxury to learn and innovate as fast as we are. So it's a balancing act that we are trying to figure out, but so far, so good with the types of customers we're bringing on board.

因為，正如我在上一次電話會議中所說，如果我們決定只把產能賣給第一個、最大的或聲音最響亮的客戶，那我們就結束了。我們就可以回家了，因為產能會被全部吃下來。但我們的意圖是把這件事像雲端一樣來營運，對吧？我們希望客戶越多越好，這樣我們才能學習、打造更好的產品，並建立更大的競爭護城河；而那些只有——或那些平台只有少數高度集中的客戶，根本沒有這種奢侈去像我們一樣快速學習與創新。所以這是一個我們正在摸索的平衡動作，但到目前為止，我們引入的客戶類型都很不錯。

In terms of the cost of the CapEx, it's certainly going to be higher than what we experienced for the 31 megawatts equipment was ordered in 2025. And you're seeing broadly across the industry, component costs are going up. But more importantly for us, we're putting in gear that has higher token kind of capacity and capabilities. And we expect to get the same or higher ROI on the investments that we're making.

就資本支出（CapEx）的成本而言，肯定會高於我們在 2025 年為 31 兆瓦設備下單時的經驗。而且你也看到整個產業普遍的趨勢是，零組件成本正在上升。但對我們更重要的是，我們正在導入具備更高 token 類型的容量與能力的設備。我們預期在所做的投資上，能取得相同或更高的投資報酬率（ROI）。

So we'll invest a bit more. We see a phenomenal opportunity in front of us. We got a very differentiated position. We're going to get more capacity out of the investments we make, and we're going to earn similar or better returns on the investments.

所以我們會多投資一些。我們看到眼前有非常驚人的機會。我們擁有非常差異化的定位。我們將從所做的投資中取得更多產能，並在這些投資上賺取相近或更好的報酬。

Josh Baer, Morgan Stanley.

Josh Baer，摩根士丹利。

Congrats on a wonderful quarter. I was hoping you could double-click a little bit on GPU and other pricing trends that you're seeing in the spot market. And wondering if you can quantify the portion of your business that's on demand and exposed to spot versus what portion is contracted and has fixed pricing? And any way that you can characterize the benefit in the quarter or the impact of the 2026 guide from spot market pricing?

恭喜你們交出非常出色的一季。我希望你們能更深入談談你們在現貨市場看到的 GPU 與其他項目的定價趨勢。另外想請問，你們業務中按需（on-demand）且暴露於現貨價格的比例，與已簽約、採固定定價的比例各是多少？以及你們是否能描述一下，本季從現貨市場定價帶來的好處，或現貨市場定價對 2026 年指引的影響？

It's interesting, Josh, that you point to the spot pricing. So we have a portion of -- a small portion right now of on-demand because most of our capacity is locked up with a customer. But if you think about the core of your question, which is how much exposure do we have to the ability to raise GPU prices along with the market. Because we don't have four or five year contracts with our customers, if we're locked into a customer, it may only be for three months or six months or a year. And as I said earlier on the call, as those contracts are coming up, we can rotate.

Josh，你提到現貨定價很有意思。所以我們目前有一部分——一小部分——是按需，因為我們大多數產能都被某個客戶鎖定。但如果回到你問題的核心，也就是我們在多大程度上能隨市場一起提高 GPU 價格的曝險。因為我們和客戶之間不是四、五年的長約，即便我們被某個客戶鎖定，可能也只是三個月、六個月或一年。而且如我先前在電話會議中提到的，當這些合約到期時，我們可以輪換。

One, we can just raise the price on that customer to whatever the current market prevailing prices. Two, we can rotate it completely out of if it's a GPU per hour price, we can say we're not going to sell that capacity in that model any longer. And if you're interested in that you've got to take our on-demand pricing or you're going to take serverless inferencing.

第一，我們可以把該客戶的價格直接提高到當前市場的主流價格水準。第二，我們也可以完全把它輪換出去——如果是按 GPU 每小時計價，我們可以說我們不再用這種模式出售該產能。如果你有興趣，那你就得採用我們的按需定價，或是採用無伺服器推理（serverless inferencing）。

So we have the ability to adjust to the market, I'd say, probably more readily than maybe some of the other folks in the industry. So we feel very, very good about our ability to adapt to pricing. And as I said, to Gabriela's question, that ability and our ability to execute that is part of the reason why we're able to raise the guidance for this year without getting any benefit from the incremental capacity that we just announced. So that's a great question.

所以我會說，我們調整以貼近市場的能力，可能比產業裡其他一些同業更為迅速。因此我們對自身適應定價的能力感到非常、非常有信心。而且如我對 Gabriela 的問題所說，這種能力以及我們執行它的能力，是我們能在不從剛宣布的新增產能中獲得任何增量效益的情況下，仍能上調今年指引的部分原因。所以這是個很好的問題。

Radi Sultan, UBS.

Radi Sultan，瑞銀。

If you think about adding more capacity and as the existing AI customer cohort scale, like how should we be thinking about the gross margin profile, this incremental capacity you're looking to add once it's fully utilized. And you mentioned, Matt, the increased component costs. But yes what are the key puts and takes there we should be keeping in mind just on the margin side of things.

如果你們考慮增加更多產能，且現有 AI 客戶群持續擴張，我們應該如何看待毛利率輪廓——也就是你們計畫新增的這些增量產能在完全利用後的毛利率表現？另外你提到，Matt，零組件成本上升。那麼在利潤率這一端，我們應該留意哪些主要的拉動與抵銷因素？

I think you'll note in our materials that we highlighted, non-GAAP operating margin. And the reason that we did that is because, again, if you think of where the industry is going and how different this business is than the business that we had several years ago, gross margin is one input, but operating margin is a better, more holistic view of what's going on in terms of the overall profitability because the revenue growth is so rapid and it's certainly at a lower gross margin, but it comes with tremendous operating expense leverage. And so the operating margins are very strong and very compelling, and we expect those to continue to be very attractive.

我想你會注意到，在我們的資料中我們特別強調了非 GAAP 的營業利益率。我們之所以這麼做，是因為——再一次——如果你思考產業的走向，以及這門業務與我們幾年前的業務有多不同，毛利率只是其中一個輸入指標，但營業利益率能更好、更全面地反映整體獲利狀況；因為營收成長非常快速，而且毛利率確實較低，但同時帶來了巨大的營業費用槓桿。因此營業利益率非常強勁、非常有吸引力，我們也預期它們會持續非常亮眼。

Will we see a small decrease in operating margin as we invest to accelerate our growth, given some of the same timing-related issues with bringing on new capacity, we certainly will. But if you look at the rate of revenue growth, if you look at the strong operating margins, if you look at the fact that we've been very, very disciplined with cash flow, and that we're earning very good returns. I think you'd agree that we're positioned very, very well for very durable and profitable growth.

當我們投資以加速成長時，營業利益率會不會小幅下降？考量到導入新產能在時點上仍有一些同樣的相關問題，確實會。但如果你看營收成長的速度、強勁的營業利益率、以及我們在現金流上的高度紀律，還有我們正在賺取非常好的報酬。我想你會同意，我們在實現非常持久且具獲利性的成長方面，處於非常、非常有利的位置。

Patrick Walravens, Citizens.

Patrick Walravens，Citizens。

It's amazing results you guys, congratulations. So Paddy, when I was at your Deploy conference, the speaker got interrupted by applause like five or six times. But two of the times were when you talked about the inference router and then also when you guys talked about support for the latest DeepSeek model. So can you just talk a little bit about why your customers are so enthusiastic about that?

你們的成果太驚人了，恭喜。所以 Paddy，我在你們的 Deploy 大會上，講者被掌聲打斷了大概五、六次。其中有兩次是你談到 inference router 的時候，還有你們談到支援最新的 DeepSeek 模型的時候。你能不能談談，為什麼你們的客戶對這些事情如此興奮？

Yes. Thank you, Patrick. And first of all, thank you for coming to deploy last week. So you bring up a really, really important point. And for those of you who have not seen the keynote video recording from last week, I encourage you to please do that.

是的。謝謝你，Patrick。首先也謝謝你上週來參加 deploy。你提出了一個非常、非常重要的點。也鼓勵還沒看過上週主題演講影片錄影的各位，請務必去看。

The two points that Patrick just mentioned are really important because AI Natives are doing something which is incredibly interesting. Number one is they are all running multiple models, right? Because as I mentioned, this is a cost of revenue line item for them, and it will be crippling if they are just beholden to one closed source model. Last week, there were two different models that were announced. One is DeepSeek version four and the other one was the latest version from OpenAI.

Patrick 剛提到的兩點非常重要，因為 AI 原生（AI Natives）正在做一件非常有意思的事。第一，他們都在同時運行多個模型，對吧？因為如我所說，這對他們而言是營收成本（cost of revenue）的一個科目，如果他們只受制於單一封閉源碼模型，成本會變得難以承受。上週有兩個不同的模型發布。一個是 DeepSeek 第四版，另一個是 OpenAI 的最新版本。

And the difference in price was 10x. In terms of the output tokens, it was literally $3 versus $30. So AI natives are doing three things: One, they are all becoming multiple models. Number two is they're running a lot of open source. And number three is, many of these AI natives are also running their own version of a model, which is distilled from an open source model or something like that.

而價格差異是 10 倍。以輸出 token 來看，真的就是 3 美元對 30 美元。所以 AI 原生正在做三件事：第一，他們都在採用多模型策略。第二，他們大量使用開源。第三，許多 AI 原生也在運行他們自己的模型版本，這些版本是從開源模型蒸餾（distilled）而來或類似方式取得的。

So there's intelligent router becomes extraordinarily important so that the router can find the right model for the task you're assigning. So we showed a demo, which was super compelling where it showed better performance at lower TCO per token by routing the incoming prompt to the right model.

因此，智慧路由器變得格外重要，讓路由器能為你指派的任務找到正確的模型。所以我們展示了一個非常有說服力的示範：透過把進來的提示詞路由到正確的模型，在每個 token 的較低總持有成本（TCO）下，呈現更好的效能。

And the second thing is Patrick mentioned that there was a lot of supplies for our DeepSeek support, which is fairly obvious because AI natives are embracing open source up and down the stack in a very pronounced manner. So that's why it is really important to understand, our target market is very different. These are AI natives that are building and monetizing software and for them, multiple models, open source and having destiny over their intelligence is an existential thing.

第二點是 Patrick 提到，我們的 DeepSeek 支援有大量供給，這其實相當明顯，因為 AI 原生族群正以非常顯著的方式，在整個技術堆疊上下全面擁抱開源。所以這也是為什麼理解我們的目標市場非常重要，它非常不同。這些是正在打造並將軟體變現的 AI 原生族群；對他們而言，多模型、開源，以及能掌握自身智慧的主導權，是攸關生存的事情。

Great. And Matt, if I could ask you a follow-up. Cursor is an amazing win, congratulations. We've all seen the news about SpaceX having an option to buy it. So just how did that fit into your guidance? How did you think about that?

很好。Matt，我可以追問一下嗎？Cursor 是一個很棒的勝利，恭喜。我們都看到 SpaceX 有選擇權可以買下它的新聞。那這件事如何納入你們的財測指引？你們是怎麼看待這件事的？

Cursor is a fantastic customer. And as you said, it's a great indication of the quality of the platform. And we're really excited by it based on the fact that they're using -- this is not a Bare Metal contract. They're using our inference services. They've made commitments around the NFS and some of the core cloud capabilities, so we're very encouraged by that, and we have a fantastic relationship with them.

Cursor 是一位非常出色的客戶。而且如你所說，這也很好地印證了平台的品質。我們對此非常興奮，因為他們使用的是——這不是一份裸機（Bare Metal）合約。他們使用的是我們的推論服務。他們也在 NFS 以及一些核心雲端能力上做出承諾，所以我們對此非常受到鼓舞，並且與他們維持非常良好的合作關係。

We haven't predicated any of our long-term guidance on any single customer. We have, as Paddy said, to the demand for the capacity that we have available and we were very confident that there'll be a good part of that, but we're not basing any of our forecasts on specific customer.

我們並未把任何長期財測指引建立在任何單一客戶之上。我們有——如 Paddy 所說——對我們可用產能的需求；我們非常有信心其中會有相當一部分被消化，但我們不會以特定客戶作為任何預測的基礎。

Raimo Lenschow, Barclays.

Raimo Lenschow，巴克萊。

Two quick questions. Going back to Gabriela's point in terms of like how big the market is. At the moment, it looks like most of the work is getting done on training models and inference is only starting. Like Paddy from your perspective, which innings are we on inference actually because it seems very, very early still to get an idea about like how long this can go on for. And then, Matt, for you, the one thing that comes up in the market is a lot of like capacity of new data centers, et cetera. You're not building 100,000 GPU to have data centers who are much smaller, but like what's the constraint of finding sites to kind of go beyond the capacity you announced today?

兩個快速問題。回到 Gabriela 的觀點，關於市場到底有多大。目前看起來大部分工作都在做模型訓練，而推論才剛開始。Paddy，從你的角度看，推論現在進行到第幾局？因為看起來仍然非常、非常早期，要判斷這能持續多久還不太容易。另外，Matt，市場上常被提到的是大量新資料中心等新增產能。你們不是在建 10 萬張 GPU 的那種資料中心，規模小得多；但要在你們今天宣布的產能之上再擴張，尋找場址的限制是什麼？

Thank you, Raimo. So to answer your question succinctly, since baseball season is just starting. I would say from an inferencing point of view, we are probably in the top of the second inning. And Agentic, we are just in the national anthem. It's just getting started.

謝謝你，Raimo。簡要回答你的問題，既然棒球季才剛開始。我會說，從推論的角度來看，我們大概在第二局上半。而 Agentic（代理式）方面，我們才剛在唱國歌。一切才剛開始。

So I think there's a lot of room for a lot of innovation. And I am the one thing that I'm super proud of with all the announcements we made last week is 15 new product launches, not just features, 15 new product launches and the velocity and the intensity from our engineering team is just -- it's going to make a difference in terms of our ability to establish a leadership position. And then Raimo, your -- what was the second question? Second part of the question?

所以我認為還有很大的創新空間。而我對上週我們所有公告中最自豪的一件事是：15 個新產品發布，不只是功能，是真正的 15 個新產品發布；我們工程團隊的速度與強度——這將在我們建立領導地位的能力上帶來差異。然後 Raimo，你的——第二個問題是什麼？問題的第二部分？

It's like, how did is it like, yes?

就是，這像是，你們是怎麼——是的？

Sorry. The -- we've been able to secure the data center capacity that we've been targeting. We're still in active conversations on additional capacity beyond the both for '27 and '28. And we've not had an issue getting capacity that we've been trying to track down.

抱歉。我們已經能夠確保我們所鎖定的資料中心產能。我們仍在就 2027 年與 2028 年的額外產能進行積極洽談。而且在取得我們一直在追蹤、想要拿到的產能方面，我們沒有遇到問題。

That concludes our Q&A session. And this also concludes today's conference call. Thank you for your participation. You may now disconnect.

我們的問答環節到此結束。今天的電話會議也到此結束。感謝各位的參與。您現在可以掛線。