DigitalOcean Holdings, Inc. (DOCN) 2026 Q2 法說會逐字稿

Hello, everyone. Thank you for joining us and welcome to the DigitalOcean second-quarter 2026 earnings conference call. (Operator Instructions)

大家好。感謝各位加入，歡迎參加 DigitalOcean 2026 年第二季財報電話會議。(接線員指示)

I will now hand the conference over to Radu Patrichi, Head of Investor Relations. Radu, please go ahead.

我現在把會議交給投資人關係主管 Radu Patrichi。Radu，請開始。

Thank you, and good morning. Thank you all for joining us today to review DigitalOcean second-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.

謝謝，各位早安。感謝各位今天與我們一同回顧 DigitalOcean 2026 年第二季業績。今天與我一同參與電話會議的還有我們的執行長 Paddy Srinivasan，以及財務長 Matt Steinfort。對於正在同步收聽的各位，網路直播上提供了隨附的簡報投影片。

Before we begin, let me remind you that certain statements made on today's call may be considered forward-looking, which reflect management's best judgment based on currently available information. Our actual results may differ materially from those projected in forward-looking statements, including our financial outlook.

在開始之前，提醒各位，今天電話會議中的某些陳述可能被視為前瞻性陳述，反映管理層基於目前可得資訊所做的最佳判斷。我們的實際結果可能與前瞻性陳述（包括我們的財務展望）中所預測者有重大差異。

I direct your attention to the risk factors contained in our SEC filing 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.

請各位留意我們向 SEC 提交文件中所載的風險因素，以及今日新聞稿（已發布於我們網站）中所提及的風險因素。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. Reconciliations to the most comparable GAAP financial measures can be found in today's earnings press release, as well as in our investor presentation that outlines the discussion on today's call. A webcast of today's call is available in 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。與最可比 GAAP 財務衡量指標的調節表可見於今日的財報新聞稿，以及我們的投資人簡報（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 turn the call over to Paddy.

那麼，我把電話交給 Paddy。

Thank you, Radu. Good morning, everyone, and thank you for joining us today. We had an exceptional Q2 as we continue to accelerate growth in a disciplined way, and I'm excited to share the highlights with all of you. Let me start with four key takeaways from the quarter.

謝謝你，Radu。各位早安，感謝各位今天加入我們。我們第二季表現非常出色，在有紀律的方式下持續加速成長，我很高興與各位分享重點。我先從本季四個關鍵重點開始。

First, our growth rate continues to accelerate. As we previewed several weeks ago, Q2 was another strong quarter for DigitalOcean. We were above guidance on every key metric. We delivered 29% year-over-year revenue growth while continuing to have strong profitability.

第一，我們的成長率持續加速。如同我們在幾週前所預告的，第二季對 DigitalOcean 而言又是強勁的一季。我們在每一項關鍵指標上都高於指引。我們實現營收年增 29%，同時維持強勁的獲利能力。

Second, our inference services, the collection of all non-bare metal inferencing capabilities on our AI-Native Cloud is getting tremendous traction and grew almost 800% year over year. Launched in late April this year, our Inference Engine, which is a managed offering that includes Serverless Inference and related technologies, is off to a flying start with over 6,000 customers, including material inference workloads from some of the most sophisticated AI-native companies.

第二，我們的推論服務（Inference Services）——也就是我們 AI 原生雲（AI-Native Cloud）上所有非裸機（non-bare metal）的推論能力集合——獲得極大市場動能，年增接近 800%。我們於今年 4 月下旬推出的 Inference Engine，是一項受管理的產品，包含無伺服器推論（Serverless Inference）及相關技術；目前已快速起飛，客戶數超過 6,000 家，其中包括一些最成熟的 AI 原生公司所帶來的具規模推論工作負載。

Third, an AI-native flywheel is emerging, driving adoption across our full AI-native cloud with a new entry point through our Inference Engine. We are already seeing early signs of this flywheel. More than half of new AI customers added year-to-date had Core Cloud attached. We believe this flywheel will drive higher margin and stickier services, further increasing our ARR per megawatt and differentiating us from bare metal Neoclouds.

第三，一個 AI 原生的飛輪效應正在形成，透過 Inference Engine 這個新的切入點，帶動我們完整 AI 原生雲的採用。我們已經看到這個飛輪的早期跡象。今年迄今新增的 AI 客戶中，超過一半同時搭配了 Core Cloud。我們相信這個飛輪將推動更高毛利且黏著度更高的服務，進一步提升我們每兆瓦（megawatt）的 ARR，並使我們有別於裸機 Neocloud。

And finally, we continue to focus on disciplined execution and durable growth. While we continue to manage the same supply chain challenges that face the entire industry, we are delivering our new 2026 capacity on time and in some cases ahead of schedule. We secured an incremental 20 megawatts, we strengthened our balance sheet, we landed our first nine figure annual commitment -- revenue commitments, and we remain focused on responsible investment and generating attractive returns.

最後，我們持續專注於有紀律的執行與可持續的成長。儘管我們仍在應對全產業共同面臨的供應鏈挑戰，我們仍按時、且在某些情況下提前交付 2026 年新增產能。我們額外取得 20 兆瓦的增量產能、強化資產負債表、拿下首筆九位數的年度承諾——營收承諾，並持續聚焦於負責任的投資與創造具吸引力的報酬。

With our meaningful progress and momentum, we are again raising our full-year 2026 outlook. We now expect revenue growth of approximately 30% for the full-year 2026, and to reach at least 35% growth by Q4 of 2026. While it is premature to give formal guidance for 2027, we are even more confident in our prior 2027 estimate of 50% plus revenue growth for the full year 2027. I'll now spend a few minutes drilling into each of these four key takeaways.

基於我們顯著的進展與動能，我們再次上調 2026 全年展望。我們目前預期 2026 全年營收成長約 30%，並在 2026 年第四季達到至少 35% 的成長。雖然現在提供 2027 年正式指引仍為時過早，但我們對先前提出的 2027 年全年營收成長 50% 以上的估計更具信心。接下來我會花幾分鐘深入說明這四個關鍵重點。

First, we delivered record Q2 revenue performance, and the topline continues to accelerate with demand well in excess of capacity. Q2 revenue was $281 million, up approximately 29% year over year, which is more than double our growth rate in the same period last year.

第一，我們第二季營收表現創下新高，在需求遠高於產能的情況下，營收端持續加速。第二季營收為 2.81 億美元，年增約 29%，是去年同期成長率的兩倍以上。

We delivered a record $93 million in incremental ARR in Q2, the most incremental ARR in a quarter in the company's history and nearly triple what we added in the same quarter last year. And we are doing all of this with strong profitability. We delivered 40% adjusted EBITDA margin, 24% adjusted operating income margin, and 17% trailing 12-month adjusted free cash flow margin in the quarter.

我們在第二季新增 ARR 達 9,300 萬美元，為公司史上單季最高的新增 ARR，幾乎是去年同季新增的三倍。而且我們在達成這些成果的同時，仍維持強勁的獲利能力。本季我們的調整後 EBITDA 利潤率為 40%，調整後營業利益率為 24%，以及過去 12 個月（TTM）的調整後自由現金流利潤率為 17%。

We are driving this growth by continuing to deliver for our highest spending customers. ARR from 100,000 plus customers grew 98% year over year. And our $500,000 plus customer ARR grew 160%, and our $1 million plus customer ARR rose 214%. The higher spend the cohort has, the faster that cohort is growing, and this has been the case for eight quarters in a row.

我們推動成長的方式，是持續為最高消費客戶交付價值。年化經常性收入（ARR）來自 10 萬美元以上客戶的部分年增 98%。而 50 萬美元以上客戶的 ARR 年增 160%，100 萬美元以上客戶的 ARR 則上升 214%。客戶群組（cohort）消費越高，該群組成長越快，且這種情況已連續八個季度如此。

Our highest spending cohort is also becoming a much bigger portion of our business. And a critical part of our growth engine, growing from 9% of total ARR a year ago to 23% in Q2. AI customer ARR reached $234 million, growing over 200% year over year.

我們最高消費的客戶群也正成為我們業務中更大的一部分。同時也是我們成長引擎的關鍵組成，從一年前占總 ARR 的 9% 成長至第二季的 23%。AI 客戶 ARR 達到 2.34 億美元，年增超過 200%。

AI customers come to DigitalOcean for more than just capacity, they come to us for software and the capabilities that help them accelerate their business. 85% of AI customer ARR in the quarter came from inference services and Core Cloud, not from bare metal. Inference services are the fastest growing component of our AI customer ARR, growing close to 800% year over year, and now represent over 70% of our total AI customer ARR.

AI 客戶選擇 DigitalOcean 不僅是為了產能，他們也為了軟體與各項能力而來，這些能力能幫助他們加速業務發展。本季 AI 客戶 ARR 中有 85% 來自推論服務與 Core Cloud，而非裸機。推論服務是 AI 客戶 ARR 中成長最快的組成部分，年增接近 800%，目前已占我們 AI 客戶 ARR 總額的 70% 以上。

We are a full-stack cloud platform with software that AI-Native companies depend on to build, run, and scale production AI.

我們是一個全棧雲端平台，提供 AI 原生公司所依賴的軟體，用以建置、運行並擴展生產環境的 AI。

The second key takeaway is the growing traction of our Inference Engine. We launched our Inference Engine, which provides the right model at the right performance and price for every task as a part of our AI native cloud in late April. Since then, over 6,000 customers have leveraged the Inference Engine while customer count grew an average of close to 60% month-over-month and the token volume increased 30x over the last 60 days.

第二個關鍵重點是我們 Inference Engine 日益增長的市場動能。我們於 4 月下旬在 AI 原生雲中推出 Inference Engine，為每一項任務提供在效能與價格上都合適的模型。自推出以來，已有超過 6,000 名客戶使用 Inference Engine；同時客戶數平均月增接近 60%，而在過去 60 天內，token 量提升了 30 倍。

We have seen open-weight models climb up from around 15% of total token volume, following our April launch to close to 75% today, highlighting the importance of open-weight models in the AI native ecosystem.

我們看到開放權重模型在我們於四月推出後，從約占總 token 量的 15% 攀升至今日接近 75%，凸顯開放權重模型在 AI 原生生態系中的重要性。

This token growth is driven by strong demand from AI natives, not from individual users looking for a badge for the most token consumption. Token maxing was the industry's first instinct, maximize usage, throw the largest frontier model at everything and let the bill compound.

這一 token 成長是由 AI 原生客戶的強勁需求所驅動，而非個別使用者為了拿到「token 消耗最多」的徽章而來。「token 極大化」曾是產業的第一直覺：把使用量拉到最大，凡事都丟給最大的前沿模型，讓帳單一路累積。

As workloads shifted from human-prompted to agent-driven, token consumption and cost exploded. For an AI-native company, tokens are both a source of value and cost, so runaway costs are an existential threat to their unit economics.

隨著工作負載從人類提示驅動轉向代理（agent）驅動，token 消耗與成本暴增。對 AI 原生公司而言，token 既是價值來源也是成本來源，因此失控的成本對其單位經濟（unit economics）構成生存威脅。

We believe that the market is shifting towards value maxing, the right model at the right cost for every task, measured in business outcomes per dollar. This shift is a tailwind for us as we believe that value creation opportunities will expand from just whoever built the model to include whoever serves it the best.

我們相信市場正轉向「價值極大化」：為每個任務以合適的成本選用合適的模型，並以每一美元所帶來的商業成果來衡量。我們認為這一轉變對我們是順風，因為價值創造的機會將從「只屬於建模型的人」擴展到「也包括把模型服務做得最好的人」。

Open-weight models make value maxing possible. Open weights let customers post train on their own data and control their cost curve. Frontier quality open-weight models at compelling cost performance characteristics has been a key adoption driver.

開放權重模型讓價值極大化成為可能。開放權重讓客戶能在自有資料上進行後訓練（post-train），並掌控其成本曲線。具備前沿品質、且成本效能具吸引力的開放權重模型，一直是採用的關鍵驅動因素。

For analysts from artificial analysis, today's best open models trail the frontier models by only a few percentage points and are over 70% of token volume per OpenRouter, the largest and most popular AI gateway.

根據 Artificial Analysis 的分析師觀點，當今最佳的開放模型僅落後前沿模型幾個百分點；而依 OpenRouter（最大且最受歡迎的 AI 閘道）資料，開放模型已占超過 70% 的 token 量。

An open-based file is necessary but not sufficient for companies to own their intelligence, turning open space into fast, reliable, economical production tokens is a systems problem our Inference Engine solves. Continuous batching, quantization, KV-cache optimization, speculative decoding, prompt cache, intelligent routing, and workload aware scheduling, all engineered as one system on infrastructure we own. Like traditional open-source software, the model may be free but making it useful and serving it well is the product.

以開放為基礎的檔案對公司擁有其智慧而言是必要但不充分的條件；把「開放」轉化為快速、可靠、經濟的生產級 token，是一個系統性問題，而我們的推理引擎（Inference Engine）正是用來解決它。連續批次處理（continuous batching）、量化（quantization）、KV-cache 最佳化、推測式解碼（speculative decoding）、提示快取（prompt cache）、智慧路由，以及工作負載感知排程（workload aware scheduling），全部在我們自有的基礎設施上以單一系統方式工程化整合。就像傳統開源軟體一樣，模型可能是免費的，但讓它變得有用並把它服務好，才是產品。

Our Inference Engine is much more than an API endpoint to an open weight model. It has become a full production runtime solving today's most pressing needs.

我們的推理引擎遠不只是通往開放權重模型的 API 端點。它已成為完整的生產級執行環境（runtime），用以解決當下最迫切的需求。

Our inference router optimizes requests in real time for quality, latency, and cost across our full open and frontier catalog behind one unified API. Close to 1,400 inference customers actively use this feature to optimize dollars per unit of intelligence.

我們的推理路由器會在即時狀態下，於單一統一 API 背後，跨我們完整的開放與前沿模型目錄，針對品質、延遲與成本最佳化請求。接近 1,400 家推理客戶正積極使用此功能，以最佳化每單位智慧的美元成本。

Model Synthesis, a new feature we just released, orchestrates a panel of models in parallel, with the synthesizer merging their outputs, delivering frontier grade quality at a fraction of frontier cost.

我們剛發布的新功能「模型合成（Model Synthesis）」可並行協調一組模型，由合成器整合其輸出，以僅為前沿成本一小部分的代價，提供前沿等級的品質。

Model Evaluations let customers test any model against their own business data. Batch inference handled high-volume asynchronous workloads. Prompt Caching cuts cost and latency with zero application changes. Server-Side Tools give agents web search, retrieval, and function calling natively inside inference requests with built-in access to Knowledge Bases and MCP servers.

模型評估（Model Evaluations）讓客戶能用自家商業資料測試任何模型。批次推理（Batch inference）可處理高量的非同步工作負載。提示快取（Prompt Caching）在不需任何應用程式變更的情況下，降低成本與延遲。伺服器端工具（Server-Side Tools）讓代理在推理請求內原生使用網頁搜尋、檢索與函式呼叫，並內建可存取知識庫（Knowledge Bases）與 MCP 伺服器。

Together, these features turn model choice from a one-time decision into a dynamic, ongoing engineering and business decision. On our platform, open-weight models grew from roughly 15% of tokens, following our initial launch to close to 75% today.

綜合而言，這些功能把模型選擇從一次性的決策，轉變為動態、持續的工程與商業決策。在我們的平台上，開放權重模型在初次推出後，從約 15% 的 token 成長到今日接近 75%。

And when Kimi-K3, the largest open-weight model ever released, went live on July 27, we were the only full-stack cloud provider to be a launch partner, delivering day zero access. Adoption has been incredible with over 400 net new customers just in the first week.

而當 Kimi-K3（史上發布過最大的開放權重模型）於 7 月 27 日上線時，我們是唯一的全棧雲端供應商擔任其發布合作夥伴，提供 Day 0 存取。採用情況非常驚人，僅第一週就新增超過 400 家淨新客戶。

Our model catalog now offers 75-plus open and closed-source models through a single endpoint, including GLM 5.2, DeepSeek V4, GPT 5.6, Opus 5, et cetera, with 14 day zero launches since April of this year.

我們的模型目錄如今透過單一端點提供 75+ 個開放與閉源模型，包括 GLM 5.2、DeepSeek V4、GPT 5.6、Opus 5 等等，且自今年四月以來已有 14 次 Day 0 上線。

Our third key takeaway is that our AI-native cloud is becoming a flywheel. Every layer a customer adopts pulls them into the next.

第三個關鍵重點是，我們的 AI 原生雲正在形成飛輪效應。客戶每採用一層，就會被帶動採用下一層。

In late April, we launched the DigitalOcean AI-Native Cloud, five fully integrated layers from silicon to inference to agents with open-source support at every layer. Since then, we shipped more than 80 releases across all layers, demonstrating innovation across the platform.

在四月下旬，我們推出 DigitalOcean AI 原生雲（AI-Native Cloud），從矽到推理到代理共五個完全整合的層級，且每一層都支援開源。此後，我們在所有層級累計交付超過 80 次發布，展現平台層面的創新。

These releases included managed agent products like Server-Side Tools, data and learning products like Knowledge Bases, the Inference Engine I just discussed, and cloud primitives like our new Insights Observability service.

這些發布包含受管代理產品（如伺服器端工具）、資料與學習產品（如知識庫）、我剛提到的推理引擎，以及雲端基礎元件（如我們新的 Insights 可觀測性服務）。

An integrated full-stack platform is foundational to AI builders because AI native applications require far more than raw GPUs or just tokens. They need a production cloud designed around inference and agent execution. Building and operating that cloud is hard. It requires deep engineering across data centers, silicon, networking, storage, Kubernetes, databases, model serving, routing, evaluations, agent runtimes, and much more.

對 AI 建構者而言，整合式全棧平台是基礎，因為 AI 原生應用需要的遠不只是原始 GPU 或只是 token。他們需要一個以推理與代理執行為核心設計的生產級雲端。建置並營運這樣的雲端很難。它需要在資料中心、矽、網路、儲存、Kubernetes、資料庫、模型服務、路由、評估、代理執行環境等多方面進行深度工程投入，還有更多。

Our integrated platform eliminates this complexity for customers and a flywheel emerging as these AI builders adopt it. Customers enter the platform through one of the three front doors: inference, agents or core compute. Most AI native customers first need inference the right model at the right performance and the right price for every task. From there, inference into agentic workflows, which use and generate data that requires databases, storage, Knowledge Bases and observability.

我們的整合式平台為客戶消除這些複雜度，並且隨著 AI 建構者採用而形成飛輪。客戶會從三個「前門」之一進入平台：推理、代理或核心運算。多數 AI 原生客戶首先需要推理：為每個任務以合適的效能與合適的價格選到合適的模型。接著，推理會進入代理式工作流程（agentic workflows），這些流程會使用並產生資料，而資料需要資料庫、儲存、知識庫與可觀測性。

That generated data becomes raw material for learning, improving, and customizing the models. Agent runtimes and learning drive demand for compute and because that compute runs on infrastructure we own and operate, every turn of the wheel improves our unit economics, better price performance for customers, spur even more tokens and the cycle accelerates.

這些產生的資料會成為學習、改進與客製化模型的原料。代理執行環境與學習會帶動對運算的需求；而由於這些運算跑在我們自有並營運的基礎設施上，飛輪每轉一圈都會改善我們的單位經濟，為客戶帶來更好的價格效能，進一步促使更多 token，循環加速。

Adoption in each layer drives the next and the effects compound. Inference is one entry point into a self-reinforcing cycle that pulls customers deeper into the platform and has been the leading indicator for full platform adoption. And this flywheel is already working. Let me give you some examples.

每一層的採用都會帶動下一層，效果相互疊加。推理是進入這個自我強化循環的其中一個入口，會把客戶更深地拉進平台，並一直是全平台採用的領先指標。而這個飛輪已經在運作。我給你一些例子。

OpenCode, a leading open-source AI coding agent with over 7.5 million monthly active developers, started by integrating with DigitalOcean Droplet to simplify agent development. Now, OpenCode is also using DigitalOcean's Inference Engine and AI-Native Cloud for its inference needs, including access to leading open-weight models.

OpenCode 是領先的開源 AI 程式碼代理，月活躍開發者超過 750 萬；它起初透過整合 DigitalOcean Droplet 來簡化代理開發。如今，OpenCode 也在其推理需求上使用 DigitalOcean 的推理引擎與 AI 原生雲，包括存取領先的開放權重模型。

In addition to OpenCode, we have also integrated DigitalOcean AI-Native Cloud into other leading coding and agent building environments like OpenCode, Codex, Hermes, and Grok Build. When developers build there, our Inference Engine is already in their workflows just one API call away. That opens the inference front door at ecosystem scale.

除了 OpenCode 之外，我們也已將 DigitalOcean AI 原生雲整合到其他領先的程式碼與代理建構環境中，例如 OpenCode、Codex、Hermes 與 Grok Build。當開發者在那些環境中建置時，我們的推理引擎已在其工作流程中，只需一次 API 呼叫即可使用。這在生態系規模上打開了推理這扇前門。

Daytona, an advanced AI sandbox company, builds secure elastic sandboxes for AI-generated code and autonomous agents on DigitalOcean. This is a textbook full-stack agentic workload running on our platform. Its workloads require GPU acceleration, isolated compute environments, fast deployment, storage, networking, and orchestration, all working together.

Daytona 是一家先進的 AI 沙箱公司，在 DigitalOcean 上為 AI 生成程式碼與自主代理建置安全、可彈性伸縮的沙箱。這是典型的全棧代理式工作負載在我們平台上運行的案例。其工作負載需要 GPU 加速、隔離的運算環境、快速部署、儲存、網路與編排，且必須協同運作。

Vercel, a scaled agentic infrastructure platform, is integrating DigitalOcean's Inference Engine into their AI gateway to provide their customers with dedicated AI platform capabilities.

Vercel 作為一個已具規模的代理式（agentic）基礎設施平台，正將 DigitalOcean 的 Inference Engine 整合進其 AI 閘道，以便為其客戶提供專屬的 AI 平台能力。

Another great example of this is OpenRouter, which is both an efficient customer acquisition channel and a platform through which we can dial up or down on-demand traffic to test, learn, and scale as we launch new models. We now serve more than 20 billion tokens per day on OpenRouter, up more than 330% over the last 60 days, with much of that traffic being generated from agents.

另一個很好的例子是 OpenRouter，它既是高效率的客戶獲取通路，也是我們可用來按需調高或調低流量、進行測試、學習並在推出新模型時擴展的平台。我們目前在 OpenRouter 上每天服務超過 200 億個 token，較過去 60 天成長超過 330%，其中相當大一部分流量由代理（agents）所產生。

These customers are examples of AI builders spinning our flywheel, and the flywheel does not stop at the first entry point. Every turn adds products to the stack we own, an integrated platform running on our own infrastructure spanning 20 global data centers. Owning the stack lowers our cost to serve. And that lower cost structure combined with the emergence of high-quality, low-cost open-weight models gives us better unit economics to serve our customers, which in turn, enables us to win more customers.

這些客戶是推動我們飛輪效應的 AI 建構者範例，而飛輪並不會在第一個切入點就停止。每一次轉動都會把更多產品加入我們所擁有的技術堆疊中——一個運行在我們自有基礎設施上的整合式平台，橫跨全球 20 座資料中心。擁有整個技術堆疊可降低我們的服務成本。而較低的成本結構，再加上高品質、低成本的開放權重模型（open-weight models）的出現，讓我們能以更佳的單位經濟效益服務客戶，進而使我們贏得更多客戶。

For AI natives, that advantage enables precisely what they value, better cost and performance on every workload, faster time to market, tight integration across inference, agents, data and compute, and freedom from having to stitch together a myriad of services across vendors. This is clearly resonating with our customers as roughly 70% of AI customers having $100,000 or more ARR in Q2 have attached a Core Cloud product to their AI workloads, showing early evidence of this flywheel in action.

對 AI 原生（AI natives）而言，這項優勢正好帶來他們所重視的價值：在每一種工作負載上更好的成本與效能、更快的上市時間、推論（inference）、代理（agents）、資料與運算之間的緊密整合，以及不必在不同供應商之間拼接大量服務的自由。這顯然正在引起客戶共鳴：在第二季 ARR 達到 10 萬美元或以上的 AI 客戶中，約有 70% 已將 Core Cloud 產品附加到其 AI 工作負載上，顯示飛輪效應開始運轉的早期證據。

This value proposition is very differentiated in the market. Hyperscalers optimize for frontier labs and large enterprises. Neoclouds have built strong GPU rental businesses for model training and are adding software mostly through acquisitions.

這項價值主張在市場上具有高度差異化。超大規模雲服務商（hyperscalers）主要為前沿實驗室與大型企業最佳化。新型雲（neoclouds）建立了強大的 GPU 租賃業務以支援模型訓練，並多半透過併購來增加軟體能力。

Assembling capabilities is not the same as building an integrated platform and customers often bear that complexity. Inference providers serve tokens well but rent their GPUs with margins stacked on margins and leaving customers to stitch together inference, agents, data, and compute.

拼湊能力不等同於打造整合式平台，而客戶往往必須承擔其中的複雜度。推論供應商能很好地提供 token 服務，但其 GPU 租賃的利潤層層疊加，並讓客戶自行把推論、代理、資料與運算拼接起來。

Our approach is different. One, purpose-built AI-native cloud tightly integrated from the ground up, enabling AI-native to start and scale their agentic applications on our cloud. We will dive deeper into our AI-native cloud at our AI Builder Summit, October 13 in San Francisco, and we hope to see you all there, which brings me to our fourth and final takeaway that we remain disciplined in our execution and continue to focus on durable growth.

我們的方法不同。第一，從零開始打造、為 AI 原生而生的雲端，並進行緊密整合，使 AI 原生公司能在我們的雲上啟動並擴展其代理式應用。我們將在 10 月 13 日於舊金山舉辦的 AI Builder Summit 更深入介紹我們的 AI 原生雲，也希望到時能與各位相見；這也帶出我們第四個也是最後一個重點：我們在執行上保持紀律，並持續聚焦於可持續的成長。

This discipline is evident not only in our financial performance, but also in our operational execution and in our responsible and profitable approach to growth. Driving growth approaching 30% in Q2 on a path to 50% plus next year requires focused execution.

這份紀律不僅體現在我們的財務表現上，也體現在營運執行，以及我們對成長採取負責任且可獲利的方法。要在第二季推動接近 30% 的成長，並走在明年達到 50% 以上的路徑上，需要聚焦且到位的執行。

We remain on time and even a little bit ahead of our previously communicated schedule on all three of our new 2026 data centers. We launched our Richmond data center in Q1, our Kansas City data center in Q2, both ahead of target, and we remain on track for the second-half launch of our Memphis data center.

我們在三座新的 2026 年資料中心上，仍按時推進，甚至比先前對外溝通的時程略為超前。我們已於第一季啟用里奇蒙（Richmond）資料中心、第二季啟用堪薩斯市（Kansas City）資料中心，兩者皆早於目標；同時我們也仍按計畫在下半年啟用孟菲斯（Memphis）資料中心。

Beyond just hitting our launch date, we've been able to allocate the majority of the capacity to specific customers or to our highly in-demand token feed before we launch these data centers.

除了如期達成啟用日期之外，在這些資料中心啟用前，我們也已能將大部分產能分配給特定客戶，或分配給需求極高的 token feed。

We also secured approximately 20 megawatts of additional capacity this quarter, which is targeted to come online over the last part of 2027 and into 2028. This brings total committed capacity to approximately 155 megawatts. The majority of which will be online by the end of 2027. We continue to actively produce additional capacity to drive further growth and meet customer demand.

本季我們也額外取得約 20 兆瓦的新增產能，預計將在 2027 年後段至 2028 年間上線。這使得我們的總承諾產能約達 155 兆瓦。其中大部分將在 2027 年底前上線。我們仍持續積極擴增更多產能，以推動進一步成長並滿足客戶需求。

Our discipline is also evident in the steps we took to strengthen our balance sheet. In July, we reduced our leverage with minimal dilution and use of cash by retiring approximately $472 million of our 2030 convertible notes, creating additional capacity to cost effectively finance our future investments.

我們的紀律也體現在強化資產負債表的措施上。7 月，我們以最小的稀釋與現金使用，透過回購並註銷約 4.72 億美元的 2030 年可轉換公司債來降低槓桿，為未來投資創造更多可用額度，以更具成本效益地進行融資。

It is worth pausing on how different our profile is from many others in the AI infrastructure market. Number one, our growth is driven by a broad set of AI-Native companies rather than by a handful of large bare metal offtake contracts with our top 25 customers representing only 20% of ARR in Q2.

值得停下來看看：我們的樣貌與 AI 基礎設施市場中的許多公司有多麼不同。第一，我們的成長由廣泛的 AI 原生公司所驅動，而非依賴少數大型裸金屬（bare metal）承購合約；我們前 25 大客戶在第二季僅占 ARR 的 20%。

Next, our largely consumption-based model gives us the flexibility to adapt to market conditions and shift capacity to where it is most valuable. This flexibility enabled us to increase list prices on numerous GPU fleets recently by approximately 30%.

其次，我們以用量為主（consumption-based）的模式，讓我們能靈活因應市場狀況，並將產能轉移到最具價值的地方。這種彈性使我們近期得以將多個 GPU 機群的牌價上調約 30%。

Third, we are profitable with 40% adjusted EBITDA margins, 24% operating income margin, and 17% last 12 months adjusted EBITDA cash flow margin.

第三，我們具備獲利能力：調整後 EBITDA 利潤率 40%、營業利益率 24%，以及過去 12 個月調整後 EBITDA 現金流利潤率 17%。

And finally, we closely match our cash outflow with our revenue by finance equipment, efficiently funding our growth.

最後，我們透過設備融資，使現金流出與營收緊密匹配，以高效率地為成長提供資金。

There are very few companies with our combination of positive adjusted operating margins and projected growth of 50% plus. This is a generational opportunity, and we will go after it responsibly, building a durable business on the foundation of our differentiated software and full-stack AI-native platform.

同時具備正向的調整後營運利潤率，以及預期 50% 以上成長的公司屈指可數。這是一個世代性的機會，我們將以負責任的方式全力把握，並以我們具差異化的軟體與全棧 AI 原生平台為基礎，打造可長可久的事業。

With this momentum continuing to build, we are again raising our 2026 outlook. For the full-year 2026, we now expect revenue growth of approximately 30% with an exit growth rate of 35% or more by Q4. That trajectory and the incremental committed capacity we've added both clearly strengthen our conviction in 50% or more revenue growth in 2027.

在這股動能持續累積之下，我們再次上調 2026 年展望。就 2026 全年而言，我們目前預期營收成長約 30%，並在第四季達到 35% 或以上的期末成長率（exit growth rate）。這樣的軌跡，以及我們新增的承諾產能，都明確強化了我們對 2027 年營收成長 50% 或以上的信心。

With that, I will turn it over to Matt.

接下來我把時間交給 Matt。

Thanks, Paddy. Good morning, everyone, and thanks for joining. As Paddy shared, Q2 was an outstanding quarter. I'm excited to take you through the results, provide further context on some of the actions we have taken, and provide some additional color on our updated outlook.

謝謝你，Paddy。各位早安，感謝大家參與。如 Paddy 所分享，第二季表現非常出色。我很期待帶各位回顧本季結果，補充我們採取的一些行動背景，並就更新後的展望提供更多說明。

Q2 revenue was $281 million, up 29% year over year, above the high end of guidance. The outperformance was broad-based by growth from our highest spending customers and our expanding AI customer base.

第二季營收為 2.81 億美元，年增 29%，高於指引區間上緣。這項優於預期的表現來自多面向的成長，包括我們最高消費客戶的成長，以及持續擴大的 AI 客戶基礎。

Our highest spending customers didn't just keep growing, they accelerated. ARR from our 100,000-plus customers grew 98%, up from 37% in the second quarter of last year. Our $500,000 grew from 64%. And our $1 million plus customer ARR grew 214%, up from 92%. Each of these highest spending customer cohorts is now growing more than twice as fast as it was a year ago.

我們最高消費的客戶不僅持續成長，還在加速。來自 ARR 超過 10 萬美元客戶的 ARR 成長 98%，高於去年第二季的 37%。ARR 超過 50 萬美元客戶的成長率為 64%。而 ARR 超過 100 萬美元客戶的 ARR 成長 214%，高於去年的 92%。這些最高消費客群如今的成長速度，皆較一年前快了兩倍以上。

We continue to gain meaningful traction with some most sophisticated AI natives. AI customer ARR reached $234 million, growing 212%, and technically, 85% of that ARR is non-bare metal. This traction is evident in the material commitments we secured during the quarter, which collectively increased remaining performance obligations to $894 million, up more than 12 times year over year with a 3.7-year average life.

我們持續在一些最成熟的 AI 原生公司中取得顯著進展。AI 客戶 ARR 達到 2.34 億美元，成長 212%；且從技術上來看，其中 85% 的 ARR 來自非裸金屬（non-bare metal）。這樣的進展也反映在我們本季取得的重大承諾上，合計使剩餘履約義務（remaining performance obligations）提升至 8.94 億美元，年增超過 12 倍，平均存續期為 3.7 年。

While changes to RPO will be lumpy, these commitments add visibility, and we expect to secure more of them in the future. They have not, however, come at the expense of our broad customer diversification. As our top 25 customers represented only 20% of ARR in Q2, and this will only modestly increase as these deals ramp up.

雖然 RPO 的變動會較為不均勻，但這些承諾提高了可見度，我們預期未來將爭取到更多此類承諾。然而，這並未以犧牲我們廣泛的客戶多元化為代價。由於我們前 25 大客戶在第二季僅占 ARR 的 20%，且隨著這些交易逐步放量，該占比也只會小幅上升。

One quick note on key financial metrics. Our business has changed dramatically over the last two years, with growth increasingly driven by our top customers and by emerging AI customers. Against that backdrop, net dollar retention, a strong indicator in the slow steady growth SaaS world, has become a less useful measure of our performance.

關於關鍵財務指標補充一點。過去兩年我們的業務已發生巨大變化，成長愈來愈由我們的頭部客戶與新興 AI 客戶所驅動。在此背景下，淨美元留存率（NDR）——在緩慢且穩健成長的 SaaS 世界中是一項強力指標——已成為較不具代表性的績效衡量方式。

While our 102% NDR in Q2 is a three-year high, we'll no longer highlight it as a key financial metric. Growth today is shaped far more by our highest spending and AI customers than by the NDR trend across our 680,000-plus customer base.

儘管我們第二季 102% 的 NDR 創下三年新高，我們將不再把它作為關鍵財務指標來強調。當前的成長更大程度上取決於我們最高消費客戶與 AI 客戶，而非橫跨我們超過 68 萬名客戶基礎的 NDR 走勢。

Profitability remains strong in Q2. Adjusted EBITDA was $114 million, and adjusted EBITDA margin of 40%. GAAP operating income was $29 million, a 10% margin and adjusted operating income was $67 million, a 24% margin. Non-GAAP diluted net income per share was $0.45.

第二季獲利能力依然強勁。調整後 EBITDA 為 1.14 億美元，調整後 EBITDA 利潤率為 40%。GAAP 營業利益為 2,900 萬美元、利潤率 10%；調整後營業利益為 6,700 萬美元、利潤率 24%。非 GAAP 稀釋後每股淨利為 0.45 美元。

Adjusted free cash flow in the quarter was $61 million. Trailing 12-month adjusted free cash flow was $175 million or 17% of revenue.

本季調整後自由現金流為 6,100 萬美元。過去 12 個月的調整後自由現金流為 1.75 億美元，約占營收的 17%。

As Paddy highlighted, we proactively strengthened our balance sheet, reducing our leverage with effectively no dilution and minimal use of cash. In July, we equitized $472 million of our 0% 2030 convertible senior notes. The underlying shares were both already reflected in our diluted share count and were highly likely to be converted given where our stock is trading. And yet, the full principal value was also reflected in our net debt, reducing our leverage capacity.

如 Paddy 所強調，我們主動強化資產負債表，在幾乎沒有稀釋且僅動用極少現金的情況下降低槓桿。7 月，我們將 4.72 億美元、2030 年到期的 0% 可轉換優先票據進行股權化。相關的基礎股份原本就已反映在我們的稀釋後股數中，且考量目前股價水準，極有可能被轉換。然而，其全部本金金額同時也被計入我們的淨負債，因而降低了我們的槓桿承載能力。

Through this proactive transaction, we retired more than half of our convertible debt four years ahead of maturity, reduced net leverage, did so with effectively no dilution and minimal use of cash, freeing up capacity to invest in further growth.

透過這項主動交易，我們在到期前四年就提前清償了超過一半的可轉債，降低淨槓桿，且幾乎沒有稀釋並僅動用極少現金，釋放出可用於進一步投資成長的空間。

Turning to guidance, we are raising our 2026 revenue outlook. For the third quarter of 2026, we expect revenue of $304 million to $307 million, representing 32% to 34% year-over-year growth.

接著談指引，我們上調 2026 年營收展望。對於 2026 年第三季，我們預期營收為 3.04 億至 3.07 億美元，代表年增 32% 至 34%。

We project adjusted EBITDA margins of 38% to 39%, and non-GAAP diluted net income per share of $0.28 to $0.30 on approximately $126.5 million weighted average fully diluted shares.

我們預估調整後 EBITDA 利潤率為 38% 至 39%，非 GAAP 稀釋後每股淨利為 0.28 至 0.30 美元，係以約 1.265 億股加權平均完全稀釋股數計算。

For the full-year 2026, we expect revenue of $1.17 billion to $1.18 billion, representing approximately 30.5% year-over-year growth, with an exit growth rate of 35% or more in Q4.

對於 2026 全年，我們預期營收為 11.7 億至 11.8 億美元，約代表年增 30.5%，且在第四季的期末（exit）成長率將達到 35% 或更高。

We expect adjusted EBITDA margins of approximately 39%, non-GAAP diluted EPS of $1.35 to $1.40, and adjusted free cash flow margin of 11% to 13%, an increase to our prior guide.

我們預期調整後 EBITDA 利潤率約為 39%，非 GAAP 稀釋後 EPS 為 1.35 至 1.40 美元，調整後自由現金流利潤率為 11% 至 13%，較先前指引上調。

While it's premature to speak to 2027 guidance, the positive momentum we're generating, and the higher projected exit growth rate give us even more confidence in our estimated 50% plus growth for the full-year 2027. Before I turn it back to Paddy, let me put our progress in perspective.

雖然現在談 2027 年指引仍為時過早，但我們正在形成的正向動能，以及更高的預估期末成長率，讓我們對 2027 全年預估 50% 以上的成長更具信心。在我把話題交回 Paddy 之前，先讓我把我們的進展放在更大的脈絡中看。

Revenue grew 14% year-over-year in the second quarter of last year. In a single year, we have doubled our growth rate to 29%. We are now projecting to nearly double it again on an annual basis next year. And we are delivering this growth with attractive margins, appropriate leverage, a strong and flexible balance sheet, and disciplined execution.

去年第二季營收年增 14%。僅一年之內，我們已將成長率加倍至 29%。我們目前預期明年在年度基礎上幾乎再加倍。而我們是在具吸引力的利潤率、適度的槓桿、強健且具彈性的資產負債表，以及嚴謹的執行下實現這項成長。

With that, I'll hand it back to Patty.

接下來我把時間交回 Patty。

Thank you, Matt. Before we move to Q&A, let me recap what we shared today. First, growth continues to accelerate. Approximately 29% revenue growth, more double the growth from a year ago, record $93 million in incremental ARR, AI customers and $1 million plus customers, each growing ARR more than 200%. We delivered this growth with strong profitability and free cash flow.

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，成長持續加速。營收約年增 29%，較一年前的成長率增加一倍以上；新增 ARR 達 9,300 萬美元創新高；AI 客戶與年 ARR 100 萬美元以上客戶的 ARR 皆成長超過 200%。我們在強勁的獲利能力與自由現金流下交出這樣的成長。

Second, our inference services are getting tremendous traction. Inference services grew nearly 800% year-over-year. Token usage on our Inference Engine is compounding monthly and open-weight models have climbed from 15% of token traffic to close to 75%. Open weight model adoption leverages our strength, turning open models into fast, reliable, economical production tokens.

第二，我們的推理（inference）服務獲得極大的市場牽引力。推理服務年增近 800%。我們 Inference Engine 的 Token 使用量按月複利成長，而開放權重（open-weight）模型占 Token 流量的比重已從 15% 攀升至接近 75%。開放權重模型的採用發揮了我們的優勢，將開放模型轉化為快速、可靠、具成本效益的生產級 Token。

Third, adoption of our Inference Engine is creating a growth flywheel. Inference is the entry point and every layer a customer adopts improves their token price performance and pulls them deeper into the platform. Leading AI builders like OpenCode, Vercel, and Daytona began spinning that flywheel, and because the entire cycle runs on infrastructure we own, it drives customers to higher margin and stickier products, increasing our potential ARR per megawatt.

第三，Inference Engine 的採用正在形成成長飛輪。推理是切入點，而客戶每多採用一層，都能改善其 Token 價格效能，並將其更深地帶入平台。像 OpenCode、Vercel 與 Daytona 等領先的 AI 建置者已開始帶動這個飛輪；且由於整個循環運行在我們自有的基礎設施上，它會推動客戶使用更高毛利、黏著度更高的產品，提升我們每兆瓦的潛在 ARR。

Finally, we remain disciplined in our execution, deploying planned capacity on or ahead of schedule, securing 20 megawatts of incremental capacity, delivering strong margins and strengthening the balance sheet. Our momentum and solid execution enables us to raise our 2026 outlook and positions us for strong performance in 2027. Before I end my comments, let me connect these four key takeaways because the connection is the real story.

最後，我們在執行上仍保持紀律，按計畫如期或提前部署產能，新增取得 20 兆瓦的增量產能，交出強勁利潤率並強化資產負債表。我們的動能與扎實執行使我們得以上調 2026 年展望，並為 2027 年的強勁表現奠定定位。在結束我的發言前，我想把這四個關鍵重點串起來，因為它們之間的連結才是真正的故事。

Software makes megawatts more valuable. Our software attracts high-quality AI native customers with insatiable demand. Those customers adopt more of the platform than just capacity and that broader adoption increases what each megawatt earns, driving durable growth, higher margins, and cash flow in future years.

軟體讓兆瓦更有價值。我們的軟體吸引了需求永不滿足的高品質 AI 原生客戶。這些客戶採用的不僅是產能，而是更廣泛的平台；而更廣泛的採用提高了每兆瓦的收益，推動未來年度更持久的成長、更高的利潤率與現金流。

Strategy is becoming results and results are building momentum. Platform shifts like this come along once in a generation. Quarters like this one show that we are becoming both an enabler and a beneficiary of that shift.

策略正在轉化為成果，而成果正在累積動能。像這樣的平台轉移是一代才會出現一次。像本季這樣的表現顯示，我們正同時成為這場轉移的促成者與受益者。

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

接下來我們開放提問。

(Operator Instructions) Gabriela Borges, Goldman Sachs.

(接線員指示) Gabriela Borges，高盛。

Hi, good morning. Thank you. Thank you for all the detail. I want to ask a little bit about DigitalOcean's ability to scale. Paddy, to your point, the hyperscalers are optimized for large enterprises. DigitalOcean has historically been optimized for smaller customers, but you're actually landing these larger flagship customers that have larger commitments, have larger backlog deals, and require perhaps a different type of sales process, a different type of operational process.

嗨，早安。謝謝。感謝你們提供這麼多細節。我想請教一下 DigitalOcean 的擴張能力。Paddy，正如你所說，超大規模雲服務商（hyperscalers）是為大型企業最佳化的。DigitalOcean 過去則是為較小型客戶最佳化，但你們實際上正在拿下這些更大型的旗艦客戶，他們有更大的承諾額度、更大的積壓訂單（backlog）型交易，並且可能需要不同類型的銷售流程與不同類型的營運流程。

So twofold questions for you. How are you meeting those demands of the larger scale to customers? And then, I think just maybe partly from that, how are you thinking as you scale these larger chunks of megawatts, talk to us about some of the operational puts and takes to being able to get those megawatts online at the right time and up and running? Thank you so much.

所以我有兩個問題想請教您。您如何滿足客戶對更大規模的需求？另外，我想也許部分承接前一題，當您把這些更大規模、以兆瓦計的容量逐步擴張時，請談談在營運上為了讓這些兆瓦能在正確時間上線並順利運轉，需要做哪些取捨與權衡？非常感謝。

Thank you, Gabriela. Good morning. It's a great question. We feel very confident in our ability to scale given our track record, like we've been doing this at a global scale, running a cloud business, managing global network of data centers or the last dozen plus years with hyperscaler SLAs and serving over a half a million paying customers along the way. So we feel very confident in our ability, and we are demonstrating that by bringing capacity on time and also before schedule.

謝謝你，Gabriela。早安。這是個很好的問題。基於我們的過往紀錄，我們對於擴大規模的能力非常有信心；我們已在全球規模上做這件事很久了，經營雲端業務、管理全球資料中心網路，在過去十多年以來都以超大規模客戶的 SLA 來運作，並一路服務超過五十萬名付費客戶。因此我們對自身能力非常有信心，而我們也透過準時、甚至提前導入產能來證明這一點。

I always work backwards from the customers we are targeting and what they are coming to us for. Right now, they're coming to us not just for capacity, as I mentioned. So they are not expecting some bespoke hardware or network configuration. They're predominantly coming to us because of the richness of our AI native cloud. So from a platform innovation perspective, our pace of innovation, as I described, is just tackling with over a major release every business day and sometimes multiple. And our engineering talent is absolutely world class, and we aggressively keep adding to it.

我一向會從我們鎖定的客戶以及他們為何選擇我們的原因倒推回來思考。目前他們來找我們不只是為了產能，如我提到的。因此他們並不期待某種客製化的硬體或網路配置。他們主要是因為我們 AI 原生雲的豐富性而選擇我們。所以從平台創新的角度來看，我剛才描述的創新速度，幾乎是每個工作日都有一次重大版本釋出，有時甚至一天多次。而我們的工程人才絕對是世界級的，我們也持續積極擴編。

To augment that engineering talent, we have also stood up a forward deployed engineering organization to work with some of our larger, more sophisticated customers with demanding workloads to ensure that they're getting the right price performance, throughput accuracy combination. But most of our core software doesn't have to be really customized to meet their needs.

為了補強這些工程人才，我們也成立了一個前線部署（forward deployed）的工程組織，與一些規模更大、成熟度更高且工作負載要求嚴苛的客戶合作，確保他們能取得正確的性價比、吞吐量與準確度的組合。但我們大多數核心軟體其實不需要高度客製化就能滿足他們的需求。

From a go-to-market point of view, we just added Kevin Van Gundy as our CRO who comes with tremendous experience working digital and now AI-native ecosystem. We added Leo as our CMO who brings a wealth of marketing experience from Google Cloud and Oracle Cloud. And they're in the process of scaling up our go-to-market muscle to help us address the next phase of our hypergrowth.

從 go-to-market 的角度來看，我們剛延攬 Kevin Van Gundy 擔任 CRO，他在數位領域以及如今的 AI 原生生態系方面都有非常豐富的經驗。我們也加入 Leo 擔任 CMO，他帶來來自 Google Cloud 與 Oracle Cloud 的大量行銷經驗。他們正在擴大我們的 go-to-market 能量，協助我們因應下一階段的高速成長。

But this is something we feel very confident we've been doing this for a number of years. And I'll let Matt answer the infrastructure question. But I think from a talent density perspective, both on core engineering and go-to-market, I feel really good. And we have demonstrated in the recent past and that's why we keep talking about our 500,000 and million-dollar customers and how that flywheel is spinning and has been doing it for about eight quarters in a row now.

但這件事我們做了很多年，我們非常有信心。我會讓 Matt 回答基礎設施的問題。不過我認為從人才密度的角度，不論是核心工程或 go-to-market，我都感覺非常好。而我們近期也已經證明了這點，這也是為什麼我們一直在談我們的 50 萬美元與 100 萬美元客戶，以及那個飛輪如何持續轉動，且已連續大約八個季度如此。

And I would just add to that, Gabriela, that the customers that we're dealing with, while they're bigger, these aren't your traditional brick-and-mortar enterprise companies. these are very sophisticated technical customers where their founders and leaders are often deeply technical. And they very much appreciate the depth and the breadth of the engineering talent that we have and our ability to work with them as Patty said, which I think uniquely and very well positions us to be able to meet their needs.

我也想補充一下，Gabriela，我們面對的客戶雖然規模更大，但並不是傳統的實體（brick-and-mortar）企業公司；他們是非常成熟的技術型客戶，他們的創辦人與領導者往往都具備深厚的技術背景。他們非常欣賞我們工程人才的深度與廣度，以及如 Patty 所說我們與他們協作的能力；我認為這讓我們在滿足他們需求方面具備獨特且非常好的定位。

From an infrastructure standpoint, as you've seen, we're working with some of the top data center operators in the industry that are very familiar with and experience bringing up capacity. We've got a deep and talented team that works alongside of them. We have great partnerships with the leading chip manufacturers. We've got a great supply chain with a diversified set of OEMs that are all global.

從基礎設施的角度來看，如你所見，我們正與業界一些頂尖的資料中心營運商合作，他們非常熟悉且具備導入產能的經驗。我們也有一支深具實力的團隊與他們並肩合作。我們與領先的晶片製造商建立了很好的夥伴關係。我們也擁有很強的供應鏈，合作的 OEM 多元且皆為全球性廠商。

And we've been able to manage the implementation schedules and turn up capacity despite some of the challenges that everyone faces in the industry. We've been able to do that on time and meet requirements that these large customers have put in front of us and we're encouraged by the partnership we have with those customers. We're doing a lot of joint development already.

即使面臨整個產業都在承受的一些挑戰，我們仍能管理導入時程並啟用產能。我們能準時完成，並滿足這些大型客戶提出的需求；我們也對與這些客戶的合作關係感到鼓舞。我們已經在進行大量的共同開發。

So I think it's more than just turning up infrastructure, it's having engineers working side by side with these very sophisticated and talented customers and we're bringing really strong talent to bear. And we're very encouraged by the progress we're making.

所以我認為這不只是把基礎設施啟用而已，更重要的是讓工程師與這些非常成熟且有才華的客戶並肩工作，而我們投入了非常強的專業人才。我們對目前取得的進展感到非常振奮。

Jason Ader, William Blair.

Jason Ader，William Blair。

Two questions. First, just if you could provide any specifics on the impact of pricing on the revenue growth in Q2 and then for the updated outlook. That's the first question.

兩個問題。第一，能否提供一些具體資訊，說明定價對第二季營收成長的影響，以及對更新後展望的影響？這是第一個問題。

The second question on equipment financing. Matt, for 2026, where do you expect net leverage to be at year-end, and could you provide any specific guidance on the free cash flow for the year, including all the leases?

第二個問題是關於設備融資。Matt，就 2026 年而言，你預期年底的淨槓桿會是多少？另外，能否就全年自由現金流提供更具體的指引，包含所有租賃在內？

Good questions. On the pricing, as you saw, we increased our list price on a number of GPU generations by about 30% a while ago. A lot of that pricing, we had already been, I'd say, upgrading as we given the short kind of contract duration for our customers.

好問題。關於定價，如你所見，我們在一段時間前將多個 GPU 世代的牌價上調了約 30%。其中很大一部分的定價，我會說，我們先前就已在調升，因為我們客戶的合約期限相對較短。

We had already been upgrading their prices and increasing their prices upon renewal or in some case pulling capacity back from a customer that we thought we have a better use for either the capacity in our token factory or with a different customer that was willing to pay a higher price. So all of that pricing is included in the '26 guide and it's part of how we went from saying we're going to exit the year around 30% to now exiting it at around 35%, and it's a good setup for us in 2027, as well.

我們已經在續約時調整並提高他們的價格；在某些情況下，我們也會把產能從某位客戶那裡收回，因為我們認為該產能有更好的用途——不論是用在我們的 token factory，或是提供給願意支付更高價格的其他客戶。因此，所有這些定價因素都已納入 2026 年指引之中，也正是我們把「年底約 30%」的說法上調到「年底約 35%」的一部分原因；同時這也為我們在 2027 年奠定了良好基礎。

The equipment financing side, we continue to get access to very attractive rates and have ample capacity to fund the growth over the committed capacity that we've taken down. If you look at the pro forma net leverage, just take the Q2 balance sheet and just simply the LTM EBITD and simply subtract the amount of debt we retired in the equitization puts us at 0.7 times net leverage. We're in very good position to stay well below that 4 times net leverage that we had articulated, and in fact, it should be well below that.

在設備融資方面，我們持續能取得非常具吸引力的利率，且有充足的額度來支應我們已承諾並已承接（taken down）的產能所帶來的成長資金需求。若看備考（pro forma）淨槓桿，只要以第二季資產負債表為基礎，取 LTM EBITDA，並簡單扣除我們在股權化（equitization）中已償還的債務金額，淨槓桿約為 0.7 倍。我們非常有把握能維持遠低於我們先前所說的 4 倍淨槓桿上限，事實上應該會明顯低於該水準。

And that's part of why we did that. We're now sitting with an incredibly strong and flexible balance sheet. We have the ability to take on incremental equipment financing and equipment-related borrowing capacity and fuel our growth. So it was a great step for us, and our leverage is going to be very comfortably below that guideline that we provided.

這也是我們之所以那樣做的一部分原因。我們現在擁有極其強健且具彈性的資產負債表。我們有能力承擔額外的設備融資與設備相關借款額度，來推動成長。因此這對我們而言是很棒的一步，而我們的槓桿將會非常舒適地低於我們提供的那個指引水準。

And just on the free cash flow?

那自由現金流呢？

Free cash flow, sorry. Yeah, no, it's great, that's a question. Yeah, so free cash flow, as we said, would be. 11% to 13% for the year. That's an adjusted free cash flow basis, which is higher than what we had guided previously. And if you take all of the principal payments and everything, we'll still generate cash in 2026.

自由現金流，抱歉。是的，這是個好問題。所以自由現金流，如我們所說，全年會是 11% 到 13%。這是以調整後自由現金流口徑計算，較我們先前的指引更高。而如果把所有本金償付等都納入考量，我們在 2026 年仍將產生現金。

So we expect it to be free cash flow positive on any metric that you use. Whether it's adjusted free cash flow or take complete cash generation and take out the principal payments, we will continue to generate cash in '26.

因此，我們預期無論你採用哪一種衡量指標，都會是自由現金流為正。不論是調整後自由現金流，或是以完整現金產生額扣除本金償付，我們在 2026 年仍將持續產生現金。

Mark Zhang, Citi.

Mark Zhang，花旗。

Hey, good morning, team. Thanks for taking the question. So wanted to actually dig in a little more into the nine-figure deals that you guys were able to assign this quarter. Number one, wanted to get a sense of I guess like the inferencing and the Core Cloud project that these logos are committed for and what the adoptions of the other aspects of the five-layer stack and monetization roadmap looks like going forward.

嗨，早安，各位團隊。謝謝讓我提問。我想再更深入了解一下你們本季能夠簽下的九位數交易。第一，想了解這些客戶（logo）承諾的推論（inferencing）與 Core Cloud 專案大概是什麼樣子，以及未來其他五層堆疊各層的採用情況與變現路線圖會如何發展。

Because I think the go-to-market philosophy here is really looking for large deals that can make good sense, that can continue to expand going forward, so just want to get a sense of the opportunity from here as we go forward with the AI stack.

因為我認為你們這裡的 go-to-market 理念，確實是在尋找合理的大型交易，並且能在未來持續擴張，所以想了解隨著 AI 堆疊往前推進，從現在起的機會有多大。

Yes, thank you, Mark. So in terms of the larger deals and any deal that we are talking about these days, I think we had a couple of different stats that I used in the prepared remarks. Over 70% of AI customers that we added this year at any significant scale are already using some aspects of the Core Cloud.

好的，謝謝你，Mark。就大型交易以及我們現在談到的任何交易而言，我想我在事先準備的發言中提到過幾個不同的統計數據。今年我們新增、且達到任何顯著規模的 AI 客戶中，超過 70% 已經在使用 Core Cloud 的某些面向。

See, some of our AI-Native Cloud layers are still new, and that's why we have another version of our AI builders conference scheduled on October 13 to talk a little bit more about more specifically the manage agents' layer of our platform. So if you take a step back and think about these types of workloads landing in our platform, they typically land on one of the three front doors that I talked about, right?

你看，我們的一些 AI-Native Cloud 分層仍然很新，這也是為什麼我們安排在 10 月 13 日再辦一場 AI builders 大會，會更具體談談我們平台中的「受管代理（managed agents）」層。所以如果你退一步來看、思考這類工作負載落在我們平台上的方式，它們通常會從我提到的三個「前門」之一進來，對吧？

And the front door is really important because that's the dominant use case for which any of these sophisticated workloads are coming to us for. And immediately, they are attaching some part of our other layers of the cloud, whether it is databases or storage or orchestration. In many cases, it's a combination of all of the above and gives us more confidence that they're coming to us not just for tokens, not just for capacity, but they're coming to us appreciating the value of the full platform. Because these workloads, they are not proof of concept, they are building agentic applications from the ground up.

而「前門」非常重要，因為那就是這些複雜工作負載來找我們時的主導使用情境。而且它們會立刻掛接我們雲端其他層的一部分，不管是資料庫、儲存，或是編排（orchestration）。很多情況下，是以上全部的組合，這讓我們更有信心：他們來找我們不只是為了 token、不只是為了容量，而是因為他們認同整個平台的價值。因為這些工作負載不是概念驗證（PoC），而是從零開始打造代理式（agentic）應用。

So by the nature of these agentic applications, they need far more than just GPUs or tokens. They need a place where they can do some post training. They need a place where they can store memory and context, they need a place where they can run agents and secure sandboxes. They need a way to orchestrate these agents.

因此，就這些代理式應用的本質而言，它們需要的遠不只是 GPU 或 token。它們需要一個可以做後訓練（post training）的地方。它們需要一個可以儲存記憶與上下文（context）的地方，需要一個可以執行代理與安全沙箱的地方。它們需要一種方式來編排這些代理。

So we feel increasingly confident and that's why I spend so much time talking about the flywheel of the more we can get these AI-native workloads to consume more aspects of our platform, we feel really good about the durability of the revenue, durability of these workloads scaling up on our platform. And the early results are really encouraging given the attack that we are seeing on the platform.

所以我們愈來愈有信心，這也是為什麼我花很多時間談到這個飛輪效應：我們愈能讓這些 AI-native 工作負載消耗我們平台更多面向，我們就愈看好營收的持久性，以及這些工作負載在我們平台上擴張的持久性。而且從我們看到的平台攻勢（attack）來看，早期成果非常令人鼓舞。

Got it. Thanks for that, Paddy. That's very helpful. And then maybe just a quick follow-up. You also mentioned that with the new CRO, Kevin, coming in, you guys are certainly in the process of scaling up the go-to-market muscles. Can you just maybe give a sense of what the early, I guess, early preview of what Kevin's plans are for the go-to-market organization? Should we expect more investments into sales and marketing and go-to-market for the enterprise at the enterprise level going forward from here?

了解。謝謝你，Paddy。這非常有幫助。接著可能一個簡短追問。你也提到新任 CRO Kevin 加入後，你們確實正在擴充 go-to-market 的能力。你能否先分享一下 Kevin 對 go-to-market 組織的初步規劃、算是早期預告？往後在企業端（enterprise level），我們是否應該預期你們會加大對銷售與行銷、以及企業 go-to-market 的投資？

So the primary focus right now is to land very high-quality AI-native workloads, right, like the ones that we discussed on the call. And these are top-tier AI-native companies. And as I described, just in the last 90 days for our Inference Engine, we've added over 6,000 customers. That is just incredible. I mean, think about it, right, 6,000 customers in 60 to 90 days. And a lot of that is still standing on the shoulders of our incredible world-class product-led growth motion.

目前的主要重點是先落地非常高品質的 AI-native 工作負載，對吧，就像我們在電話會議中討論的那些。而這些都是頂尖的 AI-native 公司。如我所述，僅在過去 90 天，我們的 Inference Engine 就新增了超過 6,000 位客戶。這真的非常驚人。你想想看，60 到 90 天就有 6,000 位客戶。而其中很大一部分，仍然是建立在我們卓越、世界級的產品驅動成長（product-led growth）動能之上。

And we are tapping into the ecosystem at scale, whether it is OpenRouter or OpenCode or Hermes Agents, we are getting customers from all kinds of ecosystem hooks and that will continue.

而且我們正在大規模切入生態系，不論是 OpenRouter、OpenCode 或 Hermes Agents，我們透過各式各樣的生態系掛鉤（hooks）獲得客戶，而且這會持續下去。

In terms of very specifically the human-based sales, yes, we will fortify our enterprise AI native enterprise go-to-market motion. But again, here, it is about nailing that motion with forward deployed engineering. It is nailing that motion with enterprise sales reps that know how to go and qualify these opportunities and hold their own with very technical founding teams rather than scaling it. We will scale it eventually, but right now it is all about quality of engagement and nailing that motion before we scale it.

至於非常具體的人力型銷售（human-based sales），是的，我們會強化我們面向企業的 AI-native 企業 go-to-market 動能。但同樣地，重點在於用前線部署工程（forward deployed engineering）把這套動能打磨到位。也在於用懂得如何外出、如何篩選與確認（qualify）這些機會、並能與高度技術導向的創辦團隊正面交鋒的企業業務代表，把這套動能打磨到位，而不是立刻去擴編規模。我們最終會擴編，但現在重點是互動品質，先把這套動能做對，再來擴大。

So in terms of investments, I don't see the investment scaling anytime soon. It is all about getting the right quality of engineering-oriented technical sales to enable us to attract and expand these AI-native workloads.

所以就投資而言，我不認為短期內會擴大投資規模。重點是找到具備工程導向、技術型銷售的正確人才品質，讓我們能吸引並擴張這些 AI-native 工作負載。

Wamsi Mohan, Bank of America.

Wamsi Mohan，美國銀行。

Yes, thank you. I appreciate the comment that it's still a bit premature for 2027, but if you look at your performance here, which has been really strong, RPO, the timing and on time or even earlier ramp of your data centers, your comments on token usage, higher exit rate for 2026 and put all of these together, should we not assume directionally that there is further upside to 2027 than what you thought 90 days ago? Any color there would be helpful, and I have a follow-up.

是的，謝謝。我理解你說對 2027 年來說現在仍稍嫌過早，但如果看你們目前的表現非常強勁：RPO、資料中心的時程與如期甚至提前的爬坡、你們對 token 使用的評論、2026 年更高的期末退出成長率（exit rate）——把這些放在一起，我們是否不應該方向性地假設，相較於 90 天前你們的看法，2027 年還有更大的上行空間？如果能提供一些補充說明會很有幫助，我後面還有一個追問。

Yeah, I think that's the appropriate conclusion that the challenge for us is the revenue growth is so predicated on the specific timing of data center implementations and the turn on the capacity. And we're sitting here in August and there's still fair bit of moving parts in terms of the dates and times for next year, so we felt it's premature to give a specific number.

是的，我認為這樣的結論是合理的；對我們而言的挑戰在於，營收成長非常取決於資料中心建置的具體時點，以及產能啟用（turn on）的時間。而我們現在是 8 月，對於明年的日期與時間點仍有不少變動因素，所以我們覺得現在給出具體數字還太早。

But clearly, the message is, well, we're exiting the year a lot faster growth than what we had said we were. We've got tons of RPO and we're landing bigger customers. So we're very bullish, and we expect there to be additional upside. It's too early to put a number on it and so we'll wait until later this year before we provide any more specifics around that.

但很明確的訊息是：我們今年的期末退出成長速度，明顯快於我們原先所說的。我們有大量的 RPO，也正在拿下更大的客戶。所以我們非常看多，也預期會有額外的上行空間。只是現在還太早無法給出數字，因此我們會等到今年稍晚再提供更具體的說明。

But all the indications are -- were -- as you saw by the fact that virtue of the fact that we increased our guidance for '26 and the exit rate, we're better positioned than we were just 90 days ago. So a good conclusion.

但所有跡象都顯示——曾經顯示——正如你從我們上調了對 '26 的指引以及退出率這一事實所看到的，我們的處境比 90 天前更有利。所以這是個不錯的結論。

Okay, thanks, Matt. And then maybe, Patty, just on the open-weight models, you, I think, quoted that it's risen from roughly 15% to nearly 75% of token volume since launch. How much of that usage is recurring production traffic versus maybe some batch inference where you have some discounts? I think you mentioned that was not batch, but I just want to make sure of that.

好的，謝謝你，Matt。接著也許請教 Patty，關於開放權重模型（open-weight models），我記得你提到自推出以來，它在代幣（token）量中的占比已從約 15% 上升到接近 75%。其中有多少使用量是可重複的正式生產流量（recurring production traffic），相對於可能是一些批次推論（batch inference）且你們有提供折扣的情況？我想你提到那不是批次，但我只是想確認一下。

And is the cloud core services attached any different between customers using closed versus open models? Thank you so much.

另外，使用封閉模型與開放模型的客戶，在雲端核心服務（cloud core services）的附掛（attach）情況上有任何不同嗎？非常感謝。

Yeah, thanks for the question, Wamsi. It's a great question. So I'll go from the reverse order. So there isn't any major difference in what these workloads are attaching based on whether they are open-weight or closed-source models. They are attaching the same kind of Core Cloud primitives.

好的，謝謝你的問題，Wamsi。這是個很好的問題。我就倒過來回答。所以，這些工作負載會附掛哪些內容，並不會因為它們是開放權重或封閉原始碼模型而有任何重大差異。它們附掛的是同一類的核心雲端基礎元件（Core Cloud primitives）。

And one pattern that we are observing is most sophisticated production workloads are now becoming a combination of open weight and closed models. It is almost always a fusion or that's why we released this new feature called Model Synthesis. where we can actually do the heavy lifting on behalf of the customer, where we run the same query in parallel across to multiple models and synthesize the results using a synthesizer rather than the customer having to stitch together these kinds of infrastructure plumbing technologies.

而我們觀察到的一個模式是：大多數成熟的正式生產工作負載，現在正變成開放權重與封閉模型的組合。幾乎總是某種融合；這也是為什麼我們推出一個新功能叫做 Model Synthesis，我們可以代表客戶做繁重的工作：把同一個查詢並行地送到多個模型，然後用一個合成器（synthesizer）來綜合結果，而不是讓客戶自己去把這些基礎設施的「管線／黏合」技術拼接起來。

So if you -- going back to the first part of your question, are these production workloads? Absolutely, yes. I can't put an exact number on this, but you can see from the combination of the throughput, latency, accuracy that these companies are demanding. It's very easy to find out whether they are running a production workload or some internal proof of concept. And I feel a lot of the traffic we are seeing is production traffic.

所以——回到你問題的第一部分——這些是正式生產工作負載嗎？絕對是。我無法給出精確數字，但你可以從這些公司所要求的吞吐量、延遲、準確度的組合看得出來。要判斷他們是在跑正式生產工作負載，還是內部的概念驗證（proof of concept），其實很容易。我覺得我們看到的流量有很大一部分是生產流量。

And as the open -weight models pick up in traffic, cost is an important factor, but it is not the only factor because as you see some of these sophisticated mixture of experts' models like a K3 or the about to be released QEN 3.8 for example, these are $2.8 trillion, $2.4 trillion parameter models. These are very big bulky models with active parameter count like $140 billion I believe was the K3 model. So these are not cheap models to serve.

而隨著開放權重模型的流量增加，成本確實是重要因素，但不是唯一因素；因為如你所見，一些成熟的專家混合（mixture of experts）模型，例如 K3 或即將發布的 QEN 3.8，這些是 2.8 兆、2.4 兆參數的模型。這些都是非常龐大笨重的模型，活躍參數（active parameter）數量我記得 K3 大約是 1,400 億。所以這些模型提供服務並不便宜。

And when you look at the cost per intelligence task, yes, it is cheaper than the frontier closed source models, but they're not cheaper by an order of magnitude. But it is creating surely a Jevon's paradox of the more open-weight models at a reasonable cost performance that we are starting to see the adoption is just going through the roof. And as I mentioned, there is just a tremendous amount of demand that far exceeds our supply.

而當你看每個智慧任務（intelligence task）的成本時，沒錯，它比最前沿的封閉原始碼模型更便宜，但並不是便宜一個數量級。但它確實在形成一種傑文斯悖論（Jevon's paradox）：隨著開放權重模型以合理的性價比提供，我們開始看到採用率飆升。而如我提到的，需求量非常驚人，遠遠超過我們的供給。

So I feel very good about these production workloads, whether it is in coding or generative media or business workflows. These are production workloads that are scaling and they have insatiable demand on our systems.

所以我對這些正式生產工作負載感到非常樂觀，不論是在程式碼、生成式媒體，或商業工作流程。這些都是正在擴張的生產工作負載，對我們系統的需求近乎無止境。

Sanjit Singh, Morgan Stanley.

Sanjit Singh，摩根士丹利。

Yeah. Thank you for taking the question. I wanted to revisit the revenue from megawatt story at DigitalOcean because we've obviously been on a huge premium to the Neoclouds. The bare metal mix is obviously coming down. You guys have previously said that as the AI mix starts to increase, the revenue per megawatt will come down a bit from its current levels.

是的。謝謝讓我提問。我想重新回到 DigitalOcean 的「每兆瓦收入」這個議題，因為我們顯然相對於 Neoclouds 一直有很高的溢價。裸機（bare metal）占比顯然正在下降。你們之前說過，隨著 AI 占比開始提高，每兆瓦收入會從目前水準略微下降一些。

Is that still the right thinking given we have the Inference Engine, given the success with attaching to the cloud portfolio? Where do you think or what are some of the levers to drive support for revenue per megawatt over time?

考量到我們有 Inference Engine，以及成功把工作負載附掛到雲端產品組合上，這樣的想法仍然正確嗎？你認為有哪些槓桿可以在時間推移下支撐每兆瓦收入？

That's a great question. So we expect the incremental ARR that we get per megawatt to increase overtime. The decline that you described is from when we were a general-purpose cloud generating north of $22 million in ARR per megawatt without much AI. As we add incremental megawatts, we're adding more ARR per megawatt than our Neocloud peers because we offer higher layer services beyond just bare metal, because we sell to a broader customer base that isn't a single customer with a multi-year commitment that's going to drive pricing and margins down. And because we offer a Core Cloud and CPU services that we attach to those AI workloads.

這是個很好的問題。所以我們預期，隨時間推移，我們每新增一兆瓦所帶來的增量 ARR 會提高。你所描述的下降，是指當我們還是通用型雲端時，在 AI 不多的情況下，每兆瓦可產生超過 2,200 萬美元的 ARR。當我們增加增量兆瓦時，我們每兆瓦增加的 ARR 會高於我們的 Neocloud 同業，因為我們提供的不只是裸機，還有更高層的服務；因為我們面向更廣泛的客戶群，而不是單一客戶用多年期承諾來壓低價格與毛利；也因為我們提供核心雲（Core Cloud）與 CPU 服務，並把它們附掛到那些 AI 工作負載上。

So we expect that to increase as the mix of Core Cloud to and attach rate increases, but we're also installing higher capacity equipment in the same megawatts going forward. So as you see the generations of NVIDIA and AMD increasing their token throughput capabilities, it gives us more revenue potential.

因此，我們預期隨著核心雲的占比與附掛率提高，這個數字會上升；同時，我們未來也會在相同兆瓦數下部署更高容量的設備。所以當你看到 NVIDIA 與 AMD 的世代提升其代幣吞吐能力時，這會帶給我們更高的收入潛力。

The costs are certainly higher per megawatt as well, but the revenue potential is also higher. So we expect it to be a combination of more attached, higher and more mix of inference services beyond just the GPU-as-a-Service and the higher token capacity of the equipment we're putting in. All of those will contribute to increasing our ARR per megawatt on an incremental basis.

每兆瓦的成本當然也更高，但收入潛力也更高。所以我們預期這會是多種因素的組合：更多附掛、更高且更多元的推論服務組合（不僅僅是 GPU-as-a-Service），以及我們所部署設備更高的代幣容量。所有這些都將在增量基礎上推動我們每兆瓦 ARR 的提升。

Tom Blakey, Cantor.

Tom Blakey，Cantor。

Thanks for taking my question. I think it's maybe a dovetail off of Sanjit's question. Could you just like talk about maybe the pricing impact to this very strong ARR number, the net new ARR number that you reported this quarter?

謝謝讓我提問。我想這可能是延伸 Sanjit 的問題。你能否談談定價對你們本季所報告的非常強勁 ARR 數字、也就是淨新增 ARR（net new ARR）數字的影響？

And then maybe give an update to the megawatt cadence that you're looking at here in calendar '26. As you mentioned, you're a little bit ahead of plan. I'm just wondering if there was any details you can give us about [Q3 '26], and if we're still looking for 25 megawatts in the second half?

另外，也請更新一下你們在曆年 '26 的兆瓦投放節奏（megawatt cadence）。如你提到的，你們目前略微超前於計畫。我想知道你們是否能提供一些關於 [Q3 '26] 的細節，以及我們是否仍預期下半年是 25 兆瓦？

Just to answer the latter part, we've got 15 megawatts that are left. We announced that the 10-megawatt Kansas City facility was launched already. So we have 15 left in one facility and it's we had said it would come on in the second-half and it's on track and we expect that to come online as we had expected over the balance of the year. And --.

先回答後半段，我們還剩下 15 兆瓦。我們已宣布 10 兆瓦的堪薩斯市（Kansas City）設施已經啟用。所以我們在一個設施中還有 15 兆瓦；我們之前說它會在下半年上線，目前進度如期，我們預期在今年剩餘期間按原先預期逐步上線。而--。

Yes, the first question was the pricing impact on the net new ARR. Yes, it's very modest in Q2 and as Matt already answered, it is baked into our guidance for the rest of the year. It's not what you might imagine right off the bat because we raised the list prices across the board for on-demand and spot instances. But as Matt mentioned previously, as some of these contracts roll out, we have been adjusting the prices to market levels for our existing contracts.

是的，第一個問題是定價對淨新增 ARR 的影響。是的，Q2 的影響非常有限，而且如 Matt 已經回答的，這已經反映在我們對今年剩餘期間的指引中。它並不像你一開始可能想像的那樣，因為我們全面上調了隨用隨付（on-demand）與現貨（spot）執行個體的牌價（list prices）。但如 Matt 先前提到的，隨著部分合約到期／續約，我們一直在把既有合約的價格調整到市場水準。

But in terms of its impact in Q2 and the $93 million in net new ARR, it had very little impact on it. So I don't want the takeaway to be that, oh, that's how we had a blowout quarter. That's not the case at all.

但就其在第二季的影響以及9,300萬美元的淨新增ARR而言，對它的影響其實非常小。所以我不希望大家的結論是：喔，這就是我們本季大爆發的原因。完全不是那樣。

Jackson Ader, KeyBanc.

Jackson Ader，KeyBanc。

Thank you. Good morning, guys. I was just curious about the -- what exactly is baked into the out-year outlooks, like if I think about all the activity that you, guys, signed or contracted in the second quarter and the impact either here on the '26 or '27, like if I just think about forward guidance.

謝謝。各位早安。我只是好奇——在更長期的展望（out-year outlooks）裡到底納入了哪些內容？例如我在想你們第二季簽署或簽約的所有活動，以及它們對2026或2027年的影響；也就是從前瞻指引的角度來看。

Is it right to think that, okay, we're at 155 megawatts, the majority online by the end of '27 and any incremental activity that happens in next few months, like, that is all incremental to the expectations for 2027 ? Or do you guys have certainly line of sight into a bunch of activity that's coming down the line and so that is also factored into what you're expecting for the 2027 numbers?

這樣理解對嗎：好，我們目前是155兆瓦，多數會在2027年底前上線，而接下來幾個月發生的任何新增活動，都會是對2027年預期的額外增量？還是說你們其實已經能清楚看到接下來會落地的一大批活動，因此那些也已經被納入你們對2027年數字的預期之中？

That's a great question. And what you'll observe about us is we're very good, I think, at having measured and appropriately conservative outlook based on what we've already communicated in terms of capacity. And so, it's a good observation that were we to add incremental capacity and were we to add incremental deals beyond what we've articulated, that there would be upside.

這是個很好的問題。你會觀察到我們在基於已經就產能所溝通的內容上，對外給出的展望一向是審慎衡量、而且適度保守的。所以你的觀察沒錯：如果我們在已經闡述的基礎上再增加額外產能、再增加額外交易，那確實會帶來上行空間。

From a '27 impact standpoint, you're getting pretty late in the year this year to have a huge impact from a capacity standpoint on the calendar year '27 just because data centers typically have a year-ish from leased signature to when you're generating revenue. So you're getting to the point where you might impact the exit growth rate, but full year, calendar year revenue might not be as impacted. And that's part of why we're saying we're not going to provide formal guidance right now, there's just a lot of moving parts.

從對2027年的影響來看，今年已經接近年底，要在產能層面對2027曆年造成很大的影響就相當晚了，因為資料中心通常從簽署租約到開始產生營收，大約需要一年左右。所以你可能會影響到期末（exit）的成長率，但對整個曆年的全年營收影響可能沒那麼大。這也是我們說目前不會提供正式指引的原因之一，因為變動因素太多。

But what you and what Wamsi also highlighted is, clearly, we've got a ton of momentum, we've made a great amount of progress in just a quarter, and all of that upside is not reflected in the prior estimate of 50% or more growth for next year. But it's too early for us to put a precise number on it other than, hey, we're exiting the year at a much higher growth rate. We've got a very strong RPO backlog. We're very active in the market looking for incremental capacity. So we certainly believe there's upside.

但你以及Wamsi也提到的重點是：很明顯我們動能非常強，在短短一季就取得了很大的進展，而所有這些上行空間並沒有反映在先前對明年「成長50%或以上」的估計之中。但現在要我們給出精確數字還太早；我們能說的是：我們將以更高的成長率結束今年。我們有非常強勁的RPO在手訂單（backlog）。我們也在市場上非常積極地尋找額外產能。所以我們確實相信還有上行空間。

Radi Sultan, UBS.

Radi Sultan，瑞銀（UBS）。

Awesome. Thanks, guys. Thanks for squeezing me in. Just one quick one. For your customers using your AMD deployment, speak to how you see the mix between NVIDIA, the GPUs and your NG and then maybe MAP. How do they unit economic on a per megawatt basis for AMDs compared to NVIDIA GPUs? Thank you.

太棒了。謝謝各位。謝謝讓我插隊提問。我只有一個很快的問題。針對使用你們AMD部署的客戶，能否談談你們如何看待NVIDIA、GPU與你們的NG，然後可能還有MAP之間的組合占比？以每兆瓦為基礎，AMD相較於NVIDIA GPU的單位經濟性如何？謝謝。

Hi, Radi. We have a good healthy mix of different types of accelerators in our farm. For obvious and competitive reasons, we don't get into the details of what we use to host, what type of models and things like that, but it is a mix of both, and we continue to keep pace with the innovation in this market. And we certainly don't want to discuss the unit economics of different hardware throughputs. And I would just stop at that because it's a good mix of different types of accelerators.

嗨，Radi。我們的機群裡有相當健康的各類加速器組合。基於顯而易見的競爭因素，我們不會深入細節，例如我們用哪些來託管、跑哪些類型的模型等等；但確實兩者都有，而且我們也持續跟上市場的創新步伐。我們也不打算討論不同硬體吞吐量的單位經濟性。我就先說到這裡，因為我們確實是各類加速器的良好組合。

And as you can imagine, we are really good at taking whatever hardware is available based on the capacity we have and running the state-of-the-art model, like, most of the state-of-the-art GLM 5.2 or K3 we run on all kinds of hardware and that is the beauty of the software optimization layer that we continue to build and refine where we are almost becoming hardware agnostic.

而且你可以想像，我們非常擅長在既有產能條件下，使用任何可取得的硬體來運行最先進的模型；例如最先進的GLM 5.2或K3，我們會在各種硬體上運行。這正是我們持續打造並精進的軟體最佳化層的優勢，使我們幾乎正在變得不依賴特定硬體。

We have reached the end of our Q&A session. I will now hand the call back to Radu.

我們的問答環節到此結束。我現在把電話交回給Radu。

Great. Thank you, Paige. Thank you, everyone, for joining, and this concludes our second-quarter earnings presentation and conference call. Apologies we couldn't get to all your questions but look forward to speaking to everyone later in the day in our follow-up calls.

很好。謝謝你，Paige。也謝謝各位的參與，本次第二季財報簡報與電話會議到此結束。很抱歉我們無法回答所有問題，但期待今天稍晚在後續電話中再與各位交流。

Thank you.

謝謝。

This concludes today's call. Thank you for attending. You may now disconnect.

今天的電話會議到此結束。感謝各位出席。您現在可以掛線。