DigitalOcean Holdings Inc (DOCN) 2024 Q2 法說會逐字稿

Thank you for standing by and welcome to the DigitalOcean second quarter 2024 earnings conference call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question-and-answer session. (Operator Instructions) Thank you.

感謝您的耐心等待並歡迎參加 DigitalOcean 2024 年第二季財報電話會議。所有線路均已靜音，以防止任何背景噪音。演講者發言後，將進行問答環節。 （操作員說明）謝謝。

I'd now like to turn the call over to Melanie Strate, Head of Investor Relations. You may begin.

我現在想將電話轉給投資者關係主管 Melanie Strate。你可以開始了。

Thank you, and good afternoon. Thank you all for joining us today to review DigitalOcean's second quarter 2024 financial results. Joining me on the call today are Paddy Srinivasan, our Chief Executive Officer, and Matt Steinfort, our Chief Financial Officer. After our prepared remarks, we will open the call to a question-and-answer session.

謝謝你，下午好。感謝大家今天與我們一起回顧 DigitalOcean 2024 年第二季的財務表現。今天與我一起參加電話會議的有我們的執行長 Paddy Srinivasan 和我們的財務長 Matt Steinfort。在我們準備好的發言之後，我們將開始問答環節。

Before we begin, let me remind you that the statements made on the call today may be considered forward-looking statements, which reflect management's best judgment based on currently available information. I refer specifically to the discussion of our expectations and beliefs regarding our financial outlook for the third quarter and full year 2024, as well as our business goals and outlook.

在我們開始之前，讓我提醒您，今天的電話會議中所做的聲明可能被視為前瞻性聲明，它反映了管理層根據當前可用資訊做出的最佳判斷。我特別指的是我們對 2024 年第三季和全年財務前景的期望和信念的討論，以及我們的業務目標和前景。

Our actual results may differ materially from those projected in these forward-looking statements. I direct your attention to the risk factors contained in our filings with the Securities and Exchange Commission and 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.

我們的實際結果可能與這些前瞻性陳述中的預測有重大差異。我請您注意我們向美國證券交易委員會提交的文件中包含的風險因素以及我們網站上發布的今天新聞稿中引用的風險因素。 DigitalOcean 明確聲明不承擔任何義務或承諾公開發布對今天所做的任何前瞻性聲明的任何更新或修訂。

Additionally, known GAAP financial measures will be discussed on this conference call, and reconciliations to the most directly comparable GAAP financial measures are also available in today's press release, as well as in our investor presentation that outlines the financial discussion on today's call. A webcast of today's call is also available on the IRS section of our website.

此外，已知的GAAP 財務指標將在本次電話會議上進行討論，並且與最直接可比較的GAAP 財務指標的調節也可在今天的新聞稿以及概述今天電話會議中財務討論的投資者演示中找到。我們網站的國稅局部分也提供了今天電話會議的網路廣播。

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

說完，我會把電話轉給帕迪。

Thank you, Melanie. Good afternoon, everyone, and thank you for joining us today as we review our second quarter results. DigitalOcean delivered a strong second quarter, building on the momentum from the first quarter and continuing to execute on all key metrics. In my remarks today, I will briefly highlight our second quarter results, provide an update on the leaders we hired recently, share tangible examples of our increasing product velocity, and discuss how we are capitalizing on our AI growth opportunity.

謝謝你，梅蘭妮。大家下午好，感謝您今天與我們一起回顧第二季的業績。 DigitalOcean 在第一季的勢頭基礎上，第二季表現強勁，並繼續執行所有關鍵指標。在今天的演講中，我將簡要介紹我們第二季度的業績，提供我們最近聘請的領導者的最新情況，分享我們不斷提高產品速度的具體例子，並討論我們如何利用人工智慧成長機會。

First, I would like to briefly recap our second quarter 2024 financial results. Revenue growth has continued to reaccelerate on the second quarter to 13% year-over-year, reflecting the growing signs of success we're seeing from both a product and go-to-market standpoint and the continued acceleration of our AI and machine learning products, where ARR has grown over 200% year-over-year from the Paperspace ARR we acquired last year. In Q2, we also saw the largest step-up in incremental total company ARR in nearly two years, excluding the quarter in which we acquired the AI/ML business.

首先，我想簡單回顧一下我們 2024 年第二季的財務表現。第二季營收成長持續加速，年增 13%，反映出我們從產品和進入市場的角度看到的越來越多的成功跡象，以及我們的人工智慧和機器學習的持續加速產品中，ARR 比我們去年收購的Paperspace ARR 年比成長了200% 以上。在第二季度，我們也看到了近兩年來公司總 ARR 增量的最大增幅（不包括我們收購 AI/ML 業務的季度）。

We also delivered strong adjusted EBITDA margins at 42% and adjusted free cash flow margins at 19%, exemplifying our ability to demonstrate ongoing cost discipline and optimization while continuing to accelerate product innovation. Our second quarter financial results highlight the progress we are making and our ability to execute on the plans we laid out at the beginning of the year. We're also encouraged by the signs of improvement within both our growth profile and our key fundamentals.

我們還實現了 42% 的強勁調整後 EBITDA 利潤率和 19% 的調整後自由現金流利潤率，這證明了我們有能力展示持續的成本控制和優化，同時繼續加速產品創新。我們第二季的財務表現凸顯了我們所取得的進展以及我們執行年初制定的計劃的能力。我們的成長狀況和關鍵基本面都有改善的跡像也令我們感到鼓舞。

Net dollar retention was flat versus the previous quarter at 97%, as expansion within our customer base continues to be lower than historical levels, given we're still navigating a challenging macro environment, which is muting the positive impact of our increased product velocity and the stability we have seen in churn and contraction from our solid execution on the various customer success motions. In addition to the increased momentum from our AI/ML products, we received healthy revenue contributions from both our managed hosting products and new customers. Matt will walk you through more details on our financial results and guidance later in the call.

淨美元保留率與上一季持平，為 97%，因為我們的客戶群擴張繼續低於歷史水平，因為我們仍在應對充滿挑戰的宏觀環境，這削弱了我們提高產品速度和技術的積極影響。由於我們對各種客戶成功行動的堅定執行，我們在流失和收縮方面看到了穩定性。除了人工智慧/機器學習產品的成長動能之外，我們還從託管產品和新客戶那裡獲得了可觀的收入貢獻。馬特將在稍後的電話會議中向您介紹有關我們財務業績和指導的更多詳細資訊。

In addition to our solid financial performance and accelerating product innovation traction, I'm also excited about the advancements we've made in building out the team. We added three critical new leaders to our executive team over the past several weeks. First is Bratin Saha, our Chief Product and Technology Officer, who will lead product strategy, product engineering, infrastructure, and security. Most recently, Bratin built AWS's multi-billion dollar AI, machine learning, and data platforms, which together represented one of its fastest growing business segments. Previously, Bratin worked at NVIDIA and Intel, running many of their software infrastructure platforms.

除了我們穩健的財務表現和加速的產品創新牽引力之外，我對我們在團隊建立方面的進步感到興奮。在過去的幾周里，我們的執行團隊增加了三位重要的新領導者。首先是我們的首席產品和技術長 Bratin Saha，他將領導產品策略、產品工程、基礎設施和安全。最近，Bratin 建立了 AWS 價值數十億美元的人工智慧、機器學習和數據平台，這些平台共同構成了其成長最快的業務領域之一。在此之前，Bratin 曾在 NVIDIA 和 Intel 工作，運行他們的許多軟體基礎設施平台。

We also announced Wade Wegner as our Chief Ecosystem and Growth Officer, which is a unique role that is highly appropriate for DigitalOcean, as we are a very unique company. Our cost-efficient self-service customer acquisition model is one of the most efficient in the industry. As I have said many times, one of DigitalOcean's strengths and a key driver of our customer acquisition model is our passionate community of developers, many of whom have grown or are growing up learning to code on our platform. Wade and his organization will be responsible for supercharging our engagement with this community and for driving our very distinct product-led growth motion.

我們也宣布 Wade Wegner 擔任我們的首席生態系統和成長官，這是一個非常適合 DigitalOcean 的獨特角色，因為我們是一家非常獨特的公司。我們經濟高效的自助客戶獲取模式是業界最高效的模式之一。正如我多次說過的，DigitalOcean 的優勢之一和我們客戶獲取模式的關鍵驅動力是我們充滿熱情的開發人員社區，其中許多人已經或正在成長，學習在我們的平台上編寫程式碼。韋德和他的組織將負責加強我們與這個社區的互動，並推動我們非常獨特的以產品為主導的成長運動。

Finally, we recently announced Larry D'Angelo, as our Chief Revenue Officer, who will bring his years of experience building and scaling high velocity go-to-market teams to drive direct sales and partner sales to augment our product-led growth engine, and also to build scalable customer success and support functions to help our customers be successful and expand their footprint on our platform.

最後，我們最近宣布拉里·德安吉洛(Larry D'Angelo) 擔任我們的首席營收官，他將利用他多年建立和擴展高速上市團隊的經驗來推動直接銷售和合作夥伴銷售，從而增強我們以產品為主導的成長引擎，並建立可擴展的客戶成功和支援功能，以幫助我們的客戶取得成功並擴大他們在我們平台上的足跡。

DigitalOcean's strong fundamentals and future potential drew these three world-class executives to come join us in our journey. Their arrivals have also created further hiring momentum, as having top talents such as these three new executives tend to attract additional world class talent. We're already seeing this dynamic play out as they fill out their respective teams. I'm very confident that we now have the right executive team in place to fuel growth, increase product velocity, help our customers be successful, and to continue to execute on our mission of making cloud and AI simple and accessible for developers.

DigitalOcean 強大的基礎和未來潛力吸引了這三位世界級主管加入我們的旅程。他們的到來也創造了進一步的招募勢頭，因為擁有像這三位新主管這樣的頂尖人才往往會吸引更多的世界級人才。當他們充實各自的團隊時，我們已經看到了這種動態的發揮。我非常有信心，我們現在擁有合適的執行團隊來推動成長、提高產品速度、幫助我們的客戶取得成功，並繼續履行我們的使命，讓雲端和人工智慧對開發人員來說變得簡單且易於使用。

Now let me give you an update on our products. As we continue to listen to our customers and incorporate their feedback, enabling them to grow and scale on our platform, we released 24 new product features throughout Q2, doubling our product velocity from the prior six months. We also revived Deploy, our virtual developer conference which was held on July 9. I'm thrilled about the success of this event and look forward to continuing to engage with our developer community as we intend to increase the frequency of our deploy events and do them on a regular cadence going forward.

現在讓我向您介紹我們產品的最新情況。隨著我們繼續傾聽客戶的意見並吸收他們的反饋，使他們能夠在我們的平台上成長和擴展，我們在整個第二季度發布了24 個新產品功能，使我們的產品速度比前六個月翻了一番。我們也恢復了Deploy，我們的虛擬開發者大會於7 月9 日舉行。做他們以固定的節奏前進。

During our July event, we announced a number of material product announcements in response to customer feedback. First, we announced GPU Droplets in early availability mode, and this launch democratizes on-demand access to Nvidia H100 GPU instances for our customers, enabling them to leverage one, eight, or more GPUs at a time, providing flexible deployment options tailored to the various use cases and budgets. A lot more on this a little later.

在 7 月的活動中，我們根據客戶回饋發布了許多重要產品公告。首先，我們在早期可用性模式中宣布了GPU Droplet，此次發布使我們的客戶能夠按需訪問Nvidia H100 GPU 實例，使他們能夠一次利用一個、八個或更多GPU，從而提供適合客戶需求的靈活部署選項。稍後會詳細介紹這一點。

During Deploy, we also announced our global load balancer product, which we refer to as GLB, which is currently in public beta. This is engineered to bolster application resiliency, eliminate single points of failure, and significantly minimize end-user latency and secure GLB traffic from denial-of-service attacks. It offers global traffic distribution based on geographical proximity of the end-user, dynamic multi-regional traffic failover, data center prioritization, edge caching, and automatic scaling of the GLBs. It is intuitive, predictably priced, and tailored to the essential needs of growing technology companies for enhancing their global resiliency.

在 Deploy 期間，我們也發布了我們的全球負載平衡器產品，我們稱之為 GLB，目前處於公開測試階段。其設計目的是增強應用程式彈性、消除單點故障、顯著最小化最終用戶延遲並保護 GLB 流量免受拒絕服務攻擊。它提供基於最終用戶地理鄰近性的全球流量分配、動態多區域流量故障轉移、資料中心優先權、邊緣快取和 GLB 自動擴展。它直觀、價格可預測，並根據成長型科技公司增強其全球彈性的基本需求量身定制。

We also recently announced that select DigitalOcean products can now be used to host electronic protective health information. This allows companies such as telehealth providers, healthcare software applications, and health tech organizations to build and scale sensitive workloads regulated under HIPAA on our developer cloud, leveraging select DigitalOcean covered products.

我們最近也宣布，部分 DigitalOcean 產品現在可用於託管電子保護性健康資訊。這使得遠距醫療提供者、醫療保健軟體應用程式和醫療技術組織等公司能夠利用精選的 DigitalOcean 覆蓋產品，在我們的開發者雲端上建立和擴展受 HIPAA 監管的敏感工作負載。

During the quarter, we also launched Managed OpenSearch, a comprehensive solution designed for in-depth log analysis, simplifying troubleshooting, and optimizing application performance. With Managed OpenSearch, customers can now pinpoint and analyze log data with a lot of ease, customize log retention, enhance security of their applications, scale to fit capacity needs, and forward these logs from multiple sources.

在本季度，我們也推出了 Managed OpenSearch，這是一款專為深入日誌分析、簡化故障排除和最佳化應用程式效能而設計的綜合解決方案。借助 Managed OpenSearch，客戶現在可以輕鬆地找出並分析日誌資料、自訂日誌保留、增強應用程式的安全性、擴展以滿足容量需求以及從多個來源轉發這些日誌。

During Q1, we announced that we offer premium memory optimized droplets and premium storage optimized droplets, and in Q2, we finished rolling this out to all of our data centers, and this was a huge milestone for us. We also announced improvements to our app platform, including auto scaling, dedicated egress, and an expanded line-up with entry level dedicated instances, higher data transfer allowances, and reduced bandwidth overage fees.

在第一季度，我們宣布提供優質記憶體優化的 Droplet 和優質儲存優化的 Droplet，在第二季度，我們完成了向所有資料中心的推廣，這對我們來說是一個巨大的里程碑。我們還宣布了對應用程式平台的改進，包括自動擴展、專用出口、入門級專用實例的擴展陣容、更高的數據傳輸限額以及降低的頻寬超額費用。

Dedicated egress provides application developers with fixed IP addresses, enabling them to meet the security needs of their customers or run applications that require whitelisting for authentication purposes. Additionally, with the new expanded line-up, customers can now start small and grow on the platform with auto scaling. Reduced bandwidth overage fees helps customers deploying bandwidth intensive applications. These updates allow customers more flexibility and features to deploy their production applications.

專用出口為應用程式開發人員提供固定 IP 位址，使他們能夠滿足客戶的安全需求或運行需要白名單進行身份驗證的應用程式。此外，透過新的擴展產品線，客戶現在可以從小規模開始，並透過自動擴展在平台上發展。降低頻寬超額費用有助於客戶部署頻寬密集型應用程式。這些更新為客戶提供了更大的靈活性和功能來部署其生產應用程式。

Now turning to our managed hosting cloud based offering, we launched Malware Protection, which detects malware and protects our customers from cyberattacks. This add-on includes critical capabilities such as phishing protection, files protection, database protection for WordPress and Joomla, automated malware cleanup, proactive defense, and cron malware cleanup.

現在轉向我們基於託管雲端的產品，我們推出了惡意軟體防護，它可以偵測惡意軟體並保護我們的客戶免受網路攻擊。此附加元件包括關鍵功能，例如網路釣魚防護、檔案保護、WordPress 和 Joomla 資料庫保護、自動惡意軟體清理、主動防禦和 cron 惡意軟體清理。

These are just a few highlights as we continue to add new capabilities and features to achieve our objective of simplifying cloud and AI infrastructure for our customers. We will continue to listen closely to our customers and accelerate our product velocity so that customers continue to scale and grow on our platform, which is our primary focus as we work to drive up expansion and improve net dollar retention.

這些只是我們不斷添加新功能和特性以實現為客戶簡化雲端和人工智慧基礎設施的目標的一些亮點。我們將繼續密切傾聽客戶的意見並加快我們的產品速度，以便客戶繼續在我們的平台上擴大規模和發展，這是我們努力推動擴張和提高淨美元保留率的首要重點。

And now I'll pivot to a part of the business that is seeing a lot of momentum, our AI/ML offerings. We continue to see very strong demand for our AI platform. To support that growing demand and to take the first step of our long-term data center optimization strategy, I'm very excited to announce that we will be opening a new state-of-the-art data center in Atlanta in Q1 of 2025.

現在我將轉向業務中發展勢頭強勁的部分，即我們的人工智慧/機器學習產品。我們繼續看到對我們的人工智慧平台的強勁需求。為了滿足不斷增長的需求並邁出我們長期資料中心優化策略的第一步，我非常高興地宣布，我們將於 2025 年第一季在亞特蘭大開設一個新的最先進的資料中心。

This not only expands our geographic footprint, providing us cost effective additional coverage across the U.S. for our core workloads, but also gives us near term incremental space and power to support our AI strategy and growth. This new data center is also a key part of our medium-term strategy to reshape our data center footprint, including consolidating workloads from DCs that are currently in expensive locations, including New York City, San Francisco, and Toronto, enabling us to improve our gross margin profile over time.

這不僅擴大了我們的地理覆蓋範圍，為我們的核心工作負載提供了在美國各地具有成本效益的額外覆蓋範圍，而且還為我們提供了短期增量空間和力量來支持我們的人工智慧策略和成長。這個新資料中心也是我們重塑資料中心足蹟的中期策略的關鍵部分，包括整合目前位於紐約市、舊金山和多倫多等昂貴地點的資料中心的工作負載，使我們能夠改善我們的資料中心業務。變化的毛利率概況。

As a reminder, opening a new data center gives us ample runway to grow into the additional capacity, and we only add equipment and spend capital as needed to meet demand. As such, the financial impact of this long term investment will appear steadily over time as we ramp capacity and leverage it for consolidation of our core workloads, and also for AI training and inferencing as that demand evolves over time. We will share additional details over the next few calls as we start building our new data center out and make further progress on our data center optimization strategy.

提醒一下，開設新的資料中心為我們提供了充足的跑道來增加額外的容量，我們只根據需要添加設備和支出資金來滿足需求。因此，隨著我們提高容量並利用它來整合我們的核心工作負載，以及隨著需求隨著時間的推移而變化的人工智慧訓練和推理，這項長期投資的財務影響將隨著時間的推移而穩定顯現。當我們開始建立新的資料中心並在資料中心優化策略上取得進一步進展時，我們將在接下來的幾次電話會議中分享更多詳細資訊。

Given this DC expansion and with the modest increase in AI related capital that Matt will detail in his remarks, it is worthwhile for me to spend a little bit of time providing some context on our AI strategy and how we view this market opportunity. Today, the majority of AI action across the industry is in the foundational infrastructure layer, with a handful of companies providing GPU infrastructure to a relatively concentrated set of customers that require GPU compute for foundational model training.

考慮到 DC 的擴張以及 Matt 將在他的演講中詳細介紹的 AI 相關資本的適度增長，我有必要花一點時間來介紹我們的 AI 戰略以及我們如何看待這個市場機會。如今，整個產業的大部分人工智慧行動都在基礎設施層，少數公司向相對集中的一組需要 GPU 運算進行基礎模型訓練的客戶提供 GPU 基礎設施。

But over time, we expect generative AI and AI overall to follow a similar progression that the market has seen with other technology evolutions, with the action shifting up stack from infrastructure to platforms to eventually applications in the coming years to deliver actual business value to customers. The heavy users of infrastructure layer today are those building foundational gen AI models or those extending those foundational models by injecting their own data.

但隨著時間的推移，我們預計生成式人工智慧和人工智慧總體上將遵循市場上其他技術發展的類似進展，在未來幾年內，行動將從基礎設施轉移到平台，最終轉移到應用程序，從而為客戶提供實際的業務價值。如今基礎設施層的大量用戶是那些建立基礎人工智慧模型的人，或者是那些透過注入自己的數據來擴展這些基礎模型的人。

This requires a lot of deep expertise in machine learning, data, and foundational models. This restricts AI and associated innovation to well-funded start-ups and large enterprise companies with very skilled staff given the limited talent pool and high costs associated with this emerging technology, leaving behind the vast majority of companies who don't have access to these capabilities.

這需要在機器學習、資料和基礎模型方面擁有大量深厚的專業知識。鑑於人才庫有限且與這項新興技術相關的成本高昂，這將人工智慧和相關創新限制在資金充足的新創公司和擁有熟練員工的大型企業公司，而絕大多數無法獲得這些技術的公司能力。

Our mission at DigitalOcean is to change this paradigm by democratizing the access to gen AI and AI infrastructure for all customers, just like we did with core cloud computing services, using simple-to-use software platform components rather than expensive CapEx-heavy hardware infrastructure. As a significant step in this direction, we announced the launch of GPU droplets, allowing customers to seamlessly leverage AI technology into their workflows and applications using as few as one or eight GPUs in an on-demand mode.

我們DigitalOcean 的使命是透過讓所有客戶對gen AI 和AI 基礎設施的存取民主化來改變這種模式，就像我們對核心雲端運算服務所做的那樣，使用簡單易用的軟體平台組件而不是昂貴的資本支出密集硬體基礎設施。作為朝這個方向邁出的重要一步，我們宣布推出 GPU Droplet，讓客戶能夠在按需模式下使用少至一到八個 GPU，將 AI 技術無縫地運用到他們的工作流程和應用程式中。

GPU droplets removes the burden of managing the full lifecycle of GPUs and the orchestration associated with its usage. This type of fractional on-demand access to GPUs is not widely available in the market today. We have seen very robust demand for this capability, which is still in early availability mode.

GPU Droplet 消除了管理 GPU 整個生命週期以及與其使用相關的編排的負擔。這種對 GPU 的部分按需存取的方式在當今市場上尚未廣泛使用。我們看到對此功能的需求非常強勁，但該功能仍處於早期可用性模式。

Additionally, applications that consume AI also need the usual cloud primitives like compute, storage, databases, security, and so on to be deployed in the real world and deliver real business value. Unlike applications that are built on pure GPU farms, software that consumes AI through GPU droplets can seamlessly take advantage of DigitalOcean's core cloud computing platform, making it easy for customers to transition from R&D mode to production very seamlessly rather than having to go through redeployment.

此外，使用人工智慧的應用程式還需要將運算、儲存、資料庫、安全性等常見的雲原語部署在現實世界中並提供真正的業務價值。與建立在純GPU農場上的應用程式不同，透過GPU Droplet消耗AI的軟體可以無縫地利用DigitalOcean的核心雲端運算平台，讓客戶可以輕鬆地從研發模式非常無縫地過渡到生產，而不必進行重新部署。

Let me give you some specific examples of customers that are building on our AI platform. First example is an advanced stage start-up building a lightweight but very fast AI code completion tool for developers with a very large context window using native neural network architecture on our platform. Another example is an AI infrastructure management company that offers a middleware layer to enable rapid training and inferencing for Gen-AI models on the DigitalOcean platform.

讓我為您提供一些在我們的人工智慧平台上建立的客戶的具體範例。第一個例子是一家高階階段的新創公司，它為具有非常大上下文視窗的開發人員建立了一個輕量級但非常快速的人工智慧程式碼完成工具，在我們的平台上使用本機神經網路架構。另一個例子是一家人工智慧基礎設施管理公司，該公司提供中間件層，以實現 DigitalOcean 平台上 Gen-AI 模型的快速訓練和推理。

To recap, our longer-term AI vision is more software-centric, with the mission of making it easy for our approximately 638,000 current customers and other companies that look like them to leverage AI in their application stack without needing super deep AI and machine learning expertise. Now, with Bratin Saha, one of the most accomplished AI leaders in the industry, leading the charge for us, we will build on this momentum we have generated over the last couple of quarters and fulfil our mission to democratize AI and make it accessible to all companies.

回顧一下，我們的長期AI 願景更加以軟體為中心，其使命是讓我們約638,000 名當前客戶和其他類似的公司能夠輕鬆地在其應用程式堆疊中利用AI，而不需要超深度的AI 和機器學習專業知識。現在，在業界最有成就的人工智慧領導者之一Bratin Saha 的帶領下，我們將在過去幾個季度所產生的勢頭的基礎上再接再厲，履行我們的使命，使人工智能民主化並讓人們可以使用它。

In conclusion, I'm very pleased with the team's performance in the first half of the year. We have seen growing signs of success in our AI machine learning business, growth in our core business is reaccelerating, and I'm excited about our near and long-term growth potential across all areas of our business. We have the right leadership team in place and are focused on accelerating our product roadmap and deliver new capabilities that we announced this year at Deploy and enhancing our go-to-market motion in the second half of the year.

總而言之，我對球隊上半年的表現非常滿意。我們已經看到人工智慧機器學習業務取得越來越多的成功跡象，我們的核心業務的成長正在重新加速，我對我們所有業務領域的近期和長期成長潛力感到興奮。我們擁有合適的領導團隊，專注於加快我們的產品路線圖並提供我們今年在 Deploy 上宣布的新功能，並加強我們在下半年的上市行動。

I will now turn the call over to Matt to provide additional details on our financial results and for our outlook in Q3 and the remainder of the year. Over to you, Matt.

我現在將把電話轉給馬特，以提供有關我們的財務業績以及第三季和今年剩餘時間的前景的更多詳細資訊。交給你了，馬特。

Thanks, Paddy. Good afternoon, everyone, and thanks for joining us today. In Q2, we continued to execute on the plans we laid out at the beginning of the year. We made progress on key metrics. We continued to see revenue growth reaccelerate, and we delivered favorable adjusted EBITDA and adjusted free cash flow margins.

謝謝，帕迪。大家下午好，感謝您今天加入我們。第二季度，我們繼續執行年初制定的計畫。我們在關鍵指標上取得了進展。我們繼續看到收入成長重新加速，並且我們實現了良好的調整後 EBITDA 和調整後自由現金流利潤率。

Revenue in the second quarter was $192.5 million, up 13% year-over-year and up 4% quarter-over-quarter. We added $32 million of annual run rate revenue, or ARR, in the quarter, which was 158% higher than the incremental ARR we generated in Q2 of 2023 and was also the highest step-up in nearly two years, excluding the quarter in which we acquired our AI/ML business.

第二季營收為1.925億美元，年增13%，季增4%。我們在本季度增加了 3,200 萬美元的年度運行率收入 (ARR)，這比我們在 2023 年第二季度產生的增量 ARR 高出 158%，也是近兩年來的最高增幅（不包括我們收購了人工智慧/機器學習業務。

Contributing to this growth was healthy incremental revenue from new customers, increased momentum from our AI/ML platform, which saw significant growth quarter-over-quarter, and contributions from our managed hosting platform, which continues to be one of our faster growing platforms, all of these together offsetting a flat quarter-over-quarter net dollar retention rate from our existing installed base.

促成這一成長的因素包括來自新客戶的健康增量收入、我們的人工智慧/機器學習平台的成長動力（該平台環比成長顯著）以及我們的託管平台的貢獻（該平台仍然是我們成長較快的平台之一），所有這些因素共同抵消了我們現有安裝基礎上季度環比持平的淨美元保留率。

Our Q2 net dollar retention rate was 97%. As we saw last quarter, we continued to see stable performance in net expansion, which is defined as expansion net of contraction on our core DigitalOcean platform. Contributing to this stability was our increased product velocity that drove an increase in ARPU, helping to offset the broader macro pressures on net expansion in our customer base.

我們第二季的淨美元保留率為 97%。正如我們上季度所看到的，我們繼續看到淨擴張的穩定表現，淨擴張被定義為我們核心 DigitalOcean 平台上的擴張淨收縮。促成這種穩定性的是我們產品速度的提高，從而推動了 ARPU 的增加，有助於抵消客戶群淨擴張所面臨的更廣泛的宏觀壓力。

Our churn levels have also remained very stable for over a year across the business. We are encouraged by the stability in NDR and the modest sequential improvements we are seeing, despite the challenging macro environment, which is muting the pace of improvement in NDR, and despite a positive but lower contribution to NDR from our managed hosting platform, now that we have fully lapped last April's price increase.

一年多來，我們整個業務的客戶流失率也保持非常穩定。我們對 NDR 的穩定性和我們所看到的適度連續改善感到鼓舞，儘管充滿挑戰的宏觀環境減緩了 NDR 的改善步伐，並且儘管我們的託管託管平台對 NDR 的貢獻雖積極但較低，但現在我們已經完全接受了去年四月的價格上漲。

We continue to expect stable NDR and expansion levels through the end of the year, despite these ongoing headwinds. To further improve our net dollar retention rate, we will continue our solid execution, accelerating our product roadmap, refining our pricing and packaging models, and enhancing our customer success motions. Beyond NDR, we continue to see acceleration within our AI/ML platform.

儘管存在這些持續的阻力，我們仍然預計到今年年底，NDR 和擴張水平將保持穩定。為了進一步提高我們的淨美元保留率，我們將繼續堅定執行，加快我們的產品路線圖，完善我們的定價和包裝模型，並增強我們的客戶成功行動。除了 NDR 之外，我們繼續看到 AI/ML 平台的加速發展。

Q2, ARR has grown over 200% year-over-year from the ARR we acquired last year. We have also successfully navigated much of the initial supply chain and implementation risk that we had identified earlier in the year and are now working aggressively to keep up with demand. We anticipate this momentum to continue for the balance of the year, given the demand for our AI solutions.

第二季度，ARR 比我們去年收購的 ARR 年比成長了 200% 以上。我們也成功地應對了今年稍早發現的大部分初始供應鏈和實施風險，現在正在積極努力以滿足需求。鑑於對我們的人工智慧解決方案的需求，我們預計這一勢頭將在今年剩餘時間內持續下去。

Turning to the P&L. Gross margin was 61%, which was consistent with the prior quarter and up 100 basis points from the prior year. The 100 basis point year-over year-improvement is primarily a result of the success of our ongoing cost optimization efforts, which to date have more than offset our continued investment in AI infrastructure. Adjusted EBITDA margin was 42% in the second quarter, which was ahead of guidance and approximately 200 basis points higher than the prior quarter. This beat was primarily driven by strength in gross margin and our ongoing operating cost discipline.

轉向損益表。毛利率為 61%，與上季持平，較上年增長 100 個基點。年比提升 100 個基點主要是我們持續的成本優化工作成功的結果，迄今為止，這些努力已經遠遠抵消了我們對人工智慧基礎設施的持續投資。第二季調整後 EBITDA 利潤率為 42%，高於指引，比上一季高出約 200 個基點。這一成長主要是由毛利率的強勁和我們持續的營運成本紀律所推動的。

Diluted net income per share was $0.20, and non-GAAP diluted net income per share was $0.48. GAAP and non-GAAP diluted earnings per share increased by $0.19 and $0.04, respectively, on a year-over-year basis. This is a result of our ability to increase our per share profitability levels by driving both operating leverage and reducing our share count. Finally, Q2 adjusted free cash flow was $37 million, or 19% of revenue.

稀釋每股淨利為 0.20 美元，非 GAAP 稀釋每股淨利潤為 0.48 美元。 GAAP 和非 GAAP 攤薄每股盈餘年增 0.19 美元和 0.04 美元。這是我們透過提高營運槓桿和減少股票數量來提高每股盈餘水準的結果。最後，第二季調整後自由現金流為 3,700 萬美元，佔營收的 19%。

Turning to our customer metrics. Our total Q2 customer count was approximately 638,000, representing an increase from 637,000 customers in Q1. The number of builders and scalers on our platform, those that spend more than $50 per month was approximately 161,000, an increase of 7% year-over-year. The revenue growth associated with builders and scalers was 15% year-over-year, ahead of our overall revenue growth rate of 13%. The number of builders and scalers on our platform, which represent 87% of our total revenue, increased by approximately 3,000 quarter-over-quarter.

轉向我們的客戶指標。我們第二季的客戶總數約為 638,000 名，較第一季的 637,000 名有所增加。我們平台上每月支出超過 50 美元的建構者和擴充者數量約為 161,000 人，較去年同期成長 7%。與建構器和擴展器相關的收入年增 15%，高於我們 13% 的整體收入成長率。我們平台上的建構器和擴展器數量占我們總收入的 87%，環比增加了約 3,000 個。

The continued growth of our largest spending cohorts is a direct result of our focusing our product development and customer success investments on these builders and scalers. The increase in our higher spend and higher growth customers also resulted in our total average revenue per user, or ARPU, increasing 9% year-over-year to $99.45. With our substantial pre-cash flow generation, our balance sheet remained very strong as we ended the quarter with $443 million of cash and cash equivalents. We also continued to execute against our ongoing share repurchase program and completed 10 million of repurchases in the quarter.

我們最大的支出群體的持續成長是我們將產品開發和客戶成功投資重點放在這些建構者和擴展者身上的直接結果。我們更高的支出和更高成長的客戶的增加也導致我們的每位用戶平均總收入 (ARPU) 年增 9% 至 99.45 美元。由於我們產生了大量的預現金流，我們的資產負債表仍然非常強勁，本季末我們的現金和現金等價物為 4.43 億美元。我們也繼續執行正在進行的股票回購計劃，並在本季完成了 1,000 萬股回購。

Moving on to guidance. We expect Q3 revenue to be in the range of $196 million to $197 million, representing approximately 11% year-over-year growth at the midpoint of our guidance range. For the third quarter, we expect adjusted EBITDA margins to be in the range of 37% to 38%, and non-GAAP diluted earnings per share to be $0.39 to $0.41, based on approximately $102 million to $103 million in weighted average fully diluted shares outstanding.

繼續指導。我們預計第三季營收將在 1.96 億美元至 1.97 億美元之間，以我們指導範圍的中位數計算，年增約 11%。對於第三季度，我們預計調整後 EBITDA 利潤率將在 37% 至 38% 之間，非 GAAP 攤薄每股收益為 0.39 美元至 0.41 美元（基於加權平均完全稀釋股票約為 1.02 億至 1.03 億美元）傑出的。

As a result of the steady performance in our core platform and strong demand we are seeing for our AI platform, we are increasing the bottom end of our full year revenue guide by 10 million, projecting revenue to be in the range of $770 million to $775 million, a $5 million increase in the midpoint of our guidance range, and representing year-over-year growth of approximately 11% to 12%. As demonstrated through the first half of 2024, we remain committed to driving continued operating leverage in our core DigitalOcean platform. Given our solid performance in the first half of the year, we are raising our adjusted EBITDA margins guidance for the full year to be in the range of 37% to 39%.

由於我們核心平台的穩定表現以及我們對人工智慧平台的強勁需求，我們將全年收入指南的下限增加了 1000 萬美元，預計收入將在 7.7 億美元至 775 美元之間百萬美元，比我們指導範圍的中點增加了500 萬美元，年增約11% 至12%。正如 2024 年上半年所證明的那樣，我們仍然致力於推動核心 DigitalOcean 平台的持續營運槓桿。鑑於我們上半年的穩健表現，我們將全年調整後 EBITDA 利潤率指引上調至 37% 至 39% 範圍內。

Turning to adjusted free cash flow. We anticipate making appropriate incremental investments through the second half of the year, as we continue to capitalize on the AI opportunity to fuel future growth, although, we anticipate these investments having only a modest impact on our cash flow margins. We expect adjusted free cash flow margins for the full year to be in the range of 15% to 17%. As a reminder, adjusted free cash flow can vary quarter-to-quarter, given the variability of our capital spend and our working capital timing.

轉向調整後的自由現金流。我們預計在下半年進行適當的增量投資，因為我們將繼續利用人工智慧機會來推動未來的成長，儘管我們預計這些投資對我們的現金流利潤率的影響不大。我們預計全年調整後自由現金流利潤率將在 15% 至 17% 之間。提醒一下，考慮到我們的資本支出和營運資金時間的變化，調整後的自由現金流可能會因季度而異。

From an overall strategic capital allocation perspective, we will continue to be good stewards of our capital and will evaluate opportunities to maximize shareholder return, maintaining financial flexibility while continuing to evaluate investments across share repurchases, incremental capacity, and balance sheet management. We are also raising the top end of our prior non-GAAP diluted earnings per share guidance and now expect this to be in the range of $1.60 to $1.70.

從整體策略資本配置的角度來看，我們將繼續做好資本管家，評估股東回報最大化的機會，保持財務靈活性，同時繼續評估股票回購、增量產能和資產負債表管理方面的投資。我們也提高了先前非 GAAP 攤薄每股收益指引的上限，現在預計該上限將在 1.60 美元至 1.70 美元之間。

That concludes our prepared remarks and we'll now open it up for Q&A

我們準備好的發言就到此結束，現在我們將進行問答

Thank you. We will now begin the question-and-answer session. (Operator Instructions).

謝謝。我們現在開始問答環節。 （操作員說明）。

Gabriela Borges, Goldman Sachs.

加布里埃拉·博爾赫斯，高盛。

Good afternoon. Thank you, and great to see the step up in ARR. Paddy, I wanted to follow up on your comments on AI strategy. Help us understand, are you saying that other GPU providers don't offer the same level of fractional access that you do with droplets? And then, broadly speaking, comment on the competitive environment. There are a number of well-funded GPU providers that are spending orders of magnitude more CapEx than you are. How do you think about the sustainability of your differentiation when you're up against competitors that have that kind of CapEx fund? Thank you.

午安.謝謝，很高興看到 ARR 的進步。 Paddy，我想跟進您對人工智慧策略的評論。請幫我們理解，您是說其他 GPU 提供者不提供與 Droplet 相同等級的部分存取嗎？然後，從廣義上講，對競爭環境進行評論。有許多資金雄厚的 GPU 提供者的資本支出比您高出幾個數量級。當您面對擁有此類資本支出基金的競爭對手時，您如何看待差異化的可持續性？謝謝。

Thank you, Gabriela. Great question. So as I started the prepared remarks by talking about our AI strategy and how it is differentiated, I want to remind everyone we are also a very strong player in the GPU infrastructure as a service today. So we have very competitive offerings all the way from bare metal to virtualized environments. And what we announced with the GPU droplets is, it's a strategy which enables access to fractional GPU access and also with the ability to orchestrate the lifecycle through the virtualized environment that we provide with GPU droplets.

謝謝你，加布里埃拉。很好的問題。因此，當我透過談論我們的人工智慧策略及其差異化來開始準備好的發言時，我想提醒大家，我們今天也是 GPU 基礎設施即服務領域的一個非常強大的參與者。因此，從裸機到虛擬化環境，我們都提供非常有競爭力的產品。我們透過 GPU Droplet 宣布的是，這是一種策略，可以實現部分 GPU 訪問，並且能夠透過我們透過 GPU Droplet 提供的虛擬化環境來編排生命週期。

So it just takes the overhead of having to manage the whole infrastructure, makes it super, super easy for those workloads which we believe are going to be more important for customers that are digital natives but also the types of customers that typically reside on our platform. So they are looking to build applications that consume AI and leverage different parts of the AI infrastructure in a way that extends existing AI models. They're not model builders per se in the sense that they're not trying to build another foundational model.

因此，它只需要管理整個基礎設施的開銷，使那些工作負載變得非常非常容易，我們相信這些工作負載對於數位原生代客戶以及通常駐留在我們平台上的客戶類型來說將更加重要。因此，他們希望建立使用人工智慧的應用程序，並以擴展現有人工智慧模型的方式利用人工智慧基礎設施的不同部分。他們本身並不是模型建構者，因為他們並沒有嘗試建立另一個基礎模型。

So we feel our AI strategy, which includes the GPU infrastructure, is tailor made for customers that are looking to consume AI, not necessarily build foundational models. So when I talked about the GPU droplets, that's an abstracted version of the core GPU as a service. And then we also in the deploy conference, we also announced and showed an early preview of what we call the next generation of our platform as a service offerings.

因此，我們認為我們的人工智慧策略（包括 GPU 基礎設施）是為希望使用人工智慧的客戶量身定制的，而不一定要建立基礎模型。因此，當我談到 GPU Droplet 時，這是核心 GPU 即服務的抽象版本。然後我們也在部署會議上宣布並展示了我們所謂的下一代平台即服務產品的早期預覽。

So these are endpoint APIs for well-known open source models like Llama 3.1 and so forth, which we will be releasing in early Q4, which enables another layer of abstraction to build applications on top of LLM platforms. So we feel our strategy is going more up stack and enabling applications that derive business value from AI rather than focusing on model builders that are building and training foundational models. So there's going to be different needs for customers that are looking to derive business value and build applications and platforms on top of our infrastructure, so that's what we are focused on.

這些是 Llama 3.1 等著名開源模型的端點 API，我們將在第四季度初發布，這使得另一個抽象層能夠在 LLM 平台之上建立應用程式。因此，我們認為我們的策略是更多地向上堆疊並啟用從人工智慧中獲取商業價值的應用程序，而不是專注於建立和訓練基礎模型的模型建構者。因此，希望獲得業務價值並在我們的基礎設施之上建立應用程式和平台的客戶會有不同的需求，這就是我們所關注的重點。

Certainly, there is going to be room and space for core infrastructure and GPU forms. And there is, as I mentioned, a very concentrated group of model training companies that are building foundational models, whether it is for question-and-answer, like a large language model for question-and-answer bots, or for text to image generation or text to video generation and things like that. But our hypothesis is that that market is very concentrated, and over time there will be few but very big model companies in the world, and the rest of the universe is going to move towards building applications on top of this infrastructure. And that's where our focus is, and that's historically been our strength is to simplify the access to complex infrastructure by providing the essential building blocks of platform and application building. And that's the strategy we are following.

當然，核心基礎設施和 GPU 形式都會有空間和空間。正如我所提到的，有一群非常集中的模型培訓公司正在建立基礎模型，無論是用於問答（例如用於問答機器人的大型語言模型）還是用於文本到圖像的模型生成或文本到視頻生成等等。但我們的假設是，這個市場非常集中，隨著時間的推移，世界上將出現很少但非常大的模型公司，而宇宙的其他部分將轉向在這個基礎設施之上建立應用程式。這就是我們的重點所在，而且歷來我們的優勢就是透過提供平台和應用程式建構的基本構建塊來簡化對複雜基礎設施的存取。這就是我們正在遵循的策略。

Thank you for the detail. Matt, the follow-up is for you on the updated outlook. How did you think about the pace of net new ARR into 3Q and 4Q? Back at the end of it, Matt, I think it implies a moderation from the result that you just put out this quarter. So help us understand how you thought about handicapping that, call it, $7 million, $8 million that you were able to deliver in 2Q into 3Q and 4Q as well.

謝謝你的詳細資料。馬特，後續內容是為您介紹最新的展望。您如何看待第三季和第四季淨新增 ARR 的步伐？最後，馬特，我認為這意味著您本季剛剛公佈的結果有所緩和。因此，請幫助我們了解您如何考慮將其在第二季交付到第三季和第四季的 700 萬美元、800 萬美元。

Yeah. As we think about the growth, particularly around the AI side of the business, it's somewhat lumpy and we talked about earlier in the year that we had some risk baked in, and as part of the reason why we gave a wide revenue range in our initial guidance was, we had some supply chain risk, and it was a new service for us, so we wanted to make sure that we were comfortable with the implementation. And we managed past that risk, and that enabled us to bring on a fair bit of incremental capacity that we had ordered last year in the second quarter, which drove up the ARR. And so as we look over the balance of the year and we're continuing to add capacity, it won't be as lumpy, so it's going to be a little bit lesser in terms of the guide than what we had in the second quarter.

是的。當我們考慮成長時，特別是圍繞業務的人工智慧方面，它有點不穩定，我們在今年早些時候談到了我們存在一些風險，這也是我們在我們的業務中給出了廣泛的收入範圍的原因之一。我們克服了這一風險，這使我們能夠增加去年第二季訂購的大量產能，從而提高了 ARR。因此，當我們審視今年的剩餘情況並繼續增加產能時，它不會那麼不穩定，因此就指南而言，它會比我們在第二季度的情況要少一些。

The other thing that you're getting when people look at the implied third quarter and fourth quarter growth rates is, from a year-over-year perspective, we've got two lapping that we're doing that are making the growth look like it's decelerating, even though we're accelerating clearly in the core and we're showing growth in the other parts of the business. And that's because this is the first quarter or third quarter will be the first quarter where the Paperspace AI revenue is in the prior period. And so that's a 200 basis point reduction in the growth rate. And then, we're lapping a price increase and customer growth, kind of nominal customer growth in the Cloudways business that's also contributing about 100 basis points to the year-over-year growth apparent deceleration from second to third quarter.

當人們看到隱含的第三季和第四季成長率時，你會得到的另一件事是，從同比的角度來看，我們正在做的兩次迭代使成長看起來像儘管我們在核心業務方面明顯加速，並且在業務的其他部分也顯示出成長，但它正在減速。這是因為這是第一季或第三季將是 Paperspace AI 收入處於上一時期的第一季。因此，成長率下降了 200 個基點。然後，我們看到了價格上漲和客戶成長，這是 Cloudways 業務的名義客戶成長，這也導致第二季到第三季的年成長率明顯減速約 100 個基點。

Got it. Thank you for the call.

知道了。謝謝您的來電。

Your next question comes from a line of Raimo Lenschow from Barclays. Your line is open.

您的下一個問題來自巴克萊銀行的 Raimo Lenschow。您的線路已開通。

Thank you. Can I go back to the first question that was asked? Just given that we are kind of early on that Gen AI journey, so we don't really fully understand how this is going to play out. So is the message from you guys that as I train a model, I need like hundreds, thousands, ten thousands GPUs, but as I run the model, then if I have an application, I can do that with a much, much smaller GPU number, like the one to eight that you were kind of suggesting today? Like, it just feels a little bit like, because you're early in the journey, it feels a little bit too good to be true. Can you speak to that a little bit more?

謝謝。我可以回到最初提出的問題嗎？鑑於我們還處於人工智慧時代的早期階段，所以我們並沒有真正完全理解這將如何發展。你們傳達的訊息是，當我訓練模型時，我需要數百、數千、一萬個 GPU，但當我運行模型時，如果我有一個應用程序，我可以使用更小的 GPU 來實現這一點數字，就像您今天建議的一到八？就像，感覺有點像，因為你還處於旅程的早期，感覺有點好得令人難以置信。能再多談一下嗎？

Yeah, Raimo. Thank you for the question. So there are three types of AI users today. One is the true foundational model builders, like, an Anthropic Mistral, folks that are building the foundation models. And the second one is, what I call the AI extenders. So they take these models, say, a Llama 3.1, an open source model, and they inject or they enhance it using their custom data. It could be a company that has, say, for example, a geospatial data, and they enhance an existing LLM with their own custom data source and create a slightly modified version of an existing foundation model. So for this class of customers, they definitely don't need hundreds of thousands of GPUs.

是的，雷莫。謝謝你的提問。所以今天的人工智慧用戶分為三種。一類是真正的基礎模型建構者，例如 Anthropic Mistral，正在建立基礎模型的人。第二個是我所謂的人工智慧擴展器。因此，他們採用這些模型，例如 Llama 3.1，一個開源模型，然後使用自訂資料注入或增強它。例如，它可能是一家擁有地理空間資料的公司，他們使用自己的自訂資料來源來增強現有的法學碩士，並創建現有基礎模型的稍微修改的版本。所以對於這一類客戶來說，他們肯定不需要幾十萬的GPU。

And the third type of user is an AI consumer. So for example, let's say you're creating a new AI native CRM application or a supply chain application for which you are relying on a very robust foundational model, again, Llama 3 or Mistral or something. But bulk of your application is to deliver that supply chain forecasting algorithm, but you're leveraging a heavy dose of AI. But you don't need the same kind of physical raw compute power that the Category 1 or Category 2 company needed. So these are different types of use cases. So the AI model builders, AI model extenders, and the AI model consumers all have different requirements and need GPU capacity at different scales. And what we are seeing is, we are definitely addressing the needs of the second and the third categories that I just talked about.

第三類用戶是AI消費者。舉例來說，假設您正在創建一個新的 AI 原生 CRM 應用程式或供應鏈應用程序，您依賴非常強大的基礎模型，例如 Llama 3 或 Mistral 等。但您的大部分應用程式是提供供應鏈預測演算法，但您正在利用大量人工智慧。但您不需要 1 類或 2 類公司所需的實體原始運算能力。所以這些是不同類型的用例。因此，AI 模型建構者、AI 模型擴充者和 AI 模型消費者都有不同的需求，需要不同規模的 GPU 容量。我們看到的是，我們肯定正在解決我剛才談到的第二類和第三類的需求。

And another way to think about this, Remo is, as with any technology wave, you have infrastructure providers. In this case, this is NVIDIA and all the foundational model builders. They are laying the infrastructure, but the true business value is going to be when this infrastructure is leveraged to build platforms like simple example would be operating systems based on x86 architecture.

另一種思考方式是，雷莫與任何科技浪潮一樣，都有基礎設施提供者。在本例中，這就是 NVIDIA 和所有基礎模型建構者。他們正在鋪設基礎設施，但真正的商業價值將在於利用該基礎設施來建立平台，例如基於 x86 架構的作業系統。

And then you have applications, which are the ones that truly deliver business value for everyone. So as this AI wave goes up stack from one layer to the other, we feel there's a tremendous amount of need to democratize the access to these GPUs and also provide other software frameworks LNB on the platform layer and infrastructure layer, which is what we are building now.

然後你就有了應用程序，它們是真正為每個人提供商業價值的應用程式。因此，隨著人工智慧浪潮從一層上升到另一層，我們認為非常需要民主化對這些 GPU 的訪問，並在平台層和基礎設施層上提供其他軟體框架 LNB，這就是我們所做的現在建設。

Okay. Perfect. That makes total sense. Thanks for the clarification. And then one for Matt, if I think about the improvement in gross margins you mentioned from just the optimization work you do around the data centers, like, what sort of magnitude are you thinking? I don't want guidance, but is this like a few basis points? Is this kind of more meaningful? How should we think about that? Thank you and congrats from me, as well.

好的。完美的。這完全有道理。感謝您的澄清。然後是馬特的一個問題，如果我考慮一下您提到的圍繞資料中心進行的最佳化工作所帶來的毛利率的改善，例如，您認為會有多大的程度？我不需要指導，但這就像幾個基點嗎？這樣的做法是不是更有意義呢？我們該如何思考這個問題？謝謝你，也恭喜我。

Thanks, Raimo. Yeah. It's a great question. And I think I've touched upon this on prior calls. When I first joined the company, one of the first things that I observed was that the data center footprint was, I'd say, interesting in that we were in tier 1 markets and tier 1 buildings. And that was pretty much the way the company had architected its data center network, which is a relatively expensive way of building your architecture. Over time, you'd want to move to some tier two locations and you'd want to be more of a wholesale than retail kind of data center kind of footprint.

謝謝，雷莫。是的。這是一個很好的問題。我想我在之前的電話會議中已經談到過這一點。當我第一次加入公司時，我觀察到的第一件事就是資料中心的足跡很有趣，因為我們位於一級市場和一級建築。這幾乎就是該公司建立其資料中心網路的方式，這是一種相對昂貴的建構架構的方式。隨著時間的推移，您可能會想要遷移到某些二級地點，並且您會希望成為批發而非零售類型的資料中心。

And so the Atlanta data center that we just announced is a great example of that, where we're going to be able to take down an incremental capacity. It'll give us room for the expansion of our AI capabilities, but we'll also be able to take workloads out of our more expensive locations and kind of reduce or consolidate those footprints and shift some of the, maybe the less latency sensitive workloads into different locations. And that's going to be a multi-year effort.

因此，我們剛剛宣布的亞特蘭大資料中心就是一個很好的例子，我們將能夠減少增量容量。它將為我們的人工智慧功能擴展提供空間，但我們也將能夠將工作負載從更昂貴的位置轉移出來，並減少或整合這些足跡，並轉移一些可能對延遲不太敏感的工作負載到不同的地點。這將是一項多年的努力。

It's not going to be something that we can do over time, but I think there's a meaningful improvement that we can drive in gross margin. And the objective is to continue to drive gross margins up, and that will offset the core business, and that will offset some of the lower gross margin mix that we'll get as our AI business continues to grow. So I'm not going to give guidance on it, but I think it's a meaningful opportunity, and Bratin and I are very focused on driving that savings and driving the gross margin up.

隨著時間的推移，這不是我們可以做到的事情，但我認為我們可以推動毛利率的有意義的改善。我們的目標是繼續提高毛利率，這將抵消核心業務的影響，也將抵消隨著我們的人工智慧業務持續增長而出現的一些較低的毛利率組合。因此，我不會對此提供指導，但我認為這是一個有意義的機會，布拉廷和我非常專注於節省開支並提高毛利率。

Kingsley Crane, Canaccord Genuity.

金斯利起重機，Canaccord Genuity。

Great. So first question would be, you cited customer success efforts as helping to create stability in churn and contraction. Especially now with Larry on board, how much could customer success be used not to just prevent churn, but also drive usage in these high potential accounts?

偉大的。所以第一個問題是，您提到客戶成功的努力有助於在流失和收縮中創造穩定性。特別是現在拉里加入後，客戶的成功可以在多大程度上不僅用於防止客戶流失，還可以提高這些高潛力客戶的使用率？

Hello, Kingsley. Good to hear from you. It's a great question. So we currently, our customer success efforts are fairly nascent, but ramping up. And they already are tasked at not only managing a relationship with our top customers, but also drive product usage and show them the breadth of our platform, which after I joined six months ago, we have put some considerable wood behind the arrow in doing two things for our core platform.

你好，金斯利。很高興收到你的來信。這是一個很好的問題。因此，目前我們的客戶成功工作還處於起步階段，但正在不斷加強。他們的任務不僅是管理與我們的頂級客戶的關係，還要推動產品使用並向他們展示我們平台的廣度，在我六個月前加入後，我們已經在這兩個方面投入了相當多的精力我們的核心平台的東西。

One is, we are rapidly closing the gap in terms of what our top end of the scalers need from a core product point of view, but also adding a lot of other new capabilities on our app platform, as I mentioned in the prepared remarks. So we are doing both, closing the gaps in the product functionality that our top end scalers need, but also expanding our platform footprint. So our customer success reps, they get trained in all the latest and greatest, and they take that message to our customers and help expand the footprint of these large customers in how they're leveraging our platform.

一是，從核心產品的角度來看，我們正在迅速縮小高端縮放器需求方面的差距，而且還在我們的應用程式平台上添加了許多其他新功能，正如我在準備好的評論中提到的那樣。因此，我們正在同時做這兩件事，縮小我們的高階縮放器所需的產品功能差距，同時也擴大我們的平台足跡。因此，我們的客戶成功代表接受了所有最新和最好的培訓，他們將這一訊息傳達給我們的客戶，並幫助擴大這些大客戶在如何利用我們的平台方面的足跡。

So that's already there, but I think it is fair to say that it's in its nascency. And with Larry coming on board, there's going to be a big push for us. So the purpose of getting someone like Larry is to ensure that he can leverage all his skills in building a high velocity motion to augment our phenomenal product-led growth and make sure that these customers that are getting onboarded to our platform, first of all, know the breadth of our platform, but also start leveraging the capabilities that we have so that we can drive up expansion.

所以這已經存在了，但我認為可以公平地說它還處於起步階段。隨著拉里的加入，我們將會得到很大的推動。因此，聘請拉里這樣的人的目的是確保他能夠利用自己的所有技能來構建高速運動，以增強我們以產品為主導的驚人增長，並確保這些客戶首先加入我們的平台，了解我們平台的廣度，同時也開始利用我們擁有的功能來推動擴張。

That makes a lot of sense. And then I want to return to the GPU topic. A few comments on the availability. It's great to see that out in the market. And the fractional demand consumption is relatively unique. We've spoken in the past about software will differentiate you from the other providers. So I just want to check in on gradient specifically. How important is that to the vision and how is that acting as an on-ramp for customers?

這很有意義。然後我想回到 GPU 話題。關於可用性的一些評論。很高興在市場上看到它。而且部分需求消費比較獨特。我們過去曾說過，軟體將使您與其他提供者區分開來。所以我只想專門檢查一下梯度。這對願景有多重要？

Yeah. So we are, so yes, I completely agree that software as a differentiator is going to be the one that we are going to bank on. And so right now, so let me again refresh everyone's memory in terms of the offerings we currently have. So at the bottom most layer, we have a bare metal GPU platform. And we just announced the GPU Droplets, which provides virtualized and fractional and on-demand access, which is also a pretty big deal. Virtualized, on-demand fractional access to GPUs is what the GPU Droplets provide.

是的。所以我們是的，所以是的，我完全同意軟體作為一種差異化因素將成為我們所依賴的。現在，讓我再次刷新大家對我們目前提供的產品的記憶。所以在最底層，我們有一個裸機GPU平台。我們剛剛發布了 GPU Droplet，它提供虛擬化、部分和按需訪問，這也是一件相當大的事情。 GPU Droplet 提供了對 GPU 的虛擬化、按需部分存取。

And then a layer up above that is our platform as a service offering, which is Gradient. And we are doing a lot of work on that platform as a service layer. And we'll be announcing a slew of enhancements over the next 90 days to add a lot of capabilities on the platform as a service layer. We believe it is going to be an important front door for customers to start adopting these AI capabilities.

再上一層是我們的平台即服務產品，即 Gradient。我們正在該平台上作為服務層做很多工作。我們將在接下來的 90 天內宣布一系列增強功能，在平台上添加大量功能作為服務層。我們相信，這將成為客戶開始採用這些人工智慧功能的重要前門。

And the abstractions that we are providing right now, like, for example, when customers come into the Gradient platform, yes, they know that they are leveraging GPUs, but that's not why they are coming to our platform as a service offering. They're coming because it is super compelling to organize their AI/ML workspaces and share it amongst their colleagues and publish it to different repos and things like that. So there are many different reasons why they come there.

我們現在提供的抽象，例如，當客戶進入 Gradient 平台時，是的，他們知道他們正在利用 GPU，但這並不是他們作為服務產品來到我們的平台的原因。他們之所以來這裡，是因為組織他們的 AI/ML 工作區並在同事之間共享並將其發佈到不同的儲存庫和類似的東西是非常引人注目的。所以他們來這裡的原因有很多不同。

And by the way, when they have to push something to testing or production, they leverage the GPU infrastructure we have, in which we have a variety of different hardware, not just H100s. We also have a variety of different GPU hardware that helps customers for different use cases. So I think software is extraordinarily important as we go up the stack. And Gradient is a very differentiated offering for us. And we are working quite a bit on enhancing Gradient over the next 90 days.

順便說一句，當他們必須將某些東西推向測試或生產時，他們會利用我們擁有的 GPU 基礎設施，其中我們擁有各種不同的硬件，而不僅僅是 H100。我們還擁有各種不同的 GPU 硬件，可以幫助客戶應對不同的用例。因此，我認為隨著我們的發展，軟體變得異常重要。 Gradient 對我們來說是一個非常差異化的產品。在接下來的 90 天內，我們將投入大量精力來增強 Gradient。

Really encouraging. Thank you.

真的很鼓舞人心。謝謝。

Patrick Walravens, JMP Securities.

帕特里克·瓦爾拉文斯 (Patrick Walravens)，JMP 證券。

Great. Thank you. And first of all, I want to commend you on your recruiting. I mean, the background, like Bratin in particular, is just fabulous. So someday I'd like to hear how you did that. But my more specific question is, what are the bottlenecks to growing your AI footprint in terms of the data centers? So data centers that were built for CPUs don't work very well for GPUs, right? So how are you thinking through that?

偉大的。謝謝。首先，我要讚揚您的招募工作。我的意思是，背景，尤其是像布拉廷一樣，真是太棒了。所以有一天我想聽聽你是如何做到的。但我更具體的問題是，在資料中心方面擴大人工智慧足跡的瓶頸是什麼？因此，為 CPU 建置的資料中心對於 GPU 來說效果不佳，對嗎？那你是怎麼想的呢？

Yeah, Pat. Thank you so much for the kind words. Yes, you're right. There is surely some constraints, but all of, so just to be clear, the new data center that I just talked about, we are not live yet on that. So all of the AI workloads that we are seeing now, we have crammed into our existing data centers, whether it is New York or Amsterdam and other places. Is it easy? No. But we have figured out a way to do that. See, there are multiple constraints. One is the stuff that you read in the press is power and cooling.

是的，帕特。非常感謝您的客氣話。是的，你說得對。肯定存在一些限制，但所有這些，所以需要明確的是，我剛才談到的新資料中心，我們還沒有上線。因此，我們現在看到的所有人工智慧工作負載，我們都塞進了現有的資料中心，無論是紐約還是阿姆斯特丹和其他地方。容易嗎？不，但我們已經找到了一種方法來做到這一點。你看，有很多限制。一是您在媒體上讀到的內容是電源和冷卻。

Yes, those are really important. The other one is network stack. It's very different from our CPU offering. So we have to build parallel network stacks to put in these GPUs. And then real estate. The density of our ability to rack and stack GPUs is very different from our traditional density of our racks for CPUs because of the power and the heat that these GPUs throw out.

是的，這些確實很重要。另一種是網路堆疊。它與我們的 CPU 產品非常不同。所以我們必須建立平行網路堆疊來放入這些 GPU。然後是房地產。由於這些 GPU 產生的功率和熱量，我們對 GPU 進行機架和堆疊的能力密度與傳統 CPU 機架密度有很大不同。

So there are some physical constraints, networking constraints, and of course, it is just super expensive and super hard to find more data center space in our existing facilities. So that's why we decided to go to Atlanta. And by the way, it also helps us consolidate and load balance some of our very expensive data center footprint in the short term.

因此存在一些物理限制、網路限制，當然，在我們現有的設施中找到更多的資料中心空間非常昂貴且非常困難。這就是我們決定去亞特蘭大的原因。順便說一句，它還可以幫助我們在短期內整合和負載平衡一些非常昂貴的資料中心佔地面積。

16 data centers across nine regions. Did you have your slide deck? Does that include the Paperspace, whatever Paperspace has?

遍佈九個地區的 16 個資料中心。你有幻燈片嗎？這是否包括 Paperspace，無論 Paperspace 有什麼？

Yes, it does. And Paperspace coincidentally happened to be in our New York City data center. So, yeah, it includes that.

是的，確實如此。巧合的是，Paperspace 恰好位於我們紐約市的資料中心。所以，是的，它包括這一點。

Okay. Great. Thank you.

好的。偉大的。謝謝。

Mike Cikos, Needham & Company.

麥克·西科斯（Mike Cikos），尼達姆公司。

Hey, guys. Thanks for taking the questions here. And I just wanted to continue the thought on the data center optimization strategy that you guys are working through. With the Atlanta data center that you guys are announcing here, should we think about that as really taking on most of the AI workloads or is it freeing up capacity for you guys to further build out those GPUs in the existing footprint that you have?

嘿，夥計們。感謝您在這裡提出問題。我只是想繼續思考你們正在研究的資料中心優化策略。對於你們在這裡宣布的亞特蘭大資料中心，我們是否應該將其視為真正承擔了大部分人工智慧工作負載，還是為你們釋放了容量，以便在現有的佔地面積中進一步建構這些 GPU？

And the reason I'm asking is I know when you had gone through those three types of categories, the AI foundational model builders, the extenders, the consumers, I would think that at the very least the extenders would probably be more concerned about security. You'd need to be in like a tier three, maybe even a tier four data center given security restrictions.

我問的原因是我知道當你經歷過這三種類別時，人工智慧基礎模型建構者、擴展者、消費者，我認為至少擴展者可能會更關心安全性。鑑於安全限制，您需要位於三級甚至四級資料中心。

And then, can you help us think about the ability to re-architect the capacity that you have for these GPUs? I know we're announcing Atlanta today, but should we expect a more significant build out even beyond Atlanta?

然後，您能否幫助我們考慮重新建置這些 GPU 容量的能力？我知道我們今天要宣布亞特蘭大，但我們是否應該期待在亞特蘭大之外進行更重要的建設？

Yes. Thanks, Mike. I'll start with the last question. Should we expect a more significant build out? I don't think so. I think we have enough capacity now for the foreseeable future. But I think the way I look at it, Mike, is slightly differently, which is as we go up the stack, the workload goes from training to what we in the industry call as inferencing. And inferencing, it is important to minimize the latency.

是的。謝謝，麥克。我將從最後一個問題開始。我們是否應該期待更大規模的擴建？我不這麼認為。我認為我們現在有足夠的能力來應對可預見的未來。但麥克，我認為我的看法略有不同，即當我們向上堆疊時，工作量從訓練轉移到我們業內所說的推理。由此推斷，最小化延遲非常重要。

So if you ask me whether all of Atlanta is going to be consumed by AI, no, because that's why Matt and I have been talking about we need to consolidate and load balance across all of our footprint, because a lot of the AI capacity will also be in the West Coast or in Canada or in Europe or Asia, because as our inferencing demand grows, some of our customers are already asking us to put GPUs in inferencing mode closest to where their customer density is, whether it is West Coast or Asia. So luckily, we have a fairly distributed data center footprint across the globe.

因此，如果你問我整個亞特蘭大是否都會被人工智慧消耗，答案不會，因為這就是為什麼馬特和我一直在談論我們需要在所有足跡上進行整合和負載平衡，因為許多人工智慧容量將也可以在西海岸、加拿大、歐洲或亞洲，因為隨著我們的推理需求的增長，我們的一些客戶已經要求我們將GPU 置於最接近其客戶密度的地方的推理模式，無論是西海岸還是亞洲。幸運的是，我們在全球擁有相當分佈的資料中心。

So, we are going to take this opportunity to optimize and load balance and consolidate some of these data centers so that we can do a couple of things. One, in our core business, we can continue to look at improving our growth margins, get out of these expensive locations and leverage larger scale data centers like the one that we just talked about. But B, as the AI world moves from training to a more inferencing mode, we want to also have the ability to deploy the inferencing GPU capacity closest to where our customer demands are so that we can reduce latency and give them the horsepower they need for inferencing. So that's how I look at it. Matt, is there something you want to add?

因此，我們將藉此機會優化和負載平衡並整合其中一些資料中心，以便我們可以做一些事情。第一，在我們的核心業務中，我們可以繼續考慮提高我們的成長利潤，擺脫這些昂貴的地點，並利用更大規模的資料中心，就像我們剛才談到的那樣。但 B，隨著人工智慧世界從訓練轉向更多的推理模式，我們也希望能夠部署最接近客戶需求的推理 GPU 容量，以便我們可以減少延遲並為他們提供所需的馬力推理。我就是這麼看的。馬特，你還有什麼要補充的嗎？

I think you answered it, Patty. I think to answer the first part of your question, Mike, it's not going to be filled with the Atlanta data center. It's not going to be filled with GPU, but it will take a big chunk of our capacity. And part of that is just getting that GPU capacity that, as Paddy said, we put in some of our more expensive locations. There's a more cost effective way for us to do that and we'll migrate a lot of that. That will free up capacity in those more expensive locations.

我想你已經回答了，派蒂。麥克，我想回答你問題的第一部分，它不會被亞特蘭大資料中心填滿。它不會充滿 GPU，但會佔用我們很大一部分容量。其中一部分就是獲得 GPU 容量，正如 Paddy 所說，我們將其部署在一些更昂貴的地點。我們有一種更具成本效益的方法來做到這一點，我們將遷移其中的許多內容。這將釋放那些更昂貴地點的容量。

For workloads that are more relevant for that, where they require that latency or it's justified to have them in the more expensive locations. So, this is just a great opportunity for us to combine two very important initiatives. One is extending our GPU runway and the capacity we have. And the other is to just load balance and cost optimize in our existing footprint.

對於與此更相關的工作負載，它們需要延遲或有理由將它們放在更昂貴的位置。因此，這對我們來說是一個很好的機會，可以將兩項非常重要的措施結合起來。一是擴展我們的 GPU 跑道和容量。另一個是在我們現有的足跡中進行負載平衡和成本優化。

And just to add one more thing, to make it clear, we are not going to rush to fill out the capacity to the brim. So, this gives us land and space and power, but we are going to fill it up as part of our consolidation strategy and as our AI demand grows over the next few years.

再補充一件事，明確一點，我們不會急於將容量填滿。因此，這為我們提供了土地、空間和電力，但作為我們整合策略的一部分以及隨著我們未來幾年人工智慧需求的增長，我們將填補這些空間。

Got it. And then just a quick follow-up for Matt. Just coming back to the prepared remarks on the net dollar retention. So the takeaway here, we're stable at 97% and based on the guide that we have, the team's assuming that that 97% remains where we are for the rest of the year. And then the second clarification here, but I know last quarter you guys had cited 19 million in AI-related ARR. Can you give us an update on what that is this quarter?

知道了。然後是馬特的快速跟進。回到準備好的有關美元淨保留的言論。因此，我們的結論是，我們穩定在 97%，根據我們現有的指南，團隊假設 97% 在今年剩餘時間內保持不變。然後是第二個澄清，但我知道上個季度你們引用了 1900 萬個與人工智慧相關的 ARR。您能否向我們介紹本季的最新情況？

We're not going to guide, we're not going to provide the details at the platform level at this point going forward. But I think with the 200% increase in growth, the 200% growth rate, not just increase, but 200% growth rate on what we acquired, you should be able to figure it out in terms of what the ARR was for the AI business. And I'm sorry, the first part of the question was around the 97%. No, what we're trying to be is, I mean, we're doing a lot to drive NDR up.

我們不會提供指導，目前我們不會提供平台層級的詳細資訊。但我認為，隨著 200% 的成長，200% 的成長率，不僅僅是成長，而是我們收購的 200% 的成長率，你應該能夠根據人工智慧業務的 ARR 來弄清楚這一點。抱歉，問題的第一部分大約是 97%。不，我的意思是，我們正在努力提高 NDR。

Clearly, our goal is to try to get that above 100 and have that be a tailwind. And we're making good progress on a lot of the initiatives as Paddy talked about and the new products and the increased product velocity is contributing. But what we're seeing is primarily the expansion is just stubborn. It's not increasing at the pace at which we'd like to see it. And a lot of that is just our customers aren't growing as fast as they were previously and their recovery is not as fast as we would like.

顯然，我們的目標是努力將其提高到 100 以上，並使其成為順風車。正如帕迪所說，我們在許多舉措上都取得了良好進展，新產品和產品速度的提高正在做出貢獻。但我們看到的主要是擴張是頑固的。它並沒有以我們希望看到的速度成長。其中很大一部分原因是我們的客戶成長速度沒有以前快，而且他們的恢復速度也沒有我們希望的那麼快。

So I don't know, if we think it's going to be flat. It's just, I mean, we only show it on a rounded basis. And so it looks very flat, but we are seeing kind of green shoots and steady improvement in that. So, I think the plan and the guidance that we have doesn't require it to get any better than it is today. And so that people shouldn't worry about, but we're clearly working aggressively to try to get it up above 100. And I'm optimistic that we'll see movement in it. It won't stay completely flat, but -- because it's such minor movements, we're just kind of saying it's going to be stable.

所以我不知道我們是否認為它會持平。我的意思是，我們只是在四捨五入的基礎上展示它。所以它看起來非常平坦，但我們看到了一些萌芽和穩定改進。因此，我認為我們的計劃和指導並不要求它比今天更好。所以人們不應該擔心，但我們顯然正在積極努力，試圖將其提高到 100 以上。它不會完全保持平坦，但是——因為它的運動很小，所以我們只是說它會保持穩定。

Terrific. Thank you.

了不起。謝謝。

Pinjalim Bora, JP Morgan.

Pinjalim Bora，摩根大通。

Hi, great. Thanks for taking the questions and congrats on the results. Paddy, you have really strengthened the leadership team, obviously with the number of hires that you highlighted. As these leaders kind of settle into their seats, how should we think about some of the changes that you might be trying to drive? And are these changes more focused on for the next year or the second half of this year and any expenses related to those changes that we should be thinking about in the model?

嗨，太好了。感謝您提出問題並對結果表示祝賀。帕迪，你確實增強了領導團隊的實力，顯然是透過你強調的員工數量來實現的。當這些領導人就位時，我們應該如何考慮您可能試圖推動的一些變革？這些變化是否更集中於明年或今年下半年，以及我們應該在模型中考慮與這些變化相關的任何費用？

Yes. Thanks, Pinjalim, for the question. I'll answer it in the reverse order. Should we expect to see a different expense profile? No. I think we are going to be super disciplined. And I don't think there's anything fundamentally different that we are going to be doing that will call for a foundationally different looking P&L or expense profile. In terms of what we should expect in terms of their impact, I would say let's take the three key functions, right, on product and technology. Bratton obviously brings a wealth of AI/ML background.

是的。謝謝 Pinjalim 的提問。我會以相反的順序來回答。我們是否應該期望看到不同的費用狀況？不，我認為我們會非常自律。我認為我們將要做的事情不會有任何根本上的不同，這將需要看起來根本不同的損益表或費用概況。就我們應該預期的影響而言，我想說，讓我們以產品和技術上的三個關鍵功能為例。布拉頓顯然帶來了豐富的人工智慧/機器學習背景。

So, as we go through the rest of the year, you should hear a lot from us in terms of both core product innovation as well as our AI strategy. So, my objective is between now and end of the year, we have to pick up even more pace. As I said, we have doubled the product velocity, but we are looking forward to pushing that even higher. And we absolutely want to make our customers super successful on our platform and give them no reason to think about leaving us or leveraging any other infrastructure. So we want to really ramp up the pace of innovation, and you will see that this year. The second thing is AI strategy.

因此，在今年剩下的時間裡，您應該會從我們的核心產品創新和人工智慧策略方面聽到很多資訊。因此，我的目標是從現在到今年年底，我們必須加快腳步。正如我所說，我們已經將產品速度提高了一倍，但我們期待將其推得更高。我們絕對希望讓我們的客戶在我們的平台上取得超級成功，並且讓他們沒有理由考慮離開我們或利用任何其他基礎設施。因此，我們希望真正加快創新步伐，今年你會看到這一點。第二件事是人工智慧策略。

I want to make sure that we continue to refine our AI strategy in response to our customers. As I've been saying for the last couple of calls, we want the AI strategy to be our strategy to serve our customers. So, you're seeing a lot of different voices in the market in terms of companies that are chasing the big foundational model builders. That's not us. We want to be very disciplined to ensure that we know exactly which customers we're going after and what they need. And I strongly believe that with Broughton here now, we will come out on the right side of this AI strategy and make sure that we are doing it in a very responsible but very expedient fashion.

我想確保我們繼續完善我們的人工智慧策略，以回應我們的客戶。正如我在過去幾次電話會議中所說的那樣，我們希望人工智慧策略成為我們為客戶服務的策略。因此，你會在市場上看到很多不同的聲音，認為公司正在追逐大型基礎模型建構者。那不是我們。我們希望嚴格遵守紀律，以確保我們確切地知道我們要尋找哪些客戶以及他們需要什麼。我堅信，布勞頓現在在這裡，我們將站在這項人工智慧策略的正確一邊，並確保我們以非常負責任但非常有利的方式來做這件事。

In terms of Wade, you already saw his team put together a really remarkable deploy event in just a few days after he joined. We'll continue the drumbeat. We are bringing in some very strong leaders to increase our presence and our voice in the developer community to drum up interest and really show them that DigitalOcean is back to simplifying their lives and have us be the real destination choice for developers to learn new complex technologies and start their AI journey from. So you will start seeing that this year. Larry, on the other hand, from a CRO point of view, as I said, his mission is to complement our incredible best-of-class product-led growth motion.

就韋德而言，您已經看到他的團隊在他加入後的短短幾天內就組織了一次非常出色的部署活動。我們將繼續敲鼓。我們正在引進一些非常強大的領導者來增加我們在開發者社群中的影響力和聲音，以激發他們的興趣，並真正向他們展示DigitalOcean 又回到了簡化他們的生活，並使我們成為開發者學習新的複雜技術的真正目的地選擇並從此開始他們的AI之旅。所以今年你就會開始看到這一點。另一方面，拉里，從 CRO 的角度來看，正如我所說，他的使命是補充我們令人難以置信的一流產品主導的成長動力。

So on the sales, on the direct sales and indirect sales, we have a lot of work to do to figure out the best ways to complement the product-led growth motion. On customer success and support, we have a running start. We have a great team. And we are going to double down on the success we already have. And with the product velocity that you're already seeing from us, the customer success team and support team have plenty to work with in taking that message to our customers and help us drive up the NDR over the next several quarters. So hopefully that gave you a lot of color, pendulum.

因此，在銷售、直接銷售和間接銷售方面，我們還有很多工作要做，以找出補充產品主導的成長動力的最佳方法。在客戶的成功和支援方面，我們已經有了良好的開始。我們有一支很棒的團隊。我們將在已經取得的成功的基礎上加倍努力。憑藉您已經從我們這裡看到的產品速度，客戶成功團隊和支援團隊有很多工作要做，可以將這一訊息傳達給我們的客戶，並幫助我們在接下來的幾個季度提高 NDR。希望這能帶給你很多色彩，鐘擺。

And that concludes our question-and-answer session. I will now turn the call back over to CEO Paddy Srinivasan for closing remarks.

我們的問答環節到此結束。現在，我將把電話轉回給執行長帕迪·斯里尼瓦桑 (Paddy Srinivasan)，讓其致閉幕詞。

Again, thank you, everyone, for spending your hour with us. As I mentioned, we are super excited with the progress. We have a lot of work to do, and we are looking forward to staying engaged. And you all have a great rest of the day.

再次感謝大家與我們共度時光。正如我所提到的，我們對進展感到非常興奮。我們還有很多工作要做，我們期待繼續參與。你們今天休息得很愉快。

This concludes today's conference call. Thank you for your participation. You may now disconnect.

今天的電話會議到此結束。感謝您的參與。您現在可以斷開連線。