(AI) 2024 Q1 法說會逐字稿

Good day, and thank you for standing by. Welcome to the C3 AI's First Quarter Fiscal Year 2024 Conference Call. At this time, all participants are in a listen only mode. (Operator Instructions) Please be advised that today's conference is being recorded.

美好的一天，感謝您的支持。歡迎參加 C3 AI 2024 財年第一季度電話會議。此時，所有參與者都處於只聽模式。 （操作員指示）請注意，今天的會議正在錄製中。

I would now like to hand the conference over to your speaker today, Amit Berry. Please go ahead.

現在我想將會議交給今天的發言人阿米特·貝里 (Amit Berry)。請繼續。

Good afternoon, and welcome to C3 AI's earnings call for the first quarter of fiscal year 2024, which ended on July 31, 2023. My name is Amit Berry, and I lead Investor Relations at C3 AI. With me on the call today is Thomas Siebel, Chairman and Chief Executive Officer; and Juho Parkkinen, Chief Financial Officer. After the market closed today, we issued a press release with details regarding our first quarter results as well as a supplemental to our results. Both of which can be accessed through the Investor Relations section of our website at ir.c3.ai. This call is being webcast, and a replay will be available on our IR website following the conclusion of this call.

下午好，歡迎參加 C3 AI 於 2023 年 7 月 31 日結束的 2024 財年第一季度財報電話會議。我叫 Amit Berry，負責 C3 AI 的投資者關係。今天與我一起參加電話會議的是董事長兼首席執行官托馬斯·西貝爾 (Thomas Siebel)；和首席財務官 Juho Parkkinen。今天收盤後，我們發布了一份新聞稿，其中詳細介紹了我們第一季度的業績以及業績的補充。兩者均可通過我們網站 ir.c3.ai 的投資者關係部分訪問。本次電話會議正在網絡直播，本次電話會議結束後，我們的 IR 網站將提供重播。

During today's call, we will make statements related to our business that may be considered forward-looking under federal securities laws. These statements reflect our views only as of today and should not be considered representative of our views as of any subsequent date. We disclaim any obligation to update any forward-looking statements or outlook. These statements are subject to a variety of risks and uncertainties that could cause actual results to differ materially from expectations.

在今天的電話會議中，我們將發表與我們的業務相關的聲明，根據聯邦證券法，這些聲明可能被視為前瞻性聲明。這些聲明僅反映我們今天的觀點，不應被視為代表我們任何後續日期的觀點。我們不承擔更新任何前瞻性陳述或展望的義務。這些陳述受到各種風險和不確定性的影響，可能導致實際結果與預期存在重大差異。

For a further discussion on the material risks and other important factors that could affect our actual results, please refer to our filings with the SEC. All figures will be discussed on a non-GAAP basis unless otherwise noted. Also, during the course of today's call, we will refer to certain non-GAAP financial measures. A reconciliation of GAAP to non-GAAP measures is included in our press release.

有關可能影響我們實際結果的重大風險和其他重要因素的進一步討論，請參閱我們向 SEC 提交的文件。除非另有說明，所有數據都將在非公認會計原則的基礎上討論。此外，在今天的電話會議中，我們將提及某些非公認會計準則財務指標。我們的新聞稿中包含了 GAAP 與非 GAAP 指標的調節表。

Finally, at times in our prepared remarks, in response to your questions, we may discuss metrics that are incremental to our usual presentation to give greater insight into the dynamics of our business or our quarterly results. Please be advised that we may or may not continue to provide this additional detail in the future.

最後，有時在我們準備好的評論中，為了回答您的問題，我們可能會討論對我們通常的演示來說是增量的指標，以便更深入地了解我們的業務動態或季度業績。請注意，我們將來可能會也可能不會繼續提供此附加詳細信息。

And with that, let me turn the call over to Tom.

接下來，讓我把電話轉給湯姆。

Thank you, Amit. Good afternoon, everyone, and thank you for joining our call today. We're off to a strong start for fiscal year '24. Our revenue came in at the high end of our guidance, exceeded analyst consensus and we're seeing significant traction across our business. This is the 11th consecutive quarter as a public company in which we have met or exceeded our revenue guidance. Following the release of ChatGPT in November of 2022, we are seeing a dramatic increase in demand for enterprise AI adoption. In Q1, we experienced strong traction with our enterprise AI applications and especially strong traction with C3 Generative AI.

謝謝你，阿米特。大家下午好，感謝您今天加入我們的電話會議。我們的 24 財年有了一個良好的開端。我們的收入達到了指導的上限，超出了分析師的共識，我們看到整個業務的巨大吸引力。作為一家上市公司，這是我們連續第 11 個季度達到或超過我們的收入指導。 2022 年 11 月 ChatGPT 發布後，我們看到企業採用人工智能的需求急劇增加。在第一季度，我們的企業人工智能應用程序受到了強烈的關注，尤其是 C3 生成人工智能的強大吸引力。

Let's take a look at our revenue highlights for the first quarter. Total revenue for the quarter was $72.4 million, coming at the high end of guidance, that was $70 million to $72.5 million and exceeding the analyst consensus. Subscription revenue for the quarter was $61.4 million, constituting 85% of total revenue. Gross profit for the quarter was $40.5 million, representing a 56% gross margin. Non-GAAP gross profit for the quarter was $49.6 million, representing a 69% non-GAAP gross margin. GAAP RPO was $334.6 million, current RPO was $170.6 million.

讓我們來看看我們第一季度的收入亮點。該季度的總收入為 7,240 萬美元，處於指導值的高端，即 7,000 萬至 7,250 萬美元，超出了分析師的共識。該季度訂閱收入為 6140 萬美元，佔總收入的 85%。該季度毛利潤為 4050 萬美元，毛利率為 56%。該季度非 GAAP 毛利潤為 4960 萬美元，相當於 69% 的非 GAAP 毛利率。 GAAP RPO 為 3.346 億美元，當前 RPO 為 1.706 億美元。

GAAP net loss per share was $0.56, our non-GAAP net loss per share was $0.09, both exceeded analyst consensus expectations substantially. We finished the quarter with $809.6 million in cash, cash equivalents and investments, exceeding the average analyst consensus of $774.3 million. Net cash provided by operating activities was $3.9 million, and free cash flow was negative $8.9 million, significantly exceeding analyst consensus that was negative $38.7 million.

GAAP 每股淨虧損為 0.56 美元，非 GAAP 每股淨虧損為 0.09 美元，均大幅超出分析師一致預期。本季度結束時，我們的現金、現金等價物和投資為 8.096 億美元，超過分析師平均預期的 7.743 億美元。經營活動提供的淨現金為 390 萬美元，自由現金流為負 890 萬美元，大大超過分析師預期的負 3870 萬美元。

The market interest in applying enterprise AI to business processes appears to be expanding exponentially fueled by the interest in ChatGPT and other consumer generative AI tools initially released in -- late last year. CEOs, business leaders, military leaders and investors are all focused on how they can take advantage of these powerful new tools to improve operational processes. In Q1, we entered into new and expanded agreements with Saudi Arabia's smart city, NEOM; Nucor, steel company; Roche; sugar producer, Pantaleon in Central America; Ball Corporation; Cargill; Con ED; Shell; Tyson Foods and the U.S. Department of Defense.

由於對 ChatGPT 和去年年底最初發布的其他消費者生成人工智能工具的興趣，市場對將企業人工智能應用於業務流程的興趣似乎呈指數級增長。首席執行官、商界領袖、軍事領導人和投資者都關注如何利用這些強大的新工具來改進運營流程。第一季度，我們與沙特阿拉伯智慧城市 NEOM 簽訂了新的和擴展的協議；紐柯鋼鐵公司；羅氏；中美洲 Pantaleon 製糖商；波爾公司；嘉吉；聯合ED；殼;泰森食品和美國國防部。

Our partner ecosystem continues to expand. In Q1, we closed 60% of our agreements with and through our partner network, including Google Cloud, AWS, Microsoft and Booz Allen and Hamilton. A qualified partner opportunity has increased by over 100% in the past year, and our qualified pipeline with our cloud providers grew by 61% just from Q4 to Q1 -- Q4 '23 to Q4 '21 (sic) [Q1 '24]. C3 AI's federal business is showing significant strength with federal bookings up 39% compared with the year ago quarter. The company continues to expand its first work with the U.S. Department of Defense with new and expanded projects with the Chief Digital and AI Office, CDAO; the U.S. Marines Corps; U.S. Air Force, the Missile Defense Agency, and the Defense Counterintelligence and Security Agency.

我們的合作夥伴生態系統不斷擴大。第一季度，我們與合作夥伴網絡（包括 Google Cloud、AWS、Microsoft 和 Booz Allen and Hamilton）達成了 60% 的協議。去年，合格的合作夥伴機會增加了100% 以上，僅從第4 季度到第1 季度——23 年第4 季度到21 年第4 季度（原文如此）[24 年第1 季度]，我們與雲提供商的合格渠道就增長了61%。 C3 AI 的聯邦業務顯示出顯著的實力，聯邦預訂量與去年同期相比增長了 39%。該公司繼續擴大與美國國防部的首次合作，與首席數字和人工智能辦公室 CDAO 開展新的和擴展的項目；美國海軍陸戰隊；美國空軍、導彈防禦局和國防反情報與安全局。

C3 AI commercial customers, including Shell, Georgia-Pacific, Koch Industries, Bank of America, and others and the U.S. Department of Defense continue to expand their C3 application footprints increasingly now including C3 Generative AI, realizing outsized economic benefit from digital transformations using C3 enterprise AI.

C3 AI 商業客戶，包括殼牌、Georgia-Pacific、Koch Industries、美國銀行等，以及美國國防部現在繼續擴大其 C3 應用足跡，其中包括 C3 Generative AI，通過使用 C3 的數字化轉型實現巨大的經濟效益企業人工智能。

Let's talk about a few of these. First, the Department of Defense. Our business relationships with the Department of Defense are extensive and rapidly expanding. The DoD uses the C3 AI platform and C3 AI applications across many services, components and combatant commands to realize significant improvement in readiness and decision advantage.

我們來談談其中的一些。首先是國防部。我們與國防部的業務關係廣泛且迅速擴大。國防部在許多軍種、組件和作戰司令部中使用 C3 AI 平台和 C3 AI 應用程序，以實現戰備狀態和決策優勢的顯著改善。

One example, beginning in 2017, we started to work for the U.S. Air Force to improve the readiness and apply predictive maintenance for the E-3 Sentry, an aircraft that you probably know of is the AWACS. By fusing the handwritten maintenance notes with the flight logs and historical inventory, okay? And pilot logs, C3 AI readiness improved the Air Force's legacy maintenance procedures substantially. Following this initial project, the United States Air Force Rapid Sustainment Office selected C3 AI for additional readiness projects -- an additional readiness project called Condition-Based Maintenance Plus, CBM plus, to apply similar analytics-based predictive maintenance approaches to the B-1B strategic bomber and other aircraft weapon systems.

舉個例子，從 2017 年開始，我們開始為美國空軍工作，以提高 E-3 Sentry 的戰備狀態並應用預測性維護，您可能知道這種飛機是預警機。將手寫的維護記錄與飛行日誌和歷史庫存融合起來，好嗎？和飛行員日誌一樣，C3 AI 準備狀態大大改善了空軍的遺留維護程序。在這個初始項目之後，美國空軍快速維持辦公室選擇C3 AI 進行額外的戰備項目，這是一個名為“基於條件的維護增強版”(CBM plus) 的附加戰備項目，旨在將類似的基於分析的預測性維護方法應用於B-1B戰略轟炸機和其他飛機武器系統。

This configuration of C3 AI readiness for the United States Air Force called the Predictive Analytics and Decision Assistant or PANDA, went live into production and is now scaled out to over 16 Air Force aircraft weapon systems. This system PANDA was subsequently selected as the system of record for all United States Air Force predictive maintenance applications. This is the only system of record for an AI application in Department of Defense that we are aware of. The goal of C3 AI PANDA is to realize up to a 25% increase in overall aircraft mission capability. And when rolled out to all aircraft in the United States Air Force, this is budgeted to realize a $3 billion cost savings in maintenance and readiness.

這種為美國空軍準備的 C3 AI 配置稱為預測分析和決策助理 (PANDA)，已投入生產，現已擴展到超過 16 個空軍飛機武器系統。該系統 PANDA 隨後被選為所有美國空軍預測維護應用的記錄系統。這是我們所知的國防部人工智能應用的唯一記錄系統。 C3 AI PANDA的目標是實現飛機整體任務能力提高25%。當推廣到美國空軍的所有飛機時，預計可在維護和戰備方面節省 30 億美元的成本。

Talk for a minute about the CDAO, the Department of Defense Chief Digital and AI Office, this is the organization that has started with choosing -- with selecting the AI platform of record for all DoD. We began working with them less than a year ago. Initially to bring the C3 AI platform into production across a number of unclassified secret and top-secret [inquiries] as part of CDAO's Advana ecosystem, a centralized data repository for the entire Department of Defense.

談一談 CDAO，即國防部首席數字和人工智能辦公室，這個組織從選擇開始，為所有國防部選擇記錄的人工智能平台。不到一年前，我們開始與他們合作。最初是為了將 C3 AI 平台投入生產，作為 CDAO 的 Advana 生態系統的一部分，該生態系統是整個國防部的集中數據存儲庫。

Our first project showed out nodal analysis and contestant logistics can radically improve when AI systems are applied to U.S. Transportation Command or TRANSCOM data. This application took a simulation-based approach to provide options in response to global logistics disruptions. We're able to accelerate the time it takes to conduct this kind of nodal analysis from days to minutes. C3 AI has now been engaged less than a year later in a dozen projects through CDO -- CDAO, including contested logistics, strategic force readiness, supply chain visibility, commander's dashboards and Combined Joint All-Domain Command and Control.

我們的第一個項目表明，當人工智能係統應用於美國運輸司令部或 TRANSCOM 數據時，節點分析和參賽者後勤可以從根本上得到改善。該應用程序採用基於模擬的方法來提供應對全球物流中斷的選項。我們能夠將進行此類節點分析所需的時間從幾天縮短到幾分鐘。不到一年後，C3 AI 已通過 CDO - CDAO 參與了十幾個項目，包括有爭議的後勤、戰略部隊戰備、供應鏈可視性、指揮官儀表板以及聯合全域指揮與控制。

Segue to Shell. Shell has been an important customer since 2018. The C3 AI applications are continuing to expand across the entire Shell asset base, including upstream, downstream, integrated gas, renewables and retail to address asset integrity, optimization, ESG and predictive maintenance.

繼續到 Shell。自2018 年以來，殼牌一直是殼牌的重要客戶。C3 AI 應用程序不斷擴展到整個殼牌資產基礎，包括上游、下游、綜合天然氣、可再生能源和零售，以解決資產完整性、優化、ESG 和預測性維護問題。

Today, C3 -- Shell C3 AI predictive maintenance program monitors almost 20,000 pieces of equipment. And because C3 AI can identify failure in advance with very high levels of accuracy, this can both increase production and prevent potential disasters such as offshore oil rig failures, the cost of which may be incalculable. The economic benefit for Shell is enormous and they have given presentations at Bank of America and other conferences where they estimate it to be in excess of $2 billion per year.

如今，C3——殼牌C3人工智能預測維護程序監控著近20,000台設備。而且由於 C3 AI 可以以非常高的準確度提前識別故障，這既可以提高產量，又可以防止潛在的災難，例如海上石油鑽井平台故障，其成本可能無法估量。殼牌公司的經濟效益是巨大的，他們在美國銀行和其他會議上做了演講，他們估計每年的經濟效益超過 20 億美元。

In the past 3 months, Shell and C3 AI have further expanded deployments applying AI-based estimation techniques in subsurface reservoir management, deployed a new C3 AI based Shell oil condition monitoring application for its customers to reduce unplanned downtime and optimize maintenance of heavy-duty assets and expanded Shell's use of the C3 AI ESG solution.

在過去的三個月中，殼牌和C3 AI 進一步擴大了在地下油藏管理中應用基於人工智能的估計技術的部署，為客戶部署了基於C3 AI 的新型殼牌油況監測應用程序，以減少計劃外停機並優化重型油藏的維護資產並擴大了殼牌對 C3 AI ESG 解決方案的使用。

Let's switch to Koch Industries. We continue to expand our partnership with Koch particularly at Georgia-Pacific and Flint Hills Resources. We generated -- we generate almost 4 million monthly predictions across 300-plus assets using our reliability and C3 AI supply chain applications. Georgia-Pacific is realizing up to 5% improvement in overall equipment effectiveness.

讓我們轉向科赫工業。我們繼續擴大與科赫的合作夥伴關係，特別是在 Georgia-Pacific 和 Flint Hills Resources 方面。我們使用我們的可靠性和 C3 AI 供應鏈應用程序，每月對 300 多種資產生成近 400 萬個預測。 Georgia-Pacific 的整體設備效率提高了 5%。

Koch also initiated 2 generative AI projects to help process data, documents and files. Georgia-Pacific is improving efficiency in triaging and resolving equipment and production and maintenance issues to automate processing for paper manufacturing. Flint Hill Resources is using C3 Generative AI to increase efficiency and improve information access in commodity trading operations.

科赫還啟動了兩個生成式人工智能項目來幫助處理數據、文檔和文件。 Georgia-Pacific 正在提高分類和解決設備以及生產和維護問題的效率，以實現造紙過程的自動化。 Flint Hill Resources 正在使用 C3 Generative AI 來提高商品交易操作的效率並改善信息訪問。

Now at Bank of America, our C3 AI applications are deployed to deliver customer insights, optimize business workflows and provide recommendations to its Liquidity Product Specialists and Treasury Sales Officers. The liquidity team is responsible for managing the bank's cash flow. Every day, over 500 liquidity and sales users log in to the C3 AI applications, the bank is applying AI best AI-based techniques to assess client responsiveness and sensitivity in a fluctuating interest-rate environment.

現在，在美國銀行，我們部署 C3 AI 應用程序來提供客戶洞察、優化業務工作流程並為其流動性產品專家和財務銷售官提供建議。流動性團隊負責管理銀行的現金流。每天，超過 500 名流動性和銷售用戶登錄 C3 AI 應用程序，該銀行正在應用基於 AI 的最佳 AI 技術來評估客戶在利率波動環境中的響應能力和敏感性。

Three applications are in production today. Bank of America and others are in development. All are expected to generate significant annual benefits, especially in a higher interest rate environment where balance retention, optimal pricing of interest rates and efficiency of sales and operations become important drivers of profitability and expense reduction.

目前，三個應用程序已投入生產。美國銀行和其他銀行正在開發中。預計所有這些都將產生顯著的年度效益，特別是在利率較高的環境下，餘額保留、利率最優定價以及銷售和運營效率成為盈利和費用減少的重要驅動力。

Let's talk for a minute about C3 Generative AI because ladies and gentlemen, this is big. Now by combining the power of the tried, tested and proven C3 AI platform that we've built in the course of the last 14 years. With large language models that you've been reading about every day. C3 Generative AI enables immediate interaction with the relevant and frequently massive [corpus] of data, documents and signals associated with enterprise domains. For example, machines, factories, systems, supply chains, natural phenomena, biological systems and operating divisions.

我們來談談 C3 生成式人工智能，女士們先生們，這很重要。現在，通過結合我們在過去 14 年中構建的經過嘗試、測試和驗證的 C3 AI 平台的強大功能。擁有您每天都在閱讀的大型語言模型。 C3 生成式 AI 能夠與企業領域相關的相關數據、文檔和信號（通常是大量數據、文檔和信號）進行即時交互。例如，機器、工廠、系統、供應鏈、自然現象、生物系統和運營部門。

We use a natural language interface to rapidly locate, retrieve and present relevant data across an entire enterprise's information systems, allowing users to use the full power of AI to optimize productivity, monitor systems, forecast to manage and in general, understand what is happening, what will happen, how to plan and how to maximize efficiency. The production adoption and customer success since our initial March 2023, C3 Generative AI release has been immediate. In the last quarter, C3 AI closed 8 new agreements for C3 Generative AI addressing use cases across multiple industries, including agriculture, consumer packaged goods, defense, intelligence, manufacturing, state and local government, oil and gas and utilities.

我們使用自然語言界面在整個企業的信息系統中快速定位、檢索和呈現相關數據，使用戶能夠利用人工智能的全部力量來優化生產力、監控系統、預測管理以及總體上了解正在發生的事情，將會發生什麼，如何計劃以及如何最大限度地提高效率。自 2023 年 3 月首次發布 C3 Generative AI 以來，產品採用和客戶成功立竿見影。上季度，C3 AI 簽署了 8 項新的 C3 Generative AI 協議，解決多個行業的用例，包括農業、消費品、國防、情報、製造、州和地方政府、石油和天然氣以及公用事業。

To date, we have closed 12 generative AI agreements and have a pipeline of more than 140 qualified generative C3 Generative AI enterprise opportunities. Over 140 now in less than 6 months. So putting in this perspective, our qualified pipeline of generative AI sales opportunities exceeds that of any other product in our product line that we've reduced -- that we've of all the products we've released in the last 14 years. This is big.

迄今為止，我們已簽署 12 項生成式 AI 協議，並擁有 140 多個合格的生成式 C3 生成式 AI 企業機會。不到 6 個月的時間，現在已超過 140 個。因此，從這個角度來看，我們合格的生成式人工智能銷售機會渠道超過了我們產品線中我們減少的任何其他產品——我們在過去 14 年中發布的所有產品。這可大了。

To meet market demand, C3 AI today announced the immediate availability of the new C3 Generative AI suite, including 28 new domain-specific generative AI solutions for industries, business processes and enterprise systems. C3 Generative AI provides fine tuned, tailored domain-specific generative AI solutions that mitigate the crippling problems that prevent the widespread industry adoption of LLMs.

為了滿足市場需求，C3 AI 今天宣布立即推出新的 C3 生成式 AI 套件，其中包括 28 個針對行業、業務流程和企業系統的新的特定領域生成式 AI 解決方案。 C3 Generative AI 提供經過微調、量身定制的特定領域的生成式 AI 解決方案，可緩解阻礙 LLM 行業廣泛採用的嚴重問題。

The market response to our generative AI offerings is simply staggering. We believe with the advent of generative AI may more than double the addressable -- the immediately addressable market opportunity available to C3 AI. And now with our Generative -- with our suite of generative AI products out the door, you can expect that we will be investing in the coming quarters to promote, market and support these initiatives.

市場對我們的生成式人工智能產品的反應簡直令人震驚。我們相信，隨著生成式人工智能的出現，C3 人工智能可獲得的可尋址市場機會可能會增加一倍以上。現在，隨著我們的生成式人工智能產品套件的推出，您可以預期我們將在未來幾個季度進行投資，以推廣、營銷和支持這些舉措。

The 28 applications that we released today and are available today include are in 3 categories: C3 Generative AI for industries. This includes generative AI for aerospace, for defense, for financial services. C3 Generative AI for health care, intelligence, manufacturing. C3 Generative AI for oil and gas, for telecommunications and for utilities.

我們今天發布並現已推出的 28 個應用程序包括 3 個類別：面向行業的 C3 生成人工智能。這包括用於航空航天、國防和金融服務的生成式人工智能。 C3 用於醫療保健、智能、製造的生成式人工智能。 C3 用於石油和天然氣、電信和公用事業的生成式人工智能。

Our family of products to address the requirements of business processes include C3 Generative AI for customer service, C3 Generative AI for energy management, C3 Generative AI for ESG, C3 Generative AI for Finance, for human resources, for process optimization, for reliability and C3 Generative AI for supply chain.

我們滿足業務流程要求的產品系列包括用於客戶服務的C3 生成式AI、用於能源管理的C3 生成式AI、用於ESG 的C3 生成式AI、用於財務、人力資源、流程優化、可靠性和C3 的C3 生成式AI供應鏈的生成式人工智能。

Finally, we're releasing a family and importantly, okay, of C3 Generative AI for enterprise systems. Okay. Ladies and gentlemen, this is not slide-ware that's being offered by software managers. This is production software available to order today available to ship today and be able to install tomorrow and will be live in 12 weeks. These products include C3 Generative AI for Databricks, C3 Generative AI for Microsoft Dynamics 365, C3 Generative AI for Oracle ERP, C3 Generative AI for Oracle NetSuite, C3 Generative AI for Palantir, for Salesforce, for SAP, for ServiceNow, for Snowflake and C3 Generative AI for Workday.

最後，我們發布了一個重要的家族，那就是用於企業系統的 C3 生成人工智能。好的。女士們先生們，這不是軟件經​​理提供的幻燈片。這是一款生產軟件，今天即可訂購，今天即可發貨，明天即可安裝，並將在 12 週內上線。這些產品包括適用於Databricks 的C3 生成式AI、適用於Microsoft Dynamics 365 的C3 生成式AI、適用於Oracle ERP 的C3 生成式AI、適用於Oracle NetSuite 的C3 生成式AI、適用於Palantir、Salesforce、SAP 、ServiceNow、Snowflake 和C3 的C3 生成式AI工作日的生成式人工智能。

LLM support is immediately available in these products Falcon 40B, Llama 2, Flan-T5, Azure GPT-3.5, AWS Bedrock Claude 2, Google PaLM 2, OpenAI GPT-3.5 and MPT-7B. Additional support will be announced for leading LLM's as the market develops. By combining the power of LLMs and generative AI. With the tried test and proven C3 AI platform, we believe C3 Generative AI solves the troubling problems endemic to all other generative AI solutions currently being proposed in the marketplace.

這些產品 Falcon 40B、Llama 2、Flan-T5、Azure GPT-3.5、AWS Bedrock Claude 2、Google PaLM 2、OpenAI GPT-3.5 和 MPT-7B 立即提供 LLM 支持。隨著市場的發展，將宣布對領先的法學碩士提供額外的支持。通過結合法學碩士和生成式人工智能的力量。憑藉經過反複測試和驗證的 C3 AI 平台，我們相信 C3 Generative AI 可以解決目前市場上提出的所有其他生成型 AI 解決方案所普遍存在的令人不安的問題。

Firstly, the answers from C3 generative AI are deterministic, not random. I mean every time you ask the same question, you get the same answer. You don't get a different answer. All answers are immediately traceable with 1 click to ground truth. So honestly, LLM's that you're applying with on ChatGPT, okay, and Google Bard or whatever, they don't tell you where the answers come from because they don't know where the answer is coming from.

首先，C3 生成式人工智能的答案是確定性的，而不是隨機的。我的意思是，每次你問同樣的問題，你都會得到同樣的答案。你不會得到不同的答案。所有答案都可以通過一鍵點擊立即追踪到真實情況。老實說，你在 ChatGPT 上申請的法學碩士，好吧，還有 Google Bard 或其他什麼，他們不會告訴你答案來自哪裡，因為他們不知道答案來自哪裡。

With C3 AI, we can tell you -- we give you a link where immediately, you can go to ground truth. No matter what the question is. How am I doing against my diversity goals in North America, okay? Which of my product lines are the least profitable? How am I doing, that's my -- how -- my readiness levels of F-35 squadrons in Central Europe. How am I doing, where the gaps in my satellite coverage into Paycom, it'll give you the answer, tell you that exactly where the answer came from.

借助 C3 AI，我們可以告訴您——我們為您提供一個鏈接，您可以立即找到真實情況。不管問題是什麼。我在北美的多元化目標做得怎麼樣，好嗎？我的哪些產品線利潤最低？我做得怎麼樣，這就是我在中歐的 F-35 中隊的戰備水平。我做得怎麼樣，我的衛星覆蓋範圍到 Paycom 的差距在哪裡，它會給你答案，告訴你答案到底來自哪裡。

With C3 AI, the LLM is by combining the LLM and use -- utilizing all the investment of the platform, the LLMs are firewalled from the data, minimizing the risk of LLM cause data [exfiltration], see Samsung for details. You've all read about it. In closing, the many LLM caused cyber attack vectors that are now becoming evident.

借助 C3 AI，LLM 是通過將 LLM 與使用相結合——利用平台的所有投資，將 LLM 與數據隔離開來，最大限度地降低 LLM 造成數據[洩露]的風險，詳情請參閱三星。你們都讀過。最後，許多法學碩士造成的網絡攻擊媒介現在變得越來越明顯。

There's a lot of research. If you look at what's going on the research that Zico Kolter is doing at Carnegie Mellon, you'll see that they're finding really troubling cybersecurity problems associated with the LLMs that do not manifest themselves in the C3 solution. The C3 AI platform -- the C3 Generative AI solution assures the enforcement of all enterprise access and cybersecurity controls, in addition providing n-factor authentication and data encryption, both in motion and at rest.

有很多研究。如果您查看 Zico Kolter 在卡內基梅隆大學所做的研究，您會發現他們發現與 LLM 相關的真正令人不安的網絡安全問題，而這些問題在 C3 解決方案中並未體現出來。 C3 AI 平台——C3 生成式 AI 解決方案可確保所有企業訪問和網絡安全控制的實施，此外還提供動態和靜態的 n 因素身份驗證和數據加密。

LLM reasoning is limited to enterprise-owned and enterprise-licensed data, mitigating the potentially unbounded risk that you're now starting to read about, okay, in the literature. Associated with IP liability provided from both LLM, virtually unlimited IP liability associated with other LLM solutions. Because C3 AI -- Generative AI is LLM agnostic, not specifically dependent, okay? We allow enterprise to interchange LLMs at will, taking advantage of the ongoing massive innovation that we're going to see in LLM's coming in the coming years, and you can just switch one in and switch one out and all the applications keep running.

LLM 推理僅限於企業擁有和企業許可的數據，減輕了您現在開始在文獻中閱讀的潛在無限風險。與兩個法學碩士提供的知識產權責任相關，與其他法學碩士解決方案相關的幾乎無限的知識產權責任。因為 C3 AI——生成式 AI 與 LLM 無關，不是特別依賴，好嗎？我們允許企業隨意交換法學碩士，充分利用未來幾年我們將在法學碩士中看到的持續大規模創新，您只需切換一位進入並切換另一位，所有應用程序都會繼續運行。

Finally, the way that C3 [AM] is structured -- C3 Generative AI structured, the fact that we have firewalled the LLM from the data itself, and we go along on this some other time. We've basically almost eliminated any risk of hallucinations. So it doesn't -- basically does not hallucinate. If it doesn't know the answer, it comes back and says, I don't know the answer, I can't tell you the answer or the answer -- I don't have access to the answer. It's not going to make up some line of creative pros that you've all seen from the LLM's that you've played with on the Internet.

最後，C3 [AM] 的結構方式——C3 生成式 AI 結構，事實上我們已經將 LLM 與數據本身隔離開來，我們會在其他時間繼續討論這一點。我們基本上已經消除了任何產生幻覺的風險。所以它不會——基本上不會產生幻覺。如果它不知道答案，它會回來說，我不知道答案，我無法告訴你答案或答案——我無法獲得答案。它不會構成你在互聯網上玩過的法學碩士中看到的一些創意專業人士。

All C3 Generative AI applications can be fully deployed within 12 weeks for $250,000, and they're available today. Okay, right now, actually on the AWS Marketplace, the Google Cloud Marketplace and the Azure Marketplace. The licensing model is straightforward. C3 AI supports the customer to bring its generative application into production. We do it in 12 weeks. After that, the customer continues to pay per vCPU or per vGCPU (sic) [vGPU] hour with volume discounts.

所有 C3 生成式 AI 應用程序均可在 12 週內完成部署，費用為 250,000 美元，並且現已上市。好的，現在，實際上是在 AWS Marketplace、Google Cloud Marketplace 和 Azure Marketplace 上。許可模式很簡單。 C3 AI 支持客戶將其生成應用程序投入生產。我們會在 12 週內完成。此後，客戶繼續按 vCPU 或每個 vGCPU（原文如此）[vGPU] 小時付費並享受批量折扣。

The generative AI market appears huge. Bloomberg Intelligence predicts this market will reach $1.3 trillion by 2032. Much of this will accrue to chip manufacturers, cloud service providers and professional service providers. The balance will accrue to generative AI applications. If we double-click on this generative AI applications box expected by Bloomberg to reach $280 billion in the same time frame. We believe the bulk of this will accrue to providers of software that enable businesses to apply LLM's to improve business processes and associated decision making. Now countless startups today are proposing companies based on generative AI for one industry niche or another, okay, whether it be doctors' offices or insurance or automotive or pharmaceutical companies and what have you.

生成式人工智能市場似乎巨大。 Bloomberg Intelligence 預測，到 2032 年，這一市場將達到 1.3 萬億美元。其中大部分將由芯片製造商、雲服務提供商和專業服務提供商獲得。餘額將用於生成人工智能應用程序。如果我們雙擊這個生成式人工智能應用程序框，彭博社預計在同一時間範圍內將達到 2800 億美元。我們相信，其中大部分將歸軟件提供商所有，這些軟件提供商使企業能夠應用法學碩士來改進業務流程和相關決策。現在，無數的初創公司正在為一個或另一個行業推薦基於生成人工智能的公司，好吧，無論是醫生辦公室、保險、汽車還是製藥公司等等。

They're taking their pitches around to venture capitalists all up and down in Silicon Valley and many are getting significant funding in some cases with private market valuations in billions of dollars. And their big idea -- in each case, a handful of former -- a handful of entrepreneurs proposed to apply LLM's to develop market-specific, business process specific, okay, and application-specific LLM solutions. Well, C3 AI offers these solutions today, and we offer them from a well-capitalized company with almost 1,000 seasoned professionals, partnered with a powerful market partner ecosystem and a global footprint.

他們向矽谷各地的風險投資家進行推介，許多人在某些情況下獲得了大量資金，私募市場估值高達數十億美元。他們的偉大想法 - 在每種情況下，少數前 - 少數企業家提議應用法學碩士來開發特定於市場、特定於業務流程、好的和特定於應用程序的法學碩士解決方案。 C3 AI 今天提供了這些解決方案，我們是一家資本雄厚的公司，擁有近 1,000 名經驗豐富的專業人士，並與強大的市場合作夥伴生態系統合作，業務遍及全球。

The market opportunity appears enormous. We have demonstrated in recent quarters that we have solid management and expense controls in place. In Q4 of last year, our cash flow operations -- from operations was a positive $27 million. In Q1 of '24, cash flow from operations was $3.9 million. Non-GAAP operating loss substantially beat market expectations in both Q4 of '23 and Q1 of '24.

市場機會顯得巨大。最近幾個季度，我們已經證明我們擁有可靠的管理和費用控制。去年第四季度，我們的運營現金流為正 2700 萬美元。 2024 年第一季度，運營現金流為 390 萬美元。 23 年第 4 季度和 24 年第 1 季度的非 GAAP 運營虧損大幅超出市場預期。

We finished Q1 of '24 with $809.6 million in cash and investments, a decrease of $2 8 million from the prior quarter. Now after careful consideration with our leadership, and our marketing partners, we have made the decision to invest in generative AI to invest in lead generation, to invest in branding, to invest in market awareness and to invest in market and customer success related to our generative AI solutions.

2024 年第一季度，我們的現金和投資為 8.096 億美元，比上一季度減少了 2800 萬美元。現在，經過與我們的領導層和營銷合作夥伴的仔細考慮，我們決定投資生成式人工智能，投資於潛在客戶開發，投資於品牌，投資於市場意識，投資於與我們的產品相關的市場和客戶成功。生成式人工智能解決方案。

The market opportunity is immediate, and we intend to seize it. So while we still expect to be cash positive in Q4, this year and in [year] '25. We will be investing in our generative AI solutions. And at this time, do not expect to be non-GAAP profitable in Q4 of '24. You can expect -- we're still -- we want to see what actually happens in the market in the next couple of quarters and how this plays out. But it looks the right now you can expect us -- and we'll update you on this as we know more, but you're going to see this happen in some place in Q2 to Q4 time frame of fiscal year '25.

市場機會就在眼前，我們打算抓住它。因此，儘管我們仍然預計第四季度、今年和 25 年將實現現金正值。我們將投資我們的生成式人工智能解決方案。目前，預計不會在 24 年第四季度實現非 GAAP 盈利。你可以期待——我們仍然——我們希望看到未來幾個季度市場實際發生的情況以及結果如何。但現在看起來您可以期待我們- 我們會在了解更多信息後向您通報這一情況，但您將在25 財年第二季度至第四季度的某個時間框架內看到這種情況發生。

We have a tight rate on our financial controls. We are operating a disciplined business, and we are making this decision to invest in generative AI because we are confident that it is in the best interest of our shareholders. C3 AI was well ahead of its time predicting the scale of the opportunity in enterprise AI applications. When we began, the market was nascent. And as the market has developed and expanded, we have expanded our branding and our marketing offers -- our market offerings to meet market expectations.

我們的財務控制非常嚴格。我們正在經營一家紀律嚴明的企業，我們做出投資生成式人工智能的決定是因為我們相信這符合股東的最佳利益。 C3 AI 遠遠領先於時代，預測了企業人工智能應用的機會規模。當我們開始時，市場還處於萌芽階段。隨著市場的發展和擴大，我們擴大了我們的品牌和營銷產品——我們的市場產品以滿足市場期望。

While we believe for over a decade that this market would be quite large, even we could not have anticipated the size and growth rate of the AI market that we now address. C3 AI has spent the last 14 years preparing for this opportunity, and now the market is coming to us. Our technology foundation is tried, tested and proven. We have a strong portfolio of enterprise AI applications in place. We have a pricing and distribution model that meets the needs of the market. We have a quality brand, a strong partner ecosystem and a long list of satisfied customers.

雖然十多年來我們都相信這個市場將相當大，但即使我們也無法預料到我們現在所關注的人工智能市場的規模和增長率。 C3 AI花了14年的時間來準備這個機會，現在市場正在向我們走來。我們的技術基礎經過嘗試、測試和驗證。我們擁有強大的企業人工智能應用程序組合。我們擁有滿足市場需求的定價和分銷模式。我們擁有優質的品牌、強大的合作夥伴生態系統和一長串滿意的客戶。

We are armed with a battalion of professional services employees -- professional employees deployed around the world, our partner ecosystem with Google Cloud, AWS, Azure, Booz Allen, Baker Hughes and others is well developed and expanding. The company is well capitalized with a senior leadership team.

我們擁有一批專業服務員工——部署在世界各地的專業員工，我們與穀歌云、AWS、Azure、博思艾倫、貝克休斯等的合作夥伴生態系統發展良好並不斷擴大。公司資本雄厚，擁有資深的領導團隊。

And now I will turn it over to my colleague, Juho Parkkinen, our Chief Financial Officer, to talk about more specific financial details associated with our performance last quarter. Juho?

現在我將把它交給我的同事、我們的首席財務官 Juho Parkkinen，討論與我們上季度業績相關的更具體的財務細節。朱霍？

Thank you, Tom. I will now provide a recap of our financial results, some color around the expected drivers of our financial results for the remainder of the year and walk you through our second quarter and full year fiscal '24 guidance. Finally, I will conclude with some additional information related to the consumption-based revenue model we introduced a year ago. All figures will be discussed on a non-GAAP basis unless otherwise noted.

謝謝你，湯姆。我現在將回顧我們的財務業績，對今年剩餘時間的財務業績的預期驅動因素進行一些闡述，並引導您了解我們的第二季度和全年 24 財年指導。最後，我將提供一些與我們一年前推出的基於消費的收入模式相關的附加信息。除非另有說明，所有數據都將在非公認會計原則的基礎上討論。

First quarter revenue increased 10.8% year-over-year to $72.4 million. Subscription revenue was up 7.6% and represented 85% of total revenue. As we discussed last quarter, we expected professional services to be within our historical range of 10% to 20% with our actual professional services coming in at 15% of the mix. Gross profit for the first quarter was $49.6 million, and gross margin was 68.6%. I would like to remind everyone on the call that we expect short-term pressure on our gross margin due to a higher mix of targets, which carry a higher cost of revenue during the target phase of our customer life cycle. We are pleased with our progress in managing expenses and our success in getting the entire employee base brought into a mission of managing our company with expense discipline.

第一季度收入同比增長 10.8% 至 7240 萬美元。訂閱收入增長 7.6%，佔總收入的 85%。正如我們上季度所討論的，我們預計專業服務將在 10% 至 20% 的歷史範圍內，而我們實際的專業服務將佔組合的 15%。第一季度毛利潤為4960萬美元，毛利率為68.6%。我想提醒與會者，由於目標組合的增加，我們預計毛利率將面臨短期壓力，這會在客戶生命週期的目標階段帶來更高的收入成本。我們對我們在管理費用方面取得的進展以及我們成功地讓整個員工群體承擔起按照費用紀律管理我們公司的使命感到高興。

Our success in expense management is reflected in our first quarter operating loss of $20.7 million, which is better than our guidance of a loss of $25 million to $30 million. Operating loss margin was 28.6%. As Tom mentioned, the generative AI opportunity is so massive that we believe it is in the best interest of our company and the shareholders to leverage our first-mover advantage, seize the market opportunity by making incremental investments in sales, marketing and customer success. As a result, we are revising our 2024 expense guidance to reflect these investments. I will provide details when I discuss guidance.

我們在費用管理方面的成功反映在我們第一季度運營虧損 2,070 萬美元，這比我們預期的虧損 2,500 萬至 3,000 萬美元要好。營業虧損率為28.6%。正如Tom 提到的，生成式人工智能機會如此巨大，我們相信利用我們的先發優勢，通過在銷售、營銷和客戶成功方面進行增量投資來抓住市場機會，符合我們公司和股東的最佳利益。因此，我們正在修訂 2024 年費用指南以反映這些投資。當我討論指導時，我會提供詳細信息。

Turning to RPO and bookings. We reported GAAP RPO of $334.6 million, which is down 27% from last year. This was expected as we transitioned to consumption-based agreements. Current GAAP RPO is $170.6 million, which is down 1.7% from last year. We continue to see positive trends diversifying our project bookings, with Q1 targets representing 8 industry sectors.

轉向 RPO 和預訂。我們報告的 GAAP RPO 為 3.346 億美元，比去年下降 27%。當我們轉向基於消費的協議時，這是預料之中的。當前 GAAP RPO 為 1.706 億美元，比去年下降 1.7%。我們繼續看到項目預訂多元化的積極趨勢，第一季度的目標代表 8 個行業領域。

Turning to cash flow. Operating cash flow was $3.9 million in the quarter and free cash flow was a negative $8.9 million, reflecting expenses related to the build-out of our new corporate headquarters. We closed the quarter with a strong balance sheet with $809.6 million of cash, cash equivalents and investments. Total cash and investments balance was decreased by only $2.8 million from last quarter. We continue to be very well capitalized. Our accounts receivables are in good shape at $122.6 million at the end of Q1 compared to $134.6 million last quarter. Total allowance for bad debt remains low at $359,000 and we have no concerns regarding collections.

轉向現金流。本季度運營現金流為 390 萬美元，自由現金流為負 890 萬美元，反映了與我們新公司總部擴建相關的費用。本季度結束時，我們的資產負債表強勁，現金、現金等價物和投資為 8.096 億美元。現金和投資餘額總額較上季度僅減少 280 萬美元。我們的資本仍然充足。我們的應收賬款狀況良好，第一季度末為 1.226 億美元，而上季度為 1.346 億美元。壞賬準備金總額仍然很低，為 359,000 美元，我們對收款不存在任何擔憂。

As it relates to our consumption business model, I would like to provide 2 key updates. First, we previously told you that we are assuming a 70% conversion rate of pilot phase engagement to production phase. At quarter end, we had signed a total of 73 pilots, 70 of these are active meaning that they were either converted in the original 6-month term, extended for 1 to 2 months or are currently negotiated for a production license. Second, regarding consumption data, our actual vCPU consumption from the last 3 quarters is slightly higher than our original estimates. Finally, our customer engagement increased to 334 from 287 in Q4 '23.

由於它與我們的消費商業模式有關，我想提供兩個關鍵更新。首先，我們之前告訴過您，我們假設試驗階段到生產階段的轉化率為 70%。截至季度末，我們總共簽署了73 個試點項目，其中70 個項目處於活躍狀態，這意味著它們要么在原來的6 個月期限內進行了轉換，要么延長了1 至2 個月，要么目前正在就生產許可證進行談判。其次，關於消耗數據，我們過去三個季度的實際 vCPU 消耗略高於我們最初的估計。最後，我們的客戶參與度從 23 年第 4 季度的 287 人增加到 334 人。

Now turning to guidance. We're guiding Q2 revenue to a range of $72 million to $76.5 million. We expect our non-GAAP loss from operations to range from negative $27 million to negative $40 million.

現在轉向指導。我們預計第二季度收入在 7200 萬美元至 7650 萬美元之間。我們預計我們的非 GAAP 運營虧損將在負 2700 萬美元到負 4000 萬美元之間。

As mentioned before, the generative AI opportunity is so massive that we have decided to invest for success. As a result, we expect to cross the non-GAAP profitability in the course of FY '25. We will provide more updates on this in future calls. We expect to be cash flow positive for Q4 '24 and the full fiscal year FY '25. For full year FY '24, we are maintaining our previous guidance for revenue in the range of $295 million to $320 million, an increase in the non-GAAP loss from operations to a range between negative $70 million and negative $100 million.

如前所述，生成式人工智能的機會如此巨大，以至於我們決定進行投資以獲得成功。因此，我們預計將在 25 財年超越非公認會計準則盈利能力。我們將在以後的電話會議中提供更多相關更新。我們預計 24 年第四季度和 25 財年整個財年的現金流將為正。對於 24 財年全年，我們維持之前在 2.95 億美元至 3.2 億美元範圍內的收入指引，非 GAAP 運營虧損增加至負 7000 萬美元至負 1 億美元之間。

I'd now like to turn the call over to the operator to begin the Q&A session.

我現在想將呼叫轉給接線員以開始問答環節。

(Operator Instructions) Our first question comes from Patrick Walravens with JMP Securities.

（操作員指令）我們的第一個問題來自 JMP 證券的 Patrick Walravens。

Great. Thank you very much. So it's great to hear about the demand levels and all the activity. Tom, can you talk a little bit about how the linearity in the quarter, how that was and how things closed out? At your investor event, you told us that you closed 16 agreements. We ended up with 32 -- but if you look back a quarter, you had 10 at the middle and you ended up with 43. So it makes it seem like maybe the second half wasn't quite as good as you would have hoped, but I don't know. Maybe I'm interpreting that wrong, too?

偉大的。非常感謝。因此，很高興聽到需求水平和所有活動。湯姆，您能談談本季度的線性情況、情況以及結果如何嗎？在您的投資者活動中，您告訴我們您達成了 16 項協議。我們最終得到了 32 分——但是如果你回顧一個季度，你會發現中間有 10 分，最終得到 43 分。所以這看起來下半場可能並不像你希望的那麼好，但我不知道。也許我也理解錯了？

Well, maybe the first half was great.

嗯，也許上半場很棒。

Right. Okay.

正確的。好的。

That's like the [half glass full] model. I would say that if the -- this might have been our best quarter ever in terms of linearity. I'm not sure, okay, in terms of being in terms of predictability. So without getting too specific, I would say -- the business volume in the course of the quarter was activity in the course of the quarter was quite consistent.

這就像[半杯滿]模型。我想說，如果——就線性而言，這可能是我們有史以來最好的季度。我不確定，好吧，就可預測性而言。因此，在不太具體的情況下，我想說的是，本季度的業務量和本季度的活動相當一致。

And then if we multiply the average TCV times the number of deals, right, then we get a total TCV number, which -- I mean, you guys are the only ones who disclose it. So thank you for that transparency. And if you look at that, that was around $26 million this quarter. And then last quarter, again, was $52 million, almost twice as much. So I just want to make sure we understand what's going on here. Is the TCV not a good indication of how we're actually doing in the quarter?

然後，如果我們將平均 TCV 乘以交易數量，對吧，我們就會得到 TCV 總數，我的意思是，你們是唯一披露該數據的人。所以感謝您的透明度。如果你看一下，就會發現本季度的金額約為 2600 萬美元。上個季度的銷售額再次達到 5200 萬美元，幾乎是兩倍。所以我只是想確保我們了解這裡發生的事情。 TCV 是否不能很好地表明我們本季度的實際表現？

Well, we used to compensate people on TCV, and that's back when we used to do [$10 million, $20 million, $30 million, $40 million, $50 million] deals, Pat. And now we're doing $250,000 projects in generative AI and $0.5 million projects in for the balance of our enterprise projects. The generative AI products last 12 weeks, the other pilots last -- projects last, generally last up to 6 months -- generally 6 months. So it's a cost -- I mean, it's sure as -- I mean, it follows directly that TCV goes down, RPO goes down. I mean -- and by the way, gross margins go down in the short run, okay?

好吧，我們過去常常通過 TCV 來補償人們，那是在我們過去做 [1000 萬美元、2000 萬美元、3000 萬美元、4000 萬美元、5000 萬美元] 交易的時候，帕特。現在，我們正在開展價值 25 萬美元的生成人工智能項目，以及 50 萬美元的企業項目餘額項目。生成式人工智能產品持續 12 週，其他試點項目持續長達 6 個月，一般為 6 個月。所以這是一項成本 - 我的意思是，它肯定是 - 我的意思是，它直接導致 TCV 下降，RPO 下降。我的意思是——順便說一句，毛利率在短期內會下降，好嗎？

Because the gross margin when you -- when we're doing these generative AI pilots for $0.25 million, whoever it may be, I mean, there is no way we are not going to succeed at any cost, let's say, on the first 50 of these guys. And if we have to overinvest to make that pilot successful, we're going to do it. And so I'm not certain that RPO is meaningful going forward. I'm not certain that TCV, I've been trying to drive that down as you're well aware, for well, 15 -- 20 quarters. 20 quarters ago, our TCV, I think, was about $15 million. Average contract revenue, it was about $15 million. And our average contract value, I think, is less than $1 million, right? So that's -- this is a good thing.

因為當我們以 25 萬美元進行這些生成式人工智能試點時，無論是誰，我的意思是，我們不可能不惜一切代價取得成功，比方說，在前 50 個項目中這些人。如果我們必須過度投資才能使試點成功，我們就會這麼做。因此，我不確定 RPO 未來是否有意義。我不確定 TCV，正如你所知，我一直在努力降低 TCV，持續 15 - 20 個季度。我認為 20 個季度前，我們的 TCV 約為 1500 萬美元。平均合同收入約為1500萬美元。我認為我們的平均合同價值不到 100 萬美元，對吧？所以這是一件好事。

Okay. Great. And then lastly, you hope probably for you. You have a footnote on the balance sheet where there's a related party, presumably Baker Hughes that still has a $75 million -- you saw a $75 million in accounts receivable from them. That's the same as last quarter. So are you guys okay with that?

好的。偉大的。最後，你可能也希望如此。資產負債表上有一個腳註，其中有一個關聯方，大概是貝克休斯，仍然擁有 7500 萬美元——您看到他們的應收賬款有 7500 萬美元。這與上季度相同。那麼你們對此還滿意嗎？

It's a lot bigger than $75 million.

這比 7500 萬美元要大得多。

No, it's total. Yes, we're okay with that. I'm not entirely sure how to interpret your question, and we have no collection concerns from any of our customers. Our bad debt reserve is only at $359,000. And all of our customers are paying on time and in full. So no concerns there.

不，是全部。是的，我們對此表示同意。我不完全確定如何解釋您的問題，而且我們的任何客戶都沒有收款問題。我們的壞賬準備金僅為 359,000 美元。我們所有的客戶都按時全額付款。所以不用擔心。

(Operator Instructions) Our next question comes from Mike Cikos with Needham.

（操作員說明）我們的下一個問題來自 Needham 的 Mike Cikos。

I appreciate the new pronunciation on the last name. A couple of questions. First on the guidance. And I appreciate this pivot and you guys are trying to take advantage of this opportunity where it really feels like the Gen AI has come online big, right? I think my question is more around the guidance, if you will. And where I'm going with this is, given the increase that we're talking to in the go-to-market investments, which is obviously acting as a drag on your operating losses, no question about it. But why aren't we seeing some sort of benefit when looking at the fiscal '24 revenues? Why maintain that guidance as we sit here today?

我很欣賞姓氏的新發音。有幾個問題。首先是指導。我很欣賞這個支點，你們正在嘗試利用這個機會，感覺 Gen AI 已經上線了，對嗎？如果你願意的話，我認為我的問題更多的是圍繞指導。我的觀點是，考慮到我們正在談論的市場投資的增加，這顯然會拖累您的運營損失，這是毫無疑問的。但為什麼我們在查看 24 財年的收入時沒有看到某種好處呢？當我們今天坐在這裡時，為什麼要維持這一指導方針呢？

Mike, I think, we've been -- we're doing the best we could do since we've been a public company to be credible in setting expectations. And we have met or exceeded expectations in every quarter that we've been a public company. Okay. Now we are in uncharted territory still with the consumption pricing model and we're definitely in uncharted territory with generative AI, okay?

邁克，我認為，自從我們成為一家上市公司以來，我們一直在盡我們所能，在設定期望方面保持可信。作為一家上市公司，我們每個季度都達到或超出了預期。好的。現在我們仍處於消費定價模型的未知領域，而且我們肯定也處於生成人工智能的未知領域，好嗎？

Now let's take -- I were to take the sum of all the spreadsheets of all my product groups and the business plans and you can be sure that they come up to a larger number than we've talked about in guidance, okay? But our position is, we feel with the guidance -- we're comfortable with the guidance that's out there today, okay?

現在讓我們來看一下——我要計算所有產品組和業務計劃的所有電子表格的總和，您可以確定它們的數字比我們在指南中討論的數字要大，好嗎？但我們的立場是，我們對指導感到滿意 - 我們對今天的指導感到滿意，好嗎？

And at the same time, we feel comfortable that after a couple of quarters of acceleration, we're going to be able to look you straight in the eye and say we are seeing -- guys, we're planning on significantly accelerated growth. But I don't want to do it prematurely. I don't want to lose credibility. And I think this is the responsible thing to do.

與此同時，我們感到很舒服的是，經過幾個季度的加速之後，我們將能夠直視你們的眼睛並說我們正在看到——伙計們，我們計劃顯著加速增長。但我不想過早這樣做。我不想失去信譽。我認為這是負責任的事情。

And, I guess, another one totally understand the commentary on RPO and even CRPO declining. I guess, it's more for Juho here, but with the transition to the consumption model, should we be seeing CRPO remain more resilient as these consumption pilots starts to convert or are consumption pilots even when they move to production, not necessarily going to be showing up in CRPO. Can you provide any more color on that, please?

而且，我想，另一位完全理解對 RPO 甚至 CRPO 下降的評論。我想，這對Juho 來說更重要，但隨著向消費模式的轉變，我們是否應該看到CRPO 保持更具彈性，因為這些消費試點開始轉換，或者即使在轉向生產時也是消費試點，不一定會表現出來在 CRPO 中。您能提供更多顏色嗎？

Yes, yes, absolutely. So effectively, the CRPO is flat, right? And the way the consumption business model works is that we start with a pilot phase, that pilot amount would be RPO in the given quarter that we signed that deal. The consumption phase unless the customer were to sign up for volume discounts is never going to be an RPO because it's going to be after consumed invoicing. So you only see ever that in revenue.

是的，是的，絕對是的。那麼實際上，CRPO 是持平的，對嗎？消費業務模式的運作方式是，我們從試點階段開始，試點金額將是我們簽署該協議的特定季度的 RPO。除非客戶簽署批量折扣，否則消費階段永遠不會成為 RPO，因為它將在消費發票之後進行。所以你只能在收入中看到這一點。

So if it were a 100% consumption model, RPO would be 0.

所以如果是100%消費模式，RPO就是0。

That is exactly right.

這是完全正確的。

Okay. And the expectation is that most of these customers would not be signing up for those larger volume commitments. So that is going to be an expected drag on the RPO and CRPO?

好的。預計這些客戶中的大多數不會簽署這些更大的批量承諾。那麼這將是對 RPO 和 CRPO 的預期拖累嗎？

Yes. Yes.

是的。是的。

And that's why makes it easier to buy rather than saying [$10 million, $20 million, $30 million, $40 million, $50 million], I think one deal we did was $0.5 billion, if I'm not mistaken, okay? Pretty my well, $300 million plus a couple of things. We're saying, hey, it's $0.5 million, so if you like it, keep it, okay? And -- so after they pay the $0.5 million if it goes that way, there's no RPO.

這就是為什麼更容易購買而不是說[1000萬美元、2000萬美元、3000萬美元、4000萬美元、5000萬美元]，我認為我們做的一筆交易是5億美元，如果我沒記錯的話，好嗎？好吧，3 億美元加上一些東西。我們說，嘿，這是 50 萬美元，所以如果你喜歡它，就保留它，好嗎？而且，如果按照這種方式，他們支付了 50 萬美元後，就沒有 RPO 了。

That's right.

這是正確的。

Got it. Got it. And maybe just one more, if I could, and apologies to be taking all the time here, but I did just want to circle up. I know that you guys are talking about the C3 Generative AI pilots being $250,000 12 weeks. And the remaining product lines, I believe, and correct me if I'm wrong, but you have typically about 6 months for those pilots. Can you help us think through what --is it just the time to value on these Gen AI pilots is so much quicker that you think that these customers can convert that much faster? Yes.

知道了。知道了。如果可以的話，也許還可以再來一次，很抱歉在這裡耽擱了所有時間，但我確實只是想繞一圈。我知道你們談論的是 C3 生成式 AI 試點項目 12 週 25 萬美元。我相信其餘的產品線，如果我錯了，請糾正我，但這些試點通常有大約 6 個月的時間。您能否幫助我們思考一下——是否只是這些 Gen AI 試點的價值時間變得如此之快，以至於您認為這些客戶可以更快地進行轉化？是的。

It is quicker, Mike. In one case, we might have to add -- load all the data, model supply chain and build machine learning models that fit the scale of the enterprise of a cargo, which is roughly $100 billion business or the United States Air Force, which is a pretty big business, okay? So generative AI, we don't have to do any in it, okay? We just load their data into a deep learning model, and it kind of takes the learnings of those data, stores the data in a vector store. And we're kind of -- we are the masters of the universe, at aggregating structured data, nonstructured data, sensor data, enterprise data, okay, images, what have you, into unified federated image.

邁克，速度更快。在一種情況下，我們可能必須添加——加載所有數據、對供應鏈進行建模並構建適合貨運企業規模的機器學習模型，該企業的規模約為1000 億美元的企業或美國空軍，該企業的規模約為1000 億美元。一筆相當大的生意，好嗎？所以生成式人工智能，我們不需要做任何事情，好嗎？我們只是將他們的數據加載到深度學習模型中，它會學習這些數據，將數據存儲在向量存儲中。我們是宇宙的主人，能夠將結構化數據、非結構化數據、傳感器數據、企業數據，好吧，圖像，等等，聚合成統一的聯合圖像。

We have 14 years of that. We're really good at that, okay? So that's easy, okay? And then, okay, we -- all the mappings are worked out by 1 deep learning model, okay, they're stored in a vector data store. And then the -- so we don't have these huge data science projects that we have at these other organizations. So yes, the time to value is faster. The implementation effort is easier, and it's technically, honestly, it's an order of magnitude easier problem.

我們已經有14年了。我們真的很擅長，好嗎？所以這很容易，好嗎？然後，好吧，我們——所有映射都是由 1 個深度學習模型計算出來的，好吧，它們存儲在矢量數據存儲中。然後，我們沒有其他組織那樣的大型數據科學項目。所以，是的，實現價值的時間更快。實施工作更容易，而且從技術上講，老實說，這是一個數量級更容易的問題。

And there is nobody who doesn't want to talk about it.

沒有人不想談論它。

(Operator Instructions) Our next question comes from Kingsley Crane with Canaccord Genuity.

（操作員說明）我們的下一個問題來自 Kingsley Crane 和 Canaccord Genuity。

Congrats on the results. It sounds like your plan is to invest more in legion, branding, market awareness, customer success. You've mentioned that you have more than 140 qualified leads in gen AI. It seems like you've done tremendously well in generative leads. So as we think about the incremental change to the profit guidance, are you balancing investments between customer success and pilot conversion with that of lead Gen and brand awareness?

祝賀結果。聽起來你的計劃是在軍團、品牌、市場知名度、客戶成功方面進行更多投資。您提到您在 gen AI 領域擁有超過 140 名合格的潛在客戶。看來您在生成線索方面做得非常好。因此，當我們考慮利潤指引的增量變化時，您是否正在平衡客戶成功和飛行員轉化之間的投資與潛在客戶和品牌知名度之間的投資？

I'm sorry, what was the -- how are we balancing between customer success and lead Gen. Okay. A lot of this is branding and lead Gen, Kingsley, is what we're looking at. Okay? Kind of like we used to do in 2021 when we established the brand for enterprise AI, they worked out pretty well. And we're going out to plant a flag on this generative AI market, and we're going to -- we're first to market, but how many companies out there have 28 enterprise-generative AI solutions in the world, okay? I know how many? Exactly one, okay?

抱歉，我們如何在客戶成功和首席總經理之間取得平衡。好的。其中很大一部分是品牌塑造，而我們正在關注的是首席一代金斯利。好的？就像我們在 2021 年建立企業人工智能品牌時所做的那樣，它們的效果非常好。我們要在這個生成式人工智能市場上插上一面旗幟，我們要——我們是第一個進入市場的公司，但是世界上有多少公司擁有 28 種企業生成式人工智能解決方案，好嗎？我知道有多少？正好一個，好嗎？

And we're going to communicate that. We're going to make it available. So that's what the bulk of it is. At the same time, if we have a customer in any one of these markets, where we need to throw in its resource to make them successful with their pilot, you can be sure we're going to make them successful with that project. And as we get down the learning curve, we'll get increasingly efficient at it. Okay. And gross margins go up.

我們將就此進行溝通。我們將使其可用。這就是大部分內容。與此同時，如果我們在這些市場中的任何一個市場有客戶，我們需要投入資源以使他們的試點取得成功，那麼您可以確信我們將使他們在該項目中取得成功。隨著學習曲線的下降，我們的效率會越來越高。好的。毛利率上升。

Okay. That makes a lot of sense. And so if I could ask one more. hoping you gain some clarity on the 28 domain-specific generative AI solutions. So -- for example, if you're an oil and gas customer, you're building a solution in sales, and this is ultimately linked into Salesforce, is that requiring 3 separate apps, like how would that be consumed and priced?

好的。這很有意義。那麼我是否可以再問一個問題。希望您對 28 個特定領域的生成式 AI 解決方案有一定的了解。例如，如果您是石油和天然氣客戶，您正在構建一個銷售解決方案，這最終會鏈接到 Salesforce，這是否需要 3 個獨立的應用程序，例如如何使用和定價？

That will be one. Basically, it's price per CPU. I mean that looks like -- I mean it's going to be on a [judgment] basis, whether it is 3 projects or whether it's a -- whether the union of them is 1 generative AI application. Well, as you've described it, the union of them is one generative AI application, it'll be $0.25 million to bring it live in 12 weeks. And after that, they pay $0.35 per vCPU hour or vGPU hour.

那將是一個。基本上，它是每個 CPU 的價格。我的意思是，這看起來像——我的意思是這將基於[判斷]，無論是3個項目還是——它們的聯合是否是1個生成式人工智能應用程序。嗯，正如您所描述的，它們的結合是一個生成式人工智能應用程序，需要 25 萬美元才能在 12 週內投入使用。之後，他們為每 vCPU 小時或 vGPU 小時支付 0.35 美元。

Okay. Very helpful. Keep up the good work.

好的。很有幫助。保持良好的工作。

And as it relates to when it gets to run-time pricing, it doesn't really matter whether it's 1 application or whether it's 3, it's going to be the same amount of run time.

由於涉及到運行時定價，因此無論是 1 個應用程序還是 3 個應用程序，運行時間都相同。

(Operator Instructions) Our next question comes from Pinjalim Bora with JPMorgan.

（操作員指令）我們的下一個問題來自摩根大通的 Pinjalim Bora。

This is Noah on for Pinjalim. So on the semi pilots that are active at the moment, if we exclude the pilots that have been extended 1 or 2 months, is there any way to parse out how many of the pilots are under production licenses? And I have a quick follow-up.

這是諾亞 (Noah) 為 Pinjalim 做的節目。那麼對於目前活躍的半試點，如果我們排除已經延期1、2個月的試點，有沒有辦法解析出有多少試點是有生產許可證的？我有一個快速的跟進。

I think -- thanks for the question. So I think at this point, the way we are looking at this that there were 73 pilot deals that we've been doing, 70 are either converted or in the process of the pilot or we're negotiating a production license on those. I think the meaningful amount or meaningful message you should take from this that out of 73 pilots, we only have 3 no's. So we have a pretty -- we feel very comfortable and very bullish about how that pilot program is currently progressing.

我想——謝謝你的提問。所以我認為，目前我們看待這個問題的方式是，我們一直在進行 73 項試點交易，其中 70 項要么已經轉換，要么正在進行試點，或者我們正在就這些交易進行生產許可證談判。我認為您應該從中得到有意義的數量或有意義的信息，在 73 名飛行員中，我們只有 3 名沒有。所以我們對試點項目目前的進展感到非常滿意和非常樂觀。

Understood. And then maybe just a double quick on the gross margins. I know you commented that with (inaudible).

明白了。然後毛利率可能會翻倍。我知道您對此發表了評論（聽不清）。

Let me comment on the no's. The no wasn't that the pilot wasn't successful. Okay. The no because I know these exactly what they are, okay? And they were hugely successful. That said, what happened is the genius CIO, okay, went to the CEO and said, oh, we're going to build this ourselves out of a bunch of tinker toys, so let him go through that.

讓我對“不”發表評論。問題並不是試點沒有成功。好的。不，因為我確切地知道它們是什麼，好嗎？他們取得了巨大的成功。也就是說，發生的事情是天才首席信息官，好吧，他去找首席執行官說，哦，我們要用一堆修補玩具自己建造這個，所以讓他經歷一下。

Okay. He's going to go do that for about 2 years. They're not going to be able to -- they're going to have cybersecurity problems. They're going to have IP infringement problems. They're going to have data exfiltration problems. They're going to have random answers and they'll be back. So for sales cycle there was just a little bit longer than we thought. They're not lost. They just lost -- they're just suspended. Sorry, could...

好的。他將繼續這樣做大約兩年。他們將無法——他們將遇到網絡安全問題。他們將面臨知識產權侵權問題。他們將會遇到數據洩露問題。他們將得到隨機答案，然後他們就會回來。因此，銷售週期比我們想像的要長一點。他們沒有迷路。他們只是輸了——他們只是被停賽了。抱歉，可以...

No, I appreciate the clarity. And just a quick follow-up on the gross margins. Just any way you could kind of help us with our model going forward in terms of how to think about gross margins? I know you laid out some commentary about this quarter's impact, but just any additional thoughts there would be helpful for the year.

不，我很欣賞這種清晰度。以及對毛利率的快速跟進。您能以任何方式幫助我們在如何考慮毛利率方面推進我們的模型嗎？我知道您對本季度的影響提出了一些評論，但任何其他想法都會對今年有所幫助。

I mean, I think -- no, the punchline is that we're still expecting some margin pressure on it. And as there's going to be more pilots, it's going to be margin pressure until the consumption becomes a more dominant portion of the revenue stream, which would then offset it and start picking up the margin. So continue to expect some pressure still on the gross margin.

我的意思是，我認為 - 不，重點是我們仍然預計會出現一些利潤壓力。隨著試點數量的增加，利潤率將面臨壓力，直到消費成為收入流中更占主導地位的部分，然後這將抵消它並開始提高利潤率。因此，繼續預計毛利率仍將面臨一些壓力。

(Operator Instructions) Our next question comes from Sanjit Singh with Morgan Stanley.

（操作員說明）我們的下一個問題來自摩根士丹利的 Sanjit Singh。

I had one for Tom and one for Juho. Tom, what's the vision around sort of multimodal. There's a lot of interest around the language models. But as you think about the different diffusion models, video, audio, image. What's the vision around supporting those types of models if multimodal becomes the dominant deployment architecture for enterprise AI?

我給 Tom 準備了一份，給 Juho 準備了一份。湯姆，多式聯運的願景是什麼？人們對語言模型很感興趣。但當你考慮不同的傳播模型、視頻、音頻、圖像時。如果多模式成為企業人工智能的主要部署架構，支持這些類型模型的願景是什麼？

Are you talking about data, Sanjit? I'm not certain I understand the question.

你在談論數據嗎，Sanjit？我不確定我是否理解這個問題。

Yes. What I was referring to is like, obviously, like the GPT models or language models and they've taken the world by storm, but they're other AI models that deal with image, audio, video with other sources of data as we think of?

是的。顯然，我指的是 GPT 模型或語言模型，它們已經席捲了世界，但它們是處理圖像、音頻、視頻以及我們認為的其他數據源的其他人工智能模型的？

So you are commenting on the fact that these large language models tend to be almost exclusively limited to, okay, text, HTML and code. So other sorts of data, they don't know how to ingest Okay. Good. Good. Okay. Now we -- so let's talk about this. We are the masters of the universe, ingesting what you call multimodal data. images, okay, images from space, trajectories of hypersonics, high-speed telemetry, trading volume, the rate at which electrons are going across the grid, enterprise data, free text.

所以你評論的是這樣一個事實：這些大型語言模型往往幾乎完全局限於文本、HTML 和代碼。所以其他類型的數據，他們不知道如何攝取好吧。好的。好的。好的。現在我們——讓我們來談談這個。我們是宇宙的主人，吸收所謂的多模式數據。圖像，好吧，來自太空的圖像、高超音速飛行軌跡、高速遙測、交易量、電子穿過電網的速率、企業數據、自由文本。

And so we're using our standard architecture to ingest those data, okay? We're using one of our standard deep learning models to basically parse out this data and store all the relationships in a vector data store. Okay? All the large language model we're using for is interacting with you and me, okay, to handle the natural language to understand what we're saying and to take the answer back from the data and give it to us in pros, okay, rather than some gibberish that might be skewed out of SAP.

所以我們使用我們的標準架構來攝取這些數據，好嗎？我們使用標準深度學習模型之一來基本上解析這些數據並將所有關係存儲在矢量數據存儲中。好的？我們使用的所有大型語言模型都是與你和我進行交互，好吧，處理自然語言以理解我們所說的內容，並從數據中獲取答案並將其提供給我們專業人士，好吧，而不是SAP 中可能歪曲的一些亂碼。

Right. No, it makes perfect sense.

正確的。不，這是完全有道理的。

That is one of the reasons why people find our generative AI solution attractive as we're -- I mean, we're tried, tested and proven at ingesting any kind of data they could think of.

這就是人們發現我們的生成式人工智能解決方案有吸引力的原因之一，因為我們——我的意思是，我們在吸收他們能想到的任何類型的數據方面都經過了嘗試、測試和證明。

Understood. And then the question for Juho is, if I sort of look at the presentation and we sort of look at where we are in the sort of transition on phase 1, Phase 2. It sounds like we've just started sort of Phase 2 and the [grass] sort of implies that we'll put to get to revenue neutral by 7 quarters in, we're about 4 quarters in. And then revenue accretive about 8 quarters -- 8 quarters in so about 3 or 4 quarters away. Is that still the time lines we should be thinking about in terms of revenue acceleration? Any color around that would be helpful.

明白了。然後 Juho 的問題是，如果我看看演示文稿，我們看看我們在第一階段、第二階段的過渡中處於什麼位置。聽起來我們剛剛開始第二階段， [草]有點意味著我們將在7 個季度（大約4 個季度）之前實現收入中性。然後大約8 個季度收入增加- 8 個季度，所以大約還有3 或4 個季度。這仍然是我們應該在收入加速方面考慮的時間線嗎？周圍的任何顏色都會有幫助。

So Sanjit, the chart that you're looking at, I think you should think about this as a kind of a per customer basis, right? Like it's not necessarily the entirety of how our business is going. But the idea is that as we now have some of the original early pilots from last year's Q2 and Q3, they're starting in the Phase 2 category. And as I mentioned in my prepared remarks, we have preliminary data on actual vCPU consumption for that first 3 quarters, and it's slightly above what we've modeled before. So we are in this fourth quarter of the transition, and we are starting to see some very positive indicators with respect to how the consumption will run for these consumption-based deals.

Sanjit，您正在查看的圖表，我認為您應該將其視為一種基於每個客戶的基礎，對吧？好像這不一定是我們業務發展的全部。但我們的想法是，由於我們現在擁有去年第二季度和第三季度的一些原始早期試點項目，因此它們將從第二階段類別開始。正如我在準備好的發言中提到的，我們有前 3 個季度實際 vCPU 消耗的初步數據，它略高於我們之前建模的數據。因此，我們正處於轉型的第四季度，我們開始看到一些非常積極的指標，這些指標表明這些基於消費的交易的消費將如何運行。

(Operator Instructions) our final question comes from Michael Turits with KeyBanc Capital Markets.

（操作員說明）我們的最後一個問題來自 KeyBanc Capital Markets 的 Michael Turits。

This is Eric Heath on for Michael. So I wanted to ask on Baker Hughes, a 2-part question. Just wonder if you can give us some color on what changed with the relationship that they're no longer considered a related party?

我是埃里克·希思 (Eric Heath) 替邁克爾發言。所以我想問關於貝克休斯的問題，這個問題分為兩部分。只是想知道您是否可以告訴我們一些關於他們不再被視為關聯方的關係發生了什麼變化的信息？

And then secondly, I hope this isn't 2 nuances, but if I take the [$16.5] million of bigger revenue contribution for 2 months in the quarter and kind of extrapolate that out for an additional month. I get about, I don't know, $24 million versus what we were thinking around $20 million. So, I guess, my question is, how is the Baker Hughes contribution in the quarter compared to your expectations? And any way to understand how the non-Baker Hughes business did relative to your guidance?

其次，我希望這不是兩個細微差別，但如果我在本季度 2 個月內獲得 [1650 萬美元] 的較大收入貢獻，並推斷出另外一個月的收入貢獻。我不知道，我得到的金額是 2400 萬美元，而我們原本的想法是 2000 萬美元左右。所以，我想，我的問題是，與您的預期相比，貝克休斯本季度的貢獻如何？有什麼方法可以了解非貝克休斯業務相對於您的指導的表現如何？

First of all, Baker Hughes is not a related party because they monetized some of their stock. Remember, they bought them stock some time ago for about $3 and they sold it for, I forgot what the rough number was. I think it would be offer, I don't know, but for nothing. Okay. And they sold it for a lot. So it's a pretty darn good trade, okay? And today, because they own less than 4 point -- for less than 5%. By definition, they're no longer a related party. As it relates to the bigger use revenue, he should actually know that. Didn't we provide that in the memo? So in other words, that we wrote like 3 quarters ago? That's right. I mean it's -- I'm sorry, I forgot to ask the question.

首先，貝克休斯不是關聯方，因為他們將部分股票貨幣化。請記住，他們不久前以大約 3 美元的價格購買了他們的股票，然後以 3 美元的價格出售，我忘記了大概的數字是多少。我想這會是一個報價，我不知道，但毫無意義。好的。他們賣了很多錢。所以這是一筆非常好的交易，好嗎？而今天，因為他們擁有的股份還不到 4 個百分點——不到 5%。根據定義，他們不再是關聯方。因為這關係到更大的使用收入，他其實應該知道這一點。我們在備忘錄中沒有提供這一點嗎？換句話說，我們是在三個季度前寫的？這是正確的。我的意思是——對不起，我忘了問這個問題。

What was your name?

你叫什麼名字？

Tom, it's Eric from KeyBanc Capital.

湯姆，我是來自 KeyBanc Capital 的埃里克。

Okay. Yes. No, we're actually -- it's on our website. It's on our IR site. You're going to be able to see what the minimum Baker Hughes revenue is. We've provided you that in great detail, and it's on the IR site.

好的。是的。不，我們實際上——它在我們的網站上。它位於我們的 IR 網站上。您將能夠看到貝克休斯的最低收入是多少。我們已在 IR 網站上為您提供了詳細信息。

Anyway, just kind of quickly frame how it was in the quarter relative to your expectations, the contribution (inaudible).

不管怎樣，只是快速地描述一下本季度相對於您的預期、貢獻（聽不清）的情況。

It was exactly what we expected.

這正是我們所期望的。

That's right. It was exactly what we expected.

這是正確的。這正是我們所期望的。

I guess, that was our last question. Ladies and gentlemen, so Tom and Juho are out. Thank you for your time. Thank you for your attention, and we look forward to providing you an update at the end of our second quarter. So thanks a lot. Stay tuned, and hopefully, we'll have some exciting things to report.

我想，這是我們最後一個問題。女士們先生們，湯姆和朱霍出去了。感謝您的時間。感謝您的關注，我們期待在第二季度末向您提供最新情況。非常感謝。請繼續關注，希望我們能報告一些令人興奮的事情。

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

謝謝。今天的電話會議到此結束。感謝您的參與。您現在可以斷開連接。