C3.ai Inc (AI) 2023 Q4 法說會逐字稿

Good afternoon, and welcome to C3 AI's earnings call for the fourth quarter fiscal year 2023, which ended on April 30, 2023. My name is Amit Barry, and I lead Investor Relations at C3 AI.

下午好，歡迎參加C3 AI 2023財年第四季（截至2023年4月30日）的財報電話會議。我是Amit Barry，負責C3 AI的投資人關係。

With me on the call today is Tom Siebel, Chairman and Chief Executive Officer; and Juho Parkkinen, Chief Financial Officer. After market close today, we issued a press release with details regarding our fourth quarter results as well as the supplemental of 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 the call.

今天與我一同參加電話會議的有董事長兼執行長湯姆‧西貝爾 (Tom Siebel) 和財務長尤霍‧帕基寧 (Juho Parkkinen)。今天收盤後，我們發布了新聞稿，詳細介紹了第四季度業績以及補充業績報告，您可以透過我們網站 ir.c3.ai 的投資者關係頁面查看這兩份文件。本次電話會議正在進行網路直播，會議結束後，您可以在我們的投資者關係網站上觀看回放。

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 be caused -- that could cause the actual results to differ materially from expectations. For a further discussion on the material risks and other important factors affect our actual results, please refer to our filings with the SEC.

在今天的電話會議中，我們將就公司業務發表一些可能被視為聯邦證券法意義上的前瞻性聲明。這些聲明僅反映我們截至今日的觀點，不應被視為代表我們未來任何日期的觀點。我們不承擔更新任何前瞻性聲明或展望的義務。這些聲明受到多種風險和不確定因素的影響，這些風險和不確定因素可能導致實際結果與預期有重大差異。有關重大風險和其他影響我們實際結果的重要因素的進一步討論，請參閱我們向美國證券交易委員會提交的文件。

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. Finally, at times in our prepared remarks, in response to your questions, we may discuss metrics that are incremental to our usual business 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.

除非另有說明，所有數據均以非公認會計準則（非GAAP）為基礎進行討論。此外，在今天的電話會議中，我們將提及某些非GAAP財務指標。 GAAP與非GAAP指標的調整表已包含在我們的新聞稿中。最後，在回答您的問題時，我們可能會在事先準備好的發言稿中討論一些補充指標，這些指標並非我們通常的業務報告內容，以便更深入地了解我們業務的動態或季度業績。請注意，我們未來是否會繼續提供這些補充資訊尚不確定。

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

那麼，現在讓我把電話交給湯姆。

Thank you, Amit. Good afternoon, everyone, and thank you for joining our call today. We finished the fourth quarter strong and the coming year looks stronger. I believe that is generally agreed that the overall market for enterprise AI now appears substantially larger and is growing at a much greater rate than most analysts and experts predicted. We have been working since 2009 to develop product leadership and established thought leadership in enterprise AI, assisting private and popular sector enterprises to apply AI to improve operational processes. C3 AI has been at the vanguard of enterprise AI of the enterprise AI market for over a decade.

謝謝Amit。大家下午好，感謝各位參加今天的電話會議。我們第四季業績強勁收官，而且來年前景一片光明。我相信大家普遍認同，企業人工智慧的整體市場規模如今已遠超預期，成長速度遠超多數分析師和專家的預測。自2009年以來，我們一直致力於在企業人工智慧領域打造產品領先地位和思想領導力，幫助私人企業和大眾企業應用人工智慧技術來改善營運流程。十多年來，C3 AI始終處於企業人工智慧市場的前沿。

As the market has developed from its roots in IoT to supervised learning -- unsupervised learning, NLP, deep learning, reinforcement learning and now Generative AI. In the past 14 years, we have developed and enhanced the C3 AI platform and now offer over 40 enterprise AI applications developed with that platform that allow our customers to rapidly take advantage of AI to improve their business processes.

隨著市場從物聯網發展到監督學習、無監督學習、自然語言處理、深度學習、強化學習，直至如今的生成式人工智慧，過去14年間，我們不斷開發並完善C3人工智慧平台，目前已推出超過40款基於該平台開發的企業級人工智慧應用，幫助客戶快速利用人工智慧技術改善業務流程。

We have been communicating for over a decade that we believe that the market for enterprise AI solutions would be quite large. And now as we enter the summer of 2023, it's become a dominant theme in technology discussions, government -- or AI has become a dominant theme in technology discussions, government discussions, media reports, defense and intelligence imperatives and government and business imperatives. I do not believe that it's an overstatement to say that there is no technology leader, no business leader and no government leader who is not thinking about AI daily.

十多年來，我們一直堅信企業級人工智慧解決方案的市場潛力巨大。如今，進入2023年夏季，人工智慧已成為科技討論、政府討論、媒體報導、國防情報需求以及政府和商業領域的熱門話題。毫不誇張地說，如今沒有一位科技領袖、一位商業領袖或一位政府領導人不在每天思考人工智慧。

AI chip makers, like NVIDIA are accelerating production to try to keep up with the very real demand that's out there. And all of this is getting accelerated by the advent of generative AI. The interest in AI and in applying AI to business and government processes has never been greater. Business inquiries are increasing. The opportunity pipeline is growing, demand is increasing. And C3 AI is well positioned to serve that increasing demand with our tried, tested and proven AI platform, our applications, our global footprint and our large global ecosystem.

像英偉達這樣的AI晶片製造商正在加速生產，以滿足市場上日益增長的市場需求。而生成式AI的出現更是加速了這個進程。人們對AI的興趣以及將AI應用於商業和政府流程的意願從未如此高漲。商業諮詢不斷增加，商機日益增多，市場需求持續成長。 C3 AI憑藉其久經考驗的成熟AI平台、應用軟體、全球佈局和龐大的全球生態系統，完全有能力滿足不斷增長的需求。

The world is in many ways now coming to us. The interest in applying AI to business processes is substantially greater than we have ever seen. In the fourth quarter, we increased our customer base, expanded our work with existing clients and saw especially strong growth in our federal business. In the fourth quarter, our total revenue was $72.4 million, our free cash flow was $16.3 million, and we ended the quarter with over $812 million in cash and cash equivalents.

世界正以多種方式向我們靠攏。人們對將人工智慧應用於業務流程的興趣遠超以往。第四季度，我們擴大了客戶群，深化了與現有客戶的合作，聯邦業務尤其實現了強勁成長。第四季度，我們的總營收為7,240萬美元，自由現金流為1,630萬美元，季末現金及現金等價物超過8.12億美元。

Importantly, we have a well-defined plan to be sustainably cash positive and non-GAAP profitable by the end of this fiscal year. For fiscal year 2024 -- I'm sorry, for the fiscal year 2023, okay, our total revenue was $266.8 million, an increase of 5.6% over fiscal year '22, okay. Subscription revenue was $230.4 million, representing an 11.4% increase over the prior year.

重要的是，我們制定了明確的計劃，力爭在本財年結束前實現可持續的現金流為正和非GAAP盈利。至於2024財年——抱歉，應該是2023財年，好吧——我們的總收入為2.668億美元，比2022財年增長5.6%。訂閱收入為2.304億美元，較上年成長11.4%。

Let's talk a little bit about the AI applications market. Now as the enterprise AI market has developed, it appears that the bulk of the demand is increasingly for turnkey enterprise AI applications rather than for development tools. This thesis is supported by an evaluation of our bookings for the past fiscal year that indicates that 83% of our bookings were driven by application sales, 17% of our bookings were driven by the sales of the C3 AI platform.

我們來談談人工智慧應用市場。隨著企業級人工智慧市場的發展，目前看來，市場對交鑰匙式企業級人工智慧應用的需求日益增長，而非開發工具。這一觀點得到了我們上一財年訂單評估的支持：83%的訂單來自應用程式銷售，17%的訂單來自C3人工智慧平台的銷售。

Importantly, we are seeing increasing diversity in the industries we serve. For fiscal year '23, an analysis of our bookings includes oil and gas was 34%, federal defense and Aerospace was 29%; hi-tech was 13%; energy and utilities, 11%; manufacturing, 4%; food processing 2%; chemicals 2%, life sciences, 1.5%; and other industries made up the remaining 3%. An important leading indicator of our increasing industry diversity is evidenced by the trial and pilot agreements closed in Q4.

值得注意的是，我們所服務的行業日益多元化。 2023財年，我們的訂單分析顯示，石油和天然氣產業佔11%，聯邦國防和航空航太產業佔29%，高科技產業佔13%，能源和公用事業佔11%，製造業佔4%，食品加工產業佔2%，化學產業佔2%，生命科學產業佔1.5%，其他產業佔剩餘的3%。第四季達成的試用和試點協議也印證了我們產業多元化程度不斷提高的一個重要先行指標。

Federal Defense and Aerospace made up almost 37%. The manufacturing comprised approximately 16% and hi-tech made up more than 10%, oil and gas also made up more than 10%. When we look at ag, state and local, chemicals, energy and financial services, each made up approximately 5% of our bookings. As a result of the increased demand for enterprise AI helped by our transition to consumption-based pricing, we are seeing a substantial increase in opportunities and shorter sales cycles.

聯邦國防和航空航太產業佔近37%。製造業約佔16%，高科技產業佔比超過10%，石油和天然氣產業也佔比超過10%。農業、州和地方政府、化學、能源和金融服務業各占我們訂單量的約5%。由於企業人工智慧需求的成長，以及我們向基於消費的定價模式的轉型，我們看到商機大幅增加，銷售週期也顯著縮短。

In Q4, we closed 43 agreements, including 19 pilots that were initiated in the quarter. The number of qualified enterprise opportunities targeted for closure within 12 months in our sales pipeline has increased by more than 100% in the past year. During fiscal year '23, we closed 126 agreements, up from 83% in the prior year. The average sales cycle for new and expansion deals was 3.7 months, down from 5 months in Q4 of the previous year.

第四季度，我們達成了43項協議，其中包括本季啟動的19個試點計畫。過去一年，我們銷售通路中計劃在12個月內完成的合格企業客戶數量成長超過100%。 2023財年，我們共達成126項協議，高於去年的83%。新客戶和拓展客戶的平均銷售週期為3.7個月，低於上年第四季的5個月。

An examination of the composition of our pilot account profile suggests there is significant opportunity for growth as these accounts convert to consumption pricing. Of the 19 pilot accounts signed in Q4, 7 were accounts greater than $100 billion in revenue. Seven were accounts between $10 billion and $100 billion in revenue. 4 were accounts between $1 billion and $10 billion, and 1 was an account less than $100 million in annual revenue.

對試點客戶概況的分析表明，隨著這些客戶轉向消費定價模式，存在巨大的成長機會。在第四季簽署的19個試點客戶中，7個客戶的年收入超過1000億美元，7個客戶的年收入在100億美元至1000億美元之間，4個客戶的年收入在10億美元至100億美元之間，還有1個客戶的年收入低於1億美元。

In fiscal year '23, we expanded our application footprint with a number of our customers, including Shell, Koch Industries, the United States Air Force Rapid Sustainment office, PwC, Ball, ExxonMobil, Con Edison, the Defense Counterintelligence and Security Agency, Baker Hughes, New York Power Authority, Duke Energy, ATB in Canada, Defense Innovation Unit, Roche, Cargill and ENGIE. We also established many new relationships during the year, including the Department of Defense common DoD AI office, Daily City, California, DOW, ExxonMobil, Flex, Hexagon, Nucor, Owens-Illinois, Pantelion, Riverside County, California, Start County, Ohio, Telus, Department of Defense, SOCOM, Department of Defense TRANSCOM and ESAB.

在2023財年，我們拓展了與眾多客戶的應用合作範圍，其中包括殼牌、科氏工業集團、美國空軍快速保障辦公室、普華永道、波爾公司、埃克森美孚、聯合愛迪生公司、國防反情報與安全局、貝克休斯公司、紐約電力局、杜克能源公司、加拿大ATB公司、國防創新部門、羅氏公司、國防創新部門和ENGIE公司。此外，我們還在這一年中建立了許多新的合作關係，包括美國國防部通用人工智慧辦公室、加州戴利城、陶氏化學公司、埃克森美孚、Flex公司、Hexagon公司、紐柯公司、歐文斯-伊利諾伊公司、潘特利翁公司、加州河濱縣、俄亥俄州斯塔特縣、Telus公司、美國國防部、特種作戰總部、加州河濱縣、俄亥俄州史塔特縣、Telus公司、美國國防部、特種作戰司令部、美國國防部作戰司令部和運輸司令部、BESA、特種作戰總部、特種作戰司令部、BESA、美國國防部和特種作戰司令部。

Many of these also expanded their AI engagements with us in the course of the year. Let's address the C3 AI partner network. The C3 AI partner ecosystem is increasingly effective at opening new doors. With our partners, we are able to provide prospects the assurance of success with -- and the highest quality service. In fiscal year '23, we closed 71 agreements with and through our partner network, including Google Cloud, AWS, Microsoft, Baker Hughes and Booz Allen. C3 AI increased its qualified pipeline with AWS by over 24% in the fourth quarter, with particular focus on state and local government.

今年，許多合作夥伴也擴大了與我們在人工智慧領域的合作。接下來，我們來談談C3 AI的合作夥伴網路。 C3 AI的合作夥伴生態系統在開拓新業務方面正發揮著越來越重要的作用。透過與合作夥伴的攜手，我們能夠為潛在客戶提供成功的保障以及最高品質的服務。在2023財年，我們透過合作夥伴網路達成了71項協議，其中包括Google雲端、AWS、微軟、貝克休斯和博思艾倫。第四季度，C3 AI與AWS的合格潛在客戶數量成長超過24%，尤其專注於州和地方政府。

With Google Cloud, our joint qualified 12-month opportunity pipeline grew from 25 opportunities at the end of fiscal year '22 to 140 opportunities at the end of fiscal year '23, a 460% increase. And importantly, we closed 10 new oil and gas accounts in the year with our strategic partner, Baker Hughes, with accounts, including ExxonMobil, ADNOC, ENI and others.

藉由Google雲，我們雙方共同篩選出的合格未來12個月的商機儲備從2022財年末的25個增長到2023財年末的140個，增幅高達460%。更重要的是，我們與策略夥伴貝克休斯公司在這一年新增了10個油氣產業客戶，包括埃克森美孚、阿布達比國家石油公司、埃尼集團等。

In Q4, we released the C3 Generative AI solution to the market. Our Generative AI solution leverages the capabilities of the C3 AI platform and is distinguished from other GPT/LLM solutions in the market in several ways. Number one, it allows enterprises to access all their enterprise and open source data, ERP, CRM, SCADA, TEXT, PDFs, Excel, PowerPoint, sensor data, you name it. Secondly, importantly, it provides traceable, deterministic, consistent answers. Thirdly, it enforces the corporate information access controls and security protocols that are currently in place. Fourthly, it has no risk of IP or data exfiltration caused by the large language model. And importantly, it is hallucination free.

第四季度，我們向市場推出了C3生成式人工智慧解決方案。我們的生成式人工智慧解決方案充分利用了C3人工智慧平台的強大功能，並在多個方面區別於市場上其他GPT/LLM解決方案。首先，它允許企業存取所有企業數據和開源數據，包括ERP、CRM、SCADA、文字、PDF、Excel、PowerPoint、感測器數據等等。其次，更重要的是，它提供可追溯、確定且一致的答案。第三，它強化了企業現有的資訊存取控制和安全協定。第四，它不存在因大型語言模型而導致的智慧財產權或資料外洩風險。最後，它不會產生任何虛假資訊。

So if the system doesn't know an answer, it doesn't fabricate it, which is clearly unacceptable for any commercial or serious government applications. After releasing the product in March, we rapidly closed 3 generative AI applications in the quarter with large enterprises, including Georgia Pacific, Flint Hills Resources and the U.S. Department of Defense Missile Defense Agency.

因此，如果系統不知道答案，它不會捏造答案，這對於任何商業應用或嚴肅的政府應用來說顯然都是不可接受的。自3月發布產品以來，我們迅速在本季與包括喬治亞太平洋公司、弗林特山資源公司和美國國防部飛彈防禦局在內的大型企業達成了3項生成式人工智慧應用合作。

We expect these applications to be live during this current quarter. We are currently working quite a substantial pipeline of additional C3 Generative AI opportunities with large corporations. The C3 AI generation is now available today available on both AWS Marketplace and the Google Cloud Marketplace. It is difficult to estimate the size of the addressable market for these generative AI solutions but it appears to be extraordinarily large.

我們預計這些應用程式將在本季上線。目前，我們正在與多家大型企業洽談大量 C3 生成式人工智慧 (C3 Generative AI) 的合作機會。 C3 生成式人工智慧解決方案現已在 AWS Marketplace 和 Google Cloud Marketplace 上架。雖然難以估算這些生成式人工智慧解決方案的潛在市場規模，但其規模似乎非常龐大。

We saw a lot of momentum last year and in the fourth quarter with the -- our U.S. federal business. The U.S. federal sector represented 29% of our bookings in fiscal year '23 and continues to show significant strength. Our predictive maintenance solution, predictive -- our predictive analytics and decision assistant, also known as PANDA, has been in production used for several years at the United States Air Force Rapid Sustainment Office. And last quarter, it was selected as the system of record for all predictive maintenance for virtually all United States Air Force assets, okay? This important designation expands our opportunity really substantially in the U.S. Air Force and their services.

去年，尤其是在第四季度，我們的美國聯邦政府業務發展勢頭強勁。 2023財年，美國聯邦政府業務占我們總訂單量的29%，並且持續維持強勁成長動能。我們的預測性維護解決方案－預測分析與決策助理（簡稱PANDA）－已在美國空軍快速保障辦公室投入生產使用多年。上個季度，PANDA被選為幾乎所有美國空軍資產的預測性維護系統。這項重要措施大大拓展了我們在美國空軍及其下屬機構的業務機會。

Let's talk about guidance. C3 AI has a consistent and solid track record of meeting or exceeding guidance as we have done in every quarter since we've been public, okay? And we are -- at this time, we are not inclined to pat on the table regarding guidance.

我們來談談業績指引。 C3 AI一直以來都保持著穩定可靠的業績記錄，自上市以來每季都達到了甚至超過了業績指引，好嗎？但是，目前我們並不打算就業績效指引做出任何樂觀的預測。

In general, we feel comfortable with the expectations that the sell-side analysts have set for the coming year, and we are not inclined to change those expectations at this time. For fiscal -- for Q1 fiscal year '24, we see revenue in the range of $70 million to $72.5 million. For the full year, fiscal year 2024, we expect revenue to be in the range between $295 million and $320 million. As it relates to non-GAAP loss from operations, we expect to fall between $25 million, $30 million in Q1 and $50 million to [$70 million] (sic, see press release, "$75 million") for the year.

整體而言，我們對賣方分析師對未來一年的預期感到滿意，目前暫不打算改變這些預期。就2024財年第一季而言，我們預計營收將在7,000萬美元至7,250萬美元之間。對於2024財年全年，我們預計營收將在2.95億美元至3.2億美元之間。關於非GAAP營運虧損，我們預計第一季虧損將在2,500萬美元至3,000萬美元之間，全年虧損將在5,000萬美元至7,000萬美元之間（原文如此，請參閱新聞稿中的「7,500萬美元」）。

As we begin fiscal year '24, C3 AI has never been better positioned. The addressable market is large and expanding. The overall business environment for enterprise AI is strong, and C3 AI is front and center in the minds of CEOs and government leaders. Our balance sheet is strong and with over $812 million in cash and cash equivalents, we are in a great position to expand market share.

2024財年伊始，C3 AI的市場地位空前優越。目標市場規模龐大且持續成長。企業人工智慧的整體商業環境強勁，C3 AI已成為執行長和政府領導人關注的焦點。我們資產負債表穩健，擁有超過8.12億美元的現金及現金等價物，為我們擴大市場佔有率奠定了堅實的基礎。

As the dynamics of the enterprise AI market are developing so rapidly, we thought it appropriate to host a mid-quarter Investor Day in New York City on June 27. We will provide -- at that time, we will provide C3 AI investors a company update, additional information about our product road map, product demonstrations direct access to the C3 AI executive team, updates on our partner ecosystem, C3 Generative AI demonstrations and additional company developing news.

鑑於企業人工智慧市場動態發展如此迅速，我們認為在6月27日於紐約市舉辦季度中期投資者日活動是恰當的。屆時，我們將向C3 AI的投資者提供公司最新動態、產品路線圖的更多資訊、產品演示、與C3 AI高管團隊的直接交流機會、合作夥伴生態系統的最新進展、C3生成式人工智慧演示以及其他公司發展新聞。

We hope you can attend either in person or online. And that Investor Day event will be available to view online live, okay, for all investors via webcast.

我們希望您能親自到現場或線上參加。投資者日活動將透過網路直播向所有投資者開放。

I will now turn this call over to my colleague, Juho Parkkinen, Chief Financial Officer, for additional details regarding our financial results. Juho?

現在我將把電話轉給我的同事，財務長尤霍·帕基寧，請他詳細介紹我們的財務表現。尤霍？

Thank you, Tom. I will now provide a recap of our financial results, add some color to the drivers of our financials, provide more detail on our first quarter and full year 2024 guidance and I will conclude with some additional color related to the consumption-based revenue model we introduced 3 quarters ago. All figures will be discussed on a non-GAAP basis, unless otherwise noted.

謝謝湯姆。接下來，我將概述我們的財務業績，詳細說明影響財務業績的因素，並進一步說明我們對2024年第一季和全年業績的預期。最後，我將簡要介紹我們三個季度前推出的基於消費的收入模式。除非另有說明，所有數據均以非GAAP準則進行討論。

Overall, the business activity is higher than we have ever seen. Our sales reps are more engaged. There are more opportunities, they're working on and there are more interest from our prospects. During Q4, our ability to close agreements was more consistent throughout the quarter compared to prior quarters this fiscal year. We ended the fourth quarter with a total revenue of $72.4 million, of which, subscription revenue was 78.5%. As we discussed last quarter, we expected professional services would be within our historical range of 10% to 20% with our actual professional services coming in at 21.5% of the mix. Gross profit for the fourth quarter was $53.9 million, and our gross margin was 74.4%. We generated $27.1 million in positive operating cash flow and $16.3 million in free cash flow for the quarter.

整體而言，業務活動比以往任何時候都更加活躍。我們的銷售代表積極性更高，他們正在跟進更多商機，潛在客戶也表現出更高的興趣。與本財年之前的幾季相比，第四季我們的成交能力更加穩定。第四季總收入為7,240萬美元，其中訂閱收入佔78.5%。正如我們上個季度所討論的，我們預期專業服務收入將維持在10%至20%的歷史區間內，而實際專業服務收入佔比為21.5%。第四季毛利為5,390萬美元，毛利率為74.4%。本季我們產生了2710萬美元的營運現金流和1630萬美元的自由現金流。

As mentioned during the prior updates, we have a short-term pressure on our gross margins due to a higher mix of targets, which carry a higher cost of revenue than production deployment. Operating loss of $23.5 million was improved due to more rigorous expense management. As a reminder though, the fourth quarter is when we host our C3 AI transform customer events, as such, our marketing expenses ramped up to support the successful execution of that event.

如同先前的更新所提到的，由於目標產品組合比例較高，我們的毛利率短期內面臨壓力，因為目標產品的收入成本高於生產部署產品。由於更嚴格的費用控制，2350萬美元的營業虧損有所改善。但要提醒的是，第四季是我們舉辦C3 AI轉型客戶活動的時期，因此，為了確保活動的成功舉辦，我們的行銷支出增加。

Operating loss margin was 32.5% in Q4 where the sequential increase was driven by our annual customer conference. For the full year fiscal 2023, our revenue was $266.8 million, an increase of 5.6% from fiscal 2022. Non-GAAP loss from operations was $68.1 million and free cash flow was negative $187 million. Our gross margin for the year was 77%. Our subscription revenue was 86% of total revenue compared to 82% in fiscal '22. We ended fiscal '23 with $812.4 million in cash and investments.

第四季營業虧損率為32.5%，季成長主要受年度客戶大會的影響。 2023財年全年，公司營收為2.668億美元，較2022財年成長5.6%。非GAAP營業虧損為6,810萬美元，自由現金流為負1.87億美元。全年毛利率為77%。訂閱收入佔總營收的86%，高於2022財年的82%。 2023財年末，公司持有現金及投資8.124億美元。

At the end of Q4, our accounts receivable, including unbilled receivables, was $134.6 million. Unbilled receivables at quarter end was $77.6 million, inclusive of $70.7 million related to Baker Hughes. During the quarter, we collected from Baker Hughes nearly $35 million.

第四季末，我們的應收帳款（包括未開立發票應收帳款）為1.346億美元。季度末未開立發票應收帳款為7,760萬美元，其中包括與貝克休斯公司相關的7,070萬美元。本季度，我們從貝克休斯公司收回了近3500萬美元。

The general health of our accounts receivable is excellent. 76% of our receivables were current or less than 30 days past due. For the entirety of FY '23, our bad debt expense was approximately $300,000.

我們的應收帳款整體狀況良好。 76%的應收帳款處於正常狀態或逾期不超過30天。 2023財年全年，我們的壞帳費用約為30萬美元。

Now turning to RPO and bookings. As consumption-based go-to-market model continues to pick up. RPO is a less important indicator of future performance. We reported GAAP RPO of $381.4 million down 20% from last year, which is expected as a result of the transition to consumption-based pricing. Current GAAP RPO of $186.3 million is up 9.8% from last year-end and up 5.7% on a sequential basis. We continue to see positive trends in target bookings diversity as we have sold pilots to a broad range of 9 different industries during the quarter.

現在來看RPO和預訂狀況。隨著基於消費的市場推廣模式持續發展，RPO作為未來績效指標的重要性有所下降。我們報告的GAAP RPO為3.814億美元，較去年同期下降20%，符合預期，是向基於消費的定價模式過渡的結果。目前的GAAP RPO為1.863億美元，較去年年底成長9.8%，較上季成長5.7%。我們持續看到目標預訂多元化的正面趨勢，本季我們已向9個不同行業的眾多客戶售出了試播集。

Regarding our outlook for fiscal '24. We're guiding Q1 revenue to range between $70 million to $72.5 million. For the full year 2024, we expect revenue to range between $295 million and $320 million. As it relates to the full year, we finished the third quarter of our transition under the consumption pricing model. As a recent remodel, we expect flatness and somewhat of a decline in revenue during the transition with an acceleration as consumption starts to have a meaningful portion of our in-quarter revenue. As such, we expect the second half of FY '24 to have higher growth rates on a sequential basis than the first half.

關於我們對2024財年的展望，我們預計第一季營收將在7,000萬美元至7,250萬美元之間。 2024財年全年，我們預計營收將在2.95億美元至3.2億美元之間。就全年而言，我們已完成向消費定價模式過渡的第三季。由於近期進行了調整，我們預計過渡期間營收將保持平穩甚至略有下降，但隨著消費定價模式在季度營收中佔據顯著份額，下降速度將會加快。因此，我們預計2024財年下半年的環比成長率將高於上半年。

We expect our non-GAAP loss from operations to range between $25 million and $30 million for Q1, and for full fiscal '24, we expect the non-GAAP loss from operations between $50 million and $75 million. As a reminder, we expect to be non-GAAP profitable for Q4 '24 and beyond. And as it relates to full fiscal '24, we are guiding to a range in operating loss due to the potential investments we made for C3 Generative AI applications. We expect our cash and investments to be at its lowest at around $700 million during fiscal '24.

我們預計2024財年第一季非GAAP營運虧損將在2,500萬美元至3,000萬美元之間，全年非GAAP營運虧損將在5,000萬美元至7,500萬美元之間。需要提醒的是，我們預計2024財年第四季及以後將實現非GAAP獲利。至於2024財年全年，由於我們對C3生成式人工智慧應用進行了潛在投資，因此我們預計營運虧損將在一個區間內波動。我們預計2024財年期間，我們的現金和投資將降至最低點，約7億美元。

Turning to customer metrics. Historically, we have provided a quarterly customer count estimate as a proxy for the adoption of our products and solutions. However, due to the complexity of our contractual and pricing structures and the involvement of resellers we believe comparing customer accounts from quarter-to-quarter based on our current methodology, we believe -- apologies, customer accounts from quarter-to-quarter based on our current methodology does not fully convey the acceptance and adoption of our products and solutions. To help address this, we retained an external Big 4 consulting firm to update our current customer accounts methodology consistent with best practices to be consistent, systematic and auditable.

接下來談談客戶指標。過去，我們一直提供季度客戶數量估算值，以此作為衡量產品和解決方案採用的指標。然而，由於合約和定價結構的複雜性以及經銷商的參與，我們認為，基於當前方法逐季度比較客戶數量並不能全面反映客戶對我們產品和解決方案的接受度和採用情況。為了解決這個問題，我們聘請了一家外部的四大顧問公司，根據最佳實務更新我們目前的客戶數量統計方法，使其更加一致、系統化且可審計。

As a result of that review and adoption of those recommendations we believe a metric that demonstrates contracted use cases that our customers are using our solutions to solve would provide a more meaningful understanding of the product adoption. This is defined as customer engagement. The customer engagement increased from 247 to 287 comparing Q3 '23 to Q4 '23. Our traditional customer account metric went from 236 to 244 for the same period. There would be additional detail included in the supplement, which is available on our website.

經過審查並採納相關建議後，我們認為，採用能夠體現客戶使用我們解決方案解決的實際用例的指標，可以更有效地了解產品採用情況。此指標定義為客戶參與度。 2023年第三季至第四季度，客戶參與度從247增加到287。同期，我們傳統的客戶帳戶指標從236增加到244。更多詳情請參閱我們網站上提供的補充資料。

We are on track with our plan for profitability for Q4 '24 and expect to have cash positive quarter starting Q4 '24 on a consistent go-forward basis. The entire executive team is managing the business to a detailed budget on our plan for profitability. We are expecting to invest aggressively to generate AI initiatives during the first half of the year, which is reflected in the operating income guidance. As it relates to the model assumptions that we provided 3 quarters ago for our consumption-based pricing, our preliminary analysis of the actual results suggest we are on that model. Overall, we're very excited about the business momentum as we start FY '24.

我們正按計畫推進2024財年第四季的獲利目標，並預計從2024財年第四季開始，現金流將持續為正。整個管理團隊正根據詳細的獲利計畫預算管理業務。我們預計將在今年上半年大力投資人工智慧項目，這已反映在我們的營業收入預期中。關於我們三個季度前提出的基於消費的定價模型假設，我們對實際結果的初步分析表明，我們目前的情況與該模型相符。總而言之，我們對2024財年伊始的業務發展動能感到非常振奮。

As a go-forward KPI for the investing community to assess our performance, we believe good KPIs to focus are the number of pilots started during the quarter, the conversion of those pilots to production; and finally, the actual vCPU consumption fees generated.

作為投資界評估我們業績的未來關鍵績效指標，我們認為值得關注的關鍵績效指標包括：季度內啟動的試點項目數量、這些試點項目轉化為生產項目的數量；以及最終產生的實際 vCPU 消耗費用。

With that, I would like to open this up for questions. Operator?

接下來，我想接受提問。接線生？

(Operator Instructions) Our first question comes from the line of Kingsley Crane of Canaccord.

（操作說明）我們的第一個問題來自 Canaccord 的 Kingsley Crane 生產線。

So Tom, you said that sales cycles were down to 3.7 months from 5 months last year. Why do you think that is? Is this entirely due to the consumption model? How much of this is due to general excitement around the potential in the space and even potentially increase sales force productivity?

湯姆，你提到銷售週期從去年的5個月縮短到了3.7個月。你認為這是為什麼？這完全是由於消費模式的改變嗎？有多少是因為人們對這個領域的巨大潛力感到興奮，甚至可能提高了銷售團隊的效率？

No, I think -- thanks, Kingsley. I think it's all of that. I mean, clearly, AI is on everybody's mind, the consumption-based pricing model that we have makes it much easier to adopt our technology. I mean in the old days, 1 and 2 years ago to do business with us was $5 million, $10 million, $20 million, $50 million to open the door. And now the transaction is pretty much -- we're bringing the app live in 6 months or $0.5 million. If you like it, keep it and paid $0.55 per CPU hour -- vCPU hour, so we're pretty easy to do business with. And so we're seeing the number of transactions increased dramatically as we'd expect.

不，我想──謝謝你，金斯利。我認為以上所有因素都有影響。我的意思是，很顯然，人工智慧是每個人都在關注的話題，而我們基於消費的定價模式讓採用我們的技術變得更加容易。我的意思是，在過去，一兩年前，與我們合作需要支付500萬美元、1000萬美元、2000萬美元甚至5000萬美元的啟動資金。而現在，交易成本幾乎是——我們只需50萬美元就能在6個月內上線您的應用程式。如果您喜歡，可以繼續使用，每CPU小時（虛擬CPU小時）支付0.55美元，所以與我們合作非常方便。因此，正如我們預期的那樣，我們看到交易數量大幅增長。

The ease of contracting with us. As you know, we have largely reconstituted the sales organization in the last 1.5 years to a sales team that is candidly much more productive and effective than our other sales organizations. So I think all of those are contributing to increased pipeline, increased business, increased business activity by which we're quite optimistic.

與我們合作非常便捷。如你所知，過去一年半以來，我們對銷售團隊進行了大幅重組，如今的銷售團隊效率和生產力都遠超其他銷售團隊。我認為所有這些因素都有助於提升銷售管道、增加業務量、促進業務活動，對此我們非常樂觀。

That's really helpful. And then 1 for Juho. When I think about the timing of the transition. So if it is the case that the vast majority of existing customers are not necessarily migrating to the consumption model, how should we think about the contribution of consumption over time and particularly in the back half? Because I think that you said revenue could accelerate as consumption increases in mix?

這真的很有幫助。然後是給Juho的1分。我在考慮轉型的時機。如果絕大多數現有客戶不一定會遷移到消費模式，那麼我們該如何看待消費模式隨時間推移，尤其是在後半段的貢獻呢？因為我記得您說過，隨著消費模式在收入結構中佔比的增加，收入可能會加速成長？

Yes, Kingsley, thanks for that question. So that's exactly as we sign and initiate more pilots within the quarter. The pilots are generally 2 quarters long, and then you start to see the consumption revenue kick in. As we finished the quarter with 19 pilots last quarter, we had a good increase in pilots with 17 pilots as well. You can start seeing those layer on to the revenue by Q3 and Q4 of this fiscal year.

是的，金斯利，謝謝你的提問。我們正是在這個季度內簽署並啟動更多試點計畫。試點計畫通常持續兩個季度，之後你就能開始看到消費收入的產生。上季末我們共有19個試點項目，本季末我們又增加了17個，數量相當可觀。到本財年的第三季和第四季度，你就能開始看到這些試點計畫帶來的營收成長。

Now to your point about renewals, we do expect our existing customers with the large enterprise agreements to continue to remain on those types of agreement structures, but you will see the RPO trickle down as these contracts enter into renewal phase, and then we would expect to see a pickup as they renew.

關於您提到的續約問題，我們確實希望現有客戶（尤其是那些簽訂大型企業協議的客戶）繼續保持這種類型的協議結構，但隨著這些合約進入續約階段，您會看到 RPO 逐漸下放，然後我們預計隨著續約的進行，RPO 將會回升。

Our next question comes from the line of Pat Walravens of JMP Securities.

我們的下一個問題來自 JMP Securities 的 Pat Walravens。

Tom, can you talk some more about the opportunity with National Security and the Department of Defense? And then also, you said something I thought was interesting about a version of Generative AI that doesn't hallucinate, if you could maybe comment a little more on what hallucinating is? And how you prevent it from doing that, I think that would be really interesting?

湯姆，你能再詳細談談國家安全和國防部的合作機會嗎？還有，你剛才提到了一種不會產生幻覺的生成式人工智慧，我覺得很有趣。你能不能再詳細解釋一下什麼是幻覺？以及如何防止它產生幻覺？我覺得這會非常有趣。

DoD, well, Pat, you asked kind of many times about the -- we have 2 basically authorities to operate contract vehicles one is for $100 million and one is $1.5 billion in DoD that are associated. It could be applicable to what we're doing at RSO, that was the Rapid Sustainment Office and the predictive maintenance application that we're doing for the United States Air Force for F-15, F-16, F-18, F-35, KC-135, et cetera.

國防部，嗯，帕特，你問過很多次了——我們基本上有兩個授權來運作合約工具，一個是1億美元，另一個是15億美元，都是國防部相關的。這可能適用於我們在RSO（快速安全辦公室）所做的工作，RSO是為美國空軍的F-15、F-16、F-18、F-35、KC-135等機型開發的預測性維護應用。

And what we made a proposal to the Secretary of the Air Force to take that into full production for all the aircraft in the Air Force, which is 5,000. I think the proposal would have increased aircraft availability for the Air Force by 25%. And I think decrease their cost of maintenance and readiness by about $6 billion. So he considered that as did his Chief of Staff, General Brown, and they went off on their own for a few months why you were asking the questions, and we didn't have the answers and these guys go into their star chamber the way they do. What they came out with was not -- was a selection of C3 as the standard -- as the system of record, not only for aircraft in the United States Air Force, but for all AI-based -- all predicted maintenance, okay, in the United States Air Force for all assets. So this is genuinely a big deal. Okay.

我們向空軍部長提出了一項提案，建議將這項技術全面投入生產，應用於空軍所有飛機，也就是5,000架飛機。我認為這項提案能使空軍飛機的可用性提高25%，並降低約60億美元的維修和戰備成本。他和他的參謀長布朗將軍都考慮了這項提案，然後他們各自進行了幾個月的獨立研究，這就是為什麼你會問這些問題，而我們當時也沒有答案。這些人就像他們一樣，各自進入秘密會議進行討論。最終，他們選擇C3系統作為標準——作為記錄系統，不僅適用於美國空軍的飛機，也適用於美國空軍所有資產的所有基於人工智慧的預測性維護。所以這確實是一件大事。

Now we have the opportunity to make this a line item in the budget. So this is -- it's hard to over describe the impact of this or overestimate the impact of this. And then not only do we have it in Air Force, we can talk now to other services like Army and the Navy and the Marines and the National Guard, what have you. So this is a big one.

現在我們有機會將這項支出列入預算。這項支出的影響之大，不管怎麼強調都不為過。而且，這項支出不僅適用於空軍，我們現在還可以與其他軍種，例如陸軍、海軍、海軍陸戰隊、國民警衛隊等等進行討論。所以，這意義重大。

The second one has to do with generative AI. So one of the problems with Generative AI is the -- is you're limited to the number of data sources that you can use with these large language typically is text, HTML and sometimes code. And the large language model will interact directly with the data. But one of the problems is you get kind of random answers. Every time you ask the question, you get a different answer. If 2 people have an question, they get a different answer.

第二個問題與生成式人工智慧有關。生成式人工智慧的問題之一是，它能使用的資料來源數量有限，而這些大型語言模型通常處理的是文字、HTML，有時也包括程式碼。大型語言模型會直接與資料互動。但問題在於，你會得到一些隨機答案。每次提出問題，你都會得到不同的答案。如果兩個人問同一個問題，他們得到的答案也會有所不同。

And the -- there's no traceability. It doesn't tell you where the answer came from, okay? And finally, if it doesn't know the answer, it makes 1 up. This is what they call hallucination. So it doesn't know it just kind of wings it makes up an answer.

而且——沒有可追溯性。它不會告訴你答案是從哪裡來的，懂嗎？最後，如果它不知道答案，它就會編造一個。這就是所謂的「幻覺」。所以它根本不知道答案，只是憑空想像一個答案。

So we've leveraged -- we're using the entire C3 platform. And the way that we do that is we incorporate -- as you, I think, all know, we're very good at aggregating enterprise data, extraprise data, code, images, text, sensor data, what have you, into a unified federated image. When we do that, those data are read by a deep learning model and they happen to be stored in a vector database that we have a kind of a firewall between that and the large language model. Now our customer uses any language model they want, be it ChatGPT, be it PaLM, be it Bard, be it FLAN-T5, whatever comes along next.

所以我們充分利用了整個C3平台。具體做法是，正如大家所知，我們非常擅長將企業資料、提取資料、程式碼、圖像、文字、感測器資料等等整合到一個統一的聯邦鏡像中。整合完成後，這些資料會被深度學習模型讀取，並儲存在一個向量資料庫中。我們在這個資料庫和大型語言模型之間設置了一道防火牆。現在，我們的客戶可以使用他們想要的任何語言模型，無論是ChatGPT、PaLM、Bard、FLAN-T5，還是未來可能出現的任何模型。

Now we built a firewall within the large language model and the data. So it will -- every time -- I mean what's really -- every time you ask the question, it will give you the same answer. Okay, if 2 people ask the same question and they have the authority, they will both get the same answer every time. Associated with the answer, it provides you traceability to see if they click on it, you can see exactly where the data can come from, okay? And very importantly, there's no risk of LLM cause data exfiltration, see Samsung for details where they find out that all of their proprietary information is not published on the Internet, okay?

現在我們在大型語言模型和資料內部建構了一道防火牆。所以，每次——我是說真的——每次你提出這個問題，它都會給你相同的答案。好，如果兩個人問同一個問題，而且他們都擁有相應的權限，那麼他們每次都會得到相同的答案。此外，它還提供可追溯性，你可以看到他們是否點擊了答案，從而準確地了解數據的來源，明白嗎？而且非常重要的是，LLM 不會導致資料外洩的風險。詳情可以參考三星的例子，他們發現所有專有資訊都不會發佈到網路上，懂嗎？

And finally, there's no risk of LLM caused hallucination. It doesn't know the answer, it tells you, I don't know the answer rather than making one up. So for these, you think would be kind of table stakes, and they are table stakes for any large commercial or government installation, and this is something that really distinguishes the C3 Generative offering. And one of the reasons that we're seeing very high levels of interest.

最後，LLM 不會導致幻覺。它不會憑空捏造答案，反而會直接告訴你「我不知道答案」。這些功能對於任何大型商業或政府機構來說都是基本要求，而這正是 C3 Generative 產品的獨特之處，也是我們看到市場對其表現出極高興趣的原因之一。

Our next question comes from the line of Sanjit Singh of Morgan Stanley. Again, our next question comes from the line of Sanjit Singh at Morgan Stanley.

我們的下一個問題來自摩根士丹利的桑吉特辛格。再次強調，我們的下一個問題來自摩根士丹利的桑吉特辛格。

I appreciate you guys squeezing me in for the question. Tom, earlier this week, you guys announced had a press release about the C3 Generative AI suite being available in the Amazon Marketplace. And it got me thinking about what the sales motion going forward is going to look like? As you sort of mentioned, Generative AI is permeating the boardroom, the C-suite in a pretty substantial way. And when we look at sort of converting this interest into deals and ultimately revenue. How much of this is going to be like flywheel kind of self-service consumption-based marketplace type deals versus you working with partners to get more consultative approach as it's helping these large enterprise customers sort of navigate the world of generative AI actually deliver value?

感謝你們抽空回答我的問題。湯姆，本週早些時候，你們發布了新聞稿，宣布C3生成式人工智慧套件已在亞馬遜商城上線。這讓我開始思考，未來的銷售模式會是什麼樣的呢？正如你剛才提到的，生成式人工智慧正在以相當大的方式滲透到董事會和高階主管。當我們考慮如何將這種興趣轉化為實際交易並最終實現收入時，其中有多少會是像飛輪效應一樣的自助式消費型市場交易，又有多少會是與合作夥伴攜手，提供更具諮詢性的方式，幫助這些大型企業客戶駕馭生成式人工智慧的世界，真正創造價值？

Great question, Sanjit. So our first 3 engagements that are valid now will be -- our organization is the order of $100 billion or greater in revenue, okay, and have -- we'll bring the application live 12 weeks. We're not doing it -- and we have like 3 people on the project. So it's pretty straightforward.

桑吉特，問得好。我們目前有效的前三個項目是——我們公司年收入在1000億美元以上，好的，而且——我們會在12週內上線這個應用。我們不是自己做的——我們這個專案只有三個人。所以很簡單。

Now the issue of going from, say, 6 customers to 60 customers to 100 customers, it's pretty straightforward. We know how to do that, okay? The real key is, okay, in terms of blowing the doors off this thing, can we go from 6 customers to 60 customers to 6,000. So for 6,000, now we have to leverage these channels like the AWS marketplace where the product is available today, the Google marketplace where they're available today. But in terms of usability, it needs to be with the Apple iPhone. You open the box, you take the cellophane off, you turn it on and it works.

現在，從6個客戶成長到60個客戶再到100個客戶，這個問題其實很簡單。我們知道該怎麼做，好嗎？真正的關鍵在於，如何讓客戶數量從6個成長到6000個，最終達到6000個。要達到6000個客戶，我們需要利用各種管道，例如AWS Marketplace（產品目前已上線）和Google Marketplace（產品目前已上線）。但就易用性而言，它必須與蘋果iPhone相容。你打開包裝盒，撕掉塑膠膜，開機就能用。

And so now we're -- the next generation -- the next -- the really serious development work that we're doing now on that product kind of relates to really product design, okay, and making it like an Apple product, you open it up, you turn it on and it works. And so that's the challenge that's before us. I think we're up to it, okay? And if we're able to hit that note, hold on to your stock.

所以現在，我們正在進行下一代產品的研發工作，這項工作真正關乎產品設計，要讓它像蘋果產品一樣，打開就能用。這就是我們面臨的挑戰。我認為我們能夠應對。如果我們能做到這一點，請繼續持有我們的股票。

I appreciate the color, Tom. And then maybe 1 follow-up. Maybe this is for Juho and Tom as well. And it sort of relates to the guidance for the full year. I'm trying to contextualize like what's really driving the guidance for next year? Because we're coming off a year, fiscal year '21, I think you guys grew north of 30%, 33%, 34%. And this past year, you guys grew sort of mid-single digits. The initial guidance calls for growth sort of mid-teens at the midpoint sort of 20% at the high end. And I want to understand, like is the acceleration you're seeing a function that you're coming off a tougher year where you had -- spending environment is more difficult, sales reorg, those types of things versus Generative AI really coming online in fiscal year '24. And so is there any way you can sort of attribute those 2 things between sort of coming off of a tougher year versus the demand that you're seeing in pilots out in the field?

湯姆，我很欣賞你提出的觀點。然後，我可能還有一個後續問題。這個問題可能也問了尤霍和湯姆。它與全年業績指引有關。我想了解一下，究竟是什麼因素真正推動了明年的績效指引？因為在剛剛過去的2021財年，你們的成長率超過了30%、33%甚至34%。而去年，你們的成長率只有個位數。最初的業績指引是，成長率中位數在15%左右，最高可達20%。我想了解的是，你們目前看到的成長加速，是否是因為你們剛剛經歷了一個比較艱難的年份——比如消費環境更加緊張、銷售重組等等——以及2024財年生成式人工智慧的真正上線。所以，你們能否將這兩個因素歸因於剛經歷的艱難年份和你們在試點計畫中看到的需求？

Sanjit, let me address the premise, okay? Before we all bring our hands about tougher year, tougher year tougher year. I think we got that in 4 times in the call for all the audience. Okay, let's remember, okay? When we are now the transition to consumption-based pricing, we made it very clear that this was going to have a short and midterm, okay, negative effect on revenue growth. It's actually met. Anybody who knows how to use a spreadsheet can figure this out. If we're closing $0.5 million deals instead of $10 million, $20 million, $30 million, $40 million, $50 million deals, the short-term impact on revenue is dampened revenue growth. So I'm not certain that's so tough. Okay, that is basically we're actually getting exactly the plan that we set. So this thing is exactly on track.

桑吉特，讓我先說明一下前提，好嗎？在我們開始抱怨「今年更難了，今年更難了，今年更難了」之前——我想我們在電話會議上已經跟所有聽眾說了四遍了——我們先來回顧一下。好，讓我們記住，好嗎？當我們開始向基於消費的定價模式過渡時，我們已經非常明確地表示，這將在短期和中期內對收入成長產生負面影響。實際上，這個預測已經實現了。任何會用電子表格的人都能明白這一點。如果我們達成的交易金額從1000萬美元、2000萬美元、3000萬美元、4000萬美元、5000萬美元降到5000萬美元，那麼短期內對收入的影響就是收入成長放緩。所以我不確定這到底有多難。好，基本上，我們實際上正在按計劃推進。所以，一切都在按計劃進行。

Now when you run this 3-year a (inaudible) spreadsheet model and you hit the carriage return, okay, and you run it out a few years -- a few quarters out there, you can do the math and you know what happens. But I'm not -- so I think we're exactly on plan with what we did. We made the investment. I think it was a great decision. It was a good investment. And now in fiscal year '24 and '25, we're going to yield the returns from that investment.

現在，當你運行這個為期三年的（聽不清楚）電子表格模型，然後按下回車鍵，好的，再運行幾年——幾個季度，你就可以計算出結果，知道會發生什麼。但我不是——所以我認為我們所做的一切都在按計劃進行。我們進行了投資。我認為這是一個很棒的決定。這是一筆不錯的投資。現在，在2024和2025財年，我們將獲得這項投資的回報。

Our next question comes from the line of John Katsingris of Wedbush.

我們的下一個問題來自 Wedbush 的 John Katsingris 家族。

John on for Dan Ives. So given the increased diversity seen, I guess, across industries served, how have you seen these use cases develop? And how do you see them playing out in the future?

約翰接替丹·艾夫斯發言。鑑於服務業日益多元化，您認為這些應用案例是如何發展的？您又認為它們未來會如何發展？

Great question, John. Right now, I mean, in terms of applying AI to enterprise we're in first half of the first inning and the first guys to do that, okay. So this is an embryotic market. I mean, where we're seeing the biggest uptake in media. First, it was in the SmartGrid. Okay. Why the SmartGrid because they had invested $2 trillion to upgrade and create infrastructure globally to make all the devices in the SmartGrid, remotely machine addressable, a huge IoT constellation. So that's where we saw it first.

約翰，問得好。就目前而言，人工智慧在企業中的應用還處於起步階段，我們才剛開始，而率先涉足這一領域的公司……好吧，所以這還是一個萌芽期的市場。我的意思是，我們看到人工智慧在媒體領域應用最為廣泛。首先，它出現在智慧電網領域。為什麼是智慧電網呢？因為他們投資了2兆美元在全球升級和建設基礎設施，使智慧電網中的所有設備都能透過遠端機器尋址，形成一個龐大的物聯網網路。所以我們最初就是在那裡看到了它的應用。

The next large -- what we're seeing in the past year, the largest market is in AI reliability, basically predictive maintenance. So they can move the military, they called readiness, okay, or in the private sector, they call reliability. So AI-based predictive maintenance is the largest segment today. How will this evolve? I mean it's clear we will be applying AI to all business processes, production optimization, demand forecasting, supply chain risk okay, stochastic optimization of the supply chain, CRM. I think there is no aspect of business operations that will not be or -- and medicine, okay, and research and the science and literature and entertainment that will not be accelerated by the use of AI. So we're just going to have to -- we're along for the ride and we're going to see where this goes in the next few years and stay on the balls of our feet as it develops, but it is a rocket ship.

下一個大市場——過去一年我們看到的最大市場是人工智慧可靠性，也就是預測性維護。它可以提升軍方所謂的戰備水平，也可以提升私部門所謂的可靠性。因此，基於人工智慧的預測性維護是目前最大的細分市場。它將如何發展？我的意思是，很明顯，我們將把人工智慧應用於所有業務流程，包括生產優化、需求預測、供應鏈風險管理、供應鏈隨機優化以及客戶關係管理（CRM）。我認為，商業運營的各個方面，以及醫療、科學研究、科學、文學和娛樂等，都將因人工智慧的應用而加速發展。所以我們只能順其自然，看看未來幾年它將如何發展，並密切關注它的發展，但它的發展速度堪比火箭。

Our next question comes from the line of Mike Cikos of Needham Company.

我們的下一個問題來自 Needham 公司的 Mike Cikos。

Maybe the first would be going to Juho. So I know that you guys have cited the 43 deals you closed this quarter, 19 of those are pilots. Can you further refine that for us? And maybe it's just the classifications or names we're using for this. But like how many of those pilots are purely consumption-based versus maybe pilots that are still coming in under the old pricing model?

或許第一個應該是去Juho。我知道你們提到本季完成了43筆交易，其中19筆是試點計畫。能否再詳細說明一下？或許只是我們對這些項目的分類或名稱有誤。例如，這些試點計畫中，有多少是純粹基於消費的，又有多少是仍沿用舊定價模式的？

Mike, these are all -- these would all follow the new consumption-based approach. These are not under the old model at all.

麥克，這些都——這些都將遵循新的消費模式。它們完全不屬於舊模式。

Okay. And so I guess the follow-up that I have on that is with the 19 deals that are consumption based, and I know that you guys have pulled -- I'm sorry, 19 pilots that you guys closed or initiated that are a consumption base this quarter and the other pilots that we've decided in previous quarters. Do we have a feel for how many of these pilots have now converted to production? And do we have a gauge for what the vCPU is for those consumption deals once they move into production?

好的。我想就此跟進一下，關於那19個基於消費量的交易，我知道你們這季度已經完成了——抱歉，是19個試點項目，都是基於消費量的，還有我們之前幾個季度決定的其他試點項目。我們能否大致了解其中有多少個試點計畫已經轉為正式上線？一旦這些基於消費量的交易進入正式上線階段，我們能否估算出它們的虛擬CPU（vCPU）佔比是多少？

That's a great question. So Michael, on the first quarter, we announced this would have been Q2, which obviously, you take 6 months from that, we get towards the end of Q4. So we are very early in that in the conversions. We are standing by with the model assumptions, i.e., whatever we provided 3 quarters ago where it is, each pilot is expected at 70% likelihood to convert into a follow-on consumption deal. But I would say that the first quarter before consumptions, we really will start seeing more of that this quarter since it was late in Q2 as we entered into those original pilot arrangements.

這是一個很好的問題。邁克爾，我們在第一季宣布，這原本應該是第二季度，但顯然，從那時起六個月，就到了第四季末。所以我們目前還處於轉化工作的早期階段。我們仍然沿用先前的模型假設，也就是說，我們三個季度前給出的預測是，每個試點計畫都有70%的可能性轉化為後續的消費協議。但我想說的是，在消費協議生效之前，我們真正開始看到轉換率上升是在本季度，因為我們最初是在第二季末才達成試點協議的。

Understood. Understood on that. And then just 1 quick follow-up, if I could. But I wanted to add just on the professional services revenue. I know it's you just a tick higher versus the typical 10% to 20% range we've been talking about. And I just wanted to see, is 10% to 20% still the appropriate range we should be thinking through? Or is there maybe more handholding for these pilots as you guys engage in them? Or is it maybe handholding of potentially federal sector customers? Like how do we think about the higher preserve revenue generation in Q4 versus what you guys are thinking about over the next year?

明白了。我明白了。如果可以的話，我還有一個後續問題。我想補充一下關於專業服務收入的問題。我知道你們的收入比我們之前討論的10%到20%的典型範圍略高一些。我想確認一下，10%到20%仍然是我們應該考慮的合適範圍嗎？或者，你們在進行這些試點計畫時是否需要更多指導？或者，你們是否需要為潛在的聯邦客戶提供更多指導？例如，我們如何看待第四季較高的保留收入，以及你們對未來一年的規劃？

I think on a go-forward basis, we expect to be in the 10% to 20% range. There's always going to be these types of projects that our customers want, and it's difficult to forecast a specific in a go-forward revenue, but we believe 10% to 20% is appropriate on a go-forward basis.

我認為未來一段時間內，我們預計這一比例會在10%到20%之間。客戶總是會需要這類項目，因此很難預測未來特定的收入，但我們認為10%到20%的比例是比較合適的。

(Operator Instructions) Our next question comes from the line of Brad Sills of Bank of America.

（操作員說明）我們的下一個問題來自美國銀行的布拉德·西爾斯。

This is Adam Bergere on for Brad Sills. So you're pretty well positioned in the current market, just given AI use cases are coming into focus. So kind of curious if it's changed your cadence for R&D investments at all?

這裡是亞當·伯格雷，代布拉德·西爾斯。鑑於人工智慧的應用案例正逐漸清晰，你們在當前的市場環境中佔據了相當有利的地位。我很好奇，這是否改變了你們的研發投資節奏？

Well, this is Tom. I mean, clearly, the investments we've made in the last 14 years are paying off, okay, in that we have over 40 applications and people want applications. And I think we're the only company in the world, but that has 40 applications. I think that -- I think the only recent change that we've made is we're a little bit shocked by the response that we had to C3 Generative AI. I mean that is -- we're a little bit overwhelmed by that. But that's a big opportunity. And so now we just came off a plane mean and we decided to really invest in that product category in a big way because it's just difficult to estimate the size of that market, but it's extraordinarily large.

我是湯姆。很明顯，過去14年的投資正在取得成效，我們現在有超過40個應用程序，而且人們需要這些應用程式。我想我們是世界上唯一一家擁有40個應用程式的公司。我認為——我認為我們最近做出的唯一改變是，C3生成式人工智慧的反應讓我們有點震驚。我的意思是，我們有點應接不暇。但這同時也是一個巨大的機會。所以我們剛下飛機，就決定大力投資這個產品類別，因為很難估計這個市場的規模，但它確實非常龐大。

Yes. Fair enough. And then for kind of the Generative AI use cases and solutions thus far, I guess, the first -- this's your first take on it. But do you see any outsized uptick or expect any outsized uptick within certain verticals over others in your view?

是的，說得有道理。那麼，就目前為止的生成式人工智慧應用案例和解決方案而言，我想，首先——這是你的第一印象。在你看來，你是否看到或預期某些垂直領域會出現比其他領域更大的成長？

It's a good question. It kind of seems like everybody is interested in this. They want -- at the level of the CEO or the person who operates manufacturing and the person to operate sales they want basically a Google-like interface where they go -- a web browser like interface where they can ask any question about their business, okay? What are the problems in our supply chain. If I'm the chair of the joint chief, what am I ready to less levels, they have 35-ish quadrants and okay, in Central Europe. I mean that's what we call Google for DoD, but their open AI initiatives can provide the Secretary of Defense or the Chair of the Joint Chiefs of Staff answers to that question in seconds. Right now, it actually takes a week for him or most people to get those answers. So it's -- I don't know any industry that will not be taking use of this technology. It's really quite amazing.

這是個好問題。似乎每個人都對此感興趣。無論是執行長、生產負責人還是銷售人員，他們都想要一個類似谷歌的介面，一個類似網頁瀏覽器的介面，可以讓他們詢問任何與業務相關的問題，例如供應鏈中存在哪些問題？如果我是參謀長聯席會議主席，我準備好提供哪些資訊給更低層級的人員？他們有大約35個像限，而且位於中歐。我的意思是，我們把這稱為國防部的“谷歌”，但他們的開放式人工智慧計劃可以在幾秒鐘內為國防部長或參謀長聯席會議主席提供這些問題的答案。而現在，他或大多數人實際上需要一週的時間才能得到這些答案。所以——我不知道哪個產業不會使用這項技術。這真是太神奇了。

Our next question comes from the line of Mike Cikos of Needham Company.

我們的下一個問題來自 Needham 公司的 Mike Cikos。

I did just have 1 quick follow-up. And maybe building on the question that Sanjit had asked earlier, but taking a different look rather than looking at the revenue, let's talk profitability for a second. But obviously, you guys are issuing guidance now, which is below Street and below what you guys had initially flagged if we go back a quarter ago, maybe for Juho. Can you help us think about the additional levers you have to pull on to ensure that C3 is achieving its target of exit fiscal '24 with non-GAAP profitability?

我還有一個後續問題。或許可以接著Sanjit之前提出的問題，但這次我們不只關注營收，而是換個角度來談談獲利能力。顯然，你們現在發布的業績指引低於市場預期，也低於你們上個季度（例如Juho）所給的預期。能否請你們談談，為了確保C3實現2024財年非GAAP獲利的目標，你們還需要採取哪些額外措施？

Before we answer it, Mike. I do want to poke at the premise a little bit. Okay. I think our guidance is pretty much in line with what the Street expectations are once you take out like 1 outlier or 2 outliers. So our current guidance is in line with what the Street currently has. I'm pretty confident in that.

在我們回答這個問題之前，麥克，我想先稍微探討一下這個前提。好的。我認為，剔除一、兩個異常值之後，我們的績效指引與華爾街的預期大致一致。所以，我們目前的業績指引與華爾街的預期相符。對此我相當有信心。

Now Juho the other question related to how are you sure you're going to get to profitability?

Juho，現在另一個問題是，你要如何確定自己能夠獲利？

Right. So Mike, 1 of the things that I had on the prepared remarks was our planned investments into Generative AI, which combine that and vendor expenses, I think we can control spending towards the end of the year, if for whatever reason, the expected revenue would not occur from those. But we're pretty bullish about the Generative AI opportunity.

好的。麥克，我準備的發言稿裡有一項內容是關於我們計劃對生成式人工智慧的投資，加上供應商費用，我認為如果由於某種原因，這些投資未能達到預期收入，我們可以在年底前控制支出。但我們對生成式人工智慧的發展前景非常樂觀。

Said, we don't need the Generative AI for (inaudible) to be, Generative AI could be 0, okay? And we're still going to run a cash positive profitable business in Q4. Well, you have a very, very detailed plan that's been distributed to all the members of the management team. They all have big budgets. They know that's going to operate and you can expect it to be in cash positive profitable business non-GAAP profitable business in Q4, hard stock.

他說，我們不需要生成式人工智慧來實現（聽不清楚），生成式人工智慧可以設為0，好嗎？而我們第四季仍會實現現金流為正、獲利的業務。嗯，你們有一個非常非常詳細的計劃，已經分發給了管理團隊的所有成員。他們都有充足的預算。他們知道這個計劃會順利進行，你可以預期在第四季度實現現金流為正、盈利的業務（非GAAP準則下的盈利業務），並且會有實體股票。

Our next question comes from the line of Noah Herman of JMP Securities.

我們的下一個問題來自 JMP Securities 的 Noah Herman。

It is great to see the average sales cycles for agreements ticked down, I think, by about 1.3 months year-over-year. What's really driving that? And where do you think a sustainable sales cycle basically concludes that maybe thinking about the rest of this year?

很高興看到平均銷售週期比去年同期縮短了約1.3個月。這主要是什麼原因造成的？考慮到今年剩餘時間，您認為可持續的銷售週期應該達到什麼程度？

Well, I think the consumption-based pricing model is driving it where it's pretty easy to do. We're not going to have a large-scale enterprise AI application live in production in 6 months for $0.5 million. I mean that's nothing guys in terms of what it costs to bring in an Accenture or an IBM or somebody to try to bring one of these things, , that's going to be scores of millions of dollars in the years. So it's a pretty easy sale. It's a shorter sales cycle. And so I'm not sure where it ends up, but as we move more into more of our products, onto the AWS marketplace, the Google marketplace and other leverage channels like this, we'd expect to see it get shorter.

我認為，基於使用量的定價模式之所以能推動這一趨勢，是因為它很容易實現。我們不可能在6個月內以50萬美元的價格將一個大型企業級AI應用投入生產環境。我的意思是，這和聘請埃森哲、IBM或其他公司來開發這類應用的成本相比，簡直微不足道。後者幾年下來將耗資數千萬美元。所以，這種模式很容易銷售，銷售週期也更短。因此，我不確定最終結果會如何，但隨著我們越來越多的產品進入AWS Marketplace、Google Marketplace以及其他類似的管道，我們預計銷售週期將進一步縮短。

At this I'd like to turn the call back over to Mr. Siebel for any closing remarks.

接下來，我想把電話轉回給西貝爾先生，請他作總結發言。

Ladies and gentlemen, we thank you so much for your attention today. Thank you for tuning in. I encourage you to mark your calendars for June 22. I think you'll find that we'll talk about some interesting developments at our Investor Conference at that time, and we hope you'll have time to join us for that exchange. So thank you very much for your time today, and we wish you all a good night.

女士們、先生們，非常感謝各位今天的關注。感謝收聽。請各位在日曆上標記6月22日。屆時，我們將在投資者大會上討論一些重要的進展，希望各位屆時能抽出時間參與交流。再次感謝各位今天抽出時間，祝大家晚安。

Thank you. Ladies and gentlemen, this does conclude today's conference. Thank you all for participating. You may now disconnect. Have a great day.

謝謝大家。女士們、先生們，今天的會議到此結束。感謝各位的參與。現在可以斷開連線了。祝您有美好的一天。