C3.ai Inc (AI) 2024 Q4 法說會逐字稿

Good day and welcome to the C3 AI’s fourth-quarter fiscal year 2024 conference call.

大家好，歡迎參加 C3 AI 2024 財年第四季電話會議。

(Operator Instructions) Please be advised that today's conference is being recorded.

（操作員指示）請注意，今天的會議正在錄音。

I would now like to hand the conference over to your speaker, Mr. Amit Berry.

現在我想將會議交給發言人阿米特·貝裡先生。

Please go ahead.

請繼續。

Good afternoon and welcome to C3 AI's earnings call for the fourth-quarter fiscal year 2024, which ended on April 30 2024.

下午好，歡迎參加 C3 AI 2024 財年第四季（截至 2024 年 4 月 30 日）收益電話會議。

My name is Amit Berry, and I lead Investor Relations at C3 AI.

我叫 Amit Berry，是 C3 AI 的投資人關係主管。

With me on the call today is Tom Siebel, Chairman and Chief Executive Officer; and Hitesh Lath, Chief Financial Officer.

今天與我一起參加電話會議的是董事長兼執行長湯姆·西貝爾 (Tom Siebel)；以及財務長 Hitesh Lath。

After the market closed today, we issued a press release with details regarding our fourth 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.

今天市場收盤後，我們發布了一份新聞稿，詳細介紹了我們的第四季度業績以及業績補充信息，您可以通過我們網站 ir.c3.ai 的投資者關係部分訪問這些信息。

This call is being webcast and a replay will be available on our IR website following the conclusion of the call.

本次電話會議將進行網路直播，會議結束後，我們將在 IR 網站上提供重播。

During today's call, we will make statements related to the business that may be considered forward-looking under federal securities laws.

在今天的電話會議中，我們將發表與業務相關的聲明，這些聲明可能根據聯邦證券法被視為前瞻性的。

These statements reflect our views only as of today and should not be considered representative of our views as of any subsequent date.

這些聲明僅反映我們截至今天的觀點，不應被視為代表我們隨後任何日期的觀點。

We disclaim any obligation to update any forward-looking statements or outlook.

我們不承擔更新任何前瞻性陳述或展望的義務。

These statements are subject to a variety of risks and uncertainties that could cause actual results to differ materially from expectations.

這些聲明受各種風險和不確定因素的影響，可能導致實際結果與預期有重大差異。

For a further discussion on material risks and other important factors that could affect our actual results, please refer to our filings with the SEC.

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.

除非另有說明，所有數據都將以非 GAAP 基礎討論。

Also, during today's call, we will refer to certain non-GAAP financial measures.

此外，在今天的電話會議中，我們將參考某些非 GAAP 財務指標。

A reconciliation of GAAP to non-GAAP measures is included in our press release.

我們的新聞稿中包含了 GAAP 與非 GAAP 指標的對帳表。

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.

最後，在我們準備好的發言中，為了回答您的問題，我們可能會討論一些比我們通常的介紹更詳細的指標，以便更深入地了解我們的業務動態或季度業績。

Please be advised that we may or may not continue to provide this additional detail in the future.

請注意，我們將來可能會或不會繼續提供這些額外的詳細資訊。

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

現在，請允許我將電話轉給湯姆。

Thank you, Amit.

謝謝你，阿米特。

Good afternoon, everyone, and thank you for joining our call today.

大家下午好，感謝大家今天的電話會議。

Hitesh and I are pleased to share with you our results for the fourth quarter and for the entire fiscal year of 2024.

Hitesh 和我很高興與您分享我們 2024 年第四季和整個財年的業績。

Q4 was a great quarter and the end of a huge year for C3 AI.

對 C3 AI 來說，第四季是一個偉大的季度，也是豐收的一年的結束。

We exceeded all expectations for revenue, cash flow, and profitability.

我們的收入、現金流和獲利能力超出了所有預期。

Let me be clear: there were no expectations that we did not exceed.

讓我明確一點：我們沒有超越任何預期。

This was our fifth consecutive quarter of accelerating revenue growth.

這是我們連續第五個季度實現營收加速成長。

Our quarterly year-over-year revenue growth has accelerated from 11% in Q1 to 17% in Q2, 18% in Q3, and now, 20% in Q4 of fiscal year '24.

我們的季度年收入成長從第一季的 11% 加速至第二季的 17%、第三季的 18%，現在，24 財年第四季的成長率為 20%。

Our quarterly subscription revenue has also significantly accelerated, going from 8% in Q1 to 12% in Q2, 23% in Q3, and 41% in Q4 on a year-over-year basis.

我們的季度訂閱收入也顯著加速，年增率從第一季的 8% 上升至第二季的 12%、第三季的 23% 和第四季的 41%。

We finished the quarter with $86.6 million in revenue, exceeding the high-end of both our guidance and analyst expectations.

本季度，我們的營收為 8,660 萬美元，超過了我們的預期和分析師的較高預期。

I'll note that this is the 14th consecutive quarter as a public company in which we have met or exceeded our revenue guidance.

我要指出的是，作為一家上市公司，這是我們連續第 14 個季度達到或超過了我們的收入預期。

For the quarter, subscription revenue was $79.9 million, accounting for 92% of total revenue and increasing 41% from a year ago.

本季訂閱營收為 7,990 萬美元，佔總營收的 92%，較去年同期成長 41%。

Our non-GAAP gross profit was $60.9 million, representing a 70% gross margin.

我們的非公認會計準則毛利為 6,090 萬美元，毛利率為 70%。

Our GAAP operating loss was $82.3 million.

我們的 GAAP 營業虧損為 8,230 萬美元。

Our non-GAAP operating loss was $23.4 million, better than our guidance for a loss of $43.5 million to $51.5 million.

我們的非公認會計準則營業虧損為 2,340 萬美元，優於我們預期的 4,350 萬美元至 5,150 萬美元的虧損。

Our non-GAAP net loss per share was $0.11. We generated a free cash flow of $18.8 million, down the quarter with $750.4 million in cash, cash equivalents and investments, again exceeding analyst consensus.

我們的非公認會計準則每股淨虧損為 0.11 美元。我們產生了 1880 萬美元的自由現金流，較上季度下降，現金、現金等價物和投資為 7.504 億美元，再次超過了分析師的預期。

Full year results exceeded both the high-end of our guidance and analyst expectations with record revenue of $310.6 million, a 16% increase over last year.

全年業績超出了我們的最高預期和分析師的預期，創下了 3.106 億美元的營收記錄，較去年同期成長 16%。

Subscription revenue was $278.1 million, a 21% increase over last year.

訂閱收入為2.781億美元，比去年成長21%。

Now, with the transition that we went through to pay-as-you-go consumption pricing, we are engaging in a much larger number of smaller transactions of shorter term.

現在，隨著我們經歷了向現收現付消費定價的轉變，我們正在參與大量短期小額交易。

This offers us greater revenue visibility and greater revenue predictability.

這為我們提供了更高的收入可見度和更高的收入可預測性。

Our average TCV has plummeted as a result from over $16 million in fiscal year '19 to $900,000 last quarter.

因此，我們的平均 TCV 從 2019 財年的 1,600 多萬美元暴跌至上季的 90 萬美元。

As we work through this pricing transition, we are seeing, as expected, okay, at first a decline, and now, a return to accelerating revenue growth.

在我們完成這項定價轉變的過程中，我們看到，正如預期的那樣，一開始是下降，但現在又恢復了加速收入成長。

Also, as expected, we are seeing a reduction in RPO.

此外，正如預期的那樣，我們看到 RPO 有所減少。

We expect RPO to continue to decline in the next few quarters as we expect revenue to increase.

我們預計收入將增加，因此RPO將在未來幾季繼續下降。

This is a mathematical certainty from the change in our go-to-market model, and I am not certain at all that RPO is a valid leading indicator of our business in the short term going forward.

這是我們市場進入模式變化所帶來的數學上的確定性，但我完全不確定 RPO 是否是我們未來短期業務的有效領先指標。

Let's take a look at the AI value stack.

讓我們來看看 AI 值堆疊。

There clearly is a market frenzy today around AI infrastructure.

如今，人工智慧基礎設施市場顯然十分狂熱。

Now, when you look at the value stack at AI, at the bottom you have silicon.

現在，當你查看 AI 的價值堆疊時，你會發現最底層就是矽。

Above that you have infrastructure.

除此之外，還有基礎建設。

Above that you have foundation models.

在其之上，有基礎模型。

And on top of all of that you have enterprise AI Applications.

最重要的是，您擁有企業 AI 應用程式。

C3 AI plays at the top of the stack, okay, focused exclusively on Enterprise AI applications.

C3 AI 處於堆疊的頂端，專注於企業 AI 應用程式。

Now, we believe that in the long run, silicon and infrastructure get commoditized and AI applications dominate the value stack.

現在，我們相信從長遠來看，矽和基礎設施將商品化，人工智慧應用將主導價值堆疊。

As an analog, think about the early stages of the personal computer market.

打個比方，想想個人電腦市場的早期階段。

At the beginning, most of the value was in the silicon and the infrastructure.

一開始，大部分價值在於矽片和基礎設施。

Think about the IBM PC/XT that you might have used in 1983, okay, it cost $7,900.

想想您可能在 1983 年使用過的 IBM PC/XT，它的價格是 7,900 美元。

In today's dollars, that would be $22,000.

以今天的美元計算，這個數字是 22,000 美元。

You might have had $200 to $300 worth of software running on that machine that you purchased from BusyCal or Lotus or wherever.

您可能在該機器上運行了從 BusyCal 或 Lotus 或其他地方購買的價值 200 到 300 美元的軟體。

Now, that PC that's on your desk today cost your company about $200 a year in depreciation expense for the hardware and another $200 a year or so for infrastructure cost.

現在，您辦公桌上的這台電腦每年會花費您公司約 200 美元的硬體折舊費用以及每年約 200 美元的基礎設施成本。

And by the time you have all the applications you're running on that computer, be it Bloomberg, SAP, CRM, okay, whatever it might be, those applications can exceed $8,000 a year in total cost.

當你在電腦上運行所有應用程式時，無論是 Bloomberg、SAP、CRM 還是其他什麼，這些應用程式每年的總成本可能會超過 8,000 美元。

Well, the AI era will be no different and the same game is going to play out as we move forward.

好吧，人工智慧時代也不會有所不同，隨著我們不斷前進，同樣的遊戲仍將上演。

The bulk of the value is going to accrue to the applications that leverage the entire AI stack and deliver value to the business.

大部分價值將歸於利用整個 AI 堆疊並為企業帶來價值的應用程式。

Silicon will get commoditized; it always gets commoditized.

矽將會商品化；它總是會被商品化。

Infrastructure will get commoditized.

基礎設施將會商品化。

It always gets commoditized.

它總是會被商品化。

What doesn't get commoditized in the long run are the applications and that's where C3 AI plays.

從長遠來看，無法商品化的是應用程序，而這正是 C3 AI 發揮作用的地方。

Let's take a look at the market dynamics in AI, okay?

我們來看看人工智慧的市場動態，好嗎？

This is proving a headwind for some companies as we're seeing, and it's proving a tailwind for some companies.

正如我們所見，這對一些公司來說是不利因素，對一些公司來說則是順風。

For us, it is clearly a tailwind.

對我們來說，這顯然是順風。

Okay, the primary competitor to C3 AI remains, try to build versus buy.

好的，C3 AI 的主要競爭對手仍然是嘗試建造而不是購買。

Building AI applications for an enterprise is incredibly difficult and unlike anything CIOs have encountered before.

為企業建立 AI 應用程式極為困難，與 CIO 以前遇到的情況不同。

In fairness, most CIOs have their hands full trying to install single sign on, trying to get their security firewalls to work, and trying to figure out how to manage over budget, delayed, sometimes multi-billion dollar SAP upgrades from Accenture and Deloitte, okay?

公平地說，大多數 CIO 都忙於嘗試安裝單一登入、嘗試使其安全防火牆正常運行，並嘗試弄清楚如何管理超出預算、延遲、有時耗資數十億美元的埃森哲和德勤 SAP 升級，好嗎？

Developing enterprise scale application software is simply not what they do.

開發企業級應用軟體根本不是他們的工作。

The extensive infrastructure and software services required to operate AI applications at scale are exceptionally complex and not feasible for most companies to manage with an in house team of IT engineers.

大規模運行人工智慧應用所需的大量基礎設施和軟體服務極其複雜，大多數公司無法透過內部 IT 工程師團隊進行管理。

Today, many companies are dabbling in trivial AI projects or relying on outside integrators to try to cobble together something that works.

如今，許多公司都在涉足瑣碎的人工智慧專案或依靠外部整合商來嘗試拼湊一些可行的東西。

These are nothing more than large and expensive experiments nobody succeeds.

這些只不過是大型且昂貴的實驗，沒有人會成功。

In reality, enterprise customers don't want to buy tools to build applications.

事實上，企業客戶並不想購買工具來建立應用程式。

They want to buy applications.

他們想購買應用程式。

We've already proven this.

我們已經證明了這一點。

We've proven it in the relational database market.

我們已經在關係資料庫市場證明了這一點。

We've proven it in the ERP market.

我們已經在 ERP 市場上證明了這一點。

We've proven it in the CRM market.

我們已經在 CRM 市場上證明了這一點。

At C3 AI, we've dedicated 15 years and a couple billion dollars worth of software engineering in building a powerful AI platform that underpins some of the largest enterprise AI deployments on earth today.

在 C3 AI，我們花了 15 年的時間和價值數十億美元的軟體工程來建立強大的 AI 平台，為當今地球上一些最大的企業 AI 部署提供支援。

We started this effort in 2009 before anybody even talked about Enterprise AI, before Azure existed, before GCP existed, okay, before the GPU existed.

我們在 2009 年就開始了這項工作，那時甚至還沒有人談論企業 AI，也沒有 Azure、GCP，甚至 GPU 出現。

With significant first mover advantage, we serve the market today with 90 Enterprise AI and Generative AI applications that offer outsized economic benefit.

憑藉顯著的先發優勢，我們目前為市場提供了 90 種企業 AI 和生成 AI 應用程序，可提供巨大的經濟效益。

Our business is focused on enterprise AI applications.

我們的業務專注於企業AI應用。

In fiscal year '24, 88% of our bookings were driven by AI application sales and 12% of our bookings were driven by the C3 AI platform.

在 24 財年，我們 88% 的訂單來自 AI 應用程式銷售，12% 的訂單來自 C3 AI 平台。

Our pilot counts surged to 123 for the year as we closed 191 agreements now across 19 different industries, underscoring the effectiveness of these products in meetings complex business needs across many business sectors.

我們目前已在 19 個不同行業達成 191 份協議，試點數量激增至 123 個，凸顯了這些產品在滿足眾多商業部門複雜業務需求方面的有效性。

Our bookings distribution for the fourth quarter was approximately 50% federal, defense and aerospace, 15% oil and gas, 11% state government, 7% manufacturing, 6% energy and utilities, 5% consumer packaged goods, 5% crofessional services.

我們第四季的訂單分佈約為 50％ 聯邦、國防和航空航天、15％ 石油和天然氣、11％ 州政府、7％ 製造業、6％ 能源和公用事業、5％ 消費包裝商品、5％ 專業服務。

This increase in bookings diversity would be a leading indicator for C3 AI.

預訂多樣性的增加將成為 C3 AI 的領先指標。

Our pilot distribution for the fourth quarter was 29% manufacturing, 21% federal, defense, and aerospace, 12% agriculture, 9% chemicals, 6% life sciences, 6% oil and gas, 6% state and local, 6% energy and utilities, and 3% logistics and transportation.

我們第四季的試點分佈為：製造業29%、聯邦、國防和航空航天21%、農業12%、化學品9%、生命科學6%、石油和天然氣6%、州和地方6%、能源和公用事業6%、物流和運輸3%。

This pilot diversity is a future indicator of where you expect this company to be going.

飛行員的多樣性可以作為您預期該公司未來發展方向的一個指標。

Now, let me provide a brief update on some of our recent product advancements: first, Version 8 of our Platform and Applications is providing customers with an order of magnitude improvement in speed, efficiency and overall performance.

現在，讓我簡要介紹我們最近的一些產品進步：首先，我們的平台和應用程式的第 8 版為客戶提供了速度、效率和整體效能的數量級提升。

With Version 8, it is now more than 20 times faster to ingest data, train machine learning models and infer time series features, and customers can run thousands of applications in a single C3 AI platform cluster to reach highly scalable deployments.

使用版本 8，現在資料擷取、訓練機器學習模型和推斷時間序列特徵的速度提高了 20 倍以上，並且客戶可以在單一 C3 AI 平台叢集中運行數千個應用程序，實現高度可擴展的部署。

The C3 AI Community is the name of our interactive training, online help and developer platform.

C3 AI 社群是我們互動式培訓、線上幫助和開發者平台的名稱。

It is becoming a thriving ecosystem for engagement and collaboration amongst C3 developers and data scientists around the world.

它正在成為一個蓬勃發展的生態系統，促進全球 C3 開發人員和資料科學家之間的參與和協作。

This year, we supercharged the C3 AI community by delivering C3 Generative AI co-pilot, which instantly answers questions and generates code for programmers to massively increase developer productivity on the C3 AI platform.

今年，我們透過推出 C3 Generative AI 副駕駛增強了 C3 AI 社群的實力，它可以立即回答問題並為程式設計師產生程式碼，從而大幅提高 C3 AI 平台上開發人員的工作效率。

Let me talk a little bit about customer traction.

讓我稍微談談客戶吸引力。

We are witnessing increased usage amongst our customers.

我們注意到客戶的使用量增加。

Cargill has expanded from 13 to 18 plants in production in the past year.

嘉吉公司在過去的一年裡從 13 家工廠擴大到了 18 家。

Baker Hughes sourcing optimization is now deployed across 855 sites and three business segments with 2,000 users, offering a potential savings of $100 million a year.

貝克休斯採購優化目前已部署在 855 個站點和三個業務部門，擁有 2,000 名用戶，每年可節省 1 億美元。

C3 AI reliability is now deployed at 12 plants at Petronas, monitoring 4,000 control valves, and realizing $25 million a year of annual loss avoidance. [Dow] is enhancing its predictive maintenance capabilities with C3 AI reliability and has announced that it's expecting to decrease downtime for steam cracking furnaces in polyethylene production facilities by 20%.

目前，C3 AI 可靠性已部署在馬來西亞國家石油公司的 12 家工廠，監控 4,000 個控制閥，每年可避免 2,500 萬美元的損失。 [陶氏] 正在透過 C3 AI 可靠性增強其預測性維護能力，並宣布預計聚乙烯生產設施中蒸汽裂解爐的停機時間將減少 20%。

Holcim, a large European construction products company, started with C3 AI reliability production pilot in May of 2023 and now has 31 facilities in production, running over 200 machine learning models to monitor 3,000 sensors from critical equipment, including vertical roller mills.

歐洲大型建築產品公司 Holcim 於 2023 年 5 月開始進行 C3 AI 可靠性生產試點，目前擁有 31 家生產設施，運行 200 多個機器學習模型來監控關鍵設備（包括立式輥磨機）的 3,000 個感測器。

According to Roze Wesby, who is Head of Plants of Tomorrow at Holcim, and this is a quote, C3 AI is playing an important role in Holcim's digital transformation, providing innovative AI solutions that drive efficiency and sustainability.

根據 Holcim 未來工廠負責人 Roze Wesby 的說法，C3 AI 在 Holcim 的數位轉型中發揮重要作用，提供推動效率和永續性的創新 AI 解決方案。

She continues, the collaboration between C3 AI and Holcim has led to advancements in operational efficiency at scale, raising the bar for predictive maintenance in our sites.

她繼續說，C3 AI 和 Holcim 之間的合作促進了大規模營運效率的提升，提高了我們工廠預測性維護的標準。

Thanks to C3 AI's platform, Holcim has achieved a step function change, okay, in asset life cycle management, improving our reliability and capacity for our customers, as well as reducing environmental impact.

借助 C3 AI 平台，Holcim 在資產生命週期管理方面實現了階躍變化，提高了我們為客戶提供的可靠性和產能，並減少了對環境的影響。

Con Edison, a C3 AI customer since 2017, uses the C3 AI platform to improve everything from operational and energy efficiency to public safety, billing performance, and customer satisfaction.

自 2017 年以來，康乃狄克愛迪生公司一直是 C3 AI 的客戶，它使用 C3 AI 平台改善從營運和能源效率到公共安全、計費性能和客戶滿意度等各個方面。

According to Tom Magee, who is general manager for Con Ed's advanced metering infrastructure project, and I quote, the AMI project, the largest in Con Edison history included the deployment of 5.3 million smart meters and resulted in significant benefits such as improved outage management and energy efficiency.

根據康尼迪克公司先進計量基礎設施項目總經​​理湯姆·馬吉 (Tom Magee) 介紹，AMI 項目是康尼迪克公司歷史上規模最大的項目，部署了 530 萬個智慧電錶，帶來了許多顯著效益，例如改善了停電管理和提高能源效率。

The use of AI and machine learning has enhanced public safety, optimized grid operations, and achieved substantial energy savings, and emissions reductions for our customers.

人工智慧和機器學習的使用提高了公共安全，優化了電網運行，並為我們的客戶實現了大幅的節能和減排。

We monitor our customer satisfaction very closely, and our customer satisfaction levels are well above industry averages for enterprise application software.

我們密切監控客戶滿意度，我們的客戶滿意度遠高於企業應用軟體產業平均。

We'll talk a little bit about the strength that we're seeing in the US federal market.

我們將稍微談論一下我們在美國聯邦市場看到的實力。

We had a strong quarter and closed out a remarkable year for the federal business, with revenue growing more than 100% in 2024.

我們本季表現強勁，為聯邦業務結束了不平凡的一年，2024 年的營收成長了 100% 以上。

Our transaction in this vertical is increasing, establishing it as a significant growth engine for C3 AI going forward.

我們在這個垂直領域的交易正在增加，這使其成為未來 C3 AI 的重要成長引擎。

Last year, we closed 65 agreements with federal agencies and made inroads into 10 new federal organizations.

去年，我們與聯邦機構達成了 65 項協議，並加入了 10 個新的聯邦組織。

In Q4, we entered into 13 new and expanded agreements with the US Air Force, the US Navy, the US Intelligence Community, the Defense Counterintelligence and Security Agency, the Chief Digital and Artificial Intelligence Office, the Thales group, and the US Marine Corps.

第四季度，我們與美國空軍、美國海軍、美國情報界、國防反間諜和安全局、首席數位和人工智慧辦公室、泰雷茲集團和美國海軍陸戰隊簽訂了 13 項新的和擴展協議。

Our expertise and leadership in predictive maintenance is clear when you look at the work we do with the US Air Force and now, the Navy.

當您查看我們與美國空軍以及現在的海軍所做的工作時，我們在預測性維護方面的專業知識和領導地位就顯而易見。

The US Air Force Rapid Sustainment Office continues to expand their C3 AI footprint by increasing the capabilities in the number of weapons systems monitored on the predictive analytics and decision-assisted application.

美國空軍快速維持辦公室繼續擴大其 C3 AI 足跡，透過增加預測分析和決策輔助應用監控的武器系統數量的能力。

This system they call Panda.

他們將這個系統稱為 Panda。

This is the system of record for all predictive maintenance projects within RSO and the United States Air Force, optimizing fleet maintenance, increasing aircraft availability, and minimizing downtime.

這是 RSO 和美國空軍內所有預測性維護項目的記錄系統，可優化機隊維護、提高飛機可用性並最大限度地減少停機時間。

This application is now being applied to monitor two new weapon systems, the T7 and the KC46, and it's been expanded to include new capability for the B-1 Bomber, the C5, or the KC-135.

該應用程式目前用於監控兩種新型武器系統，T7 和 KC46，並已擴展到包括 B-1 轟炸機、C5 或 KC-135 的新功能。

According to Jimmy Lawrence, who is the Deputy Program Executive Officer for the Rapid Sustainment Office, and this is a quote.

根據快速維持辦公室副專案執行官吉米勞倫斯的說法，這是一段引言。

C3 AI's cutting-edge technology has been a game changer for the US Air Force, driving unparalleled advancements in predictive analytics and maintenance.

C3 AI 的尖端技術改變了美國空軍的格局，推動了預測分析和維護領域前所未有的進步。

The implementation of C3 AI solutions have revolutionized the operational capabilities of the Air Force, leading to significant improvements in aircraft readiness and efficiency.

C3 AI解決方案的實施徹底改變了空軍的作戰能力，顯著提高了飛機的準備程度和效率。

We've also been working with the US Navy, building on predictive maintenance program for C3 AI and the US Air Force on the Crowdsource flight data program at Nellis Air Force Base in Nevada.

我們也與美國海軍合作，為 C3 AI 建立預測性維護計劃，並與美國空軍合作，在內華達州內利斯空軍基地開展 Crowdsource 飛行數據計劃。

This new agreement also expands the Navy into the analysis of electronic emissions on the F-35 weapon system.

這項新協議也擴大了海軍對F-35武器系統電子發射的分析範圍。

Talk a little bit about the C3 AI Partner Network, our partners remain a key driver of growth and customer success, as we continue to deepen our relationships with the major hyperscaler providers and system integration partners.

談談 C3 AI 合作夥伴網絡，隨著我們繼續深化與主要超大規模供應商和系統整合合作夥伴的關係，我們的合作夥伴仍然是成長和客戶成功的關鍵驅動力。

Last year, we closed 115 agreements through our partner network, representing a 62% increase from the prior year.

去年，我們透過合作夥伴網路達成了 115 項協議，比前一年成長了 62%。

This includes 91 agreements with AWS, Google Cloud, and Azure.

其中包括與 AWS、Google Cloud 和 Azure 達成的 91 項協定。

Our joint 12-month qualified pipeline with partners grew by 63% year-over-year.

我們與合作夥伴共同建立的12個月合格管道年增了63%。

Our business activity with Google Cloud has increased considerably.

我們與 Google Cloud 的業務活動大幅增加。

In Q4 alone, we closed 12 pilots with Google Cloud.

光是在第四季度，我們就與 Google Cloud 完成了 12 個試點。

There's a massive amount of support from GCP, in pursuing our state and local pilots, and Google has committed to invest with us in a big way in the first quarter.

GCP 在我們開展州和地方試點的過程中提供了大量支持，谷歌已承諾在第一季與我們進行大規模投資。

We've also substantially increased our partnerships with two firms, one Fractal, and the other called Paradyme, partnering with them for professional services to support our Version 8 upgrades, customer service engagements and pilot delivery.

我們也大幅加強了與兩家公司的合作關係，一家是 Fractal，另一家是 Paradyme，我們與他們合作提供專業服務，以支持我們的第 8 版升級、客戶服務參與和試點交付。

These organizations have established dedicated practices around C3 AI and are committed to train over 200 C3 AI qualified engineers and data scientists in the coming year.

這些組織圍繞著 C3 AI 建立了專門的實踐，並承諾在來年培訓超過 200 名 C3 AI 合格的工程師和資料科學家。

Let's double click on C3 Generative AI.

讓我們雙擊 C3 Generative AI。

Folks, this is a massive opportunity.

朋友們，這是一個巨大的機會。

There is substantial and growing demand for our C3 Generative AI products.

我們的 C3 生成式 AI 產品的需求量龐大，且不斷成長。

The market is very much coming our way.

市場正朝著我們想要的方向發展。

The company launched 30 generative AI products in fiscal year '24, and we are being overwhelmed with market interest for these products.

該公司在 24 財年推出了 30 款生成式 AI 產品，市場對這些產品的興趣令我們應接不暇。

In Q4 alone, we received almost 50,000 inquiries from 3,000 businesses, each with revenue greater than $500 million, all expressing interest in our generative AI Applications. 50,000, 10,500 in the 28 days of February alone.

光是在第四季度，我們就收到了來自 3,000 家企業的近 50,000 個諮詢，每家企業的收入都超過 5 億美元，他們都對我們的生成式 AI 應用程式表示了興趣。光是 2 月 28 天就有 5 萬人、10,500 人。

We currently expect this to expand to 90,000 inquiries in the first quarter of '25.

我們目前預計，到 25 年第一季度，這一數字將擴大到 90,000 個。

Over the past year, C3 Generative AI was piloted across 15 different industries, driving us deeper into new verticals and accelerating our industry diversification.

在過去的一年裡，C3 生成式人工智慧在 15 個不同的行業進行了試點，推動我們深入新的垂直領域，並加速我們的產業多元化。

If we look at the industries that we touched with these pilots, be like 21% federal, defense, and aerospace, 12% manufacturing, 10% ag, 10% state and local government, 7% financial services, 5% chemicals, 5% construction, 5% CPG, 5% energy utilities, 5% oil and gas, 5% pharmaceuticals and life sciences.

如果我們看看這些試點涉及的產業，就會發現聯邦、國防和航空航太佔 21%，製造業佔 12%，農業佔 10%，州和地方政府佔 10%，金融服務佔 7%，化學品佔 5%，建築業佔 5%，快速消費品佔 5%，能源公用事業佔 5%，石油和製藥

The C3 generative AI remains a highly differentiated product offering in the generative AI market, providing customers with safe, secure, fast, reliable information from across their enterprise.

C3 生成式人工智慧仍然是生成式人工智慧市場中高度差異化的產品，可為客戶提供整個企業的安全、快速、可靠的資訊。

It enables retrieval and reasoning across omni-modal data with deterministic responses fully traceable to ground truth sources.

它可以跨全模態資料進行檢索和推理，並提供完全可追溯到地面真實來源的確定性反應。

It offers robust enterprise controls, no incremental cybersecurity risk caused by or LLM caused data leakage, minimal hallucination risk, poses no IP liability exposure from the LLM, and provides flexibility to be completely LLM agnostic.

它提供強大的企業控制，不會因 LLM 導致的資料外洩而增加網路安全風險，將幻覺風險降至最低，不會因 LLM 而產生任何 IP 責任風險，並且提供完全不受 LLM 限制的靈活性。

And we further differentiated C3 generative AI from other market offerings in the course of the year in many, many ways.

在這一年中，我們透過多種方式進一步將 C3 生成式人工智慧與市場上的其他產品區分開來。

We have a rich product roadmap for the coming year, and we will continue to invest in this product to drive innovation in the generative AI market.

我們為未來一年制定了豐富的產品路線圖，並將繼續對該產品進行投資，以推動生成式 AI 市場的創新。

Okay, so to wrap this up, over the decades and as inflation goes up and inflation goes down and markets boom and market bust, here we see equity market mood swings, okay.

好的，總結一下，在過去的幾十年裡，隨著通貨膨脹的上升和下降，市場繁榮和蕭條，我們看到股票市場的情緒波動。

And great management teams don't build companies based upon the fad of the week.

優秀的管理團隊不會根據一時的時尚來創立公司。

As it relates to equity markets, with increased inflation, the current pendulum has swung to a demand for instant cash generation and instant profitability.

就股票市場而言，隨著通貨膨脹的加劇，當前的鐘擺已經轉向對即時現金產生和即時利潤的需求。

Now, let's put this into perspective.

現在，讓我們來客觀看待這個問題。

It took Apple over a quarter of a century to be consistently profitable, a quarter of a century.

蘋果花了超過四分之一個世紀的時間才持續獲利，四分之一個世紀。

How did that work out for Apple investors?

這對蘋果投資人有什麼影響？

It took Amazon 29 years to be consistently profitable, okay?

亞馬遜花了 29 年才持續獲利，好嗎？

That generated roughly, okay, $2 trillion in investor value, okay?

這為投資者創造了大約 2 兆美元的價值，好嗎？

These companies were going after large market opportunities and they had conviction to invest for growth and market share along the way.

這些公司正在追逐巨大的市場機遇，並且有信心在整個過程中進行投資以實現成長和市場份額。

Regardless of the current fad that happened in response to market fluctuations quarter-to-quarter and kind of day-to-day.

不管當前的流行趨勢如何，這都是為了應對每個季度和每天的市場波動。

C3 AI is looking at addressing a potentially $1 trillion addressable software market.

C3 AI 正在考慮開拓潛在的 1 兆美元軟體市場。

We believe this is the largest market opportunity in the history of software.

我們相信這是軟體史上最大的市場機會。

We raised $1 billion in December of 2020.

我們在 2020 年 12 月籌集了 10 億美元。

Think back, before the world at large was even talking about enterprise AI, and we raised that money to invest in growth, to invest in technology leadership, to invest in brand leadership, and to invest in market leadership.

回想一下，在全世界開始談論企業 AI 之前，我們就已經籌集了資金來投資成長、投資技術領導、投資品牌領導和投資市場領導。

The investments we've made since then have been well considered, prudent, and consistent with what we communicated to investors.

自那時以來，我們所做的投資都經過深思熟慮，審慎進行，並且與我們向投資者傳達的訊息一致。

Our investment plan is a lot longer than day-to-day investment cycles.

我們的投資計劃比日常投資週期長得多。

So as it relates to guidance, we are expecting additional acceleration of C3 AI revenue to approximately 23% in fiscal year '25.

因此，就指導而言，我們預計 25 財年 C3 AI 營收將進一步加速至約 23%。

At the same time, make no mistake, we plan to continue to invest in growth as necessary, to establish market share, to establish a market leadership position, and to build a long-term cash generating profitable market-leading enterprise AI software company.

同時，毫無疑問，我們計劃在必要時繼續投資於成長，確立市場份額，確立市場領導地位，並打造一家長期創造現金、盈利的市場領先企業 AI 軟體公司。

Our revenue guidance for Q1 of fiscal year '25 is going to be $84 million to $90 million.

我們對 25 財年第一季的營收預期為 8,400 萬美元至 9,000 萬美元。

For the fiscal year we're looking at $370 million to $395 million.

我們預計本財政年度的收入為 3.7 億美元至 3.95 億美元。

Our non-GAAP loss from operations, we're expecting to be for Q1 between a $22 million to $30 million loss and for the year $125 million to $95 million loss.

我們預計第一季的非公認會計準則營業虧損將在 2,200 萬美元至 3,000 萬美元之間，全年虧損將在 1.25 億美元至 9,500 萬美元之間。

And now, I'll turn the call over to our most competent CFO, Hitesh, for additional color and detail.

現在，我將把電話轉給我們最有能力的財務長 Hitesh，以了解更多詳情和細節。

Hitesh?

希特什？

Thank you, Tom.

謝謝你，湯姆。

I will now provide a recap of our financial results and additional color on our business.

我現在將回顧我們的財務表現並進一步介紹我們的業務。

All figures are non-GAAP unless otherwise noted.

除非另有說明，所有數據均為非 GAAP 數據。

As Tom mentioned, total revenue for the fourth quarter increased 20% year-over-year to $86.6 million.

正如湯姆所說，第四季的總營收年增 20%，達到 8,660 萬美元。

Subscription revenue increased 41% year-over-year to $79.9 million and representing 92% of total revenue.

訂閱營收年增41%至7,990萬美元，佔總營收的92%。

Professional services revenue was $6.7 million.

專業服務收入為 670 萬美元。

This represented 8% of total revenue in the fourth-quarter of fiscal '24 as compared to 21.5% of total revenue in the fourth quarter of fiscal '23, demonstrating an improved mix of subscription revenue.

這佔24財年第四季總營收的8%，而23財年第四季該數字為21.5%，顯示訂閱收入結構有所改善。

Gross profit for the fourth quarter was $60.9 million and gross margin was 70%.

第四季毛利為6090萬美元，毛利率為70%。

Gross margin for professional services was higher this quarter due to a greater mix of higher margin professional services like prioritized engineering services.

本季專業服務的毛利率較高，原因是優先工程服務等利潤率較高的專業服務組合較多。

Operating loss for the quarter was $23.4 million.

本季營業虧損為 2,340 萬美元。

Our operating loss was lower than guidance due to continued focus on expense management as well as the timing of additional investments we are making to capture market share.

由於持續注重費用管理以及為搶佔市場份額而進行的額外投資的時機，我們的營業虧損低於預期。

As we discussed last quarter, we expected fourth quarter free cash flow to be positive.

正如我們上個季度所討論的那樣，我們預計第四季度的自由現金流將為正值。

Free cash flow for the quarter was $18.8 million.

本季自由現金流為1880萬美元。

We continue to be very well-capitalized and closed the quarter with $750.4 million in cash, cash equivalents, and marketable securities.

我們的資本持續充足，本季結束時我們的現金、現金等價物和有價證券總額為 7.504 億美元。

Please note that the professional services mix in our revenue depends upon the nature and size of revenue deals in any given quarter.

請注意，我們收入中的專業服務結​​構取決於任何特定季度的收入交易的性質和規模。

However, we expect the professional services revenue to generally stay within 10% to 20% of total revenue.

不過，我們預期專業服務收入一般將維持在總收入的 10% 到 20% 之間。

As a reminder, we continue to expect short-term pressure on our gross margins due to higher mix of pilots, which carry a greater cost of revenue during the pilot phase of the customer life cycle.

提醒一下，我們預計，由於試點組合增加，我們的毛利率在短期內仍將面臨壓力，因為這會在客戶生命週期的試點階段帶來更大的收入成本。

We also expect short-term pressure on our operating margin due to additional investments we are making in our business, including in our sales force, research and development, and marketing spend.

我們也預計，由於我們在業務上（包括銷售團隊、研發和行銷支出）進行了額外投資，我們的營業利潤率將在短期內面臨壓力。

At the end of Q4, our accounts receivable balance was $130 million including unbilled receivables of $62.3 million.

截至第四季末，我們的應收帳款餘額為 1.3 億美元，其中包括 6,230 萬美元的未開立發票應收帳款。

Total allowance for bad debt remains low at less than $400,000 and we do not have concerns regarding collections.

壞帳準備金總額仍維持在較低水平，少於 40 萬美元，因此我們並不擔心收款問題。

The general health of our accounts receivables remains strong.

我們的應收帳款整體狀況依然良好。

During the fourth quarter, we signed 34 pilots, a 79% increase from last year and up 17% from last quarter.

第四季度，我們簽約了 34 名飛行員，比去年同期成長了 79％，比上一季成長了 17％。

At quarter end, we had cumulatively signed 172 pilots, of which, 157 are still active.

截至季末，我們累計簽約飛行員 172 名，其中 157 名仍在飛行中。

This means they are either in their original three to six-month term or extended for some duration or converted to a subscription or consumption contract, or are currently being negotiated for conversion to subscription or consumption contract.

這意味著它們要么處於原來的三到六個月的期限，要么延長一段時間，要么轉換為訂閱或消費合同，或者目前正在協商轉換為訂閱或消費合同。

Seven quarters ago, we announced the transition from subscription-based pricing to consumption-based pricing, a standard in the industry.

七個季度前，我們宣布從基於訂閱的定價轉變為基於消費的定價，這是行業標準。

We also announced that this transition would have a short to medium-term negative effect on revenue growth.

我們也宣布，這種轉變將對收入成長產生短期至中期的負面影響。

Accordingly, our GAAP RPO at the end of Q4 was $244.3 million, which is down 36% from last year.

因此，我們在第四季末的 GAAP RPO 為 2.443 億美元，比去年下降了 36%。

And our current GAAP RPO was $163.8 million, which is down 12% from last year.

我們目前的 GAAP RPO 為 1.638 億美元，比去年下降了 12%。

Now, I would like to turn the call over to the operator to begin the Q&A session.

現在，我想將電話轉給接線員，開始問答環節。

Operator?

操作員？

(Operator Instructions) Timothy Horan, Oppenheimer.

（操作員指令）蒂莫西·霍蘭，奧本海默。

Thanks, guys and congratulations.

謝謝大家，恭喜你們。

Can you talk a little bit how did you get the 20-fold increase in improvements in Version 8?

您能否稍微談談如何實現第 8 版中 20 倍的改進？

And how sustainable are those type of improvements?

那麼這些改進的可持續性如何？

How long did that take to get?

要花多長時間才能得到它？

And then secondly, obviously, the sales inquiries are off the charts.

其次，顯然，銷售諮詢量超出了預期。

I mean, how scalable are these inquiries at this point?

我的意思是，目前這些詢問的可擴展性如何？

Both, I guess, to deal with the sales operations and the implementation of these inquiries.

我認為，兩者都是為了處理銷售業務和這些詢問的實施。

Thank you.

謝謝。

Hi, it's Tom, Version 8 was a four-year engineering effort.

大家好，我是湯姆，第 8 版是經過四年的工程努力而完成的。

I mean, it was a very large scale effort, and we basically gutted the product.

我的意思是，這是一次非常大規模的努力，我們基本上毀掉了整個產品。

We re-engineered the very core of it.

我們重新設計了它的核心。

And so it was a major release of the product and it's difficult to get into all the specifics, but we were heads down for four years on this, and it's a major architectural revamp.

這是產品的重大發布，很難詳述，但我們已經為此投入了四年時間，這是一次重大的架構改造。

And we won't see performance increases like that again for a while.

我們暫時不會再看到這樣的效能提升。

Sales inquiries, well, it's just been overwhelming what's been going on in generative AI.

銷售諮詢，嗯，生成人工智慧的發展令人應接不暇。

I think we reached 10,500 in February and then almost 50,000 last quarter.

我認為我們在二月達到了 10,500，上個季度則達到了近 50,000。

How scalable is it?

它的可擴展性如何？

Right now, we can believe that we can generate order of 90,000 a quarter.

目前，我們相信每季可以產生 90,000 份的訂單。

And now, is that going to diminish at some point?

那麼現在，這種趨勢是否會在某個時候減弱呢？

We really don't know, but this is all brand new territory.

我們確實不知道，但這都是全新的領域。

But every time we look at this generative AI market, it looks bigger than it did before, so that is just a huge opportunity.

但每次我們審視這個生成式人工智慧市場時，它看起來都比以前更大，所以這是一個巨大的機會。

And it's important to note we have a highly differentiated product there because all of these issues associated with hallucination, this new thing they call ramps, IP liability, access controls, stochastic responses, we've solved all those problems by coupling the learning models with the capabilities of the C3 AI platform.

值得注意的是，我們擁有高度差異化的產品，因為所有這些與幻覺相關的問題，他們稱之為坡道、IP 責任、存取控制、隨機響應的新事物，我們都透過將學習模型與 C3 AI 平台的功能相結合來解決了所有這些問題。

So omni-modal data ingestion, we have that nailed.

所以，全模式資料擷取，我們已經搞定了。

Identity, we have that nailed.

身份，我們已經確定了。

Access control, we have that nailed.

訪問控制，我們已經搞定了。

And so the marriage of the work we did in the first 15 years of the company with these new innovations in generative AI enables us to solve the problems that all the hobgoblins that are preventing these large language models from being installed in many corporations around the world.

因此，我們將公司成立後前 15 年所做的工作與生成式人工智慧領域的這些新創新結合起來，解決了阻礙這些大型語言模型在全球許多公司安裝的所有障礙。

Thank you.

謝謝。

Pat Walravens, JMP securities.

Pat Walravens，JMP證券。

Great, thank you and congratulations, it's really impressive.

太好了，謝謝你，恭喜你，真是令人印象深刻。

So, I mean, 50% of bookings, Tom, from federal, defense, and aerospace, if you could drill into that more and talk about what you see for the pipeline for that vertical for this coming year, that would be great.

所以，湯姆，我的意思是，50%的訂單來自聯邦、國防和航空航天，如果你可以深入研究這個問題，並談談你對明年該垂直領域的渠道前景的看法，那就太好了。

Federal looks like a growth engine, Pat.

聯邦看起來像是成長引擎，帕特。

Business is good and we've had a lot of inroads in the Air Force, the Navy, the Intelligence Community, and we are investing in the Federal business in a big way.

業務發展良好，我們在空軍、海軍、情報界都取得了很大進展，而且我們正在大力投資聯邦業務。

The Federal community is investing in AI in a big way.

聯邦政府正在大力投資人工智慧。

This is kind of an existential issue where a little bit of war going on with AI against the United States and China and we're on the side of the good guys and we're on the team.

這是一種生存問題，美國和中國在人工智慧方面發生了一些戰爭，而我們站在好人這邊，我們是團隊的一員。

So I'm not sure how big it is, but it's big.

所以我不確定它有多大，但它很大。

Yeah, and as a follow-up on that, so your partnerships with AWS, Microsoft, Google, Booz Allen, I guess what's bearing the most fruit in federal?

是的，作為後續問題，您與 AWS、微軟、谷歌、博思艾倫的合作，我想哪一個在聯邦層面上取得了最大的成果？

Well, AWS is probably, they're the company by far that has the most tentacles into Federal and I would say probably 11 out of 12 of our applications are running on the AWS GovCloud.

嗯，AWS 可能是迄今為止對聯邦政府影響最大的公司，我想說我們 12 個應用程式中大概有 11 個都在 AWS GovCloud 上運行。

And so in our relationships with the Federal, AWS Federal group and the International Federal Group that deals with the allies, NATO, Five Eyes, what have you, is very deep and rich.

因此，我們與聯邦、AWS 聯邦集團以及與盟國、北約、五眼聯盟等打交道的國際聯邦集團的關係非常深厚和豐富。

And as it relates to hyperscalers, that's where we're seeing the most action.

就超大規模運算而言，我們看到了最多的行動。

And AWS it just is the dominant-installed platform.

而 AWS 只是主導的安裝平台。

All right, great.

好的，太好了。

Thanks.

謝謝。

And congratulations again.

再次恭喜你。

Thank you.

謝謝。

Sanjit Singh, Morgan Stanley.

摩根士丹利的 Sanjit Singh。

Yeah, thank you for taking the questions, congrats on a strong close to the year.

是的，感謝您回答這些問題，並祝賀您在今年取得了圓滿的成果。

Tom, I'd love to get a, like an example, your favorite example of one of the customers coming out of the gen AI C3 pilot program and the role that C3 AI did in terms of getting them into production, I think that's a clear debate in the industry about are a lot of these projects experimental and can they actually get into production?

湯姆，我很想舉個例子，你最喜歡的例子是來自新一代人工智慧 C3 試點計畫的一個客戶，以及 C3 AI 在將它們投入生產方面所發揮的作用，我認為業界存在一個明確的爭論，關於這些項目中有很多是否是實驗性的，它們是否真的可以投入生產？

It seems like you guys are getting your customers into production.

看起來你們正在讓客戶投入生產。

And so I don't know if there's one of the 58 pilots that you signed this year that sort of catches your eye and provides like an example, or a model, if you will, of how C3 AI gets its customers into production for Gen AI use cases.

所以我不知道您今年簽署的 58 名飛行員中是否有一名能吸引您的注意並提供一個例子或模型，說明 C3 AI 如何讓其客戶投入 Gen AI 用例的生產。

Well, Sanjit, it's very interesting is they're incredibly diverse.

嗯，桑吉特，非常有趣的是，它們的多樣性令人難以置信。

One example would be there's a large law firm that we all know that's very active in taking companies public, and what we did for them is that we ingested the corpus of sec.gov egger, okay, into an enterprise learning model.

一個例子是，我們都知道有一家大型律師事務所非常積極地推動公司上市，我們為他們所做的就是將 sec.gov egger 的語料庫納入企業學習模式。

This would be all the S1s, all the 10-Ks, all the 10-Qs that ever been published.

這將是所有曾經發布的 S1、所有 10-K 和所有 10-Q。

Now, what they're going to use this for, their first use is when they're taking the next company public, whoever that might be, okay, and they want to generate type in the name, they type in the financials, okay, they hit the carriage return and generate the first draft of the S1 and it does it in an hour rather than two weeks.

現在，他們要用它做什麼，他們的第一個用途是當他們將下一家公司上市時，無論那是什麼公司，好的，他們想要輸入名稱，輸入財務數據，好的，他們按下回車鍵並生成 S1 的初稿，它在一小時內而不是兩週內完成。

And this would be applicable to your business, so I can have it live.

並且這將適用於您的業務，因此我可以讓它上線。

My offer to you is I'll have the system live and in production for $0.25 million dollars in 12 weeks.

我給您的報價是，我將在 12 週內以 25 萬美元的價格讓該系統上線並投入生產。

So give me a call, send me a check and we'll have it live.

所以請給我打電話，給我寄一張支票，我們就可以讓它生效。

And another one is let's look at the application that we have in place for this application, it's called Panda; we've talked about this a lot.

另外一個是讓我們來看看我們為這個應用程式準備的應用程序，它叫做 Panda；我們已經討論過這個問題很多次了。

This is where we've loaded all the underlying information and telemetry associated with 22 weapons systems in the United States Air Force; F-15, F-16, F-18, F-35, KC-135, F-22, et cetera.

這裡我們載入了與美國空軍 22 個武器系統相關的所有基礎資訊和遙測數據； F-15、F-16、F-18、F-35、KC-135、F-22 等等。

And we use this for try to identify system and subsystem failure before it happens, predictive maintenance.

我們利用這一點，在系統和子系統故障發生之前嘗試識別它，進行預測性維護。

And so we can identify that their auxiliary power unit or the flap actuator or the igniter and the afterburner is going to fail in the next 50 or 100 flight hours.

因此我們可以確定他們的輔助動力裝置、襟翼執行器、點火器和加力燃燒器將在未來 50 或 100 個飛行小時內出現故障。

You fix it that night in Stuttgart or Munich and the plane doesn't fail.

你當晚在斯圖加特或慕尼黑修好它，飛機就不會故障。

Net-net 25% increase in aircraft availability at the scale of the United States Air Force.

美國空軍規模的飛機可用性淨增加25％。

Now, you can imagine that the human interface for this is pretty tactical, right?

現在，您可以想像這個人類介面非常具有戰術性，對嗎？

And it's designed for use by highly technical maintenance people who manage sustainment and logistics at the scale of the United States Air Force, so it's as technical as you get, like a manufacturing application or other applications that you've seen.

它是為管理美國空軍規模的維持和後勤的高技術維護人員設計的，因此它的技術性與製造應用程式或您見過的其他應用程式一樣強。

So here, where does generative AI play here?

那麼，生成式人工智慧在這裡發揮什麼作用呢？

And I think this is probably the biggest impact of Generative AI, actually, it could be used to fundamentally change the nature of the human computer interface for enterprise applications.

我認為這可能是生成式人工智慧最大的影響，實際上，它可以從根本上改變企業應用程式的人機介面的性質。

So when we put a Generative AI front-in on that, it looks like a mosaic browser, where you can ask any question in English or whatever, or 131 languages, by the way, and it gives you the answer.

因此，當我們將生成式人工智慧放在其前端時，它看起來就像一個馬賽克瀏覽器，您可以在其中用英語或其他語言（順便說一下，131 種語言）提出任何問題，它會為您提供答案。

For example, now at the level of the Secretary of the Air Force or the Secretary of Defense or the Chairman of the Joint Chiefs, he or she might ask, what are my readiness levels from F-35 squadrons in Central Europe?

例如，現在空軍部長、國防部長或參謀長聯席會議主席可能會問，我駐中歐的 F-35 中隊的戰備程度如何？

And you have to grind through a lot of data, but a minute later, it generates a map of Europe, tells you each of your F-35 squadrons are and what their readiness levels are.

你必須仔細研究大量數據，但一分鐘後，它就會產生一張歐洲地圖，告訴你每個 F-35 中隊的位置和準備程度。

Not only that, you can see ground troop.

不僅如此，你還可以看到地面部隊。

You can see right where the answer came from, and then you can continue to drill down and you get the answer right now.

您可以立即看到答案來自何處，然後您可以繼續深入研究並立即獲得答案。

And today, it takes days to weeks in the Pentagon to get answers like that.

如今，五角大廈需要花費幾天甚至幾週的時間才能得到這樣的答案。

Now, what's the impact of that?

那麼，這有什麼影響呢？

We can transfer the application from the utilization of the application from thousands of highly technical users to tens of thousands of users.

我們可以將應用程式的使用率從數千名技術含量高的用戶轉移到數萬名用戶。

I mean, every pilot on the flight line knows how to use this.

我的意思是，航線上的每個飛行員都知道如何使用它。

The Chairman of the Joint Chiefs knows how to use it.

參謀長聯席會議主席知道如何使用它。

The Chairman of the Joint Chief's mother knows how to use it.

參謀長聯席會議主席的母親知道如何使用它。

Okay?

好的？

So basically, it's a mosaic.

所以從根本上來說，它是一幅馬賽克。

You know it is the Google user interface.

您知道這是 Google 使用者介面。

I know it as mosaic, but everybody knows how to use it.

我知道它是馬賽克，但每個人都知道如何使用它。

So those are the types of applications that we're seeing in Generative AI, and it's just staggering the diversity that we're seeing in the use cases.

這些就是我們在生成式人工智慧中看到的應用類型，我們在用例中看到的多樣性令人震驚。

There's one of our very large customers has basically put it has 68,000 employees around the world, has all their HR systems in ServiceNow and Workday.

我們的一個非常大的客戶基本上在全球擁有 68,000 名員工，其所有人力資源系統都位於 ServiceNow 和 Workday 中。

So we put Generative AI on top of that so that any one of their 68,000 employees in God knows how many countries probably, order of 30, 40, 50, 60, 70 countries, and it may be Dubai, Qatar, Germany or Houston can ask any question about any of their HR policies, vacation days, insurance, what's in plan, what's out of plan, what are our holidays in, name the country.

因此，我們在此基礎上添加了生成性人工智慧，以便他們在天知道有多少個國家（大約 30、40、50、60、70 個國家，可能是迪拜、卡達、德國或休斯頓）的 68,000 名員工中的任何一位都可以詢問有關他們的人力資源政策、假期、保險、計劃內的內容、計劃外的內容、我們的任何問題、我們的任何問題。

In some place, it's Ramadan, and the other place it's Rajasthan.

在某個地方，這是齋月，而在某個地方，這是拉賈斯坦邦。

But, what are my holidays?

但是，我的假期是什麼？

And so we're seeing it as a front-end to other enterprise applications like Workday, like ServiceNow.

因此，我們將其視為其他企業應用程式（如 Workday、ServiceNow）的前端。

And those are three completely different use cases.

這是三種完全不同的用例。

But those would be examples.

但這些只是例子。

And our offer is, we'll bring the application live in 12 weeks for $0.25 million.

我們的報價是，我們將以 25 萬美元的價格在 12 週內上線該應用程式。

So if any of you need it, you all know via my email, okay.

所以如果你們當中有誰需要的話，你們都可以透過我的電子郵件知道，好的。

And we'll be happy to do it in your organization.

我們非常樂意在您的組織內進行這項工作。

No, that's great.

不，那太好了。

The breadth of use cases is super compelling.

用例的廣度非常引人注目。

I had one follow-up for Hitesh.

我對 Hitesh 進行了一次跟進。

As we're coming up on almost two years now on the transition to consumption and you guys are seeing accelerating subscription growth of 41% with a really, really nice number this quarter, what percent of that subscription revenue is now consumption?

現在我們已經進入消費轉型期快兩年了，你們看到訂閱量加速成長 41%，本季的數字非常非常好，那麼訂閱收入中有多少百分比是消費呢？

If you can sort of give us a sense.

如果您能為我們提供一下思路的話。

And is that what's driving that re-acceleration in revenue growth?

這就是推動營收再次加速成長的因素嗎？

Thank you so much.

太感謝了。

Yeah, Sanjit, we are still in early stages of our new business model.

是的，桑吉特，我們的新商業模式還處於早期階段。

We haven't disclosed our consumption revenue separately before, but that is something which we continue to see a ramp in and it will be more meaningful in the future.

我們之前沒有單獨披露過我們的消費收入，但我們看到這一數字持續成長，並且在未來將更有意義。

Kingsley Crane, Canaccord Genuity.

金斯利‧克蘭 (Kingsley Crane)，Canaccord Genuity。

Hi, thanks for taking the question and congrats on the traction, it's encouraging to hear.

你好，感謝您回答這個問題，並祝賀您的進展，聽到這個消息很令人鼓舞。

As we think about how some of the customer engagement metrics will translate to revenue growth, where would the dollar or that incremental dollar of investment be most impactful?

當我們思考某些客戶參與度指標將如何轉化為收入成長時，每一美元或每一美元增量的投資將產生怎樣的最大影響？

Is it in for deployed sales engineers?

它適用於部署的銷售工程師嗎？

Is it in partner sales motion?

這是合作夥伴銷售活動嗎？

Are you capacity constrained on the application development side?

您在應用程式開發方面的能力是否受到限制？

Just want to get a little bit more granular on the investment profile.

只是想更詳細地了解一下投資狀況。

Good question, Kingsley.

好問題，金斯利。

I think as it relates to the idea of a land grab and market share, which we plan on doing, I would say the constraints that we're certainly not constrained by the market, okay?

我認為，就我們計劃要做的土地爭奪和市場份額的想法而言，我想說的是，我們肯定不會受到市場的限制，好嗎？

We're absolutely not constrained by competitive dynamics, okay?

我們絕對不受競爭動態的限制，好嗎？

We're going to be constrained by sales capacity and service capacity to bring these pilots live.

我們將受到銷售能力和服務能力的限制，以使這些試點項目能夠順利進行。

So that's the constraint, and I think that's where we would invest, okay, in terms of to get the biggest impact for the next dollar.

這就是限制因素，而且我認為，這就是我們要投資的地方，以便為下一美元帶來最大的影響。

Great question.

好問題。

Okay, perfect.

好的，完美。

Thanks for the clarity.

謝謝你的澄清。

And Hitesh, just on the gross margins, understand that we continue to invest, and there's a mix of pilots in there.

希特什，就毛利率而言，我了解到我們會繼續投資，並且其中有多種試點。

You did improve in the quarter on the subscription side, I mean, should we expect that we've already troughed, or is this still sort of we're feeling it out on a quarter-to-quarter basis?

本季你們在訂閱方面確實有所改善，我的意思是，我們是否應該預期我們已經觸底了，還是我們仍然在按季度感受這種狀況？

Yeah.

是的。

You should expect our gross margins to decline from where they were in Q4 at 70%, as we plan to significantly increase the number of pilots and make additional investments.

由於我們計劃大幅增加飛行員數量並進行額外投資，因此您應該預計我們的毛利率將從第四季度的 70% 下降。

By the way, - my colleague, Amit, has noted an error in my comments, okay, where when I gave guidance for revenue for Q1, I misspoke.

順便說一句，我的同事阿米特 (Amit) 指出了我的評論中的一個錯誤，好吧，當我給出第一季收入指引時，我說錯了。

The guidance for revenue for Q1 is $84 million to $89 million in Q1.

第一季的預計收入為 8,400 萬美元至 8,900 萬美元。

So I made a mistake.

所以我犯了一個錯誤。

I said $84 million to $90 million, and that is an error.

我說的是 8400 萬美元到 9000 萬美元，這是錯誤的。

It's $84 million to $89 million.

介於 8,400 萬美元至 8,900 萬美元之間。

So I'm falling on my sword and correcting the wreckage.

所以我要自殺並修復這場災難。

Next question.

下一個問題。

Arvind Ramnani, Piper Sandler.

阿爾文德·拉姆納尼，派珀·桑德勒。

Hi.

你好。

Thanks for taking my question.

感謝您回答我的問題。

I wanted to ask about -- like an incredibly high number of inquiries for your product.

我想問一下——針對你們產品的問詢數量非常多。

Do you think that could drive, like, further upside on the revenue side in the next year or two or some of those inquiries are sort of less qualified and you think your guidance is kind of more realistic?

您是否認為這可能會在未來一兩年內推動收入進一步上升，或者其中一些詢問不太符合條件，而您認為您的指導更為現實？

You're talking about guidance beyond fiscal year '25.

您談論的是 25 財年之後的指導。

I don't have any comment, Arvind, on that.

阿爾文德，我對此沒有任何評論。

Right now, we're being overwhelmed by the numbers.

現在，我們被這些數字壓得透不過氣。

We are sorting through the numbers.

我們正在整理這些數字。

We're actually using Generative AI to qualify these leads with something that sometime I'll show you.

我們實際上正在使用生成式人工智慧來鑑定這些線索，有時我會向您展示。

That's pretty cool.

這太酷了。

C3 Generative marketing.

C3 生成行銷。

But it's too early to tell, and I'm not prepared to give you guidance for fiscal year '26.

但現在說還太早，我還沒準備好給你 26 財年的指引。

Yeah, yeah.

是啊是啊。

But what I'm trying to understand is with this incredible amount of kind of interest you're seeing in the product, how should we think about that impacting your income statement, revenue growth, or your margins?

但我想了解的是，鑑於您對該產品表現出如此大的興趣，我們應該如何看待這會對您的損益表、收入成長或利潤率產生影響？

Because it seems like there's languages when you're kind of staggering, just like 50,000 inquiries.

因為當你感到震驚時，似乎有多種語言，就像 50,000 個查詢一樣。

I'm just trying to qualify to take some of this commentary and can mess it up to what does that mean for either growth or for margins?

我只是想有資格接受這些評論，並且可以把它搞亂到這對成長或利潤意味著什麼？

It means that we're facing a staggeringly large addressable market, okay?

這意味著我們正面臨著一個極為龐大的潛在市場，好嗎？

It means that the game that we're playing is to establish a market leadership position in this market, okay?

這意味著我們玩的遊戲就是在這個市場上確立市場領導地位，好嗎？

I don't know what the stock trades for today, $20 or $30 or whatever it is, okay?

我不知道今天的股票交易價是多少，20 美元還是 30 美元，或其他什麼價位，好嗎？

But let's say that we succeed like we did at Oracle, okay, like we did at Seibel, and we established a market leadership position in Enterprise AI applications, I assure you, this is not a stock trading at $20,$30, okay?

但是，假設我們像在 Oracle 那樣取得了成功，就像我們在 Seibel 那樣，並且在企業 AI 應用中建立了市場領導地位，我向你保證，這不是一隻以 20 美元、30 美元的價格交易的股票，好嗎？

Okay, it's multiples of that, okay?

好的，它是這個的倍數，好嗎？

Maybe an order of magnitude larger than that.

可能比這還要大一個數量級。

Maybe we fail.

也許我們會失敗。

Maybe we fail and we end up number two or number three, okay?

也許我們會失敗，最後只排在第二或第三，好嗎？

Do some math on that.

對此做一些計算。

I know it doesn't work.

我知道這不管用。

There's no formula for this in your spreadsheet.

您的電子表格中沒有這個公式。

But I don't think a spreadsheet is the right way to look at the opportunity.

但我不認為電子表格是看待機會的正確方式。

This is a large addressable market.

這是一個巨大的潛在市場。

We have first mover advantage.

我們擁有先發優勢。

We have a strong technology foundation, and we are going for it, and that's.

我們擁有強大的技術基礎，我們正在為此努力，就是這樣。

The way to model the business honestly, I would look at what we say revenue is going to be, because what we say revenue is going to be for the last 14 quarters has been pretty accurate.

誠實地模擬業務的方法是，我會看我們所說的收入是多少，因為我們所說的過去 14 個季度的收入是相當準確的。

I think that's the best leading indicator you can have.

我認為這是你能擁有的最佳領先指標。

Terrific.

了不起。

And then, if you can maybe just double click on margins, right?

然後，您可能只需雙擊邊距，對嗎？

There's some margin degradation by kind of next year because of the number of pilots.

由於飛行員數量的原因，明年利潤率可能會下降。

How does that work?

這是如何運作的？

Like, when you do pilots, you charge less or do the professional services go up more?

例如，當您從事飛行員工作時，收費會較低還是專業服務的費用會較高？

Like, what drives lower margins by making a choice?

例如，哪些選擇會導致利潤率降低？

(multiple speakers)

（多位發言者）

Good question.

好問題。

What drives lower?

是什麼導致價格下跌？

Essentially, our market offering today is for an Enterprise application, let's say Stochastic optimization of supply chain, let's say demand forecasting for a large agribusiness or predictive maintenance for a large manufacturer.

本質上，我們今天向市場提供的是企業應用，例如供應鏈的隨機優化、大型農業綜合企業的需求預測或大型製造商的預測性維護。

We'll bring that application live at a multi-billion dollar corporation, basically, in one of their facilities for bring it live.

我們將在一家價值數十億美元的公司內推出該應用程序，基本上是在他們的一個設施內推出該應用程式。

Not a proof of concept, live, okay, in six months for $0.5 million, okay?

這不是概念驗證，而是上線，好的，六個月內花費 50 萬美元，好嗎？

And by the way, the alternative is to do this with Accenture, Deloitte, who'll charge them $100 million to do it or $30 million to do it in two years.

順便說一句，另一個選擇是與埃森哲、德勤合作，他們將向這些公司收取 1 億美元或 3,000 萬美元在兩年內完成這項工作。

We'll have it live in six months.

我們將在六個月內讓它上線。

Okay, my application, as I mentioned in generative AI, is to have the application live in 12 weeks for $0.25 million.

好的，正如我在生成式人工智慧中提到的那樣，我的應用程式將在 12 週內上線，耗資 25 萬美元。

Now, we will do, honestly, Arvind, whatever it takes to make that customer live, okay.

現在，老實說，Arvind，我們會盡一切努力讓那位顧客活下去，好吧。

And do I really look at what the profitability level of every one of these pilots is?

我真的有關注每一位飛行員的獲利水準嗎？

I do not.

我沒有。

Okay.

好的。

And if I'm looking with a Fortune 50 company about bringing their first enterprise AI application live, I'm going to invest whatever it takes, even at a loss if necessary, to make sure the customer is successful.

如果我正在與一家財富 50 強公司合作，希望他們能上線第一個企業 AI 應用程序，那麼我將不惜一切代價進行投資，即使必要時會虧本，也要確保客戶的成功。

So that's what drives the margins degradation.

這就是導致利潤率下降的原因。

Are these, in aggregate, profitable?

整體來說，這些能獲利嗎？

I'm sure they are, okay, and I'm sure they're enormously profitable.

我確信他們確實如此，而且我確信他們利潤豐厚。

But at any given one, I mean, we are not going to fail and we have the resources to back that up.

但在任何時候，我們都不會失敗，而且我們有足夠的資源來支持這一點。

(multiple speakers) Thank you very much.

（多位發言者）非常感謝。

That's where the margin degradation is coming from.

這就是利潤率下降的原因。

And I realize it's hard to model, but it's just we know that's who we are and that's what we are.

我意識到這很難模仿，但是我們知道這就是我們，這就是我們。

Yeah, terrific.

是的，太棒了。

Thank you so much, really appreciate it.

非常感謝，真的很感激。

Mike Cikos, Needham & Co.

麥克·西科斯（Mike Cikos），Needham & Co.

Hey, guys, this is Matt Calitri on for Mike Cikos over at Needham.

嘿，大家好，我是 Needham 的 Mike Cikos 的 Matt Calitri。

Thanks for taking our questions.

感謝您回答我們的問題。

I wanted to ask how have newly converted customers ramp consumption versus customers who adopted the consumption model in previous quarters?

我想問一下，與前幾季採用該消費模式的顧客相比，新轉換的顧客消費成長如何？

Are you seeing consistency across cohorts?

您是否看到了不同群體之間的一致性？

Yeah, I'm not sure I understand the question.

是的，我不確定我是否理解了這個問題。

I think we provided very specific guidance on that last quarter.

我認為我們在上個季度提供了非常具體的指導。

Okay, Matt, okay, in the supplemental last quarter, we provided very specific guidance on what we're seeing, okay, in revenue consumption in basically the first quarter they go to production to the tenth quarter they go production.

好的，馬特，好的，在上個季度的補充報告中，我們對所看到的收入消費情況提供了非常具體的指導，基本上在第一季他們開始生產，到第十季度他們開始生產。

And if I am mistaken, this is from memory, but I think the first quarter they go production, they consume about 300,000 or 400,000 GPU hours.

如果我沒記錯的話，這是根據記憶算出來的，但我認為他們投入生產的第一季會消耗大約 30 萬或 40 萬個 GPU 小時。

And by the time you get to the tenth quarter, I think it's 1.4 million.

到第十個季度時，我認為這個數字會達到 140 萬。

Yeah.

是的。

What is it?

它是什麼？

How's my memory?

我的記憶力怎麼樣？

I wish the initial assumptions, actual usage.

我希望最初的假設，實際的使用。

First quarter they go production, 370,000, and it ramps up to 1.3 million in the tenth quarter.

第一季產量為 37 萬輛，第十季產量增加到 130 萬輛。

And -- so, if you look at our supplemental from last quarter, it is in this quarter too.

所以，如果您查看我們上個季度的補充數據，您會發現它也在本季。

Last quarter.

上個季度。

It's provided there in great detail and that these are empirically accurate data.

其中提供了非常詳細的信息，而且這些都是經驗準確的數據。

Got it.

知道了。

Okay, I'll take a look there.

好的，我會去看看。

Thank you.

謝謝。

And then how are sales cycles compared to a year ago?

那麼與一年前相比，銷售週期如何？

Are customers demonstrated in a positive as they identify benchmarks and TCO to secure budget, or has it been pretty static?

當客戶確定基準和 TCO 以確保預算時，他們是否表現出積極的態度，還是一直保持穩定？

Did we talk about sales cycle this quarter?

我們討論過本季的銷售週期嗎？

No, not this quarter.

不，這個季度沒有。

Last quarter we

上個季度我們

--.

--.

Last quarter we said it was what?

上個季度我們說它是什麼？

3.5 months.

3.5個月。

3.5 months.

3.5個月。

I don't have the hard data before me, Matt, but I don't think it's changed appreciably.

馬特，我面前沒有確鑿的數據，但我認為它沒有明顯的變化。

All right, appreciate it.

好的，謝謝。

Pinjalim Bora, JPMorgan.

摩根大通的 Pinjalim Bora。

Great, thanks for taking the question.

太好了，謝謝您回答這個問題。

This is Jaiden Patel on for Pinjalim Bora at JPMorgan.

這是摩根大通 Pinjalim Bora 的 Jaiden Patel。

Just a quick one on our end.

我們這邊只需做一個簡單的介紹。

Last quarter you had mentioned that you expected positive free cash flow for the fiscal year '25.

上個季度您曾提到，預計 25 財年的自由現金流將為正值。

Just wanted to get any update on that commentary.

只是想了解該評論的最新進展。

If there's anything more this quarter.

如果本季還有更多事情的話。

Thanks.

謝謝。

As we have the business plan right now, we are expecting positive free cash flow for fiscal year '25.

根據我們目前的業務計劃，我們預計 25 財年的自由現金流將為正值。

Great, thanks.

太好了，謝謝。

Thank you.

謝謝。

Thank you.

謝謝。

I would now like to turn the call back over to Mr. Siebel for any closing remarks.

現在我想將電話轉回給西貝爾先生，請他做最後發言。

Thanks, everybody, for your time.

謝謝大家抽出時間。

Appreciate your continued attention.

感謝您一直以來的關注。

And stay tuned.

請繼續關注。

I think we're just getting started here.

我認為我們才剛開始。

So next year is looking good and we look forward to communicating with you and keep you posted on what's going on.

因此明年的前景看好，我們期待與您溝通並隨時向您通報最新進展。

We appreciate your interest and the courtesy of you following us.

我們感謝您的關注和關注我們。

So we wish you all a great day and thank you for your time.

我們祝大家有個愉快的一天並感謝你們抽出時間。

This concludes today's program.

今天的節目到此結束。

Thank you all for participating.

感謝大家的參與。

You may now disconnect.

您現在可以斷開連線。