甲骨文 (ORCL) 2026 Q1 法說會逐字稿

Hello. Thank you for standing by. My name is [Tiffany]. I will be your conference operator today.

你好。感謝您的支持。我的名字是[蒂芙尼]。今天我將擔任您的會議主持人。

At this time, I would like to welcome everyone to the Oracle Corporation Q1 FY 2026 conference call.

現在，我歡迎大家參加甲骨文公司 2026 財年第一季電話會議。

(Operator Instructions)

（操作員指示）

I would now like to turn the call over to Ken Bond, head of investor relations. Ken, please go ahead.

現在我想將電話轉給投資者關係主管肯·邦德 (Ken Bond)。肯，請繼續。

Thank you, Tiffany. Good afternoon, everyone.

謝謝你，蒂芙尼。大家下午好。

Welcome to Oracle's first-quarter fiscal year 2026 earnings conference call.

歡迎參加甲骨文2026財年第一季財報電話會議。

A copy of the press release and financial tables, which include a GAAP to non-GAAP reconciliation and other supplemental financial information, can be viewed and downloaded from our Investor Relations website. Additionally, a list of many customers who purchased Oracle Cloud services or went live on Oracle Cloud recently will be available from the Investor Relations website.

您可以從我們的投資者關係網站查看和下載新聞稿和財務表的副本，其中包括 GAAP 與非 GAAP 的對帳和其他補充財務資訊。此外，投資者關係網站也將提供許多最近購買 Oracle 雲端服務或開始使用 Oracle 雲端的客戶名單。

On the call today are Chairman and Chief Technology Officer Larry Ellison and Chief Executive Officer Safra Catz.

今天參加電話會議的有董事長兼首席技術官拉里·埃里森 (Larry Ellison) 和首席執行官薩弗拉·卡茲 (Safra Catz)。

As a reminder, today's discussion will include forward-looking statements including predictions, expectations, estimates, or other information that might be considered forward-looking. Throughout today's discussion, we will present some important factors relating to our business, which may potentially affect these forward-looking statements.

提醒一下，今天的討論將包括前瞻性陳述，包括預測、預期、估計或其他可能被視為前瞻性的資訊。在今天的討論中，我們將介紹一些與我們的業務相關的重要因素，這些因素可能會影響這些前瞻性陳述。

These forward-looking statements are also subject to risks and uncertainties that may cause actual results to differ materially from statements being made today. As a result, we caution you against placing undue reliance on these forward-looking statements.

這些前瞻性陳述也受到風險和不確定性的影響，可能導致實際結果與今天的陳述有重大差異。因此，我們提醒您不要過度依賴這些前瞻性陳述。

We encourage you to review our most recent reports, including our 10-K and 10-Q and any applicable amendments for a complete discussion of these factors and other risks that may affect our future results or the market price of our stock.

我們鼓勵您查看我們最新的報告，包括我們的 10-K 和 10-Q 以及任何適用的修訂，以全面討論這些因素以及可能影響我們未來業績或股票市場價格的其他風險。

Finally, we are not obligating ourselves to revise our results or these forward-looking statements in light of new information or future events.

最後，我們沒有義務根據新資訊或未來事件修改我們的結果或這些前瞻性陳述。

Before taking questions, we'll begin with a few prepared remarks. With that, I'd like to turn the call over to Safra.

在回答問題之前，我們先準備一些發言稿。說完這些，我想把電話轉給薩夫拉。

Thanks, Ken. Good afternoon, everyone.

謝謝，肯。大家下午好。

Clearly, we had an amazing start to the year because Oracle has become the go-to place for AI workloads. We have signed significant cloud contracts with the who's who of AI, including OpenAI, xAI, Meta, NVIDIA, AMD, and many others.

顯然，我們今年有一個驚人的開端，因為 Oracle 已經成為 AI 工作負載的首選。我們與人工智慧領域的知名企業簽署了重要的雲端合同，包括 OpenAI、xAI、Meta、NVIDIA、AMD 等。

At the end of Q1, remaining performance obligations, or RPO, now top $455 billion. This is up 359% from last year and up $317 billion from the end of Q4. Our cloud RPO grew nearly 500%, on top of 83% growth last year.

截至第一季末，剩餘履約義務（RPO）已超過 4,550 億美元。這比去年同期成長了 359%，比第四季末成長了 3,170 億美元。我們的雲端 RPO 在去年 83% 的成長基礎上又成長了近 500%。

Now, to the results, using constant currency growth rates:

現在，使用恆定貨幣成長率來得出結果：

As you can see, we've made some changes to the face of our income statement to better reflect how we manage the business and so you can understand our cloud business dynamics more directly.

正如您所看到的，我們對損益表的形式做了一些修改，以更好地反映我們如何管理業務，以便您可以更直接地了解我們的雲端業務動態。

Here it goes. Total cloud revenue, that's both apps and infrastructure, was up 27% to $7.2 billion. Cloud infrastructure revenue was $3.3 billion, up 54%, on top of the 46% growth reported in Q1 last year. OCI consumption revenue was up 57% and demand continues to dramatically outstrip supply.

就這樣。雲端運算總收入（包括應用程式和基礎設施）成長 27%，達到 72 億美元。雲端基礎設施收入為 33 億美元，成長 54%，而去年第一季的成長率為 46%。OCI 消費收入成長了 57%，需求持續大大超過供應。

Cloud database services, which were up 32%, now have annualized revenues of nearly $2.8 billion. Autonomous database revenue was up 43%, on top of the 26% growth reported in Q1 last year.

雲端資料庫服務成長了 32%，目前年收入接近 28 億美元。自治資料庫收入成長了 43%，而去年第一季的成長率為 26%。

Multi-cloud REV database revenue where OCI regions are embedded in AWS, Azure, and GCP grew 1,529% in Q1. Cloud application revenue was $3.8 billion and up 10%, while our strategic back office application revenue was $2.4 billion, up 16%.

OCI 區域嵌入 AWS、Azure 和 GCP 的多雲 REV 資料庫營收在第一季成長了 1,529%。雲端應用程式營收為 38 億美元，成長 10%，而我們的策略後台應用程式收入為 24 億美元，成長 16%。

Total software revenue for the quarter was $5.7 billion, down 2%.

本季軟體總營收為 57 億美元，下降 2%。

So all in, total revenues for the quarter were $14.9 billion, up 11% from last year and higher than the 8% growth reported in Q1 last year.

總體而言，本季總營收為 149 億美元，比去年成長 11%，高於去年第一季 8% 的增幅。

Operating income grew 7% to $6.2 billion. We have also been on an accelerated journey to adopt AI internally to run more efficiently. I expect our operating income will grow mid-teens this year and higher still in FY27.

營業收入成長 7% 至 62 億美元。我們也正在加速內部採用人工智慧，以提高營運效率。我預計今年我們的營業收入將成長百分之十五左右，2027 財年也將進一步成長。

Non-GAAP EPS was USD1.47 in US dollars while GAAP EPS was USD1.01 in US dollars. The non-GAAP tax rate for the quarter was 20.5%, which was higher than the 19% guidance and caused EPS to be $0.03 lower.

非公認會計準則每股收益為 1.47 美元，而公認會計準則每股收益為 1.01 美元。本季非公認會計準則稅率為 20.5%，高於 19% 的預期，導致每股收益下降 0.03 美元。

For the last four quarters, operating cash flow was up 13% to $21.5 billion. Free cash flow was a negative 5.9%, with 5.9 27.4 billion of Capex. Operating cash flow for Q1 was $8.1 billion, while free cash flow was a negative $362 million with CapEx of $8.5 billion.

過去四個季度，營業現金流成長 13%，達到 215 億美元。自由現金流為負5.9%，資本支出為59億2740億。第一季的經營現金流為 81 億美元，而自由現金流為負 3.62 億美元，資本支出為 85 億美元。

At quarter end, we had $11 billion in cash and marketable security and short-term deferred revenue balance was $12 billion, up 5%.

截至季末，我們擁有 110 億美元現金和有價證券，短期遞延收入餘額為 120 億美元，成長 5%。

Over the last 10 years, we've reduced the shares outstanding by 3% at an average price of $55, which is at this point, much less than 25% of our current stock price.

在過去的 10 年裡，我們以平均 55 美元的價格減少了 3% 的流通股，這遠低於我們目前股價的 25%。

This quarter, we repurchased 440,000 shares for a total of $95 million. In addition, we paid out dividends of $5 billion over the last 12 months. The Board of Directors again declared a quarterly dividend of $0.50 per share.

本季度，我們回購了 44 萬股，總計 9,500 萬美元。此外，我們在過去 12 個月內支付了 50 億美元的股息。董事會再次宣布季度股息為每股 0.50 美元。

Given our RPO growth, I now expect fiscal year '26 Capex will be around 35 billion.

鑑於我們的 RPO 成長，我現在預計 26 財年的資本支出將在 350 億左右。

As a reminder, the vast majority of our CapEx investments are for revenue-generating equipment that is going into the data centers and not for land or buildings. As we bring more capacity online, we will convert the large RPO backlog into accelerating revenue and profit growth.

提醒一下，我們的資本支出絕大部分是用於進入資料中心的創收設備，而不是用於土地或建築物。隨著我們上線更多容量，我們將把大量的 RPO 積壓轉化為加速的營收和利潤成長。

Now, before I dive into specific Q2 guidance, I'd like to share some of the overarching thoughts on fiscal year '26 and the coming year.

現在，在我深入探討具體的第二季指引之前，我想分享一些關於 26 財年和來年的整體想法。

Clearly, it was an excellent quarter. Demand for Oracle Cloud Infrastructure continues to build. I expect we will sign additional multi-billion dollar customers and that RPO will likely grow to exceed $0.5 trillion. The enormity of this RPO growth enables us to make a large upward revision to the cloud infrastructure portion of our financial plan.

顯然，這是一個出色的季度。對 Oracle 雲端基礎架構的需求持續成長。我預計我們將簽約更多價值數十億美元的客戶，而 RPO 可能會成長到超過 0.5 兆美元。RPO 的巨大成長使我們能夠對財務計劃中的雲端基礎設施部分進行大幅上調。

We now expect Oracle Cloud Infrastructure will grow 77% to $18 billion this fiscal year and then increase to $32 billion and $73 billion dollars, $114 billion, and $144 billion over the following four years. Much of this revenue is already booked in our $455 billion PO number. We are off to a fantastic start this year.

我們現在預計，Oracle 雲端基礎設施本財年將成長 77%，達到 180 億美元，並在接下來的四年內增至 320 億美元、730 億美元、1,140 億美元和 1,440 億美元。其中大部分收入已計入我們的 4,550 億美元採購訂單中。今年我們有了一個好的開始。

Now, while much attention is focused on our GPU-related business, our non-GPU infrastructure business continues to grow much faster than our competitors. We're also seeing our industry-specific cloud applications drive customers to our back office cloud apps.

現在，雖然人們將大量注意力集中在我們的 GPU 相關業務上，但我們的非 GPU 基礎設施業務的成長速度仍然遠遠快於我們的競爭對手。我們也看到，我們針對特定行業的雲端應用程式正在吸引客戶使用我們的後台雲端應用程式。

Finally, the Oracle database is booming with 34 multi-cloud data centers now live inside of Azure GCP and AWS. We will deliver another 37 data centers for a total of 71.

最後，Oracle 資料庫正在蓬勃發展，目前 Azure GCP 和 AWS 內部擁有 34 個多雲資料中心。我們將交付另外 37 個資料中心，總數達到 71 個。

All these trends point to revenue growth going higher. For fiscal year 2026, we remain confident and committed to full-year total revenue growth of 16%, in constant currency.

所有這些趨勢都表明收入成長將會更高。對於 2026 財年，我們仍然充滿信心並致力於實現全年總收入成長 16%（以固定匯率計算）。

Beyond fiscal year 26, I'm even more confident in our ability to further accelerate our top- and bottom-line growth rate. As mentioned, we will provide an update on our long-range financial targets at our financial analyst meeting at Oracle AI World in Las Vegas in October.

在 26 財年之後，我對我們進一步加快營收和獲利成長率的能力更有信心。如上所述，我們將在 10 月於拉斯維加斯舉行的 Oracle AI World 財務分析師會議上提供有關我們的長期財務目標的最新資訊。

Now, let me turn to my guidance for Q2, which I'll review on a non-GAAP basis and assuming currency exchange rates remain the same as they are now.

現在，讓我談談我對第二季的指導，我將在非 GAAP 基礎上進行審查，並假設貨幣匯率保持與現在相同。

Currency should have a $0.03 positive impact on EPS and a 1% positive effect on revenue, depending on rounding. However, the actual currency impact may be different as it was in Q1.

根據四捨五入，貨幣將對每股收益產生 0.03 美元的正面影響，對收入產生 1% 的正面影響。然而，實際的貨幣影響可能與第一季不同。

Here goes: total revenues are expected to grow from 12% to 14% in constant currency and are expected to grow from 14% to 16% at today's exchange rate.

預計總收入以固定匯率計算將成長 12% 至 14%，以今天的匯率計算將成長 14% 至 16%。

Total cloud revenue is expected to grow from 32% to 36% in constant currency and is expected to grow from USD33 to USD37.

以固定匯率計算，預計總雲端收入將從 32% 成長至 36%，預計總雲端收入將從 33 美元成長至 37 美元。

Non-GAAP EPS is expected to grow between 8% to 10% and be between $1.58 and $1.62 in constant currency. Non-GAAP EPS is expected to grow 10% to 12% and be between USD1.61 and USD1.65.

非公認會計準則每股收益預計將成長 8% 至 10%，以固定匯率計算將介於 1.58 美元至 1.62 美元之間。非公認會計準則每股收益預計將成長 10% 至 12%，介於 1.61 美元至 1.65 美元之間。

Lastly, my EPS guidance for Q2 assumes a base tax rate of [190x.] However, one-time tax events could cause actual tax rates to vary as they did this quarter.

最後，我對第二季的每股盈餘預測假設基準稅率為 [190x]。然而，一次性稅收事件可能會導致實際稅率發生變化，就像本季一樣。

Larry, over to you.

拉里，交給你了。

Thank you, Safra.

謝謝你，薩弗拉。

Eventually, AI will change everything. But right now, AI is fundamentally transforming Oracle and the rest of the computer industry, though not everyone fully grasp the extent of the tsunami that is approaching.

最終，人工智慧將改變一切。但目前，人工智慧正在從根本上改變甲骨文和整個電腦產業，儘管並不是每個人都完全了解即將到來的海嘯的嚴重程度。

Look at our quarterly numbers, some things are undeniably evident. Several world-class AI companies have chosen Oracle to build large-scale GPU-centric data centers to train their AI models. That's because Oracle builds gigawatt-scale data centers that are faster and more cost efficient at training AI models than anyone else in the world.

看看我們的季度數據，有些事情是無可否認的。多家世界級的AI公司選擇Oracle建構以GPU為中心的大規模資料中心來訓練他們的AI模型。這是因為 Oracle 建構了千兆瓦級資料中心，其訓練 AI 模型的速度比世界上任何其他公司都更快、更具成本效益。

Training AI models is a gigantic multi-trillion dollar market. It's hard to conceive of the technology market as large as that one. But if you look close, you can find one that's even larger.

訓練人工智慧模型是一個價值數兆美元的龐大市場。很難想像科技市場會如此之大。但如果仔細觀察，你會發現一個更大的。

It's the market for AI inferencing, millions of customers using those AI models to run businesses and governments. In fact, the AI inferencing market will be much, much larger than the AI training market.

這是人工智慧推理的市場，數百萬客戶使用這些人工智慧模型來經營企業和政府。事實上，人工智慧推理市場將比人工智慧訓練市場大得多。

AI inferencing will be used to run robotic factories, robotic cars, robotic greenhouses, biomolecular simulations for drug design, interpreting medical diagnostic images and laboratory results, automating laboratories, placing bets in financial markets, automating legal processes, automating financial processes, automating sales processes.

人工智慧推理將用於運行機器人工廠、機器人汽車、機器人溫室、藥物設計的生物分子模擬、解釋醫學診斷影像和實驗室結果、實驗室自動化、金融市場投注、法律流程自動化、財務流程自動化、銷售流程自動化。

AI is going to write. That is generate the computer programs called AI agents that will automate your sales and marketing processes. Let me repeat that: AI is going to automatically write the computer programs that will then automate your sales processes and your legal processes and everything else in your factories and so on.

AI會寫字。那就是產生稱為 AI 代理的電腦程序，它將自動化您的銷售和行銷流程。讓我重複一遍：人工智慧將自動編寫電腦程序，然後自動化您的銷售流程、法律流程以及工廠中的所有其他流程等等。

Think about it. It's AI inferencing that will change everything. Oracle is aggressively pursuing the AI -- and we're not doing badly in the AI training market, by the way -- but inferencing is bigger. Oracle is aggressively pursuing the inferencing market, as well as the AI training market.

想一想。人工智慧推理將改變一切。Oracle 正在積極進軍人工智慧領域——順便說一句，我們在人工智慧培訓市場的表現還不錯——但推理領域更大。Oracle 正積極進軍推理市場以及人工智慧培訓市場。

We think we are in a pretty good position to be a winner in the inferencing market because Oracle is by far, the world's largest custodian of high-value private enterprise data.

我們認為我們在推理市場中處於非常有利的地位，因為 Oracle 是迄今為止世界上最大的高價值私有企業資料保管者。

With the introduction of our new AI database, we added a very important new way for you to store your data in our database. You can vectorize it. By vectorizing it, by vectorizing all your data, all your data can be understood by AI models. Then, we made it very easy for our customers to directly connect all their databases, all their new Oracle AI databases and cloud storage, OCI cloud storage to the world's most advanced AI reasoning models.

隨著我們新的 AI 資料庫的推出，我們為您在我們的資料庫中儲存資料添加了非常重要的新方法。您可以將其向量化。透過向量化，透過向量化所有數據，所有數據都可以被人工智慧模型理解。然後，我們讓客戶可以非常輕鬆地將他們的所有資料庫、所有新的 Oracle AI 資料庫和雲端儲存、OCI 雲端儲存直接連接到世界上最先進的 AI 推理模型。

ChatGPT, Gemini, Grok, Llama, all of which are uniquely available in the Oracle Cloud. After you vectorize your data and link it to an LLM, the LLM of your choice, you can then ask any question you can think of. For example, how will the latest tariffs impact next quarter's revenue and profit? You asked that question, the large language model will then apply advanced reasoning to the combination of your private enterprise data plus publicly available data.

ChatGPT、Gemini、Grok、Llama，所有這些都是在 Oracle Cloud 中獨有的。在您將資料向量化並將其連結到您選擇的 LLM 之後，您就可以提出任何您能想到的問題。例如，最新的關稅將如何影響下一季的營收和利潤？正如你問到的這個問題，大型語言模型將對你的私人企業資料和公開資料的組合應用高階推理。

You get answers to important questions without ever compromising the safety and security of your private data. Again, I'd like you to think about this for a moment. A lot of companies are saying, we're being into AI because we're writing agents. We're writing a bunch of agents too.

您可以獲得重要問題的答案，同時又不會危及您的私人資料的安全性。再次，我希望您考慮一下這個問題。許多公司都說，我們進入人工智慧領域是因為我們正在編寫代理商。我們也正在寫一堆代理商。

But when they introduced ChatGPT almost 3 years ago, what you've got to do is have a conversation and ask questions. You weren't automating some process with an agent.

但是，當他們在大約 3 年前推出 ChatGPT 時，你要做的就是進行對話並提出問題。您沒有使用代理來自動化某些流程。

You could ask whatever question you wanted to ask and get a well-reasoned answer with all of the latest and best information and high-quality leasing go along with it.

您可以提出任何您想問的問題，並獲得合理的答案，其中包含所有最新、最好的資訊以及高品質的租賃服務。

Who's offering that to customers? We'll be the first when we deliver it and demonstrate it at AI World next month. That's what our customers have been asking for ever since the introduction of ChatGPT, 3.5 almost, 3 years ago. I wanted to ask questions about anything.

誰向顧客提供這項服務？下個月我們將在 AI World 上率先推出並展示它。自從三年前推出 ChatGPT 3.5 以來，我們的客戶一直要求這樣做。我想問任何問題。

Therefore, you need to understand my enterprise data, as well as all the publicly available data. Then you can answer the questions that are most important to me. Well, now they can ask those questions.

所以你要了解我的企業數據，還有所有公開的數據。然後你就可以回答對我來說最重要的問題。好吧，現在他們可以問這些問題了。

Back to you, Safra.

回到你身邊，薩夫拉。

Thank you, Larry. Tiffany, please pull the audience for questions.

謝謝你，拉里。蒂芙尼，請觀眾提問。

(Operator Instructions)

（操作員指示）

John DiFucci, Guggenheim Securities.

古根漢證券公司的約翰‧迪富奇 (John DiFucci)。

Listen, even I was blown away by what this looks like going forward. This question is purposely open ended.

聽著，就連我也對未來發展的樣子感到震驚。這個問題故意設成開放式的。

Larry and Safra, Oracle's become the de facto standard for AI training workloads. You make money at it and we have a lot of faith in that. But clearly, there's more here than just AI train.

拉里和薩夫拉，甲骨文已成為人工智慧訓練工作負載的事實標準。你可以透過它賺錢，我們對此很有信心。但顯然，這裡不隻隻有人工智慧訓練。

I know it's a big part of it. You talked about it. But can you talk about what else -- a little more detail about what else is driving these pretty amazing forecasts?

我知道這是其中很重要的一部分。你談到了這一點。但是您能否更詳細地談談其他因素推動了這些相當驚人的預測？

Go ahead, Larry, you were just referring to?

說吧，拉里，你剛才指的是什麼？

Yeah. Well, it's -- a lot of people are looking for inferencing capacity. People are running out of inferencing capacity. The company that called us, I mentioned I think the last quarter or the quarter before, someone called us, we'll take all the capacity you have that's currently not being used anywhere in the world. We don't care.

是的。嗯，很多人都在尋求推理能力。人們的推理能力正逐漸喪失。我提到過，打電話給我們的公司，我想是上個季度或上個季度，有人給我們打電話，我們將佔用你們目前在世界任何地方未使用的所有產能。我們不在乎。

I've never gotten a call at that time. That's very unusual call. That was for inferencing, not training.

我當時一直沒有接到電話。這是一個非常不尋常的電話。那是為了推理，而不是訓練。

There's a huge amount of demand for inferencing. If you think about it, in the end, all this money we're spending on training is going to have to be translated into products that are sold, which is all inferencing.

對推理的需求非常大。如果你仔細想想，我們花在培訓上的所有錢最終都要轉化為可銷售的產品，這都是推論。

The inferencing market, again, is much larger than the training market. And yeah, we are building -- like everybody else, we're building agents with our applications. But we're doing much more than that.

推理市場比訓練市場大得多。是的，我們正在建構——像其他人一樣，我們正在用我們的應用程式建立代理。但我們所做的遠不止這些。

No one has shown me a ChatGPT 3.5 again three years ago -- 3.5 years ago, a little less than three years ago, when ChatGPT amazed the world. You could simply talk to your computer and ask questions and get well-reasoned, well-questions based on the latest and most precise information.

沒有人再給我展示過三年前的ChatGPT 3.5——3.5年前，也就是不到三年前，ChatGPT震驚全世界的那個時候。您可以簡單地與您的電腦對話並提出問題，然後根據最新和最精確的資訊獲得合理的問題。

As long as you ask those questions about publicly available data, and there's a lot of publicly available data. But if you combine the publicly available data with the enterprise data, which companies really don't want to share, you have to do it in such a way that your private enterprise data stays private yet the large language model can still use it for reasoning.

只要你問有關公開數據的問題，就會有大量公開數據。但是，如果將公開資料與公司真正不想共享的企業資料結合起來，您必須以這樣一種方式進行：您的私人企業資料保持私密，但大型語言模型仍然可以使用它進行推理。

So as to answer your question, like how do the latest tariffs or the latest steel prices or whatever affect my quarterly results; effect my ability to deliver products; affect my revenue back to my cost?

為了回答您的問題，例如最新的關稅或最新的鋼鐵價格或其他因素如何影響我的季度業績；影響我交付產品的能力；影響我的收入回到成本？

Answer those kinds of questions. The -- to answer those kind of questions, we have to and we have. We had to change our database, fundamentally change our database so you can vectorize all data. That's the form in which large language models understand information is that after it's been vectorized and then, allowing people to ask any question they want about anything.

回答這些問題。為了回答這些問題，我們必須這樣做，而且我們已經這樣做了。我們必須改變我們的資料庫，從根本上改變我們的資料庫，以便您可以向量化所有資料。這就是大型語言模型理解資訊的形式，即在資訊被向量化之後，人們可以就任何事物提出任何問題。

That's exactly what we've done. But unless you have a database that is secure and reliable and link to all of the popular LLMs, and we've done all of that, unless you have that and you have to tell me who else has that besides Oracle.

這正是我們所做的。但是，除非你有一個安全可靠並且連結到所有流行的 LLM 的資料庫，而且我們已經做到了這一切，除非你擁有它，否則你必須告訴我除了 Oracle 之外還有誰擁有它。

Unless you have that, it's going to be very hard for you to deliver a ChatGPT-like experience on top of your data as well as publicly available data. That's a unique value proposition for Oracle.

除非你擁有這些，否則你將很難在你的數據以及公開數據上提供類似 ChatGPT 的體驗。這對 Oracle 來說是一個獨特的價值主張。

That's because, again, we're the custodian of all much more data than any of the application companies. They have their application data. They measure their customers in tens of thousands. We measure our customers in millions of databases.

這是因為，我們保管的資料比任何應用程式公司都要多得多。他們有他們的申請數據。他們的客戶數量以萬計。我們透過數百萬個資料庫來衡量我們的客戶。

So we think we're better positioned than anybody to take advantage of inferencing.

因此我們認為我們比任何人都更有能力利用推理。

In addition, aside from just our GPU and all of that, we have become the de facto cloud for many of our customers.

此外，除了我們的 GPU 和所有這些之外，我們已經成為許多客戶事實上的雲端。

Again, they want to put some things in our public cloud or in our competitors' public cloud, working with the Oracle database, but simultaneously, there are a lot of reasons why they want what's called either a dedicated region or cloud customer.

再說一次，他們想把一些東西放到我們的公有雲或競爭對手的公有雲中，與 Oracle 資料庫協同工作，但同時，他們也有很多理由想要所謂的專用區域或雲端客戶。

We give our customers so much choice that they're very unusual for us not to be able to meet a customer's needs in one way or another. And then, of course, we have every piece of the stack. We have the infrastructure. We have the database that you're going to hear a lot about as really the only reasonable store for data that you want to use AI models against.

我們為客戶提供如此多的選擇，以至於我們很少不能以某種方式滿足客戶的需求。當然，我們擁有堆疊的每一部分。我們擁有基礎設施。我們擁有一個資料庫，您會經常聽到它，它是您想要使用 AI 模型儲存資料的唯一合理儲存方式。

And then, we have all of these applications that are just taking off. So we just have a lot of different layers. They're all moving in the same direction. They all benefit our customers when used together.

然後，我們擁有所有這些剛起步的應用程式。所以我們有很多不同的層。他們都朝著同一個方向前進。當它們一起使用時，都會使我們的客戶受益。

Go ahead, John. Maybe you're going to complement us and I interrupted you. I apologize for being rude.

繼續吧，約翰。也許你正要稱讚我們，但我打斷了你。我為自己的粗魯道歉。

I was just going to say hats off to both of you. I have been doing this for a really long time. I tell my old team pay attention to this, even those that are not working at Oracle, because this is a career event happening right now.

我只是想向你們兩位表示敬意。我已經做這個很久了。我告訴我的舊團隊要注意這一點，即使那些不在 Oracle 工作的人也要注意這一點，因為這是正在發生的職業事件。

It looks -- it's just amazing. I'm just really happy for you and congrats on this. It's amazing. Keep doing it.

它看起來——真是令人驚奇。我真的為你感到高興並對此表示祝賀。太神奇了。繼續做下去。

It's been a lot of work. Let me mention two other things I think that are actually shocking.

這需要做很多工作。讓我提一下另外兩件我認為確實令人震驚的事情。

We have gotten the entire Oracle Cloud, the whole thing, every feature, every function of the Oracle Cloud down to something we can put into a handful of racks, three racks. We call it Butterfly. That cost $6 million. So we can give you a private version of the Oracle Cloud with every feature, every security feature, every function, everything we do for $6 million.

我們已經將整個 Oracle Cloud、整個系統、每個特性、每個功能都簡化到可以放入幾個機架（三個機架）的程度。我們稱之為蝴蝶。那項花了 600 萬美元。因此，我們可以為您提供 Oracle Cloud 的私人版本，其中包含所有功能、所有安全功能、所有功能以及我們所做的一切，價格為 600 萬美元。

I think the cost for the other hyperscalers is more than 100 times that. We can actually give our customers cloud (inaudible), the full cloud (inaudible). We have companies like Vodafone. I'm not sure which companies I can name, which companies I can't.

我認為其他超大規模企業的成本要高出 100 倍以上。我們實際上可以為我們的客戶提供雲（聽不清楚），完整的雲（聽不清楚）。我們有像沃達豐這樣的公司。我不確定哪些公司我可以說出名字，哪些公司我不能說出名字。

We have large companies that are buying basically their own Oracle cloud regions. In fact, multiple Oracle cloud regions because they don't want to have any neighbors in their cloud. They don't want other companies in their cloud but they want the full cloud. They want to pay as they consume. They want all the features, all the functions, all the safety to security. They don't want to have to buy it. They want us to buy and own the software and the hardware. They want us to maintain it, build the network to supply all of that. They just want a paper consumption.

我們有一些大公司基本上都在購買自己的 Oracle 雲端區域。事實上，Oracle 有多個雲端區域，因為他們不希望在自己的雲端中有任何鄰居。他們不希望其他公司進入他們的雲，而是想要完整的雲端。他們希望按消費付費。他們想要所有的特性、所有的功能、所有的安全保障。他們不想購買它。他們希望我們購買並擁有軟體和硬體。他們希望我們維護它，建立網路來提供所有這些。他們只是想要一份紙本消費。

We can do that. Add an entry-level price that's 1% of what our competitors can offer. That's one thing.

我們可以做到。增加入門價格，該價格僅為我們競爭對手的 1%。這是一回事。

Another, let me give you one more and I'll stop there. We also have the most advanced application generator of any company. It's interesting. We're an application company and a cloud infrastructure company. Therefore, we build applications.

另外，讓我再給你一個，我就到此為止。我們還擁有所有公司中最先進的應用程式生成器。這很有趣。我們是一家應用程式公司和雲端基礎設施公司。因此，我們建立應用程式。

As we build applications, we'd like to be more efficient. The way to be more efficient is to build AI application generators. We have been doing that. The latest applications that we are building, we're not building them. They're being generated by AI.

當我們建立應用程式時，我們希望提高效率。提高效率的方法是建立AI應用程式產生器。我們一直在做這件事。我們正在建立的最新應用程式並不是我們自己建立的。它們是由人工智慧產生的。

We think we're far, far ahead of any of the other application companies in terms of generating the applications. So that's another very significant advantage we have.

我們認為，在應用程式生成方面，我們遠遠領先其他任何應用程式公司。這是我們的另一個非常重要的優勢。

And of course -- and it's funny, I made the comment that we don't charge separately for our AI and our applications because our applications are AI. They're entirely AI. The new ones. The new ones that we're building.

當然——有趣的是，我說我們不會對我們的人工智慧和應用程式單獨收費，因為我們的應用程式就是人工智慧。它們完全是人工智慧。新的。我們正在建造的新產品。

There are nothing other than a bunch of AI agents that we generate that are linked together with workflow. That's all they are.

除了我們產生的一堆透過工作流程連結在一起的 AI 代理之外，什麼都沒有。他們就是這樣的。

How do you charge separately for that? That's every application that we have. But the applications are better. Hopefully, we'll sell more. That's the way we'll get paid for them.

您如何單獨收取費用？這就是我們擁有的每個應用程式。但應用程式更好。希望我們能賣更多。這就是我們獲得報酬的方式。

Thank you, John, for the very nice complement.

謝謝你，約翰，謝謝你的讚美。

Thank you, Larry. Thank you, Safra.

謝謝你，拉里。謝謝你，薩弗拉。

Thank you, John, for all these years following us so kindly, also. Thanks. Great day.

也感謝約翰這些年來對我們的關心與支持。謝謝。美好的一天。

Probably time for another question at this point.

現在也許該問另一個問題了。

Brad Zelnick, Deutsche Bank.

德意志銀行的布拉德‧澤爾尼克 (Brad Zelnick)。

Great. Thanks very much.

偉大的。非常感謝。

I think we're all in shock in a very, very good way. Larry, there's no better evidence of a seismic shift happening in computing than these results that you just put up. Oracle has a near 50-year track record of navigating transitions and coming out on top.

我想我們都感到非常非常震驚。拉里，沒有什麼比你剛才提出的這些結果更能證明計算領域正在發生巨大變化了。甲骨文在引領轉型和取得輝煌成就方面擁有近 50 年的歷史。

But as we think about enterprise applications, investors are fairly pessimistic these days. I'd love to hear your perspective. Where do you see this all going for the industry? Where does the market share go to the companies that don't have the database, don't have the advantages that you have all the way down to the silicon? Is this maybe an extension event?

但當我們考慮企業應用時，投資人如今相當悲觀。我很想聽聽你的看法。您認為這一切將對產業帶來什麼影響？那些沒有資料庫、在矽片方面沒有你們所擁有的優勢的公司，他們的市佔率又歸誰呢？這可能是延伸事件嗎？

Be curious to hear what you think.

好奇地聽聽您的想法。

Well, I think we have substantial advantages because we are an infrastructure company and we are an application company. There are two things that happen:

嗯，我認為我們有實質的優勢，因為我們是一家基礎設施公司，也是一家應用程式公司。會發生兩件事：

As an application company, we knew we had to start generating our applications. We just couldn't do with armies of people anymore. We still need people, don't get me wrong. But the number of people we need is substantially less.

作為一家應用程式公司，我們知道我們必須開始產生我們的應用程式。我們再也無法應付大批人馬了。我們仍然需要人手，別誤會我的意思。但我們需要的人數卻少很多。

We can build/generate much better applications than we can hand build. We've been working on these AI application generators for some time and we're actually using them.

我們可以建立/生成比手工構建的更好的應用程式。我們已經研究這些 AI 應用程式生成器一段時間了，並且我們實際上正在使用它們。

But the thing is we're not just building application generators. We're building application generators and then we're building the applications, which gives us insights to make the application generator better.

但問題是我們不只是建立應用程式生成器。我們正在建立應用程式生成器，然後建立應用程序，這為我們提供了使應用程式生成器更好的見解。

You it's a huge advantage to be on both sides of that equation, both being an application builder and a builder of the obligation generation technology, the underlying AI application code generators. That's a huge advantage.

如果你能站在這個等式的兩邊，既是應用程式建構者，也是義務生成技術（底層 AI 應用程式程式碼產生器）的建構者，那麼你將獲得巨大的優勢。這是一個巨大的優勢。

Let me give you another advantage, which is often a disadvantage. We're very large. We no longer sell individual discrete applications. We sell suites of applications. We decided to go into the medical business against EPIC, believing that we could solve much more of the problem because we're much bigger than they are.

讓我給你另一個優點，這通常是一個缺點。我們規模很大。我們不再銷售單獨的獨立應用程式。我們銷售應用程式套件。我們決定進軍醫療產業，對抗 EPIC，我們相信我們可以解決更多問題，因為我們比他們規模大得多。

By the way, we're much bigger than Workday and/or ServiceNow. We're solving a larger portion of the problem. We're able to do all of ERP, then we can add all of CRM. But all the pieces are engineered to fit together. That makes it so much easier for customers to consume.

順便說一句，我們比 Workday 和/或 ServiceNow 規模大得多。我們正在解決大部分問題。我們能夠完成所有的 ERP，然後我們可以新增所有的 CRM。但所有部件都經過精心設計，可以組合在一起。這使得顧客消費變得更加容易。

So we think that selling -- being good at application generation, the underlying technology makes us better, build better applications enables us to build more applications so we can solve more of the problem, so the customers don't have to do all that system integration across multiple vendors.

因此，我們認為銷售——擅長應用程式生成，底層技術使我們變得更好，建立更好的應用程式使我們能夠建立更多的應用程序，從而解決更多的問題，因此客戶不必跨多個供應商進行所有系統整合。

We can just build a suite where all the pieces are engineered to fit together. I think we have tremendous advantages in the application space. We have tremendous advantages in the AI inferencing space where we can -- again, what we'll demonstrate at Oracle AI World next month is we've taken all of our customer data, all of it.

我們可以建造一個套件，將所有部件設計成可以組合在一起。我認為我們在應用領域擁有巨大的優勢。我們在人工智慧推理領域擁有巨大的優勢——再次強調，我們將在下個月的 Oracle AI World 上展示我們已經獲取了所有客戶資料。

I won't go into all the details now. But you can ask any question you want to ask. Who's your salesperson? Who's the number 1 prospect in my territory? What product should I be selling them next? What are the reference -- what are the best references for me to use could persuade them to use our to use our product?

我現在不想討論所有細節。但你可以問任何你想問的問題。你的銷售人員是誰？我所在地區的頭號潛力新星是誰？我下一步該向他們銷售什麼產品？有哪些參考——我可以使用哪些最佳參考來說服他們使用我們的產品？

You can get all of those questions answered for you immediately if you're a salesperson, the engineers can look at which features of Oracle Financials are people making the most errors when they're using those features. But I have to fix and make easier to use.

如果您是銷售人員，您可以立即獲得所有這些問題的答案，工程師可以查看 Oracle Financials 的哪些功能是人們在使用這些功能時犯最多的錯誤。但我必須修復它並使其更容易使用。

You just asked the question because all of that data is available to AI models. We're the only -- is there anyone else doing this? Not that I know of. It's a huge event.

您剛才問了這個問題，因為所有這些數據都可以用於人工智慧模型。我們是唯一的──還有其他人這樣做嗎？據我所知沒有。這是一件大事。

I look forward to AI world, Larry. Thank you.

我期待人工智慧世界，拉里。謝謝。

It's an amazing day for Oracle. It's a remarkable day for the industry. Thanks again. Congrats.

對 Oracle 來說，這是值得紀念的一天。對於該行業來說，這是一個值得紀念的日子。再次感謝。恭喜。

Thank you. Thank you so much.

謝謝。太感謝了。

Derrick Wood, TD Cowen.

德里克伍德 (Derrick Wood)，TD 考恩。

Great. I'll echo my congratulations on this momentous quarter.

偉大的。我要對這個重要的季度表示祝賀。

Safra, the fact that you delivered over $300 billion of new RPO in Q1, just really amazing to see. But it's going to require a lot of infrastructure build out.

Safra，看到你們在第一季交付了超過 3000 億美元的新 RPO，真是令人驚嘆。但這需要大量的基礎建設。

So could you provide a bit more context on how much CapEx and operational cost structure will be needed to fully service these contracts? How we should think about the ramp of these costs, relative to the ramp in revenue over the next few years and generally, how investors should be thinking about the ROI on the spend?

那麼，您能否提供更多關於完全履行這些合約需要多少資本支出和營運成本結構的資訊？我們該如何看待這些成本的成長，相對於未來幾年收入的成長，以及投資人應該如何考慮支出的投資報酬率？

Sure. First of all, as I mentioned in the prepared remarks and as I've said very clearly beforehand, we do not own the property. We do not own the buildings. What we do own and what we engineer is the equipment. That's equipment that is optimized for the Oracle Cloud.

當然。首先，正如我在準備好的發言中提到的，並且我之前已經非常清楚地說過，我們並不擁有該財產。這些建築並不屬於我們。我們所擁有的和我們所設計的是設備。這是針對 Oracle Cloud 最佳化的設備。

It has extremely special networking capabilities. It has technical capabilities from Larry and his team that allows us to run these workloads much, much faster. And as a result, it's much cheaper than our competitors, depending on the workload.

它具有極為特殊的網路功能。它擁有 Larry 和他的團隊的技術能力，使我們能夠更快地運行這些工作負載。因此，根據工作量，它比我們的競爭對手便宜得多。

Now, because of that, what we do is we put in that equipment only when it's time and usually very quickly. Assuming that our customer accept it, we're already generating revenue right away. The faster they accept the system and that it meets their needs, the faster they start using it, the sooner we have revenue.

現在，正因為如此，我們所做的就是只在適當的時候放入該設備，而且通常速度非常快。假設我們的客戶接受它，我們就會立即產生收入。他們越快接受系統並確定其滿足他們的需求，越快開始使用它，我們就越快獲得收入。

This is, in some ways, I don't want to call it asset light from the finance world, but it's asset pretty light. And that is really an advantage for us. I know some of our competitors, they like to own buildings. That's not really our specialty. Our specialty is the unique technology, the unique networking, the storage, just the whole way we put these systems together.

從某種程度上來說，我不想從金融界的角度稱之為輕資產，但它的資產相當輕。這對我們來說確實是個優勢。我知道我們的一些競爭對手，他們喜歡擁有建築物。這其實不是我們的專長。我們的特色是獨特的技術、獨特的網路、儲存以及將這些系統整合在一起的整個方式。

By the way, they are identical and very simplified; and again, making it possible for us to be very profitable while still being able to offer our customers an incredibly compelling price.

順便說一句，它們是相同的並且非常簡單；並且再次使我們能夠獲得豐厚的利潤，同時仍然能夠為我們的客戶提供極具吸引力的價格。

What I have indicated is that CapEx looks like it's going to be about $35 billion for this fiscal year. But because we're monitoring this, we're literally putting it in right when we take possession and then, handing it over to generate revenue right away.

我所指出的是，本財年的資本支出看起來將達到約 350 億美元。但因為我們對此進行監控，所以我們在接收時就立即將其投入使用，然後立即移交以產生收入。

So we have a very good line of sight for our capabilities to put this out and basically to just spend on that CapEx right before it starts generating revenue. But at this point, I'm looking at $35 billion for the year. And

因此，我們對自己的能力有著非常好的認識，可以在它開始產生收入之前就花掉資本支出。但目前，我預計今年的支出將達到 350 億美元。和

I think -- it could be a little higher but I think -- and if it is higher, it's good news because it means more capacity has been handed over to me in terms of floor space.

我認為——可能會更高一點，但我認為——如果更高，那就是好消息，因為這意味著就建築面積而言，有更多的容量移交給了我。

As you also know, we are embedded in our competitors' cloud. Again, all we are responsible for to pay for is, in fact, our equipment, and that goes right away. There, we're moving ultimately to 71 data centers embedded in our competitors/partners.

如您所知，我們嵌入了競爭對手的雲端。再說一遍，事實上，我們所負責支付的費用只是我們的設備費用，而這些費用是立即到達的。我們最終將把資料中心轉移到嵌入我們競爭對手/合作夥伴的 71 個資料中心。

Let me add a couple of very short things. One is we just turned over a giant data hall to one of our customers. The acceptance time could have been as long as a couple of months.

讓我補充幾句非常簡短的話。一是我們剛剛將一個巨大的數據大廳移交給了我們的一個客戶。接受時間可能長達數月。

It was one week. It was one week from the time we officially owned the equipment and they were testing it to the time they started paying for it.

持續了一周。從我們正式擁有該設備、他們對其進行測試到他們開始付款，一共用了一周時間。

One week. So we have an extraordinary team that's doing an extraordinary job of making sure that we get the equipment working very quickly. And our customers can accept it, they want to accept it as fast as possible because they want to do the work, they want to train their models. And this one took a huge hall, took one week for acceptance. It was extraordinary that.

一周。因此，我們擁有一支出色的團隊，他們正在做著出色的工作，確保我們的設備能夠快速運作。我們的客戶可以接受它，他們希望盡快接受它，因為他們想做這項工作，他們想訓練他們的模型。這個佔據了一個巨大的大廳，花了一個星期的時間才被接受。這真是太不可思議了。

The other we are a very large consumer of networking equipment, GPUs, et cetera. Because we are a very large consumer, we are able, I think, to get better financing terms from the vendors than some of their people.

另外，我們是網路設備、GPU 等的巨大消費者。由於我們是一個非常大的消費者，我認為我們能夠從供應商那裡獲得比他們的一些人更好的融資條件。

So I think we have that going for us as well. I think we're going to do very well on the finance side. We have advantages there as well.

所以我認為我們也有這樣的優勢。我認為我們在財務方面會做得很好。我們在那裡也有優勢。

Thank you, Safra.

謝謝你，薩弗拉。

Mark Moerdler, Bernstein Research.

伯恩斯坦研究公司 (Bernstein Research) 的馬克‧莫德勒 (Mark Moerdler)。

Thank you very much, Larry and Safra; and frankly, Team Oracle. Amazing. Congratulations.

非常感謝 Larry 和 Safra；坦白地說，也感謝 Oracle 團隊。驚人的。恭喜。

I'd like to focus on the AI training business you've been winning. Could you please explain to us how Oracle can create enough of a differentiated moat to assure this business does not get commoditized? How do you continue to drive strong earnings and free cash flow from the training business, even if training slows? I think people really need to understand that.

我想重點談談你們一直在贏得的人工智慧培訓業務。您能否向我們解釋 Oracle 如何創建足夠的差異化護城河以確保這項業務不會被商品化？即使培訓業務放緩，您如何繼續從培訓業務中獲得強勁的收益和自由現金流？我認為人們確實需要理解這一點。

Well, let me -- I can do it with one sentence. Our networks move data very, very fast. And if we can move data faster than the other people, if we have advantages in our GPU super clusters that are performance advantages. If you're paying by the hour if we're twice as fast, we're half the cost.

好吧，讓我——我可以用一句話來表達。我們的網路傳輸資料的速度非常非常快。如果我們能夠比其他人更快地移動數據，如果我們的 GPU 超級叢集具有優勢，那麼這就是效能優勢。如果您按小時付費，如果我們的速度是您的兩倍，那麼費用就只有您的一半。

Alex Zukin, Wolfe Research.

沃爾夫研究公司的亞歷克斯·祖金（Alex Zukin）。

I really appreciate you squeezing me in. I originally was going to ask you if the new Oracle AI database really opens up the general enterprise inferencing market.

我真的很感激你擠出時間來接待我。我原本想問您，新的 Oracle AI 資料庫是否真的開啟了通用企業推理市場。

Based on your script, it sounds like the answer to that question is h*** yes. So my follow-up question would be: how do you see that pacing happening over the course of the next few years? How soon after the introduction of the Oracle AI database would you expect your enterprise customers, your sophisticated customers to really be open to interrogating their enterprise data in this fashion? How does the current supply-constrained environment stand in the way of that demand? Or is it solving as we speak?

根據您的腳本，聽起來這個問題的答案是肯定的。所以我的後續問題是：您認為未來幾年內這一步會如何發展？在推出 Oracle AI 資料庫後多久，您預期您的企業客戶、您的成熟客戶會真正願意以這種方式查詢他們的企業資料？目前供應受限的環境如何阻礙這種需求？或者說，我們說話的時候它正在解決嗎？

I don't know if Larry, you want to go for it. You covered it in prepared remarks. Go ahead.

我不知道拉里，你是否想去嘗試。您在準備好的發言中已經談到了這一點。前進。

Okay. I think who wouldn't want that? I think everyone says they want to use AI. Every -- CEOs, they don't want to use AI heads of state. Heads of government, say, they want to use AI. We've never had consumers like that.

好的。我想誰不想要這個呢？我認為每個人都說他們想使用人工智慧。每個執行長都不想使用人工智慧作為國家元首。政府首腦們說，他們想使用人工智慧。我們從未遇過這樣的消費者。

Historically, we don't deal with CEOs. Now, we deal with CEOs. Now we do with Heads of Government, Heads of States on this because AI is so important. And letting people have used AI on top of their data. That is what they want to do.

從歷史上看，我們不與執行長打交道。現在，我們與執行長打交道。現在我們與政府首長、國家元首就此進行磋商，因為人工智慧非常重要。並讓人們利用他們的數據來使用人工智慧。這就是他們想要做的。

But they didn't know how to do it securely. They didn't know how to -- well, they did know how to do it period. One of the big risks was, oh, my god, I can't share my -- BP Morgan Chase can't share all of its data. Goldman Sachs can't share all of his data with OpenAI. They won't do it. So -- or xAI or Llama or Meta. They won't -- it's got to get it private.

但他們不知道如何安全地做到這一點。他們不知道如何——好吧，他們確實知道如何做。最大的風險之一是，天哪，我不能分享我的——英國石油摩根大通不能分享其所有數據。高盛無法與 OpenAI 分享其所有數據。他們不會這麼做。所以——或者 xAI 或者 Llama 或者 Meta。他們不會——它必須私有化。

So we've got to keep your private data private. We've got to keep your private data secure. But we have to make it available for inferencing by the latest and best reasoning models from OpenAI and xAI and everyone else.

所以我們必須對您的私人資料保密。我們必須確保您的私人資料安全。但我們必須讓它能夠透過 OpenAI、xAI 和其他所有人的最新和最佳推理模型進行推理。

And we've -- because we have the database, because we can vectorize all the data in the database, because we have very elaborate security models in our database in the Oracle database, we can do all that. We can deliver all that.

而且我們——因為我們有資料庫，因為我們可以向量化資料庫中的所有數據，因為我們在 Oracle 資料庫中有非常複雜的安全模型，所以我們可以做到這一切。我們能夠實現這一切。

And then, what we chose to do was to -- with the AI database was not only make sure we can vectorize all the data so it can be understood by the AI model, we then bundled it with all of the AI models. That's why we did a deal with Google. That's why we did all of these deals, where Gemini, you can get Gemini from the Oracle Cloud, you can get Grok from the Oracle Cloud. You can get ChatGPT from the Oracle Cloud. You get Llama from the Oracle Cloud.

然後，我們選擇要做的是——使用 AI 資料庫不僅要確保我們可以將所有資料向量化，以便 AI 模型能夠理解，然後我們還將其與所有 AI 模型捆綁在一起。這就是我們與Google達成協議的原因。這就是我們進行所有這些交易的原因，您可以從 Oracle Cloud 獲得 Gemini，您可以從 Oracle Cloud 獲得 Grok。您可以從 Oracle Cloud 取得 ChatGPT。您可以從 Oracle Cloud 取得 Llama。

I could go on. So we bundled them together, so it's very easy for our customers to use these large language models on a combination. And that's what they want is a combination of all of the publicly available data and all of their enterprise data, which allows them to ask and get answered any question they can think of as question that's important to them.

我還可以繼續。因此我們將它們捆綁在一起，以便我們的客戶可以非常輕鬆地組合使用這些大型語言模型。他們想要的是所有公開數據和所有企業數據的結合，這樣他們就可以提出並得到任何他們認為重要的問題。

Everyone wants it. I think the demand is going to be insatiable. But we can deliver a lot of databases and a lot of AI across our cloud over the next several years. We're in a good position to do that.

每個人都想要它。我認為這種需求是無法滿足的。但在未來幾年內，我們可以在雲端提供大量資料庫和大量人工智慧。我們有能力做到這一點。

This is going to be one of the reasons that Oracle databases, which are still the bulk of the enterprise market by a lot, are going to finally move into the cloud. Many of them will move from the public cloud using the Oracle AI database. But many and the largest enterprises will want their own either dedicated regions or Oracle Cloud customer.

這將是 Oracle 資料庫（仍佔據企業市場的絕大部分）最終轉向雲端的原因之一。其中許多將從使用 Oracle AI 資料庫的公有雲轉移。但許多大型企業都希望擁有自己的專用區域或 Oracle Cloud 客戶。

Again, they can finally get the benefit of for their own data using any LLM that they want because they're all in our cloud, too.

同樣，他們最終可以使用他們想要的任何 LLM 來獲取他們自己的數據的好處，因為它們也都在我們的雲端中。

It sounds like very high-margin AI revenue guys. Congratulations.

這聽起來像是利潤率非常高的人工智慧收入。恭喜。

Thank you. Okay.

謝謝。好的。

Thanks, Alex.

謝謝，亞歷克斯。

A telephonic replay of this conference call will be available for 24 hours on our Investor Relations website.

本次電話會議的電話回放將在我們的投資者關係網站上提供 24 小時。

Thank you for joining us today. With that, I'll turn the call back to Tiffany for closing.

感謝您今天加入我們。就這樣，我將把電話轉回給蒂芙尼來結束通話。

Ladies and gentlemen, this concludes today's call. Thank you all for joining. You may now disconnect.

女士們、先生們，今天的電話會議到此結束。感謝大家的加入。您現在可以斷開連線。