2024-05-21 Microsoft Corporation (MSFT) J.P. Morgan's 52nd Annual Global Technology, Media and Communications Conference (Transcript)

Microsoft Corporation (NASDAQ:MSFT) J.P. Morgan's 52nd Annual Global Technology, Media and Communications Conference May 21, 2024 8:50 AM ET

微软公司（纳斯达克股票代码：MSFT）J.P.摩根第 52 届年度全球科技、媒体和通信会议 2024 年 5 月 21 日上午 8:50 美东时间

Company Participants 公司参与者

Alysa Taylor - Corporate Vice President of Azure & Industry

艾莎·泰勒 - Azure 和行业企业副总裁

Conference Call Participants

电话会议参与者

Mark Murphy - JPMorgan

马克·墨菲 - 摩根大通

Mark Murphy 马克·墨菲

Okay. Welcome, everyone. Good morning. I'm Mark Murphy, software analyst with JPMorgan. And it is a great pleasure to be here this morning with Alysa Taylor, who is CVP of Commercial Cloud and AI with Microsoft. Alysa, I was on stage with you virtually about 4 years ago in the wake of the pandemic and it's so nice to be here with you.

好的。欢迎大家。早上好。我是摩根大通的软件分析师马克·墨菲。很高兴今天早上能和微软商用云与人工智能副总裁艾莎·泰勒一起在这里。艾莎，大约 4 年前在疫情爆发后，我曾经和你一起虚拟登台，现在能和你在这里真的太好了。

Alysa Taylor 艾莉莎·泰勒

It is nice to be back and in person.

回来并亲自出席真是太好了。

Mark Murphy 马克·墨菲

Welcome. We really appreciate your time here. Perhaps we can just begin with kind of the brief 1-minute introduction of your background and your current role at Microsoft.

欢迎。我们非常感谢您在这里花费的时间。也许我们可以从您的背景和您在微软的当前角色的简短 1 分钟介绍开始。

Alysa Taylor 艾莉莎·泰勒

Absolutely. So as you indicated, I'm responsible for our commercial cloud and AI business. My role at Microsoft, I work very closely with our engineering counterparts to determine what services we're going to bring to market. And then my team does all of the pricing, packaging and go-to-market strategy across our Azure business and our global industries.

绝对。正如您所指出的，我负责我们的商业云和人工智能业务。在微软，我的角色是与我们的工程同事密切合作，确定我们将推出哪些服务。然后，我的团队负责所有定价、打包和市场推广策略，涵盖我们的 Azure 业务和全球行业。

Question-and-Answer Session

问答环节

Q - Mark Murphy Q - 马克·墨菲

Thank you. So Alysa, it's very impressive when we think back and we realize that Microsoft made its first investment into OpenAI, way back in 2019, but a half decade ago because the topic of generative AI really wasn't something that was mainstream, right? It became mainstream with -- when ChatGPT was released. That was late 2022. And then, we fast forward to today and that initiative has now blossomed into a 7-point AI tailwind in the Azure business. How do you conceptualize for this audience, the scale of the opportunity here for Microsoft at this point to be in pole position for the era of AI.

谢谢。所以 Alysa，当我们回想起微软在 2019 年早期首次投资于 OpenAI 时，这是非常令人印象深刻的，但半个世纪前，因为生成式 AI 的话题并不是主流，对吧？当 ChatGPT 发布时，它才成为主流。那是在 2022 年底。然后，我们快进到今天，这一举措现在已经发展成 Azure 业务中的 7 点 AI 助推器。您如何为这个观众概念化，微软在这一时刻处于 AI 时代的领先地位所带来的机遇规模。

Alysa Taylor 艾莉莎·泰勒

Absolutely. Well, the interesting point is we actually brought our first set of Azure AI services to market in 2019. So that was what you think about around cognitive services, traditional machine learning. But what we realized is, the barrier for enterprises to have to take all of their data, do data science work on top of it, it wasn't something that was widely accessible to organizations.

绝对。嗯，有趣的一点是，我们实际上在 2019 年推出了我们的第一套 Azure AI 服务。所以，你认为这是关于认知服务、传统机器学习的。但我们意识到的是，企业必须将所有数据进行数据科学处理，这并不是组织普遍可以轻松获得的。

At the time, working with OpenAI, what we saw was the ability for these large language models to really democratize AI. So to have pretrained models that companies could just, with an API, integrate into those models and not have to do all of the heavy lift with the data science work. And so that was our thesis around the investment into OpenAI and it's paid off as you -- with the introduction of GPT coming to market and these large language models, it's done exactly that. It's allowed organizations to be able to have AI accessible, generative AI in a way that we haven't seen possible.

当时，与 OpenAI 合作，我们看到这些大型语言模型真正实现了 AI 的民主化能力。因此，拥有预训练模型的公司可以通过 API 将这些模型集成到其中，而无需进行所有繁重的数据科学工作。这就是我们对投资 OpenAI 的论点，随着 GPT 进入市场和这些大型语言模型的推出，这一投资已经得到回报。它使组织能够以一种我们以前认为不可能的方式获得 AI 可访问的生成 AI。

Mark Murphy 马克·墨菲

It certainly has. So how do you convey -- in your role, Alysa, how do you convey to customers that Microsoft really should be their primary platform for all their Gen AI activity moving forward? As opposed to the alternative would be doing that work on a competing hyperscaler or maybe one of these GPU as a service providers, what is the marketing message around Microsoft's core differentiators that you're trying to bring to customers?

它确实有。那么，艾莉莎，在你的角色中，你如何向客户传达，微软确实应该成为他们未来所有 Gen AI 活动的主要平台？与在竞争性超大规模计算平台或者可能是这些 GPU 作为服务提供商上进行工作相比，微软的核心差异化要素的营销信息是什么，你试图向客户传达的？

Alysa Taylor 艾莉莎·泰勒

We start with the Microsoft Cloud. So we have infused AI at every layer of the Microsoft Cloud. So if you think about our first-party assets, the Microsoft 365, Dynamics, GitHub, which is our developer services, our Power platform, our security services. So that's our Copilot layer, our first party Copilot layer. We recently introduced the Copilot Studio, which is the service that allows organizations to customize and extend our first-party copilots and then at Build last year, which is kicking off today, we introduced the Copilot stack. And so that's where organizations that want to build their own unique AI solutions and that's everything from the infrastructure layer, the data layer, what we do around the foundational models, as well as the AI orchestration and tool chain.

我们从微软云开始。因此，我们在微软云的每一层都注入了人工智能。所以，如果你考虑我们的第一方资产，微软 365、Dynamics、GitHub（我们的开发者服务）、我们的 Power 平台、我们的安全服务。这就是我们的 Copilot 层，我们的第一方 Copilot 层。我们最近推出了 Copilot Studio，这是一项服务，允许组织定制和扩展我们的第一方 Copilot，然后在去年的 Build 大会上，也就是今天开始的，我们推出了 Copilot 堆栈。因此，那些想要构建自己独特 AI 解决方案的组织，这包括基础架构层、数据层、我们围绕基础模型所做的工作，以及 AI 编排和工具链。

And so when you ask about differentiation, it is really the completeness of everything from the first-party copilots, the extensibility of those copilots and then the copilot stack to have organizations build their own unique AI solutions.

所以当你问到差异化时，实际上是从第一方副驾驶员的完整性，这些副驾驶员的可扩展性，以及副驾驶员堆栈，让组织构建他们自己独特的人工智能解决方案。

助手只是存量业务的优化。

Mark Murphy 马克·墨菲

We're trying to track all those Build announcements in real time, while we're over here at the conference and it's been really impressive what we have been able to catch on the side. When you think about, Alysa, what is going to be happening with the foundation models, do you expect that we're going to see some convergence in the capabilities across those -- people probably -- obviously have the GPT models, Anthropic is out there and others. Or do you suspect that we're going to see the release of GPT-5, presumably sometime fairly soon and that this would show some kind of a sustained performance differential. And I'm wondering because I think we're trying to think through all those scenarios.

我们正在尝试实时跟踪所有这些 Build 公告，同时我们在会议上，我们已经能够在一边捕捉到的东西令人印象深刻。当你考虑到，Alysa，基础模型会发生什么，你是否期待我们会看到一些能力上的收敛--人们可能--显然有 GPT 模型，Anthropic 等。或者你是否怀疑我们会很快看到 GPT-5 的发布，这将显示某种持续的性能差异。我在想，因为我认为我们正在尝试思考所有这些情景。

In the convergence scenario, how would Microsoft perpetuate a structural advantage in AI? In other words, is it going to come down to what you're trying to do with the first-party silicon, would it be having a broader family of -- across all the models, you've got the small language models. Is it going to come down to something you're doing in security and governance?

在融合场景中，微软将如何在人工智能领域保持结构优势？换句话说，这是否将取决于您尝试使用第一方硅做什么，是否将拥有更广泛的家族——跨越所有模型，您拥有小语言模型。这是否将取决于您在安全和治理方面所做的事情？

first-party silicon，专门名词，指从芯片到软件系统都是自己的，比如，苹果，优点是对更容易优化，对供应链有着更好的控制。

Alysa Taylor 艾莉莎·泰勒

So there's a lot in there. So I think I'll start with the model part of it. We don't believe there is one model to rule them all. We actually believe in a variety of what we call fit-to-purpose models. So we have in our model catalog today, 1,700 models. And those are, as you indicated, across large language models, proprietary, as well as open source, third-party models and then the introduction of the small language models. So 5:3 is our open source that we just announced. And so having this range of models, we think is something that allows organizations to use those models for very specific purposes.

所以里面有很多内容。所以我想我会从模型部分开始。我们不认为有一个模型可以统治所有模型。我们实际上相信有各种我们称之为适用模型。所以我们在我们的模型目录中有 1,700 个模型。正如您所指出的那样，这些模型涵盖了大型语言模型、专有模型，以及开源、第三方模型，还有小型语言模型的引入。所以 5:3 是我们刚刚宣布的开源模型。因此，拥有这些模型范围，我们认为可以让组织为非常具体的目的使用这些模型。

And we also see organizations bringing models together to drive optimal efficiency and performance. In fact, the Microsoft Copilot is a combination of GPT-3, 3.5, 4 and Meta's Llama model. So that's a great example of where even in our first-party copilot, we are using a combination of models for that optimal performance. So that's where we are on the model side of it. To your point around then, how do you bring the governance and the security into those models. I think that is one of the things that I get most often when I'm talking to customers is, how do we govern the data that the models reason over. And so we introduced a product called Microsoft Purview, it is our data governance solution.

我们还看到组织将模型汇集在一起，以推动最佳效率和性能。事实上，微软 Copilot 是 GPT-3、3.5、4 和 Meta 的 Llama 模型的组合。这是一个很好的例子，即使在我们的第一方 Copilot 中，我们也在使用多个模型来实现最佳性能。这就是我们在模型方面的情况。至于您提到的，如何将治理和安全性引入这些模型。我认为这是我在与客户交谈时最常遇到的问题之一，即我们如何管理模型推理的数据。因此，我们推出了一个名为 Microsoft Purview 的产品，这是我们的数据治理解决方案。

每个模型相当于一个专家，3个模型相当于3个专家聚在一起讨论问题。

It is one of the things that is the most important assets for an organization to be able to use Purview to do all of the governance work. And then we are building security directly into our AI services. So we introduced things like the Azure content safety which is a tool that allows organizations to both detect and mitigate biases in the model. And so I think it is ultimately the range of models, how you bring those models together and then how you govern and secure the models.

这是组织能够利用 Purview 来进行所有治理工作的最重要资产之一。然后，我们直接将安全性融入我们的人工智能服务中。因此，我们推出了 Azure 内容安全等工具，使组织能够同时检测和减轻模型中的偏见。因此，我认为最终是模型的范围，如何将这些模型结合起来，然后如何治理和保护这些模型。

Mark Murphy 马克·墨菲

Okay. That range in breath is obviously quite impressive already. If we then try to think about, Alysa, the way that's manifesting in customer conversations around AI. Externally, so we can see, again, you've got the 7-point tailwind that has developed from AI services in Azure. We can see -- there have been these huge announcements. We've seen it with Coca-Cola. We've seen it with Cloud Software Group. There have been a bunch of others. We don't always know exactly what it is that they're building. And I thought, given you also run go-to-market for global industry, that maybe you would have a window into this to help us understand. So what is the manufacturer, retailer or an insurance firm building at the moment?

好的。呼吸范围显然已经相当令人印象深刻了。如果我们试图思考一下，艾莉莎，这种方式如何在客户关于人工智能的对话中体现。从外部来看，我们可以看到，你们在 Azure 的人工智能服务中已经获得了 7 点的顺风。我们可以看到——已经有了一些重大的公告。我们已经看到了与可口可乐合作的情况。我们也看到了与云软件集团的合作。还有其他一些公司也有类似的情况。我们并不总是确切地知道他们在构建什么。考虑到你还负责全球行业的市场推广，也许你可以帮助我们了解一下。目前制造商、零售商或保险公司在构建什么？

Alysa Taylor 艾莉莎·泰勒

I always start from the horizontal scenario. So I'll do that and then I'll go into industry, which is your specific question. We see probably 3 universal use cases across any industry, how organizations are working with their customers, particularly generative AI has enabled organizations to do like very tailored, personalized at scale, customer experiences in a way that we've never seen before. There's the employee side of it. So how do you make employees more productive, giving them tools, resources, access to information. And then on the operations side, more efficient operations, being able to rethink workflows. So those are the horizontal scenarios across any industry.

我总是从水平场景开始。所以我会这样做，然后我会进入行业，这是您的具体问题。我们可能会看到在任何行业中有 3 个通用用例，组织如何与他们的客户合作，特别是生成式人工智能使组织能够以一种我们以前从未见过的方式进行非常定制、个性化且规模化的客户体验。还有员工方面。那么如何让员工更加高效，为他们提供工具、资源、信息访问。然后在运营方面，更高效的运营，能够重新思考工作流程。所以这些是任何行业中的水平场景。

But to your very specific question, then how does that translate into opportunity for industry. We see things like in health care. Physician burnout is one of the greatest challenges within health care. And so generative AI, the combination of both ambient AI and generative AI has allowed physicians to use technology to record patient and physician interaction. The technology can then automatically analyze and generate clinical notes. So that takes a lot of the administrative burden off of the physicians, which is a big contributor to physician burnout. So that's a great health care use case scenario.

但是针对您非常具体的问题，这又如何转化为行业机会。我们看到在医疗保健领域出现了一些情况。医生的倦怠是医疗保健领域内最大的挑战之一。因此，生成式人工智能，环境人工智能和生成式人工智能的结合使医生能够利用技术记录患者和医生的互动。然后，技术可以自动分析和生成临床记录。这样就能减轻医生的行政负担，这是导致医生倦怠的一个重要因素。因此，这是一个很好的医疗保健使用案例场景。

We see in the customer engagement side, a great example is Real Madrid. So they are a Spanish football league. They have a very small set of their fans that live in Spain. They have over 500 million fans globally that they have been challenged to reach in near real time in a personalized way. They used AI to be able to create their fan engagement platform. And the interesting thing with their fan engagement platform is, they were able to not only put the matches in the hands of their fans in near real time but they could also analyze the sentiment in real time and then do targeted campaigns to their fans.

在客户参与方面，一个很好的例子是皇家马德里。所以他们是西班牙足球联赛。他们有一小部分粉丝住在西班牙。他们全球拥有超过 5 亿粉丝，他们被挑战以个性化方式在几乎实时中接触到他们。他们使用人工智能来创建他们的粉丝参与平台。而他们的粉丝参与平台的有趣之处在于，他们不仅能够将比赛几乎实时地交到粉丝手中，还能够实时分析情感，然后对粉丝进行定向宣传活动。

And the reason I love this story is that actually they've increased their fan profile base by 400% and their top line revenue by 30%. So this is an area where you see both a use case in an industry and then you also see tangible results. And I think that's the combo that we want to see. The last example I'll give you is in the automotive space. So Volvo is a great example. They used a combination of cognitive services, generative AI to digitize all of their invoices. And so if you think about not only being able to invoice their customers but all of the tracking that goes along with auto maintenance, they actually estimated that their new operational platform, took out 850 manual hours per month.

我喜欢这个故事的原因是，他们实际上将粉丝基础增加了 400％，顶线收入增长了 30％。因此，这是一个领域，在这个领域中，您可以看到用例，同时也可以看到切实的结果。我认为这是我们想要看到的组合。我将给您的最后一个例子是在汽车领域。沃尔沃是一个很好的例子。他们使用了认知服务和生成式人工智能的组合来数字化所有发票。因此，如果您考虑到不仅能够向客户开具发票，还有所有与汽车维护相关的跟踪，他们实际上估计他们的新操作平台每月节省了 850 个手动小时。

And so these are the industry use cases where we see the technology coming to bear to solve or create an opportunity and then actual -- have real top line or bottom line results as an association with that.

所以这些是我们看到技术应用于解决或创造机会的行业用例，然后实际产生真正的顶线或底线结果的地方。

Mark Murphy 马克·墨菲

Yes. I'm impressed by the range in the number of layers that where that activity is occurring. And obviously, you're laying out something that's pretty tangible across some huge organizations in terms of the ROI. And so that might be helpful as I lead into the next question where we think about the investments that Microsoft is putting into this. And so your CapEx will have risen from, let's say, roughly $25 billion to probably $60 billion to $70 billion, right? That will have happened in the course of a few years. And Amy Hood, the CFO of the company has been very clear that the investments are based upon demand signals. We've heard this. We've been hearing this repeatedly. But we do see, at the same time, there are questions, certainly in the media, there are these questions, is AI demand for real? Or are we going to find out somehow that people are overbuilding?

是的。我对活动发生的层次范围印象深刻。显然，您正在为一些大型组织提供相当具体的 ROI。这可能对我接下来要提出的问题有所帮助，我们将考虑微软正在投入的投资。因此，您的资本支出将从大约 250 亿美元上升到 600 亿至 700 亿美元，对吗？这将在几年内发生。公司的首席财务官艾米·胡德明确表示，投资是基于需求信号的。我们听说过这一点。我们一再听到这一点。但同时，我们也看到，媒体上确实存在一些问题，即 AI 需求是否真实？或者我们会发现人们是否在过度建设？

So -- what other signals could you help us with that you're seeing that may give us comfort that the CapEx surge is well informed, that it's not speculative, right. And that, that this -- that we're going to have this kind of monetization several years into the future.

那么 - 除了您已经看到的信号之外，您还能帮助我们看到哪些信号，让我们放心资本支出激增是经过深思熟虑的，而不是投机的，对吧。而且，我们将来几年会有这种货币化。

Alysa Taylor 艾莉莎·泰勒

It's a very important question. We look at demand in 3 different aspects. So the first is customer demand. And do we see the inbound of customers that want to leverage the new AI services that we've put kind of at every layer of the stack, as I talked about? And it's, we look at customer demand, both from those that are coming in, in a pilot phase. And then we also look at it from the dimension of, is that pilot then translating into an at-scale deployment because both of those components are incredibly important. So they're not just experimenting but they're actually taking it from experimentation into full-scale deployment.

这是一个非常重要的问题。我们从 3 个不同的方面来看需求。所以第一个是客户需求。我们是否看到想要利用我们在每一层堆栈中放置的新 AI 服务的客户的涌入，就像我所谈到的那样？我们看客户需求，既来自试点阶段的客户，也从另一个维度来看，即试点是否转化为规模化部署，因为这两个组成部分都非常重要。所以他们不仅仅是在试验，而是实际将其从试验转化为全面部署。

The other dimension that we look at is our ecosystem. As you know, Microsoft is a very ecosystem-driven company. And so we look at the number of partners within our ecosystem that are getting AI specializations and where they are bringing in new customers. We have within our Azure AI services, we have 53,000 active customers. 1/3 of those in the last year are new to Azure. So that is a great signal of we are not only bringing in existing customers but new customers as well. And then the last dimension that we look at is customer commitment. Do we have customers making long-term commitments to the Microsoft services, to the Microsoft platform? And our $100 million-plus contracts have increased 80% year-over-year.

我们关注的另一个维度是我们的生态系统。正如您所知，微软是一家非常注重生态系统的公司。因此，我们关注我们生态系统中获得人工智能专业化的合作伙伴数量以及他们带来新客户的情况。在我们的 Azure 人工智能服务中，我们有 53,000 个活跃客户。在过去一年中，其中有三分之一是新客户。这是一个很好的信号，表明我们不仅吸引了现有客户，还吸引了新客户。然后我们关注的最后一个维度是客户承诺。我们是否有客户对微软服务、微软平台做出长期承诺？我们的 1 亿美元以上的合同年增长率增加了 80%。

软件工程师，医生，看着专业性质的工作会早一步替换到AI。

Those demand the customer dimension, our ecosystem dimension, our long-term customer commitments are how we triangulate demand. And then we weekly, as a senior leadership team, look at that demand against supply. So it is an ongoing very fluid situation that we manage.

那些要求客户维度、我们的生态系统维度、我们的长期客户承诺是我们如何三角定位需求。然后我们每周作为高级领导团队，审视需求与供应情况。因此，这是一个我们持续管理的非常灵活的情况。

Mark Murphy 马克·墨菲

So big commitments, long term, the projects are moving from pilots to full deployment and then you're seeing all this partner buy-in. It seems like a pretty good triangulation on it. So let's spend a moment talking about the macro. We haven't heard Microsoft yet call out any kind of a pivot in macro demand. And yet, we think back to this March quarter and we had both commercial bookings in Azure grew 31% year-over-year. These are -- they're healthy results and both of those saw acceleration. There wasn't much acceleration across the software industry but there was there. How would you characterize, Alysa, business willingness to invest at the moment? If I were to say it this way, is there at least an improved sense of stability out there that might be helping on the margin?

如此重大的承诺，长期来看，项目正在从试点阶段转向全面部署，然后您会看到所有这些合作伙伴的支持。这似乎是一个相当不错的三角定位。那么让我们花一点时间来谈谈宏观情况。我们还没有听到微软提到宏观需求的任何转变。然而，我们回想一下今年三月季度，Azure 的商业预订额同比增长了 31%。这些都是健康的结果，而且这两者都加速增长。在软件行业中并没有太多加速增长，但在这方面确实有。Alysa，您如何描述企业目前投资的意愿？如果我这样说，至少在某种程度上是否有一种改善的稳定感正在帮助边际上的情况？

Alysa Taylor 艾莉莎·泰勒

Well, if we just look at it from a continuum and I kind of -- I go back to the start of the pandemic, right? As we had organizations move entire group's customer service to a remote capacity, there was an intense capital investment around digitizing foundations, foundational things, customer service environments, supporting employees from a hybrid perspective. And then we came out of the pandemic and we were in a state of cost optimization. So how do organizations take all of that investment that they made in their digitization, in the new kind of digital foundation, their IT investments, how do they then make sure that they rightsize those.

嗯，如果我们从一个连续的角度来看，我有点--我回到了疫情开始的时候，对吧？当我们看到组织将整个客户服务团队转移到远程工作时，围绕数字化基础设施、基础事务、客户服务环境以及从混合视角支持员工进行了大量资本投资。然后我们走出了疫情，进入了成本优化阶段。那么组织如何利用他们在数字化、新型数字基础设施、IT 投资方面所做的所有投资，确保他们适当地调整规模。

And we worked very closely with our customers to make sure that we were hand-in-hand helping optimize their environment. We are now in a place where they're taking new investment into generative AI, some of the examples that I have given. And there is both the -- how does AI play into their IT investments. But then you also see that translate into what I would call kind of core IT spend, so migrations, continuing to migrate on-prem data app development and the app development building new applications but the change has become a pivot to intelligent app development with the onset of generative AI data, how do you bring together disparate data sets, have an enterprise-wide data architecture and then continuing to invest in developers and making sure that developers are as productive as possible.

我们与客户密切合作，确保我们携手帮助优化他们的环境。现在我们正处于一个阶段，他们正在将新的投资引入生成式人工智能，我提到的一些例子。AI 如何融入他们的 IT 投资。但你也会看到这转化为我所说的核心 IT 支出，即迁移，继续迁移本地数据应用开发和应用开发构建新应用程序，但随着生成式 AI 数据的出现，变化已经转向智能应用程序开发，如何将不同的数据集集成在一起，拥有企业范围的数据架构，然后继续投资于开发人员，并确保开发人员尽可能高效。

And so I think it is both a spike in the -- what we're seeing around generative AI but then also a translation into kind of the core IT functions across migration, app dev, data and developers.

因此，我认为这既是对我们所看到的生成式人工智能的激增，也是对迁移、应用程序开发、数据和开发人员等核心 IT 功能的一种转化。

Mark Murphy 马克·墨菲

That's encouraging. 这是令人鼓舞的。

Alysa Taylor 艾莉莎·泰勒

It is. It is. We are also encouraged.

这是。这是。我们也受到鼓励。

Mark Murphy 马克·墨菲

So then let's go a little deeper into that in terms of the workload migrations. When we go back and look at our recent survey, Microsoft partners were calling for an uptick in Azure growth moving forward over the next 12 months. And that is quite rare because it is just such a large-scale business, where you get some law of large numbers. And we looked at what happened, again subsequently Azure growth, 31%, it accelerated by 3 points. Is there anything else in here that you would call out that is aligning to drive this rebound in Azure growth that we're seeing?

那么，让我们更深入地探讨一下工作负载迁移方面。当我们回顾最近的调查时，微软合作伙伴呼吁未来 12 个月 Azure 增长有所提升。这是相当罕见的，因为这是一个规模如此庞大的业务，你会得到一些大数定律。我们再次看到发生了什么，随后 Azure 增长了 31%，增速提高了 3 个百分点。在这里还有其他任何事情，你会指出与推动我们所看到的 Azure 增长反弹相一致的吗？

Alysa Taylor 艾莉莎·泰勒

Well, we talked about it from what our customers are doing in that time frame. I would say at that same period of time, we looked internally at where we had placed our investments, particularly in the Azure and the industry side. And the reality is, as Azure had grown as a platform, and we were invested in a number of different areas. And we took that moment to say where should we be focused that have the greatest addressable market and where we have the greatest strength. And so we went from what I would say was probably too many areas of disperse focus into a very highly focused GTM. And the core areas that we look at are around migration. We brought new migration tooling to bear. We put new programs and market in the last year.

嗯，我们从我们的客户在那个时间段所做的事情谈起。我会说在同一时间段，我们内部审视了我们的投资方向，特别是在 Azure 和行业方面。事实是，随着 Azure 作为一个平台的发展，我们在许多不同领域进行了投资。我们抓住这个时机来思考我们应该关注哪些具有最大可寻址市场和我们最大优势的领域。因此，我们从我认为可能过于分散关注的许多领域转变为一个非常高度专注的 GTM。我们关注的核心领域是迁移。我们引入了新的迁移工具，去开展了新的项目和市场。

We've had actually over 10,000 projects come through our migration, what is called Azure migrate and modernize. So we've just become, how do we make sure that we are hand-in-hand working with our customers on migration, data and making sure that we had -- we're bringing new capabilities, both to our analytical databases as well as our operational databases. So we really started to think about data, particularly in the era of AI. Bringing new services into our App Dev portfolio. And so not only on the generative AI stack but then also bringing things like GitHub Copilot for developers to be able to code faster and more efficiently.

我们实际上已经有超过 10,000 个项目通过我们的迁移，这就是所谓的 Azure 迁移和现代化。所以我们已经开始，我们如何确保我们与客户一起手牵手在迁移、数据方面共同努力，并确保我们拥有——我们正在为我们的分析数据库以及我们的运营数据库带来新的能力。因此，我们真的开始思考数据，特别是在人工智能时代。将新服务引入我们的应用开发组合中。因此，不仅仅是在生成式人工智能堆栈上，还将像 GitHub Copilot 这样的东西带入其中，以便开发人员能够更快速、更高效地编码。

And then lastly, on the hybrid space. We introduced at Ignite this past fall, this notion of an adaptive cloud centering on Azure Arc as the central control plane, allowing organizations not only to manage their on-prem but their cloud and multi-cloud environments. So we believe we have one of the strongest hybrid solutions in market. And so that's where we're focused and that's where we spend all of our time, is in those areas. Both from a, where are we innovating at the product level but then also how we are bringing those to market.

最后，关于混合空间。我们在去年秋季的 Ignite 大会上介绍了一个自适应云中心的概念，以 Azure Arc 作为中央控制平台，使组织不仅能够管理他们的本地环境，还能管理他们的云和多云环境。因此，我们相信我们在市场上拥有最强大的混合解决方案。这就是我们的重点，也是我们花费所有时间的地方，就是在这些领域。无论是在产品层面创新的地方，还是在如何将这些产品推向市场的地方。

Mark Murphy 马克·墨菲

So -- and I want to come back to that, especially on the data and analytics and the fabric layer just a moment. But to round out the thought on the migrations, you had mentioned, Alysa, an incredible stat a moment ago, the number of $100 million Azure deals being up 80% year-over-year. And our work actually was signaling an improvement in these larger cloud migrations that, that was actually beginning in the back half of the March quarter itself. What is your view on the rate in pace of those types of migrations because it's such a big revenue driver? Do you look at -- do you feel that enterprises are back in an investment mode as it relates to their cloud spend?

所以 - 我想回到这一点，特别是关于数据和分析以及基础层面。但是在迁移方面，你刚才提到了一个令人难以置信的数据，艾莉莎，即每年 1 亿美元的 Azure 交易数量增长了 80%。实际上，我们的工作表明这些更大规模的云迁移正在开始在三月季度的后半部分出现改善。你对这类迁移的速度和步伐有何看法，因为这是一个重要的收入驱动因素？你是否认为企业在云支出方面已经重新进入投资模式？

Alysa Taylor 艾莉莎·泰勒

Definitely. I think there's 2 vectors we look at or we see customers why they migrate. So the first is, particularly with AI, you are -- we have a saying around, you migrate to innovate because your AI solution is only as good as your underlying data. And that data has to be in a cloud-based environment. And so you see organizations that are migrating their data to be able to apply the new AI services on it. And the more information, the more data you have, the richer your AI solution. And so we have seen the onset of AI help fuel our migration efforts, which is fantastic to see.

肯定。我认为我们看到客户迁移的原因有两个方面。首先，特别是在人工智能方面，我们有一句话，即您迁移以创新，因为您的人工智能解决方案的好坏取决于您的基础数据。而这些数据必须在基于云的环境中。因此，您会看到组织正在迁移其数据，以便应用新的人工智能服务。拥有更多信息和数据，您的人工智能解决方案就会更加丰富。因此，我们看到人工智能的出现帮助推动了我们的迁移工作，这是非常棒的。

技术上落后的企业将进入更加困难的时期，特别是一些内部系统陈旧、混乱的大型机构，甩不掉存量是最大的障碍。

The second dimension is cost and how organizations continue to optimize for cost. And migration has been a key component of that. Sapiens is a great example of that. They are an insurance provider. They serve over 600 insurers across 30 countries. They knew that they had on-prem data kind of in different pockets, serving different countries. They migrated over into Azure Arc, as I talked about, keeping some aspects of their platform on-prem, bringing the majority of it into the cloud. They actually have a multi-cloud strategy. They're using Arc as the central control plane to be able to govern their IT and then serve those insurers across their global capacity.

第二个维度是成本以及组织如何继续优化成本。迁移一直是其中的关键组成部分。Sapiens 是一个很好的例子。他们是一家保险提供商。他们为 30 个国家的 600 多家保险公司提供服务。他们知道他们在本地数据中心中有不同的数据，为不同的国家提供服务。他们将这些数据迁移到 Azure Arc 中，保留了平台的某些方面在本地，将大部分数据迁移到云端。他们实际上有一个多云战略。他们使用 Arc 作为中央控制平台来管理他们的 IT，并为全球容量中的这些保险公司提供服务。

And they were able to take 40% of their operational cost out of the bottom line. And so that is an example of where you're migrating, you're aggregating, you're using a central IT environment to be able to bring down cost.

他们能够将 40%的运营成本削减掉。这就是一个例子，说明你正在迁移、聚合，利用中央 IT 环境来降低成本。

Mark Murphy 马克·墨菲

So part of our core thesis, Alysa, has been that Microsoft might, at some point, end up seeing what we were calling an Azure halo effect. And that, that would stem from, again, this early category leadership in generative AI that goes back at least as far as 2019. And we have heard some feedback that there could be some companies out there that had been, let's say, for instance, they were previously sole-sourced on AWS or somewhere else. And they may be thinking of a little different future road map, right? Because of -- there could be a little more consideration of Azure, right, because of these moves you've been making. Is any of that tangible to you, like do you think that you could gain a greater share of cloud workloads because big companies are going to align to your architectural view?

因此，我们核心论点的一部分，Alysa，是微软可能在某个时候会看到我们所称的 Azure 光环效应。这可能源自，再次强调，至少可以追溯到 2019 年的生成式人工智能领域的早期领导地位。我们听到一些反馈，可能有一些公司曾经，比如说，他们以前是 AWS 或其他地方的唯一供应商。他们可能在考虑一些不同的未来路线图，对吧？因为——可能会更多地考虑 Azure，对吧，因为你们一直在做这些动作。对你来说，这些是否有实质性意义，比如你认为你是否可以获得更大份额的云工作负载，因为大公司将会与你的架构观点保持一致？

Alysa Taylor 艾莉莎·泰勒

That's one of the very exciting things that we're seeing because you could just use the API into the foundational models and that's it. But we actually are seeing organizations start with the API, bringing in their unstructured data into a blob storage type capacity but then actually moving into more sophisticated analytical data services. Obviously, if you're building an app, you're bringing that into an operational data service. And in fact, of the 53,000 Azure AI customers that I talked about, as I said, 1/3 of those are new to Azure but half of them are actually using our data services as well.

这是我们看到的非常令人兴奋的事情之一，因为您可以将 API 直接用于基础模型，就这样。但实际上，我们看到组织从 API 开始，将其非结构化数据引入到一种 blob 存储类型的容量中，然后实际上转向更复杂的分析数据服务。显然，如果您正在构建一个应用程序，您会将其引入到操作数据服务中。事实上，我提到的 53,000 个 Azure AI 客户中，正如我所说的，其中有 1/3 是 Azure 的新用户，但其中一半实际上也在使用我们的数据服务。

And so it's a good stat that shows the customers are not just using the APIs but also then bringing in their data into the Microsoft platform. And so we're pulling through from the -- just the base sort of integration into the foundational models, actually pulling in our data services as well. And so to your question, the answer is yes. We are seeing customers both come to Azure that were not previously an Azure customer and using services beyond just the core AI services.

因此，这是一个很好的统计数据，显示客户不仅在使用 API，而且还将他们的数据带入了微软平台。因此，我们正在从基本的集成中提取数据，实际上也将我们的数据服务引入其中。所以对于你的问题，答案是肯定的。我们看到一些以前不是 Azure 客户的客户来到 Azure，并使用除核心 AI 服务之外的其他服务。

Mark Murphy 马克·墨菲

Okay. So there's adoption of so many services but then we think back to the recent earnings call and Amy had made a comment that near-term AI demand is a bit higher than Microsoft's available capacity, right? So the concept of the capacity constraints came up a bit there. Can you unpack that for us a bit? And one of the questions we get is, should we be somewhat handicapping the forward Azure AI services estimates due to supply constraints? Or do you think that this is something that we can overcome fairly rapidly?

好的。所以有很多服务被采用，但我们回想一下最近的收益电话，艾米曾经说过，短期内人工智能的需求略高于微软的可用容量，对吧？所以容量限制的概念在那里有点出现。你能稍微解释一下吗？我们收到的一个问题是，我们是否应该在前瞻性的 Azure 人工智能服务预估中略微考虑供应限制？或者你认为这是我们可以相当迅速克服的问题？

Alysa Taylor 艾莉莎·泰勒

And I think Amy uses that word bit very intentionally because as we talked about, we have the triangulation that we do on the demand side, the customer -- inbound customer the long-term commitments in the ecosystem. And as I indicated, we do that week over week. But I would say we are conservative in our demand. And so we want -- and we do that intentionally because we then take that demand and we marry it against this supply. And so as we make sure that we are conservative in our demand forecasting, we tend to be a bit -- we have a bit more supply constraints but it's nothing material and I would say it has no impact in future forward [indiscernible].

我认为艾米故意使用那个词“位”是因为正如我们所讨论的，我们在需求方面进行三角测量，客户——入站客户在生态系统中的长期承诺。正如我所指出的，我们每周都这样做。但我想说的是，我们在需求方面是保守的。因此，我们希望——我们故意这样做，因为我们会将这种需求与供应相结合。因此，我们确保在需求预测方面保守，我们往往会有一些供应约束，但这并不重要，我想说这对未来没有影响。

Mark Murphy 马克·墨菲

Okay. We'll try to be a bit cognizant of that going forward in our model. So then, Alysa, let's think about Microsoft fabric. We do hear this quite often that what a company is going to need to do is they're going to have to clean. They're going to have to rightsize enterprise data in the age of AI and then clean up that estate to feed it into these large language models. You have Fabric, which is the -- a newer analytics platform and it's definitely been at the forefront of all the discussions lately on the earnings calls. There was a comment about it reaching over 11,000 paid customers in less than 1 year of launch. And can you walk us through what is the customer interest in this Fabric product and are you -- should we think about Microsoft really truly positioning to try to be an end-to-end AI platform when integrated with Azure.

好的。我们将尝试在我们的模型中更加意识到这一点。那么，Alysa，让我们考虑一下微软的 Fabric。我们经常听到这样的说法，即一家公司需要做的是清理数据。在人工智能时代，他们需要调整企业数据的规模，然后清理这些数据，以供输入到这些大型语言模型中。您有 Fabric，这是一个更新的分析平台，最近在所有讨论中处于前沿。在收益电话中最近有一条评论提到，它在推出不到 1 年的时间内就吸引了超过 11,000 个付费客户。您能否向我们介绍一下客户对这款 Fabric 产品的兴趣，以及微软是否真的应该被视为一个端到端的人工智能平台，当与 Azure 集成时。

Alysa Taylor 艾莉莎·泰勒

So definitely on the integrated AI platform side and I think you'll see we are building in, across all of our services, the different AI components. Specific to Fabric, we had a thesis about 18 months ago that organizations would want a more unified environment to bring in the different analytic services, be able to aggregate their disparate data into a unified data lake and then be able to bring in AI services directly into that. And so this was a bet that we took over 1.5 years ago.

所以肯定在集成 AI 平台方面，我认为您会看到我们正在构建跨所有服务的不同 AI 组件。就 Fabric 而言，大约 18 个月前，我们有一个关于组织希望拥有更统一环境的论点，可以引入不同的分析服务，能够将他们的不同数据聚合到一个统一的数据湖中，然后能够直接引入 AI 服务。因此，这是我们在 1.5 年前做出的一项赌注。

We introduced Fabric at Build in preview a year ago and it was around the unification of the services into a SaaS environment with a unified business model. Those were all 3 major, major changes for us in how we came to market from an analytics standpoint. So it brought together things like our real-time monitoring, BI, data warehousing, all of that into this notion of Fabric, aggregating into a data lake called OneLake and then we have one meter that goes against it, which before it was all different services that you would then bring together.

我们在一年前的 Build 大会上推出了 Fabric 预览版，它围绕将服务统一到 SaaS 环境中并采用统一的商业模式展开。这些对我们来说都是 3 个重大的变化，从分析的角度来看，我们是如何进入市场的。因此，它汇集了诸如我们的实时监控、BI、数据仓库等内容，全部融入到这个 Fabric 的概念中，聚合成一个名为 OneLake 的数据湖，然后我们有一个与之对应的计量器，之前是所有不同的服务，然后再将它们整合在一起。

And so we introduced Fabric, we actually came to general availability this fall. So we've actually been in market less than 1 year and we have 11,000 paid customers. And a great example of this is Denver Motor Company. They monitor real-time racing cars. As you can imagine, detecting anomalies in the car is quite important. They adopted Fabric. And prior to bringing together their data into Fabric and being able to do real-time monitoring and the analytics on that real-time monitoring, they had about a 30-minute window before they would know if there was an anomaly with the car and they report today, they're in less than 2 minutes. And so that's the benefit of being able to aggregate into this OneLake environment and then start to bring in the different analytical services across it and then ultimately be able to then do things like vector search and build out those AI solutions. So we are integrating at the Fabric core as well as bringing in our AI services directly into Fabric as well.

因此，我们推出了 Fabric，实际上在今年秋季正式推出。所以我们实际上在市场上不到 1 年的时间，就拥有了 11,000 名付费客户。其中一个很好的例子是丹佛汽车公司。他们监控实时赛车。正如你所想象的，检测汽车中的异常非常重要。他们采用了 Fabric。在将数据整合到 Fabric 并能够进行实时监控和分析之前，他们需要大约 30 分钟的时间才能知道汽车是否存在异常，而今天他们报告说，现在不到 2 分钟。这就是能够聚合到这个 OneLake 环境中的好处，然后开始引入不同的分析服务，并最终能够做到像矢量搜索和构建 AI 解决方案等事情。因此，我们正在将 AI 服务直接集成到 Fabric 的核心，并将不同的分析服务引入 Fabric 中。

Mark Murphy 马克·墨菲

Okay. So Fabric and OneLake is having that type of an impact. I think we've spent a lot of time, Alysa, so far talking about the software stack and we haven't really gotten into the hardware side, right? sometime we like -- I think some of us would like to kind of consider the back end that is supporting this whole prior discussion. And going back to late last year, Microsoft announced a couple of very important innovations. Azure Maia and Azure Cobalt, which are chip innovations. Could you walk us through how is it that Microsoft is innovating with first-party silicon now and then what is going to be the benefit of having kind of this tightly integrated hardware and software stack?

好的。因此，Fabric 和 OneLake 正在产生这种影响。我认为我们花了很多时间，Alysa，迄今为止主要讨论软件堆栈，而我们实际上还没有深入硬件方面，对吧？有时候我们会想--我认为我们中的一些人会想考虑支持整个先前讨论的后端。回到去年底，微软宣布了几项非常重要的创新。Azure Maia 和 Azure Cobalt，这是芯片创新。您能否向我们介绍一下微软如何通过第一方硅进行创新，以及拥有这种紧密集成的硬件和软件堆栈将带来什么好处？

Alysa Taylor 艾莉莎·泰勒

So the foundational models, it is important the AI platform that they run on because they are only as efficient and effective as the infrastructure underneath. So I talked about the stack. At the core of that is our AI infrastructure. And as you indicated, we have brought first-party silicon to bear to the market but it is to complement the investments that we have with NVIDIA and AMD. So it is about a portfolio of GPUs and CPUs. And we talk about our AI platform as a systems approach. So bringing together Maia, which is our AI accelerator; Cobalt, which is our CPU; our investments with NVIDIA and AMD but then we wrapper that with networking investments as well as newly talked about liquid cooling to bring together an AI infrastructure that is the most performant for our AI solutions to run on top of.

因此，基础模型非常重要，因为它们的效率和效果取决于其运行的人工智能平台。所以我谈到了技术堆栈。在核心是我们的人工智能基础设施。正如您所指出的，我们已经将第一方硅引入市场，但这是为了补充我们与 NVIDIA 和 AMD 的投资。因此，这涉及到一系列的 GPU 和 CPU。我们将我们的人工智能平台视为系统方法。因此，将 Maia（我们的人工智能加速器）和 Cobalt（我们的 CPU）结合在一起；我们与 NVIDIA 和 AMD 的投资，然后再加上网络投资以及最新讨论的液冷技术，以打造一个最适合我们人工智能解决方案运行的人工智能基础设施。

So it really -- and all of this is opaque to a customer. So when you are a customer and you go in and select whatever Azure service you want to run, we on the back end, are firing across our different silicon investments, again, with that kind of updated networking, storage capacity, so that really the end customer, all they see is the best price to performance and we manage the system on the back end. And it really is an integrated system. And so it isn't about one chip versus the other, NVIDIA versus AMD. It's the portfolio and we do the network load balancing to be able to provide to the end customer the best price and performance.

所以这真的——所有这些对客户来说都是不透明的。所以当您是客户并且选择要运行的任何 Azure 服务时，我们在后端，通过我们不同的硅投资，再次进行更新的网络、存储容量，以便最终客户真正看到的只是最佳性能价格，我们在后端管理系统。这真的是一个集成系统。因此，这不是关于一个芯片对另一个芯片，NVIDIA 对 AMD。这是投资组合，我们进行网络负载平衡，以便为最终客户提供最佳的价格和性能。

Mark Murphy 马克·墨菲

Can you bridge that through to what it's going to mean to developers? We know there's quite a focus on developer tools. You're talking about kind of abstracting all this complexity away from the customer. How do you think about it at a high level, the ability to attract the world's developers and have them build the next generation of all these intelligent apps.

你能把这个联系到对开发者意味着什么吗？我们知道开发者工具是非常重要的。你在谈论将所有这些复杂性抽象化远离客户。你如何在高层次上考虑这个问题，吸引全球的开发者并让他们构建下一代所有这些智能应用程序。

Alysa Taylor 艾莉莎·泰勒

Obviously, developers are at the center and core of all of this. So when we think about our developer ecosystem, we have, over the years, invested in the best tooling and the best tool chain for our developers. So we have GitHub, which has 100 million developers. It is literally the home of open source development. We have Visual Studio, which is the Visual Studio IDE plus VS Code that has 40 million active developers. And then I talked about Copilot Studio, which is our low-code extensible platform for both building new AI copilots as well as extending our first-party copilots. And that actually, in less than 1 year has 30,000 organizations, active organizations.

显然，开发人员是所有这一切的中心和核心。因此，当我们考虑我们的开发人员生态系统时，多年来，我们已经投资于为我们的开发人员提供最佳工具和最佳工具链。因此，我们拥有拥有 1 亿开发人员的 GitHub。它实际上是开源开发的家园。我们有 Visual Studio，这是 Visual Studio IDE 加上拥有 4000 万活跃开发人员的 VS Code。然后我谈到了 Copilot Studio，这是我们的低代码可扩展平台，用于构建新的 AI 副驾驶员以及扩展我们的第一方副驾驶员。实际上，在不到 1 年的时间里，已经有 30,000 个活跃组织。

大型科技企业相互交织，每个公司都有自己擅长的项目，从整体看，组合在一起是一个更强大的系统，一个比S&P500更加具体的系统，持续保持领先，并领导世界的其他部分。

So we have this full range of the tool chain for developers. And then actually, we are announcing, I think, about 3 minutes ago, new enhancements for GitHub Copilot for Azure, which is allowing developers to use natural language to then be able to code in GitHub Copilot and then use Azure Resource Manager to actually then deploy directly into Azure. So connecting our large 100 million wide ecosystem of developers to build an AI solution and then deploy that directly into Azure. So enhancements we're bringing, also the Visual Studio AI toolkit. So bringing the AI development into our already existing developer base and the tools, DevSecOps tools that they use, the coding tools that they use. So it's a continued investment for us.

所以我们为开发人员提供了完整的工具链。实际上，我认为大约 3 分钟前，我们宣布了 GitHub Copilot for Azure 的新增强功能，这使开发人员可以使用自然语言编码在 GitHub Copilot 中，并使用 Azure 资源管理器直接部署到 Azure。将我们庞大的一亿开发人员生态系统连接起来，构建 AI 解决方案，然后直接部署到 Azure。我们还带来了增强功能，还有 Visual Studio AI 工具包。将 AI 开发引入我们已经存在的开发人员基础和工具，DevSecOps 工具和编码工具。这对我们来说是持续的投资。

Mark Murphy 马克·墨菲

It's moving rapidly. Alysa, in closing, as you think about the year ahead, what are you most excited about?

它正在迅速移动。艾莉莎，在结束时，当你考虑未来一年，你最期待的是什么？

Alysa Taylor 艾莉莎·泰勒

I think we've talked a lot about the innovation across the portfolio but ultimately, it comes down to what our industry is doing with it, what are organizations being able to innovate. And I think being in technology right now, we're seeing the adoption of AI services actually happening faster than cloud computing or smartphone adoption. And so it's really an incredible pace. And I think the thing I get most excited about is a lot of this, we talk at the organizational level but there's a human element to it. We look at developers that are more satisfied with their work using GitHub Copilot than they have ever been. And you see individual knowledge workers being more productive, some of the mundane tasks being taken out.

我认为我们已经谈了很多关于整个产品组合的创新，但归根结底，关键在于我们的行业如何利用它，组织能够创新到什么程度。我认为目前处于科技领域，我们看到人工智能服务的采用速度实际上比云计算或智能手机采用速度更快。因此，这真的是一个令人难以置信的速度。我最兴奋的事情是，我们谈论的很多内容是在组织层面，但其中也有人的因素。我们看到开发人员使用 GitHub Copilot 比以往任何时候都更满意他们的工作。你会看到个人知识工作者更加高效，一些单调的任务被消除了。

So it's a unique time to see technology innovation at a pace we've never seen and then actually see human satisfaction go up. So it's a really, really unique time in the industry.

所以这是一个独特的时刻，我们看到技术创新的速度是我们从未见过的，而且实际上看到人类的满足感提高。所以这是行业中一个非常非常独特的时刻。

Mark Murphy 马克·墨菲

The pace and the scale and the linkage back to the mission of the company is really incredible to behold at this moment. Alysa, I cannot thank you enough for taking the time to be here with us.

公司的步伐、规模和与公司使命的联系真的令人难以置信。艾莉莎，我无法感谢你足够花时间和我们在一起。

Alysa Taylor 艾莉莎·泰勒

Thank you. 谢谢。

2024-05-21 Microsoft Corporation (MSFT) J.P. Morgan's 52nd Annual Global Technology, Media and Communications Conference (Transcript)

2024-05-21 Microsoft Corporation (MSFT) J.P. Morgan's 52nd Annual Global Technology, Media and Communications Conference (Transcript)

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