Microsoft Corporation (MSFT) Earnings Call Transcript & Summary

Hey, welcome to our next session. I'm really happy, Rajesh. And the one thing when you have a speaker from Microsoft, it's always like you're such a big organization. And it's a positive thinking, always like, well done, and it's amazing...

Despite of the thing you have [ big elephant skin ] dance.

Yes. Maybe to get everyone on the same page, talk a little bit about yourself and your role in Microsoft.

Yes. I lead the Experiences and Devices. So that would have Microsoft 365 Office and Teams, Windows and Surface and then also our search and our browser efforts.

And so if you think about the last 12 months, it's been like a crazy journey in terms of new innovation, et cetera, like that. Can you talk a little bit like how did you experience, like -- how was this -- the last 12 months for you guys -- for you, like, and kind of funding it as well?

Yes. I mean, first of all, let me just say as you all know the last 12 months have been as exciting a time as any. I've been in Microsoft a long time, I came when it was graphical user interfaces and we thought that was a big deal, internet, mobile, I mean. This last 12 months, I actually feel generative AI is as big a technology shift, if not bigger than any or all of them put together. And so for us, in the last 12 months, we got a lot done when I reflect back. So GPT-4 has now been out for in production for about a year. So we -- In Jan we had APIs and Azure for that with all the safety and trust that our enterprise customers expect. In February, we took the large. We put that in our search in a browser because we do think this represented a new way for people to find information to reason over staff. In March, we took a step back and we felt about what about productivity, how would these generative AI, the power of these things effect or impact the way work is done. So in June, we brought a private review of that for a lot of our enterprise customers saying it's been great to be with them on this journey. In November, we launched the Microsoft 365 Copilot. But I -- when I reflect back, of course, GitHub Copilot was the first one that we went after because it was very clear that we could help the developers be incredibly productive. So we have that. In the last few months, we've also taken a look at how business processes and business workflows can be rethought in the world of generative AI, where you take systems of engagement and you take natural language and natural language interface and how you bring -- whether it be a sales person's workflow, a service provider's workflows, whether it be a doctor's workflow. So we build Copilot for those things. We've thought about how to extend the Copilot to allow customers and developers to expand these Copilots with specific information. So a lot's happened. The thing I'll just tell you is, a lot of this technology shift also intersected with the work that we have been doing anyway. On Microsoft 365 post-COVID we've spent a lot of -- we saw a lot of customer engagement intensity and that populated what we call the Microsoft Graph, which represents the customers get most important database, I think, which is the projects, who knows what in terms of knowledge, peoples calendar, e-mail documents with the right permissioning. So this is where the generative AI reasoning engine meets the Microsoft Graph to provide generative AI in the flow of work. So -- but still it's very early. Very, very exciting to see the entire company rally to this.

And you guys must be thinking a little bit more ahead, like, and I just wanted to ask, like, slightly more bigger picture, like, if you think now generative AI, what it can do and you have more insight than us like how does -- how would you think the way we work will change over time like going forward? Is there like a glimpse that you could give us there?

Yes, look, I'm not a soothsayer on this stuff, but let me just share what we are already seeing with customers. So when we did the Early Adopter Program in June with the Microsoft 365 Copilot, we had many hundreds of customers jump on the opportunity because they see the same thing. And 100 to 200 of those we've been really we went really deep. They invited Microsoft to go deep with them and analyze the impact on productivity. So we looked at 3 dimensions of productivity, one, is efficiency. Just how quickly can you get the job done? Or how much more can you go do? But we balance that by taking a look at both quality of work. So it's not just enough if AI Assistant doing something quickly. I mean what is the quality of the output in terms of accuracy, in terms of up leveling the skill level. And the third thing we looked at was effort, how much effort, how much grind did we take out? And the results have been really impressive, and we are going to publish all of this in the Work Trend Index, I think, in the coming days. But about 68% or 72% of the people felt it made them more efficient. It improved the quality of work. They felt they had to put less effort and so they had more time to do what is innately human which is more creative, more reflective work, more collaboration. And 70% of them would say, of course, the Copilot makes me more -- generative AI makes me more productive. But what's also telling is that more than 55% of people feel that AI makes them more creative. So this is today here and now. Now you asked a question, how do I see this move forward? I think workflows are going to be reimagined. So today, what it does is the Copilot or AI helps you in your existing workflows. But when a lot of the grind -- and let me just take step back in the Copilot, our vision of the Copilot is not an autopilot. It works on your agency. It works with your permissions, it works with your context. So today it is helping you in your existing workflows but because it's helping everyone, how can entire group's workflows change the reimagine and we are starting to see that, both in my team and with customers. Just one -- I want to leave you with one thought. So far AI, prior to generative AI, it was as if the human beings did all the work and AI was the editor, it would show in autocomplete grammar check, spellcheck. But it's flipped. AI is doing the work. They're doing the first draft. They're doing the summarization and the human beings are now editors. So it gone from AI big editors to now where it should be, which is the human beings are the editors and the AI is the assistant.

And Rajesh, do you have like on the reimagining workflows, et cetera. Do you have examples to make it slightly more tangible to us, like, I hear you, but I don't hear you. You know what I mean.

Yes. Yes. It's -- so let me give you just a couple of examples. Maybe a couple of customer examples and may be one from my own team. So one of the customers what they did was -- so when people started using the Copilot, the first thing they tried to figure out, hey, how should we go do this. They ask us, should I distribute a few seats to this across different departments or should we go all in, in one department and based on our work with them we tell them going into one function because we find taking a peer group and giving them all copilots helps reimagine workflows. So this customer, what they did was they give it their service department. And they're an operational company. And so they had very complicated processes for long running incidence because hundreds of people -- this incidence would be day long, this was the core of what the company does and that they had a very complicated overhead of how -- when people work 4-hour shift in the incident and the next crew came in, who was going to transfer the knowledge, how are they going to come up to speed and so on? So they reimaged this -- reimagined the entire workflow in the context of Copilot in the meeting. So when a new person would come in the Copilot, which would just summarize, they could just be in, hey, can you summarize what happened in the last 6 minutes, which ones apply to this specific function. So they were able to get rid of a lot of the manual overhead, the human overhead of transferring knowledge between shifts and so on. That as an example, just reimagining the workflow. We've another drug research company where there is a corpus of lot of data around this clinical trials that research and -- but that's in a different system. And so -- but what they did was they extended the Copilot. So in the Copilot, people could summarize quickly when they came in, what were the latest findings from the last week from their colleagues in the context of all the e-mails that have come in around that, all the chats, documents, plus what's in the system of record. In my own team, this saw as the super interesting thing, if a product manager wants to go incubate an idea the process used to be, he or she would come up with an idea and the product management team should go lobby for some resources in the engineering team and design team so they could go incubate. But now she just uses the Copilot, so this product manager early in career, she had this incubation idea. She just used the Copilot to generate. She doesn't know coding. She's not a designer. She had an idea. She used the Copilot to create a perfectly workable mobile app to incubate a bunch of stuff, got to try a lot of different things. And then once the idea [indiscernible] she brought enough to the core teams to actually then go implement it. And so this has saved that specific scenario months of just times of coordinating people. So I think what you will see is workflows are going to have to reimagine in addition augmenting the human ingenuity and creativity and taking the grind out.

Yes. And then -- and maybe I should have started actually. If you think about Copilot and the productivity gains and you started on GitHub on the Copilot side and now we had like the first guys using -- leasing it on the Office side, like, what are you seeing in terms of productivity gains and changes to work behavior coming out of that one.

So GitHub it's the -- the first thing the GitHub Copilot in many ways have blazed the trail for us in the design language. So we spent a lot of time about how should the AI instantiated for teams and users and organizations. I mean it's no secret that the AI can get somethings wrong. Of course, when you're grounded and your information through a rack pattern or what have you, then it's -- you're using it as a reasoning engine and not a fact engine. But what the GitHub Copilot thought us was a design language, of it being a copilot not an autopilot. It is working on your behalf of making sure that humans were in the loop to actually accept the changes or to commit the actions. So that -- and then when we sort of GitHub Copilot land up to 50%, 55% efficiency for developers, the other thing that we did was we created the Copilot Stack. What I mean by the Copilot Stack is, say you take a look at Microsoft 365 Copilot, it's not just the largest model, the most capable model, it's the fact that, that model is grounded with the Microsoft Graph with all your permissions and it's been brought into a copilot design language and user experiences that you use. So -- and then there needs to be extensible. So this entire thing is what we call the Copilot Stack. So all the copilots Microsoft have been built with the same orchestration, the same extensibility, same design. And we've made this thing available as an Azure service whether it would be the Azure AI Studio or for low-code, no-code with the Copilot Studio. So I think in many ways, GitHub Copilot has blazed the trail for us into this one design language.

And then what do you see in terms of the customers that have been working with the copilots and adoption curves there. And telling you where I'm coming from is like look if the productivity gains we're hearing is somewhere like [ 20%, 30% ], like 50%. And the people that are working with this are kind of more developers, white collar workers, they're expensive. So here you think, like, hang one like everyone should be rushing out getting this, like, what are things on their adoption that we kind of should consider there and like, the due kind of experience so far?

Yes. So great question. So it's clear that for end user, whether it's in the context of creation, summarization, first draft all of that pay off. I mean this is what we -- I was just talking about and what are you going to see in the Work Trend Index benefits of 50%, 60%, 70% in terms of meeting summarization and follow-up tasks about 80%, 85%. So it's clear value from the end user. For now let's start the IT admin and enterprises. They're also responsible for security, regulations, compliance and so on. So one of the things that we've done with the Microsoft 365 Copilot is actually built there to be enterprise grade. What I mean by that it understands things like conditional access. They have IT headed policy in place, which is for AMOs outside [ of this ZIP code ] and tries to access corporate data -- do not allow that access without another challenge to the authentication, the Copilot understands those policies. So things like all interactions to Copilot are discoverable for regulatory reasons or it generates an audit log. So we've done that work, but IT still has to go to the validation process of these things. So one of the pieces of feedback we got from IT early was, hey, we'd love them -- the notion of Copilot helping in meetings, but we don't want to create a Data Retention Policy where transcripts have to be retained to enable the Copilot. So we did the work. So you could now get the Copilot to perform in a meeting without creating a transcript that lasts beyond the meeting. So there is a bunch of IT evaluation and adoption, but we have an adoption guide at Microsoft -- adoption.microsoft.com for IT for those reasons. The other one that we hear from customers is how do I know the ROI. So we build a Copilot dashboard. So they can actually go take a look at the impact of the Copilot, they can join that with their custom data. So you can bring your HR's custom data, you can bring your sales performance custom data, join that with a Copilot data and see what the ROI is. So we've gone in and build that too. And then for the end users, we are having to really teach them a new way to do computing. All of have grown out key board, mouse, swipe gestures and so on. But now we are using prompts. What is a good prompt as a sales person, what's a good prompt in this organization in the manufacturing context. So we have that now in the Copilot labs. In the end of the day, the Copilot is going to go to a bit of the adoption cycle of any enterprise because we've got to work through risks. We've got lot work through compliance, procurement and make sure there is ROI but the time to value for the Copilot, I feel unlike anything that I've done in the M365 space in the last 10 years is much, much, much faster with the Microsoft Graph already exists. It's built to be enterprise grade and it shows up in flow of work.

With that like -- and you mentioned the Microsoft Graph alone and actually, I think on our side we pay not enough attention to that. Was that before and you just got lucky? Or like how did that play out?

It's super -- both I would say. So here's what happened. If you go back to the Office business, the Office business when we were on premises was a bunch of silos. So we didn't understand the user well. We understood the jobs. So with Exchange and Outlook, we understood the mail and calendaring job, with SharePoint and Office we understood what document management was. With [ Link ] at that time, we understood meetings and telephony, but they were all stuff pipes. When we moved to the cloud, we really got to be user-centered, that's what the Microsoft Graph is. We then -- and the reason we were stuff piped on premises was no customer could deploy all our products at the same time. So every silo had to be stand-alone and be able to exist even if the other pure silos were different versions. So in the cloud, now we had 1 data structure of customers. We were doing the job, keeping it up to date. So that's what allowed the graph to get created. Then what COVID did was it accelerated the use of Microsoft 365 with lots of intensity. The more use of Microsoft 365, the better your graft is. Now after COVID what we have now is tons of customers deeply immersed in Microsoft 365, the graph is held, it is remission, and now you get generative AI, you take the graph, you ground the generative AI to the graph context and you say, now bring it to the user experiences that people are using today, and that's what the Microsoft Copilot is and that's the differentiation of the Microsoft Copilot.

And then I wanted to shift gear a little bit. Like what does it mean on the way how you compute, like, first fundamentally, but then also, how do you do it in Microsoft at the moment because you have so much demand on your side on the Copilot, et cetera, but then you have all the external amount on Azure. So, like, who's getting the GPUs?

Yes. I mean -- first of all, I mean, look, this GPT-4 was built on Azure and we've been optimizing all layers of the stack for over a year now. That being said, to answer your question, you should think of it in a couple of ways. Of course, the customer when you come to Azure you can use not just the frontier models from open AI, but you can use models from Hugging Face, from Meta. We ordered -- we now have model as a service that we have announced where you can use Mistral's premium model, Cohere's model, J's model. So we can use a lots of models. But for the large model that we use in our first-party applications in Azure OpenAI API, we've done the work to leverage one implementation and one stack. So it's not that -- so I can optimize the usage of the GPUs across Bing, and Windows, and Office, and dynamics and the Azure OpenAI because we all use the same APIs. And if the third party is using our APIs, you can come and bring your model, you can use a [ plethora ] model. But if you use a large model, we are getting the leverage of the fact that we are all using the same APIs, same endpoint, and then of course underneath the cover, we are more clearly close partners with NVIDIA but of course AMD, and we have our own AI [ first ] silicon that we're bringing to the picture. So you should think about below the water line on the systems level work we've been optimizing for a long time for a year. We have silicon level to work and then above the water line, when you are hitting the large model we get to optimize the traffic across all our first bodies and anybody using the Azure OpenAI API.

Okay. Okay. Makes sense. And then I have investor relations getting a little bit nervous. But I try to ask like a number -- not a numbers question, but like more monetary question. If you think about -- you're creating with AI, a lot of value for the client. But if you look at your pricing, it's still like -- if you look at the $30 dollars per user, it's still like a very classic model of like curated price. I think it was like for the GitHub Copilot with like $9. I tell you, I talk to customers and they're like it's a steal because I got 20% productivity gains, and I'm taking $9 for a guy that cost me $150 and I'm getting 20% more out of them, like, give it to me. How do you think about that dynamic going forward? Like do you have to rethink or reimagine that? How do we think about that dynamic going forward? Could you have to rethink or reimagine that?

Well, I mean, let me just say early days yet. The thing that we believe is, take a look at Office. Before Office existed, Word was a business application, Excel was the business application. And what we did was, we said no, we want to democratize this for all the users. And so Microsoft Copilot, GitHub Copilot isn't about democratization of AI for all the users. So we basically said, hey, we want to take the core capabilities and what it used to be productivity, now AI enhanced productivity and want to take that to all the users. Just like we've done with Office, in addition to the per user, we have lots of other constructs that we have on top, whether it be E5 construct, whether it be Team's premium construct. So right now, really the way we are thinking about AI is let's bring it to every information worker, every first-line worker. We get to enhance the productivity and then I'm sure there will be lots of opportunities for us to add value and capture value.

Yes, yes, yes. And since you're kind of working on the experience side from Microsoft, how will I need to reimagine my UI going forward because the classic way, like, a good generative AI system should be more interactive, like, what's the work that you're doing here?

Super good question. For 35 years, computing hasn't changed in terms of the way. You are on the phone, you swipe, on a PC you use the keyboard and mouse and the operating system does a very basic job of abstracting away the hardware and allowing you to get to an application. That's going to change. And so the way we -- you will see us integrate the Copilot. For example, in Windows, and we think that the lines between the Shell, search, browser Copilot is going to blur because you enable something that understands your intent at a higher level with natural language. It can take a lot of the grunge out for you. The application relationship to the operating system, to the Copilot is going to evolve rapidly. And you will start to see us do this stuff of bringing the Copilot into Windows. It's going to be very exciting. But I do think it' a new paradigm for user experiences.

And that kind of gets me to the other questions around devices. Like you guys were always very good of showing your partners what's possible, like -- and actually I remember when your Surface came out, I bought it, I loved it and it was like, "Oh, this is cool" and then you kind of in a way you almost forced innovation on your partner side. This in a way -- this in a way like you need to kind of almost reimagining devices, et cetera, where -- I mean not that you announce something with me here, but like where are you in that thinking?

No, I do think this is natural user interface and things are multimodal by the way. This isn't just natural language but speech and video and all of that stuff. I do think our existing hardware form factors are going to evolve to accommodate this. And there will be new form factors.

Yes. Let's leave it like that.

Let's leave it like that. Yes.

That's right. Yes. Okay. The other thing is like -- shifting gear a little bit back to kind of adoption -- client adoption that we see on the Copilot side, like, what's your thinking in terms of the speed of adoption you can support with a given shortage of GPUs, like, how do you see that playing out? Do you give like some to every client -- like limited amount to every client? Is it like 1 client gets a lot, so you learn a lot more. How do you think that will play out in the field?

We are open for business now with customers on Copilot. It's really like the earlier conversation I was having where the customers are going through the evaluation of the security policies, governance, procurement and so on. I feel confident in our ability to optimize across all our first-party usage and third-party usage. I mean we've been at this for a while. We know how to go optimize capacity.

Okay. Yes, yes. So there wouldn't be like -- so if Barclays says, like, we are -- I think we're a big team shop for you guys. If we say we want Copilot for everyone it's not going to be like you could...

I'm happy to sign you up right after this.

Yes, it's on me. Yes.

You can talk to our IT and tell them we are an enterprise grade, who we are.

Yes, yes, yes. And the last question for me is like how do I think about you guys working with partners because one is you've very Microsoft-centric view. But on the other hand, like, it's not just view, there's like a whole broader world out there. Like, how do you see that evolving for you guys in terms of working with our people, bringing up people into the equation for going forward?

Super good question. I mean, again, back from the days that Bill used to run the company, I mean, I remember being in meetings with him. He would take incredible pride that for every dollar that Microsoft would create, we would create $3 in the ecosystem. So we are really a platform company and not just at the Azure layer but even at the M365 layer and so already with Teams we have more than 2,000 ISVs and countless other line of business applications that extend Teams. And you will see the same platform sensibility. I talked about in Build and in Ignite. And in this coming year, Build will talk about how developers -- both line of business developers inside of organizations as well as ISVs can augment the Copilot with their custom logic and their interactivity because now all productivity is so human in all its various ways. We are not going to build all the most interesting applications. And so we want the Copilot -- Microsoft 365 Copilot is a platform, and that is we are working. I don't know if you had a chance to take a look, Raimo, at Ignite from 3 weeks ago which show lots and lots of customers and partners actually extending the Microsoft Copilot.

Yes. I think, yes, and then yes. It's -- I mean the conference is really exciting. And it's like -- it's really nice to see all the innovation coming out of there. So all right. It's 58 seconds and I'm German, so I have to be on time. Thanks for joining. I really enjoyed our conversation.

Thank you Raimo. Thank you very much. Thank you, everyone. Thank you. Bye.