Executive Vice President of AI & Chief Technology Officer, Microsoft
Systems & AI | Kevin Scott, Chief Technology Officer, Microsoft
Kevin Scott, Chief Technology Officer at @Microsoft , explores when AI runs everywhere, systems-not models-decide what’s possible.
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About Kevin Scott
Kevin Scott, Microsoft's Executive Vice President of AI and Chief Technology Officer, participated in a live conversation on June 1, 2026, in San Francisco, where he discussed what he described as a gap between AI hype and real-world impact. Scott argued that AI models are often more capable than the tasks they are used for in practice, a concept he referred to as "capability overhang." He stated that deployment challenges, such as legacy infrastructure, regulatory constraints, and organizational barriers, mean that scaling up AI models alone will not solve these issues. Scott said that "there is no silver bullet" and that addressing these problems will require "a bunch of technical work, a bunch of social work, societal work, a bunch of organizational work." In earlier remarks, Scott emphasized the need for AI to function as a platform that others can use to build businesses and solve problems, rather than being controlled by a small number of companies. He also reiterated Microsoft's position on facial recognition, stating that the company believes there are uses to which it should not be put and that government regulation is needed to define boundaries. Scott expressed optimism about the future of technology, saying he is trying to encourage others to be hopeful about applying technology to solve challenging problems.
Transcript (54 segments)
I
Interviewer0:00
I don't think I've shared the story too many times, but you're probably the first person, like seven years ago, I was asking what do you think is coming down the pike and you just shook your head. I still remember this moment so distinctly and you said everything is going to get turned upside down with AI and people have no idea. And that's because you were working on a lot of this stuff back then and you had a front row seat to it. Tell us a little bit about where we are right now and how you're seeing this thing kind of lay out and did you think this, are you surprised with any of it?
K
Kevin Scott0:39
Yeah, I mean, it's hard not to be surprised. I think things have accelerated probably faster than I was expecting. So, like none of this stuff is surprising, like what is happening. I think it was relatively clear even those like seven years ago when we were having these conversations that scaling laws were going to work and that the models and the systems that we built around them were going to be very, very powerful in a general sense and they were going to behave like a platform and people were going to be able to build lots of exciting things on top of them. So, it was just a question of how fast is all of this stuff going to happen? And I think it's happening faster than I would have predicted, but what's happening doesn't seem too terribly surprising. And I think the things that are ahead of us are, I'm sure you all have been talking about this already. We really are not yet at the point of diminishing marginal return on the capability increases in the platform infrastructure that is powering all of this change.
I
Interviewer1:54
But at the same time there's a capabilities overhang where people are using...
K
Kevin Scott1:57
Oh, 100%, yeah. I've been like beating the drum about this inside and outside of Microsoft for a few years. The models are already way more powerful than what people are using them for. I think the closest glimpse that you're seeing to true, full utilization of model capability is in coding and it's an absolute frenzy right now in the state of software development. You have very, very senior people and the best coders you've ever met in your life who are just completely overwhelmed trying to keep up with the rate of progress that's happening right now.
I
Interviewer2:33
Simulate that out. What happens? Does a software engineering job, how does it change in the next...
K
Kevin Scott2:39
Well, I think it becomes more of what a good software engineer, good software engineers have always done. It's less about the mechanics of what characters are coming off of your keyboard and going into a text editor and more about do you really understand what it is that you're building and why and how is value being created and how can you move faster and recognizing what value you're creating for people and just sort of accelerating that rate of improvement. That's what the best software engineers that you and I have always worked with have been able to do. And I think that is still going to be very true. It's critically true right now because you can produce a lot of code with these coding agents right now. There's nothing to say that it's good code. So you can just sort of spray a bunch...
I
Interviewer3:30
Code review has become the bottleneck.
K
Kevin Scott3:31
Yeah. No, review is a bottleneck and people need to really make sure that they're not getting confused between activity and progress. I mean, and it's been the hard thing. I've been managing software engineering teams for a very long, like 25 years now I think. And over that 25 years, I don't think any of us have ever really been able to say what constitutes actual engineering productivity. And there's some things that you can obviously speed up a lot with AI systems and you can really see it with startups. So there are these startups that are getting funded with an order of magnitude less money than they would have just two years ago with very small teams doing crazy amounts of work and moving very quickly and that's super, super exciting. But it really is going to put the focus on choice and taste and understanding of your problem domain, your customer and what you're trying to do. That's the really, critically important thing now.
I
Interviewer4:35
And do you think the roles in the software development life cycle will fuse and change and merge?
K
Kevin Scott4:44
Yeah. Look, and it's always changed. What I hope, honestly, this is Kevin's opinion, not Microsoft's opinion. But yeah, I've been grumpy, like an old fart for a whole bunch of years now that we have turned computer science education into vocational education. Students go get a CS degree because they want to learn how to be programmers and what I want people to go back to is learning how to be computer scientists.
I
Interviewer5:15
Be thinkers about that.
K
Kevin Scott5:16
Yeah. Can you think algorithmically? Do you know how to decompose problems? Do you know how to choose the right problem to go spend your time and energy on? Do you understand how the science of what you're doing fits into society and into the broader ecosystem of science that's happening around you? Do you possess the capability and the curiosity to punch down through the layers of abstraction to debug what's going on when the machine doesn't give you the result that you want? I mean, all of those things are the critically important things and I think we'll get back to a lot of that because a bunch of the vocational aspects of the software engineering job are just going to change so radically over the next handful of years that it's going to be unrecognizable.
I
Interviewer5:59
Now, you've spent a fair amount of time thinking about the future and the good and the bad. So, paint out two futures for us. The fundamentally optimistic one with AI and one where it doesn't go quite the way that we would like it to go. And then I want to talk about Microsoft and how you folks are kind of working with that.
K
Kevin Scott6:20
Yeah. So look, I think the optimistic story is probably the more likely one because it's going to be the necessary thing that has to happen. And so, it starts with a thing that doesn't sound optimistic, but I was chatting with a friend the other day who runs one of the premier educational institutions in Japan. And he said this thing sort of off-handed to me that took me aback and he was like, 'Yeah, this year is peak high school graduation in Japan.' So, Japan will graduate as many high school students as they're ever going to graduate in a single year this year. Which means from here on out fewer high school seniors graduating, fewer going to college and it goes down and there's nothing to do about it. Unless you do some crazy thing with immigration and there's just, by looking at what the birth rates are, that's just precipitously going to go down.
I
Interviewer7:13
Is it because of the demographics?
K
Kevin Scott7:15
It's just demographics.
I
Interviewer7:16
Just pure demographics in Japan.
K
Kevin Scott7:17
Pure, pure demographics. And so we know in abstract, the demographic data is super clear that Japan is in population decline. It's a little bit ahead of where other places are but China, Korea as well, a bunch of countries in Western Europe, the United States sans immigration would also be in population decline over the next handful of decades. And so you have this aging population.
I
Interviewer7:53
It's actually a much, much bigger issue than people have.
K
Kevin Scott7:56
Yeah. No. And you can even see it in the United States. There are these rural places like where my mom lives where the population is aging faster because the demographics are kind of unfavorable in rural parts of the country. And it really has these massive implications that we don't talk about all that much. One of the implications is if you don't have as many people to do the work and you have more people who are going to create new forms of work for society to take care of this elderly population that is growing very, very rapidly. Something has to happen with the very nature of how you get work done and what productivity looks like in order for you to maintain just the level of quality of life in society and what we think of as normal in society. And it doesn't happen for free. And so you have to have technological interventions. This is always the way that we've solved our productivity problems as a species. And so I think the optimistic case is like, thank god AI has come along when it has. That we actually have this and a handful of other technological things happening in the world right now that will give us at least a partial answer for what you do when over the next handful of decades you don't have the same labor dynamics as you've had over the whole course of recorded human history.
I
Interviewer9:22
Yeah. It's like a crazy thing that's happening that we just don't talk about all that much.
K
Kevin Scott9:28
And AI becomes that kind of unlock that you need otherwise... and look, it can't do everything and it's not a substitute for all of the things that human beings do. I don't think anybody here wants it to be. But it gives us an option for how do you go sort out some of these crazy problems that otherwise would be very zero sum and very, very challenging and destabilizing. And so I am optimistic that we can put our heads together, identify all of those things that feel zero sum, we need to turn them into non-zero sum problems and then go tackle them and have the good sense to have taste about how you put the technology into practice. So that's the optimistic case. The pessimistic case, I won't give you the one, you can just sort of pick your, there's a pallet of them right now.
I
Interviewer10:25
The Terminator happens.
K
Kevin Scott10:26
Yeah. Or many, many things. So my pessimistic scenario is that we get into some superficial mode about AI that instead of using it to go solve some of these super important problems with urgency, that we use it to just further distract ourselves and a bunch of superficial... I look at my kids, they're half and half. Half the time they're using AI in really super ambitious ways to do things with the biomedical engineering projects they're working on or whatever their technical fascination is and they're just way ahead of where I was when I was their age. And then the other half of the time they're using it to make pictures of green llamas with big butts that they paste someone's face on and it's like, okay, that's a good use of compute resource.
I
Interviewer11:20
That's a good use of compute resource.
K
Kevin Scott11:22
Yeah, right. My GPUs are on fire and this is why. So I hope that we can resist the temptation to make the whole narrative about AI is like, okay, well here's the new sensational thing that happened over the weekend or the new way that a bunch of powerful people are going to throw darts at each other and we can just make it more about what does society really need from this technology.
I
Interviewer11:53
How long have you been at Microsoft now?
K
Kevin Scott11:55
A long time. This is the beginning of my 10th year. So, it's the longest I've ever worked anywhere.
I
Interviewer12:02
And what's in your mind, what's the most misunderstood thing about Microsoft in the world?
K
Kevin Scott12:08
Well, look, I think, maybe this isn't misunderstood, but you have to understand about Microsoft that it's a platform company. We don't really think about doing anything unless it's building something that someone else can pick up and then build another thing on top of. It's wired into the DNA of the company, both for good and bad. It means that we're really good at some things and we're less good at others. And as a platform company with five decades of experience doing platforms hyper successfully sometimes and less so other times, we just have infinite patience for the messiness of what comes with technological transformations like the one we've got right now. A lot of patience. We're not waiting around to go do stuff until the ideal conditions exist for things to go maximally fast. We're going to jump in early on a bunch of things. We're going to get a bunch of things wrong. We'll get a bunch of things right. And we will deal with the world as it exists, not as we wish it might be. And you know this as well, you've spent your entire career doing enterprise software. It's a messy business sometimes. You have to go make the thing that the customer wants and needs and in some cases must have. It's not about changing someone's mind. It's about doing the thing that is necessary in a situation.
I
Interviewer13:55
And what's worse, what's the really bad thing about enterprise software? What's worse than having no customers? Having one customer. You can never turn something off. Like you can only turn them on.
K
Kevin Scott14:04
Yeah. So, but look, and at the same time that's an enormous privilege. If you sort of think about all the things that Cisco runs in the world and all of the things that Microsoft runs in the world, if you took all of those things away, it would just be a grinding halt. And so yeah, and I think you have to appreciate the privilege of all of that messy complexity, the obligation that you have to make sure that all of these things work and they're up and they're available and when they are not available that you go dig in and understand what went wrong and make it better so it doesn't happen again. And for me as an engineer, it feels great because this is what I've always been drawn to as a curious technical person is these platform problems, making tools for other people to go use to make things. It's even what I do in my hobbies.
I
Interviewer15:07
I know that.
K
Kevin Scott15:10
So it's why I've been there as long as I have. I just super enjoy the challenge of all of these messy platform problems.
I
Interviewer15:18
So if you were to think of what has been the most surprising thing for you, what are you most proud of the contribution you've made at Microsoft and then I want to talk a little bit about the hyperscaler business.
K
Kevin Scott15:30
Yeah, look, I am proud, maybe the proudest thing I've done is helping to recognize the shift in AI from being a very narrow specialist thing where the way that we all did machine learning 20 years ago, to the extent that we were doing machine learning at all, is you had a group of quantitative experts who were working on a very narrow problem like predicting whether someone was going to click an ad or not in a particular context. That's a problem I worked on for a while.
I
Interviewer16:12
Long time.
K
Kevin Scott16:13
And you would have your experts and your data related to that domain and you pick an algorithm and you go train a thing and you'd have your experimental pipeline and everything was sort of specific to that problem. And being able to recognize that, okay, well finally the promise of more general AI is about to be realized and then getting it through the partnership with OpenAI and a bunch of the things that we built together with them really out into the open.
I
Interviewer16:40
I don't know if everyone realizes that Kevin was the initial architect of the partnership with OpenAI as they were first getting started. So it was like without that partnership I don't think we would have actually in the world benefited from all the things that came after it.
K
Kevin Scott16:57
Yeah. And whether or not that's true, the thing that I'm especially proud of is I like a world where you actually have the capabilities of these platforms out in the open. Where we can have a conversation like this where it's not just one Silicon Valley company that is building this thing on their infrastructure with their smart people and then they decide what is going to get done with it and it's out there for anyone who can go sign up for an API key to go start building something on top of. And that democratization of powerful AI capability is a thing, whatever small role I had in that, that I'm proud of.
I
Interviewer17:43
Yeah. So on the hyperscaler side of the business and how long do you think we stay in this constrained environment of infrastructure? What happens there?
K
Kevin Scott17:53
Yeah, I don't know. I think it's going to be constrained for a while. I keep thinking we're about to pull out and then demand keeps exploding. And I look at what's coming capability-wise over the next 12 months or so and I just can't imagine with the capabilities that I know are coming online that there's going to be less demand for the product or for the capabilities that are coming. And so I think we're just looking at coding agents. The most ambitious teams at Microsoft right now who are fully using coding agents, where the thing that limits them is their available attention to manage the full complexity of what the agents are doing, cost about 150 grand a year in inference. And so a very tiny slice of even the community of software developers have that degree of access and ambition right now for the product. But they all could benefit from it. And then you could apply that to a whole bunch of other things as well. So I just don't see how the demand for inference is going to go down. And it's hard to imagine how you get into a state, just given the silicon situation, the hardware situation, how difficult it is to build data centers and deploy power and all the things that you want to do, how you get ahead of that anytime soon.
I
Interviewer19:34
And does Microsoft build their own silicon just because there's not enough being built by all the current manufacturers so you want to continue to do that or do you see yourself where Azure is going to be 99% and build on my own?
K
Kevin Scott19:45
Yeah. Look, this is the other, going back to your question earlier about a thing people don't understand about Microsoft. We also for 50 years now have only been successful because we have all of these partnerships that we're doing.
I
Interviewer20:00
We enjoy a great one with you.
K
Kevin Scott20:02
Yeah. So yes, we have our own chips and the chips are amazing but we also have gigantic fleets of Nvidia hardware and a gigantic fleet of AMD hardware and we have a huge amount of silicon diversity. Part of that's driven by what people want us to provide for them and part of it is driven by what we know about where our workloads are going and what we need to build to be maximally efficient. But at any point in time, we really, we built an infrastructure to manage the complexity of all of this stuff and whatever is most cost-efficient is the thing that we are going to go deploy at scale.
I
Interviewer20:48
Last question. What's the one thing about the human approach to technology that you wish were different?
K
Kevin Scott20:54
So, say that one more time.
I
Interviewer20:55
What's the one thing about how humans are approaching technology in this era right now that you wish were different?
K
Kevin Scott21:02
So I just want everybody to remember that the technology hasn't transformed into anything other than a tool. And as a tool it is there for us to do interesting things with. And so I don't think that there's any kind of inexorable trend line about the technology that sort of dictates one path versus another. It's all about choices of what we choose to do with the tool and how we prioritize things. And I think it's really, really important for us to focus on how we as individuals who are wielding the tool can do that in service of our fellow human beings. That's, I wish every day we could all go into our jobs and think about what we're about to do and think, is this serving my fellow human beings well or not? And if we did that we would be in a great place at the end of the day.
I
Interviewer22:07
Last time I'd asked you this question you had said it was a great answer you had given me as well which was I wish people didn't actually think about things as zero sum as we do. You know, it's very kind of zero sum. I have to, in order for me to win you have to lose. And I just don't think that's the way that the world's going to evolve.
K
Kevin Scott22:23
Yeah. And look, the zero sum problems are miserable. They're all about scarcity and constraints and winners and losers. And the thing that I think we really want from our technology is for it to take as many of those zero sum things that are real challenges for us and turn them into non-zero sum things. Like honestly, it's a reason to go do technology.
I
Interviewer22:53
Kevin, we should do this more often. Such a pleasure to see you.
K
Kevin Scott22:55
Yeah, pleasure to see you. Thank you.