How Microsoft is Fixing the Biggest AI Agent Problem

Welcome to the Next Wave podcast. I'm Matt Wolf and today I'm joined by Kevin Scott, the Chief Technology Officer over at Microsoft. Kevin has been at the center of some of the most important technological shifts of the past decade, leading Microsoft's work on AI, cloud computing, and the partnerships that brought tools like OpenAI's models to millions of people around the world. In this conversation, we get into the big questions that everyone's asking about AI, like. Like, are we already at a point where the systems are too complex for humans to fully understand and how do we make sure that as they get smarter, they stay aligned with our goals? We'll even talk about the massive challenge of energy efficiency in AI, what Microsoft is doing to make these systems more sustainable and accessible, and even when we might see autonomous AI agents that can go off and complete entire projects while we sleep. Kevin also shares where he thinks AI is headed in the next year or two, what kinds of jobs and opportunities it might create, and why he believes the tools we're building today could unlock an explosion of creativity and entrepreneurship. It's a fascinating, wide ranging discussion with one of the most influential people in tech right now. So stick around because you won't want to miss this one.

All right, so I'll just go ahead and jump right in. My first question, do you think AI will get to a point where AI is so smart that humans truly don't understand what's going on under the hood?

Well, look, I think in certain ways we are, although I don't think that that's any different from some other really complicated systems that we built or like, you know, honestly a bunch of complicated phenomena that we don't quite understand. So, you know, in my mind, like, technology is always about, like, is there a path to being able to debug it when it doesn't behave the way that you want it to behave? And I think there are an increasingly good set of tools that we're developing to try to be able to characterize the performance of really complicated AI systems and to debug them when they aren't doing what you intended them to do. We've been talking for years and years and years about full stack developers or developers who can understand systems from top to bottom. And I think you still need that a lot. You will more successful as a developer in the age of agentic AI, like in this era where you're using AI to do significant parts of your software development job, if you understand that full system so that when it misbehaves, you're like okay. Like I'm going to punch down a level of abstraction and like go, you know, try to investigate what's going on here and then like, you know, if that doesn't do the trick, like another one and another one and like eventually getting all the way down to bare metal.

Right, right. So I mean my follow up question to that would be like, if AI gets to a point where it's like, you know, super intelligent, right. We get to that super intelligence phase where maybe it's writing code that, you know, humans haven't even figured out how to write this kind of code yet, how do we make sure that it sort of continues to stay aligned with our goals?

Yeah, I mean there is a tremendous amount of active research and development and engineering on alignment both at Microsoft and like a whole bunch of the companies that we partner with and you a whole bunch of the companies that we're big fans of and honestly a whole bunch of companies that we compete with. It's one of the things that I think all of us building this technology really want to make sure functions well. The same way that all of us have a universally high level of focus on security and classical software. No one wishes for anybody's software to be less secure or AI systems to be less safe or less responsible or less aligned than what we need them to be. So yeah, I do think that there's just a ton of activity there and I've even seen it over the past.

Three plus years as we've made really powerful AI available in APIs and as we've plumbed it through to products. I run Microsoft's Deployment Safety Board, which is this thing that.

Really makes sure that we rigorously test and investigate everything that doing to make sure that the things that we're launching adhere to Microsoft's published responsible AI standards. And like the sophistication of the tools that we use to do the job of the Deployment Safety Board has just increased exponentially over the past couple of years.

Right, right. Yeah, yeah, no, I love hearing that a lot of the sort of big tech companies are all sort of aligned on the same goal and they're all kind of working together even though they might be competitors. I wanted to ask about the whole energy efficiency kind of thing. I know there's some concern that with data centers and AI and all of that using up so much energy, what sort of things is Microsoft doing to sort of a, bring the energy usage down, but also B, kind of democratize AI so it can be more accessible to more people?

Yeah, well, look, I Think the core thing that all of us are doing and like it's been a particular focus at Microsoft over the past handful of years is AI demand has surged, is like you've got a bunch of goals that are aligned. So we just spend an extraordinary amount of energy trying to optimize the energy consuming parts of the system because like energy costs a lot and like we've made very specific, hard, sustainable energy commitments to the entire world that we intend to meet. And inside of those constraints, like you have to make sure that as the AI demand is growing that things are getting cheaper. Like that's the way to make them more accessible so that like you're just lowering the barrier of entry to people doing increasingly complicated things with the system and like you have to make them efficient so that you can run more AI computations on a fixed power budget or a fixed hardware budget or a fixed, you know, floor plan budget that you have inside of a data center, you know, and then I think outside of that we are, we're doing a couple of other things that are interesting. So one is like we've got this constant background threat running, trying to figure out whether or not there are these sort of disruptive efficiency breakthroughs. So like the Transformer for instance, which is the foundational technology that all of these modern large language model AIs are built on now was like one way to think about it is like it was a disruptive increase in the capability of the systems, but it was also a disruptive increase in the efficiency of systems. And so we're doing a bunch of basic computer science AI research on trying to find what those next disruptive things might be. It's research so you can't predict when the disruption might happen. But like we feel good about the amount of energy we're investing in that. And again, it's one of those things where incentives are all aligned. Like we just desperately, for all sorts of reasons, like need those disruptions to happen, whether you know, they're our research or you know, some research that emerges, you know, elsewhere that's publicly available. And then the other thing too that we're doing is we're trying to help the electric power industry find new sustainable, scalable sources of production so that you don't have to live in a world where you act as if there's energy scarcity. Because AI aside, you actually do want a world where there's energy abundance and sustainable energy abundance, lots of good things. Just think about some of these problems that we have in the world right now. So there Are major.

Social unrest happening in parts of the world right now because we have water scarcity. And there are all sorts of ways that you could solve water scarcity problems if for instance, desalinization were cheaper to do. But it is an energy intensive process right now, and so it's too expensive and too unsustainable to do. If all of a sudden you had an energy breakthrough where energy begin became an order of magnitude cheaper and orders of magnitude more abundant, then you could go solve problems like that.

Right, right. Yeah. I got an opportunity to go see the Applied Science Lab yesterday and talk to some of them and one of the things they were saying is that the new MPU units are actually quite a bit less expensive to develop than GPU units, making it so that maybe you don't have the best graphic cards to play Cyberpunk 2077. But you'll be able to have AI, right?

Yeah, yeah. And it's an important thing to understand that every year.

Over the past five or six years as we've been building these transformer based AI systems.

Every couple of years or so you get a new hardware generation that's given you about a 2x price performance improvement, which is enormous. It's just really, really extraordinary. But on top of that, you're getting a set of even greater efficiency improvements that are happening every single year in the software layer that's improving energy efficiency and capital efficiency in these systems. And it looks plus or minus 10x a year. And so I was at this event last night where I said, when I was a young developer, because I'm an old fart now, all I had was this crappy old Moore's Law, which again was this extraordinary exponential. But it's not the same exponential that we're on right now. And so like you just got to remember like, the hardware is getting better and the software is getting better at an incredible clip, which means like all this AI is going to get cheaper, faster, like more capable and more energy efficient at the same time.

Right.

I want to, I want to shift.

Over to Agents because it seems like agents was a big piece of, you know, what you've been talking about. How far off do you think we are to being able to sort of tell an agent before I go to bed, redesign my website, code it up and have it live for me, and then I wake up the next morning and I've got everything completed? How far off is that?

Yeah, I don't think that that is actually as far off as it might sound. It kind of sounds like A science fictional scenario, especially like if you rewind to last year's build. But like, I think it's closer than you might imagine. Like I've got a dad at my kid's school, like every school event. Like we're friends and he comes up to me and he's like, oh my God, you can't believe the crap that I'm. He runs his own business and he's a one man shop with contractors that he's hired over the years to go help him build apps. And he still has these contractors, but now he has this other thing called software development agents that are helping him do all of this stuff. And he's using everything, just a super sophisticated user. He uses our stuff, he uses OpenAI stuff, he uses anthropic stuff, he uses Google stuff. And the rate at which he is able to do his work is really incredible.

Right?

I mean like really, you know, so you just described a thing that's gonna sort of asynchronously and autonomously go off and like do a bunch of work while I'm sleeping. He's almost there right now. And like, you know, I had my kids end of year review of some social entrepreneurship work that they were doing. And like my daughter was in this group with two other girls, like building this app that trying to get people like kids to take calcium supplements. And completely unbeknownst to me, like she was off using these tools to go build an app and like the app, like, if I remember the first mobile apps that I wrote in 2008, it's probably better than the first one that I wrote by a mile. And like, my daughter has never taken a programming course. She's had a little bit of programming exposure in like a multidisciplinary design class that she took. She's in 10th grade, 16 years old. And yeah, she did all of this. And like, it also didn't ever occur to her to like, like tell me what she was doing or ask for dad's help, which is rich. So yeah, look, I think we're probably closer than most people think to that scenario you just described.

Right? Right.

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One of the things that you mentioned both last night and again today on stage was memory sort of being a bit of a bottleneck. Why is it a bottleneck and what needs to be accomplished to sort of get past the memory issues?

Well, look, you know, I think so, like, first, let me describe why it's a problem. So, you know, like, you and I are interacting, right?

Even though both you and I are busy, we will have a recollection of this interaction that we've had so that if we ever are interacting again, we can recall this conversation and what we were talking about. And we've just sort of got a foundation between the two of us for future interactions. If you're thinking about agents as things that you can delegate things to and that you can think of as collaborators, that memory is just going to be a really foundationally important part of the user experience of these things. And it's also pretty important from, again, an efficiency point of view. So right now, a lot of the times when you're using an agent because memory isn't as good as it needs to be, it just hasn't remembered anything about your previous transaction, and it doesn't even remember much about what it has done itself in taking a sequence of actions to go solve a problem, which is kind of crazy. And so you spend a bunch of time rebuilding state inside of these agents that you shouldn't have to if memory was functioning well. And so part of that is just it's kind of a constrained thing. So the way that these systems work is you have context windows. So like a prompt, you can only put so many tokens or words of instruction or information or context like into this window that you then feed to the inference system to get a response back. And the response also consumes space inside of the context window. And if you're iterating over the course of a session that consumes space in a context window. And these context windows are sort of bound because there's some ways that you can implement context and inference where the inference is quadratic in the length of the context windows, which means it gets not just expensive, you know, with one little increment per additional token, like it gets a lot more expensive per additional token of processing that you're doing. So one of the things that we've had to invent over the past handful of years are more efficient ways to use that context, more efficient attention algorithms so that you don't necessarily, for each part of the inference calculation, you have to look at the entire context of information that's there and building things that are honestly a little bit more efficient than retrieval augmented generation, which was sort of the way that we had before to take contextually or semantically relevant things to the task at hand and pulling them into those context windows for processing. So it's just been kind of a, hey, it was one of those things that was too expensive and we needed to go do some efficiency work to make it better. And also some really, if you think about how your memory and my memory work.

We have pretty good precision and recall, but it's not pretty good one shot precision and recall. So if you ask me to go remember something that happened five years ago, I am not going to be able to just like that, tell you exactly what that recollection is from. But I have a way to go get to it. It's like, okay, I kind of remember that I can put it in context. That context helps me go out to my sources of information, just kind of search around and like. And I have a way to like have really broad recall and a way to like take imprecise recollections and make them precise. And so like that's also what we need to do inside of these memory systems. So like memory in an agent isn't a database lookup. It's like an iterative process that the agent may need to do to get the right piece of information very precisely.

Right, right. I'm curious if we look ahead, let's say like a year or two from now.

Yeah.

What, what sort of things do you think an agent will be able to do that like we all do on like a daily basis right now? Is there anything you think agents are just going to take off our plate and people don't even realize agents are going to take that off our plate?

Yeah, look, I think there are things that I hope for that probably are hard, not for technical reasons, but for other reasons. And then there are, you know, certainly a whole bunch of knowledge worker toil that I hope gets resolved. And then there are things where I think they're Just sort of hard. So, like, the hard things are like the embodied AI thing. So, like, there's just a bunch of stuff that all of us have to do in the physical world, like, you know, do your laundry and, you know, like, I can't get my kids to, like, put the dishes, you know, take them from the sink and put them in the dishwasher. Like, it just really irritates me. I wish I had a robot to do that. Unfortunately, I think those are like, like really hard technological problems where we're probably not on as fast a path to getting those embodied AI problems solved as we are with some of these cognitive problems. You know, I think a whole bunch of stuff like the scenario you described is somewhat likely to happen in the next year. Like, I have a whole bunch of things that I wish an agent could go do for me while I slept, where I could wake up in the morning and, you know, it's already started writing responses to, you know, emails that I need to, like, get right to first thing in the morning where, you know, maybe someone else's agent has, like, talked to my agent to, you know, you know, try to get.

Quick responses to things that, you know, the other person needs where, you know, they no longer have to wait for me because, like, I'm blocking them. So, like, I think a bunch of this asynchronous stuff is actually gonna start happening pretty quickly. And like, the reason it hasn't happened so far is because.

You just need agents when they're taking action. Like, they have to be really precise. And like, so far we've had kind of imprecise memory and action taking actually has gotten dramatically better over the past year with these reasoning models. But, like, we need all of this MCP plumbing that I've been talking about to happen so that agents can and do this communication to other agents and systems that needs to be done. And then they have stuff that I hope for, and they're just choices that we make as society, whether or not we get them. They're not technical barriers. So I wish that medical diagnostics were more available to people everywhere over the next year, that folks like my mom, who lives in rural central Virginia, and she may not have access to the same diagnostic medicine that I have access to living in Silicon Valley.

I wish these AI systems, which are already right now plenty good enough to help give a real boost to people living in rural central Virginia where my mom lives. I wish we would get to more adoption of that. But again, that's not a technology problem, that's a set of choices that we're making.

Right. So I have two last questions for you. So with sort of every tech leap, right, new jobs are created, some jobs disappear. I'm curious, what sort of new jobs do you think will be created as a result of AI?

Well, I have.

What I think, unfortunately, is a contrarian opinion about software development. I think people sometimes think that all of this leverage that programmers are getting with, with their software development tools powered by AI means that there's going to be fewer programming jobs. I think there are actually going to be more programming jobs. Yeah. I look at that app that my daughter did. The AI tools basically turned her into a programmer. And she doesn't want to be a computer scientist and she doesn't have time to make herself a good programmer. She wants to be a biologist. And so I think we're going to have a ton of people who are effectively doing software development in places where software development couldn't happen before at all because there just aren't enough developers in the world. And I think that we're going to so lower the barrier to entry to application creation that you're just going to have a lot more folks wanting to create a lot more apps. And this is, by the way, what has happened every time in the history of software that we have made a big leap forward in leverage, like building tools for programmers that make them more productive has always resulted in us needing more programmers. So that's certainly a thing that I think we're going to need more of. And then it's sort of hard to predict what the other jobs are going to be. I do think that the shape of the jobs that we're going to need is the really, really blindingly important thing is going to be people who are really sensitive to the needs of their fellow human beings who are sort of thinking about like, okay, what are long term needs versus short term opportunities?

How do I do things that are legitimately and seriously in service to what society needs and what the people around me need and the problems they're struggling with and the solutions that they ought to be looking for that they might not be because they don't understand what the art of the possible is with this new disruptive technology. And so I think we're going to just need all sorts of new product makers.

Who have problems and they don't even realize yet what capability they have available to them to go solve those problems. And that's just going to hopefully unlock a ton of entrepreneurship and creativity that is going to benefit a ton of people. I Think.

Yeah. Yeah. Amazing. So my last question is kind of a two parter. What excites you most about what you can do with AI today? And what excites you most about what we'll be able to do with AI in the near future?

Yeah, I'm just really personally like, this is me, Kevin Microsoft. I am.

Such a curious person by nature. Like, I'm in, I got just a near schizophrenic array of hobbies that I'm working on. Like right now. Like literally I was sitting on stage waiting to go on for my bill keynote and I was texting back and forth with a bunch of friends about a hikadashi guru Japanese ceramics kiln that we're designing the second version of. And you know, one of the interest things about.

That process is like we're using AI to help with it to do a whole bunch of things. So like a lot of like a higadashi guru is like a particular type of firing process that was very common in Japan in the 16th century and earlier. And like there just isn't a lot of documentation about like how you design one of these things and like, you know what all the other considerations are in like firing ceramic objects in them. And so these deep research agents can like look at documents that aren't in your language. They can like help fill in gaps that aren't, you know, that, that are there in the published literature. And then like you can use them for doing sort of weird scientific things. Like we're trying to figure out like where to place the burner port on this kiln where what you want is like a, a vortex, a stably forming vortex of fire. And it's rectangular. And so like you gotta figure out like where the, you know, where the eddy currents are gonna be, like with this hot gas inside of the kiln. And like we're asking the system to help us figure out how to do a simulation of this combustion process.

And like, there's just no way I would have time to go into this depth on this thing, given I've got a day job without these deep research assistants. So that's just wild. It's wild that in 2025 we're already here.

And it's just sort of a great and awesome thing for a curious person to have these tools. So over the next year, the thing that I'm really hoping for is that we will have systems that are reliably taking action on our behalf, that we sort of have transitioned from this mode of.

You know, you are having to sit down with these tools synchronously and like, have a session with them. To, like, you really do say, go sort this out for me. And like, you can have a week to go do it. And like, I'm not asking you to burn a week worth of GPU time, but, like, you can make requests into systems and like, wait for the response to come back. You can, you know, sort of interact with other people and other agents on my behalf and like, like, wait for those responses and then come back to me asynchronously. Like, I get a signal when, like, you got something to show me and like, you know, we can collaborate and iterate a little bit like that. Like, I think, you know, just getting into that mode where that's what the UX of these things look like, I think is just gonna be super interesting and fantastic and we're gonna discover a bunch of problems that we'll then have to go solve. And like, that's what my talk will probably be like next year at Build is like. Like, it will be immediately obvious, like, we've got a half a dozen, like, complicated things about what I just described that we're going to have to go sort out. And like, complicated problems to go sort out is my favorite thing. Yeah.

Yeah. Amazing. Well, thank you so much for spending the time with me today. I really, really appreciate it.

Yeah, thank you. It was a pleasure chatting with you.

Well, that's it for today's episode of the Next Wave podcast. I really hope you enjoyed this conversation with Kevin Scott as much as I did. A couple of things really stood out to me. First, how much progress is being made around alignment and safety and how seriously Microsoft is treating those challenges. And second, Kevin's point that AI isn't just about efficiency, it's also opening the door for more people to build, create and solve problems, even if they've never written a line of code before. The big takeaway here is that AI isn't just a technical shift, it's a societal one. And that the people and companies who lean into curiosity, efficiency and response responsibility are the ones who will thrive in this next wave. So a huge thank you again to Kevin from Microsoft for joining me and sharing his perspective. If you enjoyed this conversation, make sure to subscribe to the Next wave over on YouTube, Spotify, Apple Podcasts, or wherever you listen to podcasts. And if you found this episode valuable, please share it with a friend. Maybe someone who's trying to wrap their head around the future of AI. Thanks so much for listening, and hopefully I'll see you in the next one.

Bye.

Bye.