I don't think, Kevin, I've shared the story too many times, but you're probably the first person. It's seven years ago. I was asking, like, 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 so 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, you know, lay out, did you think this are you surprised with any of it?
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? You know, I think it was relatively clear, even those, like, seven years ago when we were having these conversations that, like, you know, scaling laws were going to work and that, like, the models were and the systems that we built around them were going to be like very, very, powerful in a general sense. And they were going to behave like a platform and like people were going to be able to build lots of exciting things on top of them. So it was just a question of like, how fast is all of this stuff going to happen? I think it's happening faster than the new, yeah, that I would have predicted. But like, what's happening doesn't seem like too terribly surprising, you know, and I think the thing that things that are ahead of us are, you know, I'm sure you you all have been talking about this already. We really are not yet at the point of diminishing marginal return on, the like capability increases and the and the platform infrastructure that is powering all of this change. But at the same time, there's a capabilities overhang where people are using.
Yeah. Yeah. I've been like diatribe about this inside and outside of Microsoft for a few years. Like the models are already way more powerful than what people are using them for. I think the closest glimpse that you're seeing to, you know, like, true, full utilization of model capability is in coding and like, is an absolute frenzy right now in the state of software development. Like you have very, very senior people and, you know, like 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.
And simulate that, what happens? It is a software engineering job. How does it change in the next.
Well, I think it becomes more of what, like a good software engineer, good software engineers have always done. It's like less about the mechanics of, you know, like, what characters are coming off of your keyboard and going into a text editor and more about, you know, like, do you really understand what it is that you're building and why? And like, how is value being created and know, like, how can you move faster and recognizing what value you're creating for people and just sort of accelerating that rate of improvement. Like that's what the best software engineers that, you know, you and I have always worked with, like I've been able to do and, you know, I think that is still going to be, like very true. Like it's critically true right now because, you know, like, you can produce a lot of code with these coding agents right now. There's nothing to say that it's good code. So, like, you can just sort of spree and review has become the bottleneck.
Yeah. No reviews a bottleneck.
And you know, like you, you know, people need to really make sure that they're not getting confused between, activity and progress.
Yeah. I mean, it's been the hard thing, like, I've been, managing software engineering teams for a very long, like, 25 years now, I think, and over that 25 years, like, I don't think any of us have ever really been able to say what constitutes actual engineering productivity and like, there's some things, that you can obviously speed up a lot with AI systems and like, you know, you can really see it with startups. So like, there are these startups that are getting funded with an order of magnitude less money than they would have, just two years ago with like, very small teams doing like, crazy amounts of work and, like, moving very quickly and like, that's super, super exciting. But, you know, it really is going to put the focus on choice and taste and like understanding of like your problem domain, your customer and what you're trying to do. Like that's the really, really critically important thing now.
And do you think, the, the roles in the software development lifecycle will, will fuze and change and merge?
Yeah, it's it's always changed. You know like I what I hope honestly like 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, you know, like, we, we have turned, computer science education into, like, vocational education, like, we, you know, like students, like, go get a C degree because they want to learn how to be programmers and, like, what I want people to go back to is learning how to be computer scientists. Like, be thinkers about. Yeah. Can you think algorithmically? Do you know how to decompose problems? You know how to choose the right problem to go spend your time and energy on. Like, do you understand, like how the science of what you're doing fits into society and, and, yeah, the like the broader ecosystem of science that's happening around you. Like, do you possess the capability and the curiosity, like, punch down through the layers of abstraction to like, debug what's going on when the machine doesn't give you the result that you want? I mean, it's like all of those things are like the critically important things. And I think we'll get back to a lot of that because like a bunch of the vocational aspects of the software engineering job are just going to change or go slowly over the next handful of years that it's going to be unrecognizable.
You've you've spent a fair amount of time thinking about the future and the, the good and the bad. So paint, paint out two futures for us, the, the fundamentally optimistic one with AI, and one where it doesn't go quite the way that we would like it to go. And, and then I want to talk about Microsoft and how you folks are, you know, kind of working with that.
Yeah. So I think the optimistic story is probably the more likely one, because it's going to be the necessary thing that has to happen. Certainly it starts with like, I think that doesn't sound optimistic, but I was chatting with a friend the other day who runs, like one of the premier, educational institutions in Japan. And like he said, this thing sort of off handed to me that like, took me a back. 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, like, from here on out, fewer high school seniors graduating, like, fewer are going to college. And it goes like down. And there's nothing to do about it. Yes. Sans, you know, like some crazy thing with immigration and like that, there's just, you know, by looking at what the birth rates are, that's just precipitously going to go down.
Is it because of the of the demographics?
Just demographics, just pure demographics, pure, pure demographics? And so, you know, like we, we know in abstract. Right. Like the demographic data is super clear, that, Japan is in population decline. It's a little bit ahead of where, like other places are bit like China, Korea as well, like a bunch of countries in Western Europe. The United States sans, immigration would also be, like in population, or would be in population decline, over the next handful of decades. And so, like, you have this aging population, it's actually a much, much bigger issue than people have.
And like, you can even see it in the United States, like, they're these rural places, like where my mom lives, where the population is aging faster because, you know, just like the demographics are kind of unfavorable in, like, rural parts of the country. And, you know, it really has these massive implications that we don't talk about all that much. And like 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, like this elderly population that is growing very, very rapidly. Something has to happen with the very nature of like how you get work done and what productivity looks like in order for you to maintain just the level of, you know, quality of life in society and like what we think of as normal, in society and like 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 like I think the optimistic case is like, thank God I has come along when it has, that like 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, you, over the next handful of decades, don't have the same labor dynamics as you've had over the, you know, like, actually the whole course of recorded human history, you know, it's like a crazy thing that's happening that we just don't talk about all that much. And AI becomes that kind of unlock that you need otherwise, like, yeah, look, it can't do everything. And it's not a substitute for, you know, it's not a substitute for all of the things that human beings do. Like, I don't, you know, like, I don't think anybody here wants it to be, like, but but it gives us an option, for, you know, like, 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, like, I am optimistic that we can, you know, put our heads together, like, 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 like, have the good sense to have taste about how you, you know, put the technology into practice. So that's the optimistic case. You know, the pessimistic case. Like I won't give you the white. Like you can just sort of pick your, you know, like there's a palette of them right now like Terminator happens.
Or like, you know, many, many things like so my, my pessimistic scenario is that, you know, like we get into some superficial mode about AI that, you know, like we instead of using it to go solve some of these, super important problems with urgency that, like, we use it to just further distract ourselves and, you know, like a bunch of superficial, like I look at, you know, my my kids are like, half and half, right? So like, hey, you know, half the time they're using AI in like, really super ambitious ways to, like, do things, you know, with, you know, the biomedical engineering projects they're working on or like, you know, whatever their technical fascination is and like, 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 like, you know, make pictures of, you know, green llamas with big butts that they paste someone's face on. And it's like, okay, like, you know, hey, that's, that's a, that's a good use of computer resources, right?
Yeah. Like my GPUs are on fire. And like, this is why, yeah.
So like, I, I hope that, like, we can resist the temptation to make the whole narrative about AI is like, okay, well, here's, you know, the the new sensational thing that happened over the weekend or, you know, like the new way that, you know, a bunch of powerful people are going to throw darts at each other and like, we can just make it more about like, what does society really need from this technology?
