Microsoft CTO Kevin Scott, in conversation with a16z's Bob Swan, explains how AI copilots are keeping developers longer in a flow state and why AI copilots more broadly could be the start of an industrial revolution for knowledge work.

[0:00] Microsoft's approach to AI
[5:55] The next Industrial Revolution 
[10:27] Developer productivity & flow state
[15:26] Reprogramming the American Dream
[19:48] Advice to builders 

This conversation is part of our AI Revolution series, recorded August 2023 at a live event in San Francisco. The series features some of the most impactful builders in the field of AI discussing and debating where we are, where we’re going, and the big open questions in AI. Find more content from our AI Revolution series on www.a16z.com/AIRevolution.

When you're in flow state, you know it, like you're just killing it.

And so how can you use tools to preserve that as long as humanly possible?

Look, Microsoft has played such a huge role in determining the future of AI.

So maybe if you could just take a few minutes and talk about how you see the vision and the future of AI and what is the role of a large tech leader like Microsoft in this in this wave of AI.

We really do see AI not as a product and not as a research project, but as a real platform for lots of people to build many interesting new things on top of, particularly the past couple of years and maybe the past 12 months in particular with the launch of chat GPT and GPT-4, you can sort of really see, I think, the potential of the platform, maybe in the same light as the personal computer or the smartphone, just in terms of a bundle of technologies that are going to make a whole bunch of new things possible that lots of people are going to be building things on top of, most of which we probably don't even really understand right now.

And so like, I think that's the exciting thing to me about building a technology.

Like I've always been a tool builder and like the idea of building a tool and then having a bunch of people use it in ways that you didn't anticipate is like really super interesting.

And in many ways, you're not doing this on your own.

You've partnered with leaders in the industry, whether it's GitHub or OpenAI, as you mentioned, and most recently the MetaLlama 2 announcement.

Can you talk a little bit more about the role you see partners playing in your vision and how you expect the ecosystem to expand over time.

And maybe a question that you've, I'm sure you've gotten a million times is where do you get all the GPUs to support growing and expanding relationships?

Yeah, the GPU question is, I don't know, it's probably not comical, but I have to sort of laugh just the frequency of that being the number 1 question right now.

Like it's not even a weekly or monthly thing.

It's hours in the day that go by where someone's not asking for GPUs is almost non-existent.

Again, going back to this whole point of, if you're building a platform, the platform has to get built with partners and it has to get used by partners.

And so like our partners are extraordinarily important to us.

Yeah, I think if you think about AI, like by far in a way, the most important partner we have is OpenAI.

Like that partnership has been going for 4 years now and started as a, hey we are a hyperscale cloud, like we're going to build a bunch of AI supercomputing infrastructure in collaboration with you that is then going to get used to train some of the most advanced AI models and systems in the world.

And then we're going to do some work together to figure out how to take those models, those platform building blocks, and get them deployed into products that both Microsoft offers, like GitHub Copilot, as well as how do you deploy these things into environments like Azure and things like the Azure OpenAI API where people can just build their own software on top of it.

And then, you know, these other partners are super important as well.

Like GitHub obviously is I think 1 of the most interesting places to go deploy AI right now.

And it is like the first illustration of this co-pilot pattern that we are trying to build out, which is how can you take a bit of knowledge work or cognitive work that someone is doing and use AI to help them be dramatically more productive at doing that cognitive work.

And if you look at developers, we just don't have enough of them to write all of the software that the world needs.

And a lot of the work that developers do is just toil.

And so having a tool that can help both make the job of software development more delightful and to like help software developers just be more productive.

And then, you know, like back to your, you mentioned Meta and LLAMA2.

And, you know, I think LLAMA2 is just like LLAMA going to be an important building block that people are going to want to use to build AI software.

And so like we're we're doing everything that we can to like make that an easy thing for people to do.

You know, the concept of of co pilot and how you create these AI tools to assist people with the complex cognitive tasks.

How do you think AI can and will change how we all work over time?

And maybe what's your advice for how we get the most out of tools like CoPilot?

If you think about the industrial revolution, what happened is for the first time you had engineered devices, machines that were able to assist people with labor.

And like most of the labor in the 19th and 18th century and like through most of the 20th century was physical.

And the invention of these new technologies all of a sudden allowed the transformation of the physical labor that almost everyone in the world was doing.

And I think a similar thing is going to happen now with cognitive work And so knowledge work, which is becoming a bigger and bigger part of what the workforce is spending their time and energy doing.

I mean, like you and I are both knowledge workers.

And so we haven't really had a massive breakthrough in the productivity for knowledge workers.

Maybe, yes, maybe the PC was like an accelerator.

Maybe the invention of the internet was an accelerator.

I don't think the smartphone helped much at all just in terms of productivity and knowledge work.

But I think this might be the biggest thing that's ever happened for this particular type of work.

And I think the way that it will get used in early days, and you can sort of see this right now, is kind of twofold.

1 is you just sort of have these big categories of knowledge work where there is no way to do more of the particular type of work because productivity is basically not getting any better, but where you've got some kind of deficit in society because you actually could use more of it.

And then, you know, I think on the on the other hand, like you, you just sort of have like tons of drudgery in a lot of the work that we all do, where if you had some kind of productivity mechanism that could come in and do the, you know, your least favorite part of your job or the like the most repetitive, most redundant, you know, sort of work that you do.

Like I think we all would be delighted to have those sorts of tools in our lives.

And so I think lots of companies are gonna get created very soon to, you know, really help tackle some of those problems of, hey, where's the sort of crap work that people would be desperate and like happy to have automated because it would make their experience of their work better and would let them get more done.

And then, but like the interesting thing though is if you think about some of these big platform shifts that we've had in the past, The most valuable things that get done on the platforms are not the things that got deployed in the first year or 2 of the platform change.

You know, like if you think about the smartphone, the place where you spend most of your time on your smartphone is not the SMS app, it's not the web browser, it's not the mail client.

It's the new things that got created on top of the platform in the years following the availability of the platform.

It's sort of the hard stuff that you really have to sort of think about.

Like what are the difficult things that have now become possible that were impossible before?

And Like, I think that's the thing that people ought to be thinking about.

Like, what are the hard problems I can be working?

There's a lot of easy things that you can go do right now that are going to be useful.

But I don't think those are the most valuable things.

1 of the, you know, 1 of the use cases that has been pretty powerful has really been code assistant within GitHub Copilot.

Kind of going inside the 4 walls of Microsoft, can you talk a little bit about the tools that you're rolling out to your own engineers and how is it changing how engineers get their work done at Microsoft?

And any thoughts or ideas on how you track and measure that kind of productivity in the engineering community?

I'm not sure whether any of the AI systems are creating the need for new measures for developer productivity.

But I think it's really, really important, like if you are running a software development organization and you don't have good metrics and visibility into the productivity of developers, like you need to get on it right now because it will be very hard to decide which of a bewildering variety of AI, developer tools are coming your way that you want to adopt and like what order you want to adopt them in if you don't have those measurements in place.

The thing that I will say is, developer productivity is not lines of code produced.

It is how it is that you're measuring a developer's ability to very quickly deliver things to users and then measure whether or not those users are benefiting from the things that the developer is producing.

And it's sort of instrumenting that entire feedback loop and making sure that you are able to identify points of friction like throughout the entire product lifecycle development is just super important.

So I mean the thing that we're doing inside of the company right now, you know, with AI tools, like first and foremost, is like getting everybody to use GitHub, GitHub Copilot, which is like a real big productivity win.

The thing that it really does in our observation with developers more than anything else is it helps keep them in flow state longer than they otherwise would.

So rather than hitting a blocker when you're writing a chunk of code or you're trying to accomplish something and be like, okay, well, I don't know how to go get the next thing done.

I got to go ask another engineer who might be busy with something.

Being able to get yourself unblocked in the moment before you're out of flow state is just extraordinarily valuable.

And I think that's a thing for folks who are thinking about the utility of these generative AI tools that you're building for things other than software development, it's a useful thing to look at.

Like I, last fall before, you know, GPT-4 was announced, I decided, I was like, all right, well, can I use, can I use these tools to help me write a science fiction book, which is something I've wanted to do since I was a teenager?

It was terrible at like writing all of the book.

Like it just wasn't good at like, you know, giving you like chapters of well-formed prose with character development and all of the things that you want in a good book.

But it was really terrific at keeping me in flow state.

So if I measure my productivity, how many words I could write in a day versus with the tool, 2X easy.

And so I think that is a really interesting thing that everyone should be thinking about in general is like this notion of flow state or Like, you know, what are the conditions in which people are their absolute most productive?

Like they're most delighted at their job.

Like, you know, they just sort of, you know, when you're in flow state, you know it.

And so How can you use tools to preserve that as long as humanly possible?

You know, the other place, too, where we're using a bunch of AI tools is actually in the deployment of AI.

So like a lot of the testing that we're doing right now, a lot of the responsible AI work that we do, like are all using the AI tools themselves to help do that.

So that's another interesting thing that just wasn't obvious.

It hadn't been obvious to me that it was going to be as important as it is when we started doing deployments at scale about 9 months ago.

Can you talk a little bit about how you as a leader are effectively rolling out the tools to maybe to more motivate and inspire rather than stoke the fears that our employees have about what this technology means for our roles.

And then on a related basis, how do you handle the resistance to change organizationally for disruptive tools like this in your kind of daily job?

Yeah, I mean, I think those are 2 very different questions and I'll sort of dig into them separately.

So I actually wrote a book about this, like, honestly, at the worst possible time.

So the book's called Reprogramming the American Dream, and it was published.

It went to the bookstores and hit shelves in March of 2020, the week that the shutdowns for the COVID pandemic started.

But the whole point of the book was trying to assert that AI creates more opportunities for people by far than it creates the potential for harm.

And it's not to say that the potential for harm is 0, or that you can ignore it, or that you can make that the umpteen thing that you're thinking about with an AI product among a very long list of things that you're thinking about.

But it does mean, I think, that we have to be really insistent and determined around these optimistic scenarios.

Part of the book was rooted in my experience.

I grew up in rural central Virginia in a place where the economy was powered by tobacco farming, furniture manufacturing, and textiles.

In all 3 of those industries, by the time I was graduating from high school, it just collapsed.

And when folks in these communities have access to very powerful tools, like, They tend to do remarkable things that create economic opportunities for themselves and for their families and their communities.

They solve problems that you or I are not going to solve just because we don't see the whole problem landscape of the world.

I think these tools of AI are becoming more accessible now than they have been by a dramatic wide margin.

You can do interesting things with these tools right now to solve problems like create businesses, like be an entrepreneur in small town Virginia without having a PhD in computer science or expertise in classical AI.

You just need to be curious and entrepreneurial.

Your second question was about how do you get people resistant to change, to embrace some of what's happening right now with AI.

What we have seen at Microsoft over the past 9 months, and I think you all are seeing this too in the entrepreneurial community, is as soon as you have large groups of people enthusiastically building things on top of this technology, it's very easy to like get on the, I hesitate to call it, you know, the bandwagon, but like really sort of see like all of your peers.

Adapting to the change and getting excited about it and building interesting things and then just say to yourself like I gotta go do this as well And so I think part of the problem in any organization is figuring out how to get yourself to that point of critical mass of adoption, where then it just gets easy.

And then you have the opposite problem, which is the thing you started the call with, which is people screaming at you that they don't have enough GPUs.

So let me just kind of close with 1 last, 1 last more general question.

What do you see as the biggest open questions in AI at the moment?

We have proof points of, you know, a sort of a 20 watt organ that sits between our ears, that is artificial general intelligence, that is many, many, many orders of magnitude more efficient than the digital artificial neural networks that we're building right now.

And so, you know, I think there, you know, from a technology perspective, there's probably a whole handful of breakthroughs left to be discovered in figuring out how to close that efficiency gap.

The unlock that you get when you can do something on your laptop versus things where you have to go buy millions of dollars worth of computing infrastructure to do something is really enormous.

Like it does that thing for the product development that I was talking about earlier, which is like helps you turn the crank faster and like makes the iterations less expensive.

And I think just getting big efficiency breakthroughs, like it'd be great for us, obviously, because we're spending huge amounts of capital on infrastructure to run these models.

But I think it'd be great for the whole world and the pace of innovation that we're seeing in AI products.

And the thing I'll say to all of the entrepreneurs in attendance is, just go find hard stuff to work on.

It's like, don't be the, yeah, the AI moral equivalent of the fart app, you know, fart apps that were flooding the app store.

Like, just because a thing has become possible doesn't mean that it's useful.

And so just really, really focusing on the fundamentals of product building, I think is important.

Like your understanding as an entrepreneur of like, you know who is your user, like what is their problem?

And then like determining whether or not this AI stuff is actually a useful piece of infrastructure to go solve that user problem, like that is something that is unchanged.

Like it's, yeah, the AI is just like a new and interesting piece of infrastructure that has come into existence that lets you solve a new category of problems or like solve an old category of problems in better ways.

Thank you very much for taking the time and joining us today.

I greatly appreciate you sharing the vision you have there at Microsoft, some of the tools and some of the human aspects of bringing this technology to life.


Speaker 0: When you're in flow state, you know it, like you're just killing it. And so how can you use tools to preserve that as long as humanly possible?

Speaker 1: Look, Microsoft has played such a huge role in determining the future of AI. So maybe if you could just take a few minutes and talk about how you see the vision and the future of AI and what is the role of a large tech leader like Microsoft in this in this wave of AI.

Speaker 0: We really do see AI not as a product and not as a research project, but as a real platform for lots of people to build many interesting new things on top of, particularly the past couple of years and maybe the past 12 months in particular with the launch of chat GPT and GPT-4, you can sort of really see, I think, the potential of the platform, maybe in the same light as the personal computer or the smartphone, just in terms of a bundle of technologies that are going to make a whole bunch of new things possible that lots of people are going to be building things on top of, most of which we probably don't even really understand right now. And so like, I think that's the exciting thing to me about building a technology. Like I've always been a tool builder and like the idea of building a tool and then having a bunch of people use it in ways that you didn't anticipate is like really super interesting.

Speaker 1: And in many ways, you're not doing this on your own. You've partnered with leaders in the industry, whether it's GitHub or OpenAI, as you mentioned, and most recently the MetaLlama 2 announcement. Can you talk a little bit more about the role you see partners playing in your vision and how you expect the ecosystem to expand over time. And maybe a question that you've, I'm sure you've gotten a million times is where do you get all the GPUs to support growing and expanding relationships?

Speaker 0: Yeah, the GPU question is, I don't know, it's probably not comical, but I have to sort of laugh just the frequency of that being the number 1 question right now. Like it's not even a weekly or monthly thing. It's hours in the day that go by where someone's not asking for GPUs is almost non-existent. Again, going back to this whole point of, if you're building a platform, the platform has to get built with partners and it has to get used by partners. And so like our partners are extraordinarily important to us.

Yeah, I think if you think about AI, like by far in a way, the most important partner we have is OpenAI. Like that partnership has been going for 4 years now and started as a, hey we are a hyperscale cloud, like we're going to build a bunch of AI supercomputing infrastructure in collaboration with you that is then going to get used to train some of the most advanced AI models and systems in the world. And then we're going to do some work together to figure out how to take those models, those platform building blocks, and get them deployed into products that both Microsoft offers, like GitHub Copilot, as well as how do you deploy these things into environments like Azure and things like the Azure OpenAI API where people can just build their own software on top of it. And then, you know, these other partners are super important as well. Like GitHub obviously is I think 1 of the most interesting places to go deploy AI right now.

And it is like the first illustration of this co-pilot pattern that we are trying to build out, which is how can you take a bit of knowledge work or cognitive work that someone is doing and use AI to help them be dramatically more productive at doing that cognitive work. And if you look at developers, we just don't have enough of them to write all of the software that the world needs. And a lot of the work that developers do is just toil. And so having a tool that can help both make the job of software development more delightful and to like help software developers just be more productive. And then, you know, like back to your, you mentioned Meta and LLAMA2.

And, you know, I think LLAMA2 is just like LLAMA going to be an important building block that people are going to want to use to build AI software. We want AI software running on on Azure. And so like we're we're doing everything that we can to like make that an easy thing for people to do.

Speaker 1: You know, the concept of of co pilot and how you create these AI tools to assist people with the complex cognitive tasks. How do you think AI can and will change how we all work over time? And maybe what's your advice for how we get the most out of tools like CoPilot?

Speaker 0: If you think about the industrial revolution, what happened is for the first time you had engineered devices, machines that were able to assist people with labor. And like most of the labor in the 19th and 18th century and like through most of the 20th century was physical. And the invention of these new technologies all of a sudden allowed the transformation of the physical labor that almost everyone in the world was doing. And I think a similar thing is going to happen now with cognitive work And so knowledge work, which is becoming a bigger and bigger part of what the workforce is spending their time and energy doing. I mean, like you and I are both knowledge workers.

And so we haven't really had a massive breakthrough in the productivity for knowledge workers. Maybe, yes, maybe the PC was like an accelerator. Maybe the invention of the internet was an accelerator. I don't think the smartphone helped much at all just in terms of productivity and knowledge work. But I think this might be the biggest thing that's ever happened for this particular type of work.

And I think the way that it will get used in early days, and you can sort of see this right now, is kind of twofold. 1 is you just sort of have these big categories of knowledge work where there is no way to do more of the particular type of work because productivity is basically not getting any better, but where you've got some kind of deficit in society because you actually could use more of it. And then, you know, I think on the on the other hand, like you, you just sort of have like tons of drudgery in a lot of the work that we all do, where if you had some kind of productivity mechanism that could come in and do the, you know, your least favorite part of your job or the like the most repetitive, most redundant, you know, sort of work that you do. Like I think we all would be delighted to have those sorts of tools in our lives. And so I think lots of companies are gonna get created very soon to, you know, really help tackle some of those problems of, hey, where's the sort of crap work that people would be desperate and like happy to have automated because it would make their experience of their work better and would let them get more done.

And then, but like the interesting thing though is if you think about some of these big platform shifts that we've had in the past, The most valuable things that get done on the platforms are not the things that got deployed in the first year or 2 of the platform change. You know, like if you think about the smartphone, the place where you spend most of your time on your smartphone is not the SMS app, it's not the web browser, it's not the mail client. It's the new things that got created on top of the platform in the years following the availability of the platform. It's sort of the hard stuff that you really have to sort of think about. Like what are the difficult things that have now become possible that were impossible before?

And Like, I think that's the thing that people ought to be thinking about. Like, what are the hard problems I can be working? There's a lot of easy things that you can go do right now that are going to be useful. But I don't think those are the most valuable things.

Speaker 1: 1 of the, you know, 1 of the use cases that has been pretty powerful has really been code assistant within GitHub Copilot. Kind of going inside the 4 walls of Microsoft, can you talk a little bit about the tools that you're rolling out to your own engineers and how is it changing how engineers get their work done at Microsoft? And any thoughts or ideas on how you track and measure that kind of productivity in the engineering community?

Speaker 0: I'm not sure whether any of the AI systems are creating the need for new measures for developer productivity. But I think it's really, really important, like if you are running a software development organization and you don't have good metrics and visibility into the productivity of developers, like you need to get on it right now because it will be very hard to decide which of a bewildering variety of AI, developer tools are coming your way that you want to adopt and like what order you want to adopt them in if you don't have those measurements in place. The thing that I will say is, developer productivity is not lines of code produced. It is how it is that you're measuring a developer's ability to very quickly deliver things to users and then measure whether or not those users are benefiting from the things that the developer is producing. And it's sort of instrumenting that entire feedback loop and making sure that you are able to identify points of friction like throughout the entire product lifecycle development is just super important.

So I mean the thing that we're doing inside of the company right now, you know, with AI tools, like first and foremost, is like getting everybody to use GitHub, GitHub Copilot, which is like a real big productivity win. The thing that it really does in our observation with developers more than anything else is it helps keep them in flow state longer than they otherwise would. So rather than hitting a blocker when you're writing a chunk of code or you're trying to accomplish something and be like, okay, well, I don't know how to go get the next thing done. I've got to go consult documentation. I got to go ask another engineer who might be busy with something.

Being able to get yourself unblocked in the moment before you're out of flow state is just extraordinarily valuable. And I think that's a thing for folks who are thinking about the utility of these generative AI tools that you're building for things other than software development, it's a useful thing to look at. Like I, last fall before, you know, GPT-4 was announced, I decided, I was like, all right, well, can I use, can I use these tools to help me write a science fiction book, which is something I've wanted to do since I was a teenager? It was terrible at like writing all of the book. Like it just wasn't good at like, you know, giving you like chapters of well-formed prose with character development and all of the things that you want in a good book.

But it was really terrific at keeping me in flow state. So if I measure my productivity, how many words I could write in a day versus with the tool, 2X easy. And so I think that is a really interesting thing that everyone should be thinking about in general is like this notion of flow state or Like, you know, what are the conditions in which people are their absolute most productive? Like they're most delighted at their job. Like, you know, they just sort of, you know, when you're in flow state, you know it.

Like you're just killing it. And so How can you use tools to preserve that as long as humanly possible? You know, the other place, too, where we're using a bunch of AI tools is actually in the deployment of AI. So like a lot of the testing that we're doing right now, a lot of the responsible AI work that we do, like are all using the AI tools themselves to help do that. So that's another interesting thing that just wasn't obvious.

It hadn't been obvious to me that it was going to be as important as it is when we started doing deployments at scale about 9 months ago.

Speaker 1: Can you talk a little bit about how you as a leader are effectively rolling out the tools to maybe to more motivate and inspire rather than stoke the fears that our employees have about what this technology means for our roles. And then on a related basis, how do you handle the resistance to change organizationally for disruptive tools like this in your kind of daily job?

Speaker 0: Yeah, I mean, I think those are 2 very different questions and I'll sort of dig into them separately. So I actually wrote a book about this, like, honestly, at the worst possible time. So the book's called Reprogramming the American Dream, and it was published. It went to the bookstores and hit shelves in March of 2020, the week that the shutdowns for the COVID pandemic started. But the whole point of the book was trying to assert that AI creates more opportunities for people by far than it creates the potential for harm.

And it's not to say that the potential for harm is 0, or that you can ignore it, or that you can make that the umpteen thing that you're thinking about with an AI product among a very long list of things that you're thinking about. But it does mean, I think, that we have to be really insistent and determined around these optimistic scenarios. Part of the book was rooted in my experience. I grew up in rural central Virginia in a place where the economy was powered by tobacco farming, furniture manufacturing, and textiles. In all 3 of those industries, by the time I was graduating from high school, it just collapsed.

And when folks in these communities have access to very powerful tools, like, They tend to do remarkable things that create economic opportunities for themselves and for their families and their communities. They solve problems that you or I are not going to solve just because we don't see the whole problem landscape of the world. We don't have their point of view. I think these tools of AI are becoming more accessible now than they have been by a dramatic wide margin. You can do interesting things with these tools right now to solve problems like create businesses, like be an entrepreneur in small town Virginia without having a PhD in computer science or expertise in classical AI.

You just need to be curious and entrepreneurial. Your second question was about how do you get people resistant to change, to embrace some of what's happening right now with AI. What we have seen at Microsoft over the past 9 months, and I think you all are seeing this too in the entrepreneurial community, is as soon as you have large groups of people enthusiastically building things on top of this technology, it's very easy to like get on the, I hesitate to call it, you know, the bandwagon, but like really sort of see like all of your peers. Adapting to the change and getting excited about it and building interesting things and then just say to yourself like I gotta go do this as well And so I think part of the problem in any organization is figuring out how to get yourself to that point of critical mass of adoption, where then it just gets easy. And then you have the opposite problem, which is the thing you started the call with, which is people screaming at you that they don't have enough GPUs.

Speaker 1: So let me just kind of close with 1 last, 1 last more general question. What do you see as the biggest open questions in AI at the moment?

Speaker 0: We have proof points of, you know, a sort of a 20 watt organ that sits between our ears, that is artificial general intelligence, that is many, many, many orders of magnitude more efficient than the digital artificial neural networks that we're building right now. And so, you know, I think there, you know, from a technology perspective, there's probably a whole handful of breakthroughs left to be discovered in figuring out how to close that efficiency gap. The unlock that you get when you can do something on your laptop versus things where you have to go buy millions of dollars worth of computing infrastructure to do something is really enormous. Like it does that thing for the product development that I was talking about earlier, which is like helps you turn the crank faster and like makes the iterations less expensive. And I think just getting big efficiency breakthroughs, like it'd be great for us, obviously, because we're spending huge amounts of capital on infrastructure to run these models.

But I think it'd be great for the whole world and the pace of innovation that we're seeing in AI products. And the thing I'll say to all of the entrepreneurs in attendance is, just go find hard stuff to work on. Don't chase the trivial things. It's like, don't be the, yeah, the AI moral equivalent of the fart app, you know, fart apps that were flooding the app store. Like, just because a thing has become possible doesn't mean that it's useful.

And so just really, really focusing on the fundamentals of product building, I think is important. Like AI's like model's not a product. Like your understanding as an entrepreneur of like, you know who is your user, like what is their problem? Like what can you do to help them? And then like determining whether or not this AI stuff is actually a useful piece of infrastructure to go solve that user problem, like that is something that is unchanged.

Like it's, yeah, the AI is just like a new and interesting piece of infrastructure that has come into existence that lets you solve a new category of problems or like solve an old category of problems in better ways.

Speaker 1: Well, look, Kevin, this is great. Thank you very much for taking the time and joining us today. I greatly appreciate you sharing the vision you have there at Microsoft, some of the tools and some of the human aspects of bringing this technology to life. It's great to see you. I look forward to next time.

Speaker 0: Thank you so much for inviting me.

See all a16z transcripts on Youtube

AI Copilots and the Future of Knowledge Work with Microsoft's Kevin Scott