Title: Satya Nadella – Microsoft’s AGI Plan & Quantum Breakthrough
Author: Dwarkesh Patel

Transcript:
Satya, thank you so much 
for coming on the podcast. 
In a second, we're going to get to the two 
breakthroughs that Microsoft has just made,  
and congratulations, same day in Nature: the 
Majorana zero chip, which we have in front  
of us right here, and also the world human action 
models. But can we just continue the conversation  
we were having a second ago? You're describing 
the ways in which the things you were seeing in  
the 80s and 90s, you're seeing them happen again.
The thing that is exciting for me... Dwarkesh,  
first of all, it's fantastic to be 
on your podcast. I'm a big listener,  
and I love the way that you do these interviews 
and the broad topics that you explore. 
The thing that is exciting for me… It reminds me 
a little bit of my, I'd say, first few years even  
in the tech industry, starting in the 90s, where 
there was real debate about whether it's going  
to be RISC or CISC, or, "Hey, are we really 
going to be able to build servers using x86?" 
When I joined Microsoft, that was the 
beginning of what was Windows NT. So,  
everything from the core silicon platform to the 
operating system to the app tier- that full stack  
approach- the entire thing is being litigated.
You could say cloud did a bunch of that,  
and obviously distributed computing and cloud 
did change client-server. The web changed  
massively. But this does feel a little more 
like maybe more full-stack than even the past  
that at least I've been involved in.
When you think about which decisions  
ended up being the long-term winners in 
the 80s and 90s, and which ones didn't, and  
especially when you think about- you were at Sun 
Microsystems, they had an interesting experience  
with the 90s dotcom bubble. People talk about 
this data center build-out as being a bubble,  
but at the same time, we have the Internet 
today as a result of what was built out then. 
What are the lessons about what will stand 
the test of time? What is an inherent  
secular trend? What is just ephemeral?
If I go back, the four big transformations  
that I've been part of, the client and the 
client-server. So that's the birth of the  
graphical user interface and the x86 architecture, 
basically allowing us to build servers. 
It was very clear to me. I remember going to 
what is PDC in '91, in fact I was at Sun at  
that time. In '91, I went to Moscone. That's when 
Microsoft first described the Win32 interface and  
it was pretty clear to me what was going to 
happen, where the server was also going to be  
an x86 thing. When you have the scale advantages 
accruing to something, that's the secular bet you  
have to place. What happened in the client was 
going to happen on the server side, and then  
you were able to actually build client-server 
applications. So, the app model became clear. 
Then the web was the big thing for us, 
which we had to deal with in starting,  
in fact as soon as I joined Microsoft, the 
Netscape browser or the Mosaic browser came out  
what, I think, December or November of '93, right? 
I think is when Andreessen and crew had that. 
So that was a big game-changer, in an interesting 
way, just as we were getting going on what was  
the client-server wave, and it was clear that 
we were going to win it as well. We had the  
browser moment, and so we had to adjust. 
And we did a pretty good job of adjusting  
to it because the browser was a new app model.
We were able to embrace it with everything we did,  
whether it was HTML in Word or building a new 
thing called the browser ourselves and competing  
for it, and then building a web server on 
our server stack and go after it. Except,  
of course, we missed what turned out to 
be the biggest business model on the web,  
because we all assumed the web is all about being 
distributed, who would have thought that search  
would be the biggest winner in organizing the web? 
And so that's where we obviously didn't see it,  
and Google saw it and executed super well.
So that's one lesson learned for me: you have  
to not only get the tech trend right, you also 
have to get where the value is going to be created  
with that trend. These business model shifts are 
probably tougher than even the tech trend changes. 
Where is the value going to be created in AI?
That's a great one. So I think there are two  
places where I can say with some confidence. 
One is the hyperscalers that do well,  
because the fundamental thing is if you sort of 
go back to even how Sam and others describe it,  
if intelligence is log of compute, whoever 
can do lots of compute is a big winner. 
The other interesting thing is, if you 
look at underneath even any AI workload,  
like take ChatGPT, it's not like everybody's 
excited about what's happening on the GPU side,  
it's great. In fact, I think of my fleet even 
as a ratio of the AI accelerator to storage,  
to compute. And at scale, you've got to grow it.
Yeah. 
And so, that infrastructure need for the world 
is just going to be exponentially growing. 
Right.
So in fact it's manna  
from heaven to have these AI workloads because 
guess what? They're more hungry for more compute,  
not just for training, but we now know, for 
test time. When you think of an AI agent, it  
turns out the AI agent is going to exponentially 
increase compute usage because you're not even  
bound by just one human invoking a program. It's 
one human invoking programs that invoke lots more  
programs. That's going to create massive, massive 
demand and scale for compute infrastructure. So  
our hyperscale business, Azure business, and 
other hyperscalers, I think that’s a big thing. 
Then after that, it becomes a little fuzzy. You 
could say, hey, there is a winner-take-all model-  
I just don't see it. This, by the way, is the 
other thing I’ve learned: being very good at  
understanding what are winner-take-all markets 
and what are not winner-take-all markets is,  
in some sense, everything. I remember even in 
the early days when I was getting into Azure,  
Amazon had a very significant lead and people 
would come to me, and investors would come to me,  
and say, "Oh, it's game over. You'll never 
make it. Amazon, it's winner-take-all." 
Having competed against Oracle and IBM 
in client-server, I knew that the buyers  
will not tolerate winner-take-all. 
Structurally, hyperscale will never  
be a winner-take-all because buyers are smart.
Consumer markets sometimes can be winner-take-all,  
but anything where the buyer is a 
corporation, an enterprise, an IT department,  
they will want multiple suppliers. And so 
you got to be one of the multiple suppliers. 
That, I think, is what will happen even on the 
model side. There will be open-source. There  
will be a governor. Just like on Windows, 
one of the big lessons learned for me was,  
if you have a closed-source operating 
system, there will be a complement to it,  
which will be open source.
And so to some degree that's  
a real check on what happens. I think in models 
there is one dimension of, maybe there will be  
a few closed source, but there will definitely be 
an open source alternative, and the open-source  
alternative will actually make sure that the 
closed-source, winner-take-all is mitigated. 
That's my feeling on the model side. And by the 
way, let's not discount if this thing is really  
as powerful as people make it out to be, the 
state is not going to sit around and wait for  
private companies to go around… and all over the 
world. So, I don't see it as a winner-take-all. 
Then above that, I think it's going to be 
the same old stuff, which is in consumer,  
in some categories, there may be 
some winner-take-all network effect.  
After all, ChatGPT is a great example.
It's an at-scale consumer property that  
has already got real escape velocity. I go to the 
App Store, and I see it's always there in the top  
five, and I say “wow, that's pretty unbelievable”.
So they were able to use that early advantage and  
parlay that into an app advantage. In consumer, 
that could happen. In the enterprise again,  
I think there will be, by category, different 
winners. That's sort of at least how I analyze it. 
I have so many follow-up questions. We have to 
get to quantum in just a second, but on the idea  
that maybe the models get commoditized: maybe 
somebody could have made a similar argument a  
couple of decades ago about the cloud – that 
fundamentally, it's just a chip and a box. 
But in the end, of course, you and many others 
figured out how to get amazing profit margins in  
the cloud. You figured out ways to get economies 
of scale and add other value. Fundamentally,  
even forgetting the jargon, if you've got AGI 
and it's helping you make better AIs – right now,  
it's synthetic data and RL; maybe in the future, 
it's an automated AI researcher – that seems like  
a good way to entrench your advantage there. I'm 
curious what you make of that, just the idea that  
it really matters to be ahead there.
At scale, nothing is commodity.  
To your point about cloud, everybody would 
say, "Oh, cloud's a commodity." Except,  
when you scale... That's why the know-how of 
running a hyperscaler... You could say, "Oh,  
what the heck? I can just rack and stack servers."
Right. 
In fact, in the early days of hyperscale, most 
people thought “there are all these hosters,  
and those are not great businesses. Will there be 
anything? Is there a business even in hyperscale?”  
And it turns out there is a real business, 
just because of the know-how of running, in  
the case of Azure, the world's computing of 
60-plus regions with all the compute. It's  
just a tough thing to duplicate.
So I was more making the point,  
is it one winner? Is it a winner-take-all or 
not? Because that you've got to get right. I  
like to enter categories which are big TAMs, 
where you don't have to have the risk of it  
all being winner-take-all. The best news to 
be in is a big market that can accommodate  
a couple of winners, and you're one of them.
That's what I meant by the hyperscale layer.  
In the model layer, one is models need ultimately 
to run on some hyperscale compute. So that nexus,  
I feel, is going to be there forever. It's not 
just the model; the model needs state, that means  
it needs storage, and it needs regular compute for 
running these agents and the agent environments. 
And so that's how I think about why the 
limit of one person running away with  
one model and building it all may not happen.
On the hyperscaler side, and by the way, it's also  
interesting the advantage you as a hyperscaler 
would have in the sense that, especially with  
inference time scaling and if that's involved 
in training future models, you can amortize  
your data centers and GPUs, not only for the 
training, but then use them again for inference. 
I'm curious what kind of hyperscaler you 
consider Microsoft and Azure to be. Is it on  
the pre-training side? Is it on providing 
the O3-type inference? Or are you just,  
we’re going to host and deploy any single 
model that's out there in the market,  
and we are sort of agnostic about that?
It’s a good point. The way we want to  
build out the fleet is [to], in some sense 
ride Moore's law. I think this will be like  
what we've done with everything else in the 
past: every year keep refreshing the fleet,  
you depreciate it over whatever the lifetime value 
of these things are, and then get very very good  
at the placement of the fleet such that you can 
run different jobs at it with high utilization.  
Sometimes there are very big training jobs 
that need to have highly concentrated peak  
flops that are provisioned to it that also need 
to cohere. That's great. We should have enough  
data center footprint to be able to give that.
But at the end of the day, these are all becoming  
so big, even in terms of if you take pre-training 
scale, if it needs to keep going, even  
pre-training scale at some point has to cross data 
center boundaries. It's all more or less there. 
So, great, once you start crossing pre-training 
data center boundaries, is it that different than  
anything else? The way I think about it is hey, 
distributed computing will remain distributed,  
so go build out your fleet such that 
it's ready for large training jobs,  
it's ready for test-time compute, it’s ready- 
in fact, if this RL thing that might happens,  
you build one large model, and then after 
that, there’s tons of RL going on. To me,  
it's kind of like more training flops, because 
you want to create these highly specialized,  
distilled models for different tasks.
So you want that fleet, and then the  
serving needs. At the end of the day, 
speed of light is speed of light,  
so you can't have one data center in Texas and 
say, "I'm going to serve the world from there." 
You've got to serve the world based on 
having an inference fleet everywhere in  
the world. That's how I think of our 
build-out of a true hyperscale fleet. 
Oh, and by the way, I want my storage and 
compute also close to all of these things,  
because it's not just AI accelerators that are 
stateless. My training data itself needs storage,  
and then I want to be able to multiplex 
multiple training jobs, I want to be able to  
then have memory, I want to be able to have these 
environments in which these agents can go execute  
programs. That's kind of how I think about it.
You recently reported that your yearly revenue  
from AI is $13 billion. But if you look at your 
year-on-year growth on that, in like four years,  
it'll be 10x that. You'll have $130 billion in 
revenue from AI, if the trend continues. If it  
does, what do you anticipate doing with all 
that intelligence, this industrial scale use? 
Is it going to be through Office? Is it going 
to be you deploying it for others to host?  
You've got to have the AGIs to have $130 
billion in revenue? What does it look like? 
The way I come at it, Dwarkesh, it's a 
great question because at some level,  
if you're going to have this explosion, abundance, 
whatever, commodity of intelligence available,  
the first thing we have to observe is GDP growth.
Before I get to what Microsoft's revenue will look  
like, there's only one governor in all 
of this. This is where we get a little  
bit ahead of ourselves with all this AGI hype. 
Remember the developed world, which is what? 2%  
growth and if you adjust for inflation it’s zero?
So in 2025, as we sit here, I'm not an economist,  
at least I look at it and say we have a real 
growth challenge. So, the first thing that  
we all have to do is, when we say this is 
like the Industrial Revolution, let's have  
that Industrial Revolution type of growth.
That means to me, 10%, 7%, developed world,  
inflation-adjusted, growing at 5%. That's the 
real marker. It can't just be supply-side. 
In fact that’s the thing, a lot of people 
are writing about it, and I'm glad they are,  
which is the big winners here are not going to be 
tech companies. The winners are going to be the  
broader industry that uses this commodity that, by 
the way, is abundant. Suddenly productivity goes  
up and the economy is growing at a faster rate. 
When that happens, we'll be fine as an industry. 
But that's to me the moment. Us 
self-claiming some AGI milestone,  
that's just nonsensical benchmark hacking to me. 
The real benchmark is: the world growing at 10%. 
Okay, so if the world grew at 10%, the world 
economy is $100 trillion or something, if the  
world grew at 10%, that's like an extra $10 
trillion in value produced every single year.  
If that is the case, you as a hyperscaler... 
It seems like $80 billion is a lot of money.  
Shouldn't you be doing like $800 billion?
If you really think in a couple of years,  
we could be really growing the world economy 
at this rate, and the key bottleneck would be:  
do you have the compute necessary to 
deploy these AIs to do all this work? 
That is correct. But by the way, the classic 
supply side is, "Hey, let me build it and they’ll  
come." That's an argument, and after all we've 
done that, we've taken enough risk to go do it. 
But at some point, the supply and demand have 
to map. That's why I'm tracking both sides of  
it. You can go off the rails completely when 
you are hyping yourself with the supply-side,  
versus really understanding how to 
translate that into real value to customers. 
That's why I look at my inference revenue. That's 
one of the reasons why even the disclosure on the  
inference revenue... It's interesting that not 
many people are talking about their real revenue,  
but to me, that is important as a 
governor for how you think about it. 
You're not going to say they have to symmetrically 
meet at any given point in time, but you need to  
have existence proof that you are able to parlay 
yesterday's, let’s call it capital, into today's  
demand, so that then you can again invest, 
maybe exponentially even, knowing that you're  
not going to be completely rate mismatched.
I wonder if there's a contradiction in these  
two different viewpoints, because one of the 
things you've done wonderfully is make these  
early bets. You invested in OpenAI in 2019, even 
before there was Copilot and any applications. 
If you look at the Industrial Revolution, 
these 6%, 10% build-outs of railways and  
whatever things, many of those were not 
like, "We've got revenue from the tickets,  
and now we're going to..."
There was a lot of money lost. 
That's true. So, if you really think there's 
some potential here to 10x or 5x the growth  
rate of the world, and then you're like, 
"Well, what is the revenue from GPT-4?" 
If you really think that's the possibility 
from the next level up, shouldn't you just,  
"Let's go crazy, let's do the hundreds of 
billions of dollars of compute?" I mean,  
there's some chance, right?
Here’s the interesting thing, right?  
That's why even that balanced approach to the 
fleet, at least, is very important to me. It's  
not about building compute. It's about building 
compute that can actually help me not only train  
the next big model but also serve the next big 
model. Until you do those two things, you're not  
going to be able to really be in a position 
to take advantage of even your investment. 
So, that's kind of where it's not 
a race to just building a model,  
it's a race to creating a commodity that 
is getting used in the world to drive...  
You have to have a complete thought, not 
just one thing that you’re thinking about. 
And by the way, one of the things is 
that there will be overbuild. To your  
point about what happened in the dotcom era, 
the memo has gone out that, hey, you know,  
you need more energy, and you need more compute. 
Thank God for it. So, everybody's going to race. 
In fact, it's not just companies deploying, 
countries are going to deploy capital,  
and there will be clearly... I'm so excited to 
be a leaser, because, by the way; I build a lot,  
I lease a lot. I am thrilled that I'm going 
to be leasing a lot of capacity in '27,  
'28 because I look at the builds, and I'm 
saying, "This is fantastic." The only thing  
that's going to happen with all the compute 
builds is the prices are going to come down. 
Speaking of prices coming down, you recently 
tweeted after the DeepSeek model came out about  
Jevons’ Paradox. I'm curious if you can flesh that 
out. Jevons’ Paradox occurs when the demand for  
something is highly elastic. Is intelligence 
that bottlenecked on prices going down? 
Because when I think about, at least my use cases 
as a consumer, intelligence is already so cheap.  
It's like two cents per million tokens. Do I 
really need it to go down to 0.02 cents? I'm just  
really bottlenecked on it becoming smarter. If you 
need to charge me 100x, do a 100x bigger training  
run. I'm happy for companies to take that.
But maybe you're seeing something different  
on the enterprise side or something. What is the 
key use case of intelligence that really requires  
it to get to 0.002 cents per million tokens?
I think the real thing is the utility of the  
tokens. Both need to happen: One is intelligence 
needs to get better and cheaper. And anytime  
there's a breakthrough, like even what DeepSeek 
did, with the efficient frontier of performance  
per token changes, the curve gets bent, 
and the frontier moves. That just brings  
more demand. That's what happened with cloud.
Here’s an interesting thing: We used to think  
“oh my God, we've sold all the servers in the 
client-server era”. Except once we started  
putting servers in the cloud, suddenly people 
started consuming more because they could buy  
it cheaper, and it was elastic, and they 
could buy it as a meter versus a license,  
and it completely expanded.
I remember going, let’s say,  
to a country like India and talking about 
“here is SQL Server”. We sold a little,  
but man, the cloud in India is so much bigger 
than anything that we were able to do in the  
server era. I think that's going to be true.
If you think about, if you want to really have,  
in the Global South, in a developing country, 
if you had these tokens that were available  
for healthcare that were really cheap, 
that would be the biggest change ever. 
I think it's quite reasonable for somebody to 
hear people like me in San Francisco and think  
“they're kind of silly; they don't know what it's 
actually like to deploy things in the real world”. 
As somebody who works with these Fortune 
500s and is working with them to deploy  
things for hundreds of millions, billions 
of people, what's your sense on how fast  
deployment of these capabilities will be?
Even when you have working agents, even when  
you have things that can do remote work for you, 
with all the compliance and with all the inherent  
bottlenecks, is that going to be a big bottleneck, 
or is that going to move past pretty fast? 
It is going to be a real challenge because 
the real issue is change management or process  
change. Here's an interesting thing: one of 
the analogies I use is, just imagine how a  
multinational corporation like us did forecasts 
pre-PC, and email, and spreadsheets. Faxes went  
around. Somebody then got those faxes and did an 
interoffice memo that then went around, and people  
entered numbers, and then ultimately a forecast 
came, maybe just in time for the next quarter. 
Then somebody said, "Hey, I'm just going to 
take an Excel spreadsheet, put it in email,  
send it around. People will go edit it, 
and I'll have a forecast." So, the entire  
forecasting business process changed because 
the work artifact and the workflow changed. 
That is what needs to happen with AI being 
introduced into knowledge work. In fact, when we  
think about all these agents, the fundamental 
thing is there's a new work and workflow. 
For example, even prepping for our podcast, I go 
to my copilot and I say, "Hey, I'm going to talk  
to Dwarkesh about our quantum announcement and 
this new model that we built for game generation.  
Give me a summary of all the stuff that I should 
read up on before going." It knew the two Nature  
papers, it took that. I even said, "Hey, go give 
it to me in a podcast format." And so, it even  
did a nice job of two of us chatting about it.
So that became—and in fact, then I shared it  
with my team. I took it and put it 
into Pages, which is our artifact,  
and then shared. So the new workflow for me is 
I think with AI and work with my colleagues. 
That's a fundamental change management of everyone 
who's doing knowledge work, suddenly figuring out  
these new patterns of "How am I going to get 
my knowledge work done in new ways?" That is  
going to take time. It's going to be something 
like in sales, and in finance, and supply chain. 
For an incumbent, I think that this is going 
to be one of those things where—you know,  
let's take one of the analogies I like to use 
is what manufacturers did with Lean. I love  
that because, in some sense, if you look at it, 
Lean became a methodology of how one could take  
an end-to-end process in manufacturing and become 
more efficient. It's that continuous improvement,  
which is reduce waste and increase value.
That's what's going to come to knowledge.  
This is like Lean for knowledge work, in 
particular. And that's going to be the hard work  
of management teams and individuals who are doing 
knowledge work, and that's going to take its time. 
Can I ask you just briefly about that 
analogy? One of the things Lean did  
is physically transform what a factory floor 
looks like. It revealed bottlenecks that people  
didn't realize until you're really paying 
attention to the processes and workflows. 
You mentioned briefly what your own 
workflow—how your own workflow has  
changed as a result of AIs. I'm curious if we 
can add more color to what will it be like to  
run a big company when you have these AI agents 
that are getting smarter and smarter over time? 
It's interesting you ask that. I was thinking, 
for example, today if I look at it, we are very  
email heavy. I get in in the morning, and I’m 
like, man my inbox is full, and I'm responding,  
and so I can't wait for some of these 
Copilot agents to automatically populate my  
drafts so that I can start reviewing and sending.
But I already have in Copilot at least ten agents,  
which I query them different things for 
different tasks. I feel like there's a new  
inbox that's going to get created, which is 
my millions of agents that I'm working with  
will have to invoke some exceptions to me, 
notifications to me, ask for instructions. 
So at least what I'm thinking is that there's 
a new scaffolding, which is the agent manager.  
It's not just a chat interface. I need 
a smarter thing than a chat interface to  
manage all the agents and their dialogue.
That's why I think of this Copilot,  
as the UI for AI, is a big, big deal. Each of us 
is going to have it. So basically, think of it as:  
there is knowledge work, and there's a knowledge 
worker. The knowledge work may be done by many,  
many agents, but you still have a knowledge worker 
who is dealing with all the knowledge workers.  
And that, I think, is the 
interface that one has to build. 
You're one of the few people in the world who 
can say that you have access to 200,000… you  
have this swarm of intelligence around you in 
the form of Microsoft the company and all its  
employees. And you have to manage that, and 
you have to interface with that, how to make  
best use of that. Hopefully, more of the world 
will get to have that experience in the future. 
I'd be curious about how your inbox, 
if that means everybody's inbox,  
will look like yours in the morning.
Okay, before we get to that,  
I want to keep asking you more about AI, 
but I really want to ask you about the big  
breakthrough in quantum that Microsoft Research 
has announced. So can you explain what's going on? 
This has been another 30-year journey for 
us. It's unbelievable. I'm the third CEO  
of Microsoft who's been excited about quantum.
The fundamental breakthrough here, or the vision  
that we've always had is, you need a physics 
breakthrough in order to build a utility-scale  
quantum computer that works. We took the path 
of saying, the one way for having a less noisy  
or more reliable qubit is to bet on a physical 
property that by definition is more reliable and  
that's what led us to the Majorana zero modes, 
which was theorized in the 1930s. The question  
was, can we actually physically fabricate 
these things? Can we actually build them? 
So the big breakthrough effectively, and I know 
you talked to Chetan, was that we now finally  
have existence proof and a physics breakthrough 
of Majorana zero modes in a new phase of matter  
effectively. This is why we like the analogy 
of thinking of this as the transistor moment  
of quantum computing, where we effectively have a 
new phase, which is the topological phase, which  
means we can even now reliably hide the quantum 
information, measure it, and we can fabricate it.  
And so now that we have it, we feel like with that 
core foundational fabrication technique out of the  
way, we can start building a Majorana chip.
That Majorana One which I think is going to  
basically be the first chip that will be capable 
of a million qubits, physical. And then on that,  
thousands of logical qubits, error-corrected. 
And then it's game on. You suddenly have the  
ability to build a real utility-scale quantum 
computer, and that to me is now so much more  
feasible. Without something like this, you 
will still be able to achieve milestones,  
but you'll never be able to build a utility-scale 
computer. That's why we're excited about it. 
Amazing. And by the way, I 
believe this is it right here. 
That is it.
Yes. 
I forget now, are we calling it Majorana? Yes,  
that's right. Majorana One. I'm 
glad we named it after that. 
To think that we are able to build something 
like a million-qubit quantum computer in a thing  
of this size is just unbelievable. That's the 
crux of it: unless and until we could do that,  
you can't dream of building a 
utility-scale quantum computer. 
And you're saying the eventual million qubits 
will go on a chip this size? Okay, amazing. 
Other companies have announced 100 physical 
qubits, Google's, IBM's, others. When you  
say you've announced one, but you're saying 
that yours is way more scalable in the limit. 
Yeah. The one thing we’ve also done is we've 
taken an approach where we've separated our  
software and our hardware. We're building 
out our software stack, and we now have,  
with the neutral atom folks, the ion trap folks, 
we're also working with others who even have  
pretty good approaches with photonics and what 
have you, that means there'll be different types  
of quantum computers. In fact, we have what, I 
think that the last thing that we announced was 24  
logical qubits. So we have also got some fantastic 
breakthroughs on error correction and that's what  
is allowing us, even on neutral atom and ion 
trap quantum computers, to build these 20 plus,  
and I think that'll keep going even throughout 
the year; you'll see us improve that yardstick. 
But we also then said, "Let's go to the first 
principles and build our own quantum computer  
that is betting on the topological qubit." 
And that's what this breakthrough is about. 
Amazing. The million topological 
qubits, thousands of logical qubits,  
what is the estimated timeline to scale up to 
that level? What does the Moore's law here,  
if you've got the first transistor, look like?
We've obviously been working on this for  
30 years. I'm glad we now have the physics 
breakthrough and the fabrication breakthrough. 
I wish we had a quantum computer because 
by the way, the first thing the quantum  
computer will allow us to do is build 
quantum computers, because it's going to  
be so much easier to simulate atom-by-atom 
construction of these new quantum gates. 
But in any case, the next real thing is, 
now that we have the fabrication technique,  
let us go build that first fault-tolerant quantum 
computer. And that will be the logical thing. 
So, I would say now I can say, "Oh, maybe 
'27, '28, '29, we will be able to actually  
build this." Now that we have this one gate, can I 
put the thing into an integrated circuit and then  
actually put these integrated circuits into a real 
computer? That is where the next logical step is. 
And what do you see as, in '27, '28, you've got 
it working? Is it a thing you access through  
the API? Is it something you're using internally 
for your own research in materials and chemistry?
It’s a great question. One thing that I've 
been excited about is, even in today's world…  
we had this quantum program, and we added 
some APIs to it. The breakthrough we had  
maybe two years ago was to think of this HPC 
stack, and AI stack, and quantum together. 
In fact, if you think about it, AI is like an 
emulator of the simulator. Quantum is like a  
simulator of nature. What is quantum going 
to do? By the way, quantum is not going to  
replace classical. Quantum is great at what 
quantum can do, and classical will also... 
Quantum is going to be fantastic for anything 
that is not data-heavy but is exploration-heavy  
in terms of the state space. It should be 
data-light but exponential states that you  
want to explore. Simulation is a great one: 
chemical physics, what have you, biology. 
One of the things that we've started doing 
is really using AI as the emulation engine.  
But you can then train. So the way I think of it 
is, if you have AI plus quantum, maybe you'll use  
quantum to generate synthetic data that then gets 
used by AI to train better models that know how to  
model something like chemistry or physics or what 
have you. These two things will get used together. 
So even today, that's effectively what 
we're doing with the combination of  
HPC and AI. I hope to replace some of 
the HPC pieces with quantum computers. 
Can you tell me a little bit about how 
you make these research decisions which,  
in 20 years time, 30 years time, will 
actually pay dividends, especially at  
a company of Microsoft's scale? Obviously, you're 
in great touch with the technical details in this  
project. Is it feasible for you to do that 
with all the things Microsoft Research does? 
How do you know the current bet you're making 
will pay out in 20 years? Does it just have to  
emerge organically through the org, or 
how are you keeping track of all this? 
The thing that I feel was fantastic is when Bill, 
when he started MSR back in '95 I guess. I think  
in the long history of these curiosity-driven 
research organizations, to just do a research  
org that is about fundamental research and MSR, 
over the years, has built up that institutional  
strength so when I think about capital allocation 
or budgets, we first put the chips in and say,  
"Here is MSR's budget." We gotta go at it each 
year knowing that most of these bets are not  
going to pay off in any finite time frame. Maybe 
the sixth CEO of Microsoft will benefit from it.  
And in tech that is I think a given.
The real thing that I think about is,  
when the time has come for something like 
quantum or a new model or what have you, can  
you capitalize? So as an incumbent, if you look at 
the history of tech, it's not that people didn't  
invest. It's that you need to have a culture that 
knows how to take an innovation and scale it. 
That's the hard part, quite frankly, for CEOs and 
management teams. Which is kind of fascinating.  
It's as much about good judgment as it is about 
good culture. Sometimes we've gotten it right;  
sometimes we've gotten it wrong; I can tell you 
the thousand projects from MSR that we should have  
probably led with, but we didn't. And I always 
ask myself why. It's because we were not able to  
get enough conviction and that complete thought 
of how to not only take the innovation but make  
it into a useful product with a business 
model that we can then go to market with. 
That's the job of CEOs and management teams: 
not to just be excited about any one thing,  
but to be able to actually execute on a complete 
thing. And that's easier said than done. 
When you mentioned the possibility of three 
subsequent CEOs of Microsoft, if each of them  
increases the market cap by an order of magnitude, 
by the time you've got the next breakthrough,  
you'll be like the world economy or something.
Or remember, the world is going to be growing  
at 10%, so we'll be fine.
Let's dig into the other big  
breakthrough you've just made. It's amazing that 
you have both of them coming out the same day,  
in your gaming world models. I'd love if 
you can tell me a little bit about that. 
We're going to call it Muse. It's going to be the 
model of this world action, or human action model. 
This is very cool. One of the things is 
that obviously, Dall-E and Sora have been  
unbelievable in what they've been able 
to do in terms of generative models. One  
thing that we wanted to go after was 
using gameplay data. Can you actually  
generate games that are both consistent 
and then have the ability to generate the  
diversity of what that game represents, 
and then are persistent to user mods? 
That's what this is. They were able 
to work with one of our game studios,  
and this is the other publication in Nature.
The cool thing is what I'm excited about is  
bringing--we're going to have a catalog of games 
soon that we will start using these models,  
or we're going to train these models to 
generate, and then start playing them. 
In fact, when Phil Spencer first showed it 
to me, he had an Xbox controller and this  
model basically took the input and generated the 
output based on the input. And it was consistent  
with the game. That to me is a massive moment 
of “wow”. It's kind of like the first time we  
saw ChatGPT complete sentences, or Dall-E 
draw, or Sora. This is one such moment. 
I got a chance to see some of the videos 
in the real-time demo this morning with  
your lead researcher Katja on this. Only 
once I talked to her did it really hit me  
how incredible this is, in the sense that 
we've used AI in the past to model agents,  
and just using that same technique to model 
the world around the agent gives consistent  
real-time – we'll superimpose videos of what this 
looks like atop this podcast so people can get a  
chance to see it for themselves. I guess it'll 
be out by then, so they can also watch it there. 
This in itself is incredible. You, through your 
span as CEO, have invested tens of hundreds of  
billions of dollars in building up 
Microsoft Gaming and acquiring IP. 
In retrospect, if you can just merge all of 
this data into one big model that can give  
you this experience of visiting and going 
through multiple worlds at the same time,  
and if this is the direction gaming is headed, 
it seems like a pretty good investment to have  
made. Did you have any premonition about this?
I wouldn't say that we invested in gaming to  
build models. We invested, quite frankly, because- 
here's an interesting thing about our history:  
We built our first game before we built 
Windows. Flight Simulator was a Microsoft  
product long before we even built Windows.
So, gaming has got a long history at the company,  
and we want to be in gaming for gaming's 
sake. I always start by saying I hate to  
be in businesses where they're means to some 
other end. They have to be ends unto themselves. 
And then, yes, we're not a conglomerate. 
We are a company where we have to bring  
all these assets together and be better owners 
by adding value. For example, cloud gaming is  
a natural thing for us to invest in because that 
will just expand the TAM and expand the ability  
for people to play games everywhere.
The same thing with AI and gaming:  
we definitely think that it can be helpful in 
maybe changing- it's kind of like the CGI moment,  
even for gaming long-term. And it's great. 
As the biggest, world's largest publisher,  
this will be helpful. But at the same time, 
we've got to produce great quality games. I mean,  
you can't be a gaming publisher without, sort 
of, first and foremost being focused on that. 
But the fact that this data asset is going to 
be interesting, not just in a gaming context,  
but it's going to be a general action model 
and a world model, it's fantastic. I mean like,  
you know, I think about gaming data as perhaps, 
you know, what YouTube is perhaps to Google,  
gaming data is to Microsoft. And so 
therefore I'm excited about that. 
Yeah, and that's what I meant, just in the sense 
of like, you can have one unified experience  
across many different kinds of games. How does 
this fit into the other, separate from AI,  
the other things that Microsoft has worked on 
in the past, like mixed reality? Maybe giving  
smaller game studios a chance to build these AAA 
action games? Just like five, ten years from now,  
what kinds of ways could you imagine?
I've thought about these three things  
as the cornerstones of, in an interesting way, 
even five, six, seven years ago is when I said  
the three big bets that we want to place [are] 
AI, quantum, and mixed reality. And I still  
believe in them, because in some sense, 
what are the big problems to be solved? 
Presence. That's the dream of mixed 
reality. Can you create real presence?  
Like you and I doing a podcast like this.
I think it’s still proving to be the harder  
one of those challenges, quite honestly. I thought 
it was going to be more solvable. It's tougher,  
perhaps, just because of the social 
side of it: wearing things and so on. 
We're excited about, in fact, what we're 
going to do with Anduril and Palmer, now,  
with even how they'll take forward the 
IVAS program, because that's a fantastic  
use case. And so we'll continue on that front.
But also, the 2D surfaces. It turns out things  
like Teams, right, thanks to the pandemic, 
we've really gotten the ability to create  
essentially presence through even 2D. And that 
I think will continue. That's one secular piece. 
Quantum we talked about, and AI is the other one. 
So these are the three things that I look at and  
say, how do you bring these things together? 
Ultimately, not as tech for tech's sake,  
but solving some of the fundamental things that 
we, as humans, want in our life, and more, we want  
them in our economy, driving our productivity. 
And so if we can somehow get that right,  
then I think we will have really made progress.
When you write your next book, you've got to have  
some explanation of why those three pieces 
all came together around the same time,  
right? Like, there's no intrinsic reason you 
would think quantum and AI should happen in  
2028 and 2025 and so forth.
That's right. At some level,  
I look at it and say: the simple model I have 
is, hey is there a systems breakthrough? To me,  
the systems breakthrough is the quantum thing.
Is there a business logic breakthrough? That's  
AI to me, which is: can the logic tier be 
fundamentally reasoned differently? Instead of  
imperatively writing code, can you have 
a learning system? That's the AI one. 
And then the UI side of it is presence.
Going back to AI for a second, in your  
2017 book… 2019 you invest in OpenAI, very early, 
2017 is even earlier, you say in your book, "One  
might also say that we're birthing a new species, 
one whose intelligence may have no upper limits." 
Now, super-early, of course, to be talking 
about this in 2017. We've been talking in  
a granular fashion about agents, Office 
Copilot, capex, and so forth. But if you  
zoom out and consider this statement you've 
made, and you think about you as a hyperscaler,  
as the person doing research in these models as 
well, providing training, inference, and research  
for building a new species, how do you think 
about this in the grand scheme of things? 
Do you think we're headed towards 
superhuman intelligence in your time as CEO? 
I think even Mustafa uses that term. In fact he’s 
used that term more recently, this “new species”. 
The way I come at it is, you definitely need 
trust. Before we claim it is something as big  
as a species, the fundamental thing that we've 
got to get right is that there is real trust,  
whether it's personal or societal level trust, 
that's baked in. That's the hard problem. 
I think the one biggest rate limiter to the 
power here will be how does our legal… call  
it infrastructure, we’re talking about all 
the compute infrastructure, well how does  
the legal infrastructure evolve to 
deal with this? This entire world is  
constructed with things like humans owning 
property, having rights, and being liable.  
That’s the fundamental thing that one has to first 
say, okay what does that mean for anything that  
now humans are using as tools? And if humans are 
going to delegate more authority to these things,  
then how does that structure evolve? Until that 
really gets resolved, I don't think just talking  
about the tech capability is going to happen.
As in, we won't be able to deploy these kinds  
of intelligences until we figure out how to…?
Absolutely. Because at the end of the day,  
there is no way. Today, you cannot 
deploy these intelligences unless and  
until there's someone indemnifying it as a human.
To your point, I think that's one of the reasons  
why I think about even the most powerful AI 
is essentially working with some delegated  
authority from some human. You can say, oh, that's 
all alignment and this, that, and the other.  
That's why I think you have to really get these 
alignments to work and be verifiable in some way,  
but I just don't think that you can deploy 
intelligences that are out of control. For  
example, this AI takeoff problem may be a real 
problem, but before it is a real problem, the real  
problem will be in the courts. No society is going 
to allow for some human to say, "AI did that." 
Yes. Well, there's a lot of societies in the 
world, and I wonder if any one of them might  
not have a legal system that might be more 
amenable. And if you can't have a takeoff,  
then you might worry. It doesn't 
have to happen in America, right? 
We think that no society cares about it, right? 
There can be rogue actors, I'm not saying there  
won't be rogue actors; there are cyber criminals 
and rogue states; they're going to be there. 
But to think that human society at large 
doesn't care about it is also not going  
to be true. I think we all will care. We know 
how to deal with rogue states and rogue actors  
today. The world doesn't sit around and say 
“we’ll tolerate that”. That's why I'm glad  
that we have a world order in which anyone who is 
a rogue actor in a rogue state has consequences. 
Right. But if you have this picture where you 
can have 10% economic growth, I think it really  
depends on getting something like AGI working, 
because tens of trillions of dollars of value,  
that sounds closer to the total of human wages, 
around $60 trillion of the economy. Getting that  
magnitude, you kind of have to automate labor 
or supplement labor in a very significant way. 
If that is possible, and once we figure 
out the legal ramifications for it,  
it seems quite plausible, even within your 
tenure that we figure that out. Are you  
thinking about superhuman intelligence? Like, 
the biggest thing you do in your career is this?
You bring up another point. I know David Autor 
and others have talked a lot about this which is,  
60% of labor- I think the other question 
that needs to happen, let’s at least talk  
about our democratic societies. I think that 
in order to have a stable social structure,  
and democracies function, you can’t just have a 
return on capital and no return on labor. We can  
talk about it, but that 60% has to be revalued.
In my own simple way, maybe you can call it naive,  
we'll start valuing different types of 
human labor. What is today considered  
high-value human labor may be a commodity. 
There may be new things that we will value. 
Including that person who comes to me and 
helps me with my physical therapy or whatever,  
whatever is going to be the case that we value, 
but ultimately, if we don't have return on labor,  
and there's meaning in work and dignity in work 
and all of that, that's another rate limiter  
to any of these things being deployed.
On the alignment side, two years ago,  
you guys released Sydney Bing. Just to be clear, I 
think given the level of capabilities at the time,  
it was a charming, endearing, kind 
of funny example of misalignment. 
But that was because, at the time, it was like 
chatbots. They can go think for 30 seconds and  
give you some funny or inappropriate response. 
But if you think about that kind of system--that,  
I think to a New York Times reporter, tried 
to get him to leave his wife or something--if  
you think about that going forward, and you 
have these agents that are for hours, weeks,  
months going forward, just like autonomous swarms 
of AGIs, who could be in similar ways misaligned  
and screwing stuff up, maybe coordinating with 
each other, what's your plan going forward so  
that when you get the big one, you get it right?
That is correct. That's one of the reasons why  
when we usually allocate compute, let's allocate 
compute for what is that alignment challenge? 
And then more importantly, what is the runtime 
environment in which you are really going  
to be able to monitor these things? The 
observability around it? We do deal with  
a lot of these things today in the classical 
side of things as well, like cyber. We don't  
just write software and then just let it go. 
You have software and then you monitor it.  
You monitor it for cyber attacks, you monitor 
it for fault injections, and what have you. 
Therefore, I think we will have to build 
enough software engineering around the  
deployment side of these, and then inside the 
model itself, what's the alignment? These are all,  
some of them are real science problems. 
Some of them are real engineering problems,  
and then we will have to tackle it.
That also means taking our own  
liability in all of this. So that's why I'm 
more interested in deploying these things in  
where you can actually govern what the scope of 
these things is, and the scale of these things  
is. You just can't unleash something out there in 
the world that creates harm, because the social  
permission for that is not going to be there.
When you get the agents that can really just do  
weeks worth of tasks for you, what 
is the minimum assurance you want  
before you can let it run a random Fortune 500?
I think when I use something like Deep Research,  
even, the minimum assurance I think 
we want is before we especially have  
physical embodiment of anything, that I 
think is kind of one of those thresholds,  
when you cross. That might be one place.
Then the other one is, for example,  
the permissions of the runtime environment in 
which this is operating. You may want guarantees  
that it's sandboxed, it is 
not going out of that sandbox. 
I mean, we already have web search and 
we already have it out of the sandbox. 
But even what it does with web search and 
what it writes -- for example to your point,  
if it's just going to write a bunch of 
code in order to do some computation,  
where is that code deployed? And is that 
code ephemeral for just creating that output,  
versus just going and springing 
that code out into the world? 
Those are things that you could, in 
the action space, actually go control. 
And separate from the safety issues, as you think 
about your own product suite, and you think about,  
if you do have AIs this powerful, at some 
point, it's not just like Copilot- an example  
you mentioned about how you were prepping 
for this podcast- it's more similar to how  
you actually delegate work to your colleagues.
What does it look like, given your current suite,  
to add that in? I mean, there's one question about 
whether LLMs get commodified by other things. 
I wonder if these databases or canvases or 
Excel sheets or whatever -- if the LLM is your  
main gate point into accessing all these things, 
is it possible that the LLMs commodify Office? 
It's an interesting one. The way I think 
about the first phase, at least, would be:  
Can the LLM help me do my knowledge work using 
all of these tools or canvases more effectively? 
One of the best demos that I've seen is a doctor 
getting ready for a tumor board workflow. She's  
going into a tumor board meeting, and the first 
thing she uses Copilot for is to create an agenda  
for the meeting because the LLM helps reason about 
all the cases, which are in some SharePoint site.  
It says, "Hey, these cases -- obviously, a tumor 
board meeting is a high-stakes meeting where you  
want to be mindful of the differences in cases 
so that you can then allocate the right time." 
Even that reasoning task of creating an agenda 
that knows how to split time- super. So, I use  
the LLM to do that. Then I go into the meeting, 
I'm in a Teams call with all my colleagues. I'm  
focused on the actual case versus taking notes, 
because you now have this AI copilot doing a full  
transcription of all of this. It's not just a 
transcript, but a database entry of what is in  
the meeting that is recallable for all time.
Then she comes out of the meeting, having  
discussed the case and not been distracted 
by note-taking. She's a teaching doctor;  
she wants to go and prep for her class. And so she 
goes into Copilot and says, "Take my tumor board  
meeting and create a PowerPoint slide deck out of 
it so that I can talk to my students about it." 
So that’s the type. The UI and the scaffolding 
that I have are canvases that are now getting  
populated using LLMs. And the workflow itself is 
being reshaped; knowledge work is getting done. 
Here's an interesting thing: If someone came to me 
in the late '80s and said, "You're going to have  
a million documents on your desk," I would say, 
"What the heck is that?" I would have literally  
thought there was going to be a million physical 
copies of things on my desk. Except, we do have a  
million spreadsheets and a million documents.
I don’t, you do. 
They're all there. And so, that's what's 
going to happen with even agents. There  
will be a UI layer. To me, Office is not 
just about the office of today; it's the  
UI layer for knowledge work. It'll evolve as the 
workflows evolve. That's what we want to build. 
I do think the SaaS applications that 
exist today, these CRUD applications,  
are going to fundamentally be changed because 
the business logic will go more into this agentic  
tier. In fact, one of the other cool things 
today in my Copilot experience is when I say,  
"Hey, I'm getting ready for a meeting 
with a customer," I just go and say,  
"Give me all the notes for it that I should 
know." It pulls from my CRM database, it pulls  
from my Microsoft Graph, creates a composite, 
essentially artifact, and then it applies even  
logic on it. That, to me, is going to transform 
the SaaS applications as we know of it today. 
SaaS as an industry might be worth hundreds 
of billions to trillions of dollars a year,  
depending on how you count. If really 
that can just get collapsed by AI,  
is the next step up in your next decade 10X-ing 
the market cap of Microsoft again? Because you're  
talking about trillions of dollars...
It would also create a lot of value in  
the SaaS. One thing we don't pay as much 
attention to perhaps is the amount of IT  
backlog there is in the world.
These code gen things, plus the  
fact that I can interrogate all of your SaaS 
applications using agents and get more utility  
will be the greatest explosion of apps, they'll 
be called agents, so that for every vertical,  
in every industry, in every category, we're 
suddenly going to have the ability to be serviced. 
So there's going to be a lot of value. You can't 
stay still. You can't just say the old thing of,  
"Oh, I schematized some narrow business 
process, and I have a UI in the browser,  
and that's my thing." That's ain’t going to 
be the case. You have to go up-stack and say,  
"What's the task that I have to participate in?"
You will want to be able to take your SaaS  
application and make it a fantastic agent 
that participates in a multi-agent world.  
As long as you can do that, then I 
think you can even increase the value. 
Can I ask you some questions 
about your time at Microsoft? 
Yeah.
Is being a company  
man underrated? So you've spent most of your 
career at Microsoft, and you could say that one  
of the reasons you've been able to add so much 
value is you've seen the culture, the history,  
and the technology. You have all this context by 
rising up through the ranks. Should more companies  
be run by people who have this level of context?
That's a great question. I've  
not thought about it that way.
Through my 34 years now of Microsoft,  
each year I felt more excited about being at 
Microsoft versus thinking that, oh, I'm a company  
person or what have you. I take that seriously, 
even for anybody joining Microsoft. It's not like  
they're joining Microsoft as long as they feel 
that they can use this as a platform for their  
both economic return, but also a sense of purpose 
and a sense of mission that they can accomplish by  
using us as a platform. That's the contract.
So I think yes, companies have to create a  
culture that allows people to come in and 
become company people like me. Microsoft  
got it more right than wrong, at least in 
my case, and I hope that remains the case. 
The sixth CEO that you’re talking about, who’ll 
get to use the research you’re starting now,  
what are you doing to retain the 
future Satya Nadellas so that they're  
in a position to become future leaders?
It's fascinating. This is our 50th year,  
and I think a lot about it. The way to think about 
it is, longevity is not a goal; relevance is. 
The thing that I have to do and all 200,000 of 
us have to do every day is: Are we doing things  
that are useful and relevant for the world as we 
see it evolving, not just today, but tomorrow? 
We live in an industry where there's no franchise 
value, so that’s the other hard part. If you take  
the R&D budget that we will spend this year, 
it’s all speculation on what's going to happen  
five years from now. You have to basically go 
in with that attitude, saying, "We are doing  
things that we think are going to be relevant."
So that's what you have to focus on. Then know  
that there's a batting average, and you're not 
going to get- you have to have a high tolerance  
for failure. You have to take enough 
shots on goal to be able to say, "Okay,  
we will make it to the other side as a company." 
That's what makes it tricky in this industry.
Speaking of- you just mentioned that 
you're two months away from your 50th  
anniversary of Microsoft’s founding. If you 
look at the top 10 companies by market cap,  
or top 5, basically, everybody else but 
Microsoft is younger than Microsoft. It's  
an interesting observation about why 
the most successful companies often  
are quite young. The average Fortune 
500 company will last 10 to 15 years. 
What has Microsoft done to remain relevant for 
this many years? How do you keep refounding? 
I love that, Reed Hoffman uses that term, 
"refounding." That's the mindset. People  
talk about founder mode, but for us mere 
mortal CEOs, it's more like refounder mode. 
To be able to see things again in a fresh way 
is the key. To your question: can we culturally  
create an environment where refounding becomes 
a habit thing? Every day we come in and say,  
"We feel we have a stake in this place to be 
able to change the core assumptions of what  
we do and how we relate to the world around us. Do 
we give ourselves permission?” I think many times,  
companies feel over-constrained by either 
business model or whatever. You just have  
to unconstrain yourself.
If you did leave Microsoft,  
what company would you start?
Company I would start? Man.  
That’s where the company man and me sort 
of says, “I'll never leave Microsoft.” 
If I were thinking of doing something, I think 
picking a domain that has... When I look at the  
dream of tech, we've always said technology is 
about the biggest, greatest democratizing force. 
I feel like finally, we have that ability. 
If you say those tokens per dollar per watt  
is what we can generate, I would love 
to find some domain in which that can  
be applied, where it is so underserved.
That's where healthcare, education...  
Public sector would be another place. If you take 
those domains, which are the underserved places,  
where my life as a citizen of this country or 
a member of this society or anywhere, would I  
be better off if somehow all this abundance 
translated into better healthcare, better  
education, and better public sector institutions 
serving me as a citizen? That would be a place. 
One thing I'm not sure about, hearing 
your answers on different questions,  
is whether you think AGI is a thing. Will there 
be a thing which automates all cognitive labor,  
like anything anybody can do on a computer?
This is where I have a problem with the  
definitions of how people talk about it. Cognitive 
labor is not a static thing. There is cognitive  
labor today. If I have an inbox that is managing 
all my agents, is that new cognitive labor? 
Today's cognitive labor may be automated. 
What about the new cognitive labor that  
gets created? Both of those things have 
to be thought of, which is the shifting… 
That's why I make this distinction, at least 
in my head: Don't conflate knowledge worker  
with knowledge work. The knowledge work 
of today could probably be automated.  
Who said my life's goal is to triage my 
email? Let an AI agent triage my email. 
But after having triaged my email, give me a 
higher-level cognitive labor task of, "Hey,  
these are the three drafts I really want you 
to review." That's a different abstraction. 
But will AI ever get to the second thing?
It may, but as soon as it gets to that  
second thing, there will be a third thing. 
Why are we thinking that somehow, when we  
have dealt with tools that have changed what 
cognitive labor is in history, why are we  
worried that all cognitive labor will go away?
I'm sure you've heard these examples before, but  
the idea that horses can still be good for certain 
things, there are certain terrains you can't take  
a car on. But the idea that you're going to see 
horses around the street, they’re going to employ  
millions of horses, it’s just not happening.
And then the idea is, could a similar  
thing happen with humans?
But in one very narrow dimension?  
It's only 200 years of history of humans where 
we have valued some narrow sort of things called  
"cognitive labor" as we understand it.
Let's take something like chemistry. If  
this thing, quantum plus AI really helped us 
do a lot of novel material science and so on,  
that's fantastic to have novel material science 
being done by it. Does that take away from  
all the other things that humans can do?
Why can't we exist in a world where there  
are powerful cognitive machines, knowing that 
our cognitive agency has not been taken away? 
I'll ask this question, not about you, but in a 
different scenario, so maybe you can answer it  
without embarrassment. Suppose on the Microsoft 
board, could you ever see adding an AI to the  
board? Could it ever have the judgment, context, 
and holistic understanding to be a useful advisor? 
It's a great example. One of the things we added 
was a facilitator agent in Teams. The goal there,  
it's in the early stages, is can that facilitator 
agent use long-term memory, not just on the  
context of the meeting, but with the context 
of projects I'm working on, and the team,  
and what have you, be a great facilitator?
I would love it even in a board meeting,  
where it's easy to get distracted. After all, 
board members come once a quarter, and they're  
trying to digest what is happening with a complex 
company like Microsoft. A facilitator agent that  
actually helped human beings all stay on topic and 
focus on the issues that matter, that's fantastic. 
That's kind of literally having, to your point 
about even going back to your previous question,  
having something that has infinite memory 
that can even help us. You know, after all,  
what is that Herbert Simon thing? We are all 
bounded rationality. So if the bounded rationality  
of humans can actually be dealt with because there 
is a cognitive amplifier outside, that's great. 
Speaking of materials and chemistry stuff, 
I think you said recently that you want the  
next 250 years of progress in those fields 
to happen in the next 25 years. Now, when I  
think about what's going to be possible in the 
next 250 years, I'm thinking like space travel,  
and space elevators, and immortality, and 
curing all diseases. Next 25 years, you think? 
One of the reasons why I brought that up was, I 
love that thing of, the industrial revolution was  
the 250 years. We have to take this entire change 
from a carbon-based system to something different. 
That means you have to fundamentally reinvent 
all of what has happened with chemistry over  
the last 250 years. That's where I hope we have 
this quantum computer, this quantum computer  
helps us get to new materials, and then we can 
fabricate those new materials that help us with  
all of the challenges we have on this planet. 
And then I'm all for interplanetary travel. 
Amazing. Satya, thank you so much for your time.
Thank you so much. It's wonderful. Thanks. 
Great, thank you.