Satya and Scott Guthrie, Microsoft's EVP of Cloud and AI, give us a tour of their brand new Fairwater 2 data center, the current most powerful in the world. We've tried to 10x the training capacity every 18 to 24 months. So this would effectively be a 10x increase from what GPT-5 was trained with. So to put it in perspective in the number of optics, the network optics in this building is almost as much as all of Azure across all our data centers two and a half years ago. It's got like five million network connections.

You've got all this bandwidth between different sites in a region and between the two regions. The goal is to be able to aggregate these flops for a large training job and then put these things together across sites. The reality is you'll use it for training and then you'll use it for data gen, you'll use it for inference in all sorts of ways. It's not like it's going to be used only for one workload forever.

Fairwater 4, which you're going to see under construction nearby, will also be on that one petabit network so that we can actually link the two at a very high rate. Then we do the AI WAN connecting to Milwaukee where we have multiple other Fairwaters being built. Literally you can see the model parallelism and the data parallelism. It's kind of built for, essentially, the training jobs, the super pods across this campus. And then with the WAN, you can go to the Wisconsin data center. You literally run a training job with all of them getting aggregated.

Many folks who have been on Dwarkesh's podcast believe this is the final technological revolution or transition, and that this time is very, very different. At least so far in the markets, in three years we've already skyrocketed to hyperscalers doing $500 billion of capex next year, which is a scale that's unmatched to prior revolutions in terms of speed. The end state seems to be quite different.

I start with the excitement that I also feel for the idea that maybe after the Industrial Revolution this is the biggest thing. I start with that premise. But at the same time, I'm a little grounded in the fact that this is still early innings. We've built some very useful things, we're seeing some great properties, these scaling laws seem to be working. I'm optimistic that they'll continue to work.

I like one of the things that Raj Reddy has as a metaphor for what AI is. He's a Turing Award winner at CMU. He had this, even pre-AGI. He had this metaphor for AI, it should either be a guardian angel or a cognitive amplifier. I love that. It's a simple way to think about what this is. Ultimately, what is its human utility? It is going to be a cognitive amplifier and a guardian angel. If I view it that way, I view it as a tool.

But then you can also go very mystical about it and say this is more than a tool. It does all these things, which only humans did before so far. But that has been the case with many technologies in the past. Only humans did a lot of things, and then we had tools that did them.

As we go from that transition to where your business is today, there's also a transition going on after that. Software-as-a-service has incredibly low incremental cost per user. There's a lot of R&D, there's a lot of customer acquisition costs. This is sort of why, not Microsoft, but the SaaS companies have underperformed massively in the markets, because the COGS of AI is just so high, and that just completely breaks how these business models work. How do you, as perhaps the greatest software-as-a-service company, transition Microsoft to this new age where COGS matters a lot and the incremental cost per user is different?

It's a great question because in some sense with the business models themselves, the levers are going to remain similar. If you look at the menu of models starting from consumer all the way, there will be some ad unit, there will be some transaction, there will be some device gross margin for somebody who builds an AI device. There will be subscriptions, consumer and enterprise, and then there'll be consumption. So I still think those are all the meters. To your point, what is a subscription? Up to now, people like subscriptions because they can budget for them. They are essentially entitlements to some consumption rights that come encapsulated in a subscription. So I think that in some sense becomes a pricing decision.

The interesting thing is that at Microsoft, the good news for us is we are in that business across all those meters. At a portfolio level, we pretty much have consumption, subscriptions, to all of the other consumer levers as well. I think time will tell which of these models make sense in what categories.

During the transition from server to cloud, one of the questions we used to ask ourselves is, "Oh my God, if all we did was just basically move the same users who were using our Office licenses and our Office servers at the time to the cloud, and we had COGS, this is going to not only shrink our margins but we'll be fundamentally a less profitable company." Except what happened was the move to the cloud expanded the market like crazy.

We sold a few servers in India, we didn't sell much. Whereas in the cloud suddenly everybody in India also could afford fractionally buying servers, the IT cost. So this AI thing will be that. If you take coding, what we built with GitHub and VS Code over decades, suddenly the coding assistant is that big in one year. That I think is what's going to happen as well, which is the market expands massively.

Another vision is that over time these models which are now doing tasks that take two minutes, in the future, they'll be doing tasks that take 10, 30 minutes. In the future, maybe they're doing days worth of work autonomously. Then the model companies are charging thousands of dollars maybe for access to, really, a coworker which could use any UI to communicate with their human and migrate between platforms. If we’re getting closer to that, why aren't the model companies that are just getting more and more profitable, the ones that are taking all the margin? Why is the place where the scaffolding happens, which becomes less and less relevant as the AI becomes more capable, going to be that important?

Does all the value migrate just to the model? Or does it get split between the scaffolding and the model? I think that time will tell. But my fundamental point also is that the incentive structure gets clear. Let’s take information work, or take even coding. If you win the scaffolding—which today is dealing with all the hobbling problems or the jaggedness of these intelligence problems, which you kind of have to—if you win that, then you will vertically integrate yourself into the model just because you will have the liquidity of the data and what have you.

There are enough and more checkpoints that are going to be available. That's the other thing. Structurally, I think there will always be an open source model that will be fairly capable in the world that you could then use, as long as you have something that you can use that with, which is data and a scaffolding. I can make the argument that if you're a model company, you may have a winner's curse. You may have done all the hard work, done unbelievable innovation, except it's one copy away from that being commoditized. Then the person who has the data for grounding and context engineering, and the liquidity of data can then go take that checkpoint and train it. So I think the argument can be made both ways.

First of all, we are absolutely going to use the OpenAI models to the maximum across all of our products. That's the core thing that we're going to continue to do all the way for the next seven years, and not just use it but then add value to it.

With the MAI model, the way that I think we’re going to think about it is that the good news here with the new agreement is we can be very, very clear that we're going to build a world-class superintelligence team and go after it with a high ambition. But at the same time, we're also going to use this time to be smart about how to use both these things. That means we will, on one end, be very product-focused, and on the other end, be very research-focused. Because we have access to the GPT family, the last thing I want to do is use my flops in a way that is just duplicative and doesn't add much value.

I want to be able to take the flops that we use to generate a GPT family and maximize its value, while my MAI flops are being used for… Let's take the image model that we launched, which I think is at number nine in the image arena. We're using it both for cost optimization, it's on Copilot, it's in Bing, and we're going to use that. We have an audio model in Copilot. It's got personality and what have you. We optimized it for our product. So we will do those.

So when I think about the MAI roadmap, we are going to build a first-class superintelligence team. We are going to continue to drop, and do it in the open, some of these models. They will either be used in our products, because they're going to be latency-friendly, cost-friendly, or what have you, or they'll have some special capability. And we will do real research in order to be ready for the next five, six, seven, eight breakthroughs that are all needed on this march towards superintelligence—while exploiting the advantage we have of having the GPT family that we can work on top of as well.

Since then, let’s call it, in the second half of last year, Microsoft did this big pause, where they let go of a bunch of leasing sites that they were going to take... This goes back a little bit to, what is the hyperscale business all about? One of the key decisions we made was that if we're going to build out Azure to be fantastic for all stages of AI—from training to mid-training to data gen to inference—we just need fungibility of the fleet.

So that entire thing caused us basically not to go build a whole lot of capacity with a particular set of generations. Because the other thing you have to realize is that having up to now 10x'ed every 18 months enough training capacity for the various OpenAI models, we realized that the key is to stay on that path. But the more important thing is to have a balance, to not just train, but to be able to serve these models all around the world.

By the way, the other thing is that I didn't want to get stuck with massive scale of one generation. We just saw the GB200s, the GB300s are coming. By the time I get to Vera Rubin, Vera Rubin Ultra, the data center is going to look very different because the power per rack, power per row, is going to be so different. The cooling requirements are going to be so different. That means I don't want to just go build out a whole number of gigawatts that are only for a one-generation, one family. So I think the pacing matters, the fungibility and the location matters, the workload diversity matters, customer diversity matters and that's what we’re building towards.

But this seems quite different. Today, the political aspect of technology, of compute… We're sort of in a bipolar world, at least with the US and China, but Europe and India and all these other countries are saying, "No, we're going to have sovereign AI as well." How does Microsoft navigate the difference to the 90s—where there's one country in the world that matters, it's America, and our companies sell everywhere and therefore Microsoft benefits massively—to a world where it is bipolar?

It's a super critical piece. I think that the key, key priority for the US tech sector and the US government is to ensure that we not only do leading innovative work, but that we also collectively build trust around the world on our tech stack. Because I always say the United States is just an unbelievable place. It's just unique in history. It's 4% of the world's population, 25% of the GDP, and 50% of the market cap. I think you should think about those ratios and reflect on it. That 50% happens because quite frankly the trust the world has in the United States, whether it's its capital markets or whether it's its technology and its stewardship of what matters at any given time in terms of leading sector.

So therefore I applaud anything that the United States government and the tech sector jointly does to, for example, put our own capital at risk, collectively as an industry, in every part of the world. I would like the USG to take credit for foreign direct investment by American companies all over the world. It's the least talked about, but the best marketing that the United States should be doing is that it's not just about all the foreign direct investment coming into the United States, but the most leading sector, which is these AI factories, are all being created all over the world. By whom? By America and American companies.

And so you start there, and then you even build other agreements around it, which are around their continuity, their legitimate sovereignty concerns, around whether it's data residency, for them to have real agency and guarantees on privacy, and so on. In fact, our European commitments are worth reading. We made a series of commitments to Europe on how we will govern our hyperscale investment there such that the European Union and the European countries have sovereignty.

Ultimately, what matters is the use of AI in their economy to create economic value. That's the diffusion theory, which ultimately, it's not the leading sector, but it's the ability to use the leading technology to create your own comparative advantage. So I think that will fundamentally be the core driver. But that said, they will want continuity of that. So in some sense, that's one of the reasons why, I believe, there's always going to be a check to "Hey, can this one model have all the runaway deployment?" That's why open source is always going to be there.

Concentration risk and sovereignty, which is really agency, those are the two things that will drive the market structure. In fact, you just made the point of why trust in American tech is probably the most important feature. It's not even the model capability, maybe. It is, "can I trust you, the company, can I trust you, your country, and its institutions to be a long-term supplier?" That may be the thing that wins the world.