The New AI Arms Race Is Measured in Watts

Microsoft will scale its Maia chips to multi-gigawatt deployments in 2027 and make them the main system serving Copilot and the rest of its products. The announcement is about electricity, and electricity is what AI companies can't get fast enough.

Satya Nadella called it Microsoft's "biggest bet yet on our own silicon." In a post on Microsoft's official blog on Monday, he said Microsoft will scale its Maia chips to multi-gigawatt deployments starting next calendar year, and that Maia will become the primary inference system for Microsoft's own products and services.

He also introduced Maia 300, which he said produces 40 percent more tokens per watt than "leading GPU-based systems." Its predecessor, the Maia 200, is already deployed in the U.S., Europe and Australia, according to the post, and delivers up to 1.7 times more tokens per watt across Teams and Copilot.

Look at the units. Chip announcements usually lead with FLOPS, or with a benchmark win over Nvidia. Nadella led with tokens per watt and sized the rollout in gigawatts. Microsoft can order more chips. What it can't get quickly is more power to run them, so the chip that gets the most AI out of each watt is the one that counts.

Power is the bottleneck now

Gartner estimated in June that data center power demand will hit 132 gigawatts this year, up from 104 gigawatts in 2025, and reach 290 gigawatts by 2030. A Gartner analyst on the report said AI capacity is now limited by power availability. A Morgan Stanley analysis from August put numbers on the U.S. gap: data centers need roughly 68 gigawatts of new power between 2026 and 2028, and only about 30 gigawatts of that is covered by projects already underway or under contract.

Chips arrive in months. Grid connections take years, and new generation takes longer. Once a building's power supply is set, efficiency decides how much a company can sell out of it. A data center that runs 40 percent more tokens per watt can serve 40 percent more requests on the same allocation, with no new substation.

Nadella has been building toward this for a while. In early 2025, asked what he'd do if he ever left Microsoft, he said he'd work on "tokens per watt per dollar." Google pitched its Ironwood TPU on the same terms last year, calling power one of the main constraints on AI and claiming twice the performance per watt of the generation before it.

Copilot moves onto Microsoft's own chips

The line with the biggest consequences is the one about where Maia goes. Microsoft is making it the default for its own inference, which means Copilot, Teams and the AI inside Microsoft 365 will increasingly run on chips Microsoft designs rather than GPUs it buys.

The Information reported in August that this was Microsoft's fallback if it couldn't win a major outside customer for Maia: move its own AI onto Maia and keep renting Nvidia GPUs to the Azure customers who pay for them. Monday's post makes that plan official, at a scale of gigawatts.

Every Copilot response served on an Nvidia GPU carries Nvidia's margin. Served on Maia, it doesn't, and the GPU it would have used can be rented to an Azure customer instead. Microsoft gets cheaper inference for its own products and more sellable capacity for everyone else.

Microsoft is late, and smaller

The Maia program's history is less impressive than the post suggests. The first Maia chip was introduced in November 2023 and shipped in limited volume. When the Maia 200 launched in January, Microsoft said it was running in one Azure region near Des Moines, with Phoenix next. By June, Nadella said two data centers were using it, and The Information reported that Microsoft had built only tens of thousands of the chips. If Maia 200 is now running in Europe and Australia too, the rollout sped up sharply over the summer. Microsoft hasn't said how many chips that involves.

On volume, the gap with Google is wide. Microsoft has been negotiating with TSMC for more than 300,000 Maia 300 chips for delivery in 2027 and wants capacity for more than a million. Morgan Stanley estimates that Google planned to make more than 3 million TPUs this year and 5 million in 2027. Andrew Wall, who runs Azure Maia, told Reuters the reported figures don't reflect the program's scale. Even if the real number is several times higher, Google is producing at a different order of magnitude.

Our read

Nadella has the metric right. When power is the constraint, tokens per watt is the number that decides what AI costs, and whoever leads on it can charge less or keep more. Enterprise buyers should start asking their vendors about it.

The figures in the post are much harder to judge. "Leading GPU-based systems" doesn't name a product. Microsoft hasn't said which models, workloads or settings produced the 40 percent, or which GPUs it compared against. The 1.7x figure for Maia 200 uses a different measure from the 30 percent better performance per dollar Microsoft claimed in January, and the post doesn't say what it's measured against either. These are Microsoft's numbers until someone outside Microsoft reproduces them.

Tokens per watt also depends on the model. Custom chips tend to do best on the models they were tuned for. That works for Microsoft, because it decides what Copilot runs. It's less certain for Azure customers bringing their own models. It also sits awkwardly next to what Nadella wrote on Saturday, when he told companies that no single model should be the only dependency for an important outcome. A fleet tuned for a short list of models makes those models the cheapest to run, and cost tends to settle arguments about which model to use.

For enterprises, the bigger consequence is pricing. We've argued that the labs are serving enterprise customers at a loss and that token repricing is coming. When it arrives, vendors who run inference on their own efficient silicon will have room to hold prices. Vendors renting GPUs at market rates won't. Microsoft is trying to make sure it lands in the first group, and so are Google and Amazon.

What to watch

The first test is independent numbers. Until Maia 300 shows up in public benchmarks or a third party measures it, the 40 percent is a claim.

The second is an outside customer. Microsoft has been pitching Maia 300 to Anthropic, which already has deals for roughly a million chips from Amazon and Google and is building its own chip team. Landing a customer that size would show Maia works beyond Microsoft's own software.

The third is Copilot pricing in 2027. If Maia cuts Microsoft's serving costs as much as the post implies, the savings will show up in Microsoft's margins, in customers' bills, or in both. The split will show who the efficiency is for.

For three years, the AI race was scored in parameters and benchmark points. On Monday, Microsoft's CEO said he's keeping score in watts. Google started counting years earlier.