In the high-stakes race to dominate AI infrastructure, a tech giant has subtly shifted gears.
Since ChatGPT burst on the scene in late 2022, there’s been a mad dash to build as many AI data centers as possible. Big Tech is spending hundreds of billions of dollars on land, construction, and computing gear to support new generative AI workloads.
Microsoft has been at the forefront of this, mostly through its partnership with OpenAI, the creator of ChatGPT.
For two years, there’s been almost zero doubt in the tech industry about this AI expansion. It’s been all very UP and to the right.
Until recently, that is.
Pacing plans
Last Tuesday, Noelle Walsh, head of Microsoft Cloud Operations, said the company “may strategically pace our plans.”
This is pretty shocking news for an AI industry that’s been constantly kicking and screaming for more cloud capacity and more Nvidia GPUs. So it’s worth reading closely what Walsh wrote about how things have changed:
“In recent years, demand for our cloud and AI services grew more than we could have ever anticipated and to meet this opportunity, we began executing the largest and most ambitious infrastructure scaling project in our history,” she wrote in a post on LinkedIn.
“By nature, any significant new endeavor at this size and scale requires agility and refinement as we learn and grow with our customers. What this means is that we are slowing or pausing some early-stage projects,” Walsh added.
Microsoft has backed off a bit lately
She didn’t share more details, but TD Cowen analyst Michael Elias has found several recent examples of what he said was Microsoft backing off.
The tech giant has walked away from more than 2 gigawatts of AI cloud capacity in both the US and Europe in the last six months that was in the process of being leased, he said. In the past month or so, Microsoft has also deferred and canceled existing data center leases in the US and Europe, Elias wrote in a recent note to investors.
This pullback on new capacity leasing was largely driven by Microsoft’s decision to not support incremental OpenAI training workloads, Elias said. A recent change to this crucial partnership allows OpenAI to work with other cloud providers beyond Microsoft.
“However, we continue to believe the lease cancellations and deferrals of capacity point to data center oversupply relative to its current demand forecast,” Elias added.
This is worrying because trillions of dollars in current and planned investments are riding on the generative AI boom continuing at a rapid pace. With so much money on the line, any inkling that this rocket ship is not ascending at light speed is unnerving. (I asked a Microsoft spokesperson all about this twice, and didn’t get a response.)
An AI recalibration, not a retreat
The reality is more nuanced than a simple pullback, though. What we’re witnessing is a recalibration — not a retreat.
Barclays analyst Raimo Lenschow put the situation in context. The initial wave of this industry spending spree focused a lot on securing land and buildings to house all the chips and other computing gear needed to create and run AI models and services.
As part of this AI “land grab,” it’s common for large cloud companies to sign and negotiate leases that they end up walking away from later, Lenschow explained.
Now that Microsoft feels more comfortable with the amount of land it has on hand, the company is likely shifting some spending to the later stages that focus more on buying the GPUs and other computing gear that go inside these new data centers.
“In other words, over the past few quarters, Microsoft has ‘overspent’ on land and buildings, but is now going back to a more normal cadence,” Lenschow wrote in a recent note to investors.
Microsoft still plans $80 billion in capital expenditures during its 2025 fiscal year and has guided for year-over-year growth in the next fiscal year. So, the company probably isn’t backing away from AI much, but rather becoming more strategic about where and how it invests.
From AI training to inference
Part of the shift appears to be from AI training to inference. Pre-training is how new models are created, and this requires loads of closely connected GPUs, along with state-of-the-art networking. Expensive stuff! Inference is how existing models are run to support services such as AI agents and Copilots. Inference is less technically demanding but is expected to be the larger market.
With inference outpacing training, the focus is shifting toward scalable, cost-effective infrastructure that maximizes return on investment.
For instance, at a recent AI conference in New York, the discussion was focused more on efficiency rather than attaining AGI, or artificial general intelligence, a costly endeavor to make machines work better than humans.
AI startup Cohere noted that its new Command A model only needs two GPUs to run. That’s a heck of a lot less than most models have required in recent years.
Microsoft’s AI chief weighs in
Mustafa Suleyman, CEO of Microsoft AI, echoed this in a recent podcast. While he acknowledged a slight slowdown in returns from massive pre-training runs, he emphasized that the company’s compute consumption is still “unbelievable” — it’s just shifting to different stages of the AI pipeline.
Suleyman also clarified that some of the canceled leases and projects were never finalized contracts, but rather exploratory discussions — part of standard operating procedure in hyperscale cloud planning.
This strategic pivot comes as OpenAI, Microsoft’s close partner, has begun sourcing capacity from other cloud providers, and is even hinting at developing its own data centers. Microsoft, however, maintains a right of first refusal on new OpenAI capacity, signaling continued deep integration between the two companies.
What does this all mean?
First, don’t mistake agility for weakness. Microsoft is likely adjusting to changing market dynamics, not scaling back ambition. Second, the hyperscaler space remains incredibly competitive.
According to Elias, when Microsoft walked away from capacity in overseas markets, Google stepped in to snap up the supply. Meanwhile, Meta backfilled the capacity that Microsoft left on the table in the US.
“Both of these hyperscalers are in the midst of a material year-over-year ramp in data center demand,” Elias wrote, referring to Google and Meta.
So, Microsoft’s pivot may be more a sign of maturity, than retreat. As AI adoption enters its next phase, the winners won’t necessarily be those who spend the most — but those who spend the smartest.
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