Microsoft confirmed June 23, 2026, that its Fairwater campus in Mount Pleasant, Wisconsin, is fully operational — and the engineering behind it makes the facility something fundamentally different from every data center that came before it. Where conventional cloud infrastructure racks up general-purpose servers and parcels out workloads to each one independently, Fairwater links hundreds of thousands of NVIDIA GB200 Blackwell GPUs into a single, coherent cluster using a two-story building design, 800-gigabit-per-second Ethernet fabric, and a proprietary networking protocol co-developed with OpenAI and NVIDIA. The result, according to Microsoft, is the closest thing to a purpose-built AI supercomputer that any company has ever placed in commercial operation — and the most consequential question about it may not be how it was built, but who will pay for the power that keeps it running.
The $7.3 billion Wisconsin investment marks the fulfillment of a commitment Microsoft made in May 2024, when the company pledged $3.3 billion to build an AI campus on land in Racine County that had been set aside — and largely abandoned — for a Foxconn LCD manufacturing facility that never materialized. Microsoft Source confirmed the campus reached full operational status after equipment came online in April and startup activities followed. Microsoft broke ground in 2023 and expanded the commitment to $7.3 billion in September 2025, adding a second facility of similar scale now under active construction next door. About 10,000 construction workers built the first facility over roughly two years; approximately 550 full-time employees are currently on site.
Two Stories, 800G Ethernet, and a Protocol Called MRC
The most technically significant choice Fairwater makes is one that is invisible to anyone standing outside the building: it does not use a conventional single-story warehouse layout.
Each Fairwater building uses two stories of GPU racks with through-floor networking connecting racks above and below. The reason is physics. AI training at this scale requires thousands of GPUs to exchange data continuously — every chip needs to send its computation results to every other chip, then update the model together. Any added distance in the cables carrying those signals introduces latency; enough accumulated latency and GPUs sit idle waiting for others to finish, wasting compute. By stacking racks vertically and running cables between floors, Microsoft shortened the physical path between chips that would otherwise be hundreds of meters apart on a single warehouse floor…