The labs committing to gigawatts are still looking for capacity in tens of megawatts. That is a more interesting signal for Nebius than another announcement about the eventual size of the AI market. CNBC reports that Anthropic has explored 20–30 MW deployments in the UK and Nordics, while OpenAI has explored smaller deployments in the Nordics. My read: customers are buying two things when they buy compute. Processing capacity, and the ability to start using it at a particular time. Inference makes that second dimension especially valuable. A large training run needs many GPUs communicating closely. An inference fleet can serve separate requests through independent copies of a model. Each copy may still require substantial, tightly connected infrastructure, but separate copies can operate in different locations. That changes which sites are economically useful. A pocket of available power that cannot support a giant training cluster may still support meaningful production inference. An operator with several suitable locations has more opportunities to match a customer’s workload and deadline. This is the strongest argument for the distributed part of Nebius’s strategy. Its four announced UK deployments are expected to reach 65 MW combined in 2027. They sit alongside much larger projects, including the planned 1.2 GW Pennsylvania campus. Together, these give Nebius several ways to add capacity as demand develops. There is already a commercial signal behind this. For a customer constrained by compute, waiting can mean delayed launches, tighter usage limits and demand it cannot serve. A lower future infrastructure price has to be weighed against those costs. The advantage still has to be earned. A 20–30 MW allocation can sit inside a larger campus, so this reporting does not establish that customers prefer smaller buildings. And distributing capacity creates its own problems: duplicated model copies, uneven demand, more operational work and limits on where customer data can go. Nebius has to keep those sites well utilized and deliver consistent performance. Otherwise, a broader footprint simply becomes a more complicated one. But the strategic logic is strong: regional deployments create additional opportunities to serve demand while larger projects progress. For us, the opportunity is to make more of the world’s available power useful to customers sooner. For those customers, the cost of compute includes the cost of waiting.