The AI capacity you are planning around assumes the data centers actually get built. A lot of them will not.

Tracking in June 2026 suggests that 30 to 50 percent of roughly 140 planned US data centers, aiming at around 16 gigawatts of capacity, could miss their timelines or be cancelled. The reasons are not software problems. Transformers now carry multi-year lead times. Grid connections wait on utilities that cannot approve them fast enough. And in a growing number of counties, local opposition over power and water is stopping projects before they break ground. SpaceX even flagged water access as a constraint in its own IPO filing. When the blocker is a physical transformer and a zoning hearing, no clever code routes around it.

If you are building products on top of GPUs you do not own, this matters more than it looks. The compute roadmap you are quietly depending on is not guaranteed, and the specific region you want it in is even less so.

Capacity scarcity is a design input now

Most teams treat inference capacity like electricity from a wall socket. Always there, always cheap enough, always in the region you picked. That assumption held for a while. It is getting shaky.

When supply tightens, three things happen at once. Prices on the newest accelerators stay high or climb. Availability in popular regions gets rationed through quotas and waitlists. And the gap between providers widens, because whoever actually secured power and silicon can serve you and the others cannot. Plan as if at least one of your preferred regions is unavailable when you need to scale. That is not pessimism. It is just reading the lead times.

Build for where the power actually is

The practical move is to stop hard-wiring your product to a single provider, a single region, or a single model class.

Keep your inference layer behind an abstraction so you can shift workloads between providers without a rewrite. A thin internal interface today saves a painful migration later.
Treat model choice as configurable, not baked in. If your top model is rationed or priced out, you want to fall back to a smaller or open-weights model for the requests that can tolerate it.
Measure cost and latency per request, not just in aggregate. You cannot make good tradeoffs about what to downgrade if you cannot see which calls are expensive.
Design for graceful degradation. A feature that drops to a cheaper model or a cached answer under load beats one that simply fails.

None of this is exotic. It is the same portability discipline good teams already apply to cloud regions and databases, pointed at compute and models instead.

Efficiency is the cheapest capacity you can get

The capacity you do not consume is the only capacity nobody can ration. Before you assume you need more GPUs, look at what you are spending tokens on. Caching repeated calls, trimming bloated prompts, batching where latency allows, and routing easy requests to small models often cuts real usage by a meaningful chunk. In a tight market, that work pays back twice: lower bills today, and headroom when supply gets scarce.

The teams that struggle in a crunch will be the ones who assumed the build-out would arrive on schedule and architected as if compute were infinite. The teams that do well will have made scarcity a first-class assumption all along.

We are here to help founders and teams design and build digital products that are built to scale with you, not slow you down. If you are looking to build something, get in contact with us today!