AI Data Center Timeline
The AI buildout is a moving target. This hub connects the year archive, the global chronological feed, and operator and country feeds so readers can track where new capacity is appearing and how fast the infrastructure wave is spreading.
Facilities with at least one public timing field populated.
Distinct years currently exposed as public archive pages.
One feed per tracked operator for company-specific monitoring.
National feeds for country-level infrastructure tracking.
Buildout Waves by Year
Year pages are the broadest chronology layer. They group facilities by the strongest public timing field currently available, creating a usable archive now while milestone-specific coverage matures.
Use the Timeline Surface
Global feed for the broad chronological view across operators, countries, and facility types.
Year pages for acceleration waves, launch clusters, and archive-style browsing.
Operator pages and their feeds to watch Microsoft, Google, Meta, AWS, Oracle, and others separately.
Country pages and their feeds to track national buildouts and sovereign AI pushes.
Coverage transparency to judge how complete the milestone data is before drawing trend conclusions.
Operator Feeds to Watch
These are the biggest operator surfaces in the current index. Each has a dedicated feed page and feed XML endpoint.
Country Feeds to Watch
Country feeds make the site useful for journalists and analysts following national infrastructure races.
Milestone Coverage Readiness
The current feed surface is strongest where `start_year` is present. These deeper milestone fields determine how far the site can move from a general chronology into true announced, construction, and operational event tracking.
100% of tracked facilities.
Needed for true announcement feeds and launch waves.
Needed for broke-ground and buildout progress tracking.
Needed for live-capacity and commissioning timelines.
Related Surfaces
Timeline analysis is strongest when paired with investment tracking, location hubs, and dataset completeness. For Apple Silicon inference throughput related to the site’s hardware tools, use SiliconBench.