April 30 is the deadline FERC was given to act on the Department of Energy's directive on 'large load' interconnection — the federal rule that governs how data centers above 20 megawatts attach to the bulk transmission grid. Secretary Chris Wright told FERC in October to issue an advance notice of proposed rulemaking that would let customers file joint load-and-generation interconnection requests, accelerate study timelines, and apply a 100% participant-funding model in which the data-center customer pays for the upgrades it triggers.
The 100% participant-funding piece is the part that will reshape the AI build-out. Today, transmission upgrades are partially socialized across all ratepayers in a region, which has been a quiet subsidy to hyperscale data centers and a growing political problem in states like Virginia, Ohio, and California. Forcing the full cost onto the data-center customer changes the unit economics of where to build — moving compute toward markets with cheap interconnection, distressed industrial sites, or behind-the-meter generation.
This rulemaking sits at the intersection of three live trends: hyperscaler capex hitting roughly $725B for 2026, IEA forecasts of global data-center electricity demand reaching 1,100 TWh, and twenty-seven states advancing their own data-center bills. FERC's response sets the federal floor; states and utilities will fill in the rest. Whether the agency issues an ANOPR today or delays will shape how fast the next gigawatt of AI capacity comes online.
Takeaway for learners: AI infrastructure is no longer a software story. The bottleneck for the next two years is power — interconnection queues, transformer supply, and who pays for grid upgrades. If you are studying AI, spend at least an hour learning how electricity markets and FERC actually work; that knowledge is now adjacent to the field, not foreign to it.