Vanderbilt Law Professor Challenges Incumbent Utility Moves on Transmission Planning in MISO and SPP
An op-ed published Monday argues that incumbent utility behavior in two major US grid regions is blocking the competitive transmission buildout that AI-era power demand requires.
Jim Rossi, a professor at Vanderbilt Law School, published a direct challenge Monday (2026-08-10) in Utility Dive to how incumbent utilities in MISO and SPP conducted themselves during competitive transmission planning in April (2026), arguing that consumer and AI economy interests were the casualties of incumbent behavior in two of the country's largest grid regions.5
His argument lands as the grid faces overlapping strain. America's transmission infrastructure is aging, new generation is queuing faster than it can interconnect, and data centers and heavy manufacturing facilities are adding load at a rate the existing interconnection system was not designed to handle. Congestion and capacity shortfalls are already influencing where developers choose to site power-intensive facilities — decisions that carry billions in capital investment.5
Incumbent utilities have structural reasons to resist competitive transmission processes. Rate-base infrastructure earns regulated returns, which gives incumbents a direct financial interest in owning the solution rather than opening planning to outside developers who might build faster or at lower cost. Rossi's op-ed frames the April (2026) MISO and SPP utility moves in those terms, arguing that incumbent behavior adds cost and delay that ultimately lands on ratepayers and electricity offtakers, including the data center operators whose demand is driving the buildout.5
Maximizing the capacity of existing grid assets through advanced transmission technologies, improved energy management, and efficiency investment offers a faster complement to new construction, the Atlantic Council noted in analysis published in June (2026-06-01). Reconductoring, dynamic line ratings, and topology optimization can expand available capacity without the permitting and construction lead times attached to greenfield builds. The constraint in many cases is not physical; it is the commercial and regulatory environment that determines who plans and builds.1
Grid operators and regulators are testing demand-side tools as well. Treating flexible load as a grid asset through demand response and consumer participation programs can reduce peak capacity requirements before new wire arrives, Power Magazine reported in June (2026-06-08). Scaling those programs to the volume that data center demand requires takes market structures that incumbents have had little incentive to create on their own.2
A workforce problem runs alongside the infrastructure one. Utilities are losing engineers and project managers to hyperscalers at precisely the moment the transmission buildout requires exactly those skills. Many of the capabilities needed to deploy advanced grid technologies already sit inside utility organizations, Utility Dive reported in July (2026-07-23), but converting that institutional knowledge into project delivery requires sustained investment in upskilling and retention that few utilities have prioritized. Hyperscalers can simply outbid utilities on compensation.4
The pressure extends beyond the domestic grid. Countries are reorganizing their economies around the integration of energy supply and AI compute capacity. Abu Dhabi has structured its development strategy around combining power infrastructure and AI into a single economic model, OilPrice.com reported in July (2026-07-06). How open US grid planning becomes to competition partly shapes whether American data center developers can secure interconnection at the scale their AI build programs require.3
For independent transmission developers and competitive generators with projects in MISO and SPP queues, the FERC response to the April (2026) utility interventions is the near-term variable with the most direct commercial weight. If regulators hold competitive access principles firm, the pipeline of projects seeking to serve data center load has a clearer path to completion. If incumbents succeed in narrowing that access, the cost and timeline penalties fall on developers first, and eventually on the electricity prices paid by the AI companies the entire buildout is meant to serve.5,1