AWS Energy Lead: Utilities Must Use AI to Clear the Grid Interconnection Backlog

The United States electricity grid is carrying a backlog of more than 2,500 gigawatts of pending interconnection requests — new power plants, battery storage, and data centers waiting years for approval to connect to the grid because the review process cannot keep pace with demand. Amazon Web Services' energy lead is now making the case that AI is the fastest viable path to clearing that backlog, and that utilities not deploying AI for interconnection modeling are choosing to fall behind.

The argument, made in a piece published by Utility Dive, carries obvious self-interest — AWS is one of the largest drivers of new electricity demand in the country and has a direct financial stake in getting data centers connected faster. But the technical argument stands independently: AI tools can run thousands of grid scenarios simultaneously, something human engineering teams cannot do at any reasonable cost or timeline.

The Interconnection Backlog, Explained

When a power plant, battery storage facility, or large industrial customer wants to connect to the electrical grid, it submits an interconnection request. Grid operators must then model how that new connection would affect existing infrastructure: power flows, voltage stability, transmission congestion, and what upgrades would be required. Each study requires months of engineering work by specialized teams.

The backlog has grown faster than review capacity. As of mid-2026, the interconnection queues of the country's major grid operators — PJM, MISO, CAISO, and others — collectively held more than 2,500 gigawatts of pending requests, roughly double the entire installed generating capacity of the United States. Projects routinely wait three to five years for their interconnection studies to be completed. Many drop out of the queue entirely without ever connecting.

AI data centers are a significant driver of this surge. AWS, Microsoft Azure, and Google Cloud have been filing interconnection requests across the country at a pace unprecedented in the history of electricity infrastructure. But the backlog predates the AI infrastructure build-out. The fundamental problem is a systemic capacity mismatch between how many new connections are being requested and how many engineering teams exist to review them.

How AI Changes the Math

AWS's energy lead describes AI tools that can model thousands of grid scenarios simultaneously — testing how a proposed new connection would interact with every existing generation source, transmission line, and load center in the relevant region. Traditional engineering processes test scenarios sequentially, with human engineers reviewing and validating each one.

The acceleration this enables is not marginal. Current interconnection studies that take 18 to 36 months could be completed in weeks with AI-assisted modeling under AWS's framing. That's a change in kind, not degree — shifting the interconnection process from a structural bottleneck into a system that can match the pace of new energy development.

The AI approach also enables better optimization. Human engineers, working sequentially through a limited number of scenarios, will identify an interconnection path that works. AI modeling of thousands of scenarios simultaneously can identify the path that works best — minimizing required grid upgrades, reducing cost for both the applicant and the ratepayer, and finding connection points that weren't obvious from a smaller scenario set.

The Utility Adoption Challenge

The technical case is strong; the implementation is harder. Grid operators are regulated entities with significant risk aversion built into their institutional structure. Interconnection studies directly affect grid reliability — errors in modeling, whether from human engineers or AI systems, can contribute to outages affecting millions of customers. The regulatory incentive structure rewards accuracy and caution, not speed.

Several grid operators have begun experimenting with AI-assisted modeling, but adoption is uneven. PJM launched a pilot in Q2 2026 to use AI for initial screening of interconnection requests before full engineering review. MISO has been testing AI tools for load forecasting, a related but distinct modeling task. None has moved AI into the core interconnection study workflow at full scale.

AWS's framing — that utilities need AI to solve the backlog — is intended to accelerate the regulatory conversation as much as the technical one. Grid operators make interconnection decisions under Federal Energy Regulatory Commission oversight, and any significant change to the review process requires FERC engagement. AWS is effectively lobbying regulators, not just utilities.

The Irony of AI as Both Problem and Solution

AWS is simultaneously one of the largest drivers of new electricity demand in the country and the company making the loudest public case for AI-driven grid management. The same technology creating the demand surge is being positioned as the primary solution to the infrastructure crisis the surge is generating.

That's not inherently a contradiction — there are examples throughout industrial history of technologies creating problems they then help manage. But it does mean the case for AI-driven interconnection modeling is being made most forcefully by the parties with the most financial interest in seeing the backlog cleared quickly. That's worth factoring into how the argument is evaluated.

Impact for the Energy Sector

For utilities and grid operators, the question is no longer whether to explore AI for grid modeling — several already have AI tools running in planning and operations. The question is whether to extend those tools into the interconnection review process, which carries higher regulatory scrutiny and higher consequences for errors than demand forecasting or outage management.

For developers of renewable energy, battery storage, and data center infrastructure, faster interconnection reviews would materially accelerate project timelines and reduce the capital cost of multi-year waiting periods. A two-year wait for interconnection approval carries real financing costs and opportunity cost. Cutting that to three months changes the economics of entire project categories.

What to Watch

FERC's engagement with AI-assisted interconnection modeling is the regulatory signal to watch. The commission is expected to issue guidance on AI use in interconnection studies in 2027. Watch also for the results of PJM's current AI pilot — if the pilot demonstrates accuracy and speed improvements, it will accelerate adoption conversations at other grid operators. If it surfaces accuracy concerns, it will push the timeline back significantly.

The outcome will determine whether AI-driven interconnection modeling becomes a mainstream grid management tool within two years or remains an experiment at the margins of utility operations for the rest of the decade.