Midterm Voters Reject Data Centers in 40 of 41 Ballot Measures, Making Grid Flexibility the Price of AI Expansion

In the November 3 midterms, voters approved restrictions or rejected data center expansion in 40 of 41 local ballot measures across 10 states, a verdict on the social costs of the AI buildout. Futurum's view is that NVIDIA and Emerald AI anticipated this moment, building a flexibility coalition around the 100 GW of grid capacity that sits stranded outside peak hours, and that OpenAI's post-election pledge to accept up to 1,000 MW of curtailment in Georgia shows the stranded-power outlet becoming the industry's standard answer to its political problem.

What is Covered in this Article

• Midterm voters backing restrictions in 40 of 41 local data center ballot measures across 10 states
• The AI Energy Management Alliance founded by Emerald AI, Google, and NVIDIA around flexible data centers
• Emerald AI's Phoenix demonstration cutting a 256-GPU cluster's power 25% for 3 hours during a grid peak
• Duke University research finding 100 GW of grid capacity unlockable through limited data center flexing
• OpenAI's National Community Compact covering electricity costs and permitting up to 1,000 MW of curtailment in Georgia
• Schneider Electric's record Q3 with triple-digit data center revenue growth and its generator-to-chip model with Wärtsilä

The News: In the November 3 midterm elections, voters approved restrictions on data center development or rejected expansion measures in 40 of 41 local ballot initiatives across 10 states, according to Ballotpedia data reported by CNBC. The results sharpen the relevance of the AI Energy Management Alliance, founded September 16 by Emerald AI, Google, and NVIDIA with 18 launch partners, which argues that data centers making verifiable flexibility commitments should receive faster grid interconnection.

Two days after the election, OpenAI published a National Community Compact pledging to cover the full cost of electricity and related infrastructure at future data centers it owns and operates, protect utility customers if projects stall, publish water and power consumption data, and avoid nondisclosure agreements with public officials. The compact builds on OpenAI's agreement with Georgia Power, which allows deliveries to its planned Effingham County campus to be reduced by up to 1,000 MW during periods of high grid demand. The same week, Steven Carlini, Vice President of Innovation and Data Center at Schneider Electric, told The Register that transparency on power procurement, water use, carbon emissions, and noise is becoming a de facto permitting requirement.

Analyst Take: The 2026 midterms put a high price on data centers' externalities, yet NVIDIA and Emerald AI have already built an outlet. A 40-to-1 ballot sweep makes community consent the scarcest input in AI infrastructure as Futurum's AI Supply Chain Market Model projects data center capex nearly doubling to $3.07 trillion in 2027 and reaching $4.00 trillion in 2028. The NVIDIA energy coalition's answer is in stranded power. The grid is built for a few hundred peak hours a year, and Duke University research finds that data centers willing to flex 25% of consumption for 2 hours at a time, fewer than 200 hours annually, could unlock 100 GW of capacity that already exists. Flexibility can convert the social costs voters just punished, higher bills and new transmission, into costs the operator pays for with scheduling software. OpenAI conceded the terms within 48 hours of the election, agreeing to pay its own power bills and accept up to 1,000 MW of curtailment in Georgia, while Schneider Electric sells the electrical layer that makes such commitments enforceable.

Election Results Convert Grid Citizenship From a Technical Standard Into a License to Operate

The ballot results end the debate about whether community resistance to data centers is a fringe concern. A 40-to-1 outcome across 10 states is a mandate, and it arrived alongside incoming Democratic legislative majorities that have signaled scrutiny of AI's effect on electricity bills.

The stakes compound from here. Futurum's AI Supply Chain Market Model, in its base case published November 2, projects total data center capex of $1.69 trillion in 2026, $3.07 trillion in 2027, and $4.00 trillion in 2028, growth of 81.7% in 2027 and 30.4% in 2028. The election put every forward dollar of that spending on notice that siting now has a political price. The implication is that whoever supplies a credible mechanism for aligning data center growth with community costs controls the pace of the buildout.

NVIDIA and Emerald AI Built the Stranded Power Outlet Before Voters Demanded One

A mechanism to address voter concerns exists and NVIDIA funded it. Emerald AI, founded by Varun Sivaram and backed by NVIDIA's NVentures, built Conductor, an orchestration platform that mediates between the grid and the AI factory by slowing flexible workloads while time-sensitive jobs run at full performance. Its proof point came in Phoenix, where Conductor cut a 256-GPU Oracle Cloud cluster's power draw 25% for 3 hours during Salt River Project's evening peak, ramping down over 15 minutes as part of EPRI's DCFlex initiative.

The economic logic is the Duke finding scaled to the national buildout. The 100 GW that limited flexing could unlock corresponds to over $2 trillion in data center investment by NVIDIA's accounting, capacity that otherwise waits in interconnection queues AEMA puts at 5 to 10 years. AEMA's 100 GW claim traces to academic modeling rather than a grid operator study and no utility has yet written flexible interconnection into a standard tariff.

Power availability now gates GPU shipments more than packaging or HBM supply, so every gigawatt freed from the existing grid is addressable market NVIDIA does not have to wait for. Stranded power is the one resource that scales faster than new generation, and the alliance structure lets NVIDIA socialize the flexibility standard across Google, Constellation, NRG, and National Grid rather than selling it alone. The social alignment is the product. A data center that sheds load during the hours that set ratepayer prices stops showing up in the electricity bills that decided these ballot measures.

OpenAI's 1,000 MW Curtailment Commitment Turns the Flexibility Thesis Into Contract Language

OpenAI's compact is the first major post-election proof point, and its substance exceeds its public relations function. Covering the full cost of electricity and infrastructure at future owned sites directly answers the ratepayer argument that decided races in Virginia, Georgia, and New Jersey in 2025 and the ballot measures this week.

The Georgia Power curtailment provision matters more for the market. A frontier AI operator has accepted, in a binding interconnection agreement, that up to 1,000 MW of its load can be shed when the grid needs the capacity. That is the Emerald AI operating model written into an executed utility agreement, even though OpenAI did not frame it in the alliance's terms.

The bear case is execution. OpenAI's compact applies to future sites it owns and operates, a minority of its contracted capacity, and its roughly $600 billion in cumulative computing commitments through 2030, based on OpenAI's investor communications, still depends on partners such as Oracle and CoreWeave whose interconnection terms remain conventional. Whether the compact becomes an industry floor depends on whether hyperscalers with older, firmer power contracts match it.

Schneider Electric Supplies the Electrical Layer That Makes Flexibility Enforceable

Orchestration software needs switchgear to act through. Schneider Electric is absent from AEMA's membership list, and the absence looks deliberate, because the company sells the metering, power distribution, and controls that execute flexibility commitments whichever coalition writes the rules.

Its Q3 2026 results give the clearest read on how much the buildout is paying. Schneider posted record quarterly revenue of €11.5 billion, up 17% organically, with data center revenue growing by triple digits and North America up 20%. Its September announcement with Wärtsilä and Stanley Consultants packages onsite engine generation, electrical architecture, and power management software into a generator-to-chip delivery model supporting grid-connected, islanded, and hybrid configurations.

The addressable pool expands with the buildout itself. Futurum's AI Supply Chain Market Model puts physical infrastructure, the power, cooling, and electrical distribution layer Schneider sells into, at 18.9% of total data center capex in 2027 and 18.0% in 2028, roughly $580 billion and $720 billion computed against the model's capex totals. Where Emerald AI schedules the flex and NVIDIA defines it at the rack, Schneider's equipment is what physically sheds the megawatts and proves it to the utility.

Flexibility Targets the Right Cost Channel and Has Yet to Deliver a Cent of It

The best-quantified link between data centers and household bills runs through peak demand, which is exactly what curtailment attacks. Monitoring Analytics, PJM's independent market monitor, attributed 63% of the price increase in PJM's 2025/26 capacity auction to data centers, roughly $9.3 billion in added consumer costs in a single year, per IEEFA's analysis, as capacity prices rose from $28.92 per MW-day to $269.92 and then $329.17 across two auctions. Capacity prices are set by projected coincident peak, so a load that verifiably sheds during the roughly 200 hours that matter stops driving the procurement behind those increases.

Delivery is where the thesis stalls. No market rule yet converts a flexibility commitment into a lower consumer charge, and a flexible data center in PJM today is accredited and billed like firm load, which leaves the 100 GW as a grid-planning argument the ratepayer cannot see. Flexing under 200 hours a year leaves annual energy consumption essentially unchanged, and transmission and distribution buildout for connected load gets socialized regardless. The 41 ballot measures were mostly about land use, water, noise, property values, and secrecy, and curtailment is invisible to a neighbor. OpenAI's own compact concedes the point, since the noise studies, water disclosure, and no-NDA pledge address everything the grid mechanism cannot. New York halted data center buildouts in summer 2026 and a Pew Research Center survey found more than half of Americans believe data centers harm the environment and household energy bills.

The counterargument is that flexibility makes blanket opposition untenable. A campus that pays its own power costs, curtails during peaks, and publishes its consumption data gives local officials something to approve. Schneider, Emerald AI, and Wärtsilä provide a mechanism by which the industry complies with its new political constraints, and compliance spending is the one line communities just voted to make mandatory.

What to Watch

• Whether Emerald AI's Conductor moves from the Phoenix pilot to production deployments at AEMA member sites in 2027
• Whether any utility writes verifiable flexibility into a standard interconnection tariff
• Whether additional hyperscalers match OpenAI's cost-coverage and curtailment terms in new agreements
• Whether incoming state legislatures convert ballot sentiment into statutory disclosure and ratepayer protection requirements in 2027 sessions
• Whether NVIDIA attaches flexibility features to its rack architecture the way it standardized liquid cooling

---

Sources

1. Setting a new standard for responsible AI infrastructure, Openai, November 2026

2. Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers, Nvidia

---

Declaration of generative AI and AI-assisted technologies in the writing process: This content has been generated with the support of artificial intelligence technologies. Due to the fast pace of content creation and the continuous evolution of data and information, The Futurum Group and its analysts strive to ensure the accuracy and factual integrity of the information presented. However, the opinions and interpretations expressed in this content reflect those of the individual author/analyst. The Futurum Group makes no guarantees regarding the completeness, accuracy, or reliability of any information contained herein. Readers are encouraged to verify facts independently and consult relevant sources for further clarification.

Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.

Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.

Read the full Futurum Group Disclosure.

---

Other Insights from Futurum:

Oracle Makes the Supercluster Multi-Silicon at AI World 2026 Cadence Maestro Puts Frontier-Class Reinforcement Learning Behind… Synopsys GPU-Accelerated EDA Shows up to 40x Gains at GTC Berlin