PANTHERFIELD RESEARCH
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Power at the point of compute

Flagship thesis · 29 September 2026 · AI infrastructure

The scarce asset in AI infrastructure is not electricity in the abstract. It is reliable power deliverable to a qualified compute site, at an economic price, on the date it is needed.

The investment thesis

AI capacity is being built on a timeline measured in quarters. Grid connections, power equipment and permitting move on different clocks. The research case is selective: assets with verified access to power, a workable cost of service and customers able to pay for that capacity. A broad bet that every generator or data centre wins from AI demand is not the thesis.

Evidence that anchors the view

1
Demand is rising, but the forecast is a scenarioIEA's April 2026 central case moves global data-centre electricity use from 485 TWh in 2025 to 950 TWh by 2030, about 3% of world demand. This includes non-AI workloads. AI-focused centres grew faster than the whole sector in 2025.
2
The bottleneck has a physical locationIEA's 2025 analysis warned that around 20% of planned projects could face delays if grid risks are not addressed. Connection queues, transformers and cables are constraints, not marketing claims.
3
Announced capacity is not delivered loadEPRI separates nominal IT capacity, utility nameplate request and realized peak demand; project completion and ramp rates determine the gap. Do not value a queue position as energized capacity.
4
Regulators are allocating costsFERC ordered PJM in December 2025 to make rules for co-located large loads that protect reliability and consumers. Commercial advantage must survive the eventual tariff and interconnection terms.

Where to look

Screen for sites with binding interconnection milestones, traceable equipment orders, buildable cooling and credible off-take. Then examine grid equipment, power distribution, storage and flexible-load systems that solve a defined constraint. A pipeline of announced megawatts is not the same as contracted, energized, billable megawatts.

The economic test is blunt: revenue and utilization must support the full delivered-power cost, construction capital, curtailment risk and a margin of safety. Evaluate each stage of the power chain, not the AI label attached to it.

Underwriting questions

1
ConnectionWhat is signed, paid and scheduled with the utility or grid operator? Which milestones could slip?
2
Power economicsWhat are the tariff, upgrade contribution, backup, cooling and reliability costs? Who bears overruns and curtailment?
3
Demand qualityWho is the customer, what capacity is contracted, when does billing begin and what supports their credit?
4
Asset and exitWhat is replacement cost, remaining useful life, financing exposure and the downside value without the hoped-for AI premium?

Countercase and what changes the call

Efficiency per AI task is improving quickly, and project pipelines can double-count requests. Capital spending may outrun profitable AI use; local opposition, water constraints, tariffs and financing can impair delivery. The IEA's 2026 update says bottlenecks make the most aggressive near-term demand cases less likely even as the central case remains strong. These are reasons to demand project-level proof, not to ignore the structural shift.

Reduce conviction if successive IEA updates show sustained demand deceleration, energization and contracted utilization fall well short of announced capacity, equipment lead times normalize without a durable service premium, or tariff changes transfer economics away from the project. Increase conviction only when independently verified connections, off-take and unit economics improve together.

Method and sources

Desk research using primary energy-system analysis, a national laboratory scenario report, industry grid modelling and a regulator's order. No site, contract, tariff, off-take or management interview was independently verified for a specific investment. Figures are source estimates or scenarios, not forecasts by Pantherfield.