Yesterday's signals, distilled.

Yesterday's signals, distilled, A look back at May 15, 2026.

Power prices spiked 76% in the Eastern US as AI data centers soaked the grid. Semiconductor stocks sold off on a Trump–Xi summit that delivered no chip détente. Arm landed in the crosshairs of a US antitrust probe. SpaceX moved toward a public listing that will reprice the entire space and satellite stack.

At the same time, Heathrow bought its AI agent from Salesforce, not a startup. Google quietly gained share in LLM usage on Vercel. YouTube turned deepfake detection into a platform feature. EY pulled a client study over AI hallucinations.

The throughline: AI is no longer a “software” story. It is an infrastructure story, power, chips, orbits, and a distribution story, clouds, incumbents, platforms. Capital is rotating into hard assets and entrenched channels. The margin is in owning the rails, not the app.

If your 2026 plan assumes cheap power, frictionless access to GPUs, and greenfield AI distribution, it is mispriced. You’re not competing with other apps. You’re competing with grids, regulators, hyperscalers, and platforms that now treat AI as core infrastructure.

INFRASTRUCTURE / COMPUTE

AI demand is now a power and geopolitics problem, not just a cloud bill

Eastern US power prices spike 76% on AI data center demand

Wholesale power prices in the Eastern US jumped 76% year-over-year, driven in large part by AI data center load, per Gizmodo. Grid operators are warning of capacity constraints as hyperscale buildouts cluster near existing transmission and cheap baseload.

The same regions are also facing rising political pressure over siting, water use, and reliability as AI and crypto compete with industrial and residential demand.

The Bet: Hyperscalers are assuming they can buy their way through grid constraints faster than regulators and communities can push back.

So What? Compute is now physically constrained by power and permitting, not just chip supply. If your product depends on low-latency, GPU-heavy inference, your effective COGS is now tied to regional energy markets and local politics. Site selection, colocation, and even which cloud regions you target are now strategic decisions, not procurement details.

The Risk: If regulators move to protect residential and industrial customers, AI data centers become the marginal buyer that gets curtailed first, or taxed hardest. That can show up as sudden price hikes, throttled capacity, or forced migration between regions with little notice.

Action:

• Map your workloads to specific cloud regions and tie them to local power and permitting risk, not just to “US-East” as an abstraction.
• Stress-test your unit economics against a 50–100% increase in underlying power costs over the next