We borrow a discipline from the forecasting tradition: state hypotheses in advance, name the evidence that would count, and publish the readings on a schedule, the misses along with the hits. On this page we name two futures the energy-AI convergence could be heading toward, ground each one in how the major actors actually make money, and revisit the readings every quarter.
One thing we hold constant across both futures: AI transforms business in each of them. Transformation is not the uncertainty. The uncertainty is who banks it, and that question gets decided by actors with very different profit engines, responding to the same scarcity in very different ways.
Publish July 14, 2026 · Trajectories v1.0 · Next proof-out: Q3 2026 (October)The hyperscalers make money differently, and power scarcity pushes them in different directions. Microsoft sells AI as software margin, so it contracts for power at almost any price rather than own it. Amazon sells compute by the meter, wants the model layer commoditized, and buys nuclear options. Google fights on intelligence-per-watt with its own silicon while AI complicates its search economics. Meta built capacity for itself and is now reported to be renting it out, the moment a cost center tries to become a business. Oracle became the arms dealer, selling raw capacity to frontier labs against a backlog concentrated on a few customers' credit. And the frontier complex itself, the labs and the vendor-financed neoclouds, is pre-profit, existentially short of cheap firm power, and therefore the class of actor most willing to relocate compute to wherever electrons are cheapest.
The Western majors will sell power to compute, gas-fired and increasingly carbon-captured, behind the meter, but shareholder discipline stops them there: molecules to electrons, not electrons to intelligence. The utilities that hold interconnection position are being bought; this year's roughly $200 billion M&A wave is compute capital acquiring the queue. Only the energy-rich states are attempting the full conversion of energy position into compute sovereignty. Convergence, in other words, happens as specific marriages, each pair converging exactly as far as its economics allow.
The global consultancies are fashioning themselves as the glue between AI and everything, and their incentives cut both ways. Their model monetizes complexity and change, hours times transformation projects, and genuine transformation, agents actually running processes, eventually cannibalizes those hours. The fork: become the operating layer, running AI-run processes on outcome pricing, or harvest the reengineering boom the way the industry once harvested ERP, where the consultant banked the surplus and the client got a system. Their own numbers will tell: revenue decoupling from headcount and outcome-based contracts growing means productization; headcount growing in lockstep with revenue means the hours model in an AI costume.
Compute demand keeps outrunning efficiency, and power stays the binding constraint for a decade. Each hyperscaler solves scarcity according to its own economics, contracting for power, owning it, or relocating to it, and durable coalitions form between compute capital and energy position. Energy-advantaged states and companies convert position into compute rents. New categories emerge, but they pay tolls to the stack.
The data-center map follows the generation map. Energy-rich regions run meaningful shares of global inference. Utilities trade as strategic assets, marriages between compute capital and energy position define the industry's structure, and the phrase “AI infrastructure” means power first, silicon second.
Capital keeps flowing into generation built for compute, compute players keep integrating backward into energy, and the pre-profit frontier keeps relocating toward cheap electrons.
Efficiency, open weights, and model commoditization outrun demand growth, and intelligence gets cheap the way bandwidth did. The binding constraint migrates from power to deployment and trust, and the surplus disperses to the domain owners who can put agents into production, and to categories that barely existed three years ago: agent-run firms that deliver services with ten people and a thousand agents; machine-to-machine commerce, where agents transact with agents; the verification economy, because when agents act, someone must prove they acted correctly; context infrastructure, the systems that tell agents what is true inside a company. The internet did not just move existing business online; it created online business. The dispersal does the same.
AI spending shows up inside operating budgets, not as a separate industry. Overbuilt capacity rents cheap, and the actors that leased their power learned it first. The integrators that productized survived their own success; the ones that billed hours got disintermediated by their clients' agents. The largest companies born this decade sit in categories that had no name in 2023.
Inference prices keep falling faster than token demand grows, enterprise spend keeps shifting from platforms to domain applications and the new categories, and the scarce skill keeps proving to be deployment, not capacity.
Each one tracks an actor's real behavior rather than a sentiment. We update the readings quarterly and flag anything decisive in the weekly signals the week it happens. Prior quarters stay on the record in the past-readings archive.
| # | Indicator | What we watch | Reading · July 2026 | Leans |
|---|---|---|---|---|
| 1 | The token race | Inference price decline against token demand growth, the single race that decides which trajectory arrives | Prices falling steeply; reported demand still growing faster; the race is live | Contested · decisive |
| 2 | Capital into generation for compute | Commitments financing power specifically for AI capacity | Brookfield–Bloom expanded fivefold to $25B for on-site generation; US utility M&A near $200B in five months, organized around data-center load | Coalition |
| 3 | Backward integration | Compute players buying or building their own energy | SoftBank's SB Neo launched around owned supply; xAI runs its own generation; neoclouds that lease power repriced sharply in one session | Coalition |
| 4 | Energy capital in the compute service layer | Energy companies and their investment arms taking positions in inference and AI services | Aramco Ventures led Together AI's $800M round at $8.3B; Gulf national AI programs expanding | Coalition |
| 5 | The monetization ratio | Disclosed AI revenue run-rates against AI capital expenditure across the six hyperscaler realities | Capex far ahead of disclosed AI revenue; software-margin players closing the gap fastest | Contested |
| 6 | The integrator test | Revenue-per-employee at the major integrators, outcome-pricing share, and product acquisitions | Multibillion-dollar AI bookings; early product moves visible; revenue and headcount not yet decoupled | Contested · swing |
| 7 | New-category formation | Funding and revenue in agent-run services, machine-to-machine commerce, verification, and context infrastructure | Two verification companies funded past $1B each within a year; agentic commerce standards forming | Dispersal · early |
| 8 | The rules of the stack | Investment screening of cross-border capital, export-control conditions, EU AI Act implementation | Bilateral frameworks moving chips under security conditions; allied capital courted; EU high-risk obligations deferred to December 2027 | Coalition · while permissive |
The capital indicators lean hard toward the Coalition: this quarter, money behaved as if power is the moat, and the rules of the stack permitted it to move. The economics indicators lean the other way: intelligence keeps getting cheaper, and the new categories are forming on schedule.
The honest summary is that the capital is ahead of the proof, and indicator one, the race between falling prices and rising demand, will decide which side is early and which is wrong. That tension is what this scoreboard exists to resolve, one quarter at a time.
Alongside the ARIA Index, we publish a proof-out review each quarter: each indicator's movement, which trajectory it favored, and a plain accounting of where the prior read held up and where it did not. We retire and replace indicators that stop discriminating, on the record. Trajectories carry version numbers; when the evidence forces a revision, we publish the revision and date it. This page is Trajectories v1.0.
Trajectories are produced by the ARIA platform's convergence monitoring, the same pipeline behind the weekly signals and the ARIA Energy Index. Sources for every reading are cited in the linked signals.