Methodology · v0.1

How the grades are assigned

A price can move at the same time AI demand grows without AI causing the change. The grades on this site are about that causal link.

The rubric

Four grades for a specific claim

AI-driven

AI demand is the main documented cause in the market and period being studied.

AI-contributing

AI demand has a measurable effect alongside other documented causes.

Contested

The available measures do not isolate AI’s effect or produce a consistent causal result.

Scapegoated

The cited evidence contradicts the claim or does not show how AI caused the result.

Unit of analysis

Use the narrowest claim the evidence can answer

“Data centers increase electricity demand” and “AI raised my bill” are different claims. The first has national evidence. The second requires utility-level evidence. Each page keeps the original wording in view so a narrow finding does not turn into a broader claim when it is repeated.

Evidence hierarchy

Start with primary sources

  1. Measured public seriesBLS, EIA, FRED, queue and tariff data.
  2. Agency analysisMethods and models with definitions, ranges and dates.
  3. Filings and producer statementsCapacity and order-book evidence from commercially interested sources.
  4. Peer-reviewed researchEvidence for mechanisms and uncertainty; publication dates define its market period.
  5. Trade press and anecdotesLead sources that require confirmation from primary evidence.

Causal checklist

Five questions on every report card

  1. What exactly moved: price, wage, allocation, lead time or capacity?
  2. What is the proposed AI-demand pathway?
  3. Which non-AI buyers and supply shocks share the market?
  4. What fair comparison would help isolate the effect?
  5. What evidence would change the grade?

Index policy

How the index is calculated

The Crowding Index reports basket coverage, each component’s contribution and sensitivity under different weights. Missing observations remain blank. The changelog preserves every revision.

Review process

A person reviews every update

Software fetches releases, flags changed values and drafts summaries. A named editor approves every publication and grade change. Each update records what changed, why it changed, the source, the check date and the editor’s approval.

Conflicts and corrections

The capital-markets ledger gets extra scrutiny

The editor is a partner in a crypto fund whose returns benefit when capital moves away from competing AI infrastructure. The capital-markets ledger therefore requires a preregistered comparison method and external review. Corrections are judged on the evidence and logged whether they strengthen or weaken an AI attribution.