series dict | issues list | indicators list |
|---|---|---|
{
"title": "AI Cost Watch",
"subtitle": "A recurring, dated note on the unit economics of the AI buildout: whether its cost and demand assumptions are holding. Each issue states a falsifiable forward call.",
"concept_doi": "10.5281/zenodo.20541643",
"orcid": "0009-0003-4213-7769",
"author": "NM AI Research",
... | [
{
"n": 1,
"week": "Week of 2 June 2026",
"pub": "4 June 2026",
"doi": "10.5281/zenodo.20541644",
"status": "EXPANSION",
"trigger": false,
"thread": "The week stayed bifurcated: spend did not roll over, while the demand and price side kept accumulating deflation evidence and the physical ... | [
{
"name": "Core signal: capex-guidance down-revision",
"unit": "trigger state",
"note": "The one thing this series watches. A down-revision in the Big Four's forward capex guide is the trigger.",
"readings": [
{
"issue": 1,
"value": "no trigger",
"note": "guidance not t... |
AI Cost Watch
Reproducible, primary-source analysis of the AI industry: whether the buildout's unit economics are holding. A recurring, dated note, where each issue states a falsifiable test for its forward call. Every figure is published with its data and a script that regenerates it, so any number can be checked at source.
- Author: NM AI Research (independent analyst)
- ORCID: 0009-0003-4213-7769
- Concept DOI: https://doi.org/10.5281/zenodo.20541643
- Interactive tool: https://nmairesearch.github.io/cost-watch/
- Source and code: https://github.com/NMAIResearch/cost-watch
What this is
The dataset behind the AI Cost Watch series. costwatch.json holds the frozen data: the series metadata, each issue with its status and developments, and the tracked indicators with their per-issue readings. build.py regenerates the interactive front-end from that JSON using only the Python standard library, so the published output cannot carry an unchecked number.
The tracked signal
The series watches one indicator: a down-revision in the Big Four hyperscalers' forward capital-expenditure guidance. That is the trigger it is built to catch. It has not fired in any issue to date.
Files
costwatch.json: the frozen dataset (series metadata, issues, indicators, readings).build.py: standard-library reproducer that reads the JSON and writes the front-end.LICENSE: Creative Commons Attribution 4.0 International.
Method
Separate an announced figure from the delivered one, tag each source by incentive, keep human judgement over the model's output, and set a falsifiable test for every forward call. Drafting is AI-assisted; the judgement is not.
Citation
NM AI Research. AI Cost Watch. Zenodo. https://doi.org/10.5281/zenodo.20541643 . Licensed CC BY 4.0.
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