---
license: cc-by-4.0
language:
- en
tags:
- ai
- ai-economics
- finance
- reproducible-research
- primary-source
pretty_name: AI Cost Watch
---

# 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.