AI Wattch - Track Your AI Footprint

June 22, 2026 · View on GitHub

Discover how your AI usage impacts the planet. Measure, compare, and optimize your AI footprint in real time.

Available on:

  1. Chrome Web Store
  2. Firefox Store

Project Summary

AI Wattch is an open-source browser extension powered byAntarctica’s One Token Model (OTM) that estimates the energy use and carbon footprint of end-user interactions with LLM-powered chat interfaces such as ChatGPT, Claude and Gemini. It combines token-based and time-based estimation, regional infrastructure mapping, and model-specific parameters to deliver transparent, science-backed emissions reporting per session.

What's New

Persistent Usage History (v1.2)

  • Period selector — the Sessions card now has a dropdown to switch between Today, This week (rolling 7 days), This month (rolling 30 days), and All time views. Data persists across browser restarts.
  • Usage history — daily energy, CO₂ and water records are stored locally (365-day rolling window) so you can track trends over time.
  • All-time totals — a running cumulative total is maintained separately and never pruned.
  • Per-model breakdown — history is tracked per product (Claude / ChatGPT / Gemini) so the period views show per-model splits.
  • Session vs. history reset — the refresh button now clears only the current live session; a new "Clear history" button in Settings wipes the stored history after confirmation.

Why AI Wattch

  • Transparency: Makes invisible energy costs visible - per session, per token, per model.
  • Efficiency: Helps people prompt more efficiently and choose more efficient models.
  • Privacy: Prioritizes privacy; no chat text leaves the browser.
  • Scalability: Built for extensibility: multi-model, multi-region, multi-browser roadmap.

How it works (high level)

  1. The extension parses the page DOM for supported chat UIs (ChatGPT, Claude and Gemini).
  2. It captures lightweight telemetry (timestamps, token counts, model selection) - never full chat text. Here is the full API documentation.
  3. Two estimation approaches run (token-based & time-based). Both can be combined into a hybrid estimate.
  4. Regional factors (PUE, grid carbon intensity) and model-specific hardware stats (TDP, quantization) convert energy → emissions.
  5. The UI surfaces real-time metrics, session summaries, model comparisons, and prompt-efficiency tips.

Supported Models & Platforms

Initially supported (V1.5 / V2.0 planned mapping):

  • ChatGPT model family (manual selection for ChatGPT Pro/Plus; automatic detection for free-tier where possible)
  • Claude family (automatic detection via DOM parsing)
  • Gemini (manual selection for Gemini Pro/Plus; automatic detection where possible)
  • Planned: other LLMs (modular architecture supports adding new detectors)

Browsers

  • Chrome (MV3) - current release
  • Firefox - current release

Methodologies (overview)

AI Wattch runs two complementary estimators and a hybrid orchestration:

I. Token-based estimator (DOM-derived token proxy)

  • Counts characters/tokens from DOM, converts to tokens (default 4 chars/token, configurable 3–5).
  • Uses token energy factors (input/output) and infrastructure multipliers (PUE, grid intensity) to compute energy and emissions.

II. Time-based estimator (timestamp-derived compute duration)

  • Uses timestamps T1 (request), T2 (first token), T3 (last token) to derive computation time.
  • Maps computation time to GPU power, utilization, server baseline, and PUE → energy → emissions.

III. Hybrid & model-aware approach (Antarctica enhancements)

  • Uses model-specific metadata (total/active params, quantization, estimated active GPUs, GPU TDP & memory, token generation rate) to refine active GPU count and per-token energy.
  • Dynamically applies regional PUE and carbon intensity based on IP region or manual selection.
  • Handles edge cases (summarization, streaming vs. batch, cached responses fallback logic).

Install / Build / Run (developer)

Prerequisites

  • Node.js (LTS recommended)
  • npm or yarn
  • Chrome (for load-unpacked development)

Clone & Install

git clone https://github.com/AIWattch/AI-Wattch.git
cd ai-wattch-ext
npm install

Development Build (watch)

npm run dev

Load dist/ via chrome://extensions → Developer mode → Load unpacked → select browser-extension/dist.

Production Build

npm run build:extension

Artifact appears in dist/. Use packaged release workflow for Chrome Web Store.

Release

  1. Create a release branch release/vX.Y.Z
  2. Update CHANGELOG.md and package.json version
  3. npm run build:extension → create zip → upload to GitHub Release and Chrome Web Store.
  • Language: TypeScript (strict mode)
  • Linting: ESLint + Prettier (pre-commit hook)
  • Commit style: Conventional commits (feat/fix/chore/docs)
  • Branching: feature branches, one feature per PR
  • PR checklist (required): build passes, tests pass, docs updated, minimal surface area

Testing

Unit tests: Vitest Run tests:

npm test

Tests cover:

  • Token counting & character → token mapping
  • Timestamp-based computation logic
  • Basic model-detection flows (mock DOM)
  • Regional lookup fallbacks
  • Daily record upsert & per-model accumulation
  • All-time total increment and isolation from daily records
  • Quota guard (storage near-full skips daily write)
  • 365-day pruning boundary (day 364 kept, day 366 removed)

End-to-end simulation with recorded DOM payloads (place in tests/fixtures/)

Contributing & Governance

We welcome contributions. Please follow these steps:

  1. Fork the repo.
  2. Create branch feature/<short-description>.
  3. Commit with a clear message; open a PR.
  4. Add tests and update docs.
  5. One feature per PR; link relevant issue.

Pre-PR: For major architectural or methodology changes, open an issue to discuss design and data assumptions (methodology is research-sensitive). Maintain transparency in how variables are chosen and cite sources in PR descriptions.

Docs to Add/Maintain

Privacy & Security

  • AI Wattch does not send chat contents off-device.
  • The extension collects minimal telemetry (token counts, timestamps, model id) used only for computation.
  • IP-based region detection is optional - users can manually set the region (privacy-first).
  • For any detected security/privacy issue: follow SECURITY.md and do not open a public issue; contact maintainers.

Short Glossary & Variable Origin

  • EcoLogits: baseline token energy literature.
  • Artificial Analysis: latency & generation rate estimates.
  • ArXiv: academic sources used for deriving token → latency relations and GPU utilization assumptions.
  • Quantization Q: bytes per parameter mapping (INT4=0.5, INT8=1, FP16=2, FP32=4). Overhead factor ~1.2 applied.

Detection & Model Identification (implementation notes)

  • Claude: DOM parsing available - use robust selectors and feature flags; test extensively against different Claude UIs.
  • ChatGPT: detection possible via DOM; provide a manual model dropdown and clear UX to set model if detection fails.
  • Gemini: detection possible via DOM
  • Fallbacks: assume global average PUE & carbon intensity if location/model cannot be resolved.

Example Test Cases (to include under tests/fixtures)

  • Short QA exchange (50 tokens output): verify token and time estimators align within tolerance.
  • Long summarization (15,000-word doc): ensure chunking recommendation triggers and energy spike is reported.
  • Rapid retries (3 prompts within 3 minutes): ensure repetitive/iterative category detection and nudge.

License & Credits

  • License: MIT (see LICENSE file).
  • Built by Antarctica & IT Climate Ed with contributions from the open-source community. See AUTHORS.md.