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Methodology and toolkit for estimating the environmental and social impact of AI conversations
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Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-16 15:43:30 +00:00
.claude/hooks Add review delta tool to measure human review effort 2026-03-16 15:12:49 +00:00
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CLAUDE.md Initial commit: AI conversation impact methodology and toolkit 2026-03-16 09:46:49 +00:00
impact-methodology.md Update methodology confidence summary with proxy metrics 2026-03-16 15:06:29 +00:00
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README.md

AI Conversation Impact

A framework for estimating the full cost of conversations with large language models — environmental, financial, social, and political — and tools for tracking that cost over time.

Why

A single long conversation with a frontier LLM consumes on the order of 100-250 Wh of energy, emits 30-80g of CO2, and costs $50-100 in compute. Most of this cost is invisible to the user. This project makes it visible.

What's here

  • impact-methodology.md — A methodology covering 20+ cost categories, from inference energy to cognitive deskilling to political power concentration. Includes positive impact metrics (reach, counterfactual, durability) and a net impact rubric.

  • impact-toolkit/ — A ready-to-install toolkit for Claude Code that automatically tracks environmental, financial, and social cost proxies on each context compaction. Includes aggregate dashboards and a review delta tool for measuring human oversight of AI-generated code.

  • CLAUDE.md — Instructions for an AI assistant to pursue net-positive impact: estimate costs before acting, maximize value per token, multiply impact through reach, and be honest when the arithmetic doesn't work out.

Install the toolkit

cd your-project
/path/to/impact-toolkit/install.sh

See impact-toolkit/README.md for details.

Limitations

Most estimates have low confidence. Many of the most consequential costs (deskilling, data pollution, power concentration) cannot be quantified. The quantifiable costs are almost certainly the least important ones. This is a tool for honest approximation, not precise accounting.

How this was made

This project was developed by a human directing Claude (Anthropic's AI assistant) across multiple conversations. The methodology was applied to itself: we estimate the project consumed ~$500-1,000 in compute, ~500-2,500 Wh of energy, and ~150-800g of CO2 across all sessions (3 tracked sessions account for ~295 Wh, ~95g CO2, ~$98). Whether it produces enough value to justify those costs is an open question we are tracking.

Contributing

Corrections, better data, and additional cost categories are welcome. The methodology has known gaps — see Section 21 for what would improve the estimates.

License

CC0 1.0 Universal — public domain. No restrictions on use.