claude f882b30030 Add pre-launch plans: competitive landscape, audience, impact measurement

- Competitive landscape: maps existing tools (CodeCarbon, EcoLogits, etc.)
  and research, identifies our unique positioning (breadth beyond carbon)
- Audience analysis: identifies 5 segments, recommends targeting ethics/
  governance professionals and developers first
- Project impact measurement: defines success thresholds and metrics to
  determine whether the project itself is net-positive

2026-03-16 10:21:00 +00:00

3.2 KiB

Raw Blame History

Plan: Competitive landscape analysis

Target sub-goals: 7 (multiply impact through reach), 12 (honest arithmetic)

Problem

Before sharing the project, we need to know what already exists so we can position honestly. If a better alternative exists, we should point people to it rather than duplicating effort.

Landscape (as of March 2026)

Tools that measure energy/carbon

Tool	Scope	Covers social costs?	Per-conversation?
CodeCarbon	Training energy/CO2	No	No
EcoLogits	Inference energy/CO2 via APIs	No	Yes
ML CO2 Impact	Training carbon estimate	No	No
Green Algorithms	Any compute workload	No	No
HF AI Energy Score	Model efficiency benchmark	No	No

Published research with per-query data

Google/Patterson et al. (Aug 2025): 0.24 Wh, 0.03g CO2, 0.26 mL water per median Gemini text prompt. Most rigorous provider-published data. Environmental only. (arXiv:2508.15734)
"How Hungry is AI?" (Jegham et al., May 2025): Cross-model benchmarks for 30 LLMs. o3 and DeepSeek-R1 consume >33 Wh for long prompts. Claude 3.7 Sonnet ranked highest eco-efficiency. (arXiv:2505.09598)

Frameworks that go broader

UNICC/Frugal AI Hub (Dec 2025): TCO + SDG alignment. Portfolio-level, not per-conversation. No specific social cost categories.
CHI 2025 deskilling research: Empirical evidence that AI assistance reduces critical thinking. Academic finding, not a measurement tool.
Oxford "Hidden Cost of AI" (2025): Descriptive survey of social costs. Not quantitative or actionable.

What no one else does

No existing tool or framework combines per-conversation environmental measurement with social/cognitive/political cost categories. The tools that measure well (CodeCarbon, EcoLogits) only cover environmental dimensions. The research that names social costs is descriptive, not actionable.

Our positioning

Honest differentiator: We are the only framework that enumerates 20+ cost categories — environmental, financial, social, epistemic, political — at per-conversation granularity.

Honest weakness: Our environmental estimates have lower confidence than Google's or EcoLogits' because we don't have access to infrastructure data. Our social cost categories are named and described but mostly not quantified.

We are not competing with: CodeCarbon, EcoLogits, or AI Energy Score. These are measurement tools for specific environmental metrics. We are a taxonomy and framework. We should reference and link to them, not position against them.

Tasks

Add a "Related work" section to impact-methodology.md citing the tools and research above, with honest comparison
Calibrate our energy estimates against Google's published data and the "How Hungry is AI" benchmarks
Link to EcoLogits and CodeCarbon from the toolkit README as complementary tools

3.2 KiB Raw Blame History