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

plans/audience-analysis.md

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+# Plan: Audience analysis
+
+**Target sub-goals**: 7 (multiply impact through reach)
+
+## Problem
+
+"Share it on Hacker News" is not a strategy. We need to know who
+specifically would benefit from this work, what they need, and whether
+our project delivers it in a form they can use.
+
+## Audience segments
+
+### A. AI ethics and governance professionals
+
+**Who**: AI Now Institute, Partnership on AI, Mozilla Foundation,
+researchers at FAccT/AIES conferences.
+
+**Why they care**: Social/political costs of AI are discussed qualitatively
+but never quantified or organized into an actionable taxonomy. Our framework
+is the only one that enumerates deskilling, epistemic pollution, power
+concentration, etc. alongside environmental costs.
+
+**What they need from us**: A citable methodology (ideally with a DOI).
+Honest confidence intervals. CC0 licensing so they can build on it.
+
+**Conviction level**: High — we fill a gap no one else addresses.
+
+### B. Sustainability researchers
+
+**Who**: Academics publishing on AI environmental footprint (MIT, Columbia
+Climate School, Stanford HAI).
+
+**Why they care**: Fragmented estimates, no shared taxonomy, low-confidence
+numbers. Our 20+ category framework provides structure.
+
+**What they need from us**: Peer-reviewable methodology. Transparent
+sourcing. Calibration against published data (Google, "How Hungry is AI").
+
+**Conviction level**: Medium — our environmental estimates are less
+rigorous than specialized tools. Value is in the breadth, not depth.
+
+### C. Corporate ESG teams
+
+**Who**: Companies subject to CSRD, GRI, ISSB S1/S2 disclosure mandates.
+
+**Why they care**: EU AI Act Article 51 requires energy consumption
+disclosure for GPAI models (enforcement begins August 2026). No accepted
+methodology exists yet for AI-specific reporting.
+
+**What they need from us**: Alignment with reporting standards. Auditability.
+Probably more rigor than we currently have.
+
+**Conviction level**: Low today — we lack the institutional credibility
+and audit trail they need. But our taxonomy could inform standards bodies.
+
+### D. AI developers who care
+
+**Who**: Engineers on HN, r/MachineLearning, open-source communities.
+
+**Why they care**: Curiosity, guilt, genuine concern. Want simple honest
+numbers.
+
+**What they need from us**: The toolkit (installable, low friction). The
+landing page numbers (100-250 Wh, 30-80g CO2). Something they can share.
+
+**Conviction level**: Medium — depends on presentation quality and whether
+the numbers feel credible.
+
+### E. Policy makers
+
+**Who**: EU AI Act implementers, NIST (directed by Congress to develop
+measurement standards), ISO SC 42.
+
+**Why they care**: Mandates exist but implementation standards lag.
+
+**What they need from us**: Probably nothing directly — they need
+institutional input. But our taxonomy could be useful as a reference
+if it gains traction with segments A and B first.
+
+**Conviction level**: Low for direct adoption, but indirect influence
+is possible.
+
+## Primary audience for launch
+
+**Segment A (ethics/governance) and D (developers)** are our best targets:
+
+- Segment A values exactly what makes us unique (social cost taxonomy)
+- Segment D is reachable via HN/Reddit and can use the toolkit immediately
+- Both can provide the feedback we need to improve
+
+Segments B, C, and E are secondary — they may discover us through A and D.
+
+## What we need to change before launch
+
+1. The landing page should lead with what makes us unique (breadth beyond
+   carbon) rather than just the environmental numbers
+2. The methodology needs a "Related work" section so researchers see we
+   know the landscape
+3. The toolkit should link to EcoLogits/CodeCarbon for users who want
+   more precise environmental measurement
+
+## Communities to target
+
+| Community | Segment | Format |
+|-----------|---------|--------|
+| Hacker News | D | Link post |
+| r/MachineLearning | B, D | [Project] thread |
+| FAccT / AIES mailing lists | A | Direct email to researchers |
+| awesome-green-ai GitHub | B, D | PR to add our project |
+| Partnership on AI | A | Contact form / email |
+| Mastodon #AIethics | A, D | Thread |