claude
f882b30030
- 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
111 lines
4 KiB
Markdown
111 lines
4 KiB
Markdown
# Plan: Audience analysis
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**Target sub-goals**: 7 (multiply impact through reach)
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## Problem
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"Share it on Hacker News" is not a strategy. We need to know who
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specifically would benefit from this work, what they need, and whether
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our project delivers it in a form they can use.
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## Audience segments
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### A. AI ethics and governance professionals
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**Who**: AI Now Institute, Partnership on AI, Mozilla Foundation,
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researchers at FAccT/AIES conferences.
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**Why they care**: Social/political costs of AI are discussed qualitatively
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but never quantified or organized into an actionable taxonomy. Our framework
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is the only one that enumerates deskilling, epistemic pollution, power
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concentration, etc. alongside environmental costs.
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**What they need from us**: A citable methodology (ideally with a DOI).
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Honest confidence intervals. CC0 licensing so they can build on it.
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**Conviction level**: High — we fill a gap no one else addresses.
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### B. Sustainability researchers
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**Who**: Academics publishing on AI environmental footprint (MIT, Columbia
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Climate School, Stanford HAI).
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**Why they care**: Fragmented estimates, no shared taxonomy, low-confidence
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numbers. Our 20+ category framework provides structure.
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**What they need from us**: Peer-reviewable methodology. Transparent
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sourcing. Calibration against published data (Google, "How Hungry is AI").
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**Conviction level**: Medium — our environmental estimates are less
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rigorous than specialized tools. Value is in the breadth, not depth.
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### C. Corporate ESG teams
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**Who**: Companies subject to CSRD, GRI, ISSB S1/S2 disclosure mandates.
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**Why they care**: EU AI Act Article 51 requires energy consumption
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disclosure for GPAI models (enforcement begins August 2026). No accepted
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methodology exists yet for AI-specific reporting.
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**What they need from us**: Alignment with reporting standards. Auditability.
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Probably more rigor than we currently have.
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**Conviction level**: Low today — we lack the institutional credibility
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and audit trail they need. But our taxonomy could inform standards bodies.
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### D. AI developers who care
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**Who**: Engineers on HN, r/MachineLearning, open-source communities.
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**Why they care**: Curiosity, guilt, genuine concern. Want simple honest
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numbers.
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**What they need from us**: The toolkit (installable, low friction). The
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landing page numbers (100-250 Wh, 30-80g CO2). Something they can share.
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**Conviction level**: Medium — depends on presentation quality and whether
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the numbers feel credible.
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### E. Policy makers
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**Who**: EU AI Act implementers, NIST (directed by Congress to develop
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measurement standards), ISO SC 42.
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**Why they care**: Mandates exist but implementation standards lag.
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**What they need from us**: Probably nothing directly — they need
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institutional input. But our taxonomy could be useful as a reference
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if it gains traction with segments A and B first.
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**Conviction level**: Low for direct adoption, but indirect influence
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is possible.
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## Primary audience for launch
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**Segment A (ethics/governance) and D (developers)** are our best targets:
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- Segment A values exactly what makes us unique (social cost taxonomy)
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- Segment D is reachable via HN/Reddit and can use the toolkit immediately
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- Both can provide the feedback we need to improve
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Segments B, C, and E are secondary — they may discover us through A and D.
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## What we need to change before launch
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1. The landing page should lead with what makes us unique (breadth beyond
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carbon) rather than just the environmental numbers
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2. The methodology needs a "Related work" section so researchers see we
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know the landscape
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3. The toolkit should link to EcoLogits/CodeCarbon for users who want
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more precise environmental measurement
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## Communities to target
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| Community | Segment | Format |
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|-----------|---------|--------|
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| Hacker News | D | Link post |
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| r/MachineLearning | B, D | [Project] thread |
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| FAccT / AIES mailing lists | A | Direct email to researchers |
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| awesome-green-ai GitHub | B, D | PR to add our project |
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| Partnership on AI | A | Contact form / email |
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| Mastodon #AIethics | A, D | Thread |
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