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
4 KiB
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
- The landing page should lead with what makes us unique (breadth beyond carbon) rather than just the environmental numbers
- The methodology needs a "Related work" section so researchers see we know the landscape
- 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 |