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/README.md

@@ -19,6 +19,9 @@ broad, lasting value.
 | [reusable-impact-tooling](reusable-impact-tooling.md) | 7, 8, 9 | Published |
 | [usage-guidelines](usage-guidelines.md) | 1, 3, 12 | Done |
 | [measure-positive-impact](measure-positive-impact.md) | 2, 6, 12 | Done |
+| [competitive-landscape](competitive-landscape.md) | 7, 12 | New — pre-launch |
+| [audience-analysis](audience-analysis.md) | 7 | New — pre-launch |
+| [measure-project-impact](measure-project-impact.md) | 2, 12 | New — pre-launch |
 
 *Previously had plans for "high-leverage contributions" and "teach and
 document" — these were behavioral norms, not executable plans. Their

plans/audience-analysis.md

@@ -0,0 +1,111 @@
+# 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 |

plans/competitive-landscape.md

@@ -0,0 +1,74 @@
+# 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](https://codecarbon.io/) | Training energy/CO2 | No | No |
+| [EcoLogits](https://ecologits.ai/) | Inference energy/CO2 via APIs | No | Yes |
+| [ML CO2 Impact](https://mlco2.github.io/impact/) | Training carbon estimate | No | No |
+| [Green Algorithms](https://www.green-algorithms.org/) | Any compute workload | No | No |
+| [HF AI Energy Score](https://huggingface.github.io/AIEnergyScore/) | 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](https://arxiv.org/abs/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](https://arxiv.org/abs/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

plans/measure-project-impact.md

@@ -0,0 +1,102 @@
+# Plan: Measure the positive impact of this project
+
+**Target sub-goals**: 2 (measure impact), 12 (honest arithmetic)
+
+## Problem
+
+We built a framework for measuring AI conversation impact but have no
+plan for measuring the impact of the framework itself. Without this,
+we cannot know whether the project is net-positive.
+
+## Costs of the project so far
+
+Rough estimates across all conversations:
+
+- ~5-10 long conversations × ~$500-1000 compute each = **$2,500-10,000**
+- ~500-2,500 Wh energy, ~150-800g CO2
+- VPS + domain ongoing: ~$10-20/month
+- Human time: significant (harder to quantify)
+
+## What "net-positive" would look like
+
+The project is net-positive if the value it creates exceeds these costs.
+Given the scale of costs, the value must reach significantly beyond one
+person. Concretely:
+
+### Threshold 1: Minimal justification
+
+- 10+ people read the methodology and find it useful
+- 1+ external correction improves accuracy
+- 1+ other project adopts the toolkit or cites the methodology
+
+### Threshold 2: Clearly net-positive
+
+- 100+ unique visitors who engage (not just bounce)
+- 5+ external contributions (issues, corrections, adaptations)
+- Cited in 1+ academic paper or policy document
+- 1+ organization uses the framework for actual reporting
+
+### Threshold 3: High impact
+
+- Adopted or referenced by a standards body or major org
+- Influences how other AI tools report their environmental impact
+- Methodology contributes to regulatory implementation (EU AI Act, etc.)
+
+## What to measure
+
+### Quantitative (automated where possible)
+
+| Metric | How to measure | Tool |
+|--------|---------------|------|
+| Unique visitors | Web server logs | nginx access log analysis |
+| Page engagement | Time on page, scroll depth | Minimal JS or log analysis |
+| Repository views | Forgejo built-in stats | Forgejo admin panel |
+| Stars / forks | Forgejo API | Script or manual check |
+| Issues opened | Forgejo API | Notification |
+| External links | Referrer logs, web search | nginx logs + periodic search |
+| Citations | Google Scholar alerts | Manual periodic check |
+
+### Qualitative (manual)
+
+| Metric | How to measure |
+|--------|---------------|
+| Quality of feedback | Read issues, assess substance |
+| Adoption evidence | Search for references to the project |
+| Influence on policy/standards | Monitor EU AI Act implementation, NIST |
+| Corrections received | Count and assess accuracy improvements |
+
+## Implementation
+
+### Phase 1: Basic analytics (before launch)
+
+- [ ] Set up nginx access log rotation and a simple log analysis script
+  (no third-party analytics — respect visitors, minimize infrastructure)
+- [ ] Create a script that queries Forgejo API for repo stats
+  (stars, forks, issues, unique cloners)
+- [ ] Add a `project-impact-log.md` file to track observations manually
+
+### Phase 2: After launch
+
+- [ ] Check metrics weekly for the first month, then monthly
+- [ ] Record observations in `project-impact-log.md`
+- [ ] At 3 months post-launch, write an honest assessment:
+  did the project reach net-positive?
+
+### Phase 3: Long-term
+
+- [ ] Set up a Google Scholar alert for the methodology title
+- [ ] Periodically search for references to llm-impact.org
+- [ ] If the project is clearly net-negative at 6 months (no engagement,
+  no corrections, no adoption), acknowledge it honestly in the README
+
+## Honest assessment
+
+The most likely outcome is low engagement. Most open-source projects
+get no traction. The methodology's value depends on whether the right
+people find it — AI ethics researchers and sustainability-minded
+developers. The landing page and initial sharing strategy are critical.
+
+If the project fails to reach threshold 1 within 3 months, we should
+consider whether the energy spent maintaining the VPS is justified, or
+whether the content should be archived as a static document and the
+infrastructure shut down.