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/competitive-landscape.md

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+# 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