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
74 lines
3.2 KiB
Markdown
74 lines
3.2 KiB
Markdown
# Plan: Competitive landscape analysis
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**Target sub-goals**: 7 (multiply impact through reach), 12 (honest arithmetic)
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## Problem
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Before sharing the project, we need to know what already exists so we can
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position honestly. If a better alternative exists, we should point people
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to it rather than duplicating effort.
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## Landscape (as of March 2026)
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### Tools that measure energy/carbon
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| Tool | Scope | Covers social costs? | Per-conversation? |
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|------|-------|---------------------|-------------------|
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| [CodeCarbon](https://codecarbon.io/) | Training energy/CO2 | No | No |
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| [EcoLogits](https://ecologits.ai/) | Inference energy/CO2 via APIs | No | Yes |
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| [ML CO2 Impact](https://mlco2.github.io/impact/) | Training carbon estimate | No | No |
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| [Green Algorithms](https://www.green-algorithms.org/) | Any compute workload | No | No |
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| [HF AI Energy Score](https://huggingface.github.io/AIEnergyScore/) | Model efficiency benchmark | No | No |
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### Published research with per-query data
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- **Google/Patterson et al. (Aug 2025)**: 0.24 Wh, 0.03g CO2, 0.26 mL
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water per median Gemini text prompt. Most rigorous provider-published
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data. Environmental only.
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([arXiv:2508.15734](https://arxiv.org/abs/2508.15734))
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- **"How Hungry is AI?" (Jegham et al., May 2025)**: Cross-model
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benchmarks for 30 LLMs. o3 and DeepSeek-R1 consume >33 Wh for long
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prompts. Claude 3.7 Sonnet ranked highest eco-efficiency.
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([arXiv:2505.09598](https://arxiv.org/abs/2505.09598))
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### Frameworks that go broader
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- **UNICC/Frugal AI Hub (Dec 2025)**: TCO + SDG alignment. Portfolio-level,
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not per-conversation. No specific social cost categories.
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- **CHI 2025 deskilling research**: Empirical evidence that AI assistance
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reduces critical thinking. Academic finding, not a measurement tool.
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- **Oxford "Hidden Cost of AI" (2025)**: Descriptive survey of social costs.
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Not quantitative or actionable.
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### What no one else does
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No existing tool or framework combines per-conversation environmental
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measurement with social/cognitive/political cost categories. The tools
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that measure well (CodeCarbon, EcoLogits) only cover environmental
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dimensions. The research that names social costs is descriptive, not
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actionable.
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## Our positioning
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**Honest differentiator**: We are the only framework that enumerates 20+
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cost categories — environmental, financial, social, epistemic, political —
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at per-conversation granularity.
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**Honest weakness**: Our environmental estimates have lower confidence than
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Google's or EcoLogits' because we don't have access to infrastructure data.
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Our social cost categories are named and described but mostly not
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quantified.
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**We are not competing with**: CodeCarbon, EcoLogits, or AI Energy Score.
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These are measurement tools for specific environmental metrics. We are a
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taxonomy and framework. We should reference and link to them, not
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position against them.
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## Tasks
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- [ ] Add a "Related work" section to `impact-methodology.md` citing the
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tools and research above, with honest comparison
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- [ ] Calibrate our energy estimates against Google's published data
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and the "How Hungry is AI" benchmarks
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- [ ] Link to EcoLogits and CodeCarbon from the toolkit README as
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complementary tools
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