Exponential Progress Atlas

TL;DR

Feed this atlas CSV tables, JSON source notes, and plots_manifest.json. Python scripts render nine static PNG and SVG charts, interactive HTML timelines, and an offline-safe dashboard. Each chart separates measured history from proxies and forecasts across compute, energy, memory, coordination, and adoption.

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Published Inventory (9)

plots_manifest.json drives card order, README links, build steps, dashboard lanes, and repo checks. Compute charts report Floating-Point Operations (FLOPs).

• AI Compute Timeline
• Adoption Timeline
• Energetic Scaling
• Civilization Scaling
• Energy Leverage
• Model Sizes
• AI Benchmark Progress
• Cost to Train
• Unified Dashboard

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1. AI Compute Timeline

Training compute from early electronic computing to frontier AI, with proxies and speculative projections labeled separately.

Key figure: 10^27+ FLOPs. Data confidence: mixed.

• Interactive chart: AI Compute Timeline
• Static files: PNG | SVG
• Source CSV: ai-compute-timeline/data/ai_milestones.csv
• Source metadata: ai-compute-timeline/data/meta.json
• Plot folder: ai-compute-timeline/

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2. Adoption Timeline

Time-to-scale proxies across computing, connectivity, mobile, cloud, and AI paradigms.

Key figure: 60x faster. Data confidence: mixed.

• Interactive chart: Adoption Timeline
• Static files: PNG | SVG
• Source CSV: adoption-timeline/data/tech_adoption.csv
• Source metadata: adoption-timeline/data/meta.json
• Plot folder: adoption-timeline/

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3. Energetic Scaling

Biology, hardware efficiency, AI training compute, and foraging energetics compared with clean source datasets.

Key figure: 10^6x+ efficiency. Data confidence: mixed.

• Interactive chart: Energetic Scaling
• Static files: PNG | SVG
• Source CSV: energetic-scaling/data/scaling_data.csv
• Source metadata: energetic-scaling/data/meta.json
• Plot folder: energetic-scaling/

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4. Civilization Scaling

Five civilizational lanes: energy, coordination, memory, replication, and latency over log-time.

Key figure: 5 lanes. Data confidence: mixed.

• Interactive chart: Civilization Scaling
• Static files: PNG | SVG
• Source CSV: civilization-scaling/data/civilization_metrics.csv
• Source metadata: civilization-scaling/data/meta.json
• Plot folder: civilization-scaling/

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5. Energy Leverage

Per-person energy command relative to the metabolic baseline, with period anchors labeled explicitly.

Key figure: 17x body energy. Data confidence: high.

• Interactive chart: Energy Leverage
• Static files: PNG | SVG
• Source CSV: energy-leverage-per-person/data/energy_leverage_datapoints.csv
• Source metadata: energy-leverage-per-person/data/meta.json
• Plot folder: energy-leverage-per-person/

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6. Model Sizes

Language model parameter counts over time, separating disclosed counts from estimates and unreleased projections.

Key figure: 1.5B -> 5T params. Data confidence: speculative.

• Interactive chart: Model Sizes
• Static files: PNG | SVG
• Source CSV: model-sizes/data/llm_model_sizes.csv
• Source metadata: model-sizes/data/meta.json
• Plot folder: model-sizes/

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7. AI Benchmark Progress

Benchmark progress against human baselines across knowledge, coding, software engineering, and reasoning tasks.

Key figure: 4 benchmark lanes. Data confidence: mixed.

• Interactive chart: AI Benchmark Progress
• Static files: PNG | SVG
• Source CSV: ai-benchmark-progress/data/benchmark_data.csv
• Source metadata: ai-benchmark-progress/data/meta.json
• Plot folder: ai-benchmark-progress/

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8. Cost to Train

Training cost, FLOPs, and capability over time, showing the efficiency paradox at the frontier.

Key figure: $/FLOP collapse. Data confidence: mixed.

• Interactive chart: Cost to Train
• Static files: PNG | SVG
• Source CSV: cost-to-train/data/training_costs.csv
• Source metadata: cost-to-train/data/meta.json
• Plot folder: cost-to-train/

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9. Unified Dashboard

A synchronized overview of the atlas inventory using the same manifest as the homepage, README, build, and validator.

Key figure: 9 atlas entries. Data confidence: mixed.

• Interactive chart: Unified Dashboard
• Source CSV: plots_manifest.json
• Source metadata: plots_manifest.json
• Dashboard folder: dashboard/

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Data Contracts

• ai-compute-timeline/data/ai_milestones.csv uses normalized columns: year,event,category,value_numeric,value_low,value_high,value_unit,estimate_status,source_id,confidence,display_label,notes.
• adoption-timeline/data/tech_adoption.csv stores adoption_metric_type, comparability_level, source_id, confidence, and notes so distinct adoption proxies are never treated as identical metrics.
• energetic-scaling/ keeps cross-domain rows in scaling_data.csv and splits primary tables into biology_neural_scaling.csv, hardware_efficiency.csv, ai_training_flops.csv, and foraging_lht.csv.

Repository Structure

Each plot directory follows this layout:

<plot-name>/
├── index.html
├── data/
│   ├── <slug>.csv
│   └── meta.json
├── output/
│   ├── *_interactive.html
│   ├── *_highres.png
│   └── *.svg
├── src/
│   ├── *.py
│   └── *_plotly.py
└── README.md

Development

python -m pip install -r requirements.txt
python build_all.py
python scripts/generate_homepage.py
python scripts/generate_readme_links.py
python scripts/generate_sitemap.py
python scripts/validate_repo.py --check
python scripts/check_links.py
python scripts/check_accessibility_static.py
python -m unittest tests/test_doc_hygiene.py -v
python -m pytest tests -q

Adding a New Plot

1. Create the plot directory layout shown above.
2. Add CSV data, meta.json, generator scripts, output files, and a plot README.md.
3. Add the entry to plots_manifest.json with status: "draft" until outputs and sources pass validation.
4. Run the build, site generators, repo validator, link check, accessibility check, and test suite.
5. Set status to "published" only when the plot is ready for the homepage and dashboard.

Deployment

GitHub Pages builds run these same checks in CI before publishing. Any failed build, broken link, missing alt text, stale output, doc-hygiene error, or manifest mismatch blocks release.

License

MIT