Exponential Progress Atlas

Interactive timelines showing how compute, energy, coordination, memory, and adoption compound into civilizational acceleration.

The root inventory is plots_manifest.json. Homepage cards, README links, build ordering, dashboard lanes, and validation all read from that manifest.

Published Inventory (9)

• 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.

Hero stat: 10^27+ FLOPs. Data confidence: mixed.

• Interactive: AI Compute Timeline
• Static: PNG | SVG
• Data: ai-compute-timeline/data/ai_milestones.csv
• Metadata: ai-compute-timeline/data/meta.json
• Details: ai-compute-timeline/

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2. Adoption Timeline

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

Hero stat: 60x faster. Data confidence: mixed.

• Interactive: Adoption Timeline
• Static: PNG | SVG
• Data: adoption-timeline/data/tech_adoption.csv
• Metadata: adoption-timeline/data/meta.json
• Details: adoption-timeline/

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3. Energetic Scaling

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

Hero stat: 10^6x+ efficiency. Data confidence: mixed.

• Interactive: Energetic Scaling
• Static: PNG | SVG
• Data: energetic-scaling/data/scaling_data.csv
• Metadata: energetic-scaling/data/meta.json
• Details: energetic-scaling/

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4. Civilization Scaling

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

Hero stat: 5 lanes. Data confidence: mixed.

• Interactive: Civilization Scaling
• Static: PNG | SVG
• Data: civilization-scaling/data/civilization_metrics.csv
• Metadata: civilization-scaling/data/meta.json
• Details: civilization-scaling/

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5. Energy Leverage

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

Hero stat: 17x body energy. Data confidence: high.

• Interactive: Energy Leverage
• Static: PNG | SVG
• Data: energy-leverage-per-person/data/energy_leverage_datapoints.csv
• Metadata: energy-leverage-per-person/data/meta.json
• Details: 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.

Hero stat: 1.5B -> 5T params. Data confidence: speculative.

• Interactive: Model Sizes
• Static: PNG | SVG
• Data: model-sizes/data/llm_model_sizes.csv
• Metadata: model-sizes/data/meta.json
• Details: model-sizes/

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7. AI Benchmark Progress

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

Hero stat: 4 benchmark lanes. Data confidence: mixed.

• Interactive: AI Benchmark Progress
• Static: PNG | SVG
• Data: ai-benchmark-progress/data/benchmark_data.csv
• Metadata: ai-benchmark-progress/data/meta.json
• Details: 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.

Hero stat: $/FLOP collapse. Data confidence: mixed.

• Interactive: Cost to Train
• Static: PNG | SVG
• Data: cost-to-train/data/training_costs.csv
• Metadata: cost-to-train/data/meta.json
• Details: 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.

Hero stat: 9 atlas entries. Data confidence: mixed.

• Interactive: Unified Dashboard
• Data: plots_manifest.json
• Metadata: plots_manifest.json
• Details: dashboard/

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Data Contracts

• ai-compute-timeline/data/ai_milestones.csv uses normalized fields: 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 includes adoption_metric_type, comparability_level, source_id, confidence, and notes so unlike adoption proxies are not treated as perfectly comparable.
• Energetic Scaling keeps comparison-level data in scaling_data.csv and splits clean source contracts into biology_neural_scaling.csv, hardware_efficiency.csv, ai_training_flops.csv, and foraging_lht.csv.

Repository Structure

Each plot should follow this structure:

<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/validate_all.py
python scripts/check_links.py
python scripts/check_accessibility_static.py

Adding a New Plot

1. Create the standard plot directory structure.
2. Add data, metadata, generator scripts, output paths, and README.
3. Add the entry to plots_manifest.json with status: "draft" until outputs and sources pass validation.
4. Run the build, generators, validators, link checker, and accessibility checker.
5. Change status to "published" only when the plot should appear on the homepage and dashboard.

Deployment

GitHub Pages deploys should run the same validation commands in CI before publishing. A failed build, broken relative link, missing alt text, stale output, or manifest mismatch should block deployment.

License

MIT