Tracking AI's displacement of human labor in professional services — with data, not opinion.
An open-source dashboard that aggregates publicly available market signals to answer one question: Where are we on the displacement curve?
The Composite Displacement Index synthesizes eight independent signals into a single 0-100 score:
| Score | Phase | What It Means |
|---|---|---|
| 0–25 | Pre-disruption | AI is a topic, not a force |
| 26–50 | Productivity | AI making firms more efficient; employment stable |
| 51–75 | Erosion | Revenue per employee diverging from headcount |
| 76–100 | Displacement | Employment declining; funding pouring into replacements |
| Signal | Source | Frequency | What It Measures |
|---|---|---|---|
| Professional Services Employment | BLS (CES) | Monthly | Sector headcount trends |
| AI Search Interest | Google Trends | Daily | Public attention to AI tools |
| Open Source AI Activity | GitHub API | Daily | Developer momentum in AI |
| AI Revenue Reporting | SEC EDGAR (XBRL) | Quarterly | How firms report AI revenue |
| Revenue Per Employee | SEC EDGAR (10-K) | Quarterly | Productivity divergence signal |
| VC Funding: AI Services | SEC Form D | Quarterly | Capital flowing to AI replacements |
| AI vs Traditional Hiring | BLS JOLTS | Monthly | Job opening mix shift |
| Regulatory Guidance | Fed / OCC / SEC / NIST / EU | Quarterly | Government response velocity |
Every signal uses free, public data sources — no proprietary APIs, no paywalls.
- Data, not opinion. The dashboard presents signals. It does not editorialize.
- Reproducible. Every data point traces to its source. Every collector is open source.
- Automated. GitHub Actions collects fresh data on schedule.
- Durable. Only signals with reliable, ongoing public sources are included.
- Accessible. Single page. No login. No paywall. Mobile-friendly.
collectors/ Python scripts for each data source
normalizers/ Derived metrics (composite index, earnings normalization)
data/ Versioned JSON (raw + processed)
docs/ Static dashboard (HTML/JS/CSS — GitHub Pages)
.github/workflows/ Automated collection schedules
| Layer | Technology |
|---|---|
| Dashboard | Vanilla HTML/JS/CSS |
| Charts | Chart.js (CDN) |
| Collectors | Python 3 + requests |
| Scheduling | GitHub Actions (cron) |
| Hosting | GitHub Pages |
# Install dependencies
pip install -r requirements.txt
# Generate mock data for development
python3 data/generate_mock_data.py
# Or run a live collector (example: BLS employment)
python3 collectors/bls_employment.py --api-key YOUR_BLS_KEY
# Serve the dashboard
cd docs && python3 -m http.server 8000All collectors support --mock for offline development.
See METHODOLOGY.md for detailed documentation of data sources, collection methods, normalization, and composite index weights.
See CONTRIBUTING.md for guidelines.
Short version: fork, branch, follow existing patterns, test locally, submit a PR.
MIT — see LICENSE