The Doomsday Clock for Jobs
A single, transparent, reproducible number updated daily — tracking how close AI is to making white-collar work economically obsolete.
What is Takeover Tracker?
Think of it as a composite index — like the S&P 500 for job displacement. Instead of tracking stock prices, we track signals from labor markets, corporate adoption, AI capabilities, economic indicators, public sentiment, and regulation. The result is a single score from 0% to 100%.
0%
Pre-AI baseline
circa 2019
~20%
Current estimate
AI assists, early automation
50%
Role restructuring
Entry-level hiring halved
100%
Economic non-viability
No cost advantage for humans
Daily Pipeline
Every morning at 6:00 AM UTC, an automated pipeline collects fresh data, analyzes it with AI, scores 24 sector-category pairs, and produces one number. The entire process takes about 15 minutes.
Collect
Pull data from 9 sources: RSS feeds, BLS, FRED, GDELT, Reddit, Hacker News, SEC filings, AI benchmarks, and more.
Extract
AI classifies each signal by category, affected sectors, sentiment, impact magnitude, and direction.
Score
Tier 1 hard data z-scores + Tier 2 math aggregation of displacement levels — no LLM scoring calls.
Calculate
Pure math: two-tier 50/50 blend, hype discount, single EMA smoothing, 0.5 rounding.
Summarize
AI generates a headline, summary, and key developments for the day's score movement.
Design Principles
Hype Resistant
Reality signals (labor data + corporate adoption + economic indicators) are compared against hype signals. When hype exceeds 2× reality, capability and sentiment scores are discounted.
Fully Transparent
Every data source, weight, formula, and rubric prompt is published. The full methodology is available for anyone to audit, critique, or reproduce.
Source Credibility
Not all data is equal. Peer-reviewed research and BLS data count at full weight (1.0×), while CEO predictions (0.5×) and social media (0.3×) are heavily discounted.
Two-Tier Hybrid
Hard quantitative data (50%) is blended with AI-classified signals (50%). Neither tier can dominate, and the hard data serves as a reality anchor.
Self-Consistency
Each scoring call runs 3 times at low temperature. The median is taken, filtering out outlier responses and producing more stable, reproducible scores.
EMA Smoothing
A single exponential moving average (α=0.08, ~25-day window) filters noise while responding to genuine trends. Scores are rounded to the nearest 0.5.
What Feeds the Index
Every signal is categorized into one of six categories, each with a specific weight reflecting its reliability and relevance.
Disclaimer
This index is an experimental research project, not financial or career advice. The score reflects a model-based estimate using available public data and AI analysis. It should not be used as the sole basis for any employment, investment, or policy decisions.
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