Intelligence Disruption Index
Measuring whether AI is replacing human work
The Intelligence Disruption Index measures whether AI is actually replacing human work — not whether models are getting smarter, but whether jobs are disappearing, costs are falling enough to automate more tasks, AI is penetrating serious fields, and robots are replacing physical workers. It aggregates 14 signals across 4 categories into a single 0-100 score, updated monthly. Each signal is chosen for its proximity to AI-driven labor displacement, with broader economic indicators included for context.
4 Categories, 14 Signals
Job Displacement
The core question: are jobs disappearing in sectors directly exposed to AI? Tracks cumulative employment decline in 9 AI-exposed occupations, real-time hiring demand from Indeed across 7 AI-exposed sectors (Media & Comms down 42%, Software Dev down 24%), total US job postings, and information sector unemployment (5.0% vs 3.2% national).
BLS OEWS, Indeed Hiring Lab (CC-BY-4.0), FRED (BLS CPS)
AI Cost Floor
AI disrupts when it gets cheap enough to automate tasks that weren't worth automating before. Tracks the cheapest frontier model price and count of models below $1/MTok. When AI gets 10x cheaper, new automation use cases become viable.
sanand0/llmpricing (OpenRouter data)
AI Penetration
Is AI being used in real work? Over 50% of new web articles are now AI-generated (Graphite/Surfer, Ahrefs). In hard science, the share of papers using AI/ML methods is climbing toward 15%. Plus ecosystem creation velocity from HuggingFace model uploads.
Graphite/Surfer, Ahrefs, Spennemann (arXiv:2504.08755), arXiv REST API, HuggingFace
Physical Automation
Are robots and autonomous vehicles replacing physical human workers? Amazon's robot-to-employee ratio went from 0 to 642 per 1,000 in 13 years. CA autonomous vehicle miles hit 4.5M in 2024. Warehouse and transport employment shares are declining.
Amazon data, CA DMV, FRED (2 BLS series)
Scale
Methodology
The IDI aggregates 14 signals across 4 categories. Each signal is chosen for its proximity to AI-driven labor displacement. Some signals (like total job postings or sector unemployment) include broader economic context — they're weighted lower but help distinguish AI-specific trends from macro noise.
Demand-side categories (Displacement + Physical Automation) carry 60% of the weight. Supply-side categories (Cost + Penetration) carry 40%. This is deliberate: the thesis is about labor displacement, not technical progress.
The composite formula: IDI = 0.35D + 0.15C + 0.25P + 0.25A. Each signal is normalized to 0-100 with economically meaningful anchor points, then weighted within its category. A 3-month EMA is applied for smoothing.
All data sources are free and public: BLS OEWS (9 occupations), FRED (BLS CPS unemployment), Indeed Hiring Lab (CC-BY-4.0, 7 AI-exposed sectors + total US), arXiv, HuggingFace, Amazon press releases, CA DMV, Counterpoint/Omdia (humanoid robots), and published LLM pricing. Zero proprietary data.
Licensed under CC BY 4.0. Free to share and adapt with attribution — please link back to this page.