The Anthropic Economic Index, released in January 2026, provides the first empirical measurement of AI’s economic impact based on observed behavior rather than theoretical exposure. Combined with Federal Reserve research and macroeconomic data, we can now see the patterns that will define the next decade of work.
This isn’t about whether AI will transform the economy. That question is settled. The question now is: what kind of transformation, at what speed, and who benefits?
The Seven Patterns
- AI Inverts the Automation Assumption — Complex tasks see 12x speedup; simple tasks see 9x
- The Productivity Unlock is Real—But Contested — 1.0-2.6pp depending on task complementarity
- Augmentation is Winning (For Now) — 52% consumer augmentation vs 75% enterprise automation
- The Skills Paradox — Education makes you better at using AI AND more exposed to it
- Door-Closing, Not Layoffs — Entry-level positions shrinking invisibly
- Geographic Convergence Within, Divergence Between — US states equalizing in 2-5 years; international gaps widening
- The Infrastructure Paradox — Massive capital investment, minimal employment
Key Statistics
| Metric | Value |
|---|---|
| Occupations with 25%+ AI task coverage | 49% |
| Education-AI collaboration correlation | 0.92 |
| US geographic convergence timeline | 2-5 years |
| AI capex as % of US GDP | ~2% |
The Bottom Line
The race is not between humans and AI. It’s between humans who collaborate effectively with AI and those who don’t.
The latter group faces an increasingly narrow set of economically valuable contributions. Not because AI takes their jobs overnight—but because the doors into valuable work are quietly closing while they wait for the disruption that already arrived.
This is part of a comprehensive analysis. Read the full analysis on The Business Engineer.









