AI Economics & Labor Market
The Anthropic Economic Index report: Learning curves
This report from Anthropic's Economic Index studies Claude usage in February 2026, focusing on learning curves in Claude adoption. It documents slight increases in augmentation, diversification of Claude.ai usage to lower-wage tasks, and persistent global inequality in adoption. Critically, high-tenure users demonstrate greater success, more collaborative interaction, and engagement with higher-value tasks, suggesting a 'learning-by-doing' effect rather than just early adopter sophistication.
Executive Impact: Key Metrics
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Augmentation Rate (Claude.ai)
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Top 10 Tasks Concentration (Claude.ai)
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Average Task Value (Claude.ai)
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High-Tenure Success Rate Increase
Deep Analysis & Enterprise Applications
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Key Insight
19%
Top 10 O*NET tasks share in Claude.ai (February 2026), down from 24% in November 2025, indicating diversification.
Claude.ai Use Case Composition Shifts
| Category | Nov 2025 (%) | Feb 2026 (%) | Change (pp) |
|---|---|---|---|
| Work | 46 | 45 | -1 |
| Personal | 35 | 42 | +7 |
| Coursework | 19 | 12 | -7 |
Key Insight
$47.9/hr
Average task value on Claude.ai (Feb 2026), slightly down from $49.3, due to increased simple queries.
Emergent Automation Patterns in API
Sales Enablement Generation
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B2B Lead Qualification Research
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Customer Data Enrichment
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Cold-Email Drafting
Key Insight
4pp
Percentage point increase in success rate for high-tenure users, even after controlling for task, model, and country.
High vs. Low Tenure User Characteristics
| Characteristic | Low Tenure | High Tenure | Difference |
|---|---|---|---|
| Directive Mode | 38.1% | 29.4% | ▼ -8.7 pp |
| Task Iteration Mode | 24.5% | 28.2% | ▲ +3.6 pp |
| Work Use Case | 41.6% | 48.9% | ▲ +7.3 pp |
| Personal Use Case | 44.3% | 40.3% | ▼ -4.0 pp |
| Task Success Rate | 66.7% | 73.1% | ▲ +6.4 pp |
Learning-by-Doing: The Experience Advantage
Learning-by-Doing: The Experience Advantage
The report provides compelling evidence for 'learning-by-doing' with AI. High-tenure users demonstrate significantly greater success in their conversations (up to 4 percentage points), engage in more collaborative interaction modes, and tackle more challenging, higher-value tasks. This suggests that continuous interaction with AI helps users develop specific skills and strategies to harness its capabilities more effectively, leading to improved outcomes.
- Experienced users better match model capabilities to tasks.
- Higher tenure correlates with higher success rates and more sophisticated usage.
- Potential for skill-biased technological change due to AI proficiency.
User Skill Development Pathway
Early Adoption (Simpler Tasks)
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Experimentation & Learning
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Higher-Value Tasks & Collaboration
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Increased Success & Efficiency
Key Insight
12.24%
Human can't do alone (%), slightly up from 12.09%, indicating increased AI indispensability for some tasks.
Model Selection by Occupational Domain (Opus Over/Under-representation)
| Occupational Domain | Over/Under-representation (pp) |
|---|---|
| Educational instruction and library | -6.5 pp |
| Arts, design, entertainment, sports, and media | -5.7 pp |
| Sales and related | -1.6 pp |
| Office and administrative support | -0.6 pp |
| Life, physical, and social science | +0.9 pp |
| Management | +1.9 pp |
| Business and financial operations | +2.3 pp |
| Computer and mathematical | +4.4 pp |
Key Insight
24%
Share of per-person usage going to top five US states (Feb 2026), down from 30% in Aug 2025, showing continued, but slower, convergence.
Skill-Biased Technological Change & Inequality
Skill-Biased Technological Change & Inequality
The report highlights the potential for AI to deepen labor market inequalities through 'skill-biased technological change'. Early adopters, often engaging with high-skill tasks, show more successful interactions with AI. This suggests that proficiency in using AI could become a critical new skill, raising wages for those who master it while potentially depressing wages for others. The initial augmentative waves of AI adoption appear to disproportionately benefit those already performing high-skill tasks, who are both most exposed to AI disruption and most aided by it.
- AI proficiency may become a new determinant of labor market success.
- Early adopters with high-skill tasks benefit most from initial AI waves.
- Risk of deepening inequalities if AI skills are unevenly distributed.
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