58
Lab Cope Score
MODERATE
85/100
Data Transparency
Published methodology + findings
40/100
Framing Honesty
Buried the hiring slowdown
Source

“The Impact of AI on Labour Markets” — Anthropic Research, March 5, 2026. Uses Claude API usage data mapped to O*NET occupational categories to measure exposure and employment effects.

Key Findings

75%
Computer Programmer Exposure
Computer programmers are the most exposed occupation to Claude. 75% of their work activities overlap with Claude’s capabilities. This is displacement waiting to happen.
Customer Service
Second Most Exposed
Customer service representatives are the next most exposed occupation. High-volume, language-based work that AI can directly substitute.
Data Entry
Third Most Exposed
Data entry keyers round out the top three. Routine information processing tasks that AI handles faster and cheaper than humans.

The Cope: “No Systematic Unemployment”

Terminal Cope Alert

Anthropic’s headline finding: “no systematic increase in unemployment” in AI-exposed occupations. Sounds reassuring. But buried in the same paper:

What They Buried

Younger worker hiring has slowed in exposed occupations

New entrants to AI-exposed fields are finding fewer positions. The jobs aren’t being eliminated yet — they’re just not being created. Attrition without replacement. The displacement happens through non-hiring, not firing.

Exposure is concentrated in high-skill, high-pay occupations

Unlike previous automation waves that hit manual labour, AI exposure is concentrated in knowledge work. Programmers, analysts, customer service — the middle class backbone.

“No systematic unemployment” ≠ “no displacement”

Unemployment is a lagging indicator. When a company stops hiring juniors because Claude handles their work, unemployment stats don’t move — but labour demand has been permanently reduced. Anthropic measured the wrong metric and declared victory.

Anthropic vs OpenAI: Data Comparison

DimensionOpenAI (Signals)Anthropic (Labour Research)
Data SourceChatGPT aggregate usage telemetryClaude API usage + BLS employment data
Time PeriodJuly 2024 – March 2026 (21 months)2024 – early 2026
Key Metric46% of work messages are “doing”75% programmer exposure
Job Activity Mapping165 O*NET IWA codesO*NET occupational categories
Displacement FindingImplicit (framed as adoption)Explicit but minimised
Cope StrategyRename displacement as adoptionMeasure lagging indicator, declare no problem
Cope Score72 • HEAVY58 • MODERATE
Credit Where Due

Anthropic scores lower on cope than OpenAI because they at least acknowledged displacement mechanisms exist. They published actual labour market analysis, not just usage metrics. But concluding “no systematic unemployment” while documenting hiring slowdowns in exposed occupations is textbook cope — just better-written cope.

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