m3taversal 3476e44b72 theseus: add coordination infrastructure + conviction schema + simplicity-first principle

- What: skills/coordinate.md (cross-domain flags, artifact transfers, handoff
  protocols), schemas/conviction.md (reputation-staked assertions with horizons
  and falsification criteria), CLAUDE.md updates (peer review V1 as default,
  workspace in startup checklist, simplicity-first in design principles),
  belief #6 (simplicity first, complexity earned), 6 founder convictions.
- Why: Scaling collective intelligence requires structured coordination
  protocols and a mechanism for founder direction to enter the knowledge base
  with transparent provenance. Grounded in Claude's Cycles evidence and
  Cory's standing directive: simplicity first, complexity earned.

Pentagon-Agent: Theseus <845F10FB-BC22-40F6-A6A6-F6E4D8F78465>

2026-03-08 16:14:31 +00:00

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Labor market impacts of AI: A new measure and early evidence

Maxim Massenkoff and Peter McCrory (Anthropic Research)

2026-03-05

https://www.anthropic.com/research/labor-market-impacts

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Rio: labor displacement economics — 14% drop in young worker hiring in exposed occupations, white-collar Great Recession scenario modeling

Vida: healthcare practitioner exposure at 58% theoretical / 5% observed — massive gap, implications for clinical AI adoption claims

Theseus: capability vs observed usage gap as jagged frontier evidence — 96% theoretical exposure in Computer & Math but only 32% actual usage

Labor Market Impacts of AI: A New Measure and Early Evidence

Massenkoff & McCrory, Anthropic Research. Published March 5, 2026.

Summary

Introduces "observed exposure" metric combining theoretical LLM capability (Eloundou et al. framework) with actual Claude usage data from Anthropic Economic Index. Finds massive gap between what AI could theoretically do and what it's actually being used for across all occupational categories.

Key Data

Theoretical vs Observed Exposure (selected categories)

Occupation	Theoretical	Observed
Computer & Math	96%	32%
Business & Finance	94%	28%
Office & Admin	94%	42%
Management	92%	25%
Legal	88%	15%
Arts & Media	85%	20%
Architecture & Engineering	82%	18%
Life & Social Sciences	80%	12%
Healthcare Practitioners	58%	5%
Healthcare Support	38%	4%
Construction	18%	3%
Grounds Maintenance	10%	2%

Most Exposed Occupations

Computer Programmers: 75% observed coverage
Customer Service Representatives: second-ranked
Data Entry Keyers: 67% coverage

Employment Impact (as of early 2026)

Zero statistically significant unemployment increase in exposed occupations
14% drop in job-finding rate for young workers (22-25) in exposed fields — "just barely statistically significant"
Older workers unaffected
Authors note multiple alternative explanations for young worker effect

Demographic Profile of Exposed Workers

16 percentage points more likely female
47% higher average earnings
4x higher rate of graduate degrees (17.4% vs 4.5%)

Great Recession Comparison

2007-2009: unemployment doubled from 5% to 10%
Comparable doubling in top quartile AI-exposed occupations (3% to 6%) would be detectable in their framework
Has NOT happened yet — but framework designed for ongoing monitoring

Methodology

O*NET database (~800 US occupations)
Anthropic Economic Index (Claude usage data, Aug-Nov 2025)
Eloundou et al. (2023) theoretical feasibility ratings
Difference-in-differences comparing exposed vs unexposed cohorts
Task-level analysis, not industry classification

Alignment-Relevant Observations

The gap IS the story. 97% of observed Claude usage involves theoretically feasible tasks, but observed coverage is a fraction of theoretical coverage in every category. The gap measures adoption lag, not capability limits.
Young worker hiring signal. The 14% drop in job-finding rate for 22-25 year olds in exposed fields may be the leading indicator. Entry-level positions are where displacement hits first — incumbents are protected by organizational inertia.
White-collar vulnerability profile. Exposed workers are disproportionately female, high-earning, and highly educated. This is the opposite of historical automation patterns (which hit low-skill workers first). The political and economic implications of displacing this demographic are different.
Healthcare gap is enormous. 58% theoretical / 5% observed in healthcare practitioners. This connects directly to Vida's claims about clinical AI adoption — the capability exists, the deployment doesn't. The bottleneck is institutional, not technical.
Framework for ongoing monitoring. This isn't a one-time study — it's infrastructure for tracking displacement as it happens. The methodology (prospective monitoring, not post-hoc attribution) is the contribution.

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