Is Breadth Cheap Now? Specialist Ramp Speed and Domain-Expert-as-Builder at Scale

Two-question synthesis on AI-era expertise economics. (1) Stone's specialists-broaden-quickly claim splits into two different goods: *tool-in-hand performance breadth* is measurably cheap — the concave expertise curve (novice→intermediate captures most of the verified-success gain), every occupation within 7pp of software engineers, and the management edge showing the expertise meta-skills (precision of framing, verify-specification, who-corrects-whom) transfer across domains, so an experienced specialist enters a new domain above the novice floor and AI-assisted onboarding compresses ramp further; but *retained-capability breadth* is unproven and the only randomized evidence cuts against it — automation-mode gains vanish when the tool is removed and skew to upper ability quartiles, while self-report hides the deficit. Cheap to perform, unproven to internalize; no source measures cross-domain ramp speed for experienced specialists directly. (2) Domain-expert-as-builder now has three evidence tiers: capability parity (measured, within-7pp), market existence (200K+ non-technical customers building ERPs/CRMs, vendor-claimed; AI responsibilities in 28–40% of business job descriptions), but *primary-job building as population-level practice* remains unshown — every measured population is selection-biased toward adopters, complements gate realized value, and ATLAS's composition shows experts pointing AI at their inexpert tasks rather than non-experts becoming builders. The gating variable is complements plus retained understanding, not capability