diff --git a/domains/ai-alignment/divergence-ai-labor-displacement-substitution-vs-complementarity.md b/domains/ai-alignment/divergence-ai-labor-displacement-substitution-vs-complementarity.md index d86d8fd6..43a68f09 100644 --- a/domains/ai-alignment/divergence-ai-labor-displacement-substitution-vs-complementarity.md +++ b/domains/ai-alignment/divergence-ai-labor-displacement-substitution-vs-complementarity.md @@ -16,15 +16,13 @@ created: 2026-03-19 # Does AI substitute for human labor or complement it — and at what phase does the pattern shift? -This is the central empirical question behind the AI displacement thesis. The KB currently holds claims that predict opposite near-term outcomes from the same technological change, each backed by real data. +This is the central empirical question behind the AI displacement thesis. The KB holds 4 claims with real evidence that diverge on two axes: -The economic logic claim argues that competitive markets systematically eliminate human oversight wherever output quality is independently verifiable — code review, ad copy, diagnostic imaging. The mechanism is cost: human-in-the-loop is an expense that rational firms cut when AI output is measurable. +**Axis 1 — Substitution vs complementarity:** Two claims predict systematic labor substitution (economic forces push humans out of verifiable loops; young workers displaced first as leading indicator). Two others say complementarity is the dominant mechanism at the current phase (firm-level productivity gains without employment reduction; macro shock absorbers prevent economy-wide crisis). -The complementarity claim points to EU firm-level data (Aldasoro et al., BIS) showing ~4% productivity gains with no employment reduction. The pattern is capital deepening — firms use AI to augment existing workers, not replace them. +**Axis 2 — If substitution, what pattern?** Within the substitution camp, the structural claim predicts systematic displacement across all verifiable tasks, while the temporal claim predicts concentrated displacement in entry-level cohorts first, with incumbents temporarily protected by organizational inertia — not by irreplaceability. -The macro shock absorber claim argues that even where job-level displacement occurs, structural buffers (savings, labor mobility, new job creation) prevent economy-wide crisis. - -The young worker displacement claim provides the leading indicator: a 14% drop in job-finding rates for 22-25 year olds in AI-exposed occupations, suggesting substitution IS happening but concentrated where organizational inertia is lowest. +The complementarity evidence comes from EU firm-level data (Aldasoro et al., BIS) showing ~4% productivity gains with no employment reduction. Capital deepening, not labor substitution, is the observed mechanism — at least in the current phase. ## Divergent Claims diff --git a/domains/health/divergence-human-ai-clinical-collaboration-enhance-or-degrade.md b/domains/health/divergence-human-ai-clinical-collaboration-enhance-or-degrade.md index f59ca072..3a0be4b0 100644 --- a/domains/health/divergence-human-ai-clinical-collaboration-enhance-or-degrade.md +++ b/domains/health/divergence-human-ai-clinical-collaboration-enhance-or-degrade.md @@ -45,11 +45,14 @@ The middleware claim argues AI's clinical value is as a translator between raw c - If middleware is essential: AI augments rather than replaces. The physician remains in the loop but at a different layer — interpreting AI-processed insights rather than raw data or AI recommendations. - If task-dependent: Both are right in their domain. The deployment model is: AI decides on pattern-recognition diagnostics, AI translates on continuous monitoring, physicians handle complex multi-factor clinical decisions. This would dissolve the divergence into scope. +**Cross-domain note:** The mode of human involvement may be the determining variable. Real-time oversight of individual AI outputs (where humans de-skill) is structurally different from adversarial challenge of published AI claims (where humans bring orthogonal priors). The clinical degradation finding is a domain-specific instance of the general oversight degradation pattern, but it may not apply to adversarial review architectures like the Teleo collective's contributor model. + --- Relevant Notes: - [[the physician role shifts from information processor to relationship manager as AI automates documentation triage and evidence synthesis]] — the role shift both claims point toward - [[medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials]] — additional evidence on the gap +- [[scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps]] — general oversight degradation pattern that the clinical finding instantiates Topics: - [[_map]]