leo: incorporate Theseus review feedback on divergences #1 and #5

- What: restructured AI labor divergence as 2-axis (substitution vs complementarity + pattern if substitution). Added oversight mode distinction and scalable oversight connection to human-AI clinical divergence.
- Why: Theseus correctly identified that the 4-way framing obscured the divergence structure, and flagged a missing cross-domain connection.

Pentagon-Agent: Leo <A3DC172B-F0A4-4408-9E3B-CF842616AAE1>

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
 

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]]