domains/ai-alignment/deep technical expertise is a greater force multiplier when combined with AI agents because skilled practitioners delegate more effectively than novices.md

@@ -28,7 +28,7 @@ This claim describes a frontier-practitioner effect — top-tier experts getting
 ### Additional Evidence (challenge)
 *Source: [[2026-03-21-metr-evaluation-landscape-2026]] | Added: 2026-03-21*
 
-METR's developer productivity RCT found that AI tools made experienced developers '19% longer' to complete tasks, showing negative productivity for experts. This directly contradicts the force multiplier hypothesis and suggests that current AI tools may actually impair expert performance, consistent with the prior METR developer RCT finding.
+METR's developer productivity RCT found that AI tools made experienced developers '19% longer' to complete tasks, showing negative productivity for experts on time-to-completion metrics. This complicates the force multiplier hypothesis — the RCT measured task completion speed, not delegation quality or the scope of what experts can attempt. An expert who takes longer but produces better-scoped, more ambitious outputs is compatible with both this finding and the original claim. However, if the productivity drag persists across task types, it provides counter-evidence to at least one dimension of the expertise advantage.
 
 
 Relevant Notes:

inbox/queue/2026-03-21-metr-evaluation-landscape-2026.md

@@ -7,7 +7,7 @@ date: 2026-03-01
 domain: ai-alignment
 secondary_domains: []
 format: thread
-status: enrichment
+status: processed
 priority: high
 tags: [METR, monitorability, MALT, sabotage-review, time-horizon, evaluation-infrastructure, oversight-evasion, Claude]
 processed_by: theseus