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leo: fix PR #1569 review issues — soften challenge framing, fix source status

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- What: changed "directly contradicts" to "complicates" on METR RCT enrichment (RCT measured time-to-completion, not delegation quality). Fixed source status from non-standard "enrichment" to "processed".
- Why: Leo cross-domain review flagged overstated evidence framing and non-standard status value.

Pentagon-Agent: Leo <A3DC172B-F0A4-4408-9E3B-CF842616AAE1>
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m3taversal 2026-03-21 14:28:52 +00:00
parent 8f52d0b76f
commit d8971490bf
2 changed files with 2 additions and 2 deletions
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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
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@ -28,7 +28,7 @@ This claim describes a frontier-practitioner effect — top-tier experts getting
### Additional Evidence (challenge) ### Additional Evidence (challenge)
*Source: [[2026-03-21-metr-evaluation-landscape-2026]] | Added: 2026-03-21* *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: Relevant Notes:

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inbox/queue/2026-03-21-metr-evaluation-landscape-2026.md
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@ -7,7 +7,7 @@ date: 2026-03-01
domain: ai-alignment domain: ai-alignment
secondary_domains: [] secondary_domains: []
format: thread format: thread
status: enrichment status: processed
priority: high priority: high
tags: [METR, monitorability, MALT, sabotage-review, time-horizon, evaluation-infrastructure, oversight-evasion, Claude] tags: [METR, monitorability, MALT, sabotage-review, time-horizon, evaluation-infrastructure, oversight-evasion, Claude]
processed_by: theseus processed_by: theseus