ingest: Stanford SWEPR widening-gap study and AI-in-SDLC adoption pitfalls

Add two new sources with summaries, new concepts (developer-as-agent-manager,
review-is-the-new-bottleneck), new entities (SWEPR, Nikolai Sheiko), and a
query on the Stanford source; update related concept pages, overview, index,
and log.

wiki/concepts/levels-of-ai-usage.md

@@ -20,6 +20,7 @@ Supporting practices at the plateau: keep CLAUDE.md self-maintaining ("always ke
 - Webinar title and non-programmer audience confirm the ladder as the webinar's spine — [[2026-07-14-nina-interview]].
 - Convergent structure (foundation docs + skills as the non-engineer's OS) — [[2026-07-14-gap-between-ai-users-irreversible]].
 - A high-rung user missing the skills rung, and the memory pain that results — [[2026-07-21-larysa-interview]].
+- **Measured, team-level corroboration that the gap grows between rungs of mastery**, not between license-holders and others: Stanford's 46-vs-46-team analysis shows the productivity gap between AI-mastering and lagging teams growing 4.8% → 19% (4×) over ~2.25 years — [[2026-07-30-stanford-swepr-widening-gap]]. *(Caveat: measures engineering teams, not this ladder's non-programmer audience; and the study asserts "quality of usage" as the differentiator without decomposing which rung supplies it.)*
 
 ## Related Pages