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/context-as-scarce-resource.md

@@ -22,6 +22,8 @@ The human role has climbed prompt-engineer → **context-engineer** → harness-
 
 **Context now has a price — at fleet scale.** The same source names **token budget** as one of two variables separating winners from losers, alongside knowing how to use agents. Context has always been scarce per-request; this is the corpus's first claim that it is also scarce per-*wallet*. Scope, corrected 2026-07-28: the claim comes from **metered** usage (parallel remote sandboxes), and under a flat consumer subscription the corpus's own heavy users report no ceiling — so per-request scarcity remains the binding constraint for individuals, and per-wallet scarcity is a fleet and enterprise concern. See [[enterprise-ai-reality]] and [[explosion-of-internal-software]].
 
+**The compaction curse — scarcity at codebase scale** ([[2026-07-30-rakes-in-ai-sdlc-adoption]]). On a huge codebase the agent gathers context → the window overflows → compaction → it re-gathers → compaction again, and the task barely completes. [[nikolai-sheiko]]'s cure inverts the "agents mean best practices don't matter" fallacy: **best practices exist for the agent now** — locality, isolated modules with interfaces, so that *the codebase stores the context*. This converges with [[thorsten-ball]]'s context-lives-in-the-codebase position from the opposite direction (Thorsten skips skills because his codebase carries context; Sheiko says make your codebase able to). Practical additions: **AST search instead of grep** on colossal projects (grep returns a wall of noise, AST returns the relevant slice), and a flat rejection of embeddings/RAG over code. The mechanism matches Stanford's measured gains-collapse toward 10M LOC (evidence below).
+
 **The supply-side facet** ([[2026-07-22-ai-is-stupid]]): before context is *scarce* it is usually *absent*. "Intelligence without context loses to context without intelligence" — ten Nobel laureates asked about your sales month can only cite industry averages, while your rank-and-file employee answers better because they see your funnel, clients, and deals. The default "stupid AI" experience is a strong model given neither business context nor a [[harness]]; the fix is investing in context infrastructure (data, memory, integrations) before reaching for a bigger model.
 
 ## Evidence
@@ -31,6 +33,8 @@ The human role has climbed prompt-engineer → **context-engineer** → harness-
 - "Intelligence without context loses"; Nobel-vs-employee analogy; invest in context before model upgrades — [[2026-07-22-ai-is-stupid]].
 - Information > model choice > effort level; the two information sources; token budget as a winner/loser variable — [[2026-07-28-agentic-engineering-10x-developer]].
 - Reading costs attention — the human-side analog of the same scarcity — [[make-more-cheap-code]], [[2026-07-24-youre-reading-way-too-much-code]].
+- **First outside quantitative support:** Stanford SWEPR finds AI productivity gains collapse as codebases grow from ~10k to ~10M lines, attributing it to context-window limits and signal-to-noise degradation — the corpus's context-is-the-constraint claim, measured at scale — [[2026-07-30-stanford-swepr-widening-gap]].
+- Compaction curse; best-practices-for-the-agent (locality, interfaces, codebase-stores-context); AST search over grep; embeddings/RAG over code rejected — [[2026-07-30-rakes-in-ai-sdlc-adoption]].
 
 ## Related Pages
 
@@ -40,7 +44,7 @@ The human role has climbed prompt-engineer → **context-engineer** → harness-
 ## Contradictions / Uncertainty
 
 - "First third = smart zone" is a heuristic, not a measured boundary; likely model-dependent. Status: tentative.
-- [[2026-07-22-ai-is-stupid]] names **RAG** and long-term assistant memory as the practical context mechanisms; [[2026-07-14-skills-based-on-git]] argues [[skills-as-memory|skills]] beat RAG (load-on-activation vs pre-injection). Possibly audience-driven (business data vs procedures) rather than a real disagreement. Status: tentative.
+- [[2026-07-22-ai-is-stupid]] names **RAG** and long-term assistant memory as the practical context mechanisms; [[2026-07-14-skills-based-on-git]] argues [[skills-as-memory|skills]] beat RAG (load-on-activation vs pre-injection). Possibly audience-driven (business data vs procedures) rather than a real disagreement. Status: tentative. *(2026-07-30: [[nikolai-sheiko]] adds a hard anti-RAG data point for the code domain specifically — "embeddings over code don't work" — which supports the audience-driven reading: the anti-RAG votes are both about code/procedures, the pro-RAG vote is about business data.)*
 
 ## Next Questions