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/async-by-default.md

@@ -20,11 +20,12 @@ If agent work takes sixteen minutes and you are doing something else, latency st
 
 - Orbs, sleep/wake, one-URL packaging, multiplayer handoff, the 16-minute live demo, the collapsed cloud-IDE objections, Quinn's proof line, paper-cut velocity, local-dev prediction, infra-margin prediction — [[2026-07-28-agentic-engineering-10x-developer]].
 - Fire-and-forget as a native harness mode; completion notifications as what makes background agents usable — [[2026-07-14-skills-based-on-git]], [[harness]].
+- The same mode as a job description: the AI-developer is IO-bound, runs tasks in parallel, and "if you launched Claude Code and sit watching it work — you're a bad employee" — [[2026-07-30-rakes-in-ai-sdlc-adoption]] (the role-shift side lives at [[developer-as-agent-manager]]).
 - Scheduled agents producing while you sleep (the non-engineer version) — [[personal-ai-operating-system]].
 
 ## Related Pages
 
-- Concepts: [[harness]] (async is one of its two modes), [[agentic-loops]], [[make-more-cheap-code]] (proof artifacts are throwaway code with a job), [[shedding-weight]] (async is what makes killing the backlog possible — parked agents replace queued tickets), [[personal-ai-operating-system]], [[context-as-scarce-resource]]
+- Concepts: [[harness]] (async is one of its two modes), [[agentic-loops]], [[make-more-cheap-code]] (proof artifacts are throwaway code with a job), [[shedding-weight]] (async is what makes killing the backlog possible — parked agents replace queued tickets), [[personal-ai-operating-system]], [[context-as-scarce-resource]], [[developer-as-agent-manager]] (the human role this mode implies), [[review-is-the-new-bottleneck]] (where the parallel diffs pile up)
 - Entities: [[thorsten-ball]], [[amp]], [[claude-code]]
 
 ## Contradictions / Uncertainty

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
 

wiki/concepts/developer-as-agent-manager.md

@@ -0,0 +1,37 @@
+# Developer as Agent Manager
+
+#concept
+
+## Summary
+
+[[nikolai-sheiko]]'s "universal error #0": treating AI-assisted development as the same job at higher speed. A good developer is **CPU-bound** — 3–5 hours of deep focus on one feature. A good AI-developer is **IO-bound** — a *manager of an agent-employee*, running several tasks in parallel, spending their time on planning and verification instead of typing. His blunt test: "If you launched Claude Code and sit watching it work — you're a bad employee."
+
+## Current Understanding
+
+- **The switch is psychological, not technical** — and not everyone can make it. Sheiko is explicit that this is *fine*: don't drag everyone into AI under one brush; conservatives "guard the company from the vibe-coders." Compare [[2026-07-14-nina-interview|Nina's finding]] that adoption blocks on friction, not resistance — this page is the case where genuine resistance exists and is legitimate.
+- **Where the time goes instead:** "planning on the left, verification on the right" — the coding middle collapsed. Hence 20-minutes-minimum planning, explicit goals the agent self-checks against, and the review discipline of [[review-is-the-new-bottleneck]].
+- **The role splits further.** Today the people who *use* agents and the people who *tune the harness* are the same; Sheiko predicts a split into **users** (plan + verify a feature) and **Agentic Operations** (own the SDLC configuration, feedback loops, prompts, skills). A **Product engineer** also emerges — answers *why* we build it this way, what to simplify, what to ignore.
+- **What stays human: Judgment over Intelligence.** AI absorbs *Intelligence* (action sequences requiring intellect); *Judgment* — taste built over years, or deep domain expertise (oil & gas, medicine) — remains human for now. We detect slop precisely because it lacks human touch. This is the corpus's [[seniority-and-the-junior-squeeze|judgment-as-risk-reduction]] claim restated as a capability boundary.
+- **Corpus convergence.** The same working mode appears as [[async-by-default|orbs and parallel delegation]] ([[thorsten-ball]]), Eugene's 7 parallel project-agents, Allie's ~100 agents, and Karpathy's "80% Claude Code, 20% by hand" (cited within the talk). Sheiko's contribution is naming the *identity* shift and its HR consequences: the middle dev who mastered this, asked for a raise, was refused, and left for far more.
+
+## Evidence
+
+- CPU-bound vs IO-bound framing; "sit watching = bad employee"; don't-force-everyone; users vs Agentic Operations; Product engineer; Intelligence vs Judgment; the raise-refusal mini-case — [[2026-07-30-rakes-in-ai-sdlc-adoption]].
+- The same mode practiced at the frontier (parallel remote sandboxes, delegation, proof-asking) — [[2026-07-28-agentic-engineering-10x-developer]] via [[async-by-default]].
+- Non-engineer versions of parallel delegation (~100 agents, scheduled workflows) — [[2026-07-14-gap-between-ai-users-irreversible]].
+
+## Related Pages
+
+- Concepts: [[async-by-default]] (the infrastructure this role runs on), [[review-is-the-new-bottleneck]] (where the manager's verification time goes), [[product-ownership]] (the webinar's human-side twin: own outcomes, not tickets), [[seniority-and-the-junior-squeeze]] (Judgment as the durable half), [[levels-of-ai-usage]] (the non-engineer's version of the same climb), [[harness]]
+- Entities: [[nikolai-sheiko]], [[thorsten-ball]], [[eugene]], [[allie-miller]]
+
+## Contradictions / Uncertainty
+
+- **"Don't force everyone" vs the widening gap.** Sheiko permits opting out; [[2026-07-14-gap-between-ai-users-irreversible|Allie]] calls the gap irreversible and [[2026-07-30-stanford-swepr-widening-gap|Stanford measured it quadrupling]]. If both are right, opting out is legitimate *and* costly, and the source doesn't reconcile the two. Status: tentative.
+- The users / Agentic-Operations split is a prediction, not an observation — today's evidence (Eugene, Thorsten) is of people doing both. Status: tentative.
+- Intelligence-vs-Judgment is a moving boundary asserted "for now"; the corpus has no criterion for where it stops moving.
+
+## Next Questions
+
+- What does the manager's day actually look like — is there a source with a concrete parallel-task routine (queue depth, check-in cadence) rather than the mode's name?
+- Does the Agentic-Operations role match the webinar's promise that non-engineers can self-serve ([[levels-of-ai-usage]]), or does it re-centralize harness-tuning in specialists?

wiki/concepts/enterprise-ai-reality.md

@@ -14,17 +14,19 @@ The indie/practitioner world and the regulated-enterprise world diverge sharply.
 - **Adjacent constraints:** the [[seniority-and-the-junior-squeeze|"read what you approve"]] security concern is amplified at scale; safety-critical/regulated code is the clear exception to [[code-as-throwaway|"most code isn't high-value"]].
 - **A second divide: the token budget** (added 2026-07-28; **scope corrected 2026-07-28** — see below). [[thorsten-ball]] names two variables separating winners from losers — knowing how to use agents, and **having the token budget to do it**. It cuts both ways for this page: an enterprise can buy budget an individual cannot, while a locked-down enterprise may withhold it from the people who would use it best. Whoever controls the budget controls how far [[explosion-of-internal-software|internal software]] spreads. Thorsten names the variable and says nothing about who pays.
   - **Scoping correction.** This was first written here as "the divide is also a *spending* gap," which overstates it. Thorsten's pricing regime is **metered**: [[amp]] sells usage, and his working pattern is parallel remote sandboxes and parked orbs ([[async-by-default]]) — a fleet cost, not a seat cost. Under a **flat consumer subscription** the corpus's own evidence points the other way: [[eugene]] runs 7 project-agents in parallel on a $200 plan, [[allie-miller]] runs ~100 agents and 36 workflows, and neither reports hitting a cost ceiling — while [[2026-07-14-sebastian-eugene-interview]] frames levelling as "a 20-year veteran and a fresh grad **on the same subscription**." For individual and small-team use the budget is one subscription; the token-budget variable bites at fleet scale and under metered pricing, which is where Thorsten sits and where enterprises will land.
+- **The market claim, seconded — and sharpened into a quote** (added 2026-07-30). [[nikolai-sheiko]], from multi-company adoption work: *"Companies no longer need custom AI development. Come in, install Claude Code or Codex, configure everything, attach connectors, think about security — and it works better than any custom build."* This is Sebastian's company-managed-harness market stated as a service playbook. Two adoption anti-patterns attached: **the external configurator** who leaves a "magic artifact" nobody on the team owns (what a company should buy is a *teacher/curator*; the team must configure its own tools — the [[solve-first-then-skillify|evolution]] has to happen in their hands), and **metered pricing shaping behaviour** — a team on Cursor's per-token billing economizes instead of experimenting (~30% dearer than subscriptions at the same level), which is the metered-vs-subscription split from the scoping correction above observed as an organizational failure mode. His pricing prediction — tokens get dearer near-term, cheaper later; "experiment at full throttle while subscriptions are cheap" — matches [[eugene]]'s price-rise prediction already flagged in the token-budget question.
 - **The frontier's advice does not transfer.** [[shedding-weight]] — kill the backlog, kill CI that repeats the agent's tests, kill local dev in favour of remote sandboxes ([[async-by-default]]) — describes a startup that owns its own process. In a regulated shop the pipeline, the audit trail and the ticket history frequently *are* the deliverable to a regulator, and code sitting in a vendor's remote sandbox is precisely what Sebastian's clients forbid. The gap between what the frontier recommends and what compliance permits is the same gap this page calls the market.
 
 ## Evidence
 
 - Managed VMs / zero self-install, Roche ~1,200 engineers, banks banned→adopting, "company-managed resource," "the interesting market" — [[2026-07-14-sebastian-eugene-interview]].
 - Token budget as a winner/loser variable; the frontier playbook (kill backlog/CI/local dev, remote sandboxes) that compliance cannot follow — [[2026-07-28-agentic-engineering-10x-developer]].
+- "No custom AI development needed" quote; external-configurator anti-pattern vs teacher/curator; Cursor per-token billing → team economizes; tokens-dearer-then-cheaper prediction — [[2026-07-30-rakes-in-ai-sdlc-adoption]].
 
 ## Related Pages
 
 - Concepts: [[harness]], [[seniority-and-the-junior-squeeze]], [[code-as-throwaway]], [[shedding-weight]], [[async-by-default]], [[explosion-of-internal-software]], [[context-as-scarce-resource]]
-- Entities: [[sebastian]], [[virtido]], [[eugene]], [[thorsten-ball]]
+- Entities: [[sebastian]], [[virtido]], [[eugene]], [[thorsten-ball]], [[nikolai-sheiko]]
 - Tools: [[claude-code]], [[amp]]
 
 ## Contradictions / Uncertainty

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
 

wiki/concepts/make-more-cheap-code.md

@@ -23,10 +23,11 @@
 - All claims, ratios, tier table, slop patterns, Dax/Shao citations — [[2026-07-24-youre-reading-way-too-much-code]].
 - Groundwork (code disposable, kill without guilt, G-brain markdown tier) — [[2026-07-14-everything-we-knew-about-software-has-changed]].
 - 15 icon variants, Midjourney rounds, "ask the agent for proof" — [[2026-07-28-agentic-engineering-10x-developer]].
+- **External measurement of the cost moving downstream:** Stanford SWEPR coverage reports +91% PR review time and ~2.6× rework in AI-heavy workflows — writing got cheaper, reviewing got dearer, which is this page's premise measured rather than asserted — [[2026-07-30-stanford-swepr-widening-gap]]. *(Secondary-coverage numbers; and note the same study's negative gains in complex brownfield code — "code is cheap" holds least where most code lives.)*
 
 ## Related Pages
 
-- Concepts: [[code-as-throwaway]] (parent claim: cost → zero; this page is its *discipline* — what cheap code is actually for), [[think-wider-not-bigger]] (same breadth logic applied to generation volume rather than ambition), [[product-ownership]] (verifying as the human's remaining job), [[solve-first-then-skillify]] (contrast: slop is frozen into nothing; skills freeze the procedure), [[leave-less-room-for-imagination]] (tension — see below), [[context-as-scarce-resource]], [[async-by-default]] (proof artifacts as the delegated form of the same move)
+- Concepts: [[code-as-throwaway]] (parent claim: cost → zero; this page is its *discipline* — what cheap code is actually for), [[think-wider-not-bigger]] (same breadth logic applied to generation volume rather than ambition), [[product-ownership]] (verifying as the human's remaining job), [[solve-first-then-skillify]] (contrast: slop is frozen into nothing; skills freeze the procedure), [[leave-less-room-for-imagination]] (tension — see below), [[context-as-scarce-resource]], [[async-by-default]] (proof artifacts as the delegated form of the same move), [[review-is-the-new-bottleneck]] (the org-level form of the cost shift this page manages individually — with the completed-without-rework metric as its answer)
 - Entities: [[theo-browne]], [[eugene]], [[thorsten-ball]]
 
 ## Contradictions / Uncertainty

wiki/concepts/review-is-the-new-bottleneck.md

@@ -0,0 +1,38 @@
+# Review Is the New Bottleneck
+
+#concept
+
+## Summary
+
+When agents write the code, the SDLC doesn't collapse completely — it collapses *around the humans*. [[nikolai-sheiko]]'s two remaining "red squares" are the **reviewer** and the **planner**: tasks pile up in the review queue, reviewers burn out, quality drops, and headline output metrics (PRs, LoC) rise while real throughput barely moves. The organizational fix is twofold: **review with the agent** (not fully manual, not fully delegated) and **measure completed tasks without rework** rather than anything volume-based.
+
+## Current Understanding
+
+- **The mechanism.** Generation got ~free, so the cost moved downstream to verification — and at team scale, downstream is a *person* with a queue. Sheiko's European-outsourcer case: more PRs than ever, net gain +1%, because rework consumed the difference. Manual-only review starts a spiral (queue → burnout → rubber-stamping → more rework); fully delegated review is the opposite error ([[async-by-default]]'s "proof produced by the thing being checked").
+- **The middle path: review together with the agent.** Treat the model as a smart student — direct it, pose hypotheses, locate problems jointly. This is the org-level sibling of Theo's reading economics ([[make-more-cheap-code]]: AI reviews before humans, per-file summaries, read only what's worth reading).
+- **The metric that resists gaming:** a task counts as done only if it **doesn't come back for rework**; track task lifetime and rework time. LoC, commit count and PR count are all trivially hacked and all rise *because* of the bottleneck, not despite it.
+- **Externally measured:** Stanford SWEPR coverage reports **+91% PR review time** and ~2.6× rework in AI-heavy workflows, and finds gross code volume up 30–40% while net gains are ~15–20% — the same rework-eats-half story Sheiko tells anecdotally ([[2026-07-30-stanford-swepr-widening-gap]]).
+- **Planning is the other red square.** Time redistributes from coding to "planning on the left + verification on the right" — which is why Sheiko prescribes 20-minutes-minimum planning and why a **Product engineer** role emerges ([[developer-as-agent-manager]]).
+
+## Evidence
+
+- Reviewer/planner as the remaining red squares; review-with-the-agent; the metrics table; the +1% outsourcer case — [[2026-07-30-rakes-in-ai-sdlc-adoption]].
+- +91% PR review time, 2.6× rework, gross-vs-net gap — [[2026-07-30-stanford-swepr-widening-gap]] *(secondary-coverage numbers)*.
+- Reading as the scarce human resource; AI-review-before-human-review; tiered reading discipline — [[2026-07-24-youre-reading-way-too-much-code]] via [[make-more-cheap-code]].
+- Parallel agents multiplying diffs without multiplying review capacity — the attention pile-up already logged on [[async-by-default]]; this page names that open question as the bottleneck it becomes at team scale.
+
+## Related Pages
+
+- Concepts: [[make-more-cheap-code]] (individual-level discipline for the same cost shift), [[async-by-default]] (the generation side that feeds the queue), [[developer-as-agent-manager]] (the role shift on the human side of the queue), [[seniority-and-the-junior-squeeze]] ("read what you approve" — why review can't just be dropped), [[maintenance-is-the-real-cost]] (kindred move: the visible activity was never the expensive part)
+- Entities: [[nikolai-sheiko]], [[swepr]], [[theo-browne]]
+
+## Contradictions / Uncertainty
+
+- **How much review survives?** Theo/Dax hold that agent diff-summaries surface anomalies; [[eugene]] holds that drift is precisely what summaries miss ([[leave-less-room-for-imagination]]); Sheiko's review-with-the-agent is a third position between them — asserted, not tested. Status: tentative.
+- The completed-without-rework metric is better than LoC/PRs but still gameable (e.g. by inflating task granularity or quietly reclassifying rework as new tasks); the source doesn't address it. Status: tentative.
+- Whether review-as-bottleneck is transitional (until verification is agentized) or structural (a human must always sign off — the [[seniority-and-the-junior-squeeze|accountability]] view) is open across the corpus.
+
+## Next Questions
+
+- What does review-with-the-agent look like concretely — a checklist, a dialogue pattern, a skill? The corpus has the prescription but no transcript of it done well.
+- Is there a non-engineer analog (the webinar audience reviews documents, not PRs)? The checker-skill design in [[2026-07-24-non-engineer-throwaway-verification]] may be it.

wiki/concepts/skills-as-memory.md

@@ -27,6 +27,7 @@ The **method** for populating skills is [[solve-first-then-skillify]]: reach the
 - ~3-message correction-loop heuristic; skills as the non-programmer ceiling (with CLAUDE.md) — [[2026-07-14-yulia-interview]].
 - Cross-session memory loss as the #1 practitioner pain; "memory is the worst thing agents have"; skills committed as the webinar remedy — [[2026-07-21-larysa-interview]].
 - Counter-evidence: no skills, no MCP, no slash commands at a 99%-AI-written company; `AGENTS.md` + codebase + rich prompt as the substitute — [[2026-07-28-agentic-engineering-10x-developer]].
+- **A second practitioner vote *for* the skills layer** (2026-07-30): [[nikolai-sheiko]]'s "Agentic Evolution" makes skill-building-plus-verification the difference between living on defaults and "vertical growth," and he flatly rejects embeddings/RAG over code ("don't use them unless you understand *very* well why") — siding with the load-on-activation camp in the skills-vs-RAG contradiction below. His verification protocol (context-free subagent re-solves the task from the skill alone) is the closest thing yet to the falsification test this page asks for — [[2026-07-30-rakes-in-ai-sdlc-adoption]].
 
 ## Related Pages
 
@@ -43,10 +44,10 @@ The **method** for populating skills is [[solve-first-then-skillify]]: reach the
   1. **Situational.** He works daily in *one codebase he controls*, where context can live in the code and `AGENTS.md`. Skills earn their keep when work is spread across many ad-hoc tasks with no codebase to encode into — which is exactly the corpus's HR/BA audience ([[nina]], [[yulia]], [[larysa]]). Under this reading both are right and the disagreement is about who is speaking.
   2. **The abstraction is premature.** Skills are scaffolding for models that needed it; a strong model plus a rich prompt plus a good repo may simply beat a skills library, making the whole layer a 2025 artifact. This is the uncomfortable reading for the webinar's central promise.
   3. **He has skills under another name.** AMP's Oracle/Painter/Puck sub-agents and a maintained `AGENTS.md` *are* curated, reusable, two-stage context — just authored by the vendor and the team rather than the user. Under this reading the dispute is about who curates, not whether curation is needed.
-  Status: tentative. Note the evidential asymmetry — his is a first-hand report of daily practice at scale, where the pro-skills case rests on Konstantin's architecture argument plus self-reported individual workflows. **Presentation-safe restatement:** [[2026-07-28-webinar-theses]] reframes the claim as *context you author beats context that's inferred*, which holds under all three readings — Konstantin's skills, Allie's foundation docs, Eugene's anti-memory position and Thorsten's `AGENTS.md` are all authored context.
+  Status: tentative. Note the evidential asymmetry — his is a first-hand report of daily practice at scale, where the pro-skills case rests on Konstantin's architecture argument plus self-reported individual workflows. *(Asymmetry softened 2026-07-30: [[nikolai-sheiko]] adds a second practitioner voice on the pro-skills side, from multi-company adoption work rather than one codebase — though his cases are anonymous anecdotes, so the readings above remain unsettled.)* **Presentation-safe restatement:** [[2026-07-28-webinar-theses]] reframes the claim as *context you author beats context that's inferred*, which holds under all three readings — Konstantin's skills, Allie's foundation docs, Eugene's anti-memory position and Thorsten's `AGENTS.md` are all authored context.
 
 ## Next Questions
 
 - ~~What's a starter skill set for a non-engineer?~~ Answered in [[2026-07-14-best-first-skill-for-beginner]] (skill-creator as meta-skill; tone-of-voice + anti-AI-language as first content skill).
 - Do skills actually solve *cross-project* context, or only per-procedure recall? Larysa's complaint may be the former, which skills don't obviously address.
-- Is there a test that would separate reading 1 from reading 2 above? The cheapest one available: give a non-engineer the same task with and without a skill and compare drift — the corpus has never run it, and the webinar's promise rests on the answer.
+- Is there a test that would separate reading 1 from reading 2 above? The cheapest one available: give a non-engineer the same task with and without a skill and compare drift — the corpus has never run it, and the webinar's promise rests on the answer. *(Adjacent evidence 2026-07-30: Sheiko's context-free-subagent protocol — [[solve-first-then-skillify]] — runs the with-skill half in practice, but never the without-skill control, so the question stands.)*

wiki/concepts/solve-first-then-skillify.md

@@ -15,17 +15,28 @@ The recurring beginner mistake is writing the skill first and then trying to "sh
 
 The payoff goes beyond reuse: a packaged skill is a **handoff and de-risking asset** — "a person with not even a third of your HR experience can deliver a decent result," which cuts onboarding and lets the expert take a vacation. This is how [[skills-as-memory]] gets *populated* in practice — the method side of that architecture, and the fix for "don't teach the AI abstractly."
 
+**Agentic Evolution — the strongest formulation, plus the missing verification step** (added 2026-07-30). [[nikolai-sheiko]] frames the same method as onboarding an employee: asking the expert "how do you do this?" yields theory; instead **take the new employee (the agent) by the hand through hard real tasks, show it the rakes, then say: "remember all of this and write the manual for the next one."** Without this you live on defaults; with it "vertical growth begins." He then adds what the corpus's earlier heuristics lacked — a **verification protocol** for the frozen skill:
+
+1. Write the skill together with the agent.
+2. Don't go to lunch.
+3. Launch a **subagent with no context** — it must solve the same task from scratch using only the skill.
+4. The main agent watches what fails and fixes the skill.
+5. The mentor agent thus onboards the next agent.
+
+This is the first source to describe actually *running* something close to the skills falsification test proposed on [[skills-as-memory]] (same task, with-skill vs from-scratch) — though it tests the skill's completeness for one task, not whether the skill beats no-skill. His do-tomorrow extension: a skill that analyses your own sessions daily, automated via schedules/routines — evolution as a standing loop rather than a one-time freeze.
+
 ## Evidence
 
 - "You first solve a task with Claude; the moment you reach the final solution, you say — now create a skill from this"; ~3-message heuristic — [[2026-07-14-yulia-interview]].
 - Do-the-task-then-freeze framing; skill-as-handoff to a junior hire; vacation/de-risking angle — [[2026-07-14-nina-interview]].
 - >5-tool-calls auto-creation heuristic and curator pruning — [[2026-07-14-skills-based-on-git]].
 - Skills prescribed specifically as the workaround for cross-session memory loss, and as the constraint on drift — [[2026-07-21-larysa-interview]].
+- Agentic Evolution (walk the agent through tasks → have it write the manual); the context-free-subagent verification protocol; session-analysis skill as a daily loop — [[2026-07-30-rakes-in-ai-sdlc-adoption]].
 
 ## Related Pages
 
 - Concepts: [[skills-as-memory]] (the architecture this method feeds), [[levels-of-ai-usage]] (skills are the top practical rung), [[personal-ai-operating-system]], [[leave-less-room-for-imagination]] (why a *proven* spec beats a written-ahead one)
-- Entities: [[eugene]], [[nina]], [[konstantin]], [[larysa]]
+- Entities: [[eugene]], [[nina]], [[konstantin]], [[larysa]], [[nikolai-sheiko]]
 
 ## Contradictions / Uncertainty
 
@@ -33,5 +44,6 @@ The payoff goes beyond reuse: a packaged skill is a **handoff and de-risking ass
 
 ## Next Questions
 
-- What does a good "create a skill from this" prompt look like — does the corpus contain a concrete example transcript?
+- What does a good "create a skill from this" prompt look like — does the corpus contain a concrete example transcript? *(Partially answered 2026-07-30: Sheiko's "remember all of this and write the manual for the next one" after a guided run is the best prompt-shape the corpus has.)*
 - How do the ~3-message and >5-tool-call heuristics compare in practice; is one strictly better for non-programmers?
+- Does the context-free-subagent check catch skill *quality*, or only completeness for the one task it was frozen from?

wiki/entities/nikolai-sheiko.md

@@ -0,0 +1,36 @@
+# Nikolai Sheiko
+
+#entity
+
+## Summary
+
+Speaker of the talk "Грабли во внедрении ИИ в SDLC" ([[2026-07-30-rakes-in-ai-sdlc-adoption]]). Russian-speaking AI-adoption practitioner/consultant who works with client companies (a frontend-migration team, a European outsourcer, a large-codebase company) on getting real results from AI in the software lifecycle. Background beyond the talk unknown.
+
+## Current Understanding
+
+His through-line: the models are already good enough — **people, companies and metrics are what throttle the gains**. Signature positions:
+
+- The AI-developer is a **manager of an agent-employee**, IO-bound and parallel ([[developer-as-agent-manager]]).
+- **Review is the new bottleneck**; review *with* the agent; measure completed-tasks-without-rework, never LoC/PRs ([[review-is-the-new-bottleneck]]).
+- **Agentic Evolution**: walk the agent through hard tasks, then have it write the manual; verify skills with a context-free subagent ([[solve-first-then-skillify]]).
+- Companies should install and configure Claude Code / Codex rather than build custom AI tooling; buy a **teacher/curator**, not an external configurator ([[enterprise-ai-reality]]).
+- Best practices matter *more* with agents (compaction curse, AST search over grep); embeddings/RAG over code don't work ([[context-as-scarce-resource]]).
+- AI eats **Intelligence**; **Judgment** (taste or domain expertise) stays human for now.
+
+## Evidence
+
+- All positions — [[2026-07-30-rakes-in-ai-sdlc-adoption]].
+
+## Related Pages
+
+- Concepts: [[developer-as-agent-manager]], [[review-is-the-new-bottleneck]], [[solve-first-then-skillify]], [[enterprise-ai-reality]], [[context-as-scarce-resource]]
+- Entities: [[swepr]] (he cites their Stanford chart as the stakes — "be in the top half"), [[thorsten-ball]] (fellow frontier practitioner; they disagree on skills — Sheiko builds skill evolution, Thorsten uses none)
+
+## Contradictions / Uncertainty
+
+- His client cases are anonymous and self-reported; no numbers are verifiable. Status: tentative.
+- Identity/affiliation beyond the talk unknown (the raw doc names only the talk itself). Status: tentative.
+
+## Next Questions
+
+- Who is he professionally — independent consultant, agency, vendor? Affects how to weigh the "you don't need custom AI development" claim (it is also a consultant's pitch).

wiki/entities/swepr.md

@@ -0,0 +1,35 @@
+# SWEPR (Stanford Software Engineering Productivity Research)
+
+#entity
+
+## Summary
+
+Research group at Stanford University measuring software-engineering productivity from private Git data — 600+ companies, ~100k–120k engineers since 2022 — using an ML model that replicates a panel of expert code reviewers. Public face: researcher **Yegor Denisov-Blanch**. Source of the corpus's only quantitative outside study of AI's productivity impact.
+
+## Current Understanding
+
+- Site: https://softwareengineeringproductivity.stanford.edu/ — offers an "AI Practices Benchmark" and "AI Impact" research to participating companies.
+- Known for three results: the **widening-gap DiD analysis** (46 vs 46 teams, gap 4.8% → 19%, 4×, Apr 2023–Jul 2025), the **~15–20% net average gain** figure (after rework; 30–40% gross), and the earlier **"ghost engineers"** finding (~9.5% of engineers show virtually no verifiable output).
+- Methodology is peer-reviewed (arXiv 2409.15152, 2502.20747); the headline AI-impact analyses are talk/deck-published (Sept 2025 AI Conference deck "Will AI Replace Software Engineers?").
+- Their measurement philosophy — functionality delivered, not commits or LOC — aligns with the corpus's own suspicion of volume metrics ([[make-more-cheap-code]]: generation volume is the *cheap* part).
+
+## Evidence
+
+- All claims and links — [[2026-07-30-stanford-swepr-widening-gap]].
+
+## Related Pages
+
+- Sources: [[2026-07-30-stanford-swepr-widening-gap]]
+- Concepts: [[levels-of-ai-usage]] (team-level twin of the mastery gap), [[context-as-scarce-resource]] (codebase-size finding), [[make-more-cheap-code]] (review-time shift)
+- Queries: [[2026-07-30-stanford-widening-gap-source]]
+- Entities: [[allie-miller]] (her prediction, their measurement)
+
+## Contradictions / Uncertainty
+
+- Data is proprietary and opt-in — no outside replication possible; participating companies may skew toward the measurement-friendly. Status: tentative.
+- The widening-gap analysis itself is not peer-reviewed as of 2026-07-30. Status: tentative.
+
+## Next Questions
+
+- Track whether the DiD analysis lands in a peer-reviewed venue.
+- What do they say *causes* the gap (tooling vs hygiene vs practices)? The talks assert "quality of usage" without decomposing it.

wiki/overview.md

@@ -10,7 +10,7 @@ A high-signal personal knowledge base. `raw/` holds immutable source materials;
 
 ## The through-line
 
-Across eleven sources — five talks/videos/interviews from practitioners (two of them Theo's), five interviews conducted for this project, and a business-facing short — one spine recurs:
+Across thirteen sources — six talks/videos/interviews from practitioners (two of them Theo's), five interviews conducted for this project, a business-facing short, and one quantitative outside study ([[swepr|Stanford SWEPR]]) — one spine recurs:
 
 > **As the cost of writing code goes to zero, value migrates from *producing* software to *directing and verifying* it — and the durable human assets become judgment, ownership, taste, and in-person relationships.**
 
@@ -20,23 +20,24 @@ Everything else hangs off that:
 - **The human side** — what stays yours: [[product-ownership]] over outcomes, [[connections-as-moat|in-person connections]] as the last non-commoditized asset, [[seniority-and-the-junior-squeeze|judgment as risk-reduction]], and the need to [[decoupling-identity-from-profession|decouple identity from profession]].
 - **The strategy side** — where to point it: [[think-wider-not-bigger|think wider not bigger]], treat [[code-as-throwaway|code as throwaway]], and mind [[enterprise-ai-reality|enterprise compliance reality]] (the company-managed-harness market). Theo's second video supplies the *verifying* half of the spine its method: [[make-more-cheap-code]] — keep hand-verification of what ships, and generate orders of magnitude more never-shipped code to verify and explore.
 - **The frontier side** — what it looks like at the far end, from [[thorsten-ball]] at [[amp]] (99% of their code AI-written): [[shedding-weight|shed weight]] by deleting every process that only existed because humans were the bottleneck; [[build-for-the-agent-not-the-human|build for the agent, not the human]]; work [[async-by-default|async by default]] in remote sandboxes and ask for proof rather than claims. His two mechanisms for software becoming *personal* — [[emacsification-of-software|remixing what exists]] and [[explosion-of-internal-software|building what never did]] — are the corpus's strongest outside validation of the webinar's own thesis, "little tools you make for yourself." He is also its sharpest dissenter: he uses **no skills, no MCP, no slash commands**. Both mechanisms now carry a sourced counterweight — [[maintenance-is-the-real-cost]]: writing code was never the bottleneck, maintenance is, and an internal service is a second business. The reconciliation is a threshold, not a winner: tiny personal tools pass, replacing your Jira does not.
+- **The adoption side** — what goes wrong when organizations try this, from [[nikolai-sheiko]]'s multi-company casework ([[2026-07-30-rakes-in-ai-sdlc-adoption]]): the SDLC collapses *around the humans* — [[review-is-the-new-bottleneck|review becomes the bottleneck]] and volume metrics (LoC, PRs) go anti-informative, so measure **completed tasks without rework**; the developer's job flips from CPU-bound coding to [[developer-as-agent-manager|IO-bound agent management]]; and the winning company move is not custom AI development but installing and *evolving* a standard harness ([[enterprise-ai-reality]]) — with skills grown by walking the agent through real tasks and verified by a context-free subagent ([[solve-first-then-skillify]]).
 - **The demand side** — three interviews ground it all in a real audience. The two HR ones ([[2026-07-14-nina-interview|Nina]], [[2026-07-14-yulia-interview|Yulia]]) supply pain points (interview write-ups, job descriptions, sourcing) that collapse into "a candidate knowledge base plus search," teachable via [[levels-of-ai-usage]] and [[solve-first-then-skillify]]. Their key finding: **adoption is blocked by friction, not resistance.** The [[2026-07-21-larysa-interview|Larysa interview]] adds the *advanced* user's version of the same story: past the friction, the remaining walls are structural — no durable memory, [[integration-dead-ends|integrations that dead-end]], and drift on loose specs ([[leave-less-room-for-imagination]]). Her diagnosis matters because she is technically deep yet skipped the skills rung, which is exactly what her "the agent forgot" complaint reduces to.
 
 See [[ai-agent-evolution]] for how the capability curve got here.
 
 ## Where sources agree vs diverge
 
-- **Agree:** code is cheap/disposable; harnesses are the unit of work; skills-as-memory (Konstantin ↔ Allie ↔ Eugene); human relationships rise in value (Sebastian ↔ Allie ↔ Eugene, who lands there independently in the Yulia interview); solve-first-then-skillify (Eugene ↔ Konstantin's heuristics); context is the constraint — Thorsten's version is the bluntest: **the dominant variable in output quality is the information you put in**, not the model or the effort level. The [[2026-07-22-ai-is-stupid|"AI is stupid!" short]] independently compresses the machine-side spine into a business one-liner: **model + context + harness = employee-level answer**. Slop is a human problem, not an AI defect (Theo ↔ Thorsten, from verification discipline and from taste respectively). Software becomes personal — "little tools you make for yourself" (Eugene's webinar arc ↔ Thorsten's club app and bespoke forks ↔ Allie's personal OS).
-- **Diverge:** personal vs company-managed vs vendor-managed harness ([[eugene]] vs [[sebastian]] vs [[amp]]); online vs in-person networking (Eugene/Sebastian); OSS as marketing vs OSS growth; built-in agent memory as anti-feature (Eugene) vs persistent context docs used without complaint (Allie); tight specs ([[leave-less-room-for-imagination]]) vs wide latitude ([[think-wider-not-bigger]]); agent diff-summaries as sufficient review (Theo/Dax) vs invisible drift as the core danger (Eugene); model choice as a real lever (Eugene runs 4.7 over 4.8) vs a distraction past the frontier (Thorsten); local consolidated workspace (Eugene) vs local dev disappearing into remote sandboxes (Thorsten); build-your-own-tools ([[thorsten-ball]], the webinar arc) vs [[maintenance-is-the-real-cost|buy anything that needs ongoing support]] (the vibe-coding video, with the corpus's only observed reversal: an in-house Jira clone abandoned for Linear in four months). These live under "Contradictions" on the relevant pages.
-- **The one that matters most for the webinar:** [[thorsten-ball]] runs a 99%-AI-written codebase with **no skills, no MCP servers and no slash commands** — his context lives in the codebase and `AGENTS.md`. That is the corpus's first credible rejection of the mechanism the webinar's central promise rests on. Three readings (situational / premature abstraction / same thing under another name) are logged on [[skills-as-memory]]; none is settled, and the evidential asymmetry favours him — his is first-hand daily practice at scale.
+- **Agree:** code is cheap/disposable; harnesses are the unit of work; skills-as-memory (Konstantin ↔ Allie ↔ Eugene); human relationships rise in value (Sebastian ↔ Allie ↔ Eugene, who lands there independently in the Yulia interview); solve-first-then-skillify (Eugene ↔ Konstantin's heuristics); context is the constraint — Thorsten's version is the bluntest: **the dominant variable in output quality is the information you put in**, not the model or the effort level. The [[2026-07-22-ai-is-stupid|"AI is stupid!" short]] independently compresses the machine-side spine into a business one-liner: **model + context + harness = employee-level answer**. Slop is a human problem, not an AI defect (Theo ↔ Thorsten, from verification discipline and from taste respectively). Software becomes personal — "little tools you make for yourself" (Eugene's webinar arc ↔ Thorsten's club app and bespoke forks ↔ Allie's personal OS). And the corpus's central *stakes* claim now has outside measurement: [[2026-07-30-stanford-swepr-widening-gap|Stanford SWEPR]] finds the productivity gap between AI-mastering and lagging teams grew 4.8% → 19% (4×) from April 2023 to July 2025 — Allie's prediction, measured; the same study's codebase-size finding independently supports [[context-as-scarce-resource|context as the binding constraint]]. [[2026-07-30-rakes-in-ai-sdlc-adoption|Sheiko]] cites that same Stanford chart as his stakes slide and lands on the spine independently — "companies no longer need custom AI development, install Claude Code or Codex and configure it" is harness-over-model as a service playbook, and his codebase-stores-context prescription converges with Thorsten's from the opposite direction. His review-bottleneck casework (+1% net despite more PRs) is SWEPR's +91%-review-time finding told anecdotally.
+- **Diverge:** personal vs company-managed vs vendor-managed harness ([[eugene]] vs [[sebastian]] vs [[amp]]); online vs in-person networking (Eugene/Sebastian); OSS as marketing vs OSS growth; built-in agent memory as anti-feature (Eugene) vs persistent context docs used without complaint (Allie); tight specs ([[leave-less-room-for-imagination]]) vs wide latitude ([[think-wider-not-bigger]]); agent diff-summaries as sufficient review (Theo/Dax) vs invisible drift as the core danger (Eugene); model choice as a real lever (Eugene runs 4.7 over 4.8) vs a distraction past the frontier (Thorsten); local consolidated workspace (Eugene) vs local dev disappearing into remote sandboxes (Thorsten); build-your-own-tools ([[thorsten-ball]], the webinar arc) vs [[maintenance-is-the-real-cost|buy anything that needs ongoing support]] (the vibe-coding video, with the corpus's only observed reversal: an in-house Jira clone abandoned for Linear in four months); permit opting out of the agent-manager switch ([[nikolai-sheiko]] — "don't force everyone") vs the gap is irreversible and compounding ([[allie-miller]], [[swepr|Stanford]]) — see [[developer-as-agent-manager]]. These live under "Contradictions" on the relevant pages.
+- **The one that matters most for the webinar:** [[thorsten-ball]] runs a 99%-AI-written codebase with **no skills, no MCP servers and no slash commands** — his context lives in the codebase and `AGENTS.md`. That is the corpus's first credible rejection of the mechanism the webinar's central promise rests on. Three readings (situational / premature abstraction / same thing under another name) are logged on [[skills-as-memory]]; none is settled. The evidential asymmetry that favoured him narrowed on 2026-07-30: [[nikolai-sheiko]] is a second practitioner voice on the pro-skills side — his "Agentic Evolution" (guided tasks → agent writes the manual → context-free-subagent verification) is the corpus's first described *test* of a skill, though his cases are anonymous anecdotes where Thorsten's is first-hand daily practice at scale.
 
 ## Navigation
 
 - **[[index]]** — content catalog
-- **Sources (11):** [[2026-07-14-everything-we-knew-about-software-has-changed|Theo Browne]] · [[2026-07-14-gap-between-ai-users-irreversible|Allie Miller]] · [[2026-07-14-sebastian-eugene-interview|Sebastian interview]] · [[2026-07-14-skills-based-on-git|Konstantin (git skills)]] · [[2026-07-14-nina-interview|Nina interview]] · [[2026-07-14-yulia-interview|Yulia interview]] · [[2026-07-21-larysa-interview|Larysa interview]] · [[2026-07-22-ai-is-stupid|"AI is stupid!" short]] · [[2026-07-24-youre-reading-way-too-much-code|Theo Browne (reading code)]] · [[2026-07-28-agentic-engineering-10x-developer|Thorsten Ball (agentic engineering)]] · [[2026-07-29-what-if-we-vibe-code-it|"What if we vibe-code it?" (maintenance trap)]]
-- **People:** [[theo-browne]] · [[allie-miller]] · [[sebastian]] · [[eugene]] · [[konstantin]] · [[nina]] · [[yulia]] · [[larysa]] · [[thorsten-ball]]
-- **Tools/orgs:** [[claude-code]] · [[amp]] · [[hermes]] · [[virtido]] · [[inspectron]]
-- **Concepts:** see the through-line above (25 pages) · **Timeline:** [[ai-agent-evolution]] · **Comparison:** [[theo-konstantin-allie]]
+- **Sources (13):** [[2026-07-14-everything-we-knew-about-software-has-changed|Theo Browne]] · [[2026-07-14-gap-between-ai-users-irreversible|Allie Miller]] · [[2026-07-14-sebastian-eugene-interview|Sebastian interview]] · [[2026-07-14-skills-based-on-git|Konstantin (git skills)]] · [[2026-07-14-nina-interview|Nina interview]] · [[2026-07-14-yulia-interview|Yulia interview]] · [[2026-07-21-larysa-interview|Larysa interview]] · [[2026-07-22-ai-is-stupid|"AI is stupid!" short]] · [[2026-07-24-youre-reading-way-too-much-code|Theo Browne (reading code)]] · [[2026-07-28-agentic-engineering-10x-developer|Thorsten Ball (agentic engineering)]] · [[2026-07-29-what-if-we-vibe-code-it|"What if we vibe-code it?" (maintenance trap)]] · [[2026-07-30-stanford-swepr-widening-gap|Stanford SWEPR (widening gap)]] · [[2026-07-30-rakes-in-ai-sdlc-adoption|Nikolai Sheiko (rakes in SDLC adoption)]]
+- **People:** [[theo-browne]] · [[allie-miller]] · [[sebastian]] · [[eugene]] · [[konstantin]] · [[nina]] · [[yulia]] · [[larysa]] · [[thorsten-ball]] · [[nikolai-sheiko]]
+- **Tools/orgs:** [[claude-code]] · [[amp]] · [[hermes]] · [[virtido]] · [[inspectron]] · [[swepr]]
+- **Concepts:** see the through-line above (27 pages) · **Timeline:** [[ai-agent-evolution]] · **Comparison:** [[theo-konstantin-allie]]
 
 ## Open Questions (vault-level)
 

wiki/queries/2026-07-28-webinar-theses.md

@@ -61,7 +61,8 @@ The audience's real objection is "can I trust it?", and the current script has n
 
 **T7. The gap between AI users and everyone else compounds — and is becoming irreversible.**
 The person who builds their OS this week fears no release, because each capability slots into a system that already knows them. *(Thorsten names **token budget** as a second winner/loser variable, but that is a claim about metered agent-fleet work; for this audience the budget is one consumer subscription — keep the thesis on the skill gap. See [[enterprise-ai-reality]].)*
-— [[2026-07-14-gap-between-ai-users-irreversible]]
+*(Measured 2026-07-30: this thesis is no longer prediction-only — Stanford SWEPR's 46-vs-46-team analysis shows the gap growing 4.8% → 19% (4×) from April 2023 to July 2025. One citable stage line: "Stanford measured it: the gap quadrupled in two years." See [[2026-07-30-stanford-swepr-widening-gap]]. **Added to the script's closing arc 2026-07-30** — T7 is now dramatized, upgrading it from Q&A material to an on-stage beat.)*
+— [[2026-07-14-gap-between-ai-users-irreversible]] · [[2026-07-30-stanford-swepr-widening-gap]]
 
 **T8. The more the world is mediated by AI proxies, the more valuable real human connection becomes.**
 The "market of one" raises, not lowers, the price of being human.

wiki/queries/2026-07-30-stanford-widening-gap-source.md

@@ -0,0 +1,55 @@
+# Stanford "widening gap" chart — original research located
+
+#query
+
+## Question asked
+
+The user saved a chart screenshot (`raw/assets/G6g3O60bkAE05ZW.png`, filename pattern = X/Twitter image) of a Stanford slide titled *"Teams that master AI are accelerating their productivity gains, widening the gap with laggards"* and asked: **find the original Stanford research behind it.**
+
+## Answer
+
+The slide is from **Stanford's Software Engineering Productivity Research group (SWEPR)**, led by researcher **Yegor Denisov-Blanch**. Since 2022 the group has analyzed private Git repositories from 600+ companies and 120,000+ engineers, scoring every commit with an ML model trained to replicate a panel of human expert reviewers.
+
+The specific chart is their **causal difference-in-differences analysis**: 46 teams that adopted AI, matched against 46 similar non-AI teams, with net productivity gains measured quarterly.
+
+- **April 2023:** 4.8% gap between top-quartile and bottom-quartile AI adopters.
+- **July 2025:** 19% gap — a **4× increase** in ~2.25 years.
+- Slide footer: Stanford University / SWEPR; the specific slide (page 3) matches Denisov-Blanch's September 2025 AI Conference deck.
+
+**Primary sources:**
+
+- Research group home: <https://softwareengineeringproductivity.stanford.edu/> (AI Impact research, AI Practices Benchmark)
+- Talk the slide comes from: *"Will AI Replace Software Engineers?"*, AI Conference, Sept 2025 — [slide deck PDF](https://aiconference.com/wp-content/uploads/2025/09/Yegor-Denisov-Blanch-Will-AI-Replace-Software-Engineers_-.pptx.pdf)
+- Video walkthrough: [Can you prove AI ROI in Software Eng? (Stanford 120k Devs Study)](https://www.youtube.com/watch?v=JvosMkuNxF8)
+- Researcher site: <https://yegordb.com/>
+- Peer-reviewed methodology paper: [Predicting Expert Evaluations in Software Code Reviews](https://arxiv.org/pdf/2409.15152)
+
+**Caveat (Status: tentative):** the 46-vs-46 difference-in-differences result itself has been presented via talks, webinars and decks — not (yet) a peer-reviewed paper. The peer-reviewed publications cover the *measurement methodology*, not this specific analysis.
+
+**Surrounding findings from the same study** (useful nuance): AI raises developer productivity ~15–20% on average, with high variance — largest gains on greenfield/simple tasks in popular languages; AI can *decrease* net productivity in complex legacy codebases (rework eats the gains, ~2.6× increase in rework reported).
+
+## Why this matters to the vault
+
+This is the **first quantitative, external, longitudinal measurement** of a claim the corpus so far held only as practitioner assertion:
+
+- [[2026-07-14-gap-between-ai-users-irreversible]] — [[allie-miller]]'s central prediction ("in 12 months the gap will be irreversible") is the same shape as this curve, asserted from advisory experience. Stanford now supplies measured team-level data pointing the same direction.
+- [[2026-07-28-webinar-theses]] — the "stakes" thesis group (irreversible gap) gains a citable number: *4.8% → 19%, 4× in about two years*. A Stanford chart is far stronger webinar ammunition than "an ex-Amazon AI leader predicts…".
+- The "AI can decrease productivity in complex legacy codebases" finding is honest-caveat material aligning with the vault's recorded tensions ([[maintenance-is-the-real-cost]], rework costs; [[make-more-cheap-code]]'s verification burden — cf. the study's 91% increase in PR review time).
+- The mechanism Stanford implies (teams that *master* AI compound, laggards stall) is the team-level twin of [[levels-of-ai-usage]] — the gap grows between rungs, not between haves and have-nots of licenses.
+
+## Evidence trail
+
+- Screenshot: `raw/assets/G6g3O60bkAE05ZW.png` (raw asset; likely captured from an X/Twitter post sharing the talk)
+- Web search + fetch of the SWEPR site and the AI Conference deck (2026-07-30); slide title, footer, chart annotations and page number all match the deck's era (data ends July 2025)
+
+## Follow-up questions
+
+- ~~Ingest-worthy?~~ **Done, same day:** the user authorized a new raw source file; the dossier lives at `raw/sources/Stanford SWEPR - AI and the widening productivity gap.md` and is ingested as [[2026-07-30-stanford-swepr-widening-gap]] — concept pages now cite it directly.
+- Does the webinar want the number? One line — "Stanford measured it: the gap 4×'d in two years" — would upgrade the stakes beat from prediction to measurement.
+- Watch for a peer-reviewed version of the difference-in-differences analysis; the claim's status upgrades from tentative when it lands.
+
+## Whether this output changed existing pages
+
+- [[2026-07-14-gap-between-ai-users-irreversible]] — added an external-corroboration pointer to this page under Connections.
+- `index.md` (Queries section) and `log.md` updated.
+- No concept pages changed — deliberately, since the underlying talk is not yet ingested as a source (citation policy: concept evidence should point at `wiki/sources/*`).

wiki/sources/2026-07-14-gap-between-ai-users-irreversible.md

@@ -37,6 +37,7 @@
 - **Concepts:** [[personal-ai-operating-system]] · [[skills-as-memory]] · [[context-as-scarce-resource]] · [[connections-as-moat]] (human-relationship side effect)
 - **Related sources:** [[2026-07-14-skills-based-on-git]] (skills-as-memory from the engineering side — strong overlap) · [[2026-07-14-everything-we-knew-about-software-has-changed]] (markdown-as-skill ≈ G-brain markdown tier)
 - **Tools mentioned:** [[claude-code]], Claude Cowork, Codex, Flint
+- **External corroboration:** [[2026-07-30-stanford-swepr-widening-gap]] — Stanford SWEPR's difference-in-differences study measured the gap between AI-mastering and lagging teams growing 4.8% → 19% (4×) from April 2023 to July 2025; the title claim's first measured, non-practitioner support. (Traced via [[2026-07-30-stanford-widening-gap-source]].)
 
 ## Open Questions
 

wiki/sources/2026-07-30-rakes-in-ai-sdlc-adoption.md

@@ -0,0 +1,55 @@
+# Грабли во внедрении ИИ в SDLC — Rakes in AI Adoption in the SDLC (Nikolai Sheiko)
+
+#source
+
+## Source Metadata
+
+- **Date:** talk published 2026 (references events through Dec 2025); conclusions doc saved 2026-07-30
+- **Raw path:** `raw/sources/Грабли во внедрении ИИ в SDLC.md`
+- **Source type:** viewer's conclusions from a Russian-language YouTube talk (45:59) — https://www.youtube.com/watch?v=Nm3MsnngCJg — "Грабли во внедрении ИИ в SDLC — почему ИИ есть, а результата нет и как это лечить" ("why the AI is there but the results aren't, and how to treat it"). Not a transcript.
+- **Speaker:** [[nikolai-sheiko]] — AI-adoption consultant/practitioner (works with client companies on SDLC adoption; background otherwise unknown)
+- **Ingestion date:** 2026-07-30
+
+## Core Claims
+
+- **AI in development already delivers real gains, but people, companies and metrics throttle it by an order of magnitude.** The real jump started Dec 2025 (Opus 4.5 / GPT-5.2 + Claude Code / Codex); the SDLC collapsed into days/hours — but *not fully*: two human "red squares" remain — the **reviewer** (tasks queue at review) and the **planner/product person**. Developer time redistributed from "coding in the middle" to "planning on the left + verification on the right." See [[review-is-the-new-bottleneck]].
+- **Universal error #0: a developer is not a manager.** A good developer is 3–5 hours of CPU-bound focus on one feature; a good AI-developer is an IO-bound **manager of an agent-employee**, running several tasks in parallel. "If you launched Claude Code and sit watching it work — you're a bad employee." Not everyone can make the psychological switch, *and that's fine* — don't force everyone. See [[developer-as-agent-manager]].
+- **Measure completed tasks without rework** — never LoC, commit count or PR count (all trivially hacked; the European-outsourcing case shipped more PRs for a +1% gain because rework ate everything). A task counts only if it doesn't come back for fixes; also track task lifetime + rework time.
+- **Review with the agent, not instead of it and not fully by hand.** Manual-only review → queue → burnout → quality collapse; fully delegated review is the opposite error. Treat the model as a smart student: direct it, pose hypotheses, find problems together.
+- **Companies no longer need custom AI development.** Key quote: *"Come in, install Claude Code or Codex, configure everything, attach connectors, think about security — and it works better than any custom build."* Corollary anti-pattern: hiring an external configurator who leaves behind a "magic artifact" nobody owns — teams must configure their own tools; what a company should buy is a **teacher/curator**, not a setup.
+- **Agentic Evolution** — the key concept. Don't ask the expert to explain how they work (you get theory); instead take the new employee (the agent) by the hand through hard tasks, show it the rakes, then say: *"remember all of this and write the manual for the next one."* Verify a skill by launching a **context-free subagent** that must solve the same task from scratch using only the skill; the mentor agent watches what fails and fixes the skill. Without evolution you live on defaults; with it, vertical growth begins.
+- **Best practices matter more with agents, not less.** The "compaction curse": on a huge codebase the agent gathers context → window overflows → compaction → re-gathers → compaction again, and the task barely completes. The cure is locality, isolated modules with interfaces — **the codebase stores the context**. Use AST search instead of grep on colossal projects. And **embeddings/RAG over code do not work** — don't use them unless you understand *very* well why.
+- **Role futures:** a **Product engineer** emerges (answers *why* we build it this way, what to cut, what to ignore); users vs **Agentic Operations** (who tune the SDLC, feedback loops, prompts, skills) gradually split; AI eats **Intelligence** (action sequences requiring intellect) while **Judgment** (taste built over years, or domain expertise — oil & gas, medicine) stays human for now.
+- **Tokens get more expensive near-term, cheaper later. It's the wild west — experiment at full throttle while subscriptions are cheap**; the goal is to land in the top half of the Stanford chart. Don't chase every new tool: what Claude Code / Codex doesn't absorb within a couple of months is probably useless.
+
+## Key Evidence / Details
+
+- **Timeline the talk builds on:** METR study (Jul 2025, measured a *slowdown*, but methodologically contested) → Stanford study (Aug 2025, +20% speed but top-performers pull away — this is [[swepr]]'s research, cited independently) → Karpathy's tweet (Nov–Dec 2025: "80% Claude Code, 20% by hand") → "SDLC is dead" article (stages collapse into days/hours).
+- **Case 1 (frontend migration):** no feedback loop → give the agent a browser (Playwright / Chrome DevTools) to compare old vs new front; crunch → invest in planning (20 min minimum, hours are normal; every 10 min of planning saves hours; target one-shot implementation); Cursor with per-token billing → team economizes instead of experimenting (~30% dearer than subscriptions at the same level); training rollout: record sessions → expert reviews → *writes feedback, team fixes their own agent instructions* → focus on the top 2 performers (an hour with them is worth 10× more).
+- **Case 2 (European outsourcer):** more PRs, +1% — rework was the cause; the metrics table (don't: LoC/commits/PRs; do: completed-tasks-without-rework).
+- **Case 3 (large codebase):** the compaction curse; "agents mean we can drop best practices" is exactly wrong.
+- **Mini-cases:** the middle dev who sped the team up by tens of %, was refused a raise, and left for much more ("if you're that middle — think; if you're the manager — think twice"); the startup doing spec-driven development without knowing what it wants — build the UI first (even with an in-memory browser DB), click around, *then* plan.
+- **Do-tomorrow list:** close the feedback loop; write a skill that analyses your own sessions daily; automate it (Codex schedules / Anthropic routines); voice input (more context, and Russian gives more context than English); ignore tool churn.
+- **Q&A notes:** GLM (good, no vision), Kimi (works, has vision), Xiaomi's agent (interesting memory implementation; ex-DeepSeek Head of AI) as the Chinese-model trend; hardware deficit (H100s unrentable); speaker's prediction that AI will be classed as a cyber-weapon with biotech-style licensing.
+- **One-shot recipe:** feedback loop (must-have) + explicit real *goal* the agent self-checks against + skill verification via the context-free subagent.
+
+## Connections
+
+- **Entities:** [[nikolai-sheiko]], [[swepr]] (its Stanford study is the talk's central chart — "be in the top half"), [[claude-code]]
+- **New concepts:** [[review-is-the-new-bottleneck]], [[developer-as-agent-manager]]
+- **Corroborates:** [[2026-07-30-stanford-swepr-widening-gap]] (independent practitioner citation of the widening-gap result *and* of review-as-downstream-cost); [[solve-first-then-skillify]] (Agentic Evolution is its strongest formulation, plus the missing verification step); [[skills-as-memory]] (a second frontier-practitioner vote *for* the skills/evolution layer, against [[thorsten-ball]]'s dissent — and a vote *against* embeddings/RAG over code, siding with Konstantin in the skills-vs-RAG contradiction); [[context-as-scarce-resource]] (compaction curse; codebase-stores-context converges with Thorsten from the opposite direction); [[async-by-default]] (IO-bound parallel management as the working mode); [[enterprise-ai-reality]] ("no custom AI development needed" names the same managed-harness market; per-token billing shapes behaviour — the metered-vs-subscription split already logged there); [[harness]] (install-and-configure beats custom builds); [[leave-less-room-for-imagination]] (explicit goal + planning discipline).
+- **Complicates:** [[make-more-cheap-code]] (rework-free-completion as the metric is the org-level answer to "generation moved the cost downstream").
+
+## Open Questions
+
+- The cases are anonymous client anecdotes with self-reported numbers (+1%, "tens of %"); none are verifiable. Status: tentative.
+- "Embeddings/RAG over code don't work" is stated flatly with no mechanism given — strong claim, no evidence in the doc. Status: tentative.
+- Does the context-free-subagent verification protocol actually measure skill quality, or only skill *completeness* for one task? Closely related to the corpus's proposed skills falsification test ([[skills-as-memory]]) — this is the first source to describe running one.
+- The Dec-2025 "real jump" periodization is the speaker's own; the corpus's other timeline ([[ai-agent-evolution]]) slices eras differently.
+- Predictions (AI as licensed cyber-weapon; token prices up then down) are speculation. Status: tentative.
+
+## Change Impact on Wiki
+
+- Created [[nikolai-sheiko]] (entity), [[review-is-the-new-bottleneck]] and [[developer-as-agent-manager]] (concepts).
+- Updated [[solve-first-then-skillify]] (Agentic Evolution + skill-verification protocol), [[skills-as-memory]] (second practitioner vote for skills; anti-RAG-for-code), [[context-as-scarce-resource]] (compaction curse, AST search, codebase-stores-context), [[async-by-default]] (IO-bound manager evidence), [[enterprise-ai-reality]] (no-custom-AI-dev quote; external-configurator anti-pattern; token-price prediction), [[make-more-cheap-code]] (related link), [[2026-07-30-stanford-swepr-widening-gap]] (independent citation).
+- Updated [[overview]] (12 → 13 sources), `index.md`, `log.md`.

wiki/sources/2026-07-30-stanford-swepr-widening-gap.md

@@ -0,0 +1,48 @@
+# Stanford SWEPR — AI and the widening productivity gap
+
+#source
+
+## Source Metadata
+
+- **Date:** research presented through 2025 (chart data April 2023 → July 2025; deck Sept 2025); dossier compiled 2026-07-30
+- **Raw path:** `raw/sources/Stanford SWEPR - AI and the widening productivity gap.md` (screenshot: `raw/assets/G6g3O60bkAE05ZW.png`)
+- **Source type:** research dossier — a slide read first-hand plus public coverage of the underlying study; **not** a talk transcript. The corpus's first *quantitative outside study* (every other source is practitioner testimony or synthesis).
+- **Authors:** [[swepr]] (Stanford Software Engineering Productivity Research group), public face Yegor Denisov-Blanch
+- **Ingestion date:** 2026-07-30
+
+## Core Claims
+
+- **The gap between AI-mastering teams and laggards is widening, measurably.** Difference-in-differences analysis of 46 AI-adopting teams vs 46 matched non-AI teams: net productivity difference between top and bottom quartiles grew from **4.8% (April 2023) to 19% (July 2025) — a 4× increase** in ~2.25 years. Early quarters hover near zero or negative; the curve only takes off from mid-2024, then accelerates.
+- **Average net gain from AI is ~15–20%, not 10×.** Gross delivered code volume rises 30–40%, but rework (fixing AI-introduced bugs) eats roughly half.
+- **Gains are strongly context-dependent:** greenfield/low-complexity 30–40%; brownfield/high-complexity 0–10% and can be *negative*. Popular languages gain more than niche ones; gains collapse as codebases grow 10k → 10M lines (context-window limits, signal-to-noise).
+- **The cost moved downstream:** coverage reports +91% PR review time and ~2.6× rework in AI-heavy workflows — writing got cheaper, reviewing got more expensive.
+- **Proposed mechanism for the gap:** quality of AI usage beats volume; teams with clean, modular, well-tested code compound gains, teams with poor hygiene accumulate debt and lose trust in the tools.
+
+## Key Evidence / Details
+
+- Data: private Git repos, 600+ companies, ~100k–120k engineers, since 2022; ML model replicating a panel of expert reviewers, measuring *functionality delivered* (not commits/LOC). Methodology peer-reviewed (arXiv 2409.15152); the DiD result itself is talk-published only.
+- The slide: "Causal Impact of AI on Software Engineering Productivity: Difference-in-Differences Analysis," DID covariate balance < 0.25, 95% CI band, model-release markers on the time axis. Deck: "Will AI Replace Software Engineers?", AI Conference, Sept 2025.
+- Full numbers, links, and per-claim provenance in the raw dossier.
+
+## Connections
+
+- **Entity:** [[swepr]]
+- **Corroborates:** [[2026-07-14-gap-between-ai-users-irreversible]] — Allie Miller's "irreversible gap" prediction is this curve, asserted 18 months earlier from advisory experience; Stanford supplies the measurement. Also the team-level twin of [[levels-of-ai-usage]] (the gap grows between *rungs of mastery*, not between license-holders and others).
+- **Corroborates:** [[context-as-scarce-resource]] — the codebase-size finding (gains collapse toward 10M LOC, attributed to context-window limits and signal-to-noise) is the corpus's first outside quantitative support for context as the binding constraint.
+- **Complicates:** [[make-more-cheap-code]] / [[code-as-throwaway]] — +91% PR review time and 2.6× rework externally confirm that generation moved the cost to review/verification, which is Theo's premise; but the *negative* gains in complex brownfield code sharpen the honest caveat that "code is cheap" holds least where most code lives.
+- **Independently cited by a practitioner source:** [[2026-07-30-rakes-in-ai-sdlc-adoption]] builds its stakes on this study ("the Stanford study, Aug 2025: +20% speed, but the top performers pull away — the goal is to be in the top half of the chart") and anecdotally mirrors its downstream-cost finding (the +1%-despite-more-PRs rework case; review as the bottleneck — see [[review-is-the-new-bottleneck]]).
+- **Queries:** [[2026-07-30-stanford-widening-gap-source]] (how this source was traced), [[2026-07-28-webinar-theses]] (thesis T7 gains its number)
+
+## Open Questions
+
+- Does the 46-team DiD analysis ever get a peer-reviewed publication? (Claim status upgrades when it does.)
+- What exactly distinguishes the mastering teams — tooling, codebase hygiene, or skills/context practices? The proposed mechanism ("quality of usage") is asserted in talks, not decomposed. Directly relevant to whether the webinar's skills rung is *the* differentiator. Status: tentative.
+- The study measures teams of engineers; how far do team-level results transfer to the webinar's non-engineer audience? Status: tentative.
+
+## Change Impact on Wiki
+
+- Created [[swepr]] entity.
+- Added measured external evidence to [[levels-of-ai-usage]], [[context-as-scarce-resource]] and [[make-more-cheap-code]].
+- Upgraded the corroboration pointer on [[2026-07-14-gap-between-ai-users-irreversible]] to cite this page.
+- Added the "Stanford measured it: 4× in two years" note to thesis T7 in [[2026-07-28-webinar-theses]].
+- Updated [[overview]] (11 → 12 sources; first quantitative outside study), `index.md`, `log.md`.