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wiki/concepts/context-as-scarce-resource.md

@@ -18,6 +18,10 @@ Context pressure explains several otherwise-separate design choices:
 
 The human role has climbed prompt-engineer → **context-engineer** → harness-builder → loop-engineer, tracking exactly this concern.
 
+**Information beats tuning** ([[2026-07-28-agentic-engineering-10x-developer]]). [[thorsten-ball]] states the strongest version: once you have a frontier model, **the dominant variable in output quality is the information you put in** — not which model, and not the effort level (medium vs high vs ultra). "If you're mad your model doesn't use camelCase, rethink your software engineering, not the model." He names the agent's only two information sources — **training data** (a senior engineer who's seen it all, but lossy and possibly stale) and **the context window** (your prompt, plus whatever the codebase and `AGENTS.md` supply) — and the operative asymmetry: a model cannot turn a thin prompt into a good one. Note what he does *not* conclude: the fix is a better-tended codebase and a longer prompt, not [[skills-as-memory|skills]] (contested there).
+
+**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 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
@@ -25,11 +29,13 @@ The human role has climbed prompt-engineer → **context-engineer** → harness-
 - Smart zone, summarization decay, "context is the most valuable resource," tool/skill loading mechanics — [[2026-07-14-skills-based-on-git]].
 - Context engineering vs prompt engineering; foundation docs as durable context — [[2026-07-14-gap-between-ai-users-irreversible]].
 - "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]].
 
 ## Related Pages
 
-- Concepts: [[harness]], [[skills-as-memory]], [[agentic-loops]], [[evolution-of-agent-tooling]], [[personal-ai-operating-system]]
-- Entities: [[konstantin]], [[allie-miller]]
+- Concepts: [[harness]], [[skills-as-memory]], [[agentic-loops]], [[evolution-of-agent-tooling]], [[personal-ai-operating-system]], [[make-more-cheap-code]], [[explosion-of-internal-software]], [[enterprise-ai-reality]]
+- Entities: [[konstantin]], [[allie-miller]], [[thorsten-ball]]
 
 ## Contradictions / Uncertainty