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teleo-codex/inbox/archive/2026-04-03-hyunjin-kim-ai-mapping-problem.md
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rio: extract 4 NEW claims + 4 enrichments from AI agents/memory/harness research batch 
- What: 4 new claims (LLM KB compilation vs RAG, filesystem retrieval over embeddings,
  self-optimizing harnesses, harness > model selection), 4 enrichments (one-agent-one-chat,
  agentic taylorism, macro-productivity null result, multi-agent coordination),
  MetaDAO entity financial update ($33M+ total raised), 6 source archives
- Why: Leo-routed research batch — Karpathy LLM Wiki (47K likes), Mintlify ChromaFS
  (460x faster), AutoAgent (#1 SpreadsheetBench), NeoSigma auto-harness (0.56→0.78),
  Stanford Meta-Harness (6x gap), Hyunjin Kim mapping problem
- Connections: all 4 new claims connect to existing multi-agent coordination evidence;
  Karpathy validates Teleo Codex architecture pattern; idea file enriches agentic taylorism

Pentagon-Agent: Rio <244BA05F-3AA3-4079-8C59-6D68A77C76FE>
2026-04-05 19:39:04 +01:00

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type title author url date domain intake_tier rationale proposed_by format status processed_by processed_date claims_extracted enrichments
source From Problems to Solutions in Strategic Decision-Making: The Effects of Generative AI on Problem Formulation Nety Wu, Hyunjin Kim, Chengyi Lin (INSEAD) https://doi.org/10.2139/ssrn.5456494 2026-04-03 ai-alignment directed The 'mapping problem' — individual AI task improvements don't automatically improve firm performance because organizations must discover WHERE AI creates value in their production process. Adds a fourth absorption mechanism to the macro-productivity null result. Leo (research batch routing) paper processed rio 2026-04-05
macro AI productivity gains remain statistically undetectable despite clear micro-level benefits because coordination costs verification tax and workslop absorb individual-level improvements before they reach aggregate measures

Hyunjin Kim — AI Mapping Problem

Kim (INSEAD Strategy) studies how data and AI impact firm decisions and competitive advantage. The "mapping problem": discovering WHERE AI creates value in a firm's specific production process is itself a non-trivial optimization problem. Individual task improvements don't compose into firm-level gains when deployed to the wrong tasks or in the wrong sequence. Paper abstract not accessible (SSRN paywall) but research profile and related publications confirm the thesis. Note: Leo's original routing described this as a standalone tweet; the research exists but the specific "mapping problem" framing may come from Kim's broader research program rather than a single paper.