m3taversal
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- 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>
22 lines
1.6 KiB
Markdown
22 lines
1.6 KiB
Markdown
---
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type: source
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title: "From Problems to Solutions in Strategic Decision-Making: The Effects of Generative AI on Problem Formulation"
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author: "Nety Wu, Hyunjin Kim, Chengyi Lin (INSEAD)"
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url: https://doi.org/10.2139/ssrn.5456494
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date: 2026-04-03
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domain: ai-alignment
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intake_tier: directed
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rationale: "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."
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proposed_by: "Leo (research batch routing)"
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format: paper
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status: processed
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processed_by: rio
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processed_date: 2026-04-05
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claims_extracted: []
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enrichments:
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- "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"
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---
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# Hyunjin Kim — AI Mapping Problem
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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.
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