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The Paradox of Why AI Code Is Failing Us - 3 Pillars
An in-depth technical breakdown of the paradox surrounding AI-generated code, software demand elasticity, and why large language models struggle when scaling enterprise codebases. In this video, we explore the economic backdrop of Jevons Paradox in software engineering, examine how transformer attention mechanisms process tokens, and break down the 3 core architectural failure modes limiting AI code generation. TIMESTAMPS: 00:00 - The Problem: AI Generates Too Much Code 01:04 - Demand Elasticity & Software Features 04:00 - Jevons Paradox in Software Development 04:48 - How AI Reads Code: Tokens & High-Dimensional Vectors 08:01 - The Attention Mechanism & Context Windows 10:19 - Pillar 1: Context Window Limits & Quadratic Scaling (O(n²)) 14:13 - Pillar 2: Sparse Attention & Missed Connections 15:02 - Pillar 3: Retrieval (RAG) & Decoupled Index Blind Spots 16:32 - The Self-Reinforcing Loop & Conclusion