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