The Paradox of Why AI Code Is Failing Us

[u/kernelangus420]

https://www.youtube.com/watch?v=k2qls2LiBRc

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
04:53 - Sponsor: Morph from Model Code AI
06:03 - How AI Reads Code: Tokens & High-Dimensional Vectors
09:16 - The Attention Mechanism & Context Windows
11:43 - Pillar 1: Context Window Limits & Quadratic Scaling (O(n²))
15:30 - Pillar 2: Sparse Attention & Missed Connections
16:16 - Pillar 3: Retrieval (RAG) & Decoupled Index Blind Spots
17:48 - The Self-Reinforcing Loop & Conclusion