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

AI coding agents frequently ignore long rule documents. Asking them to hold on to an entire book's worth of coding advice is at best futile, at worst makes the agent's performance worse by polluting the context window.

Humans don't need to hold the same information in their head because humans can form habits through repetition. However, AI agents can't do this.

Human habits form when an easy-to-detect cue triggers a complex sequence of actions with the desired effect. This is the inspiration for habit hooks.

Linters provide a deterministic metric, but Goodhart's law postulates that a metric ceases to be a good metric if it becomes a target. AI agents are very good at gaming these metrics when they are only provided the metric.

Habit hooks wraps your linter to create the trigger, but instead of providing only the metric, it gives actionable advice on how to fix the issue. This creates AI behaviour that looks like human habits, and has similar effects.

The use of habit hooks:

• Increases code quality
• Improves AI performance ensuring that the AI always starts with good code quality
• Reduces token usage, since good quality code also means the AI doesn't need to read as much context to complete the task.

Adam Tornhill

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