When talking about AI at scale, I have always thought about it from the model-serving infrastructure perspective: How many GPUs do we need? How do we scale inference? How do we reduce latency and cost? How do we serve millions of concurrent requests? How do we efficiently distribute models across GPU clusters? But there is another way to think about AI at scale. Instead of scaling the AI model itself, what if we scale software development across ordinary users?

Introduction Spec-driven development is becoming increasingly popular in the era of AI-assisted software engineering. Instead of starting directly from implementation, teams define specifications, domain rules, contracts, and architectural intentions first, allowing Large Language Models (LLMs) and automation tools to generate significant parts of the system. This approach can dramatically improve development speed, documentation quality, and alignment between business and engineering. However, one important challenge emerges quickly: Architecture drift.