Principles / The Acceleration Problem
§ Principle 11 of 23
The Acceleration Problem
Rapid increases in model capability create rate-of-change that exceeds most organizations' ability to adapt.
The Acceleration Problem is about the pace gap between AI capability growth and organizational adaptation. Models improve dramatically every 6-12 months. Organizations adapt their workflows, training, and systems every 12-24 months. This mismatch means most organizations are using AI systems designed for a previous generation of models — over-constraining new models with old architectures, or under-utilizing new capabilities because their workflows haven't caught up.
Why it matters
The Acceleration Problem means that AI system architecture must be designed for change, not for the current state. Systems that are tightly coupled to a specific model's capabilities will be obsolete in 6 months. Systems designed as flexible substrates — with pluggable models, adaptable workflows, and modular components — can absorb capability improvements without full redesign.
In practice
Design your AI systems with model abstraction layers. Don't hard-code assumptions about model capability. Build evaluation systems that can test new models against your specific workloads. Make model switching a configuration change, not an architecture change.