Model Commoditization

Every major model now performs the same operations on the same principles: tokenize input, attend across context, predict continuations, return text. The differences that once separated them — reasoning depth, context length, instruction-following, tool use — narrow with each release cycle, and a capability exclusive to one provider in spring is standard across all of them by autumn. Prices fall as the capability converges. This is the pattern of any technology moving from differentiated product to infrastructure: electricity generation, database engines, cloud compute. What follows commoditization is not that the component stops mattering but that advantage migrates to what is built on top of it, because the component itself is available to every competitor on equivalent terms. An organization whose AI strategy is a choice of model has chosen something its competitors can choose tomorrow.

Centralpoint treats the model as a called service selected at runtime, which is the architectural expression of this view. OpenAI, Anthropic, Google Gemini and Microsoft Copilot are interchangeable positions in a configuration, alongside embedded Llama, Qwen and ONNX. What does not move is the layer above: classification applied at index time, audiences carried by records, skills and prompts held in the organization's own SQL environment. The accumulated asset is the control plane, and it compounds while the models beneath it converge.