It's certainly charitable to consider how easily developers can technically swap out one API call for another when AI models evolve, suggesting a world where switching costs are minimal and demand fluidly reallocates.
However, this overlooks the deeper, more entrenched investments that make actual migrations far from trivial; a fair reading shows that the custom prompt engineering and integrated workflows aren't just boilerplate, but often highly specific, creating significant practical friction.
For instance, if a firm has spent months fine-tuning an AI for intricate legal document review, teaching it to discern nuances in UK property law, the "cost" of switching isn't just redirecting an API; it’s re-training, re-validating, and re-integrating a new model to match that precise level of specialised output, which is a considerable undertaking.