This has been true of machine learning APIs from the early renaissance of neural networks. Computer vision APIs have been used for years to grow datasets and fine-tune models on the data.

As a matter of fact, the API/SaaS business for machine learning 'direct' model results have suffered from this since the early days. Now, what may make more sense and survive longer are full data-storage-oriented platforms, and niche products that encapsulate all underlying model calls, including to publicly available models. Sometimes glue code or even devops becomes the true solid business case of AI/ML apps ;)