Major advances in machine learning (ML) and artificial intelligence (AI) increasingly take the form of developing and releasing general-purpose models. These models are designed to be adapted by other businesses and agencies to perform a particular, domain-specific function. This process has become known as adaptation or fine-tuning. In order to understand questions about responsibility and regulation in an ecosystem where multiple parties produce applications in this way, we need reasonable models of the incentives that drive this type of production. Here we offer a model of this multi-party process, in which a general provider of machine learning technology brings the system to a certain level of performance, and one or more Domain-specialists adapt it for use in particular domains. Our model provides high-level takeaways for how incentives operate in this setting, and in this way it suggests how we might think about responsible development and regulation of these technologies. This is joint work with Ben Laufer and Hoda Heidari.