Contracting, pricing, and data collection under the AI flywheel effect

This paper explores how firms that lack expertise in machine learning (ML) can leverage the so-called AI flywheel effect. This effect designates a virtuous cycle by which, as an ML product is adopted and new user data are fed back to the algorithm, the product improves, enabling further adoptions. However, managing this feedback loop is difficult, especially when the algorithm is contracted out. Indeed, the additional data that the AI Flywheel effect generates may change the provider's incentives to improve the algorithm overtime. We formalize this problem in a simple two-period moral hazard framework that captures the main dynamics between machine learning, data acquisition, pricing and contracting. We find that the firm's decisions crucially depend on how the amount of data on which the machine is trained interacts with the provider's effort. If this effort has a more (resp. less) significant impact on accuracy for larger volumes of data, the firm underprices (resp. overprices) the product. Further, the firm's starting dataset, as well as the data volume that its product collects per user, significantly affect its pricing and data collection strategies. The firm leverages the virtuous cycle less for larger starting datasets and sometimes more for larger data volumes per user. Interestingly, the presence of incentive issues can induce the firm to leverage the effect less when its product collects more data per user.