Data production and the coevolving AI trajectories: an attempted evolutionary model

This paper contributes to the understanding of the relationship between the nature of data and the artificial intelligence (AI) technological trajectories, on the one hand, and on the dynamic processes triggered by demand during the evolution of an industry, on the other hand. We develop an agent-based model in which firms are data producers that compete on the markets for data and AI. The model is enriched by a public sector that fuels the purchase of data and trains the scientists that will populate firms as workforce. Through several simulation experiments, we analyze the determinants of each market structure, the corresponding relationships with innovation attainments, the pattern followed by labor and data productivity, the quality of data traded in the economy, and in which forms demand does affect innovation and the dynamics of industries. We question the established view in the literature of industrial organization according to which technological imperatives are enough to experience divergent industrial dynamics on both the markets for data and AI blueprints. Although technical change behooves if any industry pattern is to emerge, the actual unfolding is not the outcome of a specific technological trajectory, but the result of the interplay between technology-related factors and the availability of data-complementary inputs such as labor and AI capital, the market size, preferences, and public policies.

Automated summary

This paper contributes to the understanding of the relationship between the nature of data and AI technological trajectories, and the dynamic processes triggered by demand during the evolution of an industry. Through simulation experiments, the determinants of market structure, innovation attainments, labor and data productivity, data quality, and the impact of demand on innovation and industry dynamics are analyzed.