This study examines whether investments in artificial intelligence (AI) and Big Data technologies are associated with changes in firm-level market power. Drawing on the Resource-Based View and the economics of intangible assets, we hypothesize that the unique cost structure of AI, characterized by large sunk costs and near-zero marginal costs, enables early adopters to structurally expand their price-cost margins. Using a representative sample of Italian firms, we implement a difference-in-differences model to obtain estimates avoiding the negative weighting biases of staggered technology adoption. Our findings indicate that early AI integration is followed by a substantial post-adoption increase in the accounting-based Lerner index, equal to a 17% rise relative to the sample mean. A detailed analysis reveals that this markup expansion is mechanically driven by a reduction in the variable-cost share of sales, with no evidence of immediate market share consolidation. Furthermore, AI adoption is heavily scale-dependent and concentrated among large, financially resilient firms across diverse sectors. Our findings provide quasi-experimental evidence that early AI investments reward firms through internal cost efficiency rather than market-share consolidation, underscoring the need for policies that accelerate AI diffusion among smaller enterprises to prevent a structural digital divide.
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Forgione et al. (2026) studied this question.
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