Data and Computing Power: The New Frontiers of Competition in Generative AI

Digital markets are increasingly dominated by entities that leverage technical specificities such as network effects, economies of scale, and scope, as well as significant advantages in data access and critical infrastructure, including computing power and cloud capacities. The advent of generative artificial intelligence (AI) marks a potential inflection point in this landscape. In this context, the primary barriers to entry are no longer merely data and open source foundation models but the availability of large, high-quality datasets and substantial computing power. This paper examines whether these barriers will entrench the dominant positions of Big Tech companies or if they will catalyze a reshuffling of competitive dynamics. By focusing on the dual challenges of data and computing power, this study identifies the key factors that will shape the future competitive landscape of the generative AI industry. This article contributes to the ongoing debate in industrial economics and strategic management regarding the potentially disruptive effects of generative AI on the market power of Big Tech firms. Can this technological shift recalibrate competitive dynamics, or will it ultimately serve to entrench existing power structures? At its core, the article seeks to interrogate a prevailing narrative - namely, the notion that innovation inherently sustains competitive processes, even in the face of short-term lock-in effects.