When Everyone Has the Same AI: Rethinking Startup Competitive Advantage in the Age of Generative AI

Abstract

Generative artificial intelligence (AI), delivered through general-purpose foundation models accessible on demand at low cost, has become available to firms of all sizes. This accessibility challenges the dominant view in which AI capability, built from scarce data, infrastructure, and talent, is a source of competitive advantage: when the same frontier capability can be rented by any firm, the model satisfies neither the rarity nor the inimitability conditions that the resource-based view requires of an advantage-conferring resource. This paper develops a conceptual framework explaining where competitive advantage resides once generative AI becomes, in effect, a shared utility. Engaging the precedent of the information-technology commoditization debate, it argues that generative AI differs in kind: it commoditizes capability rather than infrastructure, displacing advantage from the technology to the complements it cannot replicate. The paper names this regularity the advantage-relocation mechanism, the displacement of advantage, when a capability-like technology is commoditized, toward co-specialized complements such as proprietary data, domain judgment, customer relationships, and the orchestration capability that converts a common model into a distinctive value proposition. Stated at the level of the firm, with resource-constrained startups as its sharpest boundary condition, the framework yields falsifiable propositions on the relocation of advantage, the role of absorptive capacity in differentiating firms that use identical models, and the accelerated erosion of AI-derived advantage. It contributes a strategic account of competitive advantage under technological commoditization and an agenda for empirical testing.