Generative AI for market entry: entrant strategies and uncertainty in new drug development
研究了新药开发中，潜在进入者如何利用生成式AI（GenAI）进入市场，发现GenAI的采用激励随竞争强度呈倒U型，且可能对利润和患者剩余产生双刃剑效应。
Generative AI (GenAI) has been suggested as a promising approach to lowering R&D costs and enhancing drug efficacy, though its ultimate impact on therapeutic effectiveness remains uncertain. This paper investigates competition between an incumbent and a potential entrant, focusing on how the entrant leverages GenAI for market entry. The entrant’s entry-adoption decision depends on fixed cost thresholds, where low cost induces entry with GenAI, high cost precludes entry, and intermediate cost leads to entry without GenAI. The GenAI adoption incentive exhibits an inverted-U pattern in competition intensity. Specifically, with weak competition, outcome uncertainty dominates and the entrant forgoes GenAI. With moderate competition, GenAI is adopted to secure a quality advantage. In intense competition, aggressive price rivalry dissipates the returns to quality, and adoption again ceases. We further reveal that GenAI exerts a double-edged effect on both firms. It raises profits under mild competition, but may counterintuitively reduce them in relatively intense competition, creating a prisoner’s dilemma. Intriguingly, when fixed cost is relatively large but entry occurs, the incumbent may resort to defensive pricing to deter entry, sacrificing its own profit. Finally, we show that GenAI may diminish patient surplus when the entrant monopolises the market or when competition is weak, despite its quality benefits.