The rapid advancement of artificial intelligence (AI) has reshaped corporate innovation, yet the existing literature has largely overlooked the non-linear boundary conditions of AI’s innovation effects. This study asks: what is the functional form of the AI–innovation relationship, and through which mechanisms does it operate? Using a sample of 25,204 firm-year observations from Chinese A-share manufacturing companies (2010–2023), we employ fixed-effects models, U-tests, bootstrap mediation, and text similarity analysis. The findings reveal an inverted-U-shaped relationship with a turning point at 2.948. Absorptive capacity partially mediates this relationship, while industry concentration negatively moderates it. Patent text similarity analysis confirms the “homogenization trap.” Heterogeneity analysis shows AI’s enabling effect is more sustainable in non-state-owned and high-tech firms. This study extends the TOE framework by identifying the optimal AI adoption range and empirically validating the homogenization trap, offering guidance for firms to invest in proprietary AI models and for governments to promote open data initiatives. Future research should test these findings across different institutional contexts, particularly European economies.
Fan et al. (Thu,) studied this question.