The application of general artificial intelligence is gradually undergoing concept verification and scenario exploration across various industries, yet whether it can effectively boost labor productivity remains questionable. Therefore, whether the application of artificial intelligence can mitigate the New Solow Paradox has become a topic of considerable concern. Based on data from A share listed companies spanning from 2012 to 2023, the two way fixed effects model and the double machine learning model were applied to examine the mechanism by which the application of artificial intelligence mitigates the New Solow Paradox. Empirical evidence reveals that artificial intelligence applications can mitigate the New Solow Paradox. Mechanism analysis indicates that artificial intelligence applications mitigate the New Solow Paradox by optimizing labor structure, reducing organizational redundancy, and strengthening organizational capability, with incentive mechanisms playing a positive moderating role in this process. Heterogeneity analysis reveals that the application effects of artificial intelligence are more pronounced in non-high-tech industries than in high-tech industries, and significantly enhance total factor productivity in the eastern and central regions, while their efficacy is constrained in the western region. Furthermore, in terms of sectoral heterogeneity, the application of artificial intelligence significantly improves total factor productivity in manufacturing, services, and the production and supply of electricity, heat, gas, and water, while its productivity enhancing effects are constrained in agriculture, mining, and construction. Practical implications and policy recommendations are offered for enterprises to cope with the challenges of the New Solow Paradox and achieve leapfrog growth of enterprise total factor productivity driven by artificial intelligence.