Alleviating Baumol’s Cost Disease with Artificial Intelligence: Mechanisms from an Economic Sustainability Perspective

The service sector accounts for a growing share of economic activity, yet relatively slow productivity growth leaves firms facing wage-driven cost pressures that constrain long-term investment. From an economic sustainability perspective, alleviating Baumol’s cost disease can help service firms improve productivity and reduce reliance on additional factor inputs. Drawing on the resource-based view and dynamic capabilities theory, this study applies double machine learning to Chinese A-share-listed service firms from 2011 to 2024 to examine the alleviating effect of artificial intelligence (AI) on Baumol’s cost disease and its potential pathways. Higher AI use intensity is significantly associated with a higher Baumol’s cost disease alleviation indicator (BMR). Greater capital investment efficiency and lower long-term debt financing dependence are two potential pathways, while supplier concentration and growth opportunities positively moderate this relationship. The association is stronger among loss-making firms and firms with greater ownership balance. These findings provide firm-level evidence that AI may help service firms cope with rising wages through productivity growth, supported by more efficient capital allocation and improved financing structures, and contribute to efficiency-driven growth.