This paper examines how AI investment and data assets affect corporatecredit risk. Using Chinese listed firms, we construct four complementary measures ofAI investment, asset-based, labor-based, LLM-based, and text-based, and link them tofirms’ distance-to-default. We find that benchmark-level AI investment reduces defaultrisk, while excessive ffrm-speciffc investment increases it by eroding profitability andreffecting risk-taking and competitive pressure. The dominance of this adverse effectyields a negative overall relation between AI investment and credit risk. Cash flow riskis the transmission channel: benchmark-level AI improves cash ffow quality, whereasexcessive investment worsens it. High-quality data assets complement benchmark-levelAI by stabilizing cash ffow, but this benefit fades once investment becomes excessive.Overall, the impact of AI on credit risk depends on both investment intensity and dataquality, operating primarily through cash flow dynamics.

We examine the relationship between customer concentration and capital structure adjustment speed using a sample of US listed firms from 1977 to 2020. We found that the customer-concentrated firms have a lower speed of leverage adjustment. Customer concentration affects leverage adjustment speed mainly through increased cash flow volatility and asset specificity. The negative association is more pronounced in firms with high relationship-specific investments and low switching costs for their customers. Stock market reacts to leverage deviation strongly for firms with concentrated customers. Our findings highlight the vital role of customers as key stakeholders in capital structure decisions.