AI in Corporate Governance: Can Machines Recover Corporate Purpose?

A key question in automating governance is whether machines can recover the corporate objective. We develop a corporate recovery theorem that establishes when this is possible and provide a practical framework for its application. Training a machine on a large dataset of firms' investment and financial decisions, we find that most neoclassical models fail to explain the data since the machine learns from managers to underestimate the shadow cost of capital. This bias persists even after accounting for financial frictions, intangible intensity, behavioral factors, and ESG. We develop an alignment measure that shows why managerial alignment with shareholder-value remains imperfect and how to debias managerial decisions.