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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 firms’ investment and financial decisions, we find that most neoclassical models fail since machines learn 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 managers deviate from shareholder-value and guides how AI can debias managerial decision-making.
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