A key question in automating governance is whether machines can recover the corporate objective. We develop a corporate recovery theorem that establishes when machines can do this and provide a framework for its application. Training a machine on a large dataset of firms' investment and financial decisions, we find that managers systematically underestimate investment costs, leading to over-investment and under-exploration. This bias persists even when accounting for intangibles, managerial compensation, and ESG scores. While social and governance concerns influence corporate objectives beyond materiality, environmental concerns do not. Last, we observe that managerial alignment with shareholder value is imperfect but has improved over time.

Institutional investors are less likely to support shareholder proposals involving environmental and social issues for firms headquartered in Republican-led states. The lower support concentrates in recent years, when politicians became more vocal about firms’ social responsibility activities, and among larger institutions and firms, which tend to attract more attention from politicians. Investor support also shifts within states following changes in their leadership. Support for such proposals is 10 percentage points lower in the same state when it is led by Republicans instead of Democrats. The findings suggest that state-level politics and the politicization of an issue impacts institutional investors’ votes.

This paper examines AI washing — firms exaggerating or falsely claiming AI investment. Using large language models, we quantify firms’ AI talk (i.e., claims of AI investment) from conference call transcripts and AI walk (i.e., actual AI investment) from employee resume data among U.S. public firms from 2016 to 2024. Despite rapid growth in AI talk, it has little predictive power for future AI investment, suggesting widespread AI washing. AI walk is positively associated with future AI patenting and institutional holdings, while AI talk boosts short-term stock returns, potentially incentivizing AI washing. These findings reveal a disconnect between firms’ AI disclosure and substantive investment.