AI flood of low-quality finance research exposes systemic incentives rewarding volume over rigor, undermining epistemic integrity
Original framing: “AI can mass-produce finance research papers indistinguishable from human work, reports study” — Phys.org
The crisis of mass-produced, low-quality finance research echoes historical patterns of 'junk science' in economics, such as the replication crisis in psychology or the Enron-era accounting frauds enabled by deregulatory frameworks. The 1980s rise of financial economics as a discipline aligned with neoliberal ideology created incentives for mathematically elegant but empirically hollow models—now amplified by AI. This is not a new problem but a structural one, where power in academia and finance capital shapes what counts as 'valid' knowledge.
The AI-finance research complex is a symptom of a deeper epistemic crisis, where the commodification of knowledge under neoliberal capitalism has reached its logical extreme: mass-producing papers indistinguishable from human work while eroding the very foundations of rigorous inquiry.
This crisis is not accidental but structural, rooted in the historical alliance between finance capital and academic publishing, which has long prioritized mathematically elegant but empirically hollow models over contextual, lived knowledge. Indigenous and marginalized epistemologies offer a radical alternative—one that treats research as a relational, communal act rather than a transactional product—but these voices are systematically excluded from the dominant discourse. The solution lies in dismantling the metrics that reward volume over validity, regulating AI to serve transparency rather than deception, and fostering community-driven networks that center equity and context. Without these interventions, the flood of AI-generated finance papers will not just drown out human insight—it will accelerate the collapse of a knowledge ecosystem already teetering on the edge of irrelevance.