The Innovation Tax: Generative AI Adoption, Productivity Paradox, and Systemic Risk in the U.S. Banking Sector

TL;DR

This paper investigates how Generative AI adoption impacts productivity and systemic risk in the U.S. banking sector, revealing a paradox where AI improves performance but also introduces significant costs and systemic vulnerabilities.

Contribution

It introduces a novel dataset and employs innovative causal inference methods to analyze AI's effects on banking productivity and systemic risk, highlighting the emergence of algorithmic coupling.

Findings

AI-adopting banks are high performers but face a 4.28% ROE decline due to implementation costs

Smaller banks experience larger ROE declines, indicating scale advantages in AI adoption

Positive spillovers among large banks suggest increased systemic coupling and contagion risk

Abstract

This paper evaluates the causal impact of Generative Artificial Intelligence (GenAI) adoption on productivity and systemic risk in the U.S. banking sector. Using a novel dataset linking SEC 10-Q filings to Federal Reserve regulatory data for 809 financial institutions over 2018--2025, we employ two complementary identification strategies: Dynamic Spatial Durbin Models (DSDM) to capture network spillovers and Synthetic Difference-in-Differences (SDID) for causal inference using the November 2022 ChatGPT release as an exogenous shock. Our findings reveal a striking ``Productivity Paradox'': while DSDM estimates show that AI-adopting banks are high performers ($\beta > 0$), the causal SDID analysis documents a significant ``Implementation Tax'' -- adopting banks experience a 428-basis-point decline in ROE as they absorb GenAI integration costs. This tax falls disproportionately on smaller institutions, with bottom-quartile banks suffering a 517-basis-point ROE decline compared to 129 basis points for larger banks, suggesting that economies of scale provide significant advantages in AI implementation. Most critically, our DSDM analysis reveals significant positive spillovers ($\theta = 0.161$ for ROA, $p < 0.01$; $\theta = 0.679$ for ROE, $p < 0.05$), with spillovers among large banks reaching $\theta = 3.13$ for ROE, indicating that the U.S. banking system is becoming ``algorithmically coupled.'' This synchronization of AI-driven decision-making creates a new channel for systemic contagion: a technical failure in widely-adopted AI models could trigger correlated shocks across the entire financial network.