When Cash Wakes Up: Inertia in Institutional Money Market Funds and the Potential Effects of AI Disruption

We study institutional cash management by constructing a novel dataset that records the specific money market funds (MMFs) held by each mutual fund in each month from 2010 to 2024. We document remarkably high inertia: more than half of mutual funds never switched their MMF choice throughout the sample period, despite non-negligible yield dispersion. To decompose the sources of this inertia, we develop a two-stage structural model that separately identifies the determinants of portfolio managers’ attentiveness (“waking up”) and their conditional preferences once attentive. We estimate the model using a methodology that combines BLP-style contraction mapping with maximum likelihood. We establish three main findings. First, MMFs with less attentive investor bases engage in greater liquidity transformation while offering lower yields. As a result, they earn higher profits and exhibit significantly higher survival rates, especially during periods of low interest rates. Second, we find that aggregate flows appear more yield-sensitive during low interest rate periods (the well-documented “reaching for yield” phenomenon) not because individual investors become more yield-seeking, but because a larger fraction of investors becomes attentive—a composition effect rather than a preference shift. Third, counterfactual analysis reveals a potential tension between market efficiency and financial stability if AI-driven automation reduces investor inattention. In the absence of inattention, fees fall by approximately half, yield dispersion collapses to near zero, and market concentration declines by roughly 20%, but monthly fund flows increase by about three times and the incidence of fund-months in which redemptions exceed a fund’s weekly liquid assets rises by about six times, raising the risk of fire sales and MMF runs.