Abstract

We test whether passively-traded AI-themed exchange-traded funds (ETFs) have delivered risk-adjusted excess returns relative to broad and sector benchmarks over the period 2019–2025. Our sample comprises the four largest AI/robotics ETFs available to US retail investors—BOTZ (Global X Robotics & AI), ROBO (ROBO Global), AIQ (Global X Artificial Intelligence & Technology), and IRBO (iShares Robotics and AI Multisector)—and three benchmarks: SPY (S&P 500), QQQ (Nasdaq 100), and XLK (S&P 500 Technology). Return series are constructed from auto-adjusted monthly closing prices retrieved from Yahoo Finance. Over the 83-month sample, an equal-weighted basket of the four AI ETFs earned an annualized return of 15.11 percent against an annualized volatility of 22.34 percent, for a Sharpe ratio of 0.63. Over the same period SPY earned 17.66 percent (Sharpe 0.98), QQQ earned 23.71 percent (Sharpe 1.08), and XLK earned 27.56 percent (Sharpe 1.15). A capital-asset-pricing-model regression of the AI basket on SPY yields a beta of 1.18 (R² = 0.78) and an annualized alpha of -5.01 percent, statistically indistinguishable from zero (t = -1.23). Splitting the sample at the November 2022 capability shock yields a higher post-shock annualized return for the AI basket (20.89 percent) than pre-shock (10.63 percent), but the difference is statistically insignificant (Welch t = 0.53, p = 0.595). The expense-ratio drag, the included-stock weighting differences (large-cap technology firms in the benchmarks vs.\ smaller pure-play AI firms in the thematic ETFs), and the trade-off between exposure purity and diversification together produce the documented underperformance gap. The data do not support the widely-discussed thesis that thematic AI ETF exposure has captured a generative-AI equity premium; over the available sample period, AI ETFs have underperformed the broad market on a Sharpe-adjusted basis and substantially underperformed the technology sector benchmark. We discuss implications for retail-investor portfolio construction, for the AI-investment thesis as it has been retailed to investors, and for the construction of AI-focused factor exposures.