David Bahnsen explains how range-bound, volatile markets can feel productive yet deliver flat or mediocre returns over long stretches, citing the S&P 500 from 2000–2009 (about -0.9%/year with dividends) and the Dow from 1966–1981 (flat on price but roughly 4–4.5%/year with dividends). He notes periods when dividends comprise most or all of total return, but shifts to today’s math: recent S&P gains (about 15% annualized over 15 years and 22.9% over three) imply either a higher long-run average or future below-mean returns via sharp declines or prolonged choppiness. Applying this to AI, he highlights NVIDIA’s flat year despite strong fundamentals, with Palantir down 7% and CoreWeave down ~60% amid 100% revenue growth, arguing that unknowability, capital constraints, and high valuations can yield unsatisfying stock returns even if AI succeeds, and he advocates dividend growth as a way to navigate volatility and reduce left-tail risk.
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