Bob Elliott, founder of Unlimited Funds and former executive at hedge fund giant Bridgewater Associates, is sounding the alarm on the stock market’s AI-fueled rally, warning that a 20% correction could materialize within three to six months. Elliott argues that investors have priced in a best-case scenario, with the S&P 500 expecting 17% forward earnings growth while GDP forecasts sit at 2.2% for 2025—significantly higher than the barely-above-1% expectations entering 2024.
The market’s elevated P/E ratios are approaching levels not seen since the dot-com bubble and 2021 market peaks, creating what Elliott describes as an especially fragile environment. “It wouldn’t take much normalization of P/Es to create a 20% decline in stocks in the near term,” he told Business Insider, emphasizing the dangerous misalignment between sky-high expectations and more modest economic realities.
Central to Elliott’s thesis is a fundamental misconception about AI’s economic impact. While AI has driven frothy valuations and investor enthusiasm, Elliott argues that even if the technology meets expectations, it will ultimately hurt income growth and consumer spending by displacing workers. He points out that robust economic growth in recent years has been fueled by an “income-driven cycle,” with Americans experiencing approximately 5% annual income growth in 2024, which has powered consumer spending.
The paradox, according to Elliott, is that AI’s promise of massive margin expansion through automation directly threatens this growth engine. “Investors extrapolate extreme things around company profitability without recognizing that if you fire 50% of your workforce, then those people don’t have the income necessary to spend,” he explained. If AI lives up to its hype and renders large portions of the labor market redundant, the resulting decline in consumer activity could tank economic growth.
Elliott’s Unlimited HFND Multi-Strategy Return Tracker ETF reflects his cautious positioning, with 34.6% allocated to high-yield bonds, 13% to S&P 500 E-mini Futures, and 9.3% to emerging markets. Beyond AI concerns, Elliott cites additional warning signs: declining housing construction, weakening business investment as pandemic-era government spending slows, and a troubling drop in prime-age workforce participation during the second half of 2024. While not predicting a full recession, Elliott advises investors to avoid getting swept up in excessive optimism and stick to disciplined portfolio strategies.
Key Quotes
What creates the ultimate adjustment in prices is when the data ends up being below what people expected.
Bob Elliott, founder of Unlimited Funds and former Bridgewater Associates executive, explains the mechanism that could trigger a market correction as overly optimistic AI and economic expectations meet more modest reality.
It wouldn’t take much normalization of P/Es to create a 20% decline in stocks in the near term.
Elliott warns that with price-to-earnings ratios approaching dot-com bubble levels, even a modest return to normal valuations could trigger a significant market downturn within months.
Investors extrapolate extreme things around company profitability without recognizing that if you fire 50% of your workforce, then those people don’t have the income necessary to spend.
Elliott identifies the fundamental paradox in AI investment thesis: automation-driven profit margins could destroy the consumer spending that fuels economic growth, creating a self-defeating cycle.
The profit extrapolation that’s happening essentially is assuming that companies are going to experience a massive margin expansion as a result of AI.
Elliott critiques the market’s assumption that AI will deliver pure upside to corporate profits without considering the broader economic consequences of workforce displacement and reduced consumer income.
Our Take
Elliott’s analysis represents a sobering counterpoint to AI euphoria and deserves serious consideration. The income-spending paradox he identifies isn’t theoretical—it’s Economics 101. If AI delivers on productivity promises by replacing workers at scale, the resulting income shock could dwarf any efficiency gains. What’s particularly compelling is Elliott’s positioning: he’s not an AI skeptic questioning the technology’s capabilities, but rather warning that AI’s success could be economically destabilizing. This perspective should prompt investors to distinguish between companies that will profit from AI and the broader economic environment those profits depend upon. The 3-6 month timeline for potential correction aligns with corporate earnings seasons where AI implementation costs and workforce impacts will become more visible. The comparison to dot-com bubble valuations is especially relevant—that era also featured transformational technology whose long-term impact was real, but whose short-term valuations were unsustainable. Smart investors will heed Elliott’s advice: maintain disciplined strategies rather than chasing AI momentum.
Why This Matters
This analysis matters because it challenges the prevailing narrative around AI as an unambiguous economic positive, highlighting a critical paradox that investors may be overlooking. The warning from a former Bridgewater Associates executive carries significant weight, given the firm’s reputation for macroeconomic analysis. Elliott’s perspective reveals how AI’s productivity gains could create a self-defeating cycle: companies boost profits through automation while simultaneously undermining the consumer spending that drives economic growth. This has profound implications for AI investment strategies, labor market policy, and economic planning. As companies race to implement AI solutions, policymakers and business leaders must grapple with the tension between technological advancement and economic stability. The timing is particularly relevant as AI adoption accelerates across industries while stock valuations reach historically elevated levels. If Elliott’s thesis proves correct, the next three to six months could see a significant market correction that reshapes investor sentiment toward AI companies and forces a more realistic assessment of AI’s near-term economic impact versus its long-term transformational potential.
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