Major AI hyperscalers including Amazon, Microsoft, Alphabet, and Meta are pouring hundreds of billions of dollars into artificial intelligence development, but historical data suggests this massive capital expenditure could negatively impact their stock performance in the coming years. According to a recent Morningstar analysis, tech giants are expected to spend $364 billion on AI development in 2025 alone, adding to the hundreds of billions already invested since the AI arms race began.
Philip Straehl, chief investment officer at Morningstar Wealth, warns that historical market data from the past 60 years reveals a troubling pattern: companies in the top 20% of capital expenditure relative to current sales have consistently underperformed compared to every other capex quintile. The reason, Straehl explains, is that massive spending increases technology supply and availability, which paradoxically makes it harder for the big spenders to monetize their investments due to increased competition.
The financial impact is already becoming visible. Since 2024, AI-related spending has begun cutting into the free cash flow of Alphabet, Microsoft, and Amazon, three of the four major AI hyperscalers. Despite this concerning trend, investors have largely shrugged off the implications so far, likely because the core businesses of these tech giants remain strong and profitable.
However, both Straehl and Bob Doll, chief investment officer at Crossmarket Global Investments, caution that the ability to monetize generative AI will become the critical factor determining future stock performance. Doll emphasizes that companies substantially increasing their capex typically lag in stock performance until the spending is complete and returns on investment become measurable.
The analysis comes at a time when market valuations are historically high and the macroeconomic backdrop remains uncertain. Straehl’s investment philosophy prioritizes the price paid for cash flow as the primary driver of investment decisions, and he’s currently taking a cautious approach, reducing risk and waiting for more attractive opportunities. The warning serves as a reality check for investors caught up in AI enthusiasm, suggesting that the massive infrastructure investments required to develop cutting-edge AI technology may not translate into proportional shareholder returns in the near term.
Key Quotes
Higher capex generally increases supply. It’s more difficult to monetize on those investments.
Philip Straehl, chief investment officer at Morningstar Wealth, explains why massive AI spending by tech giants could hurt their stock performance, noting that increased supply from capital expenditure makes monetization more challenging.
The core businesses of these companies are still doing well. But I think what’s going to matter in the future is the ability to monetize generative AI.
Straehl acknowledges that while Big Tech’s traditional revenue streams remain strong, the critical factor for future stock performance will be whether these companies can successfully generate returns from their massive generative AI investments.
Companies that substantially increase their capex usually lag for a while, until the capex is spent and you can start to see what’s the return on that capex. So I share that concern.
Bob Doll, chief investment officer at Crossmarket Global Investments, agrees with Morningstar’s analysis and emphasizes the importance of free cash flow, warning that heavy capital spending typically leads to stock underperformance until returns become measurable.
A core tenet of our investment philosophy is that the price paid for a given cash flow should be the primary driver of investment decisions.
Straehl outlines his cautious investment approach amid high market valuations, indicating that Morningstar is reducing risk and waiting for better opportunities rather than chasing AI-related stocks at current prices.
Our Take
This Morningstar analysis provides a crucial counterbalance to AI euphoria dominating tech markets. The $364 billion spending figure for 2025 alone is staggering, representing one of the largest coordinated technology investments in history. However, the historical precedent is sobering: heavy capex spending has consistently led to underperformance, suggesting that AI hyperscalers may be setting themselves up for disappointing returns despite technological breakthroughs.
The irony is profound—by racing to build AI infrastructure, these companies may be commoditizing the very technology they’re investing in. This creates an opening for more capital-efficient competitors and raises questions about whether the current AI business model is sustainable. The erosion of free cash flow at Alphabet, Microsoft, and Amazon signals that this isn’t just theoretical concern but an emerging financial reality that investors can no longer ignore.
Why This Matters
This analysis carries significant implications for the AI industry and investors alike. The warning from Morningstar challenges the prevailing narrative that AI spending automatically translates into stock market success, forcing a more nuanced evaluation of how tech giants monetize their AI investments. As the AI arms race intensifies, the historical pattern of capex-heavy companies underperforming suggests that not all players will emerge as winners, despite their technological achievements.
The impact extends beyond stock performance to broader questions about AI industry consolidation and competition. If massive spending makes AI technology more widely available, it could democratize access and prevent any single company from establishing a monopolistic position. This has profound implications for startups, enterprises, and consumers who may benefit from increased competition and lower costs.
For businesses and workers, the analysis highlights that the AI revolution’s economic benefits may take longer to materialize than the hype suggests. The lag between investment and monetization means companies and employees should prepare for a potentially extended transition period before AI’s full economic impact becomes clear.
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