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A key point often missed in the debate over whether tech stocks — and the broader market — are in a bubble is that an important segment of tech is clearly not. Tech hardware stocks, in particular, have clearly underperformed. Using equal-weighted, weekly-rebalanced portfolios over the past three years, the top 500 market-cap stocks returned 13% annualized (12% YTD). The equal-weighted tech sector within this universe delivered a 25% annualized return (20% YTD). By contrast, the tech-hardware subgroup posted an annualized return of -6% and a YTD return of -5%. Within this struggling hardware segment, however, some companies report using AI to boost revenue and reduce costs -- and their stocks have performed exceptionally well. Using OpenAI tools implemented by Finsera, a prompt was applied to earnings-call transcripts and 10-K filings to identify companies that report using AI to boost revenue and reduce costs. The 25 companies whose disclosures most closely matched the prompt were used to construct an equal-weighted portfolio, rebalanced weekly. Over the past three years, this AI-adopter portfolio has produced a 35% annualized return and is up 35% year-to-date. The chart highlights the impact: tech-hardware companies adopting AI in this way have far outperformed the broader market, the tech sector, and the tech-hardware subsector. This is a concrete example of how AI adoption that improves a company's economics can translate into strong stock performance. If you'd like to explore this or related analysis in more depth, feel free to contact me.

October’s layoffs were the worst in twenty years, and a significant portion can be traced to companies accelerating their adoption of artificial intelligence. The cuts have continued. How should investors interpret this? On one hand, the surge in layoffs signals stress for workers and potential headwinds for the broader economy. Combined with massive AI-related Capex that has not yet translated into higher profits, the near-term picture can look troubling. On the other hand, the layoffs may reflect the efficiency and productivity gains that AI is expected to deliver. One useful lens for understanding this development is a core productivity metric: sales per employee. With fewer workers, companies must generate more revenue per employee, making this ratio a natural way to gauge whether AI-linked efficiencies are materializing. The charts show the returns to sector-neutral sales-per-employee factor portfolios for stocks in both the S&P 500 and the S&P 500 Growth indices. Since the AI boom began in late 2022, the market has increasingly rewarded companies with higher sales-to-employee ratios. The charts track the cumulative return of a long-short, sector-neutral portfolio that buys companies with high sales per employee and shorts those with low figures. Before the release of ChatGPT on November 30, 2022, the market was largely indifferent to this factor. After that point, firms with higher sales per employee began to outperform those with lower ratios in a meaningful way. Given the timing, it is reasonable to attribute this shift to the market’s expectation of AI-driven productivity gains. Through early 2024, the sales-per-employee factor behaved similarly for growth stocks and the broader S&P 500. Since then, however, the advantage has become more pronounced for growth companies. The top chart highlights the divergence, and the bottom chart zooms in over the past year to show that the growth-stock premium continues. The message seems clear: the market believes AI-enabled productivity improvements are especially strong for large-cap growth firms, while remaining positive across the S&P 500 more broadly. AI has been boosting measured productivity—captured here through the sales-per-employee factor—since the end of 2022, with a powerful effect on growth stocks since early 2024. And while the recent wave of layoffs is painful, the market’s substantial reward for high-productivity firms suggests investors can view these workforce reductions as tangible evidence that AI-driven efficiency matters.

Understanding the Current Landscape of Tech Stocks Over the past year, sales growth has emerged as a dominant factor in selecting tech stocks. A long-short portfolio of S&P 500 tech names, sorted by trailing one-year sales growth, has returned an impressive 49% through August 20. In comparison, the sector overall only managed a return of 15% . This stark contrast highlights the importance of sales growth in today's market. Conversely, a long-short portfolio based on trailing one-year earnings growth returned 0% , marking a significant reversal from previous periods. This shift raises questions about the sustainability of earnings growth as a reliable indicator for tech investments. The Impact of the AI Boom As illustrated in the accompanying chart, from the onset of the AI boom in late 2022 to mid-May 2025, the earnings growth factor yielded a return of 41% , which translates to an annualized rate of 14% . While this is a strong performance, it pales in comparison to the 49% surge in sales growth factor returns over the past year. Since mid-May, however, the earnings growth factor has turned negative, indicating a shift in investor sentiment. This change suggests that the market may have already priced in good earnings growth, leaving revenue momentum as the primary driver of investor rewards. The Shift in Investor Focus Sales growth, which was once a drag on performance, flipped positive about a year ago and has been accelerating steadily since. By mid-May, sales growth had already surpassed earnings growth as a key driver of tech returns, and the gap has only widened since then. The returns from sales growth have surged ahead, while earnings growth has transitioned from a contributor to a detractor. Investors are now favoring companies that can scale revenues quickly, even if it means sacrificing short-term profitability. This trend poses a risk to tech stocks, as it may not be sustainable in the long run. The Future of Tech Stocks: What Lies Ahead? As we look to the future, the question remains: will this trend continue? The tech industry is known for its volatility, and the current emphasis on sales growth could shift again. Investors must remain vigilant and adaptable to changing market conditions. The Role of Innovation Innovation plays a crucial role in driving sales growth. Companies that invest in research and development are often better positioned to capture market share and drive revenue. As technology continues to evolve, those that can innovate effectively will likely see sustained growth. The Importance of Market Trends Understanding market trends is essential for making informed investment decisions. The tech landscape is constantly changing, and staying updated on emerging technologies and consumer preferences can provide valuable insights. Conclusion: Navigating the Tech Stock Landscape In conclusion, the current emphasis on sales growth over earnings growth marks a significant shift in the tech stock landscape. Investors are rewarding companies that demonstrate strong revenue momentum, even at the expense of profitability. As this trend continues, it will be crucial for investors to monitor market dynamics and adapt their strategies accordingly. For more insights on tech investments, consider exploring the benefits of diversifying your portfolio .

It is ironic that the most important passive index in the world--the S&P 500--is, in fact, managed. For example, the minimum eligibility requirement is now a market capitalization of $22.7 billion; yet 29% of current members fall below this threshold. A committee decides what to include or exclude to minimize turnover, with enough mystery to prevent hedge funds from front-running adds and deletes. The question for investors is what this subtle form of active management achieves compared to a purely mechanical index of the top 500 stocks by market capitalization. Are S&P 500 constituents better performers than the simpler top 500? Market-cap weighting obscures the behavior of individual stocks, whereas equal weighting offers a clearer lens. The top chart, which displays the equal-weighted returns of the stocks of the top 500 companies by market capitalization relative to the equal-weighted S&P 500, shows that the equal-weighted top 500 and the equal-weighted S&P 500 have taken turns outperforming each other. The COVID-19 market shock in March 2020 sparked sharp outperformance of the top 500, followed by two years of favoring S&P 500 constituents. Over the past two years, however, the top 500 have steadily done better. This difference matters for factor returns. Consider the low-volatility factor shown in the bottom chart. Sector-neutral long-short portfolios based on low volatility for S&P 500 stocks have performed poorly over the past five years. In contrast, the same factor applied to the top 500 has behaved quite differently — sometimes resembling the S&P 500's low-volatility factor performance, other times diverging meaningfully. For investors, comparing the equal-weighted versions of the S&P 500 and the top 500 by market cap shows how much "passive" investing depends on committee choices. As the low-volatility example illustrates, that distinction can materially reshape factor returns.

Theories explaining momentum typically fall into behavioral, risk-based, or market structure categories, often avoiding the harder question of what economic force actually drives momentum. Yet for U.S. large-cap stocks, data suggest that momentum has closely tracked returns to sales growth over the past eight years. The charts here show cumulative factor returns for 1-year price momentum and sales growth in the S&P 500, using sector-neutral, long-short portfolios rebalanced monthly. The top chart covers the past two years; the bottom spans nearly nine years from July 2016. While there are periods of divergence, the two factors tend to revert toward each other. Based on the data, it's reasonable to view momentum as a proxy for the return to sales growth, augmented by exposure to other short-lived factors. The data are sector-neutral and span all sectors, so the proxy relationship reflects an aggregate pattern that may differ at the sector level. Sales growth's return has trended higher for over a year. Momentum should, in general, follow that directional trend. You can explore this and other factor relationships at  Metafoura.com .

Its impact on employment is among the most widely discussed potential consequences of the AI revolution. But will this shift have a meaningful effect on investing? I believe it already does. A recent Bloomberg article by Walter Frick highlights a telling trend: startups are increasingly succeeding with lean teams. Low headcount has become a strategic advantage, and AI is at the center of that shift. Companies can scale without expanding their workforce as AI takes on more operational functions. This makes sales per employee the new holy grail—a metric that captures both productivity and efficiency in the AI era. Keeping teams small is now a deliberate goal for startups, elevating sales per employee to a critical performance measure. But the trend extends beyond startups. A recent Barron's article by Ben Levisohn notes that AI's soaring demand for power is reshaping the energy sector, positioning even traditional oil majors like Exxon and Chevron as unexpected AI beneficiaries. In fact, according to our data, sales per employee has become an important signal for energy stock performance. Clear evidence of this broader dynamic appears in the S&P 500 Growth Index. In this group, sales per employee has become one of the most potent drivers of stock returns. Over the past three years, it has delivered an annualized risk-adjusted factor return of 1.6 and an even stronger 2.1 over the past year, outpacing competing factors. The chart showing cumulative factor returns for a monthly rebalanced long-short portfolio based on sales per employee highlights a clear turning point in early 2023, coinciding with the rise of AI adoption. What's striking is the broader transformation: a metric once associated with nimble startups is now driving performance in large-cap growth stocks. This reflects more than an operational shift—it marks a strategic one. As AI reshapes how companies scale, sales per employee is emerging as a powerful signal for investors, translating operational efficiency into market outperformance.

Shifts in factor effectiveness signal deeper market narrative changes--specifically, what investors are pricing. The charts here show how ROE, ROIC, and R&D/Sales have influenced Tech stock performance in the S&P 500 over the past three years and the last three months. What do these shifts suggest now? A brief exchange with ChatGPT helped clarify the story. An intriguing idea emerged: 'Tariff Uncertainty and Strategic Autonomy' - see bottom of the transcript. It's an example of AI supporting equity research. I used  Metafoura.com  to uncover these factor shifts. You can explore sector and market-wide factor narratives at  Metafoura.com . Bring your favorite AI. THE TRANSCRIPT Prompt:   I've noticed a shift in tech stock selection factors. From late 2022 to mid-2024, the market was strong. In tech, ROIC outperformed ROE, while R&D/Sales was heavily rewarded. After a sharp drop in July 2024, the market didn't stabilize until September. Since then, R&D/Sales lost its edge, ROE became effective, and ROIC lagged. What explains this shift--especially with AI in the backdrop? ChatGPT: Late 2022 to Summer 2024: The AI-Driven Innovation Boom  AI enthusiasm surged after late 2022, favoring ROIC and R&D/Sales as investors rewarded reinvestment in innovation. ROE lagged, reflecting less concern for near-term shareholder returns or financial leverage. Post-September 2024: A Return to Fundamentals  As the market stabilized, the narrative shifted from "AI moonshots" to execution and monetization. Investors favored firms delivering returns now, not just future growth. Follow-up Prompt:   Over the past three months, ROE and ROIC have lagged, while R&D/Sales has outperformed. Thoughts? ChatGPT: The outperformance of R&D/Sales suggests investors are again prioritizing strategic reinvestment--especially in AI. Likely drivers: 1. AI Capital Spending Surge  - Major tech firms ramped up spending on AI infrastructure--custom chips, data centers, models.  - Short-term ROE and ROIC dipped due to upfront costs, but investors see such spending as a necessary moat.  - R&D/Sales signals innovation and long-term dominance--ROE can't capture forward-looking value. 2. Tariff Uncertainty and Strategic Autonomy  - Rising trade policy concerns (since Feb. 2025) tilt attention toward self-reliance and IP ownership.  - High R&D/Sales firms are better positioned to develop in-house capabilities, reduce supply chain risk, and align with industrial policy shifts.  - Firms optimizing for ROIC or ROE--often outsourcing--may appear fragile in this environment.

Tracking the returns of thematic baskets is a helpful way to see how the tariff turmoil is affecting equities. The chart showing the cumulative excess return of four important themes reveals a potential place to hide and possibly prosper in this environment. The themes are AI, Cybersecurity & Defense, Fintech & Digital Finance, and Industrial Renaissance. Baskets of stocks representing these themes were derived from several ETFs for each theme.  The S&P 500 peak on February 19 was roughly when outperformance in Cybersecurity/Defense and Fintech/Digital Finance peaked this year. AI held on a bit longer, peaking in mid-March (see red arrows). In contrast, the Industrial Renaissance theme bottomed as the Cybersecurity and Fintech/Digital Finance themes peaked.  Industrial Renaissance outperformance has been trending higher since the end of February. That includes stocks such as Union Pacific (UNP), Deere (DE), and others from the Industrials, Non-energy Materials, Utilities, and Energy sectors. Industrial Rennaisance is a goal of the tariffs and looks like a relatively tariff-resilient zone.

Tech stocks are performing poorly in 2025, with February's tariff-driven market shock accelerating the decline. I made a short video examining whether a defining trait of Tech exceptionalism—its ability to monetize innovation—is losing relevance. Take a look.

Value investing's performance has diverged sharply between large-cap and small-cap stocks in recent years. While value strategies have been effective in small caps, they have struggled in large caps. At the same time, investor attitudes toward debt differ: large-cap investors have benefited from highly leveraged companies, while small-cap investors have not. This contrast is evident in charts tracking cumulative returns over the past three years for long-short, sector-neutral, monthly-rebalanced portfolios sorted by free cash flow (FCF) yield, Sales/Price, Earnings/Price, and Debt/Equity in the S&P 500 and Russell 2000. A bracket highlights the three value factors in each chart. Why Is This Happening? Small-cap companies typically carry more relative debt than large caps. As shown in the chart, FCF yield has delivered strong returns for Russell 2000 stocks—27% per year over the past three years and 32% in the last year. Small-cap investors likely prioritize FCF yield because it mitigates debt risk. Strong sales and earnings also reinforce financial stability. Highly leveraged small caps are less attractive. Conversely, investors have been rewarded in large caps for favoring high debt-to-equity companies. This strategy has worked (so far) because large firms generally have stronger cash flows and financial stability, cushioning the risks of high leverage. Given this dynamic, it is unsurprising that FCF yield has not been a priority for large-cap investors. In short, leverage and value investing have played distinct roles in large-cap and small-cap stocks.

AI-based techniques, such as natural language processing (NLP) analysis of earnings calls, can reveal management or analyst sentiment about a company, potentially helping to predict market behavior. But what about investors' sentiment? Factor trends—the patterns of which factors generate returns and which factors are dismissed by the market—provide insights into investors' sentiment based on their factor narrative.

Is money better spent on research to drive innovation in small or large companies? This question is significant for government policies promoting innovation and investors seeking factors that provide an advantage. The prevailing belief is that innovation occurs more often in small companies than in large ones. Since innovation relies on funding, smaller companies should yield greater rewards from research expenditures. However, the charts here show that, on average, research spending has produced significantly better investment returns in large companies.