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As investors seek opportunities beyond megacap tech giants, small-cap technology stocks are posting significant gains, driven by improving earnings and AI exposure.
U.S. Small-cap technology stocks are experiencing a remarkable surge after years of underperformance, a clear sign that the artificial intelligence (AI) frenzy is reshaping investment strategies. Investors are increasingly looking beyond the Nvidias and Intels of the world to smaller companies with strong potential to benefit from the growing adoption of AI.
The Invesco S&P SmallCap Information Tech ETF (PSCT.O) has seen $49.7 million in inflows this year, reversing a four-year trend of outflows, according to LSEG Lipper data. This shift is not just about chasing short-term gains; it reflects a broader recognition of the long-term value these smaller firms can offer.
"The AI trade has broadened quite materially," noted Oren Shiran, portfolio manager for Lazard US Systematic Small Cap Equity ETF. "Small-caps have become a real part of the second and third order of AI." Smaller technology companies are attracting attention due to their improving earnings prospects, relatively cheap valuations, and diverse business models that align with the AI buildout.
The S&P 600 small-cap tech index has gained almost 54% this year, significantly outpacing the 20.1% rise in the S&P 500 technology index. This performance gap is the widest it has been since before 1995, according to Trivariate Research. Companies like MaxLinear and VIAVI have posted triple-digit gains, driven by improving earnings, cheap valuations, and AI exposure.
Investors are particularly interested in small-cap firms that can provide essential components for the AI ecosystem, including chipmakers, data center suppliers, and network equipment makers. These companies are often overlooked but play crucial roles in enabling the infrastructure required for advanced AI applications.
However, some analysts warn that the current rally is more speculative than fundamental. While the potential for growth is undeniable, investors must carefully evaluate the underlying business models and financial health of these smaller firms to avoid overpaying for hype.
The surge in small-cap tech stocks presents both opportunities and risks for investors. On one hand, these companies offer exposure to emerging technologies and potentially higher returns compared to their larger counterparts. On the other hand, the sector is more volatile, and not all small-cap firms will succeed in capitalizing on AI trends.
For portfolio managers like Oren Shiran, the key is to identify companies with strong fundamentals and a clear path to profitability. "We are looking for businesses that can sustain growth over the long term," he said. "It's not just about riding the wave; it's about finding the right waves to ride."
As the AI market continues to evolve, investors should remain cautious and conduct thorough due diligence. While the potential rewards are significant, the risks of investing in smaller, less established firms cannot be ignored.
In the context of broader energy trends, it's worth noting that while renewable energy sources like solar have gained traction, they still face challenges in regions with abundant fossil fuels. For instance, in Ohio, the reality of plentiful, relatively inexpensive, and reliable fossil fuels means that solar energy has yet to become a significant source of power.
The surge in small-cap tech stocks driven by AI investment is a promising trend, but it requires careful navigation. Investors who can identify and invest in the right companies stand to benefit from the ongoing technological revolution, while those who chase speculative gains may face significant risks.
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Original Sources
Investors hunt for AI winners in small-cap US tech stocks
↗ https://www.reuters.com/business/investors-hunt-ai-winners-small-cap-us-tech-stocks-2026-05-27
What if the AI boom goes into reverse?: Joachim Klement | Reuters
↗ https://www.reuters.com/commentary/reuters-open-interest/what-if-ai-boom-goes-into-reverse-2026-05-29
About the author
Marcus began tracking AI's market implications in 2016, noticing AI-related patent filings accelerating ahead of earnings upgrades before most of the sell-side had caught on. A former fixed-income quantitative analyst, he spent two decades building models that priced risk across emerging markets before pivoting to cover the economic impact of AI full-time. His writing translates opaque technical developments into clear risk/reward terms — and he's rarely diplomatic about the gap between AI valuations and underlying fundamentals. He believes most market participants still underestimate AI's long-run deflationary effect on knowledge work.
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