Semiconductor ETF Gains Traction as AI Infrastructure Demand Reshapes 2026 Investment Thesis

The Case for Chip Sector Concentration

As artificial intelligence reshapes corporate infrastructure spending, the semiconductor industry has emerged as a critical intermediary capturing outsized gains from this massive capital reallocation. The VanEck Semiconductor ETF (SMH) exemplifies this positioning, holding a concentrated portfolio of companies at the epicenter of AI hardware buildout.

SMH’s core holdings—Nvidia, Taiwan Semiconductor Manufacturing Company (TSMC), Broadcom, Micron Technology, and Advanced Micro Devices—represent nearly half of the fund’s assets. Expanding to the top 10 holdings, which additionally include ASML, Lam Research, KLA, Texas Instruments, and Qualcomm, these positions account for over 73% of total assets. Rather than representing concentration risk, this structure provides investors with pure-play exposure to the dominant beneficiaries of AI data center expansion without dispersing capital across marginal players.

Performance Metrics Paint a Compelling Picture

The numbers speak for themselves. SMH generated approximately 49% returns during 2025, substantially outperforming the S&P 500’s 16.4% gain. More impressively, the ETF’s decade-long track record reveals annualized returns of roughly 30.9% since January 2016, compared to the broader market’s 12.9% annualized return over the same interval. This consistent outperformance across multiple semiconductor cycles—not merely during the recent AI enthusiasm—demonstrates the sector’s structural advantages.

The Inference Inflection Point

While initial AI infrastructure investments emphasized training massive language models, the 2026 landscape is shifting toward inference workloads—the phase where trained models execute across production applications. Industry projections suggest inference will represent two-thirds of total AI compute demand by 2026, escalating from approximately one-third in 2023.

This transition carries profound implications for SMH investors. Training demands are episodic and heavily front-loaded, whereas inference creates durable, scalable demand that expands alongside adoption rates. Consequently, the requirement for GPUs, memory solutions, networking infrastructure, and power-efficient chipsets will intensify substantially, establishing a multi-year tailwind for semiconductor exposure.

Capital Expenditure Backdrop

Wall Street consensus projects global AI-focused data center capital expenditures will reach $527 billion in 2026, according to Goldman Sachs research. These expenditures funnel primarily toward a concentrated group of semiconductor manufacturers—chip architects, foundry operators, equipment suppliers, and memory providers. SMH’s portfolio architecture aligns directly with this capital flow dynamic.

Valuation Assessment

Trading at approximately 33 times trailing-12-month earnings, SMH commands a valuation multiple comparable to major technology companies. For investors seeking concentrated semiconductor exposure without cherry-picking individual holdings, this pricing appears reasonable relative to the underlying growth drivers and competitive positioning of portfolio constituents.

Forward-Looking Considerations

The semiconductor industry’s trajectory remains tethered to sustained AI infrastructure deployment. Whether examining total addressable market expansion, competitive moat strength among holding companies, or the durability of inference-driven demand, multiple angles support the notion that chip sector exposure will remain structurally attractive throughout 2026. The key distinction for investors is whether broad semiconductor participation outperforms targeted single-stock selection—a calculation favoring diversified approaches during periods of sector-wide tailwinds.