Is the AI Trade Entering Its Second Wave? ETF Beneficiary

Hooking the Reader: A New Chapter for AI Investing

Few investment themes have moved as fast or drawn as much attention as artificial intelligence. In recent years, chipmakers, software platforms, and AI-enabled services have led to big-cap rallies and sector leadership. But markets don’t stay red-hot forever; they shift from first-buzz narratives to scalable, sustainable growth. The question on many investors’ minds is Is the trade entering second wave? This piece examines the evidence, the likely beneficiaries, and how to position a portfolio for what could come next.

Tech cycles tend to move in stages: hype, deployment, and scale. The initial AI surge was led by a handful of mega-cap chipmakers and cloud players. Today, the focus is broadening to the broader ecosystem—equipment makers, foundries, memory, software, and AI accelerators. If the second wave takes hold, we could see a more stable, longer-lasting earnings trajectory rather than a single headline surge. This isn’t a claim of a guaranteed uptrend, but a framework to assess the next phase of AI-driven demand and the ETFs most likely to benefit.

What Defines a Wave in AI Investing?

Investors often talk about waves in sectors like AI as a mix of demand growth, technology maturation, and capital expenditure cycles. A typical AI wave lasts several years and is driven by three forces:

• End-to-end AI adoption across industries (healthcare, finance, manufacturing, logistics, and more).
• Capex cycles in semiconductors and equipment needed to scale AI workloads (chips, GPUs, memory, networking, and lithography).
• Software and services monetization from AI-accelerated platforms, data services, and AI-as-a-service revenue streams.

Early waves are often led by a few heavyweights. The next wave tends to broaden to the entire ecosystem, creating a wider base of beneficiaries and, potentially, smoother earnings growth. The key question is whether current AI enthusiasm translates into sustained demand, not just a short-term surge in hype-driven investment. That’s where the phrase "trade entering second wave?" becomes a useful guidepost for investors weighing risk vs. reward.

Is the AI Trade Entering Second Wave?

The short answer is: the setup is plausible, but not guaranteed. Here’s how to evaluate the argument behind a possible second wave of AI demand:

• AI hardware demand is expanding beyond a few leaders. While Nvidia remains a dominant AI accelerator supplier, a broader constellation of players—Taiwan Semiconductor Manufacturing Company (TSMC), Broadcom, Lam Research, ASML, and Applied Materials—are benefiting from higher-capacity AI compute and packaging needs.
• Foundry and materials capex are rising. Major foundries and equipment suppliers have signaled multi-year investment cycles to meet AI and HPC workloads, which can translate into steadier revenue streams for suppliers rather than one-off spikes.
• Software and services scale with data volumes. As more organizations deploy AI, data infrastructure, security, and platform services unlock recurring revenue, providing ballast to earnings even if hardware demand wobbles temporarily.
• Forecasts hint at large TAM expansion. Independent analyses project AI-related revenue climbing toward the trillions, with major segments—semiconductors, cloud infrastructure, AI software, and services—driving a long-run growth trajectory, even if near-term growth moderates from peak AI hype.

So, does the trade entering second wave? hinge on durable demand across the AI stack, not merely the height of a single quarter’s results. For investors, the indicators to watch include capex guidance from leading chipmakers and equipment suppliers, order backlogs, and the evolution of AI workloads across industries. If these signals point to multi-year expansion rather than a short-lived cycle, the thesis for a second wave gains credibility.

Which ETF Could Be a Major Beneficiary?

Among the investable tools, semiconductor-focused exchange-traded funds (ETFs) stand out as a way to access broad AI-enabled growth within the tech hardware realm. The VanEck Semiconductor ETF (SMH) has been a cornerstone for many AI and semis enthusiasts, delivering exposure to the supply chain that powers AI-ready infrastructure. While no single ETF is a crystal ball, an intelligent allocation to semis can capture both AI hardware demand and the equipment ecosystem fueling it.

Beyond SMH, investors can look at related vehicles that tilt toward AI hardware, materials, and capital equipment—areas most likely to benefit if the second wave of AI spends accelerates. A practical approach is to blend broad semis exposure with select names or sub-sectors that play a critical role in AI deployment, such as:

• Leading chipmakers that own AI accelerators and high-margin product lines.
• Equipment and materials suppliers that enable scale, yield improvements, and node advancement.
• Foundries and packaging firms enabling tighter AI compute integration.

A Real-World Look at the AI Stack and Its Impacts

To grasp why the AI trade could be entering a second wave, it helps to connect the dots from the silicon stage to the software layer. Here are real-world dynamics at each layer of the AI stack:

• Chipmakers and accelerators: Nvidia remains the poster child for AI acceleration, but the broader ecosystem—TSMC for manufacturing, Broadcom for connectivity, and memory suppliers—benefits from the sustained demand for AI models and data centers.
• Equipment and materials: Lam Research, Applied Materials, and ASML are at the center of enabling higher-density, faster AI chips, with capacitated capacity courses and robust backlog visibility supporting multi-year growth prospects.
• Cloud and data infrastructure: AI workloads require massive compute and memory, driving capex at hyperscalers and cloud providers. This, in turn, supports AMAT, LRCX, and ASML exposure as suppliers to the build-out accelerates.
• Software and services: Once AI models are deployed, data platforms, software automation, and AI-as-a-service revenue streams become material, providing recurring income and defense against cyclical hardware volatility.

Consider this scenario: if AI adoption continues to scale across industries, the addressable market for AI-enabled hardware and services could move from a roughly trillion-dollar annual pace to multi-trillion as the next five to ten years unfold. In such an environment, ETFs that give broad exposure to the AI-enabled hardware chain can offer a more resilient path to participation than stocks tied to a single AI giant.

How to Build a Practical AI-Investment Plan

Investors often ask how to position for a trade that might be entering its second wave without overconcentration risk. Here is a simple, actionable framework designed for real-world portfolios:

1. Define your AI exposure target. Decide whether you want broad semiconductor exposure (SMH-like) or a tilt toward AI hardware and equipment. A balanced approach could be 60% broad semis, 20% AI-specific equipment, and 20% thematic AI software/solutions.
2. Set a time horizon. A 3- to 5-year window aligns with multi-year capex cycles and AI deployment across industries. Shorter horizons can lead to whipsaw volatility if sentiment shifts on headlines rather than fundamentals.
3. Use a dollar-cost averaging (DCA) plan. Invest monthly or quarterly to smooth entry points, reducing the risk of buying at a peak. For example, commit $500 per month into a semis ETF for a year and adjust based on performance and risk tolerance.
4. Establish a rebalancing cadence. Review your AI exposure every 6–12 months and rebalance back toward your target allocation if one segment has run up or down by more than 15–20%.
5. Incorporate risk controls. Set a maximum drawdown threshold (e.g., -25%) and determine in advance how you’ll respond—whether by rebalancing, trimming, or rotating into non-cyclical areas to preserve capital.

Numbers Backing the Long-Term AI Thesis

Investors love numbers, and AI provides a mix of long-run potential and near-term catalysts. Here are several data points and scenarios to ground the discussion:

• Market forecasts: Industry analyses commonly cite nearly $1 trillion in annual AI-related revenue in the near term, with potential growth to around $2 trillion by the mid-2030s as AI becomes embedded in everyday workflows and mission-critical systems.
• Chip and equipment capex cycles: Major capex cycles in AI accelerators and lithography equipment are expected to extend through the next several years, supporting a longer runway for suppliers rather than a fleeting spike in orders.
• Earnings visibility: When AI demand becomes a mainstay, the revenue base for AI-enabled hardware tends to provide more predictable earnings growth and improved visibility for product cycles, which can cushion volatility during market pullbacks.

Of course, numbers are only one part of the equation. The actual trajectory depends on government policy, supply chain resilience, geopolitical tensions, and the pace of AI adoption across sectors. Still, the multi-year horizon looks favorable for the players in the AI hardware and equipment space, making a case for a second wave narrative if fundamentals remain supportive.

Which ETF Stands to Benefit Most?

There isn’t a single magic wand in investing, but certain ETFs provide a solid proxy for the AI hardware ecosystem and its second-wave potential. A diversified semiconductor ETF like SMH captures the breadth of the AI supply chain—from chip design and fabrication to testing equipment and packaging. If the second wave takes hold, SMH could benefit from a broader set of winners beyond the early AI leaders.

Here are some practical considerations when evaluating an ETF strategy around the AI wave:

• Concentration vs. breadth: A broad semis ETF offers diversification across multiple sub-sectors (foundries, memory, logic, equipment) which can reduce idiosyncratic risk.
• Quality of holdings: Check the top holdings and their AI exposure. Heavyweights like Nvidia may dominate, but the spread to equipment and materials names can improve resilience during cyclic downturns.
• Expense ratios and liquidity: Lower costs help compound returns over the long run, and high liquidity reduces trading friction when you rebalance.
• Sector tailwinds alignment: Ensure the ETF’s exposure aligns with the AI hardware and software tailwinds described in this guide for a coherent bet on the second wave.

Real-World Scenarios: How the Wave Might Unfold

Let’s translate the theory into two practical scenarios investors can relate to. These are plausible paths the AI trade could follow if the second wave gains traction:

1. Scenario A — Durable Demand Uptick: AI adoption becomes a standard business capability. Data centers expand compute capacity, leading to sustained orders for GPUs, memory, packaging, and lithography equipment. In this scenario, SMH and peers can see a steady earnings cadence, with periodic pullbacks due to macro noise that get bought back by ongoing capex growth.
2. Scenario B — Mixed Signals with Momentum Windows: Short-to-mid-term volatility appears as macro headwinds sync with AI headlines. Yet, backlogs stay healthy, and the supply chain remains tight enough to push equipment providers to raise guidance multiple times over a multi-quarter horizon. The ETF could exhibit drawdowns, followed by stronger recoveries as demand centers reassert themselves.

In both cases, execution matters. The second wave thesis relies on the idea that hardware and equipment demand remains less volatile than some hype-driven software plays and that the ecosystem can scale with AI workloads rather than riding a single model’s fortune.

Putting It All Together: A Practical Checklist

Before you deploy capital in the AI space, use this checklist to separate the signal from the noise:

• Is the demand broad-based? Look for capex guidance bleeding through multiple players, not just a single company’s commentary.
• Are budgets growing with scope? Track indications that AI deployment is crossing from pilot projects to enterprise-wide adoption across industries.
• Is the earnings cycle improving? Look for improved visibility, stable gross margins in hardware suppliers, and healthier backlog levels for equipment makers.
• What is the risk-reward profile? Evaluate exposure to macro risk, supply chain resiliency, and how much of the portfolio is insulated from a sharp AI sentiment pullback.

Conclusion: The Path Forward for the AI Trade

Is the AI trade entering its second wave? The evidence suggests a credible case for continued, multi-year expansion across the AI hardware ecosystem, supported by a broader base of beneficiaries beyond the earliest AI leaders. An ETF focused on semiconductors and equipment—such as SMH—offers a practical way to harvest this potential, with the added cushion of diversification across the supply chain. The key is to approach with a disciplined framework: define AI exposure, set a multi-year horizon, use DCA, rebalance, and manage risk with clear guidelines. If the second wave materializes as envisioned, investors who have prepared a thoughtful allocation now could reap the benefits of a more persistent, not just a momentary, AI-driven growth cycle.

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