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AI’s next investment phase may extend beyond the chips and data-center construction that dominated the first wave. David Dierking’s October 6, 2026 article argues that spending could spread into cloud delivery, software applications, cybersecurity and the electrical infrastructure needed to power expanding compute. It names five exchange-traded funds (ETFs) that provide those different kinds of exposure: SMH, SKYY, IGV, CIBR and GRID.
These are possible ways to express a theme, not predictions that any fund will outperform. They are also not interchangeable “AI ETFs.” Each follows a different sector, index or group of companies, and a fund’s name does not tell you how much of its portfolio is directly tied to AI. Check current holdings, methodology, fees and overlap with investments you already own before buying.
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What “AI’s next phase” means for investors
The first stage of the AI build-out centered on scarce computing capacity: advanced processors, servers, networking, data centers and the capital spending required to install them. The thesis behind the five-fund list is that the economic benefits and costs of AI could broaden as organizations move from experimentation to routine deployment.
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In that view, companies may consume computing and AI capabilities through cloud platforms rather than owning all the hardware themselves. Businesses may buy software that embeds AI into productivity and workflow tools. More capable systems could create additional cybersecurity requirements, while data centers and other high-performance computing loads could require transmission, distribution and electrical-equipment upgrades.
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That is a scenario, not a measured forecast. The article uses “could” and “may” because the pace of adoption, the profitability of AI products, regulation, competition and electricity availability remain uncertain. The five ETFs represent separate slices of the argument rather than a complete portfolio or a single diversified AI strategy.
The five ETFs and the role each one is meant to play
| Theme | ETF | What the article’s thesis is targeting | Important qualification |
|---|---|---|---|
| Semiconductor supply chain | VanEck Semiconductor ETF (SMH) | Chips that provide the processing foundation for training and inference. | The article treats semiconductors as foundational infrastructure and suggests continuing inference demand could sustain computing needs; it does not establish a future return. |
| Cloud computing | First Trust Cloud Computing ETF (SKYY) | Businesses accessing computing resources and AI services through cloud providers. | Cloud exposure can include companies with broader activities than AI, so the ticker alone does not measure AI revenue. |
| Software | iShares Expanded Tech-Software Sector ETF (IGV) | Productivity and other applications that incorporate AI. | The issuer describes IGV as tracking North American software companies and select interactive-media and services firms; holdings can change. |
| Cybersecurity | First Trust Nasdaq Cybersecurity ETF (CIBR) | Security products and services needed as digital systems and AI capabilities become more valuable targets. | The article presents higher cybercrime risk as its rationale, not as a quantified forecast. |
| Smart grid and electrical infrastructure | First Trust Nasdaq Clean Edge Smart Grid Infrastructure Index Fund (GRID) | Grid upgrades and electrical-equipment companies that could support rising data-center electricity demand. | The article specifically says GRID does not own data-center real-estate investment trusts (REITs); it is an infrastructure-equipment exposure. |
SMH: semiconductor exposure at the foundation
SMH is the list’s most direct expression of the idea that AI requires more processing capacity. Semiconductor businesses supply the processors and related components used in training models, serving inference requests and operating the wider computing stack. If inference becomes a routine workload across many applications, the article argues that demand for computing could remain substantial after the initial training build-out.
That thesis comes with industry-specific risks. Semiconductor demand is cyclical, production is capital-intensive, and supply chains can be affected by export controls, geopolitics and manufacturing bottlenecks. A chip-focused ETF can therefore rise or fall for reasons unrelated to near-term AI adoption, including inventory corrections or a downturn in other electronics markets. Before treating SMH as an AI allocation, inspect its current constituents and concentration: a fund can hold companies whose businesses span consumer electronics, communications or automotive markets as well as AI-related products.
SKYY: cloud delivery rather than a hardware purchase
SKYY represents the next link in the proposed chain. Instead of buying processors directly, an enterprise may rent computing capacity, storage, model access and software through cloud providers. The article’s argument is that broader AI use could increase demand for these services, especially among organizations that cannot economically build their own infrastructure.
Cloud companies often operate multiple businesses, so the fund’s exposure may include hosting, enterprise software, data services or other technology activities. Revenue growth also does not automatically produce shareholder returns: providers may need to spend heavily on data centers, networking and energy, and competition can pressure prices. Read the fund’s current index rules and holdings to determine whether its portfolio fits a cloud thesis, an AI thesis or a broader technology allocation.
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IGV: software that puts AI into everyday workflows
IGV is the software implementation angle. The article frames it as a way to participate in productivity applications and other tools that incorporate AI after the underlying models and infrastructure are available. The issuer says IGV tracks an index of North American software companies and select interactive media and services firms, so it is not limited to companies whose primary business is generative AI.
The issuer-reported expense ratio is 0.38%, but fees and portfolio details should be confirmed in the current prospectus and fund materials before purchase. Holdings are subject to change. Software economics can differ sharply from chip economics: recurring subscriptions may provide predictable revenue, while product cycles, customer adoption, licensing costs and the ability to charge for AI features determine whether usage becomes profit.
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CIBR is included because AI can expand both the value of digital assets and the tools available to attackers. Dierking’s article argues that more capable AI could increase cybercrime risks and, in turn, the need for security products and services. That is a logical risk-response thesis, not a stated estimate of future spending or ETF performance.
Cybersecurity companies may sell endpoint protection, identity controls, network security, cloud security or related services. Their results can depend on government and corporate budgets, breach news, renewal rates and the speed at which customers consolidate vendors. A cybersecurity fund may also hold businesses whose growth is driven by threats or regulations unrelated to AI. Review the current portfolio and sector weights rather than assuming every holding benefits equally from AI-related security demand.
GRID: the power-delivery bottleneck
AI workloads consume electricity, and large data centers can require new substations, transmission capacity, cooling systems and other electrical equipment. GRID is the list’s way to express the possibility that utilities and data-center operators will need grid modernization and that equipment suppliers could benefit from that investment.
GRID should not be confused with a data-center REIT fund. The article says it does not own data-center REITs; its intended exposure is to smart-grid and electrical-infrastructure companies. That distinction matters because property owners, utilities and equipment manufacturers have different revenue drivers, leverage and regulatory risks. Grid projects can also take years to permit and build, so increased electricity demand does not guarantee immediate sales or profits for every constituent.
Why these are not five equivalent AI ETFs
The funds differ in what they own, how narrowly they define their industries and how concentrated their portfolios may be. SMH is tied to semiconductor businesses; SKYY to cloud computing; IGV to North American software and selected interactive-media and services companies; CIBR to cybersecurity; and GRID to smart-grid infrastructure. Their returns can diverge even when the same AI headline affects all five.
A thematic label also does not establish direct AI exposure. A company can be classified in a relevant sector while earning most of its revenue from products with no AI connection. Conversely, a diversified technology company may benefit from AI without being classified as a pure-play AI business. The only reliable way to assess the connection is to read each fund’s current methodology, holdings and issuer disclosures.
Current information you should verify before trading
The article’s search-result figures for some funds were point-in-time snapshots, not current October 13, 2026 data. They should not be reused as present-day expense ratios, assets, yields, holdings weights or performance figures. The available issuer material establishes IGV’s objective and a 0.38% expense ratio, while current comparable issuer facts for SMH, SKYY, CIBR and GRID were not established in the cited material.
For every fund, open the latest issuer page and prospectus and record:
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- the current expense ratio and any fee waiver or expiration date;
- the index methodology, rebalancing schedule and eligibility rules;
- the complete holdings list, top-position weights and number of holdings;
- geographic exposure and the sectors that sit outside the headline theme;
- assets, trading volume, bid-ask spreads and any securities-lending or tax considerations relevant to you;
- the fund’s stated risks, including concentration, currency, regulatory and industry-cycle risks.
Do not substitute an old search snippet for a current prospectus. Fees, holdings and weights change, and an ETF’s name can remain the same while its exposure shifts with index reconstitutions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare the five funds with your existing portfolio
1. Define the exposure you actually want
Decide whether you want semiconductor manufacturing, cloud delivery, software applications, cybersecurity or power infrastructure. If your objective is simply broad participation in technology growth, a sector-specific fund may be a more concentrated bet than you need.
2. Map overlap before adding anything
Broad S&P 500 and total U.S. market funds may already own many of the same large technology companies. The article explicitly cautions that investors can have existing exposure through broad-market funds. Compare your current holdings with each ETF’s top positions and sector weights; owning several of the five can increase concentration rather than diversify it.
3. Check concentration and methodology
Read how the index caps individual holdings, whether it uses equal or market-cap weighting, and how often it rebalances. Two funds with similar labels can have very different risk because one may be dominated by a handful of companies while another spreads weight across more firms.
4. Match the fund to a portfolio role
A fund could serve as a small satellite position around a diversified core, a way to emphasize a sector you understand, or a research candidate you ultimately do not buy. The article recommends that these ETFs should not make up a large portfolio allocation. The appropriate size depends on your time horizon, risk capacity, tax situation and existing technology exposure; no universal percentage follows from the article.
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5. Set a review rule
Because the thesis depends on adoption, capital spending and regulation, decide in advance what would make you reassess: a methodology change, a material shift in holdings, a fee increase, a need for the money or a portfolio becoming too concentrated. A review rule is more disciplined than buying after a headline and selling after a short-term decline.
Risks that apply across the theme
- Valuation risk: Expectations for AI growth can be reflected in share prices before profits arrive.
- Execution risk: Companies may spend heavily on chips, cloud capacity, software development or grid projects without earning attractive returns.
- Competition: Falling prices or open alternatives can reduce the value captured by today’s leaders.
- Regulation and policy: Export controls, privacy rules, electricity permitting and AI-specific regulation can alter costs and market access.
- Concentration: A narrow ETF can behave very differently from a broad-market fund and may experience larger drawdowns.
- Correlation: Holding several technology-themed ETFs can leave you exposed to the same macro factors, even when the themes sound different.
A practical decision framework
Start with your current asset allocation, not with the ticker symbols. If a broad-market fund already gives you substantial exposure to major chip, cloud and software companies, adding a thematic ETF is an active tilt. Write down the specific gap you are trying to fill, then test each candidate against three questions:
- Does the current portfolio actually contain the businesses that express my thesis?
- Is the fund’s concentration and fee acceptable for the role I want it to play?
- What evidence would show that the thesis or my personal need has changed?
If you cannot answer those questions from current issuer documents, postpone the trade rather than treating the ETF name as sufficient analysis. The five funds can be watch-list candidates for different parts of the AI ecosystem, but they do not remove the need for diversification, position sizing and ongoing review.
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SMH, SKYY, IGV, CIBR and GRID map to a plausible “next phase” story: computing components, cloud access, AI-enabled software, security and electricity infrastructure. The story remains a hypothesis. Verify current holdings, methodology and costs, account for overlap with broad-market funds, and use any thematic position sparingly enough that an incorrect forecast does not derail your overall plan.
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