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The most useful way to diversify beyond Nvidia and Micron is to spread exposure across the AI supply chain, not simply buy a third chip designer. That means combining alternative compute, semiconductor manufacturing and packaging, complete server systems, networking, cooling, electricity infrastructure and—where appropriate—a broad semiconductor exchange-traded fund (ETF). Each layer has different customers, capital needs, geography, regulation and sensitivity to the data-center investment cycle.
AI is an expanding infrastructure build-out. Invesco’s 2026 outlook separates semiconductors from data-center infrastructure, energy and commodities, a useful reminder that “AI stocks” are not one industry. The choices below are research categories, not a personal recommendation or a fixed allocation.
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Start with the AI value chain
Nvidia gives an investor substantial exposure to AI accelerators, while Micron adds memory exposure. Buying another company with nearly identical revenue drivers may leave the portfolio less diversified than it appears. A better test is to ask what a company supplies, who pays for it, where it operates and what must happen before it earns revenue.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute| Value-chain layer | What it supplies | Main diversification benefit | Principal risk |
|---|---|---|---|
| Accelerator and server CPU | Compute chips, interconnects and software | Alternative architecture and product mix | Rapid product cycles and hyperscaler concentration |
| Foundry and advanced packaging | Manufacturing capacity and chip integration | Exposure to production and packaging demand rather than one chip brand | Geopolitical, capital-intensity and capacity risk |
| Broad semiconductor ETF | A basket of semiconductor companies | Reduces single-company and single-product risk | Retains semiconductor-cycle and sector valuation risk |
| Complete AI systems | Servers, racks, storage, networking, software and support | Captures system integration and deployment spending | Hardware margins, working capital and customer concentration |
| Networking, cooling and power | Connections, heat removal, electricity delivery and generation | Business models less tied to a particular processor | Industrial, utility and construction cycles can dominate AI demand |
| Manufacturing and policy optionality | Domestic fabrication and strategic semiconductor investment | Exposure to supply-chain re-shoring and public support | Execution, subsidy conditions and very high capital requirements |
These layers can still move together when hyperscalers slow capital spending. Diversification lowers dependence on one company; it does not remove the common AI-infrastructure cycle.
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Alternative compute exposure: AMD
Advanced Micro Devices (AMD) is the clearest alternative-compute category in the supplied evidence. Its 2025 Form 10-K describes a stack that includes fifth-generation EPYC server processors, Instinct MI350 GPUs, Pensando AI networking, rack-scale systems and ROCm software. The company says, “AI systems will require not only powerful chips, but also full-stack innovation across compute, networking, systems architecture and software.”
AMD reported 2025 revenue of $34.6 billion, up 34% from 2024. Data Center revenue was $16.6 billion, up 32%, according to its 2025 filing. Those figures describe reported company performance for that fiscal year; they do not guarantee future growth or indicate that AMD’s exposure is independent of large cloud customers.
Why AMD can diversify a two-stock portfolio
- Its exposure spans CPUs, GPUs, networking and software instead of memory alone.
- EPYC server processors participate in general-purpose data-center computing, while Instinct targets accelerators.
- ROCm and systems work create a software and integration component alongside silicon.
What to investigate before buying
- How much future revenue depends on a small number of hyperscalers or large system customers.
- Whether software adoption and developer support keep pace with hardware launches.
- Competitive pricing, supply availability and the time required for customers to qualify new systems.
- AMD’s stated risks around data-center construction delays and customer-financing conditions.
AMD is therefore a differentiated compute exposure, not a non-cyclical asset. It can diversify product architecture while retaining semiconductor, export-control and hyperscaler-spending risks.
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Taiwan Semiconductor Manufacturing Company (TSMC) adds manufacturing and packaging exposure rather than another branded accelerator. Its foundry model serves chip designers, and advanced packaging is increasingly important when AI systems combine large compute dies, high-bandwidth memory and complex interconnects.
Nasdaq Global Indexes reported TSMC 2025 revenue of NT$3.8 trillion (US$120.6 billion), up 32% year over year, and described its position in advanced packaging and AI accelerators. The currency conversion and growth rate are the figures reported by Nasdaq for 2025.
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What makes foundry exposure different
- Revenue is linked to customers’ chip designs and production volumes, not only to the success of one merchant-chip product.
- Advanced process technology and packaging can benefit from several AI designers at once.
- Manufacturing scale and process know-how may give the company a different competitive position from fabless designers.
Risks specific to TSMC
Fabrication and packaging require enormous, long-lived capital commitments. Taiwan geography introduces country and geopolitical risk, while export controls can restrict which products are made for which markets. A foundry also remains exposed to semiconductor inventories and customers’ capital-spending decisions. TSMC diversifies the business role in a portfolio, but not the sector’s cycle.
Use a semiconductor ETF for basket exposure
A semiconductor ETF that tracks a broad index such as the PHLX Semiconductor Index can replace a single-company bet with a basket. Nasdaq lists funds tracking that index and notes that investor attention may shift toward broad-based exposure. One example identified in that coverage is SOXQ.
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An ETF can spread company-specific risks such as a product delay, a factory problem or a management error. It does not make the holdings independent: most constituents still respond to chip inventories, equipment spending, export rules, interest rates and the pace of data-center construction.
Questions to answer before selecting an ETF
- Read the fund’s current prospectus to confirm the index, holdings methodology, geographic limits and rebalancing schedule.
- Check the expense ratio, bid-ask spread, assets, trading volume and tax treatment for your account and country.
- Inspect the latest holdings. A “semiconductor” label may still produce heavy exposure to a few large companies.
- Compare overlap with Nvidia, Micron and any individual stocks already owned.
- Confirm whether the fund distributes income, how often it rebalances and whether securities lending or derivatives are used.
ETF diversification is most useful when it changes the portfolio’s concentration. Buying a fund whose largest holdings mirror your existing positions may add convenience without materially changing risk.
Complete AI systems and liquid cooling: Supermicro
Super Micro Computer (Supermicro) represents a system- and facility-level approach. Its FY2026 Form 10-K says its data-center building-block solutions integrate servers, storage, networking, racks, liquid-cooling infrastructure, power distribution, software, services and support. That is a broader deployment bundle than a processor or memory component.
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Why a system supplier changes the exposure
- Revenue can reflect the customer’s decision to deploy a complete rack or cluster, not merely to purchase a component.
- Integration, services and support may add value around the hardware sale.
- Liquid cooling and power distribution connect AI demand to facility efficiency and construction budgets.
Risks to examine
- Server demand can be lumpy, and large customers may negotiate aggressively.
- Working-capital needs, component availability and delivery timing can affect cash flow.
- Customer concentration and dependence on hyperscaler or specialist data-center spending remain relevant.
- Cooling performance claims should be evaluated against a specific facility’s engineering requirements rather than treated as a guaranteed return.
Manufacturing and policy optionality: Intel
Intel adds advanced-manufacturing and public-policy exposure. Its 2025 Form 10-K records $5.7 billion of accelerated CHIPS Act disbursements and a completed $5.0 billion NVIDIA share purchase. Those facts show both government-program involvement and a strategic investment relationship, but they do not establish that the manufacturing strategy will meet a particular timetable or return target.
Intel’s potential diversification value comes from fabrication, process development and the strategic importance of domestic semiconductor capacity. Its risks are unusually capital intensive: new fabs require sustained spending before utilization and profitability arrive. Investors must also analyze execution, customer commitments, subsidy conditions, strategic investments and the company’s capital structure. Policy support can reduce financing pressure, but it can also impose milestones, restrictions or political uncertainty.
Look beyond chips: networking, cooling, grid and energy
Invesco’s 2026 outlook identifies networking, cooling, grid interconnection, energy generation and commodities such as copper and rare earths as separate beneficiaries of data-center construction. These are ways to gain AI-infrastructure exposure without buying another processor or memory maker.
Networking
AI clusters require high-bandwidth links between accelerators, servers and storage. Networking suppliers may benefit when customers expand the number and size of clusters, but demand can still be tied to the same hyperscaler budgets that drive chip purchases.
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Cooling
Higher rack densities increase the importance of heat removal. Air cooling, direct-to-chip liquid cooling and facility-level systems have different engineering, installation and maintenance requirements. A cooling company may be driven as much by commercial construction and industrial replacement cycles as by AI.
Grid interconnection and power generation
Data centers need available electricity, substations, transmission connections and generation capacity. Utilities, electrical-equipment suppliers and power developers can therefore participate in the build-out. Their returns are affected by regulation, permitting, local demand, fuel costs and broader industrial activity—not just server orders.
Copper and rare earths
These commodities can benefit from power, wiring and equipment demand, but they are especially indirect AI exposures. Prices respond to mining supply, China-related demand, construction, currency movements and the global business cycle. Treat them as commodity or industrial positions with an AI linkage, not as substitutes for a software or semiconductor business.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical diversification framework
Before adding a ticker, score the candidate on the following dimensions. The objective is to identify a different risk driver, not to collect familiar AI labels.
| Test | Questions to ask |
|---|---|
| Value-chain role | Is it an accelerator, CPU, foundry, packaging provider, server integrator, networking supplier, cooling provider, power business or ETF basket? |
| Revenue quality | Are sales recurring through software and services, or mainly one-time hardware shipments? |
| Customer concentration | How much revenue or growth depends on a few hyperscalers, original-equipment manufacturers or government customers? |
| Geography and regulation | Which countries contain factories, customers and key suppliers? How do export controls, subsidies or national-security rules apply? |
| Capital intensity | Does the business need fabs, packaging plants, racks, construction, power connections or inventory financing? |
| Valuation and cycle | What growth is already reflected in the price, and how would margins look if AI capital spending pauses or inventories rise? |
Common failure modes
Buying several names with the same customer
Three companies can appear different while all depending on a handful of cloud providers. Read concentration disclosures and identify whether the same capital-spending decision drives each holding.
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Confusing a theme with diversification
An accelerator designer, memory supplier, networking company and server maker may all fall together during a semiconductor or data-center digestion phase. Add layers with genuinely different economics, such as regulated power or a broad ETF, only after understanding their own cycles.
Ignoring geography and policy
Taiwan foundry exposure, export controls, CHIPS Act arrangements and domestic-factory projects create risks that a simple “AI growth” label hides. Country and regulatory exposure should be recorded alongside sector exposure.
Paying for growth without a valuation check
Strong revenue growth can already be reflected in a share price. Compare the implied expectations with capacity plans, customer commitments, margins, cash generation and the possibility of delayed data-center construction.
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- List current holdings and classify each by value-chain layer, geography, largest customers and principal capital need.
- Measure overlap. Note shared hyperscaler customers, shared semiconductor cycles and ETF holdings that duplicate individual stocks.
- Choose the missing layer rather than the most fashionable ticker. A portfolio already heavy in chip designers may need systems, power, cooling or a basket.
- Read the latest annual report and fund prospectus. Verify the current fiscal year, holdings, fees, concentration, debt, capital spending and risk factors.
- Set a position-size and rebalancing rule before buying. A written rule reduces the temptation to increase exposure after a price surge.
- Review the thesis when a material fact changes: a product launch, customer concentration, factory delay, subsidy condition, export restriction or major capital-spending revision.
What this approach can—and cannot—do
Spreading holdings across compute, manufacturing, systems and enabling infrastructure can reduce the damage from a single product failure, factory outage or company-specific decision. It cannot protect against a broad reduction in hyperscaler spending, a semiconductor inventory correction, tighter export controls, higher financing costs or an overvalued AI trade. The strongest diversification is therefore a combination of different business models, customers, regions and cash-flow drivers—not simply a longer list of AI tickers.
Frequently Asked Questions
Is an AI ETF automatically safer than buying individual AI stocks?
No. An ETF reduces single-company risk, but a semiconductor fund can still be concentrated in a few large holdings and remains exposed to chip cycles, export controls and data-center spending. Check the current prospectus and holdings before investing.
Can power and cooling stocks benefit even if a particular GPU loses market share?
Potentially. Grid connections, generation, cooling and facility equipment serve the data center rather than one processor design. Their results can nevertheless be driven by permitting, utility regulation, construction and broader industrial cycles.
What is the biggest hidden concentration risk in an AI portfolio?
Shared dependence on a small number of hyperscalers. Different suppliers may all rely on the same customers’ capital budgets, so map customer exposure—not just company names—when testing diversification.
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