There is no single “best” AI semiconductor stock. Each company captures a different part of the build-out: NVIDIA and AMD design accelerators and CPUs, Broadcom supplies custom AI silicon and networking, TSMC manufactures chips for many designers, and ASML provides critical lithography equipment. The right choice depends on whether you want direct AI-compute exposure, a broader semiconductor revenue mix, supply-chain leverage, or less single-customer risk.
Customer concentration, export controls, power availability, manufacturing capacity, product transitions and hyperscaler capital spending can all interrupt growth. The comparisons below use each company’s own reporting definitions; their percentages are not a directly comparable measure of “AI revenue.”
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The five semiconductor layers behind AI infrastructure
AI systems require more than a processor. A useful way to evaluate semiconductor stocks is to identify where each company sits in the supply chain and how directly its reported revenue is tied to AI deployments.
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| Company | Primary layer | How it participates in AI | Main diversification |
|---|---|---|---|
| NVIDIA | Accelerators and CPUs | Data-center GPUs, accelerated-computing platforms, networking and software used to train and run AI models. | Most exposure is tied to data-center and accelerated-computing demand; other businesses are smaller relative to that activity. |
| AMD | Accelerators and CPUs | EPYC server CPUs and Instinct GPUs, including fifth-generation EPYC and Instinct MI350 products reported as drivers of 2025 Data Center growth. | Client and Gaming, plus other segments, provide a larger non-data-center mix than NVIDIA’s. |
| Broadcom | Custom silicon and networking | Custom AI accelerators or XPUs, Ethernet switching and routing silicon, NICs, PHYs, optical components, racks and systems for hyperscalers and AI-model companies. | Infrastructure and connectivity businesses serve communications, enterprise and other markets, but large customers remain important. |
| TSMC | Foundry manufacturing | Manufactures advanced chips designed by many customers. AI demand increases utilization of leading-edge and advanced packaging capacity. | 534 customers and multiple end markets spread exposure across AI and non-AI products. |
| ASML | Lithography equipment | Supplies the lithography tools needed to manufacture advanced chips, giving it indirect exposure to AI-capacity expansion. | Its equipment serves the semiconductor industry broadly, but the customer base is relatively small and concentrated. |
This layering matters. A chip designer can grow rapidly when a particular architecture wins, but it may be exposed to a small number of buyers. A foundry or equipment supplier can benefit from industry-wide capacity investment, yet it depends on customers actually funding new fabs and technology ramps.
Why “AI revenue percentage” is not a clean comparison
Company filings do not use one standardized definition of AI revenue. NVIDIA reports direct-customer shares and estimates some indirect-customer exposure. AMD reports segment revenue rather than a separate AI line. Broadcom describes AI-related products across custom silicon, networking and optical components. TSMC reports revenue by technology node and customer base, while ASML reports equipment sales. A percentage from one company therefore cannot be ranked against a percentage from another without creating false precision.
Use three questions instead:
- What product is being sold? An accelerator, CPU, networking component, wafer-manufacturing service or lithography tool has a different demand cycle.
- Who is counted as the customer? A direct customer, an end customer served through channels and an estimated indirect customer are not equivalent.
- How diversified is the rest of the business? Non-AI segments can cushion a slowdown, but they may also dilute upside during an AI spending surge.
NVIDIA: the most direct accelerator exposure, with measurable concentration
Revenue exposure
NVIDIA is the most direct way among these companies to participate in demand for AI accelerators and the surrounding accelerated-computing platform. Its data-center business supplies GPUs, CPUs, networking and related systems used by large cloud providers and AI-model companies. That direct exposure can produce exceptional operating leverage when customers expand AI clusters, but it also makes results sensitive to the timing and scale of a relatively small group of buyers’ projects.
Customer concentration
NVIDIA’s fiscal-2026 filing reported that one direct customer represented 22% of total revenue and another represented 14%. The filing also says indirect-customer revenue is estimated and that some indirect customers individually represent 10% or more. These figures should not be added together: the two disclosed percentages are direct-customer shares, while indirect exposure is measured differently.
The company states that revenue is concentrated among a limited number of direct and indirect customers and that this trend may continue. A large cloud customer delaying a deployment can therefore affect quarterly revenue even when long-term AI demand remains intact.
Key risks
- Export controls: NVIDIA recorded a $4.5 billion charge related to H20 excess inventory and purchase obligations after U.S. export-license requirements reduced China demand.
- Customer capital availability: Data-center power, financing and access to capital are crucial to customers’ AI build-outs. Constraints in any of these can defer orders.
- Product transitions: New accelerator generations can shift demand quickly. Customers may delay purchases while waiting for a newer platform, while NVIDIA must manage inventory and supply commitments across generations.
- Concentration: The disclosed 22% and 14% direct-customer shares show why a strong market can still produce volatile company-specific results.
AMD: a broader mix, but not a concentration-free alternative
Revenue exposure
AMD reported 2025 net revenue of $34.6 billion, up 34% from $25.8 billion in 2024. Data Center revenue was $16.6 billion, up 32%, driven primarily by fifth-generation EPYC processors and Instinct MI350 GPUs. Client and Gaming revenue was $14.6 billion, up 51%.
This mix gives AMD more non-data-center exposure than a pure AI-accelerator thesis. Client and Gaming can support results when cloud infrastructure spending pauses, although those markets have their own cycles and competitive pressures. Data Center still represented a substantial portion of reported revenue, so AMD remains exposed to server and AI-capital spending.
Customer and product risks
AMD warns that a small number of customers will continue to account for a substantial part of revenue and receivables. A broader segment mix therefore reduces, but does not eliminate, concentration risk.
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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 minuteU.S. export controls also affect AMD. The company reported approximately $440 million of inventory and related charges associated with controls on MI308 products. This illustrates how restrictions can create costs even when end-market demand exists: products may need to be redesigned, redirected or written down.
When AMD may fit
AMD may suit investors who want exposure to AI accelerators and server CPUs but prefer a larger contribution from client and gaming products. The trade-off is that the company must execute across several product cycles at once, and its AI position is not insulated from customer concentration, export rules or competition.
Broadcom: custom AI silicon and the networking toll road
Revenue exposure
Broadcom participates in AI infrastructure beyond the GPU itself. It describes custom AI accelerators or XPUs, Ethernet switching and routing silicon, network interface controllers, physical-layer chips, optical components, racks and systems supplied to hyperscalers and AI-model companies.
This position can benefit from customers designing their own accelerators or expanding the amount of networking required to connect large clusters. It also means the revenue profile depends on a limited set of sophisticated buyers and on the success of their internal hardware programs.
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Customer concentration
In fiscal 2025, one customer accounted for 32% of Broadcom’s net revenue. Broadcom estimated that its top five end customers represented approximately 40% through all channels. These are different measurements: the 32% figure is a reported customer share, while the approximately 40% figure covers top-five end customers across channels.
Broadcom warns that losing, or seeing a significant decrease in demand from, any of its top five end customers could materially affect its business, results and financial condition. Investors should also examine payment terms: the company notes that leasing or deferred-payment models can increase credit risk if a customer’s finances weaken.
Key risks
- Hyperscalers may reduce, delay or cancel custom-accelerator and networking orders.
- Customer-specific designs can create strong relationships but may make individual programs more important to results.
- Deferred payments can lengthen the time between shipment and cash collection.
- Networking demand may remain strong while a particular accelerator program is delayed, but the reverse can also occur; the businesses should not be treated as one uniform AI cycle.
TSMC: broad foundry exposure and advanced-node leverage
Revenue exposure
TSMC is the manufacturing layer. It produces chips designed by many companies, so its AI exposure is distributed across customers rather than tied to one branded accelerator. TSMC reported 2025 U.S.-dollar revenue growth of 35.9% and said 74% of wafer revenue came from 7-nanometer-and-more-advanced technologies.
Advanced-node demand is relevant to AI because leading accelerators, server CPUs and other high-performance components generally require advanced process technology and sophisticated packaging. The 74% figure is a technology mix, not an AI-revenue percentage: it includes AI and non-AI products.
Diversification and risks
TSMC manufactured for 534 customers in 2025 and says its broad, diverse customer base and multiple end markets help smooth demand fluctuations. Chairman and CEO C.C. Wei described the company’s strategy this way: “Our success is predicated on our steadfast adherence to the pure-play foundry business model.”
That model avoids competing with customers’ chip designs, but it requires heavy investment in fabs, equipment, advanced packaging and utilities. TSMC expects AI-related demand to remain robust entering 2026, while remaining exposed to capacity constraints, technology-ramp execution, energy availability, geopolitical conditions and macroeconomic demand.
ASML: indirect AI exposure through the lithography bottleneck
How the exposure works
ASML sells lithography equipment required to manufacture advanced semiconductors. It does not receive a direct share of every AI-chip sale; instead, it benefits when chipmakers invest in additional capacity or new process generations. This can make ASML a way to express a longer-cycle view on semiconductor capital spending rather than a quarter-to-quarter accelerator view.
Customer concentration
ASML’s 2025 filing describes a relatively small customer base. Its largest customer represented 23.9% of 2025 net sales, up from 16.6% in 2024. The increase shows that even a supplier serving the whole industry can be highly exposed to the spending plans of a few major manufacturers.
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- Fab operators can delay equipment orders when utilization, financing or end-market demand weakens.
- Export rules can limit where advanced equipment may be shipped.
- Large systems have long production and acceptance cycles, so order timing can make quarterly results uneven.
- Capacity expansion may take years, making the stock sensitive to changes in long-term capital-expenditure expectations.
Cross-company comparison: the risks that matter most
| Evaluation axis | NVIDIA | AMD | Broadcom | TSMC | ASML |
|---|---|---|---|---|---|
| Direct AI visibility | High: accelerators and data-center platforms. | High in Data Center, with CPU and GPU exposure. | High but spread across custom silicon and networking. | Indirect: manufactures chips for many AI and non-AI customers. | Indirect: sells equipment needed for advanced-chip capacity. |
| Customer concentration measure | Two direct customers were 22% and 14% of fiscal-2026 revenue; indirect exposure is estimated. | Small number of customers account for a substantial share of revenue and receivables; exact shares were not stated. | One customer was 32% of fiscal-2025 revenue; top five end customers were approximately 40% through all channels. | 534 customers; broad and diverse base. | Largest customer was 23.9% of 2025 net sales. |
| Non-AI diversification | More limited relative to its data-center focus. | Meaningful Client and Gaming contribution. | Networking and infrastructure markets beyond AI. | Multiple end markets and process nodes. | Equipment demand across semiconductor applications. |
| Export and geographic exposure | China demand was reduced by licensing requirements; $4.5 billion H20-related charge. | MI308 controls led to approximately $440 million of inventory and related charges. | Customer locations and cross-border supply chains create policy exposure; the cited filing did not quantify a comparable charge. | Geopolitical and regional manufacturing considerations affect capacity and demand. | Export rules can restrict advanced-equipment shipments. |
| Capital-intensity sensitivity | Customer data-center power and capital availability are critical. | Depends on customer server and AI investment, while managing several product lines. | Depends on hyperscaler program commitments and payment terms. | Very high fab, packaging, energy and equipment requirements. | Highly sensitive to semiconductor-fab capital expenditure. |
These comparisons identify different risk types rather than a single winner. NVIDIA has the clearest direct accelerator exposure but the most visible disclosed direct-customer concentration. AMD offers a broader segment mix. Broadcom combines custom silicon with networking but reports substantial end-customer concentration. TSMC spreads manufacturing demand across hundreds of customers. ASML sells a critical bottleneck product while relying on a comparatively small customer base.
Should you buy a semiconductor ETF instead of one stock?
A semiconductor ETF can provide exposure to AI infrastructure while reducing the effect of one company’s product miss, customer loss or export-control problem. Current investing overviews commonly show diversified funds holding companies such as NVIDIA, AMD and Broadcom, but the exact holdings and weights change.
Before buying any fund, check these items in its current prospectus and portfolio:
- Holdings and weights: A fund marketed as semiconductor-focused may still be heavily dominated by a few large positions.
- Expense ratio: Fees reduce returns every year and should be compared with the diversification benefit.
- Index methodology: Equal-weight, market-cap-weight and factor-based funds behave differently when one segment rallies.
- Liquidity and spread: Trading costs matter, especially for smaller or less frequently traded funds.
- Tax treatment: Distributions and account type can change the after-tax result.
- Geographic scope: Some funds concentrate on one market; others include foundries, equipment makers and overseas issuers.
An ETF does not remove semiconductor risk. A broad fund can still fall when chip demand, capital spending or valuations contract across the industry. It does reduce single-company execution and customer-concentration risk when its holdings are genuinely diversified.
A practical buying framework
- Define the exposure you want. Choose between direct AI compute (NVIDIA or AMD), custom silicon and networking (Broadcom), manufacturing breadth (TSMC), or semiconductor-capital-equipment exposure (ASML).
- Separate growth exposure from diversification. A company with more non-AI revenue may be less sensitive to an AI pause, but it may also have less immediate upside from a single AI spending surge.
- Read the customer disclosure precisely. Record whether each percentage refers to direct customers, end customers, channels or estimates. Do not combine unlike figures.
- Review policy sensitivity. Identify which products and regions could be affected by export licenses, tariffs or changing technology restrictions.
- Check the product cycle. Look for upcoming accelerator, CPU, networking, process-node or equipment transitions and assess inventory exposure during the changeover.
- Test the capital-spending dependency. Ask whether the thesis requires continued hyperscaler data-center spending, foundry expansion or equipment orders. Then consider what happens if projects are delayed by power, financing or macroeconomic constraints.
- Compare valuation separately and currently. Price-to-earnings, price-to-sales, free-cash-flow yield and growth-adjusted measures change with market prices and estimates. Use the latest quote and consensus data at the time of purchase rather than relying on a fixed multiple.
- Set position limits. Even a compelling company can suffer a sharp drawdown from one customer decision or policy change. Size a single stock accordingly, or use an ETF if diversification is more important than maximum company-specific upside.
What could break an AI semiconductor thesis?
Customer capital pauses
AI demand forecasts assume customers can obtain enough electricity, data-center space, financing and networking capacity. A shortage in any one of these can move orders from one quarter to a later period or cancel a project.
Export-control changes
Rules can affect both sales and inventory. NVIDIA’s H20 charge and AMD’s MI308-related charges demonstrate that restrictions can create a financial cost even when the underlying technical demand remains strong. Treat every regional revenue assumption as conditional on the rules in force at the time.
Capacity and execution
TSMC must ramp advanced nodes and packaging while managing energy and geographic risks. ASML’s customers must approve and install expensive equipment. A bottleneck can limit industry growth, while an overbuild can leave suppliers with weaker utilization and pricing.
Product transitions
AI hardware evolves quickly. Customers may delay purchases before a new generation, and suppliers can accumulate inventory or purchase obligations if demand shifts faster than production plans.
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Concentration and credit
Large customers can negotiate aggressively, change suppliers or redesign systems. Broadcom specifically notes that leasing and deferred-payment structures can increase credit risk. Revenue growth should therefore be checked alongside receivables, inventory and cash collection.
How to monitor a position after buying
- Recalculate each company’s largest-customer exposure whenever a new annual or quarterly filing is published.
- Track Data Center growth separately from AMD’s Client and Gaming results rather than treating all revenue as AI-linked.
- Watch inventory, purchase obligations and export-control charges for evidence that product or policy transitions are disrupting demand.
- For TSMC and ASML, follow customer capital-expenditure plans, advanced-node utilization, equipment orders and capacity-ramp commentary.
- Review ETF holdings and weights after index rebalances; diversification can change without a fund changing its name.
- Compare valuation with updated earnings and cash-flow estimates, not with the price or multiple at which you originally bought.
Bottom line
Choose the stock that matches the risk you actually want. NVIDIA offers the clearest direct AI-accelerator exposure but reports substantial customer concentration and policy sensitivity. AMD provides a broader business mix while retaining meaningful Data Center and export-control risk. Broadcom targets custom AI silicon and networking with high end-customer concentration. TSMC offers diversified foundry exposure and advanced-node leverage, while ASML provides indirect exposure to the equipment bottleneck with a concentrated customer base. A semiconductor ETF is the simpler route when reducing single-stock risk matters more than selecting one layer of the AI supply chain.
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