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The most important comparison is not a headline “AI revenue percentage.” Company filings define AI-related sales differently, and some sales flow through indirect customers. Use segment revenue as a proxy, then examine concentration, supply-chain position, geographic exposure and valuation before buying.
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What you are buying when you buy an AI semiconductor stock
AI infrastructure is a supply chain rather than a single product. A data-center operator may buy accelerators and CPUs, custom application-specific chips, high-speed networking, advanced wafers and the lithography tools used to manufacture those wafers. Semiconductor stocks therefore provide different kinds of AI exposure.
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| Supply-chain layer | Representative company | How AI demand reaches the business | Main trade-off |
|---|---|---|---|
| Accelerators and CPUs | NVIDIA, AMD | Sell GPUs, AI accelerators and server CPUs directly or through system partners to cloud and enterprise customers. | High operating leverage and product-cycle risk; revenue can be concentrated in a few large buyers. |
| Custom silicon and networking | Broadcom | Provides custom AI accelerators, Ethernet switching and routing silicon, network interface chips, optical components, racks and systems. | Hyperscaler demand can be large but lumpy; a design win can create dependence on one customer. |
| Advanced foundry manufacturing | TSMC | Manufactures chips designed by many customers, including AI processors, at advanced process nodes. | Broader customer mix, but heavy exposure to fab capacity, energy, geopolitics and technology ramps. |
| Lithography equipment | ASML | Sells the equipment required to produce advanced chips, benefiting when manufacturers expand capacity. | Equipment orders are cyclical and the customer base is relatively small. |
This structure matters because a company can benefit from AI growth without selling an AI-branded processor. A foundry earns when customers place wafer orders; an equipment maker earns when those customers build or expand fabs. Conversely, an accelerator designer may have the clearest AI demand signal but also the greatest sensitivity to one product generation, one export rule or a few cloud customers.
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How much AI revenue does each stock really have?
Public filings do not provide a standardized AI-revenue measure. NVIDIA reports data-center activity but also sells data-center products used for non-AI workloads. AMD’s Data Center segment includes EPYC server CPUs as well as Instinct accelerators. Broadcom reports infrastructure products that serve AI and non-AI networking. TSMC and ASML manufacture or supply equipment for many end markets. Treating one company’s segment as equivalent to another company’s “AI revenue” would create a misleading comparison.
NVIDIA: the clearest direct AI exposure, with concentrated demand
NVIDIA is the most direct way in this group to obtain exposure to accelerator demand. Its data-center platform includes GPUs, networking, software and complete systems used to train and run AI models. That direct exposure also creates high expectations for continuous product launches, software support and customer spending.
NVIDIA’s fiscal-2026 filing reported that one direct customer represented 22% of total revenue and another represented 14%. The company estimates some indirect-customer sales, and certain indirect customers individually represented 10% or more. These percentages are not a complete map of end-user exposure: a cloud provider, system builder or distributor can sit between NVIDIA and the organization ultimately using the hardware.
The filing explicitly warns that revenue is concentrated among a limited number of direct and indirect customers and that this trend may continue. A pause in one hyperscaler’s data-center build-out can therefore affect quarterly results even if long-term AI demand remains strong.
Export controls are another demonstrated risk. NVIDIA recorded a $4.5 billion charge related to excess H20 inventory and purchase obligations after U.S. export-license requirements reduced demand in China. The episode shows how a policy change can create costs before a company can redirect inventory or redesign a product.
AMD: a broader mix, but not a concentration-free alternative
AMD’s 2025 net revenue was $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 exposure outside AI accelerators than a company whose growth is dominated by one data-center platform. Client CPUs, gaming products and server processors can cushion an AI-spending pause, although they bring their own PC and console cycles.
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AMD still has meaningful AI exposure through Instinct accelerators and server CPUs, and its filing says a small number of customers will continue to account for a substantial part of revenue and receivables. A diversified segment mix therefore reduces, but does not remove, customer-concentration risk.
Export restrictions also have a direct financial cost. AMD reported approximately $440 million of inventory and related charges associated with U.S. export controls on MI308 products. Investors should distinguish this one disclosed charge from a recurring estimate of lost sales; the filing does not establish a single ongoing percentage of revenue affected by the controls.
Broadcom: AI infrastructure beyond the GPU
Broadcom supplies custom AI accelerators, sometimes called XPUs, as well as Ethernet switching and routing silicon, network interface controllers, physical-layer chips, optical components, racks and systems. Its opportunity is tied to hyperscalers and AI-model companies that want customized compute or high-bandwidth networking alongside merchant accelerators.
Broadcom’s fiscal-2025 filing reported that one customer accounted for 32% of net revenue. Broadcom estimated that its top five end customers represented about 40% through all channels. The two figures use different definitions: the 32% figure is a reported customer share, while the approximately 40% figure is an end-customer estimate that includes indirect routes to market.
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The company warns that a major AI customer could reduce, delay or cancel orders. Leasing or deferred-payment arrangements can also increase credit risk if a customer’s expansion plan changes or its financing weakens. Broadcom’s concentration is therefore not only a sales issue; it can affect receivables, inventory commitments and working capital.
TSMC: diversified foundry exposure to AI and other markets
TSMC is the manufacturing layer. It fabricates chips designed by customers rather than selling a competing branded accelerator. In 2025, TSMC reported U.S.-dollar revenue growth of 35.9%, 74% of wafer revenue from 7-nanometer-and-more-advanced technologies, and manufacturing relationships with 534 customers.
The 534-customer base and multiple end markets provide more diversification than a single-product designer. TSMC says this breadth helps smooth demand fluctuations. AI still matters: advanced-node demand from accelerators, CPUs and networking can increase wafer volumes and support a richer technology mix.
TSMC’s model does not make it immune to risk. Capacity must be brought online and ramped successfully, advanced tools and power must be available, and customers must commit to volumes. Energy prices, geopolitical tensions, macroeconomic conditions and technology-ramp problems can all affect utilization and margins. Chairman and CEO C.C. Wei describes the company’s approach as adherence to the pure-play foundry model, which means TSMC does not compete by selling its own branded chips.
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ASML: the equipment bottleneck
ASML sells lithography equipment used by advanced-chip manufacturers. It benefits when foundries and integrated-device manufacturers invest in additional capacity or new process generations, including capacity needed for AI chips. This is a less direct form of AI exposure: the company sells manufacturing tools, not the processors consumed by AI services.
ASML’s 2025 filing reported that its largest customer represented 23.9% of net sales, up from 16.6% in 2024. The increase illustrates why equipment suppliers can remain concentrated even when the long-term market opportunity is broad. A small number of customers place very large, expensive orders, and those orders can move between periods as fabs adjust capital budgets.
Customer concentration: the numbers and what they mean
Concentration percentages are useful only when you identify what is being measured. A direct-customer percentage may understate dependence on an end customer that buys through a system maker. An estimated end-customer figure may be more economically meaningful but less precise.
| Company | Reported concentration | Definition and period | Investor interpretation |
|---|---|---|---|
| NVIDIA | 22% and 14% | Two direct customers’ shares of fiscal-2026 total revenue; some indirect customers were estimated at 10% or more individually. | Two direct accounts are large, and indirect exposure may be higher than the direct-customer table suggests. |
| AMD | Not stated as a single percentage in the cited filing | 2025 filing says a small number of customers will continue to account for a substantial part of revenue and receivables. | Do not infer low concentration from AMD’s broader segment mix. |
| Broadcom | 32%; approximately 40% | One customer was 32% of fiscal-2025 net revenue; top five end customers were estimated at about 40% through all channels. | Custom-chip and networking wins can create substantial dependence on a few hyperscalers. |
| TSMC | 534 customers | Customers manufacturing with TSMC in 2025; no single comparable revenue percentage is provided here. | Customer breadth is a diversification benefit, but capacity and regional risks remain. |
| ASML | 23.9% | Largest customer’s share of 2025 net sales, versus 16.6% in 2024. | Equipment demand is concentrated among a small number of very large buyers. |
Concentration is not automatically bad. A large customer can provide predictable volume, engineering collaboration and efficient scale. The risk rises when the customer can delay orders, negotiate pricing, develop an internal alternative or influence the supplier’s inventory commitments.
Is AMD less risky than NVIDIA?
AMD is less concentrated in business mix because Client and Gaming contributed $14.6 billion of 2025 revenue alongside $16.6 billion from Data Center. That does not establish that AMD’s stock is less risky overall. AMD still depends on a limited set of customers, competes in fast product cycles and faces export-control exposure, including the approximately $440 million of MI308-related inventory and other charges.
NVIDIA has a more concentrated AI growth engine and disclosed two direct customers at 22% and 14% of fiscal-2026 revenue, plus material estimated indirect exposure. Its $4.5 billion H20 charge demonstrates the potential size of a policy shock. NVIDIA may offer greater operating momentum when accelerator demand is strong, while AMD’s mix may provide more non-AI offsets during a pause. The relative stock risk depends on price paid, execution, competitive progress, customer commitments and the investor’s tolerance for volatility; segment mix alone cannot settle the question.
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The key risks that can interrupt AI semiconductor growth
Export controls and geopolitics
Export rules can remove an addressable market, require a product redesign or leave inventory that cannot be shipped. NVIDIA’s H20 charge and AMD’s MI308-related charges are concrete examples. Rules can also affect equipment sales and the location of new fabs. Check the latest U.S. and other jurisdictional requirements before relying on a company’s geographic growth assumptions; the figures above describe disclosed historical effects, not a forecast of future policy.
Customer capital spending
AI infrastructure requires enormous data-center, power and networking investments. NVIDIA identifies data-center, energy and capital availability as crucial to customers’ AI build-outs. Broadcom warns that AI customers may reduce, delay or cancel orders. A customer can remain enthusiastic about AI while slowing the timing of a particular data-center project, creating a sharp quarterly fluctuation.
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Accelerators cannot generate revenue if customers cannot obtain electricity, buildings, cooling, networking equipment or financing. Foundries face the parallel challenge of securing clean-room capacity, advanced tools, skilled workers and reliable energy. Capacity additions can arrive too late, too early or at lower yields than planned.
Product transitions and execution
AI hardware changes quickly. A new accelerator, CPU, interconnect or process node can shift demand between generations. Suppliers must manage qualification, software compatibility, packaging, memory availability and inventory while customers migrate systems. Delays can create an abrupt revenue gap; an early ramp can produce excess inventory if the customer’s deployment schedule slips.
Manufacturing and supply-chain constraints
Design leaders rely on foundries, advanced packaging, memory, substrates, networking components and equipment suppliers. A bottleneck at any layer can limit shipments even when orders are strong. TSMC’s advanced-node mix and ASML’s equipment position show why manufacturing capacity is strategically important, but they also expose those companies to ramp, energy and geopolitical risks.
Gross-margin and capital-intensity sensitivity
High-performance chips and equipment require substantial research, development and manufacturing commitments. Gross margin can change when a company introduces a new product, uses expedited logistics, writes down inventory or gives concessions to a large customer. Foundries and equipment makers must spend heavily before capacity generates revenue. Review cash-flow statements, capital-expenditure plans, inventory and purchase obligations rather than relying on revenue growth alone.
Valuation and expectations
A strong business can still be a poor investment if the share price already assumes years of exceptional growth. The cited company filings support operating and concentration analysis, but they do not provide current share prices or valuation multiples. Before buying, check the latest price, forward and trailing earnings multiples, free-cash-flow yield, revenue-growth assumptions and the sensitivity of the valuation to a slower AI build-out. Use the same date and methodology across companies; do not compare a current multiple for one stock with an outdated figure for another.
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How to choose among these stocks
- Choose the exposure you actually want. Select accelerator designers for direct AI-compute sensitivity, a custom-silicon and networking supplier for hyperscaler infrastructure, a foundry for diversified manufacturing exposure, or an equipment maker for fab-investment exposure.
- Separate reported facts from proxies. Record each company’s relevant segment revenue, then label it as AI-specific, AI-adjacent or broadly diversified. Do not create a single cross-company AI percentage from incompatible definitions.
- Map customer dependence. Note whether each percentage refers to a direct customer, an estimated indirect customer or an end customer across channels. Read receivables, purchase obligations and inventory disclosures for evidence of financial exposure beyond the revenue table.
- Stress-test policy and geography. Ask what happens if a major market becomes restricted, a product requires a new license or a factory ramp is delayed. NVIDIA’s H20 and AMD’s MI308 disclosures show why this is a financial question, not only a compliance question.
- Check execution indicators. Follow product launch timing, customer qualification, advanced-node yields, packaging availability, inventory days, gross margin and operating cash flow. Compare management’s commitments with subsequent filings.
- Apply a valuation discipline. Build a base case, a slower-growth case and a policy-shock case using current prices and estimates. If the stock works only under the most optimistic case, position size should reflect that fragility.
- Set a portfolio limit. Decide in advance how much single-company and single-theme exposure you can withstand. AI demand can be structurally strong while individual stocks experience large drawdowns.
Should you buy a semiconductor ETF instead of one stock?
A semiconductor ETF can spread exposure across chip designers, foundries, equipment makers and other suppliers. An investing overview identifies NVIDIA, AMD and Broadcom as examples of holdings in broad AI-infrastructure-oriented semiconductor funds. The diversification can reduce the damage from one product failure, customer loss or export restriction, but it does not remove sector-wide risks such as a data-center spending slowdown, a semiconductor recession or tighter trade rules.
ETF selection still requires analysis. Check the fund’s current holdings and weights, expense ratio, trading liquidity, index methodology, rebalancing schedule, geographic exposure, tracking difference and tax treatment. Holdings change, so an old fact sheet is not evidence of today’s allocation. A concentrated “semiconductor” ETF may behave much like a basket of a few large stocks, while an equal-weight or broader fund may provide more balanced exposure.
Use a single stock when you have a specific thesis, can follow the company’s filings and accept company-specific volatility. Use an ETF when your thesis is about the semiconductor and AI-infrastructure ecosystem but you cannot reliably identify which supplier will capture the profits. Some investors combine a diversified core fund with a smaller position in a company whose risks they understand; the appropriate mix depends on the rest of the portfolio, time horizon and tolerance for loss.
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- Revenue by segment: Is growth coming from the AI-adjacent area you intended to own, or from a different business?
- Customer disclosures: Are concentration percentages rising, and are direct-customer figures masking larger end-customer dependence?
- Inventory and purchase commitments: Are products accumulating because of a delayed deployment, transition or export rule?
- Gross margin and cash flow: Are expedited supply, pricing concessions or capacity investments absorbing the revenue gains?
- Capital spending by customers and suppliers: Are hyperscalers, foundries and equipment buyers still funding the capacity implied by the valuation?
- Policy changes: Could a new license requirement alter the addressable market or strand inventory?
- Valuation: Has the share price moved faster than realistic earnings and cash-flow growth?
Reassess the original thesis when the exposure changes, not merely when the stock price falls. A lower price does not repair a lost customer, an obsolete product or a permanently restricted market.
Bottom line for an AI semiconductor allocation
NVIDIA offers the most direct accelerator exposure but disclosed substantial direct and indirect customer concentration and a $4.5 billion H20-related charge. AMD has a more balanced revenue mix, with $16.6 billion of 2025 Data Center revenue and $14.6 billion from Client and Gaming, yet it remains exposed to concentrated customers, product cycles and export controls. Broadcom reaches AI through custom silicon and networking, with one customer at 32% of fiscal-2025 revenue and top-five end customers at about 40%. TSMC provides broader foundry exposure across 534 customers and 74% advanced-node wafer revenue, while ASML supplies the equipment bottleneck but had one customer at 23.9% of 2025 sales.
The prudent choice is the one whose layer, concentration and downside scenarios fit your portfolio. If you cannot distinguish those risks or monitor current valuations and holdings, a diversified semiconductor ETF may be a more robust starting point than betting on one company.
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