Compare AI-chip stocks as different supply-chain businesses, not as interchangeable gauges of AI demand. Start by identifying each company’s role, then match the period and definition of its reported revenue, test how dependent sales are on a small group of customers, and trace wafer, packaging, testing and geographic risks. NVIDIA and AMD sell computing products, Broadcom combines custom accelerators and AI networking with software, and TSMC manufactures chips designed by other companies.
Issuer-reported growth is historical operating data. It does not establish which stock is cheap, which company will keep growing, or what a share price should be worth.
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What an AI-chip stock comparison should measure
The phrase “AI chip” covers several businesses. A designer may sell GPUs, CPUs or custom accelerators; a networking supplier may count AI interconnect products inside a semiconductor segment; a foundry may manufacture many customers’ designs while reporting revenue by technology and end market. Comparing their total revenue as though it represented the same exposure produces a misleading ranking.
A useful comparison answers four separate questions:
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- What does the company sell? Identify its position in the chain and the products included in any AI category.
- How much revenue is disclosed? Record the fiscal period, currency, accounting scope and year-over-year change exactly as the issuer reports them.
- Who pays for that revenue? Look for major-customer percentages, customer categories, commitments, prepayments and evidence that buyers can finance and power new data centers.
- Can the company make and deliver the product? Trace foundries, advanced packaging, assembly, testing, capacity allocation, yields, supplier pricing and geographic concentration.
These dimensions should remain separate. A strong demand signal can coexist with a constrained supplier, and rapid revenue growth can coexist with valuation risk or an approaching product transition.
Begin with business role and disclosure scope
| Company | Supply-chain role | Disclosed evidence to anchor the comparison |
|---|---|---|
| NVIDIA | Accelerated-computing and networking platforms | Data-center revenue grew 142% year over year in fiscal 2025. Fiscal 2026 results were driven by data-center compute and networking. |
| AMD | CPU, GPU and other chip designer using external foundries and third-party assembly, packaging and testing | The fiscal 2026 second-quarter Form 10-Q discusses AI and broader computing products but does not state a comparable single AI-revenue figure in the cited material. |
| Broadcom | Semiconductor and infrastructure-software supplier; custom AI accelerators and AI networking are part of its semiconductor activity | AI semiconductor revenue was $16.7 billion in fiscal 2026 third quarter, up 221% year over year. |
| TSMC | Foundry and advanced-packaging manufacturer for customers’ designs | 2025 revenue increased 35.9% year over year in U.S.-dollar terms; 3 nm technology represented 24% of total wafer revenue in 2025. |
NVIDIA: a platform supplier with a broad data-center measure
NVIDIA’s fiscal 2025 Form 10-K reported 142% year-over-year growth in data-center revenue. “Data center” is a company reporting category, not a pure accelerator tally: it includes computing and networking platforms sold into data-center infrastructure. The fiscal 2026 Form 10-K describes results driven by data-center compute and networking but does not turn that entire category into a universally comparable AI percentage.
For analysis, pair the growth rate with the category definition, the fiscal year and the factors that could delay customer deployments. NVIDIA identifies customers’ access to data-center capacity, energy and capital as prerequisites for infrastructure plans. It also discusses production scaling, product transitions and export-licensing requirements.
AMD: product exposure must be reconstructed from segments and filings
AMD designs CPUs, GPUs and related chips but relies on outside manufacturing partners. The cited fiscal 2026 second-quarter filing does not provide a single AI-revenue number that can be placed beside NVIDIA’s data-center growth or Broadcom’s AI-semiconductor figure. Use AMD’s segment results, product commentary and customer disclosures without inventing an AI percentage.
The absence of a directly comparable figure is itself an analytical limitation. It means an investor should track reported data-center, embedded, gaming and client results separately, then identify which products and customers are associated with AI workloads.
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Broadcom: keep AI semiconductors separate from software
Broadcom reported $16.7 billion of AI semiconductor revenue in fiscal 2026 third quarter, an issuer-defined measure that rose 221% from the same quarter a year earlier. The figure covers custom AI accelerators and AI networking; Broadcom also owns a substantial infrastructure-software business. Do not divide the AI-semiconductor number by a combined semiconductor-and-software total and call the result an AI share unless the denominator is defined consistently.
Chief executive Hock Tan said, “Demand for our custom AI accelerators and networking continues to be very strong.” That is management commentary, not independent confirmation that demand will persist. Broadcom’s filings also highlight significant-customer reliance, outsourced manufacturing and a financing arrangement connected with infrastructure deployments.
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TSMC: a manufacturer whose revenue spans many end markets
TSMC’s 2025 annual report said revenue rose 35.9% year over year in U.S.-dollar terms, and that 3 nm technology generated 24% of total wafer revenue. Neither figure is an AI-revenue measure. TSMC serves high-performance computing, smartphones, automotive and Internet-of-Things applications, among others.
TSMC can benefit when AI customers order leading-edge wafers and advanced packaging, but its total revenue should not be compared directly with a chip designer’s AI-product revenue. The relevant questions are technology mix, high-performance-computing exposure, fab utilization, packaging capacity, capital requirements and customer concentration.
Normalize revenue before comparing growth
Write every growth number with its period and definition attached. The following figures are not a league table because they cover different companies, periods and scopes.
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| Issuer-reported measure | Period | Change | What it includes |
|---|---|---|---|
| NVIDIA data-center revenue | Fiscal 2025 versus fiscal 2024 | Up 142% year over year | Company data-center category, including accelerated computing and networking platforms |
| Broadcom AI semiconductor revenue | Fiscal 2026 third quarter versus the year-earlier quarter | $16.7 billion; up 221% year over year | Custom AI accelerators and AI networking as defined by Broadcom |
| TSMC total revenue | Calendar 2025 versus 2024, U.S.-dollar terms | Up 35.9% year over year | Foundry revenue across multiple end markets and technologies |
| TSMC 3 nm wafer revenue | Calendar 2025 | 24% of total wafer revenue | Wafer revenue from the 3 nm technology family; not an AI-only measure |
Before calculating a trend, check whether the comparison is fiscal-year to fiscal-year, quarter to quarter or a full-year mix. A large percentage can reflect a small prior-year base, a production ramp, temporary supply availability or customer deployment timing. Growth does not reveal gross margin, free cash flow, inventory, capital spending or valuation.
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- Record total company revenue for the same stated fiscal period.
- Record the issuer’s expressly named AI, data-center, high-performance-computing or semiconductor category, if one exists.
- Copy the issuer’s definition into your notes; do not merge networking, software or foundry revenue without a reason.
- Calculate any share only when numerator and denominator use the same period and accounting scope.
- Flag categories that are not disclosed rather than estimating them from market-share articles or chip popularity.
Measure customer exposure from filings
Customer risk is not the same as product popularity. A chip may be widely discussed while sales remain concentrated among a few cloud operators, system builders or AI laboratories. Conversely, a supplier can have broad end-market exposure but depend on a small number of customers for its newest process technology.
Look for four types of evidence:
- Concentration percentages: annual or quarterly filings may state that one customer or a group of customers represents a material share of revenue or accounts receivable.
- Customer categories: disclosures may identify leading cloud providers, hyperscalers, frontier AI laboratories, original-equipment manufacturers or distributors without naming each buyer.
- Commitments and prepayments: purchase obligations, capacity reservations, deposits and backlog show what has been ordered, but they are not the same as delivered revenue.
- Customer financing capacity: data-center construction depends on capital, electricity and completed facilities. A customer’s inability to finance or power a project can delay a supplier’s expected sales.
| Company | Customer information to examine | What it means for comparison |
|---|---|---|
| NVIDIA | Data-center buyers and infrastructure partners; filing discussion of customer capital, energy and data-center availability | Deployment can slip even when interest in the architecture is strong. Check concentration disclosures in the latest filing. |
| AMD | Customer and supplier disclosures in the fiscal 2026 second-quarter Form 10-Q | The cited material does not establish a comparable customer-percentage table; use the latest filing for concentration and commitments. |
| Broadcom | Leading frontier AI laboratories and other significant customers; infrastructure-financing arrangement and related commitments | Customer funding and lease-related obligations are important timing questions, not proof of guaranteed demand. |
| TSMC | Broad set of customer end markets, including high-performance computing, smartphones, automotive and IoT | Diverse end markets can cushion a single AI cycle, but the latest filing is needed to quantify individual-customer concentration. |
Do not infer a customer relationship from a product photograph, a cloud-service announcement or a headline about a model-training project. A filing-based comparison should state when a company does not publish a comparable percentage.
Trace manufacturing and delivery risk
Manufacturing exposure extends well beyond access to silicon wafers. Advanced packaging, assembly, testing, substrate supply, process yields, equipment availability, supplier pricing, delivery schedules and geographic concentration can all determine whether demand becomes recognized revenue.
| Company | Key production dependencies | Questions to ask |
|---|---|---|
| AMD | TSMC supplies all AMD microprocessor and GPU wafers at 7 nm and smaller; third parties handle assembly, packaging and testing. | Can TSMC and packaging partners provide enough capacity? How are wafers allocated among customers? Can AMD pass supplier-cost increases through? |
| NVIDIA | Complex accelerated-computing and networking platforms require leading-edge manufacturing, advanced packaging and production ramps. | Will a new architecture transition smoothly? Could ramp problems, infrastructure constraints or export licenses delay shipments? |
| Broadcom | Outsourced manufacturing for custom accelerators and networking products, with significant-customer reliance. | Are foundry, packaging and substrate commitments aligned with customer schedules? Could a customer delay or redesign a program? |
| TSMC | Owns fabs and advanced-packaging operations serving several technologies and end markets; expansion includes facilities in multiple geographies. | What utilization, capital spending and geographic concentration risks accompany leading-edge and packaging capacity? |
AMD’s explicit foundry and allocation risk
AMD’s fiscal 2026 second-quarter Form 10-Q states that it relies on TSMC for all microprocessor and GPU wafers manufactured at 7 nm or smaller. AMD uses third parties for assembly, packaging and testing, so a bottleneck after wafer fabrication can still constrain shipments. The filing warns that limited supplier capacity may force allocation among customers and that supplier price changes can pressure margins.
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This creates a two-sided test: strong orders do not guarantee sales if wafers or packages are unavailable, and securing capacity may require commitments before end demand is certain.
Advanced packaging is a separate bottleneck
AI accelerators often combine large dies with high-bandwidth memory and sophisticated substrates. Even when a company has wafer capacity, packaging and testing throughput may limit finished systems. Ask whether the filing names specific packaging partners, reservations, qualification requirements or yield risks. Treat “fab capacity” and “finished-product capacity” as different measurements.
Foundry diversification does not eliminate concentration
A designer can use more than one supplier while remaining dependent on one process node, package, substrate or region. Conversely, a foundry’s geographic expansion can reduce location risk without changing customer concentration. Review where each critical step occurs and whether the company can substitute a supplier without redesigning the product.
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Customer infrastructure constraints
NVIDIA identifies data-center availability, energy and customer access to capital as prerequisites for infrastructure plans. These constraints can shift revenue between quarters or cancel planned deployments. Broadcom’s financing arrangement for infrastructure deployments makes the same issue visible from another angle: the ability to fund a build-out matters alongside technical demand.
Architecture transitions and inventory
New architectures can create a powerful upgrade cycle, but the transition can also leave older products in inventory, require software and system requalification, or expose production-ramp problems. NVIDIA’s filings discuss product transitions and scaling risks. Examine channel inventory, customer acceptance timing and the length of overlap between generations.
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Export controls and licensing
NVIDIA’s fiscal 2025 filing documented the effect of export-licensing requirements. Policy changes can restrict a product, require a modified version, strand inventory or shift sales toward another region. Model this as a potential revenue and inventory event, not merely as a footnote to demand.
Shared risks can travel through the chain
A chip designer and its foundry may both be exposed to the same earthquake risk, power shortage, packaging constraint or trade rule. Diversification at one company does not automatically diversify the supply chain. Map dependencies at the component, process, facility and country level.
A filing-based workflow you can repeat
- Define the date and horizon. Choose the latest annual or quarterly filings available and state whether you are analyzing the next quarter, one year or a multiyear cycle.
- Classify the business role. Mark the company as a product designer, custom-silicon and networking supplier, foundry, or a combination. This determines which revenue denominator is meaningful.
- Build the revenue sheet. Enter total revenue, disclosed AI-related revenue or segment revenue, period, currency and year-over-year change. Copy the issuer’s wording.
- Test comparability. Separate fiscal years from calendar years, quarters from full years, semiconductor revenue from software revenue, and foundry revenue from product revenue.
- Extract customer evidence. Record concentration percentages, named categories, backlog, purchase obligations, prepayments and any discussion of customer financing or infrastructure constraints.
- Draw the manufacturing map. List wafer foundries, process nodes, advanced-packaging and test providers, substrates, major facilities and regions. Note single-source dependencies.
- List conversion risks. Add production ramps, yields, allocation, pricing, product transitions, export licenses, power and capital constraints.
- Stress-test timing. Ask what happens if a customer delays a build, a package is unavailable, a new product slips by a quarter, or a trade rule blocks a shipment.
- Only then examine valuation. Use current share price, diluted share count, earnings and cash-flow forecasts, balance-sheet commitments and an explicit investment horizon. Operating growth alone cannot answer the valuation question.
How to interpret the four companies together
NVIDIA offers the clearest disclosed data-center growth figure in this set, but its category is broader than an AI-accelerator-only measure and remains exposed to customer infrastructure, production and policy constraints.
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Broadcom provides a large, explicitly labeled AI-semiconductor figure, but the number must be separated from software revenue and read alongside significant-customer, outsourced-manufacturing and infrastructure-financing risks.
TSMC is the manufacturing exposure. Its reported 2025 growth and 3 nm mix show technology momentum, not a pure measure of AI demand. High-performance computing is one end market among several, so utilization, capital intensity, packaging and geographic plans matter as much as AI headlines.
Common mistakes that distort an AI-chip comparison
- Ranking percentages without matching periods. A fiscal-year figure and a quarterly figure are not directly comparable.
- Treating issuer categories as standardized. “Data center,” “AI semiconductor” and “3 nm wafer revenue” describe different scopes.
- Using total foundry revenue as AI revenue. TSMC’s sales include smartphones, automotive, IoT and other applications.
- Assuming demand equals shipments. Wafers, packages, substrates, testing and customer facilities can delay delivery.
- Ignoring customer funding. Data-center projects require capital, electricity and completed buildings.
- Counting management optimism as a forecast. Strong-demand comments are statements of expectation, not independent confirmation.
- Turning growth into a buy signal. Stock performance also depends on valuation, dilution, margins, cash flow, balance-sheet commitments and the chosen time horizon.
- Assuming a second supplier removes all risk. A company may still depend on one process node, package, substrate or region.
Keep the comparison current
Revenue definitions, customer commitments, product roadmaps, capacity plans and trade rules change quickly. Update the worksheet after every annual and quarterly filing, and date each figure. When a company changes a segment definition, preserve the prior definition beside the new one rather than creating a false historical trend.
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For a disciplined conclusion, describe the exposure in plain language: whose revenue is directly disclosed, whose customers are concentrated, which manufacturing step can constrain delivery, and which risks are shared across the chain. That produces a more useful comparison than labeling one company “the AI winner” from a single growth rate.
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