AI-chip stocks are not interchangeable bets on one market. NVIDIA and AMD design and sell computing products, Broadcom combines custom AI accelerators and networking with a wider semiconductor and infrastructure-software business, and TSMC manufactures chips designed by other companies. Compare them by business role, issuer-defined revenue, customer dependence, and the ability to secure wafers and advanced packaging—not by a single growth percentage.
The figures below come from company filings and releases available through October 13, 2026. They describe historical operating results, not forecasts of stock returns or recommendations to buy or sell.
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Start with the business role, not the ticker
An AI accelerator designer, a networking supplier, and a contract foundry can all benefit from the same data-center buildout while carrying very different risks. A designer may have strong orders but be unable to obtain enough wafers or advanced packaging. A foundry may report record growth while serving smartphones, automobiles, Internet-of-Things devices, and high-performance computing in addition to AI. A diversified semiconductor company may label only part of its sales as “AI semiconductor” revenue while reporting software separately.
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- Compute and platform designers: NVIDIA and AMD sell processors, accelerators, systems, software and related platforms. Their results depend on product launches, customer deployments, supply availability and pricing.
- Custom silicon and connectivity supplier: Broadcom reports custom AI accelerators and AI networking inside a broader semiconductor and infrastructure-software portfolio.
- Foundry and packaging manufacturer: TSMC manufactures wafers and provides advanced technologies and packaging for customers whose products may target several end markets.
- Integrated design-and-manufacturing case: Intel can be studied separately, but the evidence summarized here does not establish comparable current AI-revenue or customer metrics for a numerical row alongside the other companies.
This classification prevents a common error: treating a foundry’s total revenue as directly comparable with a designer’s AI-product revenue.
What the disclosed figures actually measure
| Company | Business role | Issuer-reported measure | Period and change | What the measure includes or omits |
|---|---|---|---|---|
| NVIDIA | Accelerated-computing and networking platforms | Data-center revenue growth | Fiscal 2025: up 142% year over year | Data-center revenue as defined in NVIDIA’s fiscal 2025 Form 10-K; a dollar amount and a separately defined “AI-only” total are not stated here. |
| NVIDIA | Accelerated-computing and networking platforms | Data-center compute and networking drove fiscal 2026 results | Fiscal 2026 Form 10-K discussion; no comparable percentage is stated here | Company-level results include more than one product and platform category. |
| AMD | CPU, GPU and other chip designer | AI-specific revenue figure | Not stated in the reviewed evidence | AMD’s filing provides important manufacturing and supplier disclosures, but no directly comparable AI-revenue number is established here. |
| Broadcom | Custom accelerators, AI networking, semiconductors and infrastructure software | AI semiconductor revenue | Fiscal 2026 Q3: $16.7 billion, up 221% year over year | AI semiconductor revenue in the September 2, 2026 earnings release; it is not total Broadcom revenue and excludes the company’s separately reported software activity. |
| TSMC | Foundry and advanced-packaging manufacturer | Total company revenue and wafer mix | 2025 revenue rose 35.9% year over year in U.S.-dollar terms; 3 nm was 24% of total wafer revenue | Revenue spans smartphone, high-performance computing, automotive, IoT and other end markets. The 3 nm percentage is a technology mix, not an AI-revenue percentage. |
| Intel | Potential integrated design-and-manufacturing comparison | Comparable current AI revenue and customer metrics | Not established in the reviewed evidence | Use Intel’s latest annual and quarterly filings before making a numerical comparison. |
These numbers are not a league table. NVIDIA’s 142% figure is a fiscal-2025 year-over-year change in a data-center category. Broadcom’s $16.7 billion and 221% figure is fiscal 2026 third-quarter AI-semiconductor revenue under Broadcom’s definition. TSMC’s 35.9% figure is total-company revenue growth for calendar 2025, while its 24% figure is 3 nm wafer mix. Different periods, denominators and category boundaries make direct ranking misleading.
How to read each company’s revenue disclosure
NVIDIA: a large data-center category, plus execution and policy exposure
NVIDIA’s fiscal 2025 Form 10-K reported data-center revenue growth of 142% year over year. The category reflects NVIDIA’s data-center business as the issuer defines it; it should not be relabeled as a pure measure of AI accelerators. NVIDIA’s fiscal 2026 filing says results were driven by data-center compute and networking.
For analysis, separate three questions:
- How much of the reported data-center category comes from compute, networking, systems and related platform products?
- Is growth being supplied on schedule, or are customers waiting for new products and systems to ramp?
- Can customers obtain the data-center capacity, electricity and capital required to deploy what they have ordered?
NVIDIA identifies customer infrastructure constraints, production scaling, product transitions and export licensing as risks. A strong reported quarter can therefore coexist with delayed deployments, inventory timing or restrictions on particular markets.
AMD: examine the product mix and the supply chain behind it
AMD designs CPUs, GPUs and other chips but uses external manufacturing partners. The reviewed fiscal 2026 second-quarter Form 10-Q does not establish an AI-revenue figure comparable with NVIDIA’s data-center percentage or Broadcom’s AI-semiconductor amount. Avoid estimating AMD’s AI exposure from third-party market-share claims when AMD has not disclosed a matching category.
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Instead, read AMD’s segment and product disclosures alongside its manufacturing notes. Determine which sales are data-center products, which are broader client or embedded products, and whether the period includes a major product launch or transition. Then connect that mix to wafer and packaging availability.
Broadcom: keep AI semiconductors separate from software
Broadcom reported $16.7 billion of fiscal 2026 third-quarter AI semiconductor revenue, up 221% year over year, in its September 2, 2026 earnings release. The measure covers custom AI accelerators and AI networking within Broadcom’s semiconductor business. Broadcom also operates an infrastructure-software business, so the AI-semiconductor number must not be treated as total company revenue or total operating profit.
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TSMC: foundry growth is not the same as AI growth
TSMC’s 2025 Annual Report said revenue rose 35.9% year over year in U.S.-dollar terms and that 3 nm technology represented 24% of total wafer revenue in 2025. Those are useful indicators of scale and technology adoption, but neither is an AI-revenue measure. TSMC describes demand across smartphone, high-performance computing, automotive and IoT applications as well as AI.
When using TSMC as an AI-exposure comparison, look for the share of high-performance-computing or AI-related demand within its broader mix, utilization and pricing, advanced-packaging capacity, and the capital required for fabs and geographic expansion. A foundry can benefit from AI while another end market weakens, or it can report broad growth without revealing how much comes from any one customer or application.
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Compare customers from filings, not headlines
Chip popularity does not prove customer concentration. A useful customer analysis records what each filing actually discloses:
- Percentage of revenue from a major customer, if reported.
- Named customer categories, such as leading cloud providers, frontier AI laboratories, system companies or distributors.
- Backlog, purchase commitments, prepayments and cancellation terms.
- Whether a small number of buyers can finance data-center construction and keep projects on schedule.
- Exposure to a customer’s own power, permitting, networking and capital constraints.
The reviewed evidence does not provide a consistent customer-concentration percentage for every company, so a blank or non-comparable disclosure should remain visible rather than being replaced with an estimate.
Broadcom’s customer-funding questions
Broadcom’s fiscal 2026 filing discusses demand from leading frontier AI laboratories and describes a financing arrangement intended to support infrastructure deployments. That makes customer funding, lease-related commitments and the timing of infrastructure construction important diligence questions. The arrangement is evidence of a financing structure, not proof that every planned deployment will occur or that revenue is guaranteed.
NVIDIA and the ability to build infrastructure
NVIDIA specifically identifies access to data-center capacity, energy and capital as prerequisites for customers’ infrastructure plans. A customer may want accelerators but still defer acceptance if a facility lacks power, financing or usable space. Track customer deployment schedules and infrastructure constraints rather than assuming an announced order becomes immediate recognized revenue.
AMD, TSMC and indirect customer exposure
AMD’s customers can be exposed to the same cloud and AI-infrastructure budgets that affect NVIDIA’s customers, while TSMC’s customer base spans several end markets. A customer category in a filing is not equivalent to a named buyer, and a broad foundry customer base can reduce dependence on one application without eliminating concentration in a particular process node or packaging technology.
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Manufacturing risk extends beyond wafer supply
“Who makes the chips?” is only the first manufacturing question. Compare the complete production chain:
- Which foundries produce leading-node wafers?
- Who performs assembly, testing and advanced packaging?
- How much capacity is available at the required node and package?
- Are yields and production ramps meeting expectations?
- Can the company obtain substrates, memory, networking components and other constrained inputs?
- Can supplier price increases be passed through to customers?
- How concentrated are facilities geographically, and how quickly can capacity be shifted?
AMD’s explicit foundry and packaging dependence
AMD’s fiscal 2026 second-quarter Form 10-Q says it relies on TSMC for all microprocessor and GPU wafers at 7 nanometers and smaller. AMD also uses third parties for assembly and packaging. The filing warns that constrained supplier capacity and allocation decisions can limit supply, force allocation among customers and affect sales even when demand is strong.
This creates several distinct risks. A wafer may be available while advanced packaging is constrained. A supplier may allocate output to another customer or product. A yield problem at a leading node can delay a launch. Higher supplier pricing can reduce margins if contracts do not allow AMD to pass costs through. Analyze each link instead of treating “TSMC dependence” as one undifferentiated risk.
TSMC’s capacity, packaging and geographic decisions
For TSMC, the relevant questions are utilization, process-node ramps, advanced-packaging availability, capital intensity and geographic footprint. Expanding fabs or packaging facilities can improve resilience over time but requires substantial capital and may initially raise costs or create execution risk. A customer’s supply security can improve only when the required wafer and package capacity come online with acceptable yields.
Outsourced production at Broadcom and platform supply
Broadcom identifies outsourced manufacturing among its reported risks. Its custom accelerators and networking products may depend on coordinated supply across wafers, packaging, substrates and other components. A customer commitment is more valuable when the company can show capacity access and delivery timing, not merely strong end-market interest.
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Infrastructure bottlenecks
AI deployments require data-center buildings, electricity, cooling, networking, financing and operational capacity. NVIDIA’s filings identify these prerequisites directly. If any one is delayed, chip shipments may move to a later quarter even when long-term demand remains intact.
Product transitions and inventory
New architectures can create a temporary surge, but transitions can also cause customers to pause purchases, manufacturers to rebalance inventory or older products to lose demand. NVIDIA’s fiscal 2025 filing documented the effects of product transitions. Compare launch timing with reported revenue and inventory rather than assuming a growth rate is a durable run rate.
Export licensing and trade policy
NVIDIA’s fiscal 2025 and fiscal 2026 filings discuss export licensing and related policy exposure. Restrictions can limit which products may be sold into a market, require redesigns, strand inventory or alter customer road maps. Policy risk is different from ordinary demand risk and should be tracked by product and geography.
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Shared risks across suppliers
A chip designer and its foundry can be exposed to the same bottleneck. If advanced packaging is scarce, both the designer’s shipments and the foundry’s ability to convert wafer demand into revenue may be affected. Diversification at one layer does not remove a shared constraint at another layer.
A repeatable comparison workflow
- Set the period. Write the fiscal or calendar period beside every number. Do not put NVIDIA fiscal 2025, Broadcom fiscal 2026 Q3 and TSMC calendar 2025 into one unlabeled ranking.
- Define the denominator. Mark each figure as total-company revenue, a segment, a product category, AI semiconductor revenue or wafer-technology mix.
- Record the business role. Note whether the company designs products, supplies custom silicon and networking, manufactures wafers, provides packaging, or combines several roles.
- Capture the issuer’s exact scope. List what the category includes and what it excludes, especially software, networking, systems and non-AI end markets.
- Read customer disclosures. Record major-customer percentages, categories, commitments, prepayments and any statement about customer financing. Enter “not stated” when a comparable percentage is absent.
- Map production dependencies. Identify leading-edge foundries, assembly and test providers, advanced-packaging sources, substrates and geographic concentration.
- Check conversion risks. Look for customer power and capital constraints, production ramps, yield issues, product transitions, export licenses and inventory exposure.
- Separate operating growth from valuation. A growth rate cannot show whether a stock is attractively priced. A valuation analysis requires current share price, share count, earnings and cash-flow forecasts, balance-sheet commitments and a defined investment horizon.
- Set an update date. Revenue categories, customer commitments, capacity plans and trade rules change quickly. Recheck the latest annual and quarterly filings before relying on an old comparison.
How to interpret growth without overclaiming
Large year-over-year growth can reflect a low comparison base, a product ramp, improved supply, customer deployment timing or a change in category mix. It does not by itself establish profitability, durability or valuation. Ask whether growth is accompanied by sustainable gross margin, operating cash flow, manageable capital commitments and capacity that can support the next period.
Also distinguish demand from recognized revenue. A customer may announce a data-center project, sign a commitment or seek financing before the company ships products. Conversely, a supplier may recognize revenue during an initial build before the customer’s utilization reaches a steady level. The timing difference matters when comparing designers, networking suppliers and foundries.
Questions to put beside each stock
| Company | Revenue question | Customer question | Manufacturing question |
|---|---|---|---|
| NVIDIA | How much of data-center growth comes from compute, networking and systems under the company’s definition? | Can customers secure capital, power and data-center capacity to deploy ordered systems? | Are production scaling, product transitions and export rules limiting shipments or raising inventory risk? |
| AMD | What portion of reported segment revenue is tied to data-center CPUs, GPUs and accelerators, and is an AI-specific figure disclosed? | How concentrated are large infrastructure buyers, and how firm are their commitments? | Can TSMC and external assembly and packaging providers supply 7 nm-and-smaller products without allocation or yield problems? |
| Broadcom | What is AI semiconductor revenue versus total semiconductor and infrastructure-software revenue? | How much demand comes from leading AI laboratories or other large buyers, and what financing or lease commitments support deployments? | Can outsourced manufacturing and packaging meet custom-accelerator and networking schedules? |
| TSMC | How much of broad revenue growth and 3 nm mix is associated with high-performance computing or AI rather than other end markets? | Is customer concentration disclosed by end market, process node or geography? | Are fab utilization, advanced packaging, capital spending and geographic expansion aligned with demand? |
| Intel | Which current filing provides a comparable AI-revenue definition? | Are customer and foundry commitments disclosed at a level that supports comparison? | What evidence supports current process, packaging and capacity claims? |
Common comparison mistakes
- Calling every data-center dollar “AI revenue.” Data-center categories can include networking, systems, software and non-AI workloads.
- Comparing a foundry’s total sales with a designer’s AI category. Their denominators and positions in the supply chain are different.
- Filling disclosure gaps with market-share estimates. If an issuer does not report an AI category or customer percentage, label it as not stated.
- Treating management commentary as proof. Statements about strong demand are useful context but are not independent confirmation of future shipments.
- Ignoring packaging. Wafer availability does not guarantee that a finished accelerator can be assembled and tested.
- Assuming orders equal immediate revenue. Infrastructure, financing, export licenses and acceptance schedules can delay recognition.
- Using growth as a valuation shortcut. Share-price attractiveness requires a separate, current valuation model.
Bottom line
The most informative AI-chip comparison is a supply-chain map with clearly labeled periods and denominators. NVIDIA offers a disclosed data-center growth measure but faces customer infrastructure, product-transition and policy risks. AMD’s AI exposure must be read through its broader product disclosures and its reliance on TSMC and external packaging. Broadcom reports a large, rapidly growing AI-semiconductor category that must be separated from software and tested against customer concentration, financing and outsourced production. TSMC provides manufacturing scale and leading-node technology, yet its total revenue and 3 nm mix span many end markets rather than AI alone.
Use the latest filings to update the numbers, preserve every issuer’s definition, and treat manufacturing capacity, customer funding and deployment timing as part of the investment comparison—not as footnotes after the revenue table.
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