Direct Answer
60 SEC filers are classified under the Standard Industrial Classification code that describes the edge ai semiconductors industry, and their latest annual reports show $542.8 billion of combined revenue and a median operating margin of -2.3%. Investors should still evaluate its revenue model, industry-specific KPIs, valuation framework and the risks that can change the group's economics.
Industry Overview
Edge AI semiconductor makers produce chips that run machine-learning models inside devices such as cameras, cars, phones and industrial equipment, with a focus on low power. Design wins and model support drive revenue. The first numbers to compare across companies here are design wins and revenue, gross margin and customer concentration.
Key Industry Metrics
- Design wins and revenue: New product selections by device makers and revenue from them. Wins convert into sales over long design cycles, so they show future share more than current results.
- Gross margin: Revenue left after wafers and packaging. Small firms often have lower volumes and costs, so margin tends to improve with scale and software content.
- Customer concentration: The share of sales from a few device makers. Design cycles are long and buyers few, so one program's change can have a large effect on small firms.
- Power efficiency: How much machine-learning work is delivered per unit of power. Edge devices run on batteries and thermal limits, so efficiency often decides which chip is chosen.
- Software ecosystem: The tools, models and developer support around the chips. Customers pick chips they can program easily, so a strong ecosystem raises switching costs and design wins.
Primary Macro Drivers
- AI at the edge adoption: Cameras, vehicles and appliances that run models locally require specialized chips, so rollouts of such features raise design wins and revenue.
- Automotive and industrial use: Driver assistance, robots and factory vision systems need on-device inference, creating long design cycles but sizable volume once chosen.
- Device upgrade cycles: Device makers that add AI features at each refresh buy newer chips, so upgrade timing drives revenue and design-win conversion.
- Model efficiency improvements: Smaller, faster models let weaker chips run useful tasks, widening the number of devices that can adopt AI and lowering required compute per unit.
Major Risks
- Competition from large vendors: Large chip vendors with software ecosystems can bundle edge features, pressuring win rates and margin; watch design wins and gross margin.
- Customer concentration: A few device makers account for much revenue, so a lost program can hit results; watch top-customer share and design win conversion.
- Long design cycles: Chips are chosen years before volume ramps, so wins convert slowly and can be lost to delays; watch design win counts versus revenue growth.
- Cash burn at small firms: Small firms spend heavily before sales arrive, so funding needs depend on capital markets; watch cash balance, operating burn and dilution.
Industry Characteristics
| Characteristic | Profile |
|---|---|
| Cyclicality | Cyclical |
| Capital Intensity | Medium |
| Regulatory Intensity | Low |
| Industry Maturity | Emerging |
| Geographic Importance | Global |
Edge AI Semiconductors Filers in the SEC Record
60 companies with annual reports in the dataset are classified by the SEC in the Standard Industrial Classification code 3674. Their latest fiscal years end between 2025 and 2026, and together they reported $542.8 billion of revenue. Median revenue growth over the prior year was 20.3%. Median operating margin was -2.3% and median net margin -1.7%. Median total liabilities were 34% of total assets, and 27 of 60 reported a profit in their latest year.
| Company | Revenue | Growth | Operating margin | Net margin | Year |
|---|---|---|---|---|---|
| Nvidia Corp | $215.9 billion | 65.5% | 60.4% | 55.6% | FY 2026 |
| Broadcom Inc. | $63.9 billion | 23.9% | 39.9% | 36.2% | FY 2025 |
| Intel Corp | $52.9 billion | -0.5% | -4.2% | -0.5% | FY 2025 |
| Micron Technology Inc | $37.4 billion | 48.9% | 26.1% | 22.8% | FY 2025 |
| Advanced Micro Devices Inc | $34.6 billion | 34.3% | 10.7% | 12.5% | FY 2025 |
| Applied Materials Inc | $28.4 billion | 4.4% | 29.2% | 24.7% | FY 2025 |
| Texas Instruments Inc | $17.7 billion | 13.0% | 34.1% | 28.3% | FY 2025 |
| Nxp Semiconductors N.V. | $12.3 billion | -2.7% | 24.8% | 16.5% | FY 2025 |
The 8 largest of 60 by revenue. Source: each company's own annual report as filed with the SEC (XBRL data), figures in U.S. dollars. A company's SIC code reflects its main business as the SEC records it, so it can differ from how an index provider or analyst classifies the company, and a diversified group appears under only one code.
Where to Go Next
The Edge AI Semiconductors guide describes how the industry earns money, what to measure and what can go wrong. To see how it compares, read the neighbouring industries in the same sector, then apply the same checks to a specific company.
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Frequently Asked Questions
What are the main KPIs for the Edge AI Semiconductors industry?
Key performance indicators for the Edge AI Semiconductors industry include: Design wins and revenue, Gross margin, Customer concentration, Power efficiency, Software ecosystem. Which matter most depends on the company's model, so compare them across several companies rather than relying on one.
How cyclical is the Edge AI Semiconductors industry?
The Edge AI Semiconductors industry is cyclical, meaning its revenues and earnings tend to expand during economic expansions and contract during downturns. Investors should monitor leading economic indicators and interest rate trends when evaluating timing.
What are the biggest risks in the Edge AI Semiconductors industry?
The primary risks for Edge AI Semiconductors companies include: Competition from large vendors, Customer concentration, Long design cycles, Cash burn at small firms. Investors should assess each company's exposure to these factors and its track record of navigating them.