AI chips are the processors that do the heavy calculations behind artificial intelligence: training large models and running them when people use AI apps. They have become one of the most talked-about parts of the stock market, because demand for them has grown very quickly. Knowing how these chips are designed, made and supplied helps you understand why some companies are in the news and what risks come with the theme.
What an AI chip does
An ordinary computer processor (CPU) is built to handle many different tasks one after another. AI work is different: it involves enormous numbers of simple calculations, mostly multiplying and adding large grids of numbers. Chips that can do thousands of these calculations at the same time are far better suited to the job.
The most common AI chip is the GPU (graphics processing unit). GPUs were first designed to draw images in video games, which also needs many calculations in parallel. Over time they became the main workhorse for AI. Other types include custom chips designed by large technology companies for their own data centers, and specialised "accelerators" built only for AI tasks. All of them need fast memory next to the chip, because AI models move huge amounts of data.
The AI chip chain: four steps
An AI chip passes through several companies before it reaches a data center. Each step is a different kind of business, with different risks and numbers.
1. Design. Some companies design chips but do not own factories. They create the chip's blueprint and software, then pay others to manufacture it. These are often called "fabless" companies. Nvidia and AMD are well-known examples.
2. Manufacturing. Making advanced chips takes extremely expensive factories, called fabs, where the most advanced can cost more than ₹1 lakh crore each. Only a handful of companies in the world can make the most advanced chips, and a large share is produced by contract manufacturers in Taiwan and South Korea.
3. Equipment. The factories themselves depend on specialised machines that print, deposit and etch patterns far smaller than a human hair. A small group of equipment makers, such as Applied Materials, supply these machines to chip factories worldwide.
4. Memory and packaging. AI chips need high-speed memory stacked very close to the processor, and advanced "packaging" that connects the pieces together. Memory makers and packaging specialists form another important link in the chain.
Why this chain matters to investors
Demand moves in cycles. Chip demand has always risen and fallen. When customers build capacity quickly, orders surge; when they pause, orders can drop sharply. AI has created strong demand, but the industry's history is one of booms and slowdowns.
A few customers can matter a lot. A large share of AI chip sales goes to a small number of very large technology companies building data centers. If a few of them slow their spending, chip makers feel it quickly.
Supply is concentrated. Because only a few companies can make or equip the most advanced chips, a problem at one of them, such as a factory outage or a natural disaster, can affect the whole chain.
Rules and geopolitics. Governments treat advanced chips as strategic. Export restrictions on selling certain chips to certain countries can change who a company is allowed to sell to, and therefore its revenue.
Technology changes fast. A leading chip today can be overtaken in a few years. Companies must spend heavily on research to stay ahead, and customers may design their own chips instead of buying them.
Example on PositoAI: Nvidia
Nvidia, listed in the US, designs GPUs and the software that lets developers use them for AI. It does not manufacture its own chips; contract manufacturers make them. Its rise shows how one link in the chain, design, can benefit strongly when AI demand grows, and also why its results are watched closely for any sign of slowing orders.
On PositoAI (18 Oct 2026): Positivity Score 10,274, ranked #3,438 of 3,999 companies in the US and #5,602 worldwide; market cap about ₹489.7 lakh crore; P/E 27.1; P/B 22.58. These figures change daily.
How to read a chip company's numbers
Revenue growth. How fast are sales growing, and is growth coming from AI products or from the rest of the business? Some companies report data-center or AI revenue separately.
Gross margin. This shows how much of each sale is left after the direct cost of making the product. High and stable margins suggest pricing power; falling margins can signal competition or weaker demand.
Customer concentration. Annual reports often say how much revenue comes from the largest customers. Heavy dependence on a few buyers raises risk.
Inventory. Rising stocks of unsold chips can be an early sign that demand is cooling.
Valuation. During periods of excitement, chip shares can trade at high price-to-earnings (P/E) and price-to-book (P/B) ratios. Comparing these with the company's own history and with peers shows how much future growth the market is already expecting.
Common misconceptions
"All chip companies are AI companies." Many chips go into phones, cars, appliances and factories. Only part of the industry makes AI chips.
"The chip designer makes the chip." Many designers outsource manufacturing entirely. Design, manufacturing and equipment are separate businesses.
"AI demand will keep rising in a straight line." Spending on AI capacity can pause or slow, and chip demand has always been cyclical.
"A leading position is permanent." Competitors and customers designing their own chips can change the picture quickly.
Frequently asked questions
What is a GPU?
A graphics processing unit: a chip that can do many calculations at once, originally for graphics and now widely used for AI.
What does "fabless" mean?
A company that designs chips but has them manufactured by others instead of owning factories.
Does India make AI chips?
India has a growing chip design industry and is building its first large manufacturing and packaging plants, but the most advanced AI chips are currently made elsewhere.
Why do export rules matter?
They can limit which countries a company may sell certain chips to, which can affect its revenue.
How to use this on PositoAI
Search any chip company on PositoAI, such as Nvidia, AMD or Applied Materials, to see its Positivity Score, rank, key ratios, flags and an AI summary of recent news. Use Go Deep to follow revenue, margins and cash flow over time, and Compare to put two chip companies side by side. PosiStep 15 of PosiCourse, PositoAI's free stock market course, explains how to think about valuation.
Suggested reading
What Are AI Stocks? · What Is an AI Chip (GPU)? · What Is a Data Center? · Data Storage for AI: How It Works · Cooling and Power for AI · What Is Risk in Investing? · What Is Market Capitalization?
More in AI & Markets: Data Centers and the Stock Market · How AI Is Changing Indian IT Companies · AI and Electricity Demand · Semiconductor Stocks Explained · How Banks Use AI · Can AI Predict the Stock Market? · Using AI for Stock Research Safely · The AI Bubble Question
Summary
AI chips, mostly GPUs and custom accelerators, perform the parallel calculations that artificial intelligence needs. Bringing one to market involves four kinds of business: design, manufacturing, equipment, and memory and packaging. Investors watching this theme face cycles in demand, dependence on a few large customers, concentrated supply, export rules and fast-changing technology. Revenue growth, margins, customer concentration, inventory and valuation tell you more about a chip company than the AI label alone.