Why the AI Boom Could Make Smartphones More Expensive in 2026

Artificial intelligence is changing far more than search engines, chatbots and image generators. It is also changing the economics of the semiconductor industry—and one of the clearest areas of pressure is memory.

Modern smartphones depend on memory for almost everything they do. DRAM provides the working space used by the operating system and applications, while NAND flash provides persistent storage for photos, videos, apps and other files.

At the same time, AI data centres require enormous amounts of high-performance computing hardware and memory. The resulting increase in demand is creating a difficult situation for a consumer-electronics industry that already operates on tightly controlled component costs.

This does not mean every smartphone will suddenly become more expensive by the same percentage. It means manufacturers are facing a new cost pressure and will have to decide how much of it they absorb, pass to customers, or offset through changes elsewhere in the product.

The numbers behind the story

The scale of the problem becomes clearer when looking at recent industry forecasts and market research. These numbers describe market-level trends, not guaranteed price changes for individual phones.

These figures are forecasts or market estimates from Gartner and Omdia. They should not be interpreted as a guarantee that a particular smartphone will increase in price by the same percentage.

The hidden part of your smartphone

When people compare smartphones, they usually focus on the processor, camera, display, battery and design. Memory often appears as two simple numbers in the specification sheet: RAM and storage.

Behind those numbers, however, is a large global semiconductor supply chain. The memory inside a phone has to be manufactured, packaged, tested and supplied alongside dozens of other components.

So how exactly does AI affect smartphone memory?

The connection begins inside AI data centres.

Modern AI systems process enormous quantities of data. AI accelerators need extremely fast access to that data, which is why high-bandwidth memory, commonly known as HBM, has become an important component of modern AI infrastructure.

HBM is not simply the same thing as smartphone RAM. It is a specialised memory technology designed for very high bandwidth. However, the important point for this story is that AI infrastructure is creating enormous demand for memory products and manufacturing capacity.

Memory manufacturers therefore have to make decisions about how they allocate production capacity and capital investment across different memory products.

Follow the money: how AI demand reaches your phone

The smartphone is several steps removed from the AI data centre, but the semiconductor supply chain connects the two.

Why memory supply cannot simply increase overnight

Semiconductor manufacturing is a capital-intensive industry. Increasing production is not as simple as adding another production shift to an existing factory.

New semiconductor capacity requires large investments in manufacturing facilities, specialised equipment, advanced packaging and qualification. Even after investment decisions are made, additional capacity takes time to become productive.

That creates a mismatch when demand changes quickly. AI infrastructure can expand rapidly, while semiconductor capacity generally responds over a much longer investment cycle.

DRAM and NAND: two different pieces of the puzzle

It is important not to treat all memory as one product. Smartphones use different types of memory for different purposes.

Will smartphones actually become more expensive?

This is where the story needs some caution.

Gartner has forecast an overall increase in smartphone prices during 2026 as memory costs put pressure on manufacturers. But a market-level forecast should not be interpreted as a fixed percentage increase for every device.

A manufacturer can respond to higher component costs in several ways.

1. Increase the retail price.
   The manufacturer can pass some of the additional component cost to consumers.
2. Absorb the cost.
   The company can accept lower margins rather than immediately raising the retail price.
3. Change RAM or storage configurations.
   Manufacturers can reconsider the number of memory and storage variants offered in price-sensitive segments.
4. Optimise other components.
   A company can look for savings elsewhere in the bill of materials.
5. Change product positioning.
   Manufacturers may place greater emphasis on products and segments where margins provide more flexibility.

Imagine a ₹15,000 smartphone

Why budget smartphones are more vulnerable

The lower end of the smartphone market has less room for component-cost increases because manufacturers operate within tighter price constraints.

Omdia reported that memory costs represented nearly 60% of the bill of materials for smartphones priced below $400 during the first quarter of 2026. For devices priced below $99, the share was reported to be above 64%.

This is important because the same absolute increase in component cost can represent a much larger percentage of the hardware budget of a low-cost smartphone than of a premium flagship.

That is why the memory situation can have a disproportionate effect on entry-level and lower-midrange devices.

Budget phone vs flagship: who has more room to absorb the pressure?

Could 12GB RAM phones become less common?

This is one of the most interesting questions for smartphone buyers, but there is no basis for saying that manufacturers will universally reduce RAM across the market.

What can be said is that memory configuration is one of the areas that manufacturers can reconsider when component costs rise, especially in price-sensitive segments.

A manufacturer might keep the same RAM configuration and accept lower margins. Another could change the combination of RAM and storage. A third could raise the price.

The exact decision depends on each company's product strategy and supply situation.

The unusual Samsung situation

Samsung provides an interesting example of how complicated this market has become.

Samsung operates both a major semiconductor business and a major smartphone business. Strong demand for memory can therefore create an opportunity for one part of the company while increasing component costs for another.

This is one of the unusual characteristics of the current AI-driven memory cycle: the companies supplying advanced semiconductor components can benefit from strong AI demand, while companies that use memory in consumer devices have to manage higher input costs.

Samsung is not alone in facing this kind of industry tension. The broader semiconductor ecosystem is increasingly being shaped by the competition between AI infrastructure demand and traditional consumer-electronics demand.

What does this mean for smartphone buyers in India?

India is an especially important market because consumers are highly price-conscious and the affordable and mid-range smartphone categories account for a significant part of the market.

Counterpoint Research reported that India's smartphone shipments declined 10% year over year during the June 2026 quarter, while rising memory costs contributed to higher component costs.

If memory prices remain elevated, manufacturers competing in India's lower-priced segments may have less flexibility than premium brands.

Don't forget storage: NAND is part of the equation too

The memory story is not limited to smartphone RAM.

NAND flash is used for internal storage, and modern smartphones increasingly ship with 128GB, 256GB, 512GB or even larger capacities.

When NAND becomes more expensive, manufacturers have the same basic choices: increase prices, reduce margins, change storage configurations or find savings elsewhere.

This is particularly relevant because storage requirements continue to grow. High-resolution photos, 4K and higher-resolution video, games and large applications all consume more storage than they did years ago.

The AI-phone paradox

There is an interesting contradiction at the centre of this story.

Smartphone manufacturers are adding more AI features to their products, while the broader AI industry is simultaneously increasing demand for the semiconductor components needed to build AI infrastructure.

Some on-device AI workloads can also require more computing power, memory and efficient data movement. This means smartphone manufacturers are becoming more dependent on advanced hardware at the same time that the broader technology industry is competing for semiconductor resources.

AI memory crisis: Myth vs Fact

Could the smartphone market become even more divided?

Omdia's research suggests that lower-priced smartphones are facing greater pressure than higher-priced devices. That could accelerate a trend already visible across the smartphone market: premium devices continue to receive increasingly expensive hardware and features, while affordable devices operate under much tighter cost constraints.

If memory remains expensive, manufacturers may become more selective about where they allocate high-capacity memory configurations.

This does not mean budget smartphones are going away. It means the specifications offered at a particular price point may become more important to watch.

3 things to watch next

How long could the memory pressure last?

There is no reliable date on which the market will suddenly return to normal. Semiconductor markets are cyclical and future conditions will depend on production capacity, AI investment, smartphone demand, PC demand, inventories and the investment decisions of memory manufacturers.

Reuters reported in August 2026 that SK Hynix expects memory supply tightness to remain a significant issue through 2030 and is investing heavily in additional AI-memory capacity.

That does not mean memory prices will rise continuously until 2030. It also does not mean smartphones will become progressively more expensive every year. It means the underlying supply-demand imbalance may take time to resolve.

What this story does NOT mean

1. Every smartphone will become 13% more expensive.
   The 13% figure is a Gartner market forecast and should not be applied directly to individual smartphone models.
2. AI uses exactly the same memory as a smartphone.
   AI systems use specialised technologies such as HBM alongside other memory and storage products. Smartphone memory is designed for a different use case.
3. Budget smartphones will disappear.
   Affordable phones will continue to exist, although their pricing, specifications and configurations can change.
4. Memory prices can only go up.
   Semiconductor markets are cyclical. New production capacity, weaker demand or inventory changes can affect future pricing.
5. AI is the only reason smartphones can become more expensive.
   Displays, processors, cameras, batteries, logistics, currency movements, taxes, tariffs and competition can all influence smartphone pricing.

Should you buy a smartphone now or wait?

The memory situation does not automatically mean that every consumer should rush to buy a smartphone.

If you genuinely need a new phone and find a model at a good current price, the decision should be based on the actual product rather than a general industry forecast.

Buyers who do not need an upgrade immediately can continue monitoring prices, new launches and specifications instead of assuming that a particular future month will definitely be cheaper.

NEXA Analysis: The real story is bigger than a smartphone price hike

The most important part of this story is not simply whether a particular smartphone becomes ₹1,000 or ₹2,000 more expensive.

The bigger development is that AI is changing the priorities of the entire semiconductor ecosystem.

For years, smartphones and PCs represented enormous sources of memory demand. Now AI data centres have created another rapidly expanding market for high-performance computing and memory.

That changes the economics of the supply chain. Memory manufacturers have strong incentives to invest in products where demand and returns are high, while consumer-electronics manufacturers have to manage the cost of the memory they purchase.

The long-term result could be more than higher prices. Smartphone companies may increasingly compete through software optimisation, power efficiency, AI efficiency and overall product design instead of simply increasing RAM and storage every year.

Fact check: what is confirmed and what remains uncertain?