The Rising Cost of iPhones: Why Prices are Set to Increase

When Apple debuts the next generation of iPhones this week, they’re likely to come with an unwanted change: a higher price tag. A price hike from the supply-chain powerhouse would be the clearest sign yet that soaring memory costs have become unavoidable — with no end to the memory crunch in sight.

Call it “chipflation” or “RAMageddon.” The shortage is reversing a decades-long decline in memory costs that helped make consumer electronics more powerful without making them dramatically more expensive. The terms “memory prices” and “memory shortage” appeared in 473 company transcripts last quarter, according to data provided by AlphaSense. The entire industry is seemingly working on the problem. And yet, we’re years away from seeing it fixed.

The common wisdom is that AI is at fault: The technology isn’t just contributing to higher energy prices and layoffs; it’s also helping drive up the cost of everything from smartphones to game consoles.

But the shortage was brewing well before ChatGPT took off — and was then amplified by AI’s unending hunger for RAM. The current market is the product of a complicated and very profitable reshuffling of the memory industry within a system that was already failing to keep up.

“We need to build more wafer capacity,” Manish Bhatia, president and COO of Micron — one of the three largest memory manufacturers — told The Verge. “[It’s] a very different challenge for the industry than it had been for many years before, where technology alone was able to keep up with the demand.”

For years, memory manufacturers could increase production by fitting more chips onto each wafer. But those gains were shrinking and taking longer to achieve. In 2021, even as pandemic-era electronics demand surged, Micron concluded that the problem was more fundamental: Technological advances alone would no longer create enough capacity to keep pace with long-term demand. Manufacturers would have to process more wafers — and build enormous new facilities to do it.

Then the memory business collapsed. Pandemic-era purchases of computers, tablets, and phones had pulled demand forward; consumer spending weakened, and manufacturers were left with excess inventory. They lost money and slowed their expansion plans.

By the time the market began recovering, generative AI had unleashed a wave of demand far larger and more memory-intensive than manufacturers had ever anticipated.

The memory business is also extraordinarily concentrated. Three manufacturers account for about 90 percent of the market, according to Counterpoint, leaving the world dependent on a handful of companies to divide limited capacity between AI infrastructure and consumer devices. Counterpoint estimates that Samsung controlled 39 percent of the memory market in the second quarter of 2026, followed by SK Hynix at 26 percent and Micron at 25 percent.

The memory inside your phone or computer broadly falls into two buckets. DRAM, or dynamic random-access memory, temporarily holds the information a device needs while opening apps, loading webpages, or running software. NAND flash memory provides longer-term storage for things like photos and files.

Inside AI data centers, specialized processors rely heavily on a form of DRAM called high-bandwidth memory, or HBM.

“It’s not as simple as saying data centers are consuming RAM,” David Naranjo, associate director at Counterpoint, told The Verge. “The RAM is not the same.”

By stacking memory chips together and using advanced connections and packaging, HBM can move enormous quantities of data to and from those processors much faster and more efficiently.

It’s more difficult to produce, but also more lucrative to sell. Deep-pocketed AI chipmakers like Nvidia and AMD and tech giants like Meta and Microsoft have an insatiable need for it to power their AI systems — and are more than happy to pay.

HBM also consumes significantly more manufacturing capacity than conventional DRAM. Memory chips are made many at a time on large, circular silicon wafers. Because a finished HBM product stacks multiple, larger chips together, it requires considerably more silicon. Micron estimates that producing a given amount of HBM requires roughly three times as many wafers as producing the same amount of conventional DRAM.

The incentives increasingly favor AI.