The High Cost of AI Memory

The Head of our Europe and Asia Technology Team, Shawn Kim, explains how AI’s appetite for memory chips is boosting the cost of everything from data centers to smartphones, with consequences that may reach far beyond the tech industry.

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Shawn Kim: Welcome to Thoughts on the Market. I’m Shawn Kim, Head of Morgan Stanley’s Europe and Asia Technology Team.

Today, we’re talking about chipflation – when memory chips stop getting cheaper over time, and become more expensive and even harder to find.

It’s Monday, June 8th, at 3pm in London.

Memory chips are easy to ignore, until your laptop slows down, your phone costs more, or your cloud bill jumps.

Memory is the computer’s workspace. It holds whatever the machine needs at that moment, whether that is a web search, a video, a spreadsheet, or an AI model answering a question. DRAM is the fast memory inside servers, PCs and phones. NAND is what stores files in solid-state drives. And HBM, or high bandwidth memory, is the high-performance version sitting right next to the AI chip, helping them move huge amounts of data quickly.

That last one – HBM – is key because AI has become intensely memory hungry. Memory prices have risen more than six-fold over the last year, a sharp break from decades when the cost of DRAM generally kept falling.

The pressure is coming from AI infrastructure buildouts. We see servers accounting for 59 percent of DRAM demand by 2028, up from 37 percent in 2023. We also see enterprise solid-state drives reaching 65 percent of NAND demand, up from 18 percent. And simply put, data centers are taking a much bigger share of the memory pie.

AI memory use is climbing fast, and at every scale. A newer AI chip uses 7.2 times more HBM than earlier generations. A full system uses about 65 times more. Across an entire AI data center buildout, the jump gets even bigger. HBM has gone from roughly 10 terabytes in 2020 to about 18 petabytes in 2026, orders of magnitude more.

This demand is running into a supply chain that cannot respond quickly. New memory capacity takes years to build, qualify and ramp up. Supply relief is a process, not a switch. And that creates a two-tier market. Large AI and cloud buyers can sign long-term agreements, prepay and secure priority access. Traditional buyers, including PC makers, smartphone makers and industrial hardware companies, must compete for what remains.

This impacts everyday products. In 2027, we see PC memory demand potentially facing a 15 percent shortfall, equivalent to about 58 million PCs. Smartphones could face a 12 percent shortfall, equivalent to about 134 million units. Companies may have to raise prices, cut specifications, delay launches, and accept lower profits.

The dollar numbers are striking. We see the memory market growing from about $220 USD billion in 2025 to about $890 billion in 2026. Expectations for 2026 memory revenue rose 71 percent in just three months. That implies roughly $600 USD billion of incremental memory revenue in 2026, more than the annual market for smartphones, PCs, or servers, each taken on its own.

The broader economy may not see a significant direct inflation shock. We estimate the direct impact on headline CPI at about 0.1 percent in 2026. But pressure is showing up in producer prices, in corporate margins, cloud costs, capital spending plans and delayed technology upgrades.

AI has turned memory from the cheapest part of the digital economy into one of its most contested resources. These tiny chips most people never think of may now decide what gets built or delayed, and how much we all end up paying.

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