The semiconductor industry is witnessing a peculiar inversion. For decades, memory has been the most typical commoditized business: massive capital requirements, volatile prices, and profits that evaporate once supply outpaces demand. However, artificial intelligence is changing this equation. AI accelerators require extremely large amounts of high-bandwidth memory (HBM), and hyperscale cloud providers are scrambling to secure supply years in advance. Gartner's April forecast predicted memory revenue would jump from $216.3 billion in 2025 to $633.3 billion in 2026, increasing its share of the overall $1.32 trillion semiconductor market to approximately 48%. Its latest August forecast is even more striking: memory revenue is now expected to reach $837.3 billion, accounting for a whopping 54% of the total $1.56 trillion semiconductor industry. This elevates memory far beyond just another semiconductor category, transforming it into core AI infrastructure.

The clearest evidence that AI has fundamentally altered the demand equation comes not from market forecasts but from the procurement side. In just one quarter, Nvidia surged its supply chain and capacity prepayment commitments from $119 billion to $279 billion. The company stated these commitments are primarily memory-related, with $92 billion to be paid over the remainder of fiscal 2027, $87 billion in fiscal 2028, and $88 billion in fiscal 2029. For memory manufacturers, this represents unprecedented order visibility. Micron Technology, SK hynix, and Samsung Electronics dominate this market as a trio. SK hynix has signed long-term agreements with approximately 10 customers; Micron stated that 16 strategic customer agreements cover about 20% of its DRAM capacity and one-third of its NAND capacity over the contract period; Samsung has also reached multi-year memory supply agreements with major clients. Compared to previous cycles, these long-term agreements provide memory companies with significantly improved demand visibility. But they cannot magically erase the industry's pricing adjustment mechanisms. Memory, once a volatile commodity, has evolved into the $837 billion core engine driving the AI revolution—tech giants are securing orders years in advance. The memory cycle is not yet终结 for investors, the uncomfortable aspect is that memory's share of semiconductor revenue is extremely price-sensitive. Previously, memory's share of total semiconductor revenue peaked at around 34% in 2018. However, the following year (2019), total memory revenue plummeted 31.5%, with DRAM average selling prices (ASP) plunging 47.4%, and memory's share of the entire semiconductor industry fell back to 26.7%. Gartner attributed the crash to overcapacity and falling prices. In other words, a revenue collapse does not require actual usage to plummet; price declines alone can cause significant damage. Long-term agreements help because they lock in volumes and sometimes prices. But they are not a permanent "absolute floor" beneath every type of memory product. Traditional standard DRAM and NAND still face the impact of new capacity coming online, inventory fluctuations, and competitive price wars. And new capacity is on the way. SK hynix plans to invest $4 billion in a next-generation HBM packaging plant in Indiana, and has approved a massive total investment plan of 54.3 trillion Korean won (approximately $38.3 billion) through 2031. Technological variables also exist. SK hynix and Sandisk have launched the High Bandwidth Flash standard, aimed at alleviating AI memory bottlenecks, potentially allowing flash-based architectures to carry workloads that currently rely heavily on scarce HBM.

The combined cost of memory has soared 340%, exceeding the price of a top-tier SoC. Advanced lithography and next-generation manufacturing processes once meant the chipset was the most expensive component inside a smartphone, but that has been completely changed by the AI boom. According to a leaker's latest estimate, the combined cost of DRAM and NAND flash has surged 340%, and their price now exceeds that of a top-tier SoC. According to Weibo blogger "Digital Chat Station," although smartphone SoCs are transitioning to TSMC's leading 2nm "N2" and "N2P" processes for desktops/mobile devices this year (starting with Apple's A20 Pro), their price is still not as expensive as DRAM and storage chips in phones. The leak notes that just last year, a 12GB RAM plus 256GB NAND flash combination would cost manufacturers around $70. By the third quarter of 2026, this price has surged to 2,200 yuan (approximately $310). Note that this is merely the price for the 12GB + 256GB combination. If smartphone makers choose 16GB + 256GB or 16GB + 512GB configurations, BOM (bill of materials) costs will surge dramatically, forcing substantial price increases across manufacturers. This is also one reason why some models of Google's latest Pixel 11 Pro not only come with only 12GB RAM (instead of 16GB) but are also priced higher than the Pixel 10 Pro.

In contrast, while the price of top-tier smartphone chipsets has also risen, the increase is far less than that of memory and storage. The leaker points out that SoC prices are currently around 2,000 yuan (approximately $280), making them the second most expensive component. Unlike Apple, which designs its own chips, Android phone makers must pay high premiums to companies like Qualcomm and MediaTek for their chips, putting them at a disadvantage in cost control. Since Apple designs its own chipsets, manufactured by TSMC in batches, it does not need to purchase from Qualcomm, MediaTek, or Samsung. This model gives the Cupertino-based giant an advantage in the smartphone industry, but even it cannot escape the impact of DRAM shortages. As for future price trends, memory and storage costs are expected to continue surging in 2028, and the end of this price wave may not come until sometime in 2029.

In conclusion, AI has undoubtedly changed the trajectory of memory. Gartner's latest forecast places memory's share of total semiconductor revenue at 54% in 2026, compared to just 27% in 2025. This strongly supports the bullish case for Micron, SK hynix, and Samsung. But smart investors should not conclude that memory has permanently escaped cyclicality. New capacity, technological substitutes, and pricing that eventually returns to normal could still transform today's shortages into tomorrow's gluts. A more reasonable inference is that AI has extended and amplified the memory cycle, but not necessarily eliminated the cycle itself. When today's $837 billion memory market eventually collides with massive future supply, this subtle distinction could prove crucial.