If you have purchased a laptop, upgraded a server, or priced out new workstations recently, you have probably noticed something painful: memory prices are through the roof. And according to new reporting from IEEE Spectrum and Fortune, the situation is about to get worse before it gets better.
DRAM prices surged 80-90% in just the first quarter of 2026, according to Counterpoint Research. The culprit is not some natural disaster or trade war. It is the insatiable appetite of AI data centers for a specialized type of memory chip called High-Bandwidth Memory, or HBM, and the ripple effects it is sending across the entire electronics industry.
What Is HBM and Why Does AI Need So Much of It?
High-Bandwidth Memory is a specialized DRAM chip designed to sit right next to a GPU on the same package, feeding data to the processor at extraordinary speeds. Each HBM chip is actually a stack of up to 12 individual DRAM dies, connected by thousands of microscopic vertical connections called through-silicon vias (TSVs).
The reason AI needs this technology is the "memory wall." Large language models like GPT-5.2, Claude Opus 4, and Qwen 3.5 require terabytes of data per second flowing into GPUs. Standard memory cannot keep up. HBM solves this by placing the memory as close to the processor as physically possible, with bandwidth exceeding 3 terabytes per second on the latest Samsung HBM4 chips.
The problem? HBM costs roughly three times more than standard DRAM and accounts for over 50% of the cost of a packaged GPU. NVIDIA's B300 GPU uses eight HBM chips, each a stack of 12 DRAM dies. That is 96 individual DRAM dies dedicated to memory for a single GPU. Multiply that across millions of GPUs being deployed in AI data centers, and you start to understand where all the world's DRAM production is going.
The Numbers Are Staggering
TrendForce estimates that HBM demand will increase 70% year-over-year in 2026 alone. Meanwhile, the three companies that dominate global DRAM production -- Samsung, SK Hynix, and Micron -- are not building new capacity fast enough to keep up.
The roots of the shortage trace back to the post-pandemic bust. After hoarding memory during COVID-era supply chain disruptions, hyperscalers pulled back on purchases in 2022-2023. Prices cratered so badly that Samsung cut production by 50% just to stay above manufacturing costs. The entire industry stopped investing in new fabrication capacity.
Then the AI infrastructure boom hit. Nearly 2,000 new data centers are planned or under construction globally, representing a 20% jump in worldwide capacity. Alphabet and Amazon have announced capital expenditure plans of $185 billion and $200 billion respectively for 2026 -- numbers without precedent in corporate history. McKinsey projects $7 trillion in data center spending by 2030, with $5.2 trillion going to AI-focused facilities.
Micron has called the bottleneck "unprecedented." Its entire 2026 HBM output is already allocated. Bernstein analyst Mark Li warns that memory chip prices are going "parabolic."
Who Is Feeling the Pain?
The shortage is not just an abstract supply chain problem. It is hitting real companies with real consequences:
- Apple warned that iPhone margins will compress due to DRAM costs
- Tesla is so frustrated that Elon Musk declared they may need to build their own memory fabrication plant
- Cisco gave a weak profit outlook citing the memory squeeze, causing its worst share loss in nearly four years
- Sony is reportedly considering pushing back the next PlayStation console to 2028 or 2029
- Lenovo CEO Yang Yuanqing said the crunch will last at least through the rest of 2026
- Xiaomi, Oppo, and other smartphone makers are trimming 2026 shipment targets, with Oppo cutting forecasts by up to 20%
Samsung has started reviewing memory supply contracts quarterly instead of annually. Retailers and middlemen are changing prices daily. In Seoul's Sunin Plaza, a major DIY PC hub, shop owners are holding off on transactions because "prices are almost certain to be higher tomorrow."
What This Means for Your Business
If you are running a small or medium business, this shortage affects you in several concrete ways:
Hardware purchases are more expensive. Whether you are buying laptops, desktops, or servers, the DRAM component of those devices costs significantly more than it did six months ago. If you have a hardware refresh planned for 2026, expect to pay a premium -- or consider accelerating purchases before prices climb further.
Cloud AI costs may rise. The same memory that is getting more expensive for consumer devices is also what powers the AI infrastructure behind cloud services. While major providers have secured supply contracts through 2028, the underlying cost pressure could eventually flow through to API pricing.
On-premise AI gets harder to justify. Running AI models locally requires GPU hardware packed with HBM. As those GPUs become more expensive, the economics shift further toward cloud-based AI services for most businesses. This makes open-source models running on efficient architectures -- like mixture-of-experts designs that use less memory per inference -- even more strategically important.
Plan ahead on procurement. The days of ordering hardware and receiving it in a week are fading for certain configurations. Lead times are extending, and some vendors are already limiting allocations. If your business depends on specific hardware, start conversations with suppliers now.
When Does It End?
The honest answer: not soon. New DRAM fabrication plants take 18 months or more to build and bring online. Even with Samsung, SK Hynix, and Micron all investing in expanded capacity, analysts say it will take years for supply to catch up with demand. And that assumes AI spending does not accelerate further -- a bet few are willing to make.
The IEEE Spectrum analysis suggests that even when new capacity comes online, prices may stay elevated. The structural shift toward AI-optimized memory means the industry's traditional boom-bust cycle may be replaced by sustained high demand. As Lam Research CEO Tim Archer put it: "What is ahead of us between now and the end of this decade, in terms of demand, is bigger than anything we've seen in the past."
The Bottom Line
The AI revolution is not just transforming software -- it is reshaping the physical infrastructure of the technology industry. The memory chip crisis is a reminder that AI progress has real hardware constraints, and those constraints have costs that ripple far beyond data centers.
For businesses, the practical takeaway is straightforward: factor rising hardware costs into your 2026 budgets, prioritize cloud and API-based AI tools over on-premise deployments where possible, and lock in procurement timelines sooner rather than later.
The AI boom is real. So is the bill.
Need help navigating AI strategy during a volatile hardware market? BaristaLabs helps businesses build practical AI solutions that work within real-world constraints. Get in touch to talk through your options.