Everpure 'takes the hit' as AI-fueled supply crunch drives prices up 70%

The Register Data Centre reported: The supply crunch gripping the storage market has pushed Everpure – the artist formerly known as Pure Storage – to reassure customers it won't make things worse. In a letter to customers published on Thursday, the storage biz paints a picture of a market still under strain, with component shortages and AI-fueled demand continuing to pull supply in the wrong direction. "On average, our prices have risen approximately 70 percent since the beginning of the year," said Charles Giancarlo, chairman and CEO of Everpure. "We expect the current environment to persist," he added, noting that the crunch is likely to last far longer than the COVID-era disruption. Everpure says "many high-volume semiconductor components have surged between 300 percent and 900 percent (4x to 10x) since mid-2025." Some suppliers couldn't deliver what they'd already committed to, it added, leaving the company scrambling for pricier alternatives just to keep orders moving. "Unless demand created by AI abates in the next year, we could see these escalated costs continue for many years to come," Giancarlo warns. Despite surging costs, Everpure points back to its February earnings call, where it flagged its margins would run at the low end of the usual range, effectively swallowing some of the extra costs rather than passing all of them on to customers. "We will not profiteer from this crisis … we are choosing t.

The story lands in a market where demand is already assumed. The more useful question is whether the supporting layer around data center leasing is flexible enough to turn that demand into available capacity. The constraint is not just chip supply. Advanced compute depends on packaging, memory, networking, power delivery, and the ability to land systems inside facilities that can actually run them at high utilization.

The pressure point is timing. The underappreciated variable is deployment readiness across networking, power, and packaging, not just chip availability.

That matters for buyers because the useful capacity is the installed, cooled, powered cluster, not the purchase order. It also matters for suppliers because component shortages can shift bargaining power quickly across the stack.

The financial question is whether this development improves pricing power, locks in scarce capacity, or exposes execution risk that the market may still be discounting, the operating question is procurement timing, facility readiness, network design, and the likelihood that adjacent constraints will slow realized deployment, and the customer question is whether this changes build sequencing, partner dependence, or the economics of scaling regions and clusters over the next few quarters.

This is where AI infrastructure differs from ordinary software growth. Capacity has to be financed, permitted, powered, cooled, connected, staffed, and then sold into real workloads before the economics are visible.

The practical read is that infrastructure advantage is becoming more local and more operational. Two companies can chase the same AI demand and end up with very different outcomes if one has better access to power, more credible delivery dates, or a cleaner path through procurement and permitting.

The next signal to watch is the next disclosures on customer commitments, infrastructure readiness, and any evidence that power, cooling, silicon supply, or permitting becomes the real gating factor. The next test is whether delivery schedules, memory availability, and deployment readiness move together or start to diverge.