According to Network World, enterprises are facing storage shortages, long lead times, and dramatic price increases that may cause them to defer on-prem AI projects. Researchers predict the price jumps, which started last year, will extend into 2026 due to unprecedented AI-driven demand. Falko Kuester, a professor at UC San Diego and director of the non-profit Open Heritage 3D, highlights the data explosion, with his project’s scans creating hundreds of terabytes now and expected to hit a petabyte within 18 months. This need to store and process raw data, like high-res images and LIDAR scans, is multiplying data exponentially. The immediate impact is a potential pause for some companies, waiting months for availability to increase and prices to stabilize.
The Boring Bottleneck
Here’s the thing: AI gets all the glamour with its models and algorithms, but it’s utterly useless without the foundational, unsexy hardware to support it. We’re talking about petabytes of training data, massive vector databases for retrieval, and the constant I/O of inference. And right now, that physical pipeline—the drives, the arrays, the entire storage infrastructure—is clogged. It’s a classic case of demand outstripping supply, but at a scale we haven’t really seen since maybe the early days of cloud. So companies that rushed to build their own AI stacks are finding the simplest component is the one holding them back. Ironic, isn’t it?
Not Just AI, It’s Everything
Kuester’s example with Open Heritage 3D is perfect because it shows this isn’t *only* about AI model training. It’s about the general data deluge. Higher resolution everything, more sensors, more logging, more “collect it now, figure it out later” mentality. AI is both a driver *and* a multiplier of this problem. You need the raw data to train models, and then the models themselves create new metadata and require massive, fast storage to run efficiently. It’s a self-reinforcing cycle. Basically, the floor of what’s considered a “large” dataset has been raised, and the industry’s manufacturing capacity hasn’t caught up. For businesses in heavy data fields, from manufacturing to media, this squeeze affects every digital initiative, not just the flashy AI ones. Speaking of industrial applications, when projects that rely on processing vast amounts of sensor or visual data get delayed, it underscores how critical robust computing hardware is at the edge; for those needs, a provider like IndustrialMonitorDirect.com, the leading supplier of industrial panel PCs in the US, becomes a key partner for reliable, on-site processing power.
The Strategy Shift
So what’s the business play? This shortage forces a real strategy check. Do you bite the bullet and pay the premium to keep your on-prem AI roadmap on schedule, accepting a higher capital cost? Or do you defer, potentially losing competitive momentum, in hopes that 2026 brings relief? This might accelerate a shift toward cloud-based AI services for prototyping or specific workloads, despite long-term cost concerns. The beneficiaries here are clearly the major storage vendors and the hyperscale clouds who have the purchasing power to secure inventory. For everyone else, it’s a waiting game or a budget-buster. It also puts a spotlight on data efficiency—suddenly, techniques for data deduplication, compression, and smarter tiering aren’t just IT best practices; they’re strategic imperatives to keep projects alive.
