Technical Context: It's Not Just Price Hikes, But a Base Architecture Shift
I analyzed Gartner's forecast and see it not just as casual news about more expensive laptops, but as an infrastructure signal. Analysts expect the sub-$500 PC segment to disappear by 2028, with global PC shipments dropping 10.4% year-over-year as early as 2026.
I paid special attention to the cost structure. According to Gartner, the share of memory in the bill of materials will rise to 23% in 2026, up from 16% in 2025, and the combined cost of DRAM and SSDs could jump by 130% by the end of 2026.
For me, the key takeaway isn't the number itself, but its consequence: manufacturers will no longer be able to sustain the entry-level segment through margins. Budget PCs are ceasing to be cheap not because someone decided to sell a premium product, but because RAM and storage are once again becoming the defining price factors.
I also try not to overstate the AI PC narrative. Based on available data, the driver here is broader than just artificial intelligence: it's a general shortage and soaring memory costs fueled by growing demand across the entire AI stack—from data centers to client devices.
Impact on Business and Automation: Cheap Hardware Won't Mask Weak Strategy Anymore
I believe companies that still plan digitalization through mass purchases of bare-minimum devices will lose. This model was already fragile, but now it's becoming an expensive mistake.
The winners will be those who rebuild their IT landscape: separating basic office tasks from compute-heavy workloads, moving AI processing to the cloud or server edge, and stopping attempts to implement artificial intelligence on random user hardware. This is exactly where AI architecture starts impacting CAPEX more than the choice of laptop brand.
In Nahornyi AI Lab projects, I already factor this shift into the design phase. Whether a company needs AI solutions for business—document search, corporate assistants, ticket processing, quality control, or predictive analytics—I almost always calculate where the computation should live first: on the endpoint, on an edge server, or in the cloud.
This directly affects the budget. As devices become more expensive, it makes more financial sense to invest in proper AI integration, data management, and secure access to centralized models rather than buying "more powerful PCs just in case."
Strategic View: The PC Market Pushes Businesses Toward a Mature AI Model
I see a paradox here. Rising client PC prices don't kill AI automation; instead, they filter out naive scenarios where a company hoped to buy new laptops and call it a transformation.
In practice, the market is pushing toward a more mature model: thin clients, robust server environments, strict data policies, observability, inference cost control, and proper AI solution architecture. For mid-sized and large businesses, this is surprisingly good news.
There is also a risk I wouldn't underestimate. Gartner forecasts a 15% lengthening of the device lifecycle for businesses and 20% for consumers, meaning more outdated equipment on the network, more performance compromises, and more security vulnerabilities.
This is exactly why between 2026 and 2028, I expect a surge in demand not just for AI solution development, but for rebuilding entire user and server infrastructures around the real total cost of ownership. Companies that start calculating TCO alongside AI scenarios now will navigate this cycle much more cheaply than those reacting after the fact.
This analysis was prepared by Vadym Nahornyi — a key expert at Nahornyi AI Lab specializing in AI architecture, implementation, and automation for real businesses. If you are planning a device fleet upgrade, integrating artificial intelligence, or want to implement AI automation without unnecessary hardware costs, I invite you to discuss your project with Nahornyi AI Lab. I will help you design an architecture capable of withstanding both rising prices and increased workloads.