A global shortage of high-bandwidth memory and server DRAM is emerging as a bottleneck for companies trying to adopt artificial intelligence, ABS-CBN News reported from the Philippines. The report, published October 21, says the crunch is driving steep price increases and supply disruptions across the technology sector. That matters beyond personal computers: AI systems require large amounts of memory, storage and server capacity, so tighter component supply can affect the timing and cost of enterprise deployments. The squeeze is therefore an infrastructure issue as well as a consumer-hardware story.
The comments came from Jimmy Go, president and CEO of VST ECS Phils., in a recorded message shown at the VST ECS CXO Tech Summit 2026. Go said AI workloads consume exponentially more memory and that shortages are rippling through the economy. ABS-CBN reported that memory and solid-state-drive prices have more than doubled in recent months, while hyperscalers and data centers consume a large share of available supply. That competition can leave smaller firms with longer procurement lead times and less flexibility in project planning.
The pressure is already reaching corporate budgets and consumer hardware. The ABS-CBN report said server-class high-speed DDR5 DRAM prices had risen nearly 100% quarter-on-quarter, pushing some enterprise deployments into 2027. Procurement and capacity planning now matter as much as software selection for local firms. It also reported notebook price increases of 30% to 50% and a sharp contraction in smartphone shipments. Those figures describe a supply shock that can make AI expansion more expensive even when organizations are willing to invest. Procurement and capacity planning now matter as much as software selection.
Go nevertheless argued that businesses cannot wait for AI adoption to become perfectly stable before moving into production. The summit’s official program, held October 20 to 23 at Shangri-La Boracay, focuses on practical AI-driven enterprise strategies, agentic AI, cloud and intelligent infrastructure. For Philippine companies, the immediate lesson is to match projects with available hardware, budget for component volatility and build a realistic deployment timetable rather than treating AI as only a software purchase. It also turns capacity planning into a strategic decision for technology leaders and finance teams.
