Intel’s share price sank by almost a quarter on Thursday after the company revealed a deep dip in sales of PC processors. The rout began after the silicon-slinger posted Q3 results that included some nice numbers, such as a 21 percent year on year revenue jump to $16. 5 billion, helped by 78 percent growth for datacenter and AI products, and gross margins of 43 percent. The revenue number was at the high end of Intel’s revenue guidance of $15. 8-16. 8 billion, and the gross margin beat its predictions by two points. Outweighing those upsides was a decline in revenue for client computing and physical AI products, which saw incoming cash reach $8. 1 billion but fall five percent year-over-year and nine percent decline from last quarter. In prepared remarks [PDF] published to coincide with Intel’s earnings announcement, CEO Lip Bu Tan said “In recent weeks, our customers began signaling a PC slowdown based on memory prices and availability. Year-to-date system end-point prices have increased significantly placing additional pressure on already limited IT budgets.” “The pace of the PC correction has accelerated over the last two months, as our OEM partners are aggressively managing inventory to match the softer sell through,” he added, before predicting that the market for PC chips will shrink by 12 percent next year and remain tough “until memory availability and prices begin to normalize.” Which may not be for years, given Korean memory-maker SK Hynix this week predicted that memory shortages will persist until the year 2030. Intel’s biggest business is therefore in trouble. AI and Xeons riding to a rocky rescue
Tan tried to give investors reasons to be cheerful, by saying Intel sees a “resurgence in demand for CPU” sparked by customers’ desire for computing systems that consume less energy. “Increasingly, energy efficiency is becoming the most important metric for the AI infrastructure build-out,” he said. That matters, Tan argued, because “While GPUs are great at ‘thinking tasks,’ deterministic CPUs are far more efficient at ‘finding’ tasks. While a GPU reasoning query can run as high as multiple watt-hours of energy, a single CPU-based query could cost only joules of energy.” The CEO reasoned that lower energy consumption is important in the context of agentic AI – the idea that we will all soon run many autonomous agents to handle personal and professional scutwork…