Gartner's $1.6 Trillion Semiconductor Forecast Is Really an AI Inference Story

Gartner projects worldwide semiconductor revenue will hit $1.6 trillion in 2026, a 92% jump from 2025’s $809 billion, and keep climbing to $1.9 trillion in 2027 — growth the firm attributes to sustained AI infrastructure investment layered on top of a stronger-than-expected memory pricing cycle. Memory alone is forecast at $837 billion in 2026, on its way past $1 trillion in 2027, and its share of total semiconductor revenue is set to roughly double, from 27% in 2025 to 54% in 2026, with DRAM revenue projected to grow 246.6% and NAND flash 371.9% this year. Non-memory chips grow more modestly, from $589 billion in 2025 to $718 billion in 2026 (up 21.9%) and $864 billion in 2027. Gartner Director Analyst Ben Lee called it a sign “the semiconductor industry is entering a fundamentally new phase of growth.”

The structural driver underneath those numbers is the AI data center buildout: Gartner expects the AI ecosystem’s share of total semiconductor revenue to expand from 36.5% in 2026 to over 53% by 2030, driven by CPUs, networking silicon, and high-bandwidth memory. That tracks with an adjacent Gartner forecast from earlier in August, which found worldwide AI-optimized infrastructure-as-a-service spending growing 96% in 2026 to $42 billion, and flagged a quieter but arguably more consequential shift inside that number: 2026 is the first year global spending on AI inference ($23.3 billion, 55% of the total) is expected to exceed spending on training ($19 billion). Gartner analyst Hardeep Singh attributed that shift to the “rapid operationalization of AI across enterprise applications” as companies move from building models to running them continuously in production.

Together, the two forecasts describe the same transition from two vantage points — one in silicon supply, one in cloud spend — and both point to the same conclusion: the AI cost center enterprises should be planning around in 2026 isn’t model training, it’s the compute required to keep already-built AI systems running.