By Luke Lango
Publication Date: 2026-04-22 12:55:00
Listen to the audio version of this article (generated by AI).
Nvidia (NVDA) built a near-monopoly on AI compute.
For the past three years, every major AI company — Alphabet (GOOGL), Meta (META), Amazon (AMZN), Microsoft (MSFT) — has relied on its GPUs to train and run models at scale.
That worked — until the economics changed.
Today, the real cost isn’t training. It’s inference — the billions of times those models are used every day.
At that scale, even small inefficiencies become massive, recurring expenses.
So Big Tech isn’t just buying chips anymore.
They’re building their own.
Why Custom AI Chips Are Replacing Nvidia GPUs
Nvidia’s GPUs are general-purpose chips. They’re powerful and flexible — they can train AI models, run video games, render 3D animations, and simulate physics. That versatility made them the backbone of the AI boom.
But versatility has a cost. A chip designed to do everything isn’t optimized for any one task.
As AI has scaled into mass consumer and enterprise adoption, inference has become the dominant — and fastest-growing — compute cost in the entire industry.
That’s the opportunity that custom chips — Application-Specific Integrated Circuits (ASICs), or XPUs — are built to capture. Instead of doing everything, these chips are built for a single task. Less flexible, yes. But the payoff is better performance-per-watt and significantly lower operating costs at the…
