AI Vocabulary · 02 AI Infrastructure
What is ASIC?
A chip designed for one specific job. In AI, Google's TPU and Amazon's Trainium are the best-known examples.
An ASIC, application-specific integrated circuit, is a chip built for one task only. Because it skips everything that task does not need, it can be much faster and more energy efficient than a general chip, as long as the task stays the same.
In AI, the large cloud providers build their own ASICs: Google's TPU, Amazon's Trainium, Meta's MTIA and Microsoft's Maia. Google's first TPU was 29 times faster and more than 80 times more energy efficient than a general-purpose processor on Google's own inference workloads, according to Hennessy and Patterson. Today Google owns about a quarter of the world's AI computing capacity, mostly on its own TPUs (Epoch AI, 2026).
Why it matters for you
Custom chips are one reason the big clouds can offer AI more cheaply and depend less on NVIDIA. You usually cannot buy them: you use them as a service inside the maker's cloud.
Own it
If a provider runs your model on its own chips, ask how easy it is to move elsewhere. The cheaper option inside one cloud can increase your vendor lock-in.
Where it comes from
The case for chips built for one domain is made in A New Golden Age for Computer Architecture by John Hennessy and David Patterson (2019). Google's share of global AI compute comes from Epoch AI (April 2026).
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