Video narration
Why does AI need different chips instead of one chip for everything? When your phone recognizes a voice or edits a photo, you want that help without watching the battery disappear. A GPU is the flexible workhorse: it handles many jobs at once, which makes it useful for AI. A TPU is built to speed up certain calculations used in AI, so it specializes rather than trying to do everything. An NPU is designed to handle the pattern-finding work behind AI efficiently, often inside a device like a phone. But these are design priorities, not strict borders: capabilities vary, and a system can combine chip types. The real difference isn't that one wins. Each trades flexibility for specialization or efficiency. So which chip should AI use? It depends on the job, the power available, and where the work happens. No single chip is best at everything.