AI chips
Processors purpose-built for the matrix math that training and running neural networks requires — trading general-purpose flexibility for raw throughput on that one workload.
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A CPU executes arbitrary instructions; an AI chip does one thing — multiply large arrays of numbers together — and does it at a scale a CPU cannot match. GPUs were the first press-ganged for the job; custom ASICs (Google's TPU, Cerebras, Groq) go further, stripping out everything except that arithmetic and the memory bandwidth to feed it. The hard part is not the chip itself but the software stack: a processor is useless without compilers and frameworks that know how to tile and schedule tensor operations across thousands of cores. That full-stack dependency is why switching chips is expensive and why vertical integration is a structural bet.
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Google forks the TPU
For the first time in a decade of TPUs, Google built two chips instead of one — a training chip and a serving chip — because the economics of the two jobs have pulled apart.
The penny-warrant template
Meta committed to roughly $100 billion of AMD chips and handed AMD a warrant for 160 million shares at a penny each — the same terms OpenAI signed five months earlier, now run a second time.
The trillion-dollar year, four years early
The world's chipmakers sold $99.5 billion in a single month in March 2026 — up nearly 80% in a year — putting the industry on pace to clear $1 trillion in annual sales for the first time.
The terafab ceiling
At his GTC keynote, Jensen Huang said NVIDIA's order book for its next two chip generations will hit $1 trillion through 2027 — double last year's figure — and that the only thing capping it now is how fast fabs can be built.
Terafab
Tesla announced its own US chip fab in March, targeting a million wafer starts a month — an order of magnitude past the world's biggest single plant — and within weeks the plan had quietly become an Intel contract.
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