ASICs

Plain English. An ASIC — application-specific integrated circuit — is a chip built to do one job rather than anything you throw at it. In AI it usually means fixed-function silicon for inference: run a model fast and cheaply, and don't bother being flexible. OpenAI's Broadcom-partnered Jalapeño and Etched's Sohu are the headline examples.

Why it moves money. Generality is what you pay Nvidia for; an ASIC throws it away to win on efficiency per dollar and per watt. Because inference is the growing majority of AI compute, a fixed-function chip that beats a GPU on tokens per megawatt can undercut the incumbent exactly where the volume is. That is the direct assault on Nvidia's margin, and part of why labs design their own custom silicon rather than only buy it. The trade-off is rigidity: an ASIC frozen around today's model can be stranded if the architecture moves.

What to watch. Third-party benchmarks rather than vendor decks, and whether ASICs deploy outside their designer's own fleet — the test of merchant viability. Contrast the flexible GPU and Google's in-house TPU.

From the signals. OpenAI's first ASIC reportedly beat Nvidia's Rubin on tokens per megawatt. OpenAI then published its own Jalapeño numbers, adding a latency claim. Etched shipped its first rack to the investor that led its US$700m round.