The biggest mistake is treating slower frontier-model development as the same thing as weaker AI demand. These are two different cycles.

If model training slows, the next growth engine could be inference: AI agents, enterprise automation, search, coding and customer service. Once millions of businesses start using AI continuously, compute demand becomes recurring rather than concentrated in giant training runs.

That shift could also benefit ASICs, networking, HBM and data-center infrastructure, especially where cost, latency and power efficiency matter.

So I wouldn’t call today’s selloff the end of the AI cycle. I’d call it a rotation from “build smarter models” to “use AI everywhere.”

The real warning sign would be falling cloud capex, weakening GPU utilization and declining HBM orders at the same time.

@Tiger_comments [贱笑]