OpenAI's Custom AI Chip Threatens Nvidia's Moat

A thread on OpenAI's Jalapeño chip, described as competitive with or better than Nvidia Blackwell for LLM inference workloads, generated real discussion about what vertical integration in AI compute means. The key observation from commenters: if OpenAI can build inference hardware optimized for its own models, it breaks the assumption that Nvidia's dominance in AI silicon is permanent.

The pattern: every major AI lab is now working on custom silicon. Google has TPUs. Amazon has Trainium and Inferentia. Apple has the Neural Engine baked into its chips. Now OpenAI is reportedly building its own. The motivation is economics, not engineering pride. Nvidia's margins on H100s and B200s are extraordinary, and every dollar spent on Nvidia hardware is a dollar that does not compound inside the lab building the models.

Commenters flagged that token prices keep falling, and a thread commenter made the point directly: continued hardware improvements make it hard to believe token prices will not keep dropping. That's good for builders who use APIs, but it compresses the window for anyone whose business model depends on being the cheapest inference provider.