Two hours ago, Anthropic reported “elevated error rates in Opus 4.5”. I immediately failed over to Gemini.  The result? Within seconds, I was seeing “Reduced capacity” and “Infrastructure load” warnings on Gemini too.

It makes me wonder: Are we witnessing a simple traffic spike, or is something deeper happening in the AI stack?

We talk a lot about “Model Redundancy”, but we rarely talk about Hardware Colocation.

Think about the architecture: Anthropic’s Opus and Sonnet are first-class citizens on Vertex AI. They aren’t just hosted there: they are trained and run on Google’s own TPU (Tensor Processing Unit) clusters.

This raises a critical question for Trust Engineering: If a specific TPU farm in a major region (like us-central1) hits a snag, does it matter whose API key you are using?

Are Claude and Gemini independent competitors, or are they neighbors sharing the exact same “oxygen tank”?

There are two possibilities for what happened today:

  1. The Thundering Herd: Thousands failedover to Gemini simultaneously, overwhelming its immediate capacity.
  2. The Shared Engine Failure: A local infrastructure issue in the underlying GCP layer affected both tenants at once.

If it’s the latter, then our “Multi-Cloud” and “Multi-Model” strategies might be an illusion. If your Model A and Model B are both breathing through the same hardware, you don’t have a backup—you have a Single Point of Failure with two different names. As Gergely Orosz often highlights in his analysis of engineering maturity: we are currently shipping AI features much faster than we are building the operational SRE foundations to support them.

I’m curious—did anyone else see this specific correlation today? Are we looking at a traffic fluke, or do we need to start auditing our AI providers for “Hardware Isolation” as part of our TDD?