As language models improve, many AI failures no longer look like hallucinations or obvious mistakes.

The more concerning failures I’ve been seeing are outputs that are fluent, contextually appropriate, and confident — but based on unstable or incorrect interpretation.

These aren’t syntax errors or fabrications. They’re meaning-level failures: ambiguity silently resolved, assumptions introduced, or context collapsed in ways that feel reasonable until they cause downstream issues.

I’ve been thinking about this as a form of “semantic risk” — the probability that an AI system produces a materially incorrect interpretation even when the output looks right.

I tried to write this up carefully here, focusing on why “hallucination” is the wrong mental model for many modern failures and why this matters for human accountability:

https://semanticrisk.io

I’m curious whether others are seeing similar patterns in production systems or workflows, and how you’re reasoning about them.