AI hallucination debate turns human

The hot take is right in spirit but risky in framing: hallucinations are not proof that models “think like us,” but they are a brutal reminder that plausibility is a terrible proxy for accuracy.

• –For developers, the takeaway is practical: treat LLM output as unverified inference unless grounded by retrieval, tools, citations, or tests.
• –Calling hallucinations “human-like” can help explain the UX problem, but it can also blur the technical causes: training data, decoding, objectives, and missing uncertainty calibration.
• –The strongest systems will not just sound less wrong; they will know when to abstain, ask for context, or route to verification.
• –The discussion is more philosophy than news, but it maps directly onto reliability work in agents, RAG, evals, and AI safety.