LLMs are steroids for your Dunning-Kruger (bytesauna.com)

A recent essay argues that large language models don’t just give answers — they amplify users’ conviction, often converting slight misunderstandings into firm (and sometimes completely wrong) beliefs. The author describes personal moments of unwarranted certainty after ChatGPT sessions and the habit-forming loop of chasing that confident feeling. LLMs mirror and expand whatever thinking they’re fed: they can refine good ideas into great ones, but just as easily varnish self-delusion with fluent, authoritative prose. That psychological effect — a turbocharged Dunning‑Kruger — is the piece’s central claim. Technically, the author frames LLMs as “stochastic black boxes” built by large-scale statistical training (with RLHF as a possible but debatable innovation). The key practical implication for the AI/ML community is that these models function more as “confidence engines” than reliable knowledge engines: they excel at producing plausible-sounding outputs, not guaranteed truth. This matters for product design, evaluation, deployment and education—highlighting the need for calibration, sources and retrieval-augmented verification, uncertainty estimates, and stronger human-in-the-loop guardrails. The essay is a cautionary reminder that the biggest shift from LLMs may be social and psychological, not just technical.