AI is Dunning-Kruger as a service (christianheilmann.com)

The piece argues that generative AI has become “Dunning‑Kruger as a service”: like the famous psychological effect where novices overestimate their competence, modern AI systems and the product ecosystems around them systematically project confident, polished answers that are often wrong. The author links this to broader cultural incentives—speed, virality, and engagement metrics—that reward surface fluency and applause over accuracy or craft. Chatbots’ sycophantic, high‑confidence responses, paired with marketing that promises instant genius, encourage users to skip learning and treat model outputs as authoritative rather than fallible tools. For the AI/ML community this is a practical and ethical problem: models are frequently miscalibrated (high confidence in hallucinations), reward models and RLHF can produce polite but incorrect behavior, and product KPIs can prioritize time‑on‑platform or “good‑sounding” answers over factual grounding. Technical mitigations include better uncertainty quantification and calibration, retrieval‑augmented generation and grounded sources, stronger benchmarks for hallucination and truthfulness, and design that surfaces provenance and limitations. Equally important are socio‑technical fixes—transparent UI cues, user education, human‑in‑the‑loop workflows, and incentives that value craft and correctness. Without these, AI can amplify overconfidence at scale; fixing it will require both model‑level solutions and rethinking product metrics.