I Do Not Want to Be a Programmer Anymore (mindthenerd.com)

A veteran programmer recounts a small domestic episode—asking an AI to judge a domain-name dispute with his wife—and realizing the bigger danger: it wasn’t that AI “stole” work, it was that its confident-sounding answers made him surrender his own judgment. The tool produced polished arguments and examples that instantly persuaded him, and the episode exposed a wider pattern: non-experts now produce detailed, AI-generated designs, flowcharts, and proposals that sound plausible and authoritative even when they gloss over trade-offs, costs, or feasibility. That dynamic amplifies authority bias and shifts the workload onto domain experts who must justify why a slick-sounding solution might take months, require more resources, or be outright wrong. For the AI/ML community this is a practical and ethical signal: model fluency and certainty are not the same as correctness. The technical implication is clear—deployments must prioritize uncertainty communication, provenance, and human-in-the-loop workflows so that users don’t mistake persuasive phrasing for expertise. Product teams should design guardrails, calibrated confidence scores, source citations, and UX that encourages skepticism and verification. More broadly, the story is a call to preserve “slow” expertise—context, judgment and the willingness to say “it depends”—because as models democratize answers, wisdom becomes the scarce currency that keeps AI useful rather than misleading.