Andrew Ng’s widely praised vision of artificial intelligence as “the new electricity” is dangerously wrong, according to a critique based on a decade of observing legacy enterprises. The article argues that Ng’s push for democratized, commoditized machine learning has created “an army of credentialed amateurs” who can fine-tune pre-trained models but lack understanding of systems architecture, data hygiene, and inference economics. This lowering of barriers has flooded the market with noise, leading junior developers to deploy black-box models into environments with real-world consequences. The central claim is that most organizations do not have an AI problem but a data engineering problem; handing business units uncleaned, siloed legacy CRM exports results in models that learn biases, hallucinate confidence intervals, and cost ten times more to maintain than the legacy databases they replace. The piece urges treating machine learning not as a civic utility but as an expensive, volatile industrial resource requiring ruthless engineering discipline.