A recent blog post from a Chinese developer challenges the prevailing narrative that AI coding assistants have made deep technical expertise obsolete. The author, Du Jinsong, provides several concrete examples: generating a high-fidelity data dashboard with Tailwind CSS, quickly building an SMS platform MVP with basic rate-limiting logic, and even writing a complex irregular-shape crowd counting algorithm. In each case, while AI can produce a first pass, the real value comes from understanding the underlying systems, edge cases, and performance trade-offs that AI models often miss. The post argues that 'prompt engineering' is not a substitute for years of hands-on experience and that the most effective developers will be those who combine AI tools with deep technical knowledge. This perspective is particularly relevant for engineering leaders and senior developers who are evaluating how to integrate AI into their workflows without losing the critical thinking that drives innovation.
A Chinese developer argues that AI coding tools haven't eliminated the need for deep technical knowledge, using concrete examples to show where AI falls short.