AI Didn't Simplify Software Engineering: It Just Made Bad Engineering Easier

6 hours ago

The software industry is attempting to downplay the necessity of software engineering, suggesting AI can replace it, but the author disagrees.
AI and large language models can generate code quickly, but they often lack accuracy and cannot replace the need for architecture, specifications, and validation.
Some companies are already reducing engineering staff, citing AI as making expertise redundant, but this is often a cover for poor business decisions.
The discipline of software engineering is being abandoned in favor of AI prompting, which the author argues is a mistake.
The author draws parallels to past cycles where new tools were believed to eliminate the need for engineering expertise, but complexity always resurfaces.
Aircraft maintenance is used as an analogy: despite advanced tools, trained mechanics are still essential due to the complexity of modern aircraft.
Professional software development differs from hobby projects, requiring reliability, security, and understanding of complex systems.
Writing code is not the hardest part of software development; design, behavior decisions, and system understanding are more critical.
AI-generated code can accelerate production but also risks increasing complexity and misalignment between specifications, tests, and implementation.
The author warns against treating AI tools as replacements for engineering discipline, emphasizing the need for expertise in building reliable systems.

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