The Identity Crisis of Software Engineering in the AI Era: Some Engineers Become 'Lazy' and Rely on AI to Generate Code, Comments, and Documentation
The identity crisis of software engineering in the AI era: some engineers have become 'lazy', relying on AI to generate code, comments, and documentation, with almost no manual testing or deep thinking; 'craftsmen' bear a heavy review burden, struggling to cope with low-quality pull requests, and may eventually also turn to a lazy mode.
This 'class differentiation' is particularly evident in some companies, where AI tools lower the entry threshold but also amplify the asymmetry of responsibility, with craftsmen becoming the actual gatekeepers, while the lazy disguise productivity through AI.
Not all companies are like this; many mature large enterprises have achieved real productivity improvements through correct practices and high talent density. However, this phenomenon is widespread, highlighting the impact of AI on engineering culture and team trust.
ABAB AI Insight
Deedy's described phenomenon reflects the rapid shift of AI code tools from auxiliary to dominant roles, similar to the restructuring of developer roles after the popularity of GitHub Copilot, with increased burdens on craftsmen coexisting with speculation from the lazy.
In terms of capital pathways, companies tend to reward visible outputs, allowing the lazy to profit in the short term, but the long-term accumulation of technical debt will force organizations to revalue code quality and review culture, with funding shifting towards engineering practices that can balance AI efficiency and human oversight.
Similar to the differentiation between artisans and machine operators during the Industrial Revolution, current software engineering is at a critical window of transformation from manual coding to AI-enhanced collaboration, and the identity crisis is an inevitable growing pain.
Essentially, this is about technological substitution and capital concentration; AI lowers the coding threshold but amplifies review costs, shifting pricing power from individual productivity to team trust and engineering culture, with long-term outcomes determined by organizations that can maintain high-quality output.
ABAB News · Cognitive Law
AI is an efficiency lever and a responsibility diluter, with lazy speculation and craftsmen under pressure; there must be warnings before cultural collapse.
Code review is the last line of defense, trust is the foundation of collaboration, and the quality of engineering in the AI era depends on human-machine collaborative design.
Software is not a mass-produced product; the spirit of craftsmanship is the core competitive advantage, and pricing power is determined by teams that can balance speed and quality.