FEATURE: CODING
Hiring managers are increasingly looking beyond language fluency in Python, Java or C ++ and asking whether applicants know how to leverage tools like GitHub Copilot, ChatGPT or Amazon CodeWhisperer effectively.
This represents a fundamental shift in software engineering – from coding as craft to coding as orchestration. The new question isn’ t“ can you write this function?” but“ can you direct AI to write it, validate the output and integrate it responsibly?”
Developers see AI as an enabler
Despite widespread concerns, the majority of respondents are not fatalistic. Four out of five developers( 80 %) said they view AI as an enabler rather than a threat.
This perspective is key. Developers increasingly believe AI’ s biggest value lies beyond automation. It’ s not just about completing tasks faster – it’ s about enabling new ways of working.
Examples include:
• Debugging: AI can suggest fixes faster than manual inspection.
• Testing: Generating exhaustive unit tests that humans wouldn’ t have time to write.
• Prototyping: Exploring multiple design patterns in minutes.
By shifting focus from repetitive work to higher-level problem-solving, AI could help developers do more creative and impactful work.
The gap between use and understanding
However, the survey uncovered a worrying statistic: 59 % of developers admitted to using AI-generated code without fully understanding what it does.
This poses risks for security, maintainability and compliance. As codebases grow more dependent on AI contributions, teams may find themselves deploying solutions they cannot fully audit.
Several industry experts have already raised the alarm about‘ black-box coding’ – a situation where software runs correctly but nobody fully understands why. If left unchecked, this could increase vulnerabilities and technical debt over time.
Industry perspectives: What comes next
The Clutch survey captures a moment of transition. Developers are both embracing and questioning
The trust gap
• 59 % use AI-generated code without fully understanding it
• Raises risks in security, maintainability and compliance
• Could lead to long-term technical debt
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