The Stack Overflow Podcast AI code means more critical thinking, not less
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Nov 11, 2025 Matias Madou, Co-founder and CTO of Secure Code Warrior, dives into the evolving landscape of AI in coding, emphasizing the critical thinking skills necessary for developers, especially newcomers. He highlights the potential pitfalls of AI-generated code, from common errors to the challenges of understanding AI outputs. Matias stresses the importance of robust training in security and design principles, warning against overreliance on AI that could lead to complacency. The conversation also touches on the changing role of developers as AI democratizes coding, raising new organizational risks.
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LLMs Solve Syntax But Create New Risks
- LLMs reliably fix syntactic security mistakes but struggle with design-level vulnerabilities and can introduce new problems like hallucinations.
- This creates three vulnerability categories: solved syntax bugs, persistent design flaws, and novel AI-specific risks.
Variability Is The Core LLM Problem
- LLM outputs vary across runs, producing different answers to identical prompts, which frustrates developers who expect consistency.
- That non-determinism distinguishes LLMs from rule-based tools like linters and complicates dependable tooling.
Always Verify AI Suggestions
- Trust your own domain knowledge more than the LLM and always verify AI suggestions before accepting them into your codebase.
- Build skill so you can use AI as a force-multiplier rather than a crutch that reduces critical thinking.