AI coding tools sound confident even when they're wrong. Here's the psychology behind why Java developers accept bad suggestions — and habits that help.

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AI coding tools like Copilot produce fluent, confident-sounding output that exploits the brain's fluency heuristic, leading developers to accept suggestions without adequate scrutiny. Sonatype's 2026 report found nearly 30% of LLM-generated dependency recommendations reference non-existent package versions. Java developers face specific risks: hallucinated Maven coordinates that enable slopsquatting attacks, invisible transitive dependency changes (e.g., CVE-2025-48976), boilerplate code with semantic bugs that compile cleanly, and outdated API patterns. While the toolchain catches obvious errors, subtler issues like known CVEs in existing packages or security flaws in valid code slip through. Practical mitigations include asking the model what it doesn't know, providing full project context, requesting alternatives and trade-offs, verifying reasoning not just output, and treating every AI suggestion as a draft. Microsoft research warns that heavy AI tool use measurably reduces critical thinking — the very skill needed to catch AI mistakes.

Why Java Developers Over-Trust AI-Generated Code