GPT-5's Impressive Performance Hides Complex Code Issues
GPT-5, the latest iteration of AI-driven code generation, is making waves with its superior functional performance. However, it also generates a vast amount of complex and potentially insecure code, raising concerns about technical debt. Xia Junxiong, in a report published on 36Kr, highlights these findings.
GPT-5's advanced reasoning capabilities enable it to outperform other models in functional correctness. It's a significant leap, according to Xia's report. However, this increased reasoning also introduces a new class of subtle, complex issues. GPT-5-minimal, a lighter version, is a top-tier performer but is extremely verbose and generates highly complex code, nearly double the rate of Claude Sonnet 4.
Xia's report, citing tests in sectors like healthcare, finance, and government, notes that GPT-5 and Claude Opus 4.1 by Anthropic are approaching the quality of work of industry experts. Yet, using such AI capabilities requires a 'trust, but verify' approach. While GPT-5's higher reasoning modes appear cleaner and more correct, they are saturated with hard-to-detect issues. Robust code governance, including rigorous automated static analysis, is crucial to manage this complexity and identify these nuanced flaws.
GPT-5's impressive functional performance is tempered by the sheer volume of complex and potentially insecure code it generates. As AI capabilities like GPT-5 become more prevalent, so too must rigorous testing and governance to ensure the quality and security of the code it produces.
