Quality Assurance in AI Assisted Software Development: Risks and Implications

"We're now cooperating with AIs and usually they are doing the generation and we as humans are doing the verification. It is in our interest to make this loop go as fast as possible. So, we're getting a lot of work done." — Andrej Karpathy: Software Is Changing (Again) This quote describes a shift that is already visible in many teams. Code generation has accelerated. Verification and validation increasingly become the bottleneck. With AI tools, writing code is often not the limiting factor anymore. The hard part is proving that what was generated is correct, safe, and maintainable. Code Volume Growth and Test Review Challenges To understand QA challenges, we should look at how code is produced. Testing is not isolated. It reflects development speed and development habits. If development accelerates, QA pressure grows too. The Main Shift: Writing Has Become Cheap, Verification Has Become Expensive A common side effect of AI coding is rapid codebase growth without matching growth in qua