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Analysis: Why AI coding tools shift the real bottleneck to review

AI-Generated Code: A New Era for Code Review

AI-Generated Code: A New Era for Code Review

The acquisition of Graphite by Cursor in late 2025 signaled a significant shift in the tech industry. As AI coding tools become more prevalent, the way we approach code review is evolving. In this article, we explore how AI-generated code changes the dynamics of code review and why it matters for the North East region and beyond.

More Code, Different Questions

When AI generates code, it often produces more lines of code than human-written code. However, the problem isn't just about volume. It's about the shift in review questions. AI-generated code is more comprehensive and defensive, which leads to a different set of questions during the review process.

Defensive Patterns and Over-Engineering

AI-generated code tends to be more defensive and comprehensive, covering edge cases that developers might overlook. While this can lead to more robust code, it also results in more lines of code and more complex structures, which can make review more time-consuming.

Necessity, Not Correctness

Instead of focusing on correctness, reviewers of AI-generated code find themselves questioning the necessity of certain abstractions, edge cases, and defensive patterns. This shift in focus can lead to a more thorough review process, but it also requires a change in mindset.

Implications for the North East and India

As AI coding tools become more prevalent, the North East region and India as a whole will need to adapt to this new era of code review. This shift will have implications for team structures, workflows, and the skills required for developers. Embracing this change will be crucial for staying competitive in the global tech landscape.

Looking Forward

As AI coding tools continue to evolve, so too will the dynamics of code review. Teams that adapt well will change how review works, using AI for first-pass checks and basic issue detection, and then routing the code to humans for judgment calls. By doing so, they can leverage the strengths of both AI and human developers, ensuring that code is not only correct but also necessary and efficient.