AI code review tools fall short of solving real developer problems

The AI code review market faces a significant disconnect between the problems engineering teams hope to solve and the actual capabilities of the tools they purchase. This misalignment stems from conflating author-focused improvements with reviewer efficiency, leading many organizations to invest in solutions that don’t address their primary bottleneck: the time senior engineers spend reviewing code rather than building new features.

The big picture: Engineering teams at growth-stage startups are experiencing a critical bottleneck where their most valuable engineers spend excessive time reviewing pull requests instead of developing new features, yet the AI code review tools they purchase often fail to solve this specific problem.

• Most teams evaluate AI code review tools based on their ability to detect bugs, identify security vulnerabilities, and suggest style improvements—focusing primarily on code quality rather than reviewer efficiency.
• When examined closely, these tools primarily help authors write better code before review, but don’t fundamentally change how reviewers must interact with and understand code changes.

The key distinction: Current AI code review tools like CodeRabbit, CodeAnt, Greptile, and Ellipsis are primarily author-focused rather than reviewer-focused, creating a mismatch between expectations and outcomes.

• Author-focused tools excel at catching issues before human review begins, suggesting improvements to code quality, and enforcing style guidelines.
• Reviewer-focused tools would instead address the fundamental inefficiency in how senior engineers must still read code line-by-line to understand changes, identify business logic issues, and evaluate architectural implications.

Why this matters: Understanding this distinction is crucial for engineering leaders making technology investments, as mistaking author improvements for reviewer efficiency leads to continued bottlenecks despite significant tool investments.

• When engineers adopt AI code review tools hoping to “save time on reviews,” they’re often unclear about which side of the equation they’re actually optimizing for.
• The review bottleneck remains as reviewers still need to perform every step they did before adopting these tools, even with AI-generated comments available.

The complementary approach: Rather than viewing these as competing solutions, engineering teams should consider how different tools work together to optimize the entire development cycle.

• The ideal approach likely combines author-focused tools that improve code before review with reviewer-focused tools that make the human review process itself more efficient.
• By addressing both sides of the equation, teams can build a more holistic approach to code reviews that truly accelerates development cycles.

Looking forward: Engineering teams need to clearly define which problem they’re trying to solve when evaluating code review tools.

• Teams should explicitly determine whether their primary goal is improving code quality before review, reducing time spent by senior engineers on reviews, or both.
• This clarity will help organizations make more informed decisions about which tools will actually deliver their desired outcomes and improve overall development efficiency.