Are Code Reviews Even Necessary?

2026-05-01 · Source: Modern Software Engineering · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

Trisha Gee and Daniel Terhorst North discuss the necessity and evolving role of code reviews, particularly contrasting them with pair programming in eXtreme Programming (XP) teams and the impact of AI-generated code. They identify three distinct purposes for code reviews: "gateway" (policy/gatekeeping for safety), "knowledge sharing" (show and tell for team awareness), and "critique" (iterative feedback for design evolution and alignment). While XP teams often forgo formal reviews due to continuous pairing, traditional organizations use them for feature branches and pull requests, often with a "default is no" mindset. The conversation highlights that AI's ability to generate significantly more code is creating bottlenecks in traditional review processes, forcing teams to re-evaluate the "why" behind their reviews. They suggest that humans excel at scanning code for structural issues or "janky stuff" rather than bug detection, and that AI serves as a "sophisticated rubber duck" for developers.

Key takeaway

For Directors of AI/ML or Software Engineering teams integrating AI code generation, you must critically re-evaluate your code review processes. Traditional gatekeeping reviews become bottlenecks when AI produces significantly more code. Instead of blindly reviewing every line, define the specific purpose for each review—whether it's for policy adherence, knowledge sharing, or design critique. Automate checks where possible and empower your team to use AI as a sophisticated "rubber duck" for adjacent technologies, freeing human reviewers for higher-value tasks like architectural alignment and complex problem-solving.

Key insights

Code reviews serve distinct purposes (policy, knowledge, critique); AI's code generation demands re-evaluating their "why" and human role.

Principles

• Pairing often negates the need for traditional code reviews.
• Code reviews must serve explicit policy, knowledge sharing, or critique.
• Junior developers reviewing code enhances its readability.

Method

Daniel's "code critique" involves monitoring commit messages for similar solutions, then a team-based review to integrate diverse approaches, capturing decisions via Architecture Decision Records (ADRs) for alignment.

In practice

• Explicitly define the "why" for each code review.
• Automate policy checks (linting, formatting, tests).
• Assign junior developers to review senior code.

Topics

• Code Review Practices
• Pair Programming
• AI Code Generation
• Software Development Workflow
• Architecture Decision Records
• Quality Gates

Best for: CTO, VP of Engineering/Data, Machine Learning Engineer, Software Engineer, Director of AI/ML, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Modern Software Engineering.