Summary of "Are Code Reviews Even Necessary?"

Main ideas / concepts

Core question: Are code reviews even necessary in modern software engineering?
Central claim: The value of code reviews depends heavily on purpose and team culture/process, not on whether teams use pull requests or pairing.

Pairing (XP-style, high-trust teams):
- Continuous collaboration while evolving design and implementation.
- Often uses iterative work patterns (e.g., small commits, trunk/main integration).
- Emphasizes trust and how to solve the problem, not whether code is permitted to pass a gate.
Traditional gatekeeping code reviews (e.g., investment bank context):
- Often “default no” mindset.
- Reviews act as a merge/production gate controlled by senior/architects.
- Can become policing-like, though the speaker distinguishes valid “quality bar” gatekeeping from abusive power dynamics.

The speakers emphasize that high-quality outcomes often result from team co-creation, not solitary authorship.

Gateway code reviews (a.k.a. “policy” / gatekeeping in the positive sense)
- Goal: Ensure code meets an agreed quality bar before it reaches the next stage (merge/commit to main/release).
- When it matters: Systems requiring high safety/assurance (example context: financial markets).
- How the quality bar can be enforced:
  - Partially via static analysis / linters / automation / robots (and potentially AI-assisted checks).
  - Decide how much human judgment is needed vs automated checks.
Knowledge sharing code reviews (a.k.a. “show and tell”)
- Goal: Share what changed and why, so the team understands patterns, approaches, and risks.
- Behavior:
  - Not always about rejecting code; often about adding further commits based on feedback.
  - Turns review into a mechanism for collective learning rather than a pass/fail stamp.
Iterative, collaborative critique during development (newer pattern, aligned with pairing)
- Goal: Collaborate on design evolution as work progresses, similar to how pairing evolves design through iteration.
- Observation from speakers: This pattern is uncommon in mainstream “PR review” workflows.
- AI connection: With agentic AI/assistants generating smaller chunks/commits, teams may need new collaborative review skills that resemble pairing/iterative critique.

Purpose: Team alignment on significant or risky decisions, not on every small change.
When critique is appropriate:
- Big new features or new design approaches
- Greenfield work where patterns need alignment
- Risky areas, such as:
  - New security profile concerns
  - New technologies adoption
When critique is less necessary:
- Simple bug fixes or UI issues where no major redesign/alignment is needed
Output of critique: Decide which approach to adopt; critique can later crystallize into policy (team-wide agreed practice).

A high-performing team (trading system) noticed multiple pairs producing different but functionally equivalent implementations.
The team reviewed the different approaches as a group, agreed on an integrated direction, then:
- Captured the decision in an Architecture Decision Record (ADR)
- Updated codebases so implementations converged to a shared model
Key point: This review was not gatekeeping or policing; it happened out of band while still allowing normal shipping.

Instruction / recommendation:
- Have juniors review seniors’ code, especially for knowledge sharing.
Rationale:
- Newer engineers ask clarifying questions that expose readability and hidden complexity gaps.
- If reviewers don’t understand the code, the code is likely not readable.
The speaker also notes this works well because “why is that there?” questions can reveal dead/unclear logic.

AI increases code volume, which can overwhelm review bandwidth.
Many teams review code as cargo-cult or regulatory checkbox behavior rather than clearly stating:
- What is the review for?
- What failure mode are we preventing?
Humans may not be good at catching bugs line-by-line, but can be good at:
- Scanning for “something doesn’t look right”
- Detecting naming/structure issues, duplication, and suspicious complexity
Shift from “review every line” to “review for purpose”:
- If reviews are slower than feature creation, the organization should question what’s broken in the process.
Rubber ducking / reflective prompting with LLMs:
- LLMs can function like sophisticated rubber ducks, but requires the human to articulate the problem well.
- The speakers imply that good questioning/clarification becomes important when using AI to assist development.

Answer framing: Code reviews are not universally necessary—the answer is “it depends.”
Decision criteria:
- Start with the review purpose (“to what end?”).
- Then determine:
  - Whether code needs review at all
  - What to look for during review (security/risks vs understanding vs alignment)
Suggested improvement: Clarify and align review practices with one of the three purposes (policy/gateway, knowledge sharing, critique/iterative collaboration).