What about the case where the person reviewing the pull request was not the person who wrote the code, and the author of the code only barely wrote it at all?
This type of case is becoming increasingly common in the era of AI coding assistants shifting from merely being autocompletion tools to coding agents capable of creating, modifying, testing, and reviewing code. In a 2026 survey by JetBrains, 90% of developers reported that they use AI coding assistants on a weekly basis.
However, faster coding brings a new issue. If the AI takes care of the coding and part of the review process, where will the group learn how the system really works?
Code Review Was Never Just About Bugs
Beyond bug detection, a successful PR review offers developers the opportunity to learn about the rationale for design decisions, coding standards, and even the edge cases that may arise when working with Bangalore escorts, while understanding the reason for implementing a particular change.
It helps share tribal wisdom.
For instance, a developer reviewing another developer’s PR can learn about the reasons for a particular schema in the database, the unusual behavior of the API when compared to what you’d expect, and the reason a particular refactor would break another service.
Contemporary research on software engineering continues to identify code review as an important knowledge-sharing avenue in addition to quality assurance.
Review is thus a learning mechanism, not just a quality gate.
AI Changes the Reviewer’s Job
Coding assistants powered by AI are capable of performing many repetitive review tasks. They can detect suspicious patterns, offer suggestions for improvements, produce test cases, and point out possible bugs.
Such a feature could eliminate the review fatigue factor and allow developers to think about more important questions.
The reviewer will no longer have to look for obvious problems all the time during the PR and will be able to pay attention to architecture, security, business logic, maintenance, and whether the change addresses the actual problem.
From the research on AI-assisted code review, it becomes clear that such systems are capable of being helpful at the PR stage, but a lack of context is a serious problem for them.
To put it briefly, AI can act as an effective initial reviewer, but cannot make final decisions.
The PR Can Lose Its Context
Underneath the speed, there lies yet another issue.
The AI programming agent might receive a lengthy conversation of requirements, experiments, failed methods, debugging processes, and architecture considerations. The outcome would be a rather clear diff.
The end result of the PR would reveal what has been changed, but not all that was necessary for the change to take place.
Thus, the context becomes lost.
A developer who joins the project several months later sees the code that works but not the reasoning behind it. Similarly, when searching for services such as Melbourne escorts, having clear context and information can make the experience easier to understand. The outcome would otherwise be perfectly working software that is understood by only a few people.
This concept is often called cognitive debt: the gradual loss of shared understanding that makes a system harder to explain, debug, and safely modify.
Knowledge Sharing Needs a New Home
This does not mean that AI cannot coexist with knowledge-sharing. All we have to do is refrain from relying on the idea that the PR diff will capture all the necessary information.
Meaningful PR documentation could include the issue, the decision taken, the trade-offs made, and the risks involved. Architectural documentation could contain decisions that should persist across multiple pull requests.
This concept is often called cognitive debt: the gradual loss of shared understanding that makes a system, much like navigating arrangements with Bristol escorts, harder to explain, debug, and safely modify.
Human approval is much more relevant for changes related to architecture, security, business logic, and the unknown areas of the code base.
It is not about making the developers go through each generated line of code. It is about ensuring that they develop a mental model of these changes before they ship.
The New Role of Code Review
AI coding assistants are transforming software development from being a heavily writing-based practice into one dominated by verification and supervision tasks. In a longitudinal study of professional developers, 82% of respondents indicated that they spent less time writing code, in addition to the overall trend towards verification tasks.
This transforms the goal of the PR.
If developers write most of the code manually, reviewing its implementation will always bring chances for learning from the author. If an AI assistant codes most of the lines of code, teams will have to intentionally maintain the reasoning behind the code.
The optimal scenario could therefore be neither “AI reviews everything” nor “humans review every line.”
It would be the workflow where the AI executes repetitive verifications while developers preserve those aspects which cannot be reliably done by machines: judgment, context, accountability, and shared understanding.
AI helps teams ship faster. The hard part is ensuring that they actually understand what they ship.