There is a slow-motion traffic jam happening inside engineering organizations right now, and very few people are addressing it directly.
AI coding assistants are generating more code, at a faster rate, than any human team ever could. Because of this, the traditional pull request queue is out of control. Code reviews that used to take ten minutes now take an hour, assuming the reviewer actually takes the time to read the code line by line.
The most common response from engineering leadership has been to throw more resources at the problem: hire more reviewers, add extra approval steps, or, worst of all, ask an AI to review the code generated by the first AI. 🤖
That last solution should be a massive red flag. If your answer to an overwhelming amount of AI-generated code is an AI-generated review, with a human blindly clicking “approve” in the middle to make it feel legitimate, you haven’t actually solved the problem. You haven’t solved the bottleneck; you’ve just added a waiting room to a self-checkout line. 🛑
This points to a much deeper, more uncomfortable question: What if the code review process was never actually the thing keeping our software high-quality, and AI is just the event that finally exposed that truth?
The Job Code Review Was Never Meant to Do 🎯
If you ask ten different engineering leaders what the purpose of a code review is, you will get a long, contradictory list of answers. Often, you will get multiple answers from the same person. They will tell you it exists for:
- 🐛 Catching bugs before they reach the public;
- 🏛 Enforcing consistent software architecture;
- 🎓 Teaching and mentoring junior engineers;
- 🤝 Sharing knowledge so no single person holds all the answers;
- 🔒 Identifying security vulnerabilities;
- 🧹 Maintaining clean code style and formatting;
- 📜 Creating a paper trail for compliance and audits;
- 🫂 Fostering a feeling of shared team ownership.
That is not a single job description. Those are seven or eight entirely different jobs, all mixed into a fifteen-minute window where a developer looks at a screen, usually while distracted and slightly annoyed about being pulled away from their own work.
We didn’t intentionally design the code review process to handle all of this. It just absorbed these responsibilities over the last fifteen years because it was the one guaranteed checkpoint where a second human being was forced to look at the project.
AI has not broken this system. It has just amplified the volume flowing through it until the cracks became impossible to ignore. A process built occasionally to catch a subtle bug is now being asked to verify system architecture, transfer knowledge, and ensure security at ten times the normal speed. Naturally, it is collapsing under the weight.
The Flawed Pattern: Build First, Think Later 🔄
The pattern causing teams the most pain right now is the tendency to build first and think later. A developer (or an AI agent) writes an entire feature, opens a request for review, and only then does the team have its first real conversation about whether it was the right approach to begin with. 🤦♂
That ordering was always backwards. It just didn’t cause massive issues when code was written at human speed, because the cost of building the wrong thing first was only a day or two of one person’s time. 🐢
Today, an AI agent can generate that same flawed solution in four minutes. A team can produce ten flawed solutions before lunch. The cost of having a conversation to revise the mistakes hasn’t gone up. It still takes the same amount of time to discuss the right approach. But the sheer volume of code waiting for that conversation has exploded, and there are simply not enough hours in the day to handle it.
The real fix is not to review code faster. It is to make the important decisions earlier in the process.
Shifting the Conversation to the Starting Line 🏁
To actually fix the math of this bottleneck, teams need to change how they work, rather than just adding more horsepower to a broken checkpoint.
1. Design out loud before the code exists 🗣: If there are two different ways to build a feature, debate them on a whiteboard or virtually. Do not wait to argue about it in a comment thread on a 900-line code review three days later. Disagreements are cheap before the code is written and incredibly expensive afterward. AI makes the coding phase happen so fast that skipping the initial planning phase is now actively dangerous.
2. Let junior developers watch the thinking, not just the output 🧠: Reading a senior engineer’s finished code only teaches a junior developer what the final decision was. Watching a senior engineer actually make those decisions, including the wrong turns, the dead ends, and the moments of realization, teaches them how to think. Pair programming and structured planning sessions accomplish this far better than reading a finished document ever could.
3. Automate the mechanical tasks 🤖: Code formatting, basic errors, and known security flaws do not require human brainpower. If your highly paid reviewers are still leaving comments about whether to use tabs or spaces, that is a failure of your automated tools, not a quality control process. Let computers handle the mechanical checks.
4. Encode your core rules into your testing suite 🛡: If you have strict rules about architecture, security boundaries, or system performance, turn them into automated tests that run continuously. A critical rule that lives only in a senior developer’s memory will eventually be forgotten on a busy day. A rule written into an automated test suite never gets tired and never takes a day off.
5. Reserve human eyes for where judgment actually matters 🎯: Not every change to your software carries the same level of risk. A one-line update to some text and a massive overhaul of your user login system are entirely different categories. Treating them with the same level of mandatory scrutiny causes review fatigue; reviewers start skimming everything because they are overwhelmed. Save deep, focused human review for major architectural shifts, sensitive data handling, unfamiliar code, or any moment when a developer honestly admits, “I am not confident about this.”
The Uncomfortable Reality of Dropping Mandatory Reviews ⚖
Telling a team that they no longer need to review every single line of code creates a very natural fear: what if the critical mistakes are the ones that slip through? What if the developer least equipped to judge the risk of their own code is the one who decides to skip the review?
Moving to a risk-based review process is a trade-off. You are trading a low-attention checkpoint for a smaller number of high-attention, highly focused reviews. This only works safely if you put guardrails in place:
- 🚩 Make the rules explicit: The triggers for requiring a human review must be set in stone. Anything touching user privacy, payments, or core authentication must always be reviewed. This cannot be left to an individual’s judgment call when they are rushing to meet a deadline.
- 📊 Watch the metrics: Someone must actively monitor the health of the system: tracking error rates, crashes, and how fast problems are detected. You cannot just assume the new process is working because nobody is complaining.
- 🎭 Be honest about rushing: Teams must be honest with themselves about the difference between being truly confident in their code and simply being in a hurry. Only genuine confidence should ever skip a review.
Loosening up on code reviews means giving up a process that felt safe. Having a mandatory review for every piece of code is the software equivalent of installing a fake security camera. It looks like it is monitoring the situation, it makes everyone feel safer, but it isn’t actually recording a thing.
If engineering teams are serious about surviving the AI transition, they have to admit that a large portion of the traditional review process was just theater. It was useful theater that sometimes caught real problems accidentally, but theater does not scale to the speed of AI development.
Engineering leaders must now focus their energy not on managing an endless queue of reviews but on constantly asking what job each step in their process is actually doing.
The teams that come out ahead will not be the ones who figure out how to review code faster. They will be the ones who figure out how to plan better, automate the busywork entirely, and save real human judgment for the moments that truly require it.