How to Win in the AI Coding Era

AI makes it easier to write code.

That does not mean it makes software easier to build.

The real challenge is no longer getting code into a pull request. It is making sure someone understands it before it ships.

That is why code review has to change.

Not because AI review apps are bad. They can be genuinely useful. But because AI writing code and AI approving that code is not a review process. It is a fast way to create software nobody really owns.

The old review process is too weak

Most teams still treat review as a gate:

1. Open a pull request
2. Get an approval
3. Merge

That worked well enough when changes were smaller and slower to produce.

Now a developer can generate a big feature, refactor several files, or add an integration in an afternoon. The code may compile. The tests may pass. An AI tool may even say it looks fine.

But none of that tells you whether the team understands the change.

Reviewsaur’s State of Code Review 2026 looked at 77,949 merged pull requests across 261 active public GitHub repositories. It found that 62% of approvals had no comment, question, or inline feedback.

That does not mean 62% of reviewers did nothing. Some review happens in Slack, calls, pairing sessions, or at a desk. But it does mean that most approvals leave no visible evidence of what was checked.

And that matters more when code is cheap to generate.

AI has increased the pressure on reviewers

The study found that the typical pull request grew from 85 changed lines in 2022 to 124 in 2026. At the same time, the median repository shipped 69% more code per half year, while the number of active authors barely changed. That means about 40% more code per engineer. See the data on larger pull requests and code volume.

Review capacity did not grow at the same rate.

The busiest reviewer in the median repository approved 47% more lines than in 2022. One person still accounted for 40% of approvals in the typical repository. See the reviewer load findings.

That is not because reviewers suddenly stopped caring.

It is because there is more code than the old process can reasonably absorb.

The result is predictable. Bigger pull requests get less attention per line. Pull requests with more than 1,000 changed lines received around four times less inline comment attention per line than mid sized pull requests. See the pull request size analysis.

At some point, review stops being reading and starts being scrolling.

Do not let AI review itself

AI review tools have a role.

They can summarise a diff, identify files that affect authentication or payments, flag missing tests, explain unfamiliar code, and point reviewers toward possible edge cases.

That is helpful. It saves time and makes the human reviewer more effective.

But an AI tool cannot own the consequences of a change.

It does not know whether a retry could create duplicate invoices during a provider outage. It does not know why a strange legacy rule exists. It cannot reliably judge whether a shortcut creates a problem for the product, the architecture, or the people supporting the system next year.

AI can spot patterns.

Humans need to judge intent, tradeoffs, and risk.

So the model should not be:

AI writes the code. AI reviews the code. Human clicks merge.

It should be:

AI helps the team understand the code. Humans decide whether it should merge.

That is a very different workflow.

What review should look like now

Code review in the AI era should be less about proving that someone clicked approve and more about creating shared understanding.

A good review does not need a long comment on every pull request. But it should make a few things clear.

Start with the reason

The author should explain what changed and why.

Not just “adds retry logic.”

Something like:

Adds retries for temporary payment provider failures. Uses the existing idempotency key to avoid duplicate charges. The main risk is a timeout after a successful payment, so we added monitoring and a feature flag for rollback.

Now the reviewer knows what to look for.

Let AI point to risk

Use AI to help answer practical questions:

• What parts of the system does this touch?
• What behaviour changes?
• Are there security, permissions, payment, or data migration risks?
• What edge cases may be missing?
• Which parts of this diff are generated, copied, or unusually complex?

This is where AI review tools are strongest. They help people find the important parts faster.

Put humans on the important decisions

The reviewer should focus on what AI cannot really own:

• Is this the right behaviour?
• Is the risk acceptable?
• Does it fit the architecture?
• What happens when the happy path breaks?
• Can another engineer maintain this later?
• Does the team understand the tradeoff it is accepting?

That is review.

Match the process to the risk

A copy change should not get the same process as a database migration or a new permissions flow.

For small, low risk changes, keep review light.

For changes involving money, security, customer data, infrastructure, public APIs, or a lot of AI generated code, slow down. Get the right domain owner involved. Ask for an explanation and a rollback plan.

The goal is not to make every merge harder.

It is to make high consequence changes harder to misunderstand.

The approval button is not the outcome

A required approval is useful. It creates a pause before code enters production.

But it is not proof that the team understands what it is shipping.

The Reviewsaur research found that company repositories were much less likely to merge with no approval than community repositories, 4% versus 44%. But their silent approval rates were similar: 68.5% for company repositories and 65.1% for community repositories. See the company and community comparison.

In other words, companies were better at ensuring that someone clicked approve.

That is not the same thing as ensuring someone engaged.

The winning teams will not measure success by how quickly a pull request gets an approval.

They will measure whether the right people understood the important parts before it merged.

Win by scaling understanding

AI will reward teams that can move quickly.

But the best teams will not use AI just to produce more code. They will use it to make code easier to understand, easier to review, and easier to own.

Use AI to summarise changes.

Use AI to highlight risk.

Use AI to suggest test cases.

Use AI to help reviewers navigate unfamiliar code.

Then let humans do the part that still matters most: decide whether the change is right.

That is how to win in the AI coding era.

Not by generating the most code.

By making sure your team can still explain the code it ships.

Sources

All data in this article comes from Reviewsaur Research’s The State of Code Review 2026, v1.1, which analysed 77,949 merged pull requests across 261 active public GitHub repositories.