The Comprehension Bottleneck

Many assume that when AI produces ten times the code, we get ten times the output.

Writing code was never the bottleneck.

The bottleneck has always been reading code. Understanding it. Deciding whether a given approach is the right one. Figuring out where new work should connect to existing work and whether that connection will hold.

AI tools have become remarkably good at generating code. They autocomplete functions. They translate intentions into syntax. They make the mechanical act of typing faster than ever.

This is genuine progress. It is also progress on the wrong constraint.

Most time spent building software is not spent typing. It is spent staring. Tracing logic through files. Asking why something was done a certain way. Wondering whether the abstraction you are looking at should be extended, replaced, or left alone.

This is comprehension work. It happens before you write anything and after someone else writes something you need to evaluate.

AI does very little to accelerate comprehension.

Generating code is easy for a model because generation has a clear target: produce something that works. Understanding code is harder because understanding has no fixed output. You are not trying to produce an artifact. You are trying to build a mental model accurate enough to make good decisions.

The difference matters. A tool that writes code for you does not automatically give you insight into that code. It does not tell you whether the approach fits the broader system. It does not explain the tradeoffs embedded in someone else’s pull request.

You still have to read. You still have to think.

This creates a strange imbalance. Teams can now produce code faster than they can evaluate it. The queue of things to review grows. The bottleneck tightens.

Reviewing code is cognitively expensive. It requires holding context in your head while predicting how a change will interact with parts of the system you cannot see on screen. It requires judgment about maintainability, edge cases, team conventions, and future evolution. None of this compresses well.

Writing more code does not make reviewing easier. If anything, it makes it harder. The volume of decisions waiting for human judgment increases while the human capacity for judgment stays fixed.

This is the hidden cost of faster generation: faster accumulation of review debt.

Multiply code output by ten. Review capacity stays the same. The math is unforgiving. Either engineers review more code per hour, or code waits longer to be reviewed, or both.

Pushing reviewers to move faster does not solve the problem. It transforms it. Rushed reviews are shallow reviews. Edge cases get missed. Subtle bugs slip through. Technical debt accumulates in places no one looked closely enough to see.

The bottleneck does not disappear under pressure. It degrades. You trade a visible queue for invisible defects.

Some hope that AI will soon review code as well as it writes it. This is plausible for surface-level checks. Style, syntax, obvious bugs. But deep review is not pattern matching. Deep review is asking whether a solution is the right solution given everything you know about the system, the roadmap, and the team.

That kind of review is downstream of comprehension. And comprehension is downstream of context that no model fully has.

The real leverage in software engineering has always been the same. It is not typing speed. It is the ability to understand a system well enough to make good decisions quickly. AI raises the floor on production. It does not raise the ceiling on understanding.

Teams that recognize this will use AI to handle the mechanical while protecting time for the cognitive. They will resist the temptation to measure productivity by output volume. They will treat code review as the rate-limiting step it has always been.

The keyboard was never the slow part. The slow part was always the space between reading something and knowing what to do about it.