The Real Bottleneck Is Not Review, It Is Reliability

Building AI code reviewers is useful, but it does not solve the core problem.

AI systems still make major mistakes. Having one AI review another AI’s work does not remove that problem. It only pushes it further down the process.

This may catch some issues, but it does not remove the need for human judgment. The system itself is still not reliable enough to trust on its own.

A common response is that humans also make serious mistakes.

That is true. Humans break things all the time. But humans also have agency. When something goes wrong, they can notice it, reason about what is happening, and change course. They can improvise and respond without being prompted.

We do not need to engineer humans into a system to monitor and fix problems. Humans adapt, react, and take responsibility.

If an AI creates a problem, it does nothing unless something asks it to. If people still have to find and fix the mistakes, then the system is not truly automating the work. We are only shifting the work around.

Even if AI were close to perfect at execution, we still do not know how to engineer systems that give it the right context at the right time and at the right cost.

The real bottleneck to large scale AI code generation is not review. It is reliability.

AI needs to produce code that is correct often enough that humans no longer have to check every important change. It also needs to be integrated into systems in a way that allows it to detect and respond to problems.

Reliability also takes time to prove. We trust airplanes because they earned that trust through decades of consistent performance. AI systems have not had that kind of time yet, and longevity cannot be rushed.

So cost needs to go down and reliability needs to go up before we even get a glimpse of the digital panacea many people are imagining.