Paradoxically, where AI is having the greatest real impact isn't in code writing, but in code review. Tools like CodeRabbit are automating the most tedious and repetitive tasks: checking for conventions, checking for associated tests, summarizing extensive changes, and identifying common vulnerabilities.
This not only reduces human effort, it also elevates the conversation. By eliminating trivial workload, humans can focus on discussing architectural decisions, readability, maintainability, and alignment with business objectives.
It also democratizes access to expertise: even if you don't have a country email list security expert on every review, AI can act as a top-level, specialized reviewer, flagging the most common errors in that area.
What comes next?
The most interesting thing is what we haven't seen yet:
AI will be better at evaluating code than generating it. Because review is based on detecting deviations from a pattern, not creating from scratch. It's easier to judge than to invent.
The engineer's focus will shift from "how" to "why." As AI automates implementations, key decisions will be about purpose, design, and alignment.
We'll have to assume certain risks and fully automate tasks. Some decisions can be handled directly by AI if the cost of reversing them is low. Not everything needs to be approved by humans.
Conclusion
Generating code has never been easier. But this abundance demands a new kind of responsibility: creating systems that ensure that code is sustainable, maintainable, and aligned with the business.
AI doesn't replace human judgment, but it can amplify it. If we know how to delegate the trivial and take care of the important, the result can be a new era of faster, more reliable, and more enjoyable development.
And that's the key: it's not about doing the same thing faster, but about doing things better.