As AI-generated code becomes more prevalent, it’s bringing to light a concerning trend: mediocrity on steroids. A colleague of mine, who was already producing low-to-medium quality code, is now churning out large PRs at an alarming rate. And, unfortunately, the quality hasn’t improved. It’s like the saying goes: ‘a fool with a tool is still a fool.’
The problem is, these AI-generated PRs often come with glaring bugs, logical flaws, or bad architectural choices. And because the tests are also generated, they’re just happily testing that subpar behavior. It’s creating more work for the rest of us, who have to review and fix these PRs.
I’m not alone in this struggle. Have you encountered similar issues? How do you deal with the influx of mediocre code, amplified by AI?
## The Double-Edged Sword of AI
AI-generated code can be a powerful tool, but it’s not a replacement for good coding practices. It’s essential to recognize that AI is only as good as the data it’s trained on and the guidance it receives. When we rely too heavily on AI, we risk perpetuating mediocrity.
## The Human Touch Still Matters
To combat this issue, we need to emphasize the importance of human oversight and review. It’s crucial to have experienced developers who can identify and correct the mistakes AI-generated code can’t catch. We must also prioritize coding standards, best practices, and continuous learning to ensure our team members are equipped to produce high-quality code.
## Finding a Balance
The key is to strike a balance between the efficiency AI-generated code provides and the critical thinking humans bring to the table. By acknowledging the limitations of AI and embracing our role in the development process, we can create better code and a more sustainable future for our industry.
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*Further reading: [The Ethics of AI-Generated Code](https://wwwTowardsDataScience.com/the-ethics-of-ai-generated-code)*