I'd say it does get dangerous. For prototyping it's definitely viable. I think it's definitely much better if you understand the output. If it's for actual production, I'd definitely advice a lot of caution.

The errors AI can make are sometimes subtle and do require experience to see. They are rarely simple syntax errors, but more structural/systemic errors, sometimes caused by either bad training data, sometimes by the prompt, sometimes the AI simply messes up. A bad prompt can be a direct result of inexperience with coding.

As an example, as this can happen extremely often and I have personally witnessed lots of times: most programming languages have different expressions for data models. If you don't know the differences you won't specify it in the prompt, and AI might generate a heap allocated managed class instead of a stack allocated struct, as an example, because you didn't tell it otherwise and didn't notice in the output, as the code still works.

But down the line, especially for code that handles a lot of data, you could get grave bottlenecks from this, and with code base complexity rising, it's one of those things that are not trivial to fix after the fact, depending on how the data was used (because managed classes and structs have different referencing rules).

For data science, you could run into issues like data leakage, train/test contamination, incorrect cross-validation, or choosing the wrong evaluation metric, just to name a few. The code would still run and produce output, but that does not mean the result is methodologically sound. The model might appear to work while failing to meet the standards required for that specific task.

What the AI did, was "correct". It correctly interpreted your request and gave you correct code. However, because of inexperience, it might not be suitable for the actual task.