We are recently thinking about how to have AI provide more information in ways we can interpret. For example, might adding some feature to hint if AI's computation use a specific formula that users didn't mention. I feel working with AI is like the first week working with a someone we never met before.

People keep trying to downplay the problem as "not a big deal" or that it happens rarely, but that is kinda the problem. It is right enough times that people get confident in it and we stop double checking the work. When it is manipulating data that makes it even more problematic.

Now if what is happening here is the AI is generating the code that makes the visualizations and that code is still working in a traditional way (accessing the data directly), than I think its fine. Sure maybe some of the code may be wrong, but at least it is not manipulating the data and coming up with wild conclusions.

So you end up checking what the AI does which negates the productivity argument entirely.

Curious how others deal with this dilemma