A dictum that always sticks with me is "the value of a software engineer is the model of the code that lives in their head".

I've been trying to square that with the AI revolution and I'd love to see more usage of projects like this one.

Ok and then go into the average person's living quarters.

There are many non-trivial differences that make these comparisons complex; GDP is about as good as you can get.

> When an agent writes code, the solution space is open-ended and tests catch hallucinations. Analytics is the opposite. "There's often only a single correct answer using a single correct source,"

As someone who is data-native and software-second, I would say the opposite is true. It's very difficult to specify desired code behavior in natural language, but good PMs occasionally manage it.

Beyond really trivial questions, it's much, much more difficult to specify an analytics question in natural language unless you are a statistics expert. The garden of forking paths is immense.