I believe choosing a well known problem space in a well known language certainly influenced a lot of the behavior. AIs usefulness is correlated strongly with its training data and there’s no doubt been a significant amount of data about both the problem space and Python.
I’d love to see how this compares when either the problem space is different or the language/ecosystem is different.
I am just not having this experience of AI being terribly useful. I don’t program as much in my role but I’ve found it’s a giant time sink. I recognize that many people are finding it incredibly helpful but when I get deeper into a particular issue or topic, it falls very flat.
And to add-on, isn’t there some market dynamics we are avoiding here with this example? If I’m an AI company and really produced a principal level engineer, why would I sell it for less than the labor market is willing to bear? Wouldn’t I price it perhaps less than the market but not so dramatically less as to lose money.
I’m genuinely curious on the point about reducing headcount because AI will be more efficient. I’ve seen it articulated here but other places too that a company will be able to have less engineers because each would be more productive. What if companies kept the same number of people engineers but now massively out produce what they used to? And I disagree with the example that this is like typewriters replacing typists. I think typists have a fixed number of things that need to be typed. Software is different - a company that has a better or more feature rich project could gain on their competitors.
Curious if anyone else thinks this. Maybe it’s just optimism but I’ve yet to be convinced that a company would want to maintain its productivity through trading engineers for AI if it had the same opportunity to grow its productivity through AI and maintaining headcount.