I've lived through several ends of programming as we know it. They almost always resulted in more work, more growth, more opportunities and more capabilities for the average dev. We're finally firmly out of the "what can we do?" portion of the information revolution and into the "how can we best do it?" portion. It's a lot less romantic, but it's the part where the rubber meets the road.
> So yes, let’s be bold and assume that AI codevelopers make programmers ten times as productive. (Your mileage may vary, depending on how eager your developers are to learn new skills.)
Has anyone ever seen this hypothetical 10x AI developer? Why do we always back into such hand-wavy arguments when talking about the efficiency of AI-supported software engineering?
Here's what I think the flaw is in all the AI hype's arguments, including the one in this article (I hope Tim O'Reilly can withstand this small amount of debate).
Currently, LLM AIs are stochastic parrots and they don't offer creating levels of abstractions, i.e. creatively and responsively packaging ideas into some higher level form that can be reused.
All the examples in the article did offer a higher level of abstraction: assembly, high-level programming languages, libraries & frameworks like React, database systems etc.
AIs don't offer abstractions. They are not creative, they don't have "better ideas" than what their training data contains. They don't take responsibility for their work.
Us engineers at our company have all tried and are using some AI tools but they don't nearly work as well as management would think so. They make us 10%, maybe in the best case 20% more efficient, but not 10x efficient or anything.
In my lifetime Tim O'Reilly has called every single major change in programming, he's even named a few. This is the single best roadmap I have read on where programming is headed in the AI era.
