A lot of what we do as programmers, writers or artists IS 'prompt engineering'. All that AI is replacing (generalizing? automating?) is execution. Prompt engineering is the 'what' of the 'how'.
"They should have sent a poet"- not NASA, but a screenplay written by some sort of poet.
If there's a singularity it will be in this: you speak of real programmers doing the work to make this AI, but it seems like a hell of a lot of this 'real programmer work' is not poetry. Someone put together the ideas that make code resonate with how people think (for good or ill) and then a lot of real programmers groveled over tedious Python code or whatever, to make it happen at scale.
That tedious groveling and scale is what took the effort, but it's that which can be replaced by machine learning.
Art is prompt engineering. 'Always has been'. We don't master penmanship in the age of the keyboard. People still buy very expensive pens and write in longhand for the beauty of it, but it ceased to be a requisite chore many years ago.
Value the correct side of the balance. If you can't beat the guy with poorer execution by having a better eye and better prompt-imagining, you're not the better artist, you just have better penmanship.
Same for programmers. Best put your effort into trying to understand… because that's going to be a seller's market. You're right that most people won't keep up with understanding, and that's going to directly set their value.
Good points. Understanding what and how it really works will be valueble.
Explaining the AI needs the knowledge of what can be done. For example, you cannot say AI "build systems that process the data". You need to explain AI what "kind of processing should be done". That by itself requires knowledge of what kind of processing exist and when to use which. Also, explaining to AI what should be done takes time and effort which should not be underestimated. Sometimes when I am exlaining someone which way data should be cleaned in many cases I think that it maybe faster to do it by myself and explainig to AI will most likly be a harder than to AI. So, the question about is it easier to implement by myself than explain what I need to AI is a valid and important question.
Also, with data important part even without AI is to verify how well system works. Does it correctly handles differents types of data and different use cases? Just having a system that processes data but you are not sure what output can be is in most cases useleless data system.
So, in any serious data systems understanding of what they can be done and how code and data systems are working are important and I think will become even more important than now.
Also, someone needs to creat AI and systems around them.
"They should have sent a poet"- not NASA, but a screenplay written by some sort of poet.
If there's a singularity it will be in this: you speak of real programmers doing the work to make this AI, but it seems like a hell of a lot of this 'real programmer work' is not poetry. Someone put together the ideas that make code resonate with how people think (for good or ill) and then a lot of real programmers groveled over tedious Python code or whatever, to make it happen at scale.
That tedious groveling and scale is what took the effort, but it's that which can be replaced by machine learning.
Art is prompt engineering. 'Always has been'. We don't master penmanship in the age of the keyboard. People still buy very expensive pens and write in longhand for the beauty of it, but it ceased to be a requisite chore many years ago.
Value the correct side of the balance. If you can't beat the guy with poorer execution by having a better eye and better prompt-imagining, you're not the better artist, you just have better penmanship.
Same for programmers. Best put your effort into trying to understand… because that's going to be a seller's market. You're right that most people won't keep up with understanding, and that's going to directly set their value.