That’s also true without AI. Engineers want more time to polish and businesses want to ship the 80/20 solution that’s good enough to sell. There's always going to be a tension there regardless of tools.
Don't you see the problem? Now engineers literally do not have any leverage. Did the model make it work? Yes? Then ship it, what are we waiting around for?
That sounds pretty much the same as it’s always been? It used to be: “Does the happy path work? Then ship it! There’s no time to make it robust or clean up tech debt.”
Now there actually is time to make things robust if you learn how to do it.
> Now there actually is time to make things robust if you learn how to do it.
What makes you think you are going to be given time to polish it? You would be pushed to another project. You have more responsibilities with none of the growth.
You’re assuming that building something robustly is significantly more time consuming than the “quick and dirty” version. But that’s not really true anymore. You might need to spend another hour or two thinking through the task up front, but the implementation takes roughly the same amount of time either way.
One cannot build something robust just by thinking about it _a priori_, and while this was somewhat at the periphery of the author's argument, it is important.
You can’t get every detail right up front, but you can build a robust foundation from the beginning.
The argument seems to be that AI is causing managers to demand faster results, and so everything has to be a one-shotted mess of slop that just barely works. My point is that it doesn’t take much longer to build something solid instead. Implementation time and quality/robustness are not tightly coupled in the way they used to be.