I do. If too many of our apprentices don’t actually learn how to work the forge, how ready will they be to take over as masters someday themselves?
I can see how ChatGPT was useful to the grandparent today, but got very disturbed by what it might portend for tomorrow. Not because of job loss and automation, like many people worry, but because of spoiled training and practice opportunities.
I liked your take, so I’d be curious to hear what you think.
Looking forward to the Y2K levels of highly paid consulting work becoming available now you mention it
We've long since reached the point at which no one can be said to be a true polymath ( https://en.wikipedia.org/wiki/The_Last_Man_Who_Knew_Everythi... ). Having lost the ability as individuals to know something about everything, we're now losing the ability to know everything about anything.
The best answer is really if you ask chatGPT "how has the forging of steel progressed since it was invented?".
To me, you are basically worried for no reason about what happens when the apprentices no longer spends their time heating and hammering iron to remove impurities and increase carbon content. There is a trade off involved here. I am sure the apprentices of old understood at a base level what was really going on in the forge better than a modern apprentice but that hardly is an argument against progress.
Lesser issues: additional strain on index rebuilding whenever that happens; messing with execution plans and causing the query planner to be inefficient; primary/secondary memory overhead; or if your DB engine uses locks you can run into a myriad of issues there.
I'm all for SWEs learning about databases, as I'm morally opposed to the proliferation of ORMs and the like, but I don't think ChatGPT is the right way to go about things long-term. It's similar to Googling or using StackOverflow: yes, you will find information that is relevant to what interests you at the moment, but it's soon forgotten and does nothing to help build long-term mental models.
Here's a session from me working on a side project yesterday:
https://chat.openai.com/share/a6928c16-1c18-4c08-ae02-82538d...
The most impressive thing I think starts in the middle:
* I paste in some SQL tables and the golang structrues I wanted stuff to go into, and described in words what I wanted; and it generated a multi-level query with several joins, and then some post-processing in golang to put it into the form I'd asked for.
* I say, "if you do X, you can use slices instead of a map", and it rewrites the post-processing to use slices instead of a map
* I say, "Can you rewrite the query in goqu, using these constants?" and it does.
I didn't take a record of it, but a few months ago I was doing some data analysis, and I pasted in a quite complex SQL query I'd written a year earlier (the last time I was doing this analysis), and said "Can you modify it to group all rows less than 1% of the total into a single row labelled 'Other'?" And the resulting query worked out of the box.
It's basically like having a coding minion.
Once there's a better interface for accessing and modifying your local files / buffers, I'm sure it will become even more useful.