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I was thinking about this the other day, and I don't know if it has a name. Please let me know if it does. For now I will call it the "power bank effect". When your phone battery is constantly low, you buy a portable power bank thinking that you will improve your phone charge as you will constantly have it on you. Actually, you have just added another device you need to charge and you end up just as bad as before, with an empty power bank and a phone with low charge.
Reminds me of the old jwz quote:
Some people, when confronted with a problem, think “I know, I'll use regular expressions.” Now they have two problems.
Both examples are ones where, if the underlying problem fits the solution, it's a pretty good solution. But if the underlying problem is misdiagnosed, the solution makes it worse.
I have a power bank, because my phone is often low. It solves the problem because the REASON it's usually low is that I use it a lot and spend a good amount of time in places where charging is inconvenient. When I'm home, I definitely follow ABC (Alec Baldwin voice: A Always. B Be. C Charging.), both for phone and battery. If your problem is that you forget to charge things, then another thing to charge of course doesn't solve it.
Practical advice: On some phones, you can set reminders by time and location, so it reminds you to charge if you're home at 7pm or whatever. It's also pretty cheap to invest in multiple chargers left around the house so it's always convenient. Even more so if it supports wireless charging and you arrange to have the ability near each place you spend significant time in.
I was thinking about non-obvious incorrect uses for LLMs, where the output is useable but not aligned to the target audience. For example, using an LLM to design a return to office distribution schedule seems like a good idea because it takes advantage of LLMs ability to solve “math” problems. In reality, however, this method is masking the actually difficult part of this task, human preferences. It won’t accurately factor in niche cultural imprints (lunch time/duration, bathroom usage, tardiness) nor will it be able to detect chaotic phenomena (traffic fluctuations, weather, sports game results).