If she told you that afterwards the failing grades had "soared", it could easily be read either way:

- The (previously failing) grades had increased, so the program must be working very well.

- The percent of grades that count as failing had increased, so the program must actually be terrible.

  My mom has been regularly beating me 
  at Scrabble since I could spell.

To be fair, if I'm reading an exabyte in a month, my hardware's pushing >3 Tbps, which I'd be very happy with.

If you squint they may look like the same thing, but their subtle difference is important. One is a tool suggesting "Hey I see you're trying to do A, but I think B might also fit your needs", and the other is "You want A? Ok, I'll eventually point you towards A, but only after you consume this message from our sponsor."

Google's not genuinely thinking "Hey this will help the user more!" and building that into their tool - it's an ad platform that mimics being helpful, in the name of growing profits.

(That's fine for them to do btw; They're a company and they need to make money somehow.)

I understand the sentiment of "the language is defined by its speakers", but this statement seems a bit overblown. According to that logic, it is literally impossible for someone to be incorrect about the meaning of a word.

But when I see people say things like "Look! I used GPT to write a functioning webapp!" - I worry that people get a false sense of "It works!" from pasting GPTs code into their compiler and seeing roughly the results they expect. That's great, but GPT in its current form spends exactly zero time "thinking" about corner cases - It's just a black box that repeatedly spits out "most likely next token". So maybe that app works 90% of the time. Or 95%. Or 99%. But you don't have much of a way to tell the difference without rigorous testing that includes thorough and well-articulated test cases. But in order to do that, you need to understand the problem you're solving in a very detailed way, and how your code reacts to it. And in order to do that, you need to... know how to write the program.

I think this latest wave of LLMs and generative AI is really awesome tech, and I play with it every day, because it's just so cool. But seeing people trust programs written with them worries me. Some day someone is gonna copy/pasta some LLM generated code into mission critical software, trusting it implicitly, and cause a tragedy.