Obviously if that's your own preference, I'm not going to tell you that you're wrong; but I think in general, most people wouldn't agree with that statement.
You use it for things which are 1) hard to write but easy to verify -- like doing drudge-work coding tasks for you, or rewording an email to be more diplomatic, or coming up with good tweets on some topic 2) things where it doesn't need to be perfect, just better than what you could do yourself.
Here's an example of something last week that saved me some annoying drudge work in coding:
https://gitlab.com/-/snippets/2567734
And here's an example where it saved me having to skim through the massive documentation of a very "flexible" library to figure out how to do something:
https://gitlab.com/-/snippets/2549955
In the second category: I'm also learning two languages; I can paste a sentence into GPT-4 and ask it, "Can you explain the grammar to me?" Sure, there's a chance it might be wrong about something; but it's less wrong than the random guesses I'd be making by myself. As I gain experience, I'll eventually correct all the mistakes -- both the ones I got from making my own guesses, and the ones I got from GPT-4; and the help I've gotten from GPT-4 makes the mistakes worth it.
1. Bulky edits. These are conceptually simple but time consuming to make. Example: "Add an int property for itemCount and generate a nested builder class."
Gpt4 can do these generally pretty well and take care of other concerns like updating the hashcode/equals without you needing to specify it.
2. Iterative refactoring. When generating utility or modular code, you can very quickly do dramatic refactoring. By asking the model to make the changes you would make yourself at a conceptual level. The only limit is the context window for the model. I have found that in java or python, the GPT4 is very capable.
I use to generate code I'd get from libraries. Graph-theory related algorithms, special datastructures, etc...
In the prompt they specifically request only the Python code, no other output. An “attempt to be helpful” that directly contradicts the user’s request seems like it should count against it.
I certainly have found quirks like this; for instance, for a while I was asking it questions about Chinese grammar; but I wanted it only to use Chinese characters, and not to use pinyin. I tried all sorts of prompt variations to get it not to output pinyin, but was unsuccessful, and in the end gave up. But I think that's a very different class of failure than "Can't output correct code in the first place".
https://news.ycombinator.com/item?id=36781968
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This paper is being misinterpreted. The degradations reported are somewhat peculiar to the authors' task selection and evaluation method and can easily result from fine tuning rather than intentionally degrading GPT-4's performance for cost saving reasons.
They report 2 degradations: code generation & math problems. In both cases, they report a behavior change (likely fine tuning) rather than a capability decrease (possibly intentional degradation). The paper confuses these a bit: they mostly say behavior, including in the title, but the intro says capability in a couple of places.
Code generation: the change they report is that the newer GPT-4 adds non-code text to its output. They don't evaluate the correctness of the code. They merely check if the code is directly executable. So the newer model's attempt to be more helpful counted against it.
Math problems (primality checking): to solve this the model needs to do chain of thought. For some weird reason, the newer model doesn't seem to do so when asked to think step by step (but the current ChatGPT-4 does, as you can easily check). The paper doesn't say that the accuracy is worse conditional on doing CoT.
The other two tasks are visual reasoning and answering sensitive questions. On the former, they report a slight improvement. On the latter, they report that the filters are much more effective — unsurprising since we know that OpenAI has been heavily tweaking these.
In short, everything in the paper is consistent with fine tuning. It is possible that OpenAI is gaslighting everyone by denying that they degraded performance for cost saving purposes — but if so, this paper doesn't provide evidence of it. Still, it's a fascinating study of the unintended consequences of model updates.