That's not the same. Whenever you do a google search, it only gives out what to do. The decision making ultimately ends up towards you.
In AI case however, its like replacing the person who used to make decisions about what to copy and implement. It makes you a proxy towards the AI's decision making. At least that is how I interpreted it.
Realistically, people do a lot of Googling "parse Json in Python" and going with whatever pops out, like "import json" probably. But there's other options out there.
With a LLM, you're almost certainly also going to get the standard json module if you just ask for a JSON parser.
But nobody says you have to stop there. For instance if I say I want "the fastest JSON module", it goes for orgjson instead.
And if I wasn't happy with that, I could ask it to just write a benchmark using every module available and actually measure what's optimal for my hardware and my data file.
Choices don't go anywhere, and a smart user can take advantage to evaluate possibilities for whatever factors might matter -- speed, size, number of dependencies, etc. I'd say smartly used LLMs make for better choices because you can do research much more easily. Benchmarks, prototypes, asking it to search for you whether a given library has a good reputation or not and is well maintained, etc.
Honestly I agree with this take. The problem is that, under the context of the people banned for using LLMs, no one makes decisions like this.
For instance, say I want to do some data analytics on a pretty large csv file. Of course before AI I would need to think of 1.How to open the CSV file 2. What is the specific data I want to extract 3. How to extract the data 4. How do I change the data into a specific format (say a list, a set etc ..) 5. How do I sort it. 6. How do I visualize it .... and so on. Those kinds of steps on how to write the code forces you to research the libraries and methods and decide what is required. (for instance using pandas in python)
Using AI for that task however, I wouldn't have done any decision for how to extract the data in question. Maybe the AI's decision was marginally better than mine (Writing a csv parser from scratch is faster for that specific data for example). Maybe in the case of data analytics, there is no point in the code cause in the end what is desired more is the visualization. But dumping the code into communities where people were interested in how you came to that decision falls short cause you didn't do it in actuality. I think that is the current issue at hand.
Yeah, but what's more likely to happen is that when you look inside the code you see thousands of lines of code of a handrolled parser and when you ask "why would you write your own rather than use some existing one here?" you only get silence in return.
Except that's exactly what the slop machine peddlers are saying. Why would you, a mere mortal, dare to question the decisions of the superhuman intelligence after all?
I haven't a clue what the peddlers are saying because I run uBlock Origin and do my best to never see an ad. I don't care or even know what they're advertising, and don't take my guidance from big tech CEOs.