* People using it as a tool, aware of its limitations and treating it basically as intern/boring task executor (whether its some code boilerplate, or pooping out/shortening some corporate email), or as tool to give themselves summary of topic they can then bite into deeper.
* People outsourcing thinking and entire skillset to it - they usually have very little clue in the topic, are interested only in results, and are not interested in knowing more about the topic or honing their skills in the topic
The second group is one that thinks talking to a chatbot will replace senior developer
And this may be fine in certain cases.
I'm learning German and my listening comprehension is marginal. I took a practice test and one of the exercises was listening to 15-30 seconds of audio followed by questions. I did terribly, but it seemed like a good way to practice. I used Claude Code to create a small app to generate short audio (via ElevenLabs) dialogs and set of questions. I ran the results by my German teacher and he was impressed.
I'm aware of the limitations: Sometimes the audio isn't great (it tends to mess up phone numbers), it can only a small part of my work learning German, etc.
The key part: I could have coded it, but I have other more important projects. I don't care that I didn't learn about the code. What I care about is I'm improving my German.
> Group 1: intern/boring task executor
Yup, that makes sense I'm in group 1.
> Group 2: "outsourcing thinking and entire skillset to it - they usually have very little clue in the topic, are interested only in results"
Also me (in this case), as I'm outsourcing the software development part and just want the final app.
Soo... I probably have thought too much about the original proposed groups. I'm not sure they are as clear as the original suggests.
The word "thinking" can be a bit nebulous in these conversations, and critical thinking perhaps even more ambiguously defined, so before we discuss that, we need to define it. I go with the Merriam-Webster definition: the act or practice of thinking critically (as by applying reason and questioning assumptions) in order to solve problems, evaluate information, discern biases, etc.
LLMs seem to be able to mimic this, particularly to those who have no clue what it means when we call an LLM a "stochastic parrot" or some equally esoteric term. At first I was baffled that anyone really thought that LLMs could somehow apply reason or discern its own biases but I had to take a step back and look at how that public perception was shaped to see what these people were seeing. LLMs, generative AI, ML, etc are all extremely complex things. Couple that with the pervasive notion that thinking is hard and you have a massive pool of consumers who are only too happy to offload some of that thinking on to something they may not fully understand but were promised that it would do what they wanted, which is make their daily lives a bit easier.
We always get snagged by things that promise us convenience or offer to help us do less work. It's pretty human to desire both of those things, but proving to be an Achilles Heel for many. How we characterize AI determines our expectations of it; so do you think of it as a bag of tools you can use to complete tasks? Or is it the whole factory assembly line where you can push a few buttons and an pseudo-finished product comes out the other side?