it’s getting to the point where I notice people say it a lot, especially IRL now for whatever reason recently.
And for clarity I’m not in research or anything, so these people just mean ‘LLM/image gen’, not utilities like OCR or (usually not) transcription.
Some have argued it’s just more efficient (which I can kind of get), while others think you’re actively hindering your intelligence somehow.
On the first point:
I’ve tried it occasionally to see how it compares to my own skill, and while it produces a functional result, it’s always very derivative work to the point where you can find things with the exact same names of other ‘public’ (but not libre) works, and often isn’t the ideal solution to what it targets. So I can see how you can get things out of it, but it never felt really that profound to me.
But for the second… isn’t this supposed to be the tool for people to do things they aren’t experienced in? If anything, you probably need to be able to understand how to write pertaining to the task so the token probabilities are biased toward writing from that area.
And even then, if all you end up doing is prompting AI, then wouldn’t you ultimately serve no purpose outside of being glorified QA?
I guess I’m trying to figure out what exactly non-users would be ‘falling behind’ in that affects them more than those who use AI?
I think a good alternative explanation is this: AI is here to stay, so knowing how to use it in whatever form it takes in your life as a tool is good knowledge to have.
Typewriters are close to extinction, but most everyone these days uses a computer to write documents. Those that refused to learn how to use a word processing application on a computer were “left behind” because now almost all documentation is written digitally.
AI is here to stay
LLMs are not AI.
Don’t be daft.
it’s like your face mate, it can’t be helped.
So realistically, it’s kinda BS pushed by FOMO farmers with hustle culture vibes.
The reality is that the acceleration of AI is reaching a speed where by the time you’d build your whole setup to work with AI there will be a new AI who can set the whole thing up in an afternoon.
There is a learning curve to things like how a model processes information and how to set them up for success. But it’s fairly easy to learn and likely to shift quickly.
The biggest gap that I see between people who have success with models and those who have a bad time is how you treat the model.
If you are a jerk, I hope you don’t like your production DB or home directory too much.
Things that seem crazy to people who aren’t following things, like giving the model breaks to avoid context burnout or encouragement vs yelling in all caps, go a long way.
TL;DR: The methodological best practices are likely to change so fast you won’t need to do much catching up, but the relational dynamics of working with AI will likely remain pertinent into the future.
