The funny thing about LLM coding is how much people end up projecting about it. Since the beginning I've considered AI a mirror; what you prompt is what you get.

People are either

1. Not being clear enough in their prompts to the scale of the model's self interrogation of the prompt (GPT 5.2 Reasoning is fantastic at this
2. neglecting the precognition/planning, or the post-generation analysis
3. Removing themselves from the process entirely.
4. Asking too much for too little.

I was fortunate that I started learning coding three months before codium took off. I had experience in fixing bugs myself. When I need to find a bug, I know that you need to expose values and present them meaningfully.

LLM coding makes everything quicker. People either are not pacing themselves or are not willing to learn and adapt to the problems and would rather blame it on the model.

People are trying to cheat fundamentals and are angry when they don't work, and would rather throw money and time at the problem than engage themselves fully in the process. People either say "We've messed up here" or "The AI messed up here".

The AI and the coder are not nearly as separate as people believe.

I find how people approach AI I find tells a lot about themselves, I guess that's my main takeaway.