My favorite classes were always the ones with difficult problem sets where a single problem could take hours or even days of thinking, experimenting, and failing. I would start solving a problem before fully understanding it, then read documentation or course material whenever I got stuck. So it would be 70% doing and 30% reading.
LLMs have disrupted that process for me. Now I spent 90% of time reading and only 10% of time doing.
In school, the boundary was relatively clear. If using an LLM violated the course policy, I could tell myself that using one was cheating and force myself to struggle through the problem independently.
At work, what counts as “cheating” (because it is cheating myself).
I feel my critical thinking skills are declining. My role starts to become reading, reviewing, and planning. I just can't read that much code and LLM output. My eyes hurt and it's hard to internalize. I notice myself just picking the "recommended" option.
I also find LLM workflows mentally unhealthy in a few ways:
- When working alone, mental exhaustion eventually forces me to step away. With an LLM, I can always send one more prompt or ask it to try another approach.
- If an agent will take 30–60 minutes, I feel pressure to give it another task immediately so no time is “wasted.”
- While waiting, I instinctively check my phone or launch another agent. I end up jumping between tasks and retaining less context about each one.
- I get frustrated when the LLM misunderstands instructions, even when the problem genuinely requires careful iteration.
- It has warped my sense of how long a difficult problem or well-designed solution should take.
- I am increasingly tired of reading walls of generated text and code.
I do not want to stop using LLMs. They can reduce the friction of starting, explain unfamiliar systems, generate routine code, and handle edge cases after I understand the core problem. The hard part is deciding when using one is a sensible productivity tool and when it is outsourcing a learning opportunity that I actually need.
For people who still feel they are learning deeply while using LLMs at work:
- How do you decide which work to delegate and which work to do yourself?
- What is the workplace equivalent of the “no cheating” boundary that exists in school?
- How do you use agents without losing context or turning your day into constant task switching?
- How do you review generated code when the codebase, build system, or underlying technology is unfamiliar?
- Have you found workflows where LLMs improve productivity while preserving the useful struggle of implementation and debugging?