AI_SLANG_GUIDE
LLM Burnout
LLM burnout is what happens when the AI stack meant to save time becomes a second workload made of subscriptions, prompts, evals, context cleanup, and trust checks.
The cost side
AI bills create a concrete version of fatigue. Teams notice the subscription stack, API usage, premium model limits, and the cost of running evals before they know whether the workflow pays for itself.
The work side
The hidden work is prompt rewriting, checking hallucinations, switching tools, preserving context, and explaining why a demo failed in production. That overhead can turn enthusiasm into skepticism.
The practical alternative
The answer is not always a bigger model. Sometimes it is prompt caching, narrower tool calling, local workflows, better evals, or deciding that a normal script is the cheaper and calmer interface.