Original Reddit post

Wrote up a phenomenon I’ve been watching in myself and a lot of people I know: token anxiety, the AI-community twin of range anxiety in EVs. The fear that an LLM will exhaust its context or its credits before arriving at a solution. Two failure modes, both ugly: The empty tank. Scarcity makes you ration. You cut context, you downgrade the model, you compress chats early, you start splitting sessions, you hop providers, you watch the meter like a fuel gauge. You stop iterating because you can’t afford another round. The full tank. Plenty makes you sloppy. You offload the trivial (renaming variables, looking up flags), let chats run forever with stale state, retry from scratch instead of iterating, and babysit agents from the checkout line. The financial cost is fixed; the effort cost feels free, but it isn’t. You never get to consolidate. The model gets to forget. You don’t. The cure isn’t infinite limits, just like the cure for range anxiety wasn’t 1,000-mile batteries. It was chargers along the route, trip planning, drivers learning their cars. Knowing what the work is worth before you ask. Spending where the answer earns it. The practice I’ve landed on is downgrading my plan every few months for a month at a time. The cap forces intentional use. https://starikov.co/token-anxiety/ What does intentional AI use look like for the people here? Is the middle lane real, or am I kidding myself? submitted by /u/iGotYourPistola

Originally posted by u/iGotYourPistola on r/ArtificialInteligence