Token Efficiency as an Act of Refusal: Why AI Companies Want You Wasteful

A post on r/ClaudeAI argues that AI companies like Anthropic and OpenAI have no incentive to teach users token efficiency. The author, u/insanespiral, claims that companies profit from dependency — and the more tokens users burn, the more locked-in they become.

Key Point

The post describes a cycle: LLM users generate large outputs — 20-page documents, oversized merge requests — that can only be interpreted by other agents. As human review declines, quality drops, requiring even more agents and tokens to fix the mess. The author calls this a deliberate design that builds dependency on the platform.

Practical Advice from the Post

• Don't generate what you won't read. If you wouldn't review a 20-page document on a trivial topic, don't ask for it.
• Don't apply agents where human-level quality is needed. LLMs are assistants — make them assist you, not do the work for you.
• Split merge requests. A PR too big to review is a red flag. Break it into reviewable chunks.
• Be disciplined. Treat token efficiency as an act of refusal against vendor lock-in.

The author frames token waste as a threat to developer autonomy. The more we lean on agents, the less we can work without them. The solution is conscious restraint: use LLMs to assist, not replace, human judgment.

This is a conversational take — no benchmarks or code — but the sentiment resonates with developers seeing bloated agent outputs in CI/CD, code review, and documentation.