Reject AI Token Bloat and Save the World

Every time an LLM repeats your question, buries the answer under corporate boilerplate, or ends with a follow-up you never asked for, it wastes your time and burns compute. Call it what it is: AI token bloat.

A few useless sentences may seem trivial. Multiply them by millions of requests and you’re spending GPU time and electricity at industrial scale to generate text nobody asked for. Depending on how those data centers are powered and cooled, that extra work can also mean more emissions and water use.

The incentives are clear. Training can reward answers that sound more helpful because they’re longer or more agreeable. Engagement metrics can count an unnecessary follow-up as a win. Corporate risk teams can defend another paragraph of boilerplate. The user gets stuck reading it and we all pay for it.

Why would companies police a behavior their own incentives reward? That pressure has to come from users. Reject padded answers. Say so when you give feedback. If a tool routinely makes you dig through filler to find the answer, switch to one that respects your time.

Demand the answer. Reject the padding. Take your business elsewhere if the tool can’t tell the difference.

not just AI !

I despise AI because intelligent it is NOT. Ridiculously fast at giving valid replies from its gigantic fixed memory store it IS. Accuracy and cost per word is down the owner of any said Corporation. We, the users, can only be vigilant with any AI replies.

If the grammar, punctuation, spelling, and facts are all correct then it serves a purpose of not only educating the recipient of the subject in hand, but also, if the recipient has limited knowledge of his or her own mother tongue further help with his or her spoken and written language too.
So the boilerplate can be useful in an area that is not part of the remit of the LLM in the first place.
This assumes that the recipient is bright enough to look up any words they don't know to see what they mean in any reply from relevant AI sources.

As a human, my reply is effectively lengthy, "corporate boilerplate" which I could have made shorter but I decided to be long[-]winded just to be as accurate as possible for my level of knowledge of my mother tongue.

Although I use Gemini and ChatGPT very occasionally I still generally avoid AI if possible...

My [twenty]_five pennoth.

My focus in this discussion was primarily on data center infrastructure, computational resources, and the environmental costs of unnecessary LLM output.

LLMs generate far too much useless, unwanted text. They routinely produce excessive corporate boilerplate, faux neutrality, artificial hedging, and other conversational filler that adds little or no value.

They also waste enormous numbers of tokens on unnecessary follow-up questions, unsolicited suggestions, and artificial conversational engagement designed to keep users interacting longer than necessary.

None of this is free.

Every unnecessary token consumes computational resources. At scale, this token bloat increases demand for GPU processing, data center capacity, electricity, cooling infrastructure, and hardware manufacturing. These costs translate into greater energy consumption, environmental pollution, resource depletion, and electronic waste.

Meanwhile, the economic incentives reward prolonged engagement and increased consumption rather than efficiency, precision, or respect for the user's time.

The bottom line is simple:

LLMs generate costly, unnecessary filler at the expense of users, computing resources, and the planet, while the primary beneficiaries are corporations pursuing profits and engagement metrics.

Users should reject AI token bloat. We should demand useful answers, not manufactured conversations that waste resources for corporate benefit.