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Yes, courtesy words use a little extra processing, but there is no published audit showing that “please” and “thank you” themselves cost tens of millions of dollars. The phrase comes from an informal April 16, 2025 reply by OpenAI CEO Sam Altman. A standalone “thank you” may trigger another response, while a polite phrase in the same prompt adds only a few input tokens. Keep being polite if you prefer; save meaningful compute by combining your task and context, removing repetition, limiting output length, and avoiding unnecessary extra turns.
At a glance
| Item | What the research supports | What to do with that information |
|---|---|---|
| “Please” or “thank you” inside a prompt | Adds a few input tokens | The marginal workload exists, but its dollar amount is not disclosed. |
| A standalone “thank you” message | Can start another inference pass and generate a reply | Combine thanks with your next request when you have one. |
| Average ChatGPT query | 0.34 watt-hours, an OpenAI estimate cited by the IEA in 2025 | Do not treat this average as the energy cost of courtesy words. |
| Sam Altman’s “tens of millions” line | An informal April 16, 2025 reply | Read it as an aggregate, colorful claim—not a user charge or audited electricity bill. |
| 2026 energy preprint | 10,000 conversations ending in “thank you,” measured on open-source models | It demonstrates mechanisms and overhead, not OpenAI’s commercial cost. |
Is Sam Altman’s “tens of millions” claim a real bill?
Not in the accounting sense. When asked how much electricity OpenAI was wasting because people said “please” and “thank you,” Altman replied, “Tens of millions of dollars well spent—you never know.” The remark was posted on April 16, 2025, and was not accompanied by an OpenAI cost breakdown that isolates courtesy words. Calcalist Tech reports the quote and the expert commentary around it.
That distinction matters. “Tens of millions” could refer to cumulative operating costs across a huge volume of conversations, including the responses generated after polite messages. It does not mean every user adds a measurable surcharge, nor that two words can be assigned a verified electricity price. Provider accounting would need to separate input-token processing, output generation, hardware depreciation, cooling, networking, staffing and other overhead; the cited evidence does not do that.
How does tokenization make a polite message use compute?
Language models do not read words as humans do. They split text into tokens—pieces that may be whole words, parts of words, punctuation or spaces—and process those tokens in context. A phrase such as “please summarize this report in five bullets” therefore contains a few more input tokens than “summarize this report in five bullets.”
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Those extra tokens require some additional work, but the total depends on the complete conversation, the model’s size, the requested reasoning mode, hardware and serving configuration. A longer prompt can also improve an answer when it supplies necessary context or constraints. The sensible target is not minimum word count at any cost; it is removing words that add no information.
Output matters too. A short answer generally requires less decode work than a long one, and an additional conversational turn requires another pass through the context. This is why a standalone message containing only “thank you” can have a larger operational effect than the same words attached to a useful request: the system may generate a fresh reply.
How much energy does one ChatGPT prompt use?
The International Energy Agency cites OpenAI’s 2025 estimate of 0.34 watt-hours per average ChatGPT query. The IEA also says data centres represented about 1.5% of global electricity consumption in 2025. The IEA’s “Key Questions on Energy and AI” document gives those figures and their broader infrastructure context.
Neither number tells you what “please” costs. The 0.34 Wh figure is an average query estimate, not a marginal measurement for a courtesy phrase; actual energy varies with model, prompt and output length, hardware utilization and workload. Converting that average into a personal carbon or dollar saving by deleting two words would claim precision the evidence does not support.
What did the 2026 study measure?
A 2026 arXiv preprint built a dataset of 10,000 chat conversations that ended with “thank you” and used repeated energy measurements on open-source models. It reports that larger models consume more energy and tend to produce longer replies, while output length affects decode energy. Read the preprint’s methods and results on arXiv.
This is useful evidence that an extra turn and a generated response have measurable overhead. It is not an OpenAI audit: the models, hardware and controlled serving setup differ from ChatGPT’s commercial infrastructure. Treat it as a mechanism study, not a conversion rate from one “thank you” to a known number of dollars.
Does politeness make ChatGPT answer better?
There is no consistent evidence in available sources that courtesy alone improves factual accuracy. Professor Lev Muchnik, quoted by Calcalist Tech, says longer input requires more computing for AI even though humans may find detailed explanations easier; the same report notes no reliable improvement from simply adding “please” or “thank you.” Another expert views polite wording as a way users sometimes add context or clarify intent.
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Style can change, however. Le Monde reports that 67% of surveyed Americans were polite with chatbots and quotes Orange Labs specialist Julia Velkovska describing politeness as a marker of novice, non-expert or playful use. Microsoft Copilot designer Kurtis Beavers says that when the system detects politeness, it is more likely to respond politely. Those observations concern conversational tone and user behavior, not proof of better factual performance.
“Polite and precise” is therefore a better goal than either stiff formality or deliberate rudeness. State the task, provide the context the model needs, and specify what a useful answer should contain.
What prompt habits reduce unnecessary work?
- Put the task and context in one message. Instead of sending a vague request and then several clarifications, write the objective, source material, audience, constraints and format together. This can avoid repeated inference passes.
- Be brief and transactional when the task is simple. “Please summarize this report in five bullets” is courteous and clear. It carries useful instructions without a long social preamble.
- Add constraints that change the answer. Include the word limit, date range, reading level, required sections, citation rules or exclusions that genuinely matter. Necessary context can improve usefulness even though it adds tokens.
- Specify prompt and output length deliberately. Ask for “five bullets,” “under 200 words,” or “a detailed explanation with examples” when that is what you need. A suitable output limit can prevent an unnecessarily long generation.
- Ask for revisions deliberately. If the first answer misses a requirement, identify the exact defect and request one focused revision. Avoid a chain of “make it better” messages that each trigger another full response.
- Combine a thank-you with the next request. After an answer, write “Thanks—now turn those bullets into a 100-word email” rather than sending “thank you” alone and then starting a second turn. You still express appreciation while making the next inference useful.
- Do not optimize for rudeness. Removing every courtesy word for a tiny, unmeasured saving can make your interaction less comfortable without producing a meaningful environmental benefit. Concise directness and concise courtesy can be equally efficient.
- Keep “please” if you want to. If saying it supports your communication habits, teaching style or comfort, the evidence gives no reason to stop. The practical environmental gain comes from reducing redundant context, long outputs and avoidable turns.
- Record the model and version when comparisons matter. Energy and answer length depend on model size and serving conditions. Note the model name, version or reasoning mode when your team is evaluating efficiency; do not assume results transfer between models.
- State environmental assumptions. If you estimate impact, label whether you mean input processing, generated output, a whole conversation or an average query. Hardware mix, utilization, electricity source and provider accounting can change the result, so avoid presenting a universal personal carbon number.
How should I interpret the two common examples?
In a single turn, “Please summarize this report in five bullets” is a good pattern: polite, precise and bounded. The request says what to do and how much to return.
A less efficient sequence is: “Please summarize this report.” The system answers; you later send “thank you.” If it replies to the thanks, that is a new output-generating turn. A better sequence is “Thanks—please now list the report’s three risks in one sentence each,” or simply omit the social follow-up when you have no next task.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteThese examples do not prove that the first phrase has zero cost or that the second always produces a response. They show why turn count and output length usually offer more practical leverage than policing two courtesy words.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does “serving efficiency” mean here?
Serving efficiency is how effectively a provider’s infrastructure handles requests: batching, hardware utilization, memory management, model choice and the amount of computation used for each generated token all matter. These details are largely provider-controlled and are not disclosed as a courtesy-word ledger.
Consequently, provider accounting is the missing link between “extra tokens require work” and a dollar total. Until a provider publishes a method that attributes energy and cost to polite wording, the honest statement is qualitative: unnecessary text and extra responses add workload, but the marginal amount is unknown.
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Should I stop being polite to ChatGPT to save electricity?
No. Keep “please” and “thank you” when they make the interaction feel natural, teach respectful communication or help you formulate a clear request. If efficiency is your priority, put the task and necessary context in one message, set an appropriate output length, and combine thanks with the next request.
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Sam Altman’s line is best understood as a lighthearted aggregate claim, not a measured bill for individual manners. Courtesy has a small token cost; conversation design has the larger, controllable effect.
Frequently Asked Questions
Can ChatGPT tell whether I am being polite?
It can detect wording patterns and may mirror a courteous tone, but that does not mean it has human feelings or that politeness improves factual reliability. Treat tone matching as a conversational behavior, not a quality guarantee.
Is 0.34 Wh the energy cost of every ChatGPT answer?
No. It is an average query estimate cited by the IEA from OpenAI, and individual requests can be higher or lower. Model choice, prompt size, generated length and infrastructure conditions all affect actual use.
Does asking for a shorter answer always save energy?
A shorter generated response generally reduces decode work, but a strict limit can force follow-up questions or revisions. Choose the shortest format that fully serves the task rather than minimizing length blindly.
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What is the most efficient way to say thanks to an AI assistant?
If you have another task, attach it to the appreciation in the same message, such as “Thanks—now extract the three deadlines.” If you have no next request, a standalone thanks is optional; it may prompt another response.
