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A New Angle on AI Etiquette and Energy
We’re used to teaching children to say “please” and “thank you” — but what if those habits come with a cost when we interact with machines? In the age of large language models (LLMs), every word you type isn’t just data — it’s a demand on real-world energy resources. A groundbreaking study has revealed that even the simplest gesture of politeness — like saying “thank you” to a chatbot — consumes measurable energy. While it may seem insignificant on the surface, scaling that across billions of daily interactions paints a very different picture.
From OpenAI’s Sam Altman’s revelation that saying “please” and “thank you” to ChatGPT has cost the company tens of millions of dollars, to a detailed energy audit of LLaMA and Qwen models, this article dives deep into the environmental and operational consequences of conversational AI etiquette.
The Energy Behind a Polite Chat: A Findings
When users type a simple “thank you” to an AI model like ChatGPT, the response it generates doesn’t come out of thin air. It requires a full inference pass through billions of parameters, activating GPUs, CPUs, and memory systems — all of which draw power.
In a recent experiment, researchers used thousands of conversations to measure the energy used just to respond to a “thank you” message. Using the LLaMA 3–8B model on an NVIDIA H100 GPU, the average response consumed around 0.245 Wh — equivalent to lighting a 5W LED bulb for about 3 minutes. Specifically:
0.202 Wh came from the GPU
0.024 Wh from the CPU
0.019 Wh from RAM
Notably, GPU energy usage dominated and displayed high variability. This wasn’t just about the hardware — longer responses and more complex conversations significantly raised energy costs.
The team then scaled their measurements across different model sizes, from Qwen-0.5B to Qwen-14B. Unsurprisingly, larger models used far more energy. For instance, Qwen-14B and LLaMA 3–8B produced longer, more nuanced responses, but their energy demands were 3–4× higher than smaller models.
The GPU energy usage did not scale linearly with model size. Smaller models were more efficient, but the larger ones offered richer outputs — at a steeper energy cost.
And this wasn’t even the whole story.
The study emphasized that real-world deployments involve many other variables. Server batching can reduce per-interaction energy by 10–15×, while infrastructure requirements like cooling, idle times, and regional energy efficiency greatly affect the carbon footprint. For hyperscale deployments, each “thank you” could realistically consume between 1–5 Wh.
Scale that up to hundreds of millions of daily interactions, and suddenly, we’re talking about several megawatt-hours of energy — enough to power hundreds of homes. In other words, mass politeness toward AI isn’t free — it’s part of our growing digital carbon footprint.
What Undercode Say: 💻 Behind the Tech and the Trade-offs
Politeness vs Performance
This study challenges a widespread perception that digital interactions are intangible and “green” by nature. In reality, every AI interaction demands power, and every polite flourish increases that demand. At small scales, it feels trivial. At global scales, it becomes substantial.
Large Models, Larger Costs
Undercode analysis aligns with the findings: large language models (LLMs) are highly expressive but extremely power-hungry. The trade-off between brevity and politeness becomes a tangible energy decision. Qwen-14B, for example, delivers complex, detailed responses — but at 4× the energy cost of its 0.5B counterpart. This means companies need to carefully weigh user satisfaction against compute and environmental expense.
Real-World Implications for AI Ethics
Politeness is traditionally seen as a social good. But in AI usage, it adds to environmental burdens. As more users interact with AI daily, the cumulative energy footprint of simple niceties could rival or exceed that of traditional IT systems.
Infrastructure Optimizations Are Key
Undercode sees hope in backend optimizations. Techniques like dynamic batching and model distillation can dramatically cut energy costs per response. However, those gains must be matched with responsible design decisions — such as response truncation, default brevity modes, and smarter user interfaces.
Regional Carbon Sensitivity
Energy isn’t the same everywhere. Serving a “thank you” response in Norway (renewables-heavy grid) has vastly different environmental implications than serving it from a coal-heavy region. Undercode notes that sustainable AI isn’t just about model design — it’s about deployment geography too.
AI Usage Norms Will Evolve
We may see a cultural shift where saying “thank you” to AI becomes unnecessary or discouraged for energy savings. This won’t be about rudeness — but about efficiency, sustainability, and redefining etiquette for the digital age.
✅ Fact Checker Results
Claim: Saying “thank you” to LLMs consumes energy.
✅ True – Quantified at ≈0.245 Wh per reply for LLaMA 3–8B.
Claim: Large models use significantly more energy than small ones.
✅ True – Up to 3–4× more in Qwen-14B vs Qwen-0.5B.
Claim: GPU is the main energy consumer.
✅ True – GPU accounted for \~82% of energy per response.
🔮 Prediction: AI Conversations Will Get Greener — or Shorter
As AI continues to scale, the cost of politeness will likely trigger a wave of innovation and cultural adaptation. We predict:
Politeness Compression: Chatbots may start recognizing and ignoring unnecessary pleasantries to save energy.
Greener Defaults: LLM platforms will shift toward lighter models and aggressive batching strategies.
New UI Patterns: Conversational apps may include “concise mode” options to promote minimal energy usage.
In the near future, AI etiquette might evolve not to be less polite — but to be more conscious. Every word will matter — both socially and electrically.
References:
Reported By: huggingface.co
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