The notion that eliminating polite phrases like "please" and "thank you" from ChatGPT queries could contribute to planetary conservation has gained traction online. While this idea appears logical on the surface, a deeper examination reveals a more complex reality regarding artificial intelligence's environmental footprint.
The Surface Logic Behind the Politeness Myth
The concept seems plausible because AI systems process text incrementally, with longer prompts requiring slightly more computational power and consequently consuming more energy. OpenAI CEO Sam Altman has acknowledged that at the scale of billions of queries, even minor additions contribute to operational expenses.
However, suggesting that courteous language carries significant environmental consequences represents a substantial exaggeration. The energy impact of a few extra words proves negligible when compared to the substantial power required to operate the underlying data centre infrastructure that supports AI systems.
Why This Persistent Idea Matters
Perhaps more significant than the myth itself is its persistence, indicating that many people intuitively recognize AI isn't as immaterial as it might appear. This instinct deserves serious consideration as artificial intelligence increasingly integrates into daily life.
Artificial intelligence depends on extensive data centres built around high-density computing infrastructure. These facilities consume substantial electricity, require continuous cooling systems, and become embedded within broader networks of energy supply, water usage, and land allocation.
As AI adoption expands globally, so does this underlying physical footprint. The crucial environmental question therefore shifts from how individual prompts are phrased to how frequently and intensively these computational systems are utilized.
Understanding AI's Unique Energy Demands
A fundamental structural difference between AI and most familiar digital services helps explain why energy consumption matters significantly. When opening a document or streaming stored video content, the primary energy cost has already been incurred, with systems mainly retrieving existing data.
By contrast, each AI model query requires fresh computation to generate responses. Technically speaking, every prompt triggers a complete "inference" process—a full computational pass through the model—with energy costs incurred during each interaction.
This characteristic makes AI behave less like conventional software and more like physical infrastructure, where usage translates directly into energy demand. The scale of this demand has moved beyond marginal significance, with research published in Science estimating data centres already account for a considerable portion of global electricity consumption, with demand rising rapidly alongside AI workload growth.
The Broader Environmental Picture
The International Energy Agency has issued warnings that electricity demand from data centres could potentially double by the decade's end under current growth trajectories. Electricity represents just one component of the environmental equation, as data centres also require substantial water volumes for cooling systems, while their construction and operation involve land use, material resources, and long-lived physical assets.
These environmental impacts manifest locally, even when the services provided operate globally. New Zealand offers a clear illustration of this dynamic, where high renewable electricity percentages attract data centre operators, yet this doesn't eliminate environmental consequences.
Large data centres can place significant pressure on local electrical grids, and claims of renewable supply don't always correspond to new generation capacity being added. Electricity consumed by servers becomes unavailable for other applications, particularly during dry years when hydroelectric generation faces constraints.
Systemic Perspectives on AI Infrastructure
Viewed through a systems lens, artificial intelligence introduces new metabolic loads into regions already experiencing strain from climate change, population growth, and competing resource demands. Energy, water, land, and infrastructure remain tightly interconnected, with changes in one system component propagating throughout the entire network.
This interconnectedness holds particular importance for climate adaptation and long-term planning. Much adaptation work focuses on land and infrastructure management: addressing flood risks, protecting water quality, maintaining reliable energy supplies, and designing resilient communities.
Yet AI infrastructure often receives planning and assessment separately, as though it represents merely a digital service rather than a persistent physical presence with ongoing resource requirements.
Why the Politeness Myth Carries Significance
From systemic perspectives, new pressures don't simply accumulate—they can drive organizational restructuring. In some instances, this reorganization produces more coherent and resilient arrangements; in others, it amplifies existing vulnerabilities.
Which outcome prevails depends largely on whether pressure receives early recognition and incorporation into system design or builds unchecked. This represents where discussions about AI's environmental footprint require maturation.
Focusing on minor behavioural adjustments, such as prompt phrasing, distracts attention from genuine structural issues. More consequential questions concern how AI infrastructure integrates into energy planning frameworks, how its water consumption receives management, how its location interacts with land-use priorities, and how its demand competes with other societal needs.
Balancing AI Benefits with Environmental Costs
None of this analysis implies artificial intelligence should face rejection. AI already delivers substantial value across research, healthcare, logistics, and numerous other domains. However, like any infrastructure system, it carries both costs and benefits.
Treating AI as immaterial software obscures these costs, while recognizing it as part of physical systems we already manage brings them into clearer view. The popularity of the "please" myth therefore represents less a factual error than an important signal.
People intuitively sense AI possesses an environmental footprint, even if precise language to describe it continues emerging. Taking this signal seriously opens pathways toward more grounded conversations about how artificial intelligence fits into landscapes, energy systems, and societies already navigating adaptation limits.
