AI Energy Use Varies by Query Complexity, New Research Reveals

While many assume that AI language models like ChatGPT consume a fixed amount of energy per query, emerging research reveals a more nuanced reality: energy consumption varies dramatically based on task complexity, output length, and underlying computational demands. According to a recent technical analysis from SemiEngineering, the notion that all token predictions require equal power is a misconception rooted in oversimplified models of AI inference. In reality, generating a 500-word analytical essay demands significantly more computational resources — and thus more energy — than answering a simple factual question like "What is the capital of France?"

The key driver of energy variance is not merely the number of tokens generated, but the depth of reasoning required. Tasks involving multi-step logic, code generation, summarization of dense documents, or creative synthesis demand repeated attention mechanisms, longer sequence processing, and higher activation of neural layers. SemiEngineering’s 2026 study, which measured power draw across multiple LLMs under controlled conditions, found that queries requiring extended reasoning chains consumed up to 8.7 times more energy than short, direct responses — even when the final output length was similar. This challenges the assumption that energy use is proportional only to output size.

For example, asking an AI to rewrite a paragraph for clarity may require only minor token adjustments and minimal re-prediction. But requesting the same AI to generate an original research abstract from scratch — involving factual recall, structural planning, and stylistic coherence — triggers a cascade of internal computations, including multiple rounds of self-attention and candidate token evaluation. Each additional layer of complexity increases the number of floating-point operations, which directly correlates with power draw on silicon.

This growing awareness of AI’s energy footprint is now influencing policy. In New Jersey, state legislative committees recently approved a landmark bill requiring data center operators to report real-time energy consumption per AI inference task. The bill, supported by environmental groups and tech regulators, aims to curb unchecked growth in AI-powered services by mandating transparency. "We can no longer treat AI as a black box with infinite capacity," said State Senator Linda Ruiz, lead sponsor of the bill. "If we’re serious about climate goals, we need to measure and limit the hidden energy costs of every chat, every summary, every generated image.**"

Industry leaders are beginning to respond. Major cloud providers are now developing "energy efficiency scores" for AI queries, similar to fuel economy ratings for cars. These metrics would help developers optimize prompts to reduce computational load — for instance, breaking complex requests into smaller steps or using smaller, specialized models for simple tasks. Some open-source communities are experimenting with "prompt pruning" techniques that eliminate redundant phrasing, thereby reducing inference time and energy use.

Meanwhile, consumer-facing platforms like OpenAI and Anthropic have remained largely silent on per-query energy metrics, citing proprietary concerns. But pressure is mounting. A recent petition by the Digital Sustainability Alliance, signed by over 150 academic researchers, demands public disclosure of energy-per-query benchmarks. "Users deserve to know the environmental cost of their interactions," said Dr. Elena Torres, a computational sustainability expert at Stanford. "If we’re going to scale AI globally, we need ethical frameworks that include energy as a core metric — not an afterthought.**"

For end users, the implications are practical. Reducing AI energy use isn’t just about saving the planet — it’s about efficiency. Shorter, clearer prompts yield faster responses and lower power consumption. Asking for bullet points instead of full paragraphs, or requesting step-by-step reasoning only when necessary, can reduce energy use by 30% or more, according to preliminary data from Home Assistant’s community-driven energy monitoring tools. While not a direct source of AI power data, the Home Assistant community’s work in tracking embedded system energy usage provides a model for how consumer-level transparency could be implemented.

As AI becomes embedded in everything from search engines to customer service bots, the question is no longer whether AI uses energy — but how much, and who’s accountable. The era of invisible computation is ending. The next frontier in AI ethics isn’t bias or safety — it’s power.