GPT-5’s Release Triggers Environmental Outcry Over Energy Demands

OpenAI’s latest marvel, GPT-5, is causing a stir, and not just because of its advanced capabilities. The new model’s potentially immense energy consumption is at the center of a growing environmental debate. While the company has remained quiet on the issue, experts are voicing serious concerns. They argue that the model’s new features—such as its ability to create websites and answer PhD-level questions—come at an unprecedented environmental cost. This lack of openness from a major AI developer is raising serious questions about the industry’s commitment to building a sustainable future.
The scale of the issue is highlighted by research from the University of Rhode Island’s AI lab, which found that generating a single medium-length response of about 1,000 tokens with GPT-5 can consume an average of 18 watt-hours. This is a dramatic increase from earlier models. To provide a clear comparison, 18 watt-hours is the same amount of energy needed to keep an incandescent light bulb running for 18 minutes. With services like ChatGPT fielding billions of requests every day, the cumulative energy consumption could be staggering, potentially reaching the daily electricity needs of millions of homes.
This sharp rise in energy use is directly linked to the model’s size and complexity. Experts believe GPT-5 is significantly larger than its predecessors, with a greater number of parameters. This is consistent with a study from the French AI company Mistral, which found a strong correlation between a model’s size and its energy consumption. The study concluded that a model that is ten times larger will have an impact that is an order of magnitude greater. This principle seems to be holding true for GPT-5, with some experts suggesting its resource use could be “orders of magnitude higher” than even GPT-3.
The situation is further complicated by the new model’s architectural design. While it employs a “mixture-of-experts” system for efficiency, its sophisticated reasoning capabilities and ability to process video and images likely counteract these gains. The new “reasoning mode,” which requires the model to perform computations for a longer time before generating a response, could make its energy footprint several times greater than text-only operations. This combination of size, complexity, and advanced features paints a clear picture of a highly power-hungry AI system, leading to urgent calls for greater transparency from OpenAI and the wider AI community.