Core Insights - The energy consumption of AI models is increasing exponentially, with GPT-5 consuming significantly more power than its predecessors, raising concerns about the environmental impact of AI technology [1][5][12] Group 1: Energy Consumption of AI Models - GPT-5 consumes an average of 18.35 watt-hours for generating a medium-length response of approximately 1000 tokens, which is about 8.6 times higher than GPT-4's 2.12 watt-hours [7][11] - The daily energy consumption of GPT-5 can reach 45 gigawatt-hours, equivalent to the daily electricity needs of 1.5 million American households or the output of 2 to 3 nuclear reactors [13][14] - The energy consumption of AI models has become a critical metric for assessing their true costs, with experts warning that the increasing scale of models leads to a corresponding rise in energy use [5][12] Group 2: Industry Implications - OpenAI has not disclosed the energy consumption data for GPT-5, which raises questions about the operational costs and sustainability of its AI products [5][14] - The reliance on cloud infrastructure, particularly Microsoft's Azure, means that energy consumption issues affect not only OpenAI but also the broader cloud computing industry, including major providers like Microsoft, Google, and Amazon [14][15] - The construction of AI infrastructure, such as a 250-megawatt "AI factory," can cost around $12 billion, highlighting the financial burden associated with the energy demands of AI technologies [12][13] Group 3: Ethical and Regulatory Considerations - The lack of transparency regarding energy consumption has led to ethical debates about the justification of using significant energy resources for AI development, especially in a world where many face energy shortages [15][16] - There is currently no unified regulatory framework addressing AI energy consumption, which allows companies to choose whether to disclose their energy usage, potentially eroding public trust in the industry [15][16] - The decision by OpenAI to withhold GPT-5's energy consumption data has sparked broader discussions about the societal costs of AI and the need for a collective decision on the future of AI development in light of environmental concerns [15][16]