Introduction
GPT-5’s arrival marks a leap forward in AI capability—but also a controversial move in environmental accountability. As OpenAI remains opaque about power consumption, environmentalists, policymakers, and enterprise users alike are calling for GPT-5 energy transparency as an urgent necessity.
Background: Model Advancements vs. Hidden Costs
Launched on August 7, 2025, GPT-5 introduces multiple model variants—fast, high-throughput and deep reasoning—with intelligent routing between them to optimize performance. ([turn0search23]) It excels in PhD-level science, coding, and reasoning, effectively summarizing years of AI innovation.
However, OpenAI has refused to share energy usage figure details. The Guardian reports responses for typical prompts may use up to 20 times more energy than earlier ChatGPT outputs. ([turn0search0]) The University of Rhode Island’s AI lab benchmark estimates 18 watt-hours per medium-length response, with peaks reaching 40 watt-hours, dwarfing previous models’ footprints. ([turn0search0])
The Transparency Gap
While Sam Altman once claimed an average ChatGPT query uses about 0.34 watt-hours, experts cautioned that this figure lacks context—such as model type, prompt complexity, or training emissions included. ([turn0news21], [turn0news18]) A recent analysis shows 84% of LLM usage lacks any public environmental disclosure, making GPT-5’s non-transparency even more troubling. ([turn0news21])
Why This Matters
- Environmental Impact: AI energy consumption is significant. By 2027, global LLM-related energy use could reach 85–134 TWh, equating to nearly half a percent of global electricity. ([turn0search22])
- Scale Amplifies Costs: According to recent studies, a single prompt on a short model may emit carbon comparable to household-scale usage—but millions of users multiply this dramatically. ([turn0academia24])
- Policy Fallout: With regulations like the EU’s AI Act urging AI sustainability, companies may face pressure to cement energy-per-query disclosures.
Expert Insights & Sector Reactions
Rakesh Kumar, an energy-computation expert, acknowledges that GPT-5 likely consumes far more power than GPT-4, due to its complexity. ([turn0search0]) Meanwhile, researchers developing the “How Hungry Is AI?” framework highlighted that models like o3 or DeepSeek-R1 consume 33 watt-hours per long prompt—70× a GPT-4 nano query. This underscores the alarming scale of modern model inference costs. ([turn0academia24])
And it’s not just electricity—water is a massive factor. MIT News emphasizes the significant water usage required for cooling AI infrastructure, which is often neglected in energy discussions. ([turn0search12])
Impacts on Stakeholders
- Enterprises may hold back on integrating GPT-5 without understanding operating costs and environmental liabilities.
- AI Practitioners need benchmarks to drive efficiency. Without guidance on energy use, efforts remain speculative.
- Consumers & Investors are increasingly influenced by ESG considerations, expecting brands to demonstrate environmental responsibility.
- Policy Makers may push for mandatory disclosures, especially in light of models’ ever-growing influence.
The Road Ahead
- Benchmark Pressure: Independent labs may publish regular energy audit results, setting de facto industry norms.
- OpenAI Response: They could preempt regulation by revealing energy-per-token metrics or efficiency benchmarks.
- Policy Evolution: Environmental auditors may become standard in AI model certification.
- Public Awareness: As energy and emissions become central to AI discourse, demand for green AI won’t subside.
