OpenAI disclosure gap on GPT-5 energy raises AI power concerns

OpenAI disclosure gap on GPT-5 energy raises AI power concerns is profiled by BTW Media because public-source evidence links it to internet infrastructure, governance, operational dependencies, or market visibility.

• GPT-5’s energy consumption remains undisclosed despite its expanded capabilities.

• Experts warn AI power demands could strain global infrastructure.

What happened:OpenAI keeps GPT-5 energy data secret

On 9 August 2025, The Guardian revealed that OpenAI “will not disclose GPT-5’s energy use. It could be higher than past models”. Independent benchmarking specialists, who have long studied the resource footprints of AI systems, caution that GPT-5’s leap in reasoning power and output complexity likely comes at the cost of substantially greater energy consumption.

Further details on the model’s energy profile remain absent. Wikipedia summarises that “the exact energy consumption for GPT-5 usage has not been disclosed by OpenAI,” but an estimate based on a system card suggests a medium-length response may require over 18 watt-hours.

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Why it’s important

GPT-5’s rollout arrives at a time when AI’s energy demands are starting to strain infrastructure and raise environmental alarms. AI workloads already contribute significantly to data-centre power use, especially in energy-hungry hubs like Northern Virginia, where grid connection delays now stretch years. Without transparency from major providers, researchers cannot accurately assess or mitigate the climate impact.

Beyond environmental consequences, energy demands have economic and operational implications. As computing scales, OpenAI is pursuing vast infrastructure expansions—aiming for over 1 million GPUs by end of 2025 and potentially up to 100 million in the long term—requiring tens of gigawatts of power, roughly three-quarters of the UK’s grid capacity.

In the race for increasingly intelligent AI, firms—including OpenAI—are integrating GPT-5 across enterprise ecosystems. Microsoft, for instance, is embedding the model into its Copilot suite and Azure AI Foundry, promising advanced reasoning capabilities alongside potential efficiencies. Yet the lack of clarity on energy use leaves stakeholders grappling with uncertainty.