The Hidden Energy cost of AI: what You Need to know
Artificial intelligence is rapidly transforming how we live and work. But behind the convenience of chatbots like ChatGPT and the power of advanced AI models lies a meaningful, and often hidden, energy cost. As AI becomes increasingly integrated into our daily lives, understanding it’s environmental impact is crucial.
The Energy Intensive Process of Building AI
Training large language models (LLMs) is a monumental undertaking.It typically requires clusters of servers, each equipped with around eight powerful GPUs, running continuously for weeks or even months.
The energy consumption is staggering. Estimates suggest training OpenAI‘s GPT-4 alone required 50 gigawatt-hours – enough to power the city of San francisco for three days. This intensive process is necessary to build the foundational knowledge base of these AI systems.
Inference: The Constant Drain of AI Usage
While training grabs headlines, inference – the process of an AI chatbot responding to your requests – also demands substantial energy. Though less resource-intensive than initial training, inference is a constant draw due to the sheer volume of interactions.
Consider this: as of July 2025, OpenAI reports over 2.5 billion prompts are sent to ChatGPT every day. Multiple servers are working constantly to deliver those instantaneous responses.And ChatGPT is just one player. Google’s Gemini is poised to become the default option within Google Search, further increasing demand.
Essentially, even after an LLM is trained, energy savings are minimal. As researcher Chowdhury explains, “It’s not really massive data. I mean, the model is already massive, but we have a massive number of people using it.”
The Transparency Problem & What We Know (and Don’t)
Quantifying the total energy footprint of AI is challenging. Researchers like Chowdhury are actively working to track inference energy consumption, maintaining resources like the ML Energy Leaderboard for open-source models.
However, major tech companies – Google, Microsoft, and Meta – largely keep their energy usage data private. the statistics they do release frequently enough lack the detail needed to accurately assess the environmental impact. This lack of transparency hinders our ability to predict future energy demands and determine if we can sustainably support the growth of AI.
What Can You Do? Demand Transparency & Responsible AI
You,as a user,have a role to play. Pushing for greater transparency from AI developers is a critical step.
Here’s how increased transparency benefits everyone:
* Informed Choices: You can make more energy-conscious decisions about your own AI usage.
* Accountability: It encourages companies to prioritize energy efficiency.
* Effective Policies: It provides policymakers with the data needed to create robust regulations.
As de Vries-Gao points out, “The ball is with policymakers to encourage disclosure so that the users can start doing something.” The impact of digital applications is often invisible, and it’s time to bring it into the light.
Ultimately, the future of AI depends on our ability to develop and deploy these powerful technologies responsibly, with a clear understanding of – and commitment to mitigating – their environmental consequences.
