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As AI tools are constantly improving, appealing to an increasing number of users, ChatGPT remains the most popular chatbot.
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ChatGPT has moved from novelty to infrastructure. With an estimated 900 million weekly active users and roughly 1.17 trillion prompts processed annually, OpenAI’s flagship chatbot now operates at a scale comparable to the largest digital platforms. But as adoption accelerates, so does a less visible metric: energy consumption.
A new analysis from the firm BestBrokers, reviewed by Digital Journal, offers a striking snapshot of how global demand for AI is distributed—and what it costs in computational terms. The findings highlight two key trends shaping the AI era: the rapid rise of emerging markets and the growing energy footprint of large-scale AI systems.
One of the most notable developments is the geographical redistribution of AI usage. While the United States remains a dominant force in the tech ecosystem, it is no longer the largest user of ChatGPT.
This shift reflects broader digital trends. Large, mobile-first populations are adopting AI tools rapidly, often leapfrogging traditional desktop computing. In contrast to earlier waves of internet growth—where Western markets dominated—AI adoption is proving far more evenly distributed.
The implications are significant: the “centre of gravity” for AI usage is moving toward the Global South, reshaping where infrastructure investment, regulation and innovation pressures will concentrate.
Within Europe, usage remains strong but more concentrated. The United Kingdom stands out as a high-intensity market:
This translates to roughly one prompt per person per day, a level of engagement that suggests AI is becoming embedded in everyday workflows. The UK ranks behind France, Germany and Spain in total traffic, but per-capita usage remains among the highest.
This pattern reflects the UK’s position as a mature digital economy, where AI is being rapidly integrated into sectors such as finance, media, education and professional services. The trend is particularly pronounced among knowledge workers, who increasingly rely on AI for drafting, coding, summarisation and research.
Behind this growth lies a critical constraint: energy. AI systems—especially large language models—require vast computational resources to process queries in real time. The energy consumption is, as of March 2026:
The survey analysis estimates:
To put this in perspective, that places AI infrastructure firmly in the category of large-scale industrial energy consumers.
For the UK alone:
This is equivalent to the output of a large nuclear power plant running continuously for weeks.
At global scale, the cost is equally striking. If powered entirely from U.S. grid electricity, operating ChatGPT at current levels would exceed $8 million per day in energy costs.
This raises a central tension in AI development: the trade-off between performance and efficiency.
Modern AI models are becoming:
All three trends increase computational demand. At the same time, industry efforts are underway to reduce energy intensity through:
However, efficiency gains are often offset by rising demand—a classic “rebound effect” seen in other technology sectors.
The data also reflects a broader shift in how work is being performed. In countries like the UK, AI is now deeply embedded in professional environments, where it is used to increase output and streamline tasks.
This has clear productivity benefits. But it also introduces new dynamics:
The result is a form of “AI augmentation” that may enhance efficiency while raising concerns about overwork, job displacement and skill erosion.
ChatGPT’s growth marks a turning point in the digital economy. Unlike earlier platforms—social media, search or streaming—AI is not just distributing content, but actively generating it. This makes it both more powerful and more resource-intensive.
The emerging picture is one of global adoption, rising energy demand and shifting economic geography. Emerging markets are driving usage growth, developed economies are integrating AI deeply into workflows, and infrastructure providers are racing to keep up with demand.
The key question is whether the underlying systems—technical, economic and environmental—can scale sustainably.
As AI becomes a permanent layer of digital infrastructure, its success will depend not only on what it can do, but on how efficiently it can do it.
Dr. Tim Sandle is Digital Journal’s Editor-at-Large for science news. Tim specializes in science, technology, environmental, business, and health journalism. He is additionally a practising microbiologist; and an author. He is also interested in history, politics and current affairs.
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