■ Plain-Language Guide
++ Not a tech expert? No problem. Here are plain-language answers to the + most common questions about AI, tokens, and why any of this matters. +
+ ++ AI (Artificial Intelligence) is software that can do tasks we used + to think only humans could do — like writing text, answering questions, translating + languages, recognising photos, or generating images. +
++ Modern AI systems (such as ChatGPT, Google Gemini, or Claude) are powered by + large language models (LLMs) — giant programs trained on billions of web + pages, books, and articles so they can predict what words should come next in a + sentence. +
++ AI models don't read words — they read tokens. A token is a small + chunk of text, roughly 3–4 characters on average. Common short words like + "the" or "is" are a single token each. Longer words get split into several tokens. +
+ ++ Every time you send a message to an AI chatbot, it reads your message (input tokens) + and writes a reply (output tokens). Both cost energy. +
++ Inference is the moment an AI actually generates a response — when + it takes your question and produces an answer, word by word (or token by token). +
++ It's different from training (which is the one-time process of teaching the + AI using vast amounts of data). Inference happens billions of times every day, around + the clock, every time anyone uses an AI tool anywhere in the world. +
++ Training is the expensive, one-time (or infrequent) process of + building an AI model. It requires running data through billions of mathematical + operations on specialised hardware for weeks or months. A single training run can + use as much electricity as hundreds of homes use in a year. +
++ Inference is the ongoing, every-second process of using that + already-trained model to answer questions. Each individual inference is cheap, but + with hundreds of millions of queries per day across every AI product on Earth, the + cumulative energy use is enormous — and this is what this site tracks. +
++ Generating each token requires running your input through a neural network with + billions of mathematical multiplications on specialised chips called GPUs + or TPUs. These chips are extremely powerful — and extremely power-hungry. +
++ A single AI server rack can draw as much electricity as 20–30 family homes. Data + centres housing thousands of those racks run 24 hours a day, 365 days a year. + The chips also generate a lot of heat, requiring large cooling systems that consume + even more electricity and water. +
++ AI data centres use enormous amounts of water to cool their servers. When chips run + at full power, they generate heat. That heat is carried away using chillers and + cooling towers that evaporate large quantities of water into the air. +
++ Microsoft's own sustainability report estimated that training a single large AI model + can consume hundreds of thousands of litres of fresh water. Inference — happening + continuously — adds billions more litres every year across the industry. +
+ ++ CO₂ (carbon dioxide) is a greenhouse gas. When power stations burn + coal, oil, or natural gas to generate electricity, CO₂ is released into the + atmosphere. This extra CO₂ traps heat from the sun, gradually warming the planet — + a process known as climate change. +
++ Because AI data centres consume huge amounts of electricity, and much of the world's + electricity grid still relies on fossil fuels, running AI produces a significant + amount of CO₂. Even data centres that use renewable energy have an indirect carbon + footprint through manufacturing, water use, and grid draw during low-renewable + periods. +
++ It's complicated. AI can be a powerful tool for environmental good — + helping model climate systems, optimise energy grids, or accelerate scientific + research. But the current rapid growth of AI token consumption is also + a significant and fast-growing source of energy and water use. +
++ The goal of this site is not to say "AI is evil" — it's to make the hidden cost + visible, so individuals and organisations can make more informed + decisions about when and how to use AI, and so that pressure builds on AI companies + to invest in efficiency and clean energy. +
++ The numbers are estimates, not official measurements. No public + real-time feed of global AI token consumption exists. We derive our figures from: +
++ Published AI usage disclosures (OpenAI, etc.) · Epoch AI compute trend research · + Academic papers on AI energy use · IEA electricity data · + Microsoft sustainability reports. +
++ The real-world figure could be higher or lower — the point is to communicate + order of magnitude. The counter is live in the sense that it extrapolates + forward in real time from a fixed anchor point at the current estimated global rate. + See the Data Sources section above for the exact figures used. +
++ + The current estimated global AI inference rate is around + 100 million tokens per second — generated by all the AI services + (chatbots, search assistants, coding tools, image generators, API calls, etc.) running + simultaneously across every country, every company, and every individual user on the + planet. +
++ That rate has grown by orders of magnitude since 2020, and is projected to keep + growing as more AI products launch and more people adopt them. Even if one company's + service is efficient, the sheer scale of global AI usage means the total climbs + relentlessly. +
++ Quite a lot, actually. Individual habits scale up when millions of people share them: +
+
+ Write shorter, clearer prompts — the more specific you are, the
+ fewer tokens the AI needs to generate a good answer.
+ Use smaller models — for simple tasks (summarising a paragraph,
+ checking grammar), a lightweight model uses a fraction of the energy of a frontier
+ model.
+ Don't regenerate unnecessarily — if an answer is good enough, use
+ it. Every regeneration is another burst of tokens.
+ Tell organisations you care — write to your AI providers asking
+ about their energy roadmap. Consumer pressure works.
+ Share this page — the more people understand the cost, the more
+ pressure there is to improve efficiency.
+