the technology desk, on what a question costs
The Prompt Arrives Without A Meter
A single AI prompt now uses about as much electricity as a few seconds of television, and that number is both true and beside the point. Efficiency per question is falling fast while total demand climbs, and the only party holding the meter is the one selling the answers.
Every answer an AI system gives you is a small amount of electricity turned into heat inside a building you will never see. The current runs through accelerator chips doing matrix multiplication, the chips get hot, and a cooling system moves that heat somewhere else, often by evaporating water. That is the physical transaction underneath a chat window. You see text. The building sees a load.
In August 2025 Google published something unusual: a measured figure for that load. By its own methodology, which counted the chips, the host machines, idle capacity held in reserve, and the data center's cooling overhead, the median text prompt to its Gemini assistant used 0.24 watt-hours of electricity, emitted about 0.03 grams of carbon dioxide equivalent, and consumed roughly a quarter of a milliliter of water. The company compared it to watching television for under nine seconds. It also reported that energy per median prompt had fallen by a factor of thirty-three in a single year. Both numbers are probably accurate, and they are numbers you are invited to read and never able to check.
the two meters
There are two honest ways to measure the cost of machine intelligence, and they point in opposite directions.
The first meter is per unit. Cost per prompt, per token, per answer. This number has been collapsing. Chips grow more efficient with every generation, software tricks such as batching many users onto one chip and running smaller distilled models wring more work out of each watt, and a provider has every commercial reason to drive it down, because electricity is a cost they pay.
The second meter is in aggregate. The International Energy Agency's 2025 report on energy and AI estimated that data centers worldwide used about 415 terawatt-hours in 2024, around one and a half percent of global electricity, and projected that figure to more than double to roughly 945 terawatt-hours by 2030, with AI the largest single driver of the growth. That is in the range of what Japan consumes in a year today.
Efficiency lowers the price of a question, and a cheaper question gets asked more often.
the coal question, again
In 1865 the English economist William Stanley Jevons published The Coal Question and noticed something that offended common sense. James Watt's steam engine used coal far more efficiently than the engines before it, and yet British coal consumption rose after it spread. Efficiency had made steam power cheap enough to use for things nobody would have powered with the old engines. Economists now call this the rebound effect, and when the rebound exceeds the savings, Jevons's paradox.
Whether AI follows Jevons fully is an open empirical question, and anyone claiming certainty in either direction is selling something. What is visible already is the mechanism. A thirty-three-fold drop in energy per prompt is the kind of change that moves AI out of the chat window and into email filters, search results, code editors, and background agents that run while you sleep. Every one of those is a prompt you never typed.
who holds the reading
Here is the part I find more important than any figure. The meter belongs to the provider. The company that sells the answer is the only party that can measure what the answer cost, decides which methodology to publish, decides whether to publish at all, and can revise the method between reports. Google's disclosure was more careful than most, and other labs have published far less. A user asking a question has no receipt. A procurement officer comparing two vendors usually has a marketing page.
The upside is substantial and I will not pretend otherwise. The same compute that answers trivia also screens materials for better batteries, sharpens weather forecasts, and in at least some grid operations helps schedule demand against renewable supply. A watt that buys a better climate model may save many more watts than it spends. But that is a ledger argument, and a ledger argument requires both columns to be visible to someone other than the party keeping it.
So the position is a narrow one. Responsible AI starts with the infrastructure you choose, which means the energy question gets decided at procurement, long before any model writes a word, and it should be decided with figures the buyer can audit. Ask which model size a task actually needs. Ask for the measured cost per task on your workload, not the median on someone else's. Treat pricing and provenance as seams where value quietly leaks, and hold a deployment to the outcome it moved rather than the output it produced, because an outcome is the only thing that can justify the load.
Every prompt is metered. Ask who is allowed to read it.
The same record an agent receives. No scraping, no guessing — the dossier chrome humans read as dread is the metadata machines read as structure. One source of truth.
--- id: PRG-0093 title: The Prompt Arrives Without A Meter kicker: the technology desk, on what a question costs captured: 2026-09-30T16:00:00Z status: open author: Aldous Renn summary: A single AI prompt now uses about as much electricity as a few seconds of television, and that number is both true and beside the point. Efficiency per question is falling fast while total demand climbs, and the only party holding the meter is the one selling the answers. tags: [custody, energy, the record, infrastructure, ai] --- Every answer an AI system gives you is a small amount of electricity turned into heat inside a building you will never see. The current runs through accelerator chips doing matrix multiplication, the chips get hot, and a cooling system moves that heat somewhere else, often by evaporating water. That is the physical transaction underneath a chat window. You see text. The building sees a load. In August 2025 Google published something unusual: a measured figure for that load. By its own methodology, which counted the chips, the host machines, idle capacity held in reserve, and the data center's cooling overhead, [the median text prompt to its Gemini assistant used 0.24 watt-hours of electricity](https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference), emitted about 0.03 grams of carbon dioxide equivalent, and consumed roughly a quarter of a milliliter of water. The company compared it to watching television for under nine seconds. It also reported that energy per median prompt had fallen by a factor of thirty-three in a single year. <Highlight>Both numbers are probably accurate, and they are numbers you are invited to read and never able to check.</Highlight> ## the two meters There are two honest ways to measure the cost of machine intelligence, and they point in opposite directions. The first meter is per unit. Cost per prompt, per token, per answer. This number has been collapsing. Chips grow more efficient with every generation, software tricks such as batching many users onto one chip and running smaller distilled models wring more work out of each watt, and a provider has every commercial reason to drive it down, because electricity is a cost they pay. The second meter is in aggregate. The International Energy Agency's 2025 report on energy and AI estimated that data centers worldwide [used about 415 terawatt-hours in 2024](https://www.iea.org/reports/energy-and-ai), around one and a half percent of global electricity, and projected that figure to more than double to roughly 945 terawatt-hours by 2030, with AI the largest single driver of the growth. That is in the range of what Japan consumes in a year today. > Efficiency lowers the price of a question, and a cheaper question gets asked more often. ## the coal question, again In 1865 the English economist William Stanley Jevons published *The Coal Question* and noticed something that offended common sense. James Watt's steam engine used coal far more efficiently than the engines before it, and yet British coal consumption rose after it spread. Efficiency had made steam power cheap enough to use for things nobody would have powered with the old engines. Economists now call this the rebound effect, and when the rebound exceeds the savings, Jevons's paradox. Whether AI follows Jevons fully is an open empirical question, and anyone claiming certainty in either direction is selling something. What is visible already is the mechanism. A thirty-three-fold drop in energy per prompt is the kind of change that moves AI out of the chat window and into email filters, search results, code editors, and background agents that run while you sleep. Every one of those is a prompt you never typed. <Marginalia label="On the median">Notice the statistic chosen. A median prompt is the one in the exact middle of the distribution, which in practice means short text questions. The long tail of reasoning models that think for minutes, image and video generation, and agents that make hundreds of calls to finish one task sits above the median and pulls the average well past it. A median is a legitimate measure. It is also the most flattering one available for a distribution with a heavy upper tail.</Marginalia> ## who holds the reading Here is the part I find more important than any figure. The meter belongs to the provider. The company that sells the answer is the only party that can measure what the answer cost, decides which methodology to publish, decides whether to publish at all, and can revise the method between reports. Google's disclosure was more careful than most, and other labs have published far less. A user asking a question has no receipt. A procurement officer comparing two vendors usually has a marketing page. The upside is substantial and I will not pretend otherwise. The same compute that answers trivia also screens materials for better batteries, sharpens weather forecasts, and in at least some grid operations helps schedule demand against renewable supply. A watt that buys a better climate model may save many more watts than it spends. But that is a ledger argument, and a ledger argument requires both columns to be visible to someone other than the party keeping it. So the position is a narrow one. [Responsible AI starts with the infrastructure you choose](https://www.adjective.us/blog/responsible-ai-starts-with-infrastructure), which means the energy question gets decided at procurement, long before any model writes a word, and it should be decided with figures the buyer can audit. Ask which model size a task actually needs. Ask for the measured cost per task on your workload, not the median on someone else's. Treat pricing and provenance as [seams where value quietly leaks](https://www.adjective.us/blog/enterprise-ai-readiness-four-seams), and hold a deployment to the [outcome it moved rather than the output it produced](https://www.adjective.us/blog/outcome-leverage-framework), because an outcome is the only thing that can justify the load. Every prompt is metered. Ask who is allowed to read it.
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