How much electricity does one AI task use? TokWatt exposes why published estimates disagree
The open database harmonises 134 estimates across 20 task types and shows a six-order-of-magnitude spread. Most reference values still rest on one source, and the boundary excludes idle capacity, hardware manufacture and user devices.
The short version
- What happened
- TokWatt version 0.1.0 curates 134 estimates from 13 public sources covering 20 task types and records which hardware, server and facility components each estimate counts.
- Who it affects
- A person choosing between a short answer and a long-running agent cannot infer the energy difference from the word 'AI' alone. Transparent task and boundary labels help organisations estimate demand without turning a partial operational figure into a full environmental claim.
- What remains unproven
- Unreviewed preprint
Keep in mind
Unreviewed preprint
Curated rather than systematic review
English-language and web-visible sources
91 of 134 rows from one modelling preprint
12 of 19 reference values depend on one source
Sparse first-party disclosures
Agent values based on two individuals' logs
No frontier closed-video measurement
Idle capacity and embodied energy excluded
Networks and user devices excluded
No audited product ranking
No separate funding or competing-interests statement
Source analysis · 2 linked sources. Primary analysed source date: 29 October 2026.
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A short answer and an agent task are not the same unit of work
TokWatt, a new open database, tries to make public claims about AI electricity use comparable before they are repeated as universal facts. Its first release contains 134 estimates from 13 sources published between 2023 and 2026, covering 20 task types. After mapping estimates to a common server-plus-facility boundary, the paper reports 0.34 watt-hours for a short chat answer, 4.74 Wh for a reasoning answer, 124 Wh for a short video and 150 Wh for one task delegated to a coding agent.
Those values span six orders of magnitude across the wider task set. That is partly a real difference in workload: generating video or running many agent calls is not equivalent to classifying one text. It is also a measurement problem. Published estimates variously count accelerators, host servers, facility overhead, idle machines or none of those explicitly. TokWatt records five boundary components and the functional unit so readers can see whether two numbers describe the same thing.[1][2]
The harmonisation is reproducible, but the evidence base is lopsided
The inclusion rules required a first-hand public measurement, model or disclosure tied to an identifiable task, convertible to watt-hours, with enough method detail to classify the boundary. Secondary accounts could be stored but not used in a reference set. The target boundary includes accelerators, host equipment and facility overhead; it excludes idle capacity and embodied hardware energy. Sensitivity tests changed five of seven calculator values by nothing and the largest by 12%, suggesting that source disagreement matters more than the chosen conversion factors.
Yet the source mix constrains the precision. One modelling preprint supplies 91 of 134 rows and nine of ten estimates behind the reasoning-answer value. Twelve of 19 reference values rely on one source. The agent numbers come from two individuals' logs, frontier closed video systems have no provider measurements, and only Google, OpenAI and Mistral supplied first-party per-query environmental figures in the reviewed set. The study is a curated English-language collection, not a systematic review.[1][2]
The distinction that matters
Why two AI energy numbers can both be honest
The answer changes with both the task and the system boundary.
Functional unit
Answer, image, video or agent task
Different amounts of computation
Operational boundary
Accelerator + host + facility
TokWatt's harmonised target
Excluded here
Idle capacity, hardware manufacture, network, user device
Reference value below a full footprint
Evidence
134 estimates from 13 sources
Many tasks still rely on one source
Reference values are useful for scenarios, not product labels
Boundary reporting remains sparse: of 132 estimates with an energy value, 127 count accelerator energy and 112 count the host, but only one fully counts idle capacity and none includes embodied hardware energy. Seven estimates from two sources cover all four operational components. Networks and user devices also sit outside the target. That means the values are deliberately narrower than a complete life-cycle footprint and cannot establish the electricity use or emissions of a named data centre.
For product teams and policymakers, TokWatt's strongest contribution is traceability: every estimate has a source locator, functional unit, method and boundary flags, while validation scripts rebuild the derived tables. Its values can support transparent scenarios if the uncertainties travel with them. They should not become claims that every prompt consumes 0.34 Wh or every agent task 150 Wh. Audited provider disclosures, repeated measurements under real serving loads, systematic searches and life-cycle accounting would materially change the assessment. The preprint lists no separate funding or competing-interests statement.[1][2]
Continue the story
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Read the next reportExplore the full source and evidence record
What this means for people
- A person choosing between a short answer and a long-running agent cannot infer the energy difference from the word 'AI' alone. Transparent task and boundary labels help organisations estimate demand without turning a partial operational figure into a full environmental claim.
Global context
Electricity and emissions depend on where and when computation runs, which the per-task reference values do not standardise. The database is globally useful as a measurement vocabulary, but its current source pool is not geographically or commercially representative of all AI services.
What the evidence does not yet show
- Unreviewed preprint
- Curated rather than systematic review
- English-language and web-visible sources
- 91 of 134 rows from one modelling preprint
- 12 of 19 reference values depend on one source
- Sparse first-party disclosures
- Agent values based on two individuals' logs
- No frontier closed-video measurement
- Idle capacity and embodied energy excluded
- Networks and user devices excluded
- No audited product ranking
- No separate funding or competing-interests statement
What to watch next
- Peer review
- Systematic-search update
- Audited provider measurements
- Frontier agent and video disclosures
- Idle-capacity accounting
- Embodied-energy estimates
- Measurements under real serving loads
- Versioned updates as models and hardware change
Living evidence record
Impact record IAI-0G4AYS4
Evidence stage
Studied
Confidence
Supported
Reporting basis
Source analysis
Independent or research support
Not yet
Record status
Monitoring
Last checked
31 October 2026
Source trail
2 direct sources across 1 source type.
People impact
Documented in this record.
Uncertainty
Limits and next checks are explicit.
Stages describe the evidence available—not whether a technology is good or bad. See the public method.
Related-source reporting disclosure
This record analyses 2 linked source records around the same underlying development. The extra records add method, date or context, but they do not by themselves constitute independent replication of every performance claim or predicted outcome.
Evidence trail
Sources used for this report
Links checked 31 October 2026
Research question, topics and regions
Open harmonisation of published per-task electricity estimates for AI inference
Topics: AI energy · Inference · Data centres · Measurement boundaries · Agentic AI · Environmental evidence
Regions: Global
This report is labelled source analysis. We summarise and analyse source material in our own words; company statements remain attributed claims until independently supported. Translated summaries preserve the meaning of the original source and link back to it. Read our editorial standards.
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