Watts and bots: the energy implications of AI adoption
Bibliographic record
Abstract
Abstract Rapid expansion of Artificial Intelligence is expected to drive proportional expansion of economic activity through productivity gains–potentially leading to higher energy consumption and associated environmental impacts, including increased carbon dioxide emissions. We combine data on economic activity with early estimates of likely adoption of AI across occupations and industries to quantify the potential change in energy use and carbon dioxide emissions for the United States. At the industry level, we estimate annual increases in energy use ranging from 0 and 12 petajoules (PJ) and carbon emissions from 0 tonnes to 272 kt (ktCO 2 ). Aggregated across the economy, AI adoption could lead to an additional 28 PJ of energy use and 896 ktCO 2 in emissions annually–equivalent to approximately 0.03% of annual national energy use and 0.02% of annual national CO 2 emissions. These results highlight the need to account not only for the aggregate energy and environmental implications of AI-driven productivity gains, but also for how these impacts vary across industries based on their specific characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".