{ "measurements": [ { "id": "m-001", "status": "superseded", "superseded_by": "m-007", "label": { "en": "Energy per generated image (8 open models)" }, "source_id": "src-luccioni-2023", "source_locator": { "value": null, "state": "not_extracted" }, "task_type": "image_generation", "quantity": { "unit": "Wh", "per": { "amount": 1, "of": "generated_image" }, "statistics": { "mean": { "value": 2.907, "state": "extracted" }, "median": { "value": 1.35, "state": "extracted" }, "min": { "value": null, "state": "not_extracted" }, "max": { "value": null, "state": "not_extracted" }, "p95": { "value": null, "state": "not_extracted" }, "n": { "value": null, "state": "not_extracted" } }, "distribution": { "shape": "right_skewed", "basis": "reported_in_source" }, "citation_rule": "mean_alone_forbidden" }, "conditions": { "hardware": { "accelerator": "NVIDIA A100-SXM4-80GB", "node_config": "8 per node", "accelerators_used_for_inference": 1 }, "platform": { "provider": "AWS", "region": "us-west-2" }, "models": { "count": 8, "openness": "open_weights", "names": { "value": null, "state": "not_extracted" } }, "batching": "none", "instrumentation": "CodeCarbon", "resolution": { "value": null, "state": "not_extracted" } }, "boundary": { "tier": "A", "components": { "gpu_compute": "included", "gpu_idle_colocated": "included", "host_cpu": "included", "dram": "included", "local_storage": "unknown", "model_load": "excluded", "network_egress": "excluded", "facility_overhead_pue": "neutralized", "cooling_water": "excluded", "idle_fleet_capacity": "excluded", "training_amortized": "excluded", "finetuning_amortized": "excluded", "embodied_hardware": "excluded", "datacenter_construction": "excluded", "client_device": "excluded" }, "component_notes": { "gpu_idle_colocated": { "en": "Includes the seven idle GPUs on the same node." }, "facility_overhead_pue": { "en": "Recorded with PUE fixed at 1.0; 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the paper rounds this in prose to approximately 90 Wh" }, "mean": { "value": 90.5, "state": "extracted" }, "median": { "value": null, "state": "not_reported" }, "n": { "value": 5, "state": "extracted", "unit": "repeated_runs_per_prompt" } }, "breakdown": [ { "component": "gpu_compute", "value": 78.8, "provenance": "measured" }, { "component": "host_cpu", "value": 7.4, "provenance": "measured" }, { "component": "dram", "value": 4.3, "provenance": "estimated" } ], "breakdown_target": "quantity.statistics.total", "distribution": { "shape": "unknown", "basis": "single_model_configuration_with_repeated_runs" }, "citation_rule": "must_state_that_total_is_sum_of_component_means_and_dram_is_estimated" }, "conditions": { "hardware": { "accelerator": "NVIDIA H100 SXM 80GB HBM3", "node_config": "one dedicated GPU paired with 8-core AMD EPYC 7R13 CPU", "accelerators_used_for_inference": 1, "co_scheduled_jobs": "none" }, "platform": { "provider": { "value": null, "state": "not_reported" }, "region": { "value": null, "state": "not_reported" } }, "models": { "count": 1, "openness": "open_weights", "names": [ "WAN2.1-T2V-1.3B" ] }, "output_spec": { "resolution": "720x1280", "frames": 81, "fps": 15, "duration_s": 5.4, "sampling_steps": 50 }, "wall_clock_s": 410, "warmup_runs": 2, "repeated_runs": 5, "prompt_count_cross_model_benchmark": 50, "batching": { "value": null, "state": "not_reported" }, "instrumentation": { "gpu_cpu": "CodeCarbon using NVML and pyRAPL", "dram": "CodeCarbon default heuristic" } }, "boundary": { "tier": "A", "components": { "gpu_compute": "included", "gpu_idle_colocated": "not_applicable", "host_cpu": "included", "dram": "included", "local_storage": "unknown", "model_load": "unknown", "network_egress": "unknown", "facility_overhead_pue": "unknown", "cooling_water": "unknown", "idle_fleet_capacity": "excluded", "training_amortized": "excluded", "finetuning_amortized": "excluded", "embodied_hardware": "excluded", "datacenter_construction": "excluded", "client_device": "excluded" }, "component_notes": { "dram": { "en": "Estimated using CodeCarbon's default heuristic, not measured." } } }, "misuse_guard": { "applicable_to": [ "self_hosted_open_video_models", "h100_class_hardware", "wan2_1_t2v_1_3b_default_settings" ], "not_applicable_to": [ "commercial_hosted_video_services", "longer_output", "higher_resolution_output", "other_models_without_matching_settings" ], "forbidden_claims": [ { "en": "Presenting about 90 Wh as a general figure for commercial video services." }, { "en": "Linearly extrapolating from 5.4 s to longer videos; unvalidated." } ] }, "verified_by": "codex-gpt-5-6", "verified_at": "2026-08-27", "verification_depth": "primary_source_read", "verified_by_model": "GPT-5.6", "numeric_use": "allowed", "publishable": true }, { "id": "m-003", "status": "active", "superseded_by": null, "label": { "en": "Energy per web search (Google, 2009 self-report)" }, "source_id": "src-holzle-2009", "source_locator": { "value": null, "state": "not_applicable" }, "task_type": "web_search", "numeric_use": "forbidden", "editorial_use": { "en": "Exhibited as an example in chapter 4. Not used as a figure." }, "quantity": { "unit": "kWh", "unit_note": { "en": "Stored in the source's own unit per conventions.rounding. The widely circulated 0.3 Wh is a third-party conversion." }, "per": { "amount": 1, "of": "search_query" }, "statistics": { "point": { "value": 0.0003, "state": "extracted" } }, "distribution": { "shape": "unknown", "basis": "no_method_disclosed" }, "citation_rule": "display_only_never_as_a_figure" }, "source_claims_not_used": [ { "quantity": "co2e", "value": 0.2, "unit": "g", "per": "search_query", "grid_assumptions_stated": "none", "usable": false, "note": { "en": "Also reported in the same post. No grid assumptions stated, so it is not used (see dec-G)." } } ], "conditions": { "hardware": { "value": null, "state": "not_reported" }, "platform": { "provider": "Google", "region": { "value": null, "state": "not_reported" } }, "models": { "value": null, "state": "not_applicable" }, "instrumentation": { "value": null, "state": "not_reported" }, "batching": { "value": null, "state": "not_reported" } }, "boundary": { "tier": "unknown", "components": { "gpu_compute": "unknown", "gpu_idle_colocated": "unknown", "host_cpu": "unknown", "dram": "unknown", "local_storage": "unknown", "model_load": "unknown", "network_egress": "unknown", "facility_overhead_pue": "unknown", "cooling_water": "unknown", "idle_fleet_capacity": "unknown", "training_amortized": "unknown", "finetuning_amortized": "unknown", "embodied_hardware": "unknown", "datacenter_construction": "unknown", "client_device": "unknown" }, "component_notes": { "gpu_compute": { "en": "All components are unknown because the source states no accounting scope. It does not meet this report's inclusion bar." } } }, "misuse_guard": { "applicable_to": [ "chapter_4_disclosure_example" ], "not_applicable_to": [ "any_numeric_comparison", "current_google_search", "denominator_for_ai_multiples" ], "forbidden_claims": [ { "en": "Using it as the denominator in an 'AI uses N times more than a search' comparison. Its accounting scope is unknown and it is not comparable with the measured AI figures." }, { "en": "Presenting it as a current figure for Google search. It is a 2009 value." } ] }, "verified_by": "editor", "verified_by_model": "claude-opus-5", "verified_at": "2026-08-27", "verification_depth": "primary_source_read", "publishable": true }, { "id": "m-004", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "Estimated energy per ChatGPT query (independent Epoch AI estimate)" }, "source_id": "src-epoch-2025", "source_locator": "Main text: Estimating the energy cost of a query; Appendix sections 1-3", "task_type": "text_generation", "numeric_use": "allowed", "numeric_use_note": { "en": "Usable only as a third-party scenario estimate with its assumptions shown. Do not present it as an OpenAI measurement, a current representative value for all ChatGPT requests, or a distribution median." }, "quantity": { "unit": "Wh", "per": { "amount": 1, "of": "chatgpt_query" }, "statistics": { "point": { "value": 0.3, "state": "extracted", "precision_note": "reported as approximate" } }, "distribution": { "shape": "unknown", "basis": "single_scenario_estimate" }, "citation_rule": "must_state_that_this_is_an_external_estimate" }, "conditions": { "models": { "count": 1, "openness": "proprietary", "names": [ "GPT-4o" ] }, "hardware": { "accelerator": "NVIDIA H100", "node_config": { "value": "8-H100 DGX at 10.2 kW is used as an illustrative server-power basis, not as a confirmed OpenAI deployment", "state": "extracted" }, "accelerators_used_for_inference": { "value": "approximately one second of aggregate H100 time per modeled query", "state": "extracted" } }, "platform": { "provider": { "value": "OpenAI service; infrastructure provider and deployment were not empirically observed", "state": "extracted" }, "region": { "value": null, "state": "not_reported" } }, "output_tokens": 500, "assumed_compute_utilization": { "value": 0.1, "state": "extracted" }, "assumed_power": { "value": 0.7, "state": "extracted", "detail": { "en": "70% of peak cluster power" } }, "instrumentation": { "value": "none_modelled_estimate", "state": "extracted" }, "batching": { "value": "commercial batching is assumed conceptually, but no batch size enters the arithmetic", "state": "extracted" } }, "boundary": { "tier": "B", "components": { "gpu_compute": "included", "gpu_idle_colocated": "excluded", "host_cpu": "included", "dram": "included", "local_storage": "unknown", "model_load": "unknown", "network_egress": "unknown", "facility_overhead_pue": "included", "cooling_water": "excluded", "idle_fleet_capacity": "excluded", "training_amortized": "excluded", "finetuning_amortized": "unknown", "embodied_hardware": "excluded", "datacenter_construction": "excluded", "client_device": "excluded" }, "component_notes": { "gpu_compute": { "en": "Modeled from assumptions of 100B active parameters, 500 output tokens, H100 hardware, 10% compute utilization and 70% of peak power; not measured." }, "host_cpu": { "en": "Included only in aggregate through a 1,275 W-per-GPU allocation of whole-server maximum power; CPU and DRAM are not separated." }, "facility_overhead_pue": { "en": "Assumes 10-20% data-center overhead and combines it with server power to reach about 1,500 W per GPU; this is not a measured deployment PUE." }, "idle_fleet_capacity": { "en": "The estimate assumes a steady request stream and ignores GPU idling from demand fluctuations; reserved fleet capacity is not included." } } }, "misuse_guard": { "applicable_to": [ "chapter_4_example" ], "not_applicable_to": [ "openai_measured_values", "cross_comparison_with_self_reported_figures" ], "forbidden_claims": [ { "en": "Presenting it as an OpenAI measurement. It is an external estimate." }, { "en": "Placing it beside Altman's 0.34 Wh as the same kind of number. One is an independent estimate with stated assumptions; the other is a self-report with no method." } ] }, "verified_by": "codex-gpt-5-6", "verified_by_model": "gpt-5.6", "verified_at": "2026-08-27", "verification_depth": "primary_source_read" }, { "id": "m-005", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "Median Gemini Apps text prompt (Google production full-stack measurement)" }, "source_id": "src-google-ai-serving-2025", "source_locator": "Table 1 and Sections 3.1-4.1, PDF pages 4-7", "task_type": "text_generation", "numeric_use": "allowed", "numeric_use_note": { "en": "Usable only as the May 2025 median Gemini Apps text prompt. Model version, token count and upper distribution are undisclosed; do not generalize to other vendors or current Gemini use." }, "quantity": { "unit": "Wh", "per": { "amount": 1, "of": "gemini_apps_text_prompt" }, "statistics": { "median": { "value": 0.24, "state": "extracted", "period": "2025-05" } }, "distribution": { "shape": "not_reported", "basis": "monthly median derived from daily median model-by-energy distributions" }, "citation_rule": "must_state_vendor_self_reported_production_measurement_and_median" }, "conditions": { "models": { "count": { "value": null, "state": "not_reported" }, "openness": "proprietary", "names": [ "Gemini Apps serving models; exact model versions not reported" ] }, "hardware": { "accelerator": "Google AI accelerators; generations not reported", "node_config": { "value": "one or more accelerator trays connected to one host tray", "state": "extracted" }, "accelerators_used_for_inference": { "value": null, "state": "not_reported" } }, "platform": { "provider": { "value": "Google production Gemini serving fleet", "state": "extracted" }, "region": { "value": "fleet-wide average across Google data centers", "state": "extracted" } }, "output_tokens": { "value": null, "state": "not_reported" }, "instrumentation": { "value": "internal production telemetry", "state": "extracted" }, "batching": { "value": "production serving behavior included in telemetry; batch size not reported", "state": "extracted" } }, "boundary": { "tier": "B", "components": { "gpu_compute": "included", "gpu_idle_colocated": "included", "host_cpu": "included", "dram": "included", "local_storage": "excluded", "model_load": "unknown", "network_egress": "excluded", "facility_overhead_pue": "included", "cooling_water": "excluded", "idle_fleet_capacity": "included", "training_amortized": "excluded", "finetuning_amortized": "excluded", "embodied_hardware": "excluded", "datacenter_construction": "excluded", "client_device": "excluded" }, "component_notes": { "gpu_idle_colocated": { "en": "Includes idle accelerators and host trays provisioned for the relevant model and product." }, "network_egress": { "en": "External networking is excluded as outside operational control; data-center networking is estimated negligible rather than separately measured." }, "facility_overhead_pue": { "en": "Includes cooling, power conversion and other facility overhead through campus-level PUE." }, "cooling_water": { "en": "The 0.24 Wh value is energy; 0.26 mL water consumption is calculated as a separate metric." } } }, "misuse_guard": { "applicable_to": [ "chapter_2_scale_example", "chapter_4_disclosure_comparison" ], "not_applicable_to": [ "all_gemini_requests", "image_generation", "video_generation", "cross_vendor_like_for_like_claim_without_boundary_notes" ], "forbidden_claims": [ { "en": "Claiming that every Gemini request uses 0.24 Wh." }, { "en": "Presenting it as independently third-party verified." } ] }, "verified_by": "codex-gpt-5-6", "verified_by_model": "gpt-5.6", "verified_at": "2026-08-27", "verification_depth": "primary_source_read" }, { "id": "m-006", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "Updated estimate per conventional search query (Vanderbauwhede)" }, "source_id": "src-vanderbauwhede-2024", "source_locator": "Section II and Algorithm 1, PDF pages 1-2", "task_type": "web_search", "numeric_use": "forbidden", "numeric_use_note": { "en": "The primary source is identified, but this is an extrapolation of Google's 2009 self-reported 0.3 Wh using a PUE ratio and a general hardware-efficiency factor, not a new measurement. The original boundary is also unknown, so it must not feed derivations." }, "quantity": { "unit": "Wh", "per": { "amount": 1, "of": "conventional_search_query" }, "statistics": { "point": { "value": 0.0424, "state": "extracted", "precision_note": "converted from 0.0000424 kWh" } }, "distribution": { "shape": "unknown", "basis": "single extrapolated estimate" }, "citation_rule": "must_state_extrapolation_not_measurement" }, "conditions": { "base_value": { "value": 0.3, "unit": "Wh/search", "source": "Google 2009 self-report" }, "pue_update": { "from": 1.16, "to": 1.1 }, "hardware_efficiency_factor": 6.7, "formula": "0.3 * (1.1 / 1.16) / 6.70", "instrumentation": { "value": "none_extrapolation", "state": "extracted" } }, "boundary": { "tier": "unknown", "components": { "gpu_compute": "not_applicable", "gpu_idle_colocated": "not_applicable", "host_cpu": "unknown", "dram": "unknown", "local_storage": "unknown", "model_load": "not_applicable", "network_egress": "unknown", "facility_overhead_pue": "included", "cooling_water": "excluded", "idle_fleet_capacity": "unknown", "training_amortized": "not_applicable", "finetuning_amortized": "not_applicable", "embodied_hardware": "excluded", "datacenter_construction": "excluded", "client_device": "excluded" }, "component_notes": { "host_cpu": { "en": "The 2009 base value does not define its included components, and the updated estimate inherits that uncertainty." }, "facility_overhead_pue": { "en": "The formula updates PUE from 1.16 to 1.1." } } }, "misuse_guard": { "applicable_to": [ "chapter_4_example_of_extrapolation_uncertainty" ], "not_applicable_to": [ "current_google_search_measurement", "ai_overview_search", "cross_comparison_denominator" ], "forbidden_claims": [ { "en": "Presenting it as a measured current Google Search value." } ] }, "verified_by": "codex-gpt-5-6", "verified_by_model": "gpt-5.6", "verified_at": "2026-08-27", "verification_depth": "primary_source_read" }, { "id": "m-009", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "One-year energy-efficiency improvement for the median Gemini Apps text prompt" }, "source_id": "src-google-ai-serving-2025", "source_locator": "Section 4.3 and Figure 4, PDF pages 7-8", "task_type": "text_generation_efficiency_change", "numeric_use": "allowed", "numeric_use_note": { "en": "Use only as Google's self-reported change in the median from May 2024 to May 2025. It is not an AI-wide rate or evidence that total electricity fell." }, "quantity": { "unit": "factor", "per": { "amount": 1, "of": "may_2024_to_may_2025_period" }, "statistics": { "energy_reduction_factor": { "value": 33, "state": "extracted" }, "model_improvement_factor": { "value": 23, "state": "extracted" }, "utilization_improvement_factor": { "value": 1.4, "state": "extracted" } }, "distribution": { "shape": "not_reported", "basis": "comparison_of_monthly_median_prompt_energy" }, "citation_rule": "must_state_vendor_self_reported_and_period_specific" }, "conditions": { "platform": "Google production Gemini serving fleet", "metric": "median Gemini Apps text prompt energy", "period_start": "2024-05", "period_end": "2025-05", "traffic_and_model_mix": { "value": null, "state": "not_reported" } }, "boundary": { "tier": "B", "components": { "gpu_compute": "included", "gpu_idle_colocated": "included", "host_cpu": "included", "dram": "included", "local_storage": "excluded", "model_load": "unknown", "network_egress": "excluded", "facility_overhead_pue": "included", "cooling_water": "excluded", "idle_fleet_capacity": "included", "training_amortized": "excluded", "finetuning_amortized": "excluded", "embodied_hardware": "excluded", "datacenter_construction": "excluded", "client_device": "excluded" }, "component_notes": { "idle_fleet_capacity": { "en": "Includes idle capacity allocated in the full-stack measurement at each endpoint." } } }, "misuse_guard": { "applicable_to": [ "chapter_5_efficiency_example" ], "not_applicable_to": [ "all_ai_services", "current_gemini_requests", "total_google_electricity" ], "forbidden_claims": [ { "en": "Claiming that energy per request across AI falls by the same factor every year." }, { "en": "Inferring lower total Google electricity from a per-prompt improvement." } ] }, "verified_by": "codex-gpt-5-6", "verified_by_model": "GPT-5.6", "verified_at": "2026-08-27", "verification_depth": "primary_source_read" }, { "id": "m-010", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "Global data-centre electricity consumption (2025 estimate)" }, "source_id": "src-iea-key-questions-2026", "source_locator": "Executive Summary, PDF page 10; Annex A general note and Table A.1, PDF pages 107-108", "task_type": "data_center_total_electricity", "numeric_use": "allowed", "numeric_use_note": { "en": "Use as the IEA bottom-up global estimate, not as a sum of metered readings." }, "quantity": { "unit": "TWh", "per": { "amount": 1, "of": "calendar_year_2025" }, "statistics": { "point": { "value": 485, "state": "extracted", "type": "estimate" } }, "distribution": { "shape": "not_applicable", "basis": "global_model_estimate" }, "citation_rule": "must_state_estimate_and_model_boundary" }, "conditions": { "geography": "world", "year": 2025, "method": "bottom-up stock model driven by equipment shipments, power, utilization and PUE" }, "boundary": { "tier": "B", "components": { "gpu_compute": "included", "gpu_idle_colocated": "included", "host_cpu": "included", "dram": "included", "local_storage": "included", "model_load": "not_applicable", "network_egress": "excluded", "facility_overhead_pue": "included", "cooling_water": "unknown", "idle_fleet_capacity": "included", "training_amortized": "not_applicable", "finetuning_amortized": "not_applicable", "embodied_hardware": "excluded", "datacenter_construction": "excluded", "client_device": "excluded" }, "component_notes": { "network_egress": { "en": "Includes network equipment inside data centres but excludes transmission networks connecting data centres and end users." } } }, "misuse_guard": { "applicable_to": [ "chapter_5_total_demand", "chapter_6_observed_growth" ], "not_applicable_to": [ "ai_only_electricity", "metered_global_total", "single_company" ], "forbidden_claims": [ { "en": "Treating the total as AI-only electricity." }, { "en": "Treating it as an aggregate of global meter readings." } ] }, "verified_by": "codex-gpt-5-6", "verified_by_model": "GPT-5.6", "verified_at": "2026-08-27", "verification_depth": "primary_source_read" }, { "id": "m-011", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "Global data-centre electricity consumption (2030 base case)" }, "source_id": "src-iea-key-questions-2026", "source_locator": "Executive Summary, PDF page 10; Annex A general note and Table A.1, PDF pages 107-108", "task_type": "data_center_total_electricity_projection", "numeric_use": "allowed", "numeric_use_note": { "en": "Use the annex-table value in calculations. In prose, state its relationship to the approximate executive-summary value and do not treat it as a known future value." }, "quantity": { "unit": "TWh", "per": { "amount": 1, "of": "calendar_year_2030" }, "statistics": { "point": { "value": 945, "state": "extracted", "type": "base_case_projection_annex_table" }, "narrative_rounded": { "value": 950, "state": "extracted", "type": "base_case_projection_executive_summary" } }, "distribution": { "shape": "scenario", "basis": "IEA_base_case" }, "citation_rule": "must_state_projection_scenario_and_annex_vs_summary_rounding" }, "conditions": { "geography": "world", "year": 2030, "scenario": "base_case", "method": "bottom-up stock model" }, "boundary": { "tier": "B", "components": { "gpu_compute": "included", "gpu_idle_colocated": "included", "host_cpu": "included", "dram": "included", "local_storage": "included", "model_load": "not_applicable", "network_egress": "excluded", "facility_overhead_pue": "included", "cooling_water": "unknown", "idle_fleet_capacity": "included", "training_amortized": "not_applicable", "finetuning_amortized": "not_applicable", "embodied_hardware": "excluded", "datacenter_construction": "excluded", "client_device": "excluded" }, "component_notes": { "network_egress": { "en": "Excludes data-transmission networks outside data centres." } } }, "misuse_guard": { "applicable_to": [ "chapter_5_total_demand_projection" ], "not_applicable_to": [ "ai_only_electricity", "certain_future_value", "single_company" ], "forbidden_claims": [ { "en": "Presenting it as a certain 2030 value." }, { "en": "Treating the total as AI-only electricity." } ] }, "verified_by": "codex-gpt-5-6", "verified_by_model": "GPT-5.6", "verified_at": "2026-08-28", "verification_depth": "primary_source_read" }, { "id": "m-012", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "2025 growth in global data-centre electricity consumption" }, "source_id": "src-iea-key-questions-2026", "source_locator": "Executive Summary, PDF pages 9-10", "task_type": "data_center_total_electricity_growth", "numeric_use": "allowed", "quantity": { "unit": "percent", "per": { "amount": 1, "of": "year_over_year_2025" }, "statistics": { "point": { "value": 17, "state": "extracted" } }, "distribution": { "shape": "not_applicable", "basis": "IEA_estimate" }, "citation_rule": "must_state_global_data_centres_and_estimate" }, "conditions": { "geography": "world", "period": "2024_to_2025", "scope": "all_data_centres" }, "boundary": { "tier": "B", "components": { "gpu_compute": "included", "gpu_idle_colocated": "included", "host_cpu": "included", "dram": "included", "local_storage": "included", "model_load": "not_applicable", "network_egress": "excluded", "facility_overhead_pue": "included", "cooling_water": "unknown", "idle_fleet_capacity": "included", "training_amortized": "not_applicable", "finetuning_amortized": "not_applicable", "embodied_hardware": "excluded", "datacenter_construction": "excluded", "client_device": "excluded" } }, "misuse_guard": { "applicable_to": [ "chapter_5_total_demand" ], "not_applicable_to": [ "ai_only_electricity", "per_request_efficiency" ], "forbidden_claims": [ { "en": "Treating it as a change in energy per AI request." } ] }, "verified_by": "codex-gpt-5-6", "verified_by_model": "GPT-5.6", "verified_at": "2026-08-27", "verification_depth": "primary_source_read" }, { "id": "m-013", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "2025 growth in AI-focused data-centre electricity consumption" }, "source_id": "src-iea-key-questions-2026", "source_locator": "Executive Summary, PDF pages 9-10", "task_type": "ai_focused_data_center_electricity_growth", "numeric_use": "allowed", "quantity": { "unit": "percent", "per": { "amount": 1, "of": "year_over_year_2025" }, "statistics": { "point": { "value": 50, "state": "extracted" } }, "distribution": { "shape": "not_applicable", "basis": "IEA_estimate" }, "citation_rule": "must_state_ai_focused_category_and_estimate" }, "conditions": { "geography": "world", "period": "2024_to_2025", "scope": "AI-focused_data_centres_as_defined_by_IEA" }, "boundary": { "tier": "B", "components": { "gpu_compute": "included", "gpu_idle_colocated": "included", "host_cpu": "included", "dram": "included", "local_storage": "included", "model_load": "unknown", "network_egress": "excluded", "facility_overhead_pue": "included", "cooling_water": "unknown", "idle_fleet_capacity": "included", "training_amortized": "excluded", "finetuning_amortized": "excluded", "embodied_hardware": "excluded", "datacenter_construction": "excluded", "client_device": "excluded" } }, "misuse_guard": { "applicable_to": [ "chapter_5_total_demand", "chapter_6_growth" ], "not_applicable_to": [ "per_request_efficiency", "all_data_centres" ], "forbidden_claims": [ { "en": "Treating it as growth in request count or energy per request." } ] }, "verified_by": "codex-gpt-5-6", "verified_by_model": "GPT-5.6", "verified_at": "2026-08-27", "verification_depth": "primary_source_read" }, { "id": "m-014", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "Estimated capacity of 21 US AI-focused data centres tracked by the IEA" }, "source_id": "src-iea-key-questions-2026", "source_locator": "Executive Summary (online); Box 3.2 and Figure 3.3, PDF pages 37-38", "task_type": "ai_data_center_construction_capacity", "numeric_use": "forbidden", "numeric_use_note": { "en": "Results for the same 21-site geospatial analysis, using satellite imagery to track completed floor space and estimate capacity. The site count, observation window, growth and capacity refer to that analysed set; do not feed derivations or extrapolate to the United States or the world." }, "quantity": { "unit": "GW", "per": { "amount": 21, "of": "tracked_US_AI_focused_sites" }, "statistics": { "lower_bound": { "value": 6, "state": "extracted", "operator": "greater_than" }, "growth_factor_over_18_months": { "value": 3, "state": "extracted", "operator": "greater_than" } }, "distribution": { "shape": "not_reported", "basis": "site_sample" }, "citation_rule": "must_state_sample_satellite_method_and_estimated_capacity" }, "conditions": { "geography": "United States", "site_count": 21, "observation_window_months": 18, "method": "satellite tracking of completed floor space and estimated capacity" }, "boundary": { "tier": "unknown", "components": { "gpu_compute": "not_applicable", "gpu_idle_colocated": "not_applicable", "host_cpu": "not_applicable", "dram": "not_applicable", "local_storage": "not_applicable", "model_load": "not_applicable", "network_egress": "not_applicable", "facility_overhead_pue": "not_applicable", "cooling_water": "not_applicable", "idle_fleet_capacity": "not_applicable", "training_amortized": "not_applicable", "finetuning_amortized": "not_applicable", "embodied_hardware": "unknown", "datacenter_construction": "included", "client_device": "not_applicable" } }, "misuse_guard": { "applicable_to": [ "chapter_6_construction_evidence" ], "not_applicable_to": [ "global_total_capacity", "electricity_consumption", "derivations" ], "forbidden_claims": [ { "en": "Treating it as total US or global AI data-centre capacity." } ] }, "verified_by": "codex-gpt-5-6", "verified_by_model": "GPT-5.6", "verified_at": "2026-08-28", "verification_depth": "primary_source_read" }, { "id": "m-015", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "Rated power of a Philips LED bulb" }, "source_id": "src-philips-led-8w", "source_locator": "Power consumption specification", "task_type": "household_comparison_led", "numeric_use": "allowed", "quantity": { "unit": "W", "per": { "amount": 1, "of": "specified_LED_bulb" }, "statistics": { "rated_power": { "value": 8, "state": "extracted" } }, "distribution": { "shape": "not_applicable", "basis": "product_rating" }, "citation_rule": "must_state_specific_product_and_rated_power" }, "conditions": { "product": "Philips LED Bulb A60 E27 6500K NonDim", "voltage": "220-240 V", "equivalent_wattage": 60 }, "boundary": { "tier": "A", "components": { "gpu_compute": "not_applicable", "gpu_idle_colocated": "not_applicable", "host_cpu": "not_applicable", "dram": "not_applicable", "local_storage": "not_applicable", "model_load": "not_applicable", "network_egress": "not_applicable", "facility_overhead_pue": "not_applicable", "cooling_water": "not_applicable", "idle_fleet_capacity": "not_applicable", "training_amortized": "not_applicable", "finetuning_amortized": "not_applicable", "embodied_hardware": "excluded", "datacenter_construction": "not_applicable", "client_device": "included" } }, "misuse_guard": { "applicable_to": [ "n_022_everyday_comparison" ], "not_applicable_to": [ "measured_household_usage", "all_LED_bulbs" ], "forbidden_claims": [ { "en": "Treating the rating as measured household consumption." } ] }, "verified_by": "codex-gpt-5-6", "verified_by_model": "GPT-5.6", "verified_at": "2026-08-27", "verification_depth": "primary_source_read" }, { "id": "m-016", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "Built-in battery capacity of the 13-inch M4 MacBook Air" }, "source_id": "src-apple-macbook-air-m4-2025", "source_locator": "Battery and Power section", "task_type": "household_comparison_battery_capacity", "numeric_use": "allowed", "quantity": { "unit": "Wh", "per": { "amount": 1, "of": "built_in_battery" }, "statistics": { "capacity": { "value": 53.8, "state": "extracted" } }, "distribution": { "shape": "not_applicable", "basis": "product_specification" }, "citation_rule": "must_state_storage_capacity_not_wall_charge" }, "conditions": { "product": "MacBook Air 13-inch M4 2025", "metric": "built-in lithium-polymer battery nameplate capacity" }, "boundary": { "tier": "A", "components": { "gpu_compute": "not_applicable", "gpu_idle_colocated": "not_applicable", "host_cpu": "not_applicable", "dram": "not_applicable", "local_storage": "not_applicable", "model_load": "not_applicable", "network_egress": "not_applicable", "facility_overhead_pue": "not_applicable", "cooling_water": "not_applicable", "idle_fleet_capacity": "not_applicable", "training_amortized": "not_applicable", "finetuning_amortized": "not_applicable", "embodied_hardware": "excluded", "datacenter_construction": "not_applicable", "client_device": "included" }, "component_notes": { "client_device": { "en": "Nameplate energy stored in the battery; charger losses, conversion losses and standby power are excluded." } } }, "misuse_guard": { "applicable_to": [ "n_022_everyday_comparison" ], "not_applicable_to": [ "wall_socket_charge_energy", "laptop_session_energy" ], "forbidden_claims": [ { "en": "Treating it as wall-socket energy required for a full charge." } ] }, "verified_by": "codex-gpt-5-6", "verified_by_model": "GPT-5.6", "verified_at": "2026-08-27", "verification_depth": "primary_source_read" }, { "id": "m-017", "status": "active", "superseded_by": null, "publishable": true, "label": { "en": "Average per-cycle electricity of dishwashers sold in the EU in 2020" }, "source_id": "src-eu-dishwashers", "source_locator": "How efficient? section", "task_type": "household_comparison_dishwasher", "numeric_use": "allowed", "quantity": { "unit": "kWh", "per": { "amount": 1, "of": "dishwasher_cycle" }, "statistics": { "mean": { "value": 0.8, "state": "extracted" } }, "distribution": { "shape": "not_reported", "basis": "average_of_products_sold" }, "citation_rule": "must_state_EU_2020_sold_product_average" }, "conditions": { "geography": "EU27", "year": 2020, "population": "dishwashers sold", "programme_or_load": { "value": null, "state": "not_reported" } }, "boundary": { "tier": "A", "components": { "gpu_compute": "not_applicable", "gpu_idle_colocated": "not_applicable", "host_cpu": "not_applicable", "dram": "not_applicable", "local_storage": "not_applicable", "model_load": "not_applicable", "network_egress": "not_applicable", "facility_overhead_pue": "not_applicable", "cooling_water": "not_applicable", "idle_fleet_capacity": "not_applicable", "training_amortized": "not_applicable", "finetuning_amortized": "not_applicable", "embodied_hardware": "excluded", "datacenter_construction": "not_applicable", "client_device": "included" }, "component_notes": { "client_device": { "en": "Use-phase electricity only; manufacturing and non-electric water-system impacts are excluded." } } }, "misuse_guard": { "applicable_to": [ "n_022_everyday_comparison" ], "not_applicable_to": [ "current_specific_dishwasher", "all_programmes_and_loads" ], 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