Atlas of the Present Atlas · v0.1

As of 12 September 2026

Dossier 02

Energy and Chips

v1.4-draft · unchecked

1. As of

Date
Version
v1.4-draft
Author / model
Atlas generator
Reviewer
unchecked (Josef)

2. In one sentence

The energy limit of AI is first a task and system limit: inference energy varies by orders of magnitude by task class; globally 2010–2018 +6% energy at +550% compute instances (Masanet); US DC power has risen again after a flat era (LBNL); IEA ~415 TWh 2024 remains E2; Epoch chips overview (E2): ~20 million AI chips by Q4 2025, sold capacity 3.3×/year since 2022 (CI 2.7–4.1) — not the same as 5.3× training compute; TSMC is producing 5.5-reticle CoWoS now, 14-reticle ~10 compute+20 HBM slated for 2028 (E2 vendor); CoWoS kwpm still E0; DGX B200 about 14.3 kW; bottlenecks in HBM, packaging, lithography.

Established now · E1 / E2

3. What works today

Inference energy depends on the task

  1. E1

    88 models, 10 tasks, 1,000 inferences: text classification 0.002 kWh / 1,000; image generation 2.907 kWh / 1,000 (σ 3.31). Factor >1,000. Generative ≫ discriminative; multi-purpose more expensive than task-specific. BLOOMz-7B: training+FT parity about 593 million inferences. Limits: sequential inferences, open models 2023, one hardware setting (A100).

    Luccioni, Jernite, Strubell. Power Hungry Processing: Watts Driving the Cost of AI Deployment?. https://arxiv.org/html/2311.16863. As of 2023-11-28. Checked 2026-08-28. Type: Paper (FAccT 2024).

Training energy of individual models

  1. E1

    BLOOM-176B: 1,082,990 GPU-h, 433,196 kWh dynamic, grid 57 gCO₂eq/kWh → 24.7 t dynamic; plus idle 14.6 t and embodied 11.2 t → 50.5 t lifecycle training. E1 for the BLOOM measurement; the paper’s cross-model table (GPT-3/OPT) is E2.

    Luccioni, Viguier, Ligozat. Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. https://arxiv.org/html/2211.02001. As of 2022-11. Checked 2026-08-28. Type: Paper.
  2. E1

    Strubell 2019: measured GPU power 2018–19 (Titan-X/1080-Ti, PUE 1.58). Transformer-big ~192 lb CO₂e; BERT-base ~1,438 lb. The NAS extrapolation (626,155 lb) is corrected by Patterson — do not read it as a world footprint.

    Strubell et al. Energy and Policy Considerations for Deep Learning in NLP. https://arxiv.org/html/1906.02243. As of 2019-06. Checked 2026-08-28. Type: Paper (ACL 2019).
  3. E2

    Patterson 2022: UMass NAS estimate 284 tCO₂e vs. actually 3.2 t (88×). Google ML energy 10–15% of Google total energy 2019–2021, of which ~⅗ inference / ⅖ training. E2 (one hyperscaler measurement); not E1 for “the world’s ML footprint is shrinking”.

    Patterson et al. The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. https://arxiv.org/abs/2204.05149. As of 2022-04-11. Checked 2026-08-28. Type: Paper (Google/Berkeley).

Datacenter power: the flat era is broken

  1. E1

    Masanet et al. Science 2020 (Policy Forum, bottom-up model): global data-center electricity around 194 TWh (2010) → 205 TWh (2018), about 1% of world electricity; +6% energy while compute instances +550%. Intensity −20%/year. IT devices 92 → 130 TWh; infrastructure (cooling/power) falls and mostly offsets IT growth. Traditional 79% of instances (2010) → cloud including hyperscale 89% (2018). Authors: “we estimated” / “around”. 2010–2018, global only — not IEA 2024/26, not LBNL US 2023/24.

    Masanet et al. Recalibrating global data center energy-use estimates. https://datacenters.lbl.gov/sites/default/files/Masanet_et_al_Science_2020.full_.pdf. As of 2020-02-28. Checked 2026-08-31. Type: Paper (Science Policy Forum; LBNL-hosted journal PDF).
  2. E1

    Masanet’s flat phase is global 2010–2018. LBNL 2024 (source 13) measures the US break afterward: ~60 TWh 2014–16 → 176 TWh 2023. Not the same series, not the same geography. 205 TWh (world 2018, Masanet) ≠ 76 TWh (US 2018, LBNL).

    Masanet et al. Recalibrating global data center energy-use estimates. https://datacenters.lbl.gov/sites/default/files/Masanet_et_al_Science_2020.full_.pdf. As of 2020-02-28. Checked 2026-08-31. Type: Paper (Science Policy Forum; LBNL-hosted journal PDF).Shehabi et al. (LBNL 2024). 2024 United States Data Center Energy Usage Report. https://eta-publications.lbl.gov/sites/default/files/2024-12/lbnl-2024-united-states-data-center-energy-usage-report.pdf. As of 2024-12. Checked 2026-08-28. Type: Official lab report (LBNL-2001637).
  3. E1

    US DC power: ~60 TWh 2014–16, 76 TWh 2018 (1.9%), 176 TWh 2023 (4.4%). GPU-accelerated servers are the break. E1 for the 2014–2023 best estimate; the 2028 range 325–580 TWh is E2.

    Shehabi et al. (LBNL 2024). 2024 United States Data Center Energy Usage Report. https://eta-publications.lbl.gov/sites/default/files/2024-12/lbnl-2024-united-states-data-center-energy-usage-report.pdf. As of 2024-12. Checked 2026-08-28. Type: Official lab report (LBNL-2001637).
  4. E2

    LBNL 2025 update: 2024 actual 192 TWh = 4.7% of US electricity (GPU shipments 2023–24 revised down). 2030 reference 649 TWh = 11.8%; compounded 521–843 TWh. One update model, June 2026.

    Smith et al. (LBNL 2025). United States Data Center Energy Usage Report: 2025 Update. https://doi.org/10.71468/P1RP4F. As of 2026-06-18. Checked 2026-08-28. Type: Official lab report (LBNL-2001758).
  5. E2

    Global 2024 ~415 TWh, ~1.5% of world electricity; +12%/year since ~2017. US 45%, China 25%, Europe 15%. IEA: uncertainty “substantial” in the demand chapter.

    IEA. Energy and AI — Executive summary. https://www.iea.org/reports/energy-and-ai/executive-summary. As of 2025-04. Checked 2026-08-28. Type: Official report.IEA Demand. Energy demand from AI. https://www.iea.org/reports/energy-and-ai/energy-demand-from-aI. As of 2025-04. Checked 2026-08-28. Type: Official report (chapter).
  6. E2

    EU-27, 2022: DCs 45–65 TWh (1.8–2.6% of EU electricity). No official statistic; literature synthesis. Not a 2026 update.

    Kamiya, Bertoldi (JRC). Energy Consumption in Data Centres and Broadband Communication Networks in the EU. https://doi.org/10.2760/706491. As of 2024-02. Checked 2026-08-28. Type: Official report (JRC135926).

Chips: specification, foundry, lithography

  1. E1

    H100 SXM: FP8 tensor 3,958 TFLOPS (with sparsity), 80 GB, 3.35 TB/s, NVLink 900 GB/s, max TDP up to 700 W (configurable). E1 for the published specification; not for field utilization (LBNL ~70% of rated).

    NVIDIA. NVIDIA H100 GPU (product specifications). https://www.nvidia.com/en-us/data-center/h100/. As of 2026-08-28. Checked 2026-08-28. Type: Manufacturer specification.
  2. E1

    DGX B200 (manufacturer page, checked 2026-08-28): 8× NVIDIA Blackwell GPUs; GPU memory 1,440 GB total, 64 TB/s HBM3e; FP4 tensor 144 PFLOPS sparse / 72 PFLOPS dense; FP8 tensor 72 PFLOPS sparse (dense = ½); NVLink 14.4 TB/s aggregate; system power about 14.3 kW max; 10 rack units. E1 for the published specification; not for field utilization, not for token cost (the page’s SemiAnalysis FAQ is not a warrant).

    NVIDIA DGX B200. NVIDIA DGX B200 (product specifications). https://www.nvidia.com/en-us/data-center/dgx-b200/. As of 2026-08-28. Checked 2026-08-28. Type: Manufacturer specification.
  3. E2

    Epoch 2023, 47 ML accelerators: FP32 FLOP/s 2× / 2.3 years; energy efficiency 2× / 3.0 years; DRAM capacity and bandwidth 2× / ~4 years (memory wall). Peak ≠ utilization. EUV reticle limit 858 mm².

    Hobbhahn, Heim, Aydos. Trends in machine learning hardware. https://epoch.ai/publications/trends-in-machine-learning-hardware. As of 2023-11-09. Checked 2026-08-28. Type: Dataset/report.
  4. E2

    Epoch Trends (as of 2026-02-05): AI chip stock 3.4×/year; frontier LM training compute 5×/year since 2020; GPU FLOP/s/W 1.34×/year since 2008. E2 (research NGO; underlying inputs not audited). The 5×/year LM-frontier dashboard figure is corroborated as of 2026-08-31 by the May 2024 Epoch report in dossier 01 (different sample/window; figures there).

    Epoch Trends. Trends in Artificial Intelligence. https://epoch.ai/trends. As of 2026-02-05. Checked 2026-08-28. Type: Dataset/dashboard.
  5. E2

    Epoch Topic Overview AI Chips (pagefind 1 May 2026): five largest designers roughly 20 million AI chips by Q4 2025; Nvidia about half of units and over 66% of computing capacity. Data insight (CSV 16 Feb 2026): sold capacity in H100-equivalents 3.3×/year since 2022 (90% CI 2.7–4.1×; doubling 7 months, CI 6–8). Not the 3.4× dashboard (source 11) and not 5.3× training FLOP (D01 Sevilla). Inputs: revenue/filings/analysts, not a chip census.

    Epoch Chips Overview. Topic Overview: AI Chips. https://epoch.ai/publications/chips-topic-overview. As of 2026-05-01. Checked 2026-09-01. Type: Topic overview (research NGO).Epoch chip production. Global AI computing capacity is doubling every 7 months. https://epoch.ai/data-insights/ai-chip-production. As of 2026-02-16. Checked 2026-09-01. Type: Data insight (CSV stamp 16 Feb 2026).
  6. E2

    Overview, ML Hardware DB: flagship release price P100 $5,700 (2016) → H100 $34,000 (2022); H100 seventeen times as much computation per dollar. Performance per dollar roughly 2× / 2.5 years since the early 2010s. Leading chips ~40% more energy-efficient per year (~2.7-year doubling); B200 (2024) roughly three times computation/watt vs A100 (2020). One chip around 1,000 W at full load; AI DC power late 2025 roughly tens of gigawatts — order of magnitude, not a point value. Jan 2026 spending analysis: chips+compute time 54–62% of spend where breakdowns exist.

    Epoch Chips Overview. Topic Overview: AI Chips. https://epoch.ai/publications/chips-topic-overview. As of 2026-05-01. Checked 2026-09-01. Type: Topic overview (research NGO).
  7. E1

    TSMC FY 2025: wafer shipments 15.0 million 12-inch equivalents; advanced (≥7 nm) 74% of wafer revenue; N3 24%. CoWoS/InFO/SoIC named, without kwpm. E1 for AR figures; E2 for the node schedule.

    TSMC. 2025 Annual Report, Letter to Shareholders (ch. 1 PDF). https://investor.tsmc.com/static/annualReports/2025/english/pdf/2025_tsmc_ar_e_ch1.pdf. As of 2025. Checked 2026-08-28. Type: Company report.
  8. E2

    TSMC 2026 North America Tech Symposium PR (22 Apr 2026, PDF opened): “The Company is now producing 5.5-reticle size CoWoS.” E2 (vendor PR) for current 5.5-reticle production. No kwpm figure in this PDF — CoWoS kwpm remains E0.

    TSMC Tech Symposium 2026. TSMC Debuts A13 Technology at 2026 North America Technology Symposium. https://pr.tsmc.com/system/files/newspdf/attachment/49337b40ff139d51d533076cf7a945b30e107e07/2026%20Tech%20Symposium%20%28E%29_Final_wmn.pdf. As of 2026-04-22. Checked 2026-09-02. Type: Company PR PDF (Tech Symposium; news HTML Cloudflare-blocked).
  9. E1

    ASML FY 2025: total net sales €32.667 billion; EUV systems recognized 48 (2024: 44). Of those, 44 NXE in 2025; High-NA EXE arithmetically 4. E1 for FY units; 2026 guidance E3.

    ASML PR. ASML reports €32.7 billion total net sales and €9.6 billion net income in 2025. https://www.asml.com/en/news/press-releases/2026/q4-2025-financial-results. As of 2026-01-28. Checked 2026-08-28. Type: Company report.ASML AR. 2025 Annual Report — Financial performance section. https://ourbrand.asml.com/m/419103cb23dfeaa4/original/asml-2025-annual-report-financial-performance-section.pdf. As of 2025. Checked 2026-08-28. Type: Company report.

Critical materials — list and concentration

  1. E1

    USGS 2025 List of Critical Minerals (60), including gallium, germanium, silicon, tungsten. The US imported 80% of rare earths used in 2024. No AI-specific tonnage in this source.

    USGS. Interior Department releases final 2025 List of Critical Minerals. https://www.usgs.gov/news/science-snippet/interior-department-releases-final-2025-list-of-critical-minerals. As of 2025-11-06. Checked 2026-08-28. Type: Official.
  2. E2

    IEA Energy and AI PDF: China ~99% of global refined gallium supply; DC gallium demand 2030 >10% of today’s supply. Energy-security HTML of the same report family: 95% — internal discrepancy 99 vs 95, do not smooth. Minerals 2026 exec: top refiner China >90% for Ga, graphite, manganese, REE.

    IEA PDF. Energy and AI (PDF, Auszug). https://iea.blob.core.windows.net/assets/40a4db21-2225-42f0-8a07-addcc2ea86b3/EnergyandAI.pdf. As of 2025-04. Checked 2026-08-28. Type: Official report PDF (excerpt).IEA Minerals. Global Critical Minerals Outlook 2026 — Executive Summary. https://www.iea.org/reports/global-critical-minerals-outlook-2026/executive-summary. As of 2026. Checked 2026-08-28. Type: Official report (exec).

Claimed · E3

4. What is claimed, not shown

IEA projections to 2030

  1. E2

    IEA base: ~945 TWh global 2030 (from 415 TWh 2024) or update 485 TWh (2025) → 950 TWh (2030), ~3% of world electricity. AI-focused DCs triple; +50% in 2025 vs. +17% all DCs. 2026 data per Key Questions: estimates. Methods annex not fully read.

    IEA. Energy and AI — Executive summary. https://www.iea.org/reports/energy-and-ai/executive-summary. As of 2025-04. Checked 2026-08-28. Type: Official report.IEA Demand. Energy demand from AI. https://www.iea.org/reports/energy-and-ai/energy-demand-from-aI. As of 2025-04. Checked 2026-08-28. Type: Official report (chapter).IEA Key Questions. Key Questions on Energy and AI — Executive Summary. https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary. As of 2026. Checked 2026-08-28. Type: Official report (exec summary).
  2. E2

    IEA Electricity 2026, demand chapter: US electricity use plus more than 420 TWh over the next five years; datacenter expansion is expected to make up about 50% of US demand growth to 2030. Global demand 2026–2030 averages 3.6%/year (past decade 2.8%); about 1,100 TWh/year added versus 700; 33,600 TWh in 2030 versus 28,200 TWh in 2025. E2 (agency forecast; 2026–2030 are projected values).

    IEA Electricity 2026. Demand — Electricity 2026. https://www.iea.org/reports/electricity-2026/demand. As of 2026. Checked 2026-08-28. Type: Official report (chapter).
  3. E2

    Energy per task down “at least an order of magnitude annually”. All conventional searches as simple AI text: <4 TWh/year. Video/reasoning/agents: “hundreds or thousands of times” more energy per query. E2 for direction and the 4 TWh illustration; E3 for the exact order of magnitude per year while methods are unread.

    IEA Key Questions. Key Questions on Energy and AI — Executive Summary. https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary. As of 2026. Checked 2026-08-28. Type: Official report (exec summary).
  4. E2

    HBM shortage “over the past six months”, expected until at least end-2027. Capex of the largest tech firms > USD 400 billion in 2025, +75% expected 2026. Grid: ~20% of planned DC projects at delay risk. Satellite method not independently replicated.

    IEA Security. AI and energy security. https://www.iea.org/reports/energy-and-ai/ai-and-energy-security. As of 2025-04. Checked 2026-08-28. Type: Official report (chapter).IEA Key Questions. Key Questions on Energy and AI — Executive Summary. https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary. As of 2026. Checked 2026-08-28. Type: Official report (exec summary).

Vendor outlook and unread splits

  1. E3

    TSMC 2026 shipments 16–17 million wafers (AR). ASML 2026 sales €34–39 billion. Guidance, not actuals.

    TSMC. 2025 Annual Report, Letter to Shareholders (ch. 1 PDF). https://investor.tsmc.com/static/annualReports/2025/english/pdf/2025_tsmc_ar_e_ch1.pdf. As of 2025. Checked 2026-08-28. Type: Company report.ASML PR. ASML reports €32.7 billion total net sales and €9.6 billion net income in 2025. https://www.asml.com/en/news/press-releases/2026/q4-2025-financial-results. As of 2026-01-28. Checked 2026-08-28. Type: Company report.
  2. E2

    Same Symposium PR: “A 14-reticle size CoWoS, capable of integrating approximately 10 large compute dies and 20 HBM stacks, is slated for production in 2028.” Then expansion beyond 14 reticles in 2029; optional SoW-X 40-reticle expected 2029. E2 as vendor roadmap (claimed schedule), not running capacity. kwpm still E0.

    TSMC Tech Symposium 2026. TSMC Debuts A13 Technology at 2026 North America Technology Symposium. https://pr.tsmc.com/system/files/newspdf/attachment/49337b40ff139d51d533076cf7a945b30e107e07/2026%20Tech%20Symposium%20%28E%29_Final_wmn.pdf. As of 2026-04-22. Checked 2026-09-02. Type: Company PR PDF (Tech Symposium; news HTML Cloudflare-blocked).
  3. E3

    Same product page: DGX B200 delivers 3× training and 15× inference versus DGX H100. The performance sections are marked “Projected performance subject to change”. E3 (vendor comparison, not measured in this log).

    NVIDIA DGX B200. NVIDIA DGX B200 (product specifications). https://www.nvidia.com/en-us/data-center/dgx-b200/. As of 2026-08-28. Checked 2026-08-28. Type: Manufacturer specification.
  4. E3

    Epoch overview: estimate 290,000–1.6 million H100-equivalents into China through end-2025 (median 660,000, “roughly a third” of China’s capacity). An estimate, not a customs census — do not collapse with the 20 million shipments.

    Epoch Chips Overview. Topic Overview: AI Chips. https://epoch.ai/publications/chips-topic-overview. As of 2026-05-01. Checked 2026-09-01. Type: Topic overview (research NGO).
  5. E3

    Industry splits cited inside Luccioni, not independently opened here: Barr 2019 (inference 80–90%); Wu 2021 Meta ~⅓. The Patterson Google split ⅗/⅖ is E2 vendor (above).

    Luccioni, Jernite, Strubell. Power Hungry Processing: Watts Driving the Cost of AI Deployment?. https://arxiv.org/html/2311.16863. As of 2023-11-28. Checked 2026-08-28. Type: Paper (FAccT 2024).

Constrained · Limit

5. Bottleneck and limit

What the sources mark as a limit

  1. E2

    Jevons: efficiency per task rises (IEA; Luccioni task classes; Epoch FLOP/s/W). At the same time more expensive tasks and more chips (dashboard 3.4×/year stock; sold-capacity 3.3×/year since 2022, sources 24–25). Do not collapse either with D01 5.3× training FLOP. IEA central path: DC power doubles by 2030. LBNL: the US era of flat kWh is over. Causal share of AI vs. rest-of-cloud vs. crypto is not finely resolved.

    IEA Key Questions. Key Questions on Energy and AI — Executive Summary. https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary. As of 2026. Checked 2026-08-28. Type: Official report (exec summary).Luccioni, Jernite, Strubell. Power Hungry Processing: Watts Driving the Cost of AI Deployment?. https://arxiv.org/html/2311.16863. As of 2023-11-28. Checked 2026-08-28. Type: Paper (FAccT 2024).Epoch Trends. Trends in Artificial Intelligence. https://epoch.ai/trends. As of 2026-02-05. Checked 2026-08-28. Type: Dataset/dashboard.Shehabi et al. (LBNL 2024). 2024 United States Data Center Energy Usage Report. https://eta-publications.lbl.gov/sites/default/files/2024-12/lbnl-2024-united-states-data-center-energy-usage-report.pdf. As of 2024-12. Checked 2026-08-28. Type: Official lab report (LBNL-2001637).
  2. E1

    Masanet 2020 itself warns: next doubling of compute instances “within the next 3 to 4 years”; “sharp energy demand growth” once that efficiency resource is tapped; “demand surge later this decade”. That is a 2020 Policy Forum projection (Cisco index), not a 2026 actual. IEA 2024 ~415 TWh global remains E2 (sources 1–2).

    Masanet et al. Recalibrating global data center energy-use estimates. https://datacenters.lbl.gov/sites/default/files/Masanet_et_al_Science_2020.full_.pdf. As of 2020-02-28. Checked 2026-08-31. Type: Paper (Science Policy Forum; LBNL-hosted journal PDF).
  3. E2

    Local ≠ global. World 2024 ~1.5%, 2030 ~3%. US 2024 4.7%, 2030 reference 11.8%. JRC is 2022 only for the EU.

    IEA. Energy and AI — Executive summary. https://www.iea.org/reports/energy-and-ai/executive-summary. As of 2025-04. Checked 2026-08-28. Type: Official report.Smith et al. (LBNL 2025). United States Data Center Energy Usage Report: 2025 Update. https://doi.org/10.71468/P1RP4F. As of 2026-06-18. Checked 2026-08-28. Type: Official lab report (LBNL-2001758).Kamiya, Bertoldi (JRC). Energy Consumption in Data Centres and Broadband Communication Networks in the EU. https://doi.org/10.2760/706491. As of 2024-02. Checked 2026-08-28. Type: Official report (JRC135926).
  4. E1

    Memory wall / packaging: compute 2×/2.3 y vs. bandwidth 2×/~4 y (Epoch 2023). Overview (source 24): HBM most binding constraint as of late 2025 (vendor quote via Epoch). IEA: HBM shortage until ≥2027. TSMC Tech Symposium 2026 (source 26): 5.5-reticle CoWoS in production; 14-reticle roadmap 2028 — reticle sizes known, capacity in kwpm still E0.

    Hobbhahn, Heim, Aydos. Trends in machine learning hardware. https://epoch.ai/publications/trends-in-machine-learning-hardware. As of 2023-11-09. Checked 2026-08-28. Type: Dataset/report.TSMC. 2025 Annual Report, Letter to Shareholders (ch. 1 PDF). https://investor.tsmc.com/static/annualReports/2025/english/pdf/2025_tsmc_ar_e_ch1.pdf. As of 2025. Checked 2026-08-28. Type: Company report.IEA Key Questions. Key Questions on Energy and AI — Executive Summary. https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary. As of 2026. Checked 2026-08-28. Type: Official report (exec summary).
  5. E1

    System vs. GPU TDP: H100 SXM up to 700 W (GPU, configurable). DGX B200 system about 14.3 kW max for 8 Blackwell GPUs plus CPU, network, memory — not 8×700 W and not field load. Peak spec ≠ utilization (LBNL ~70% of rated applies to H100 nodes, not measured here for B200).

    NVIDIA. NVIDIA H100 GPU (product specifications). https://www.nvidia.com/en-us/data-center/h100/. As of 2026-08-28. Checked 2026-08-28. Type: Manufacturer specification.NVIDIA DGX B200. NVIDIA DGX B200 (product specifications). https://www.nvidia.com/en-us/data-center/dgx-b200/. As of 2026-08-28. Checked 2026-08-28. Type: Manufacturer specification.
  6. E1

    CO₂ accounting: Strubell NAS 88× too high (Patterson). Grid intensity beats model size (BLOOM 25 t vs. GPT-3 502 t at similar size). Embodied is 22% of BLOOM’s training footprint. Standardised vendor disclosures 2026: E0.

    Strubell et al. Energy and Policy Considerations for Deep Learning in NLP. https://arxiv.org/html/1906.02243. As of 2019-06. Checked 2026-08-28. Type: Paper (ACL 2019).Patterson et al. The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. https://arxiv.org/abs/2204.05149. As of 2022-04-11. Checked 2026-08-28. Type: Paper (Google/Berkeley).Luccioni, Viguier, Ligozat. Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. https://arxiv.org/html/2211.02001. As of 2022-11. Checked 2026-08-28. Type: Paper.

6. Actors and incentives

Who measures, who fabricates, who projects

  1. E1

    LBNL (DOE) measures US datacenter power bottom-up; the 2025 update revises 2024 downward.

    Shehabi et al. (LBNL 2024). 2024 United States Data Center Energy Usage Report. https://eta-publications.lbl.gov/sites/default/files/2024-12/lbnl-2024-united-states-data-center-energy-usage-report.pdf. As of 2024-12. Checked 2026-08-28. Type: Official lab report (LBNL-2001637).Smith et al. (LBNL 2025). United States Data Center Energy Usage Report: 2025 Update. https://doi.org/10.71468/P1RP4F. As of 2026-06-18. Checked 2026-08-28. Type: Official lab report (LBNL-2001758).
  2. E2

    IEA projects global DC electricity and HBM/grid bottlenecks in executive summaries; methods annex incompletely read.

    IEA. Energy and AI — Executive summary. https://www.iea.org/reports/energy-and-ai/executive-summary. As of 2025-04. Checked 2026-08-28. Type: Official report.IEA Key Questions. Key Questions on Energy and AI — Executive Summary. https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary. As of 2026. Checked 2026-08-28. Type: Official report (exec summary).
  3. E1

    TSMC reports wafer mix and advanced share; ASML reports EUV units. CoWoS reticle sizes in 2026 Symposium PR (source 26); kwpm still not in the opened letter/PR.

    TSMC. 2025 Annual Report, Letter to Shareholders (ch. 1 PDF). https://investor.tsmc.com/static/annualReports/2025/english/pdf/2025_tsmc_ar_e_ch1.pdf. As of 2025. Checked 2026-08-28. Type: Company report.ASML PR. ASML reports €32.7 billion total net sales and €9.6 billion net income in 2025. https://www.asml.com/en/news/press-releases/2026/q4-2025-financial-results. As of 2026-01-28. Checked 2026-08-28. Type: Company report.
  4. E1

    NVIDIA publishes H100 and DGX B200 specifications; the 3×/15× comparison remains a vendor projection (E3).

    NVIDIA. NVIDIA H100 GPU (product specifications). https://www.nvidia.com/en-us/data-center/h100/. As of 2026-08-28. Checked 2026-08-28. Type: Manufacturer specification.NVIDIA DGX B200. NVIDIA DGX B200 (product specifications). https://www.nvidia.com/en-us/data-center/dgx-b200/. As of 2026-08-28. Checked 2026-08-28. Type: Manufacturer specification.
  5. E2

    IEA Electricity 2026 demand chapter projects the US datacenter share of electricity growth; not identical to LBNL TWh levels.

    IEA Electricity 2026. Demand — Electricity 2026. https://www.iea.org/reports/electricity-2026/demand. As of 2026. Checked 2026-08-28. Type: Official report (chapter).
  6. E2

    Epoch AI curates hardware trends, chip sales and the chips topic overview (sources 11, 12, 24, 25); underlying revenue/analyst inputs not audited.

    Epoch Trends. Trends in Artificial Intelligence. https://epoch.ai/trends. As of 2026-02-05. Checked 2026-08-28. Type: Dataset/dashboard.Hobbhahn, Heim, Aydos. Trends in machine learning hardware. https://epoch.ai/publications/trends-in-machine-learning-hardware. As of 2023-11-09. Checked 2026-08-28. Type: Dataset/report.

7. State of the dispute

Where sources contradict or bound each other

  1. E1

    Strubell NAS vs. Patterson: 88× difference on the same NAS story. Both opened.

    Strubell et al. Energy and Policy Considerations for Deep Learning in NLP. https://arxiv.org/html/1906.02243. As of 2019-06. Checked 2026-08-28. Type: Paper (ACL 2019).Patterson et al. The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. https://arxiv.org/abs/2204.05149. As of 2022-04-11. Checked 2026-08-28. Type: Paper (Google/Berkeley).
  2. E2

    2024 consistency IEA vs. LBNL is tight (IEA US ≈187 TWh vs. LBNL 192). 2030 levels diverge more and are not harmonised here.

    IEA. Energy and AI — Executive summary. https://www.iea.org/reports/energy-and-ai/executive-summary. As of 2025-04. Checked 2026-08-28. Type: Official report.Smith et al. (LBNL 2025). United States Data Center Energy Usage Report: 2025 Update. https://doi.org/10.71468/P1RP4F. As of 2026-06-18. Checked 2026-08-28. Type: Official lab report (LBNL-2001758).
  3. E2

    IEA gallium refining: 99% (PDF) vs. 95% (security HTML). Do not smooth.

    IEA PDF. Energy and AI (PDF, Auszug). https://iea.blob.core.windows.net/assets/40a4db21-2225-42f0-8a07-addcc2ea86b3/EnergyandAI.pdf. As of 2025-04. Checked 2026-08-28. Type: Official report PDF (excerpt).IEA Security. AI and energy security. https://www.iea.org/reports/energy-and-ai/ai-and-energy-security. As of 2025-04. Checked 2026-08-28. Type: Official report (chapter).
  4. E2

    Patterson “plateau then shrink” (Google internal, 4Ms) stands against the IEA/LBNL net rise. E2 internal; E3 as a 2026 world forecast.

    Patterson et al. The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. https://arxiv.org/abs/2204.05149. As of 2022-04-11. Checked 2026-08-28. Type: Paper (Google/Berkeley).IEA. Energy and AI — Executive summary. https://www.iea.org/reports/energy-and-ai/executive-summary. As of 2025-04. Checked 2026-08-28. Type: Official report.Shehabi et al. (LBNL 2024). 2024 United States Data Center Energy Usage Report. https://eta-publications.lbl.gov/sites/default/files/2024-12/lbnl-2024-united-states-data-center-energy-usage-report.pdf. As of 2024-12. Checked 2026-08-28. Type: Official lab report (LBNL-2001637).

8. Open questions

  1. IEA Energy and AI methods annex + supply chapter — how 415/945 are built.
  2. TSMC 20-F / CoWoS capacity in kwpm (reticle sizes opened via 2026 Symposium PR; kwpm still E0).
  3. SK hynix / Samsung HBM annual report — not TrendForce.
  4. USGS OFR 2024-1057 gallium/germanium export restrictions.
  5. NVIDIA B200 single-GPU datasheet PDF + DGX-node measurements beyond the product-page TDP.
  6. Wu et al. 2021 (Meta) + Google 2025/26 environmental reports for current splits.
  7. Epoch chips-topic-overview (retry the timeout).
  8. JRC or ENTSO-E 2025/26 update for EU DC TWh after 2022.
  9. IEA Key Questions full PDF — HBM claim and “order of magnitude/year” methods.

9. Changes

  • v1.4-draft2026-09-02: TSMC 2026 Tech Symposium PR (source 26) opened — 5.5-reticle CoWoS now in production; 14-reticle ~10 compute+20 HBM slated for 2028 (vendor roadmap). kwpm still E0.
  • v1.3-draft2026-09-01: Epoch Topic Overview AI Chips (source 24) and chip-production data insight (source 25) opened — ~20 million chips Q4 2025; 3.3×/year sold capacity since 2022 (CI 2.7–4.1). Do not mix with 5.3× training compute.
  • v1.2-draft2026-08-31: Masanet Science 2020 (source 23, LBNL-hosted journal PDF) opened — global 2010–2018 +6% energy / +550% compute instances; 194→205 TWh. IEA 415 TWh and LBNL US untouched. Epoch 5×/year dashboard figure corroborated in D01 (no new D02 source for the report).
  • v1.1-draft2026-08-28: NVIDIA DGX B200 spec (product page) and IEA Electricity 2026 demand (US datacenter share) folded in.
  • v1.0-draftFirst version from the 2026-08-28 verification log.

10. Sources

No. Source As of Checked Grade
1 International Energy Agency. Energy and AI — Executive summary. https://www.iea.org/reports/energy-and-ai/executive-summary. Type: Official report. E2
2 International Energy Agency. Energy demand from AI. https://www.iea.org/reports/energy-and-ai/energy-demand-from-aI. Type: Official report (chapter). E2
3 International Energy Agency. AI and energy security. https://www.iea.org/reports/energy-and-ai/ai-and-energy-security. Type: Official report (chapter). E2
4 International Energy Agency. Energy and AI (PDF, Auszug). https://iea.blob.core.windows.net/assets/40a4db21-2225-42f0-8a07-addcc2ea86b3/EnergyandAI.pdf. Type: Official report PDF (excerpt). E2
5 International Energy Agency. Key Questions on Energy and AI — Executive Summary. https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary. Type: Official report (exec summary). E2
6 International Energy Agency. Global Critical Minerals Outlook 2026 — Executive Summary. https://www.iea.org/reports/global-critical-minerals-outlook-2026/executive-summary. Type: Official report (exec). E2
7 Luccioni, A. S.; Jernite, Y.; Strubell, E.. Power Hungry Processing: Watts Driving the Cost of AI Deployment?. https://arxiv.org/html/2311.16863. Type: Paper (FAccT 2024). E1
8 Luccioni, A. S.; Viguier, S.; Ligozat, A.-L.. Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model. https://arxiv.org/html/2211.02001. Type: Paper. E1
9 Strubell, E.; Ganesh, A.; McCallum, A.. Energy and Policy Considerations for Deep Learning in NLP. https://arxiv.org/html/1906.02243. Type: Paper (ACL 2019). E1
10 Patterson, D.; Gonzalez, J.; Hölzle, U.; Le, Q.; Liang, C.; Munguia, L.-M.; Rothchild, D.; So, D.; Texier, M.; Dean, J.. The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. https://arxiv.org/abs/2204.05149. Type: Paper (Google/Berkeley). E2
11 Epoch AI. Trends in Artificial Intelligence. https://epoch.ai/trends. Type: Dataset/dashboard. E2
12 Hobbhahn, M.; Heim, L.; Aydos, G.. Trends in machine learning hardware. https://epoch.ai/publications/trends-in-machine-learning-hardware. Type: Dataset/report. E2
13 Shehabi, A.; Smith, S. J.; Hubbard, A.; Newkirk, A.; Lei, N.; Siddik, M. A. B.; Holecek, B.; Koomey, J.; Masanet, E.; Sartor, D.. 2024 United States Data Center Energy Usage Report. https://eta-publications.lbl.gov/sites/default/files/2024-12/lbnl-2024-united-states-data-center-energy-usage-report.pdf. Type: Official lab report (LBNL-2001637). E1
14 Smith, S. J.; Hubbard, A.; Newkirk, A.; Ganeshalingam, M.; Holecek, B.; Sartor, D.; Mills, M.; Shehabi, A.. United States Data Center Energy Usage Report: 2025 Update. https://doi.org/10.71468/P1RP4F. Type: Official lab report (LBNL-2001758). E2
15 U.S. Geological Survey. Interior Department releases final 2025 List of Critical Minerals. https://www.usgs.gov/news/science-snippet/interior-department-releases-final-2025-list-of-critical-minerals. Type: Official. E1
16 Taiwan Semiconductor Manufacturing Company. 2025 Annual Report, Letter to Shareholders (ch. 1 PDF). https://investor.tsmc.com/static/annualReports/2025/english/pdf/2025_tsmc_ar_e_ch1.pdf. Type: Company report. E1
17 ASML Holding N.V.. ASML reports €32.7 billion total net sales and €9.6 billion net income in 2025. https://www.asml.com/en/news/press-releases/2026/q4-2025-financial-results. Type: Company report. E1
18 ASML Holding N.V.. 2025 Annual Report — Financial performance section. https://ourbrand.asml.com/m/419103cb23dfeaa4/original/asml-2025-annual-report-financial-performance-section.pdf. Type: Company report. E1
19 NVIDIA. NVIDIA H100 GPU (product specifications). https://www.nvidia.com/en-us/data-center/h100/. Type: Manufacturer specification. E1
20 Kamiya, G.; Bertoldi, P. (European Commission, JRC). Energy Consumption in Data Centres and Broadband Communication Networks in the EU. https://doi.org/10.2760/706491. Type: Official report (JRC135926). E2
21 NVIDIA. NVIDIA DGX B200 (product specifications). https://www.nvidia.com/en-us/data-center/dgx-b200/. Type: Manufacturer specification. E1
22 International Energy Agency. Demand — Electricity 2026. https://www.iea.org/reports/electricity-2026/demand. Type: Official report (chapter). E2
23 Masanet, E.; Shehabi, A.; Lei, N.; Smith, S.; Koomey, J.. Recalibrating global data center energy-use estimates. https://datacenters.lbl.gov/sites/default/files/Masanet_et_al_Science_2020.full_.pdf. Type: Paper (Science Policy Forum; LBNL-hosted journal PDF). E1
24 Epoch AI. Topic Overview: AI Chips. https://epoch.ai/publications/chips-topic-overview. Type: Topic overview (research NGO). E2
25 Epoch AI. Global AI computing capacity is doubling every 7 months. https://epoch.ai/data-insights/ai-chip-production. Type: Data insight (CSV stamp 16 Feb 2026). E2
26 Taiwan Semiconductor Manufacturing Company. TSMC Debuts A13 Technology at 2026 North America Technology Symposium. https://pr.tsmc.com/system/files/newspdf/attachment/49337b40ff139d51d533076cf7a945b30e107e07/2026%20Tech%20Symposium%20%28E%29_Final_wmn.pdf. Type: Company PR PDF (Tech Symposium; news HTML Cloudflare-blocked). E2

11. Uncertainty log

Overall uncertainty of this entry, bound to the verification log of 2026-08-28 plus Masanet 2026-08-31, Epoch chips 2026-09-01 and TSMC Symposium PR 2026-09-02. 26 openings. Do not mix 3.3× sold capacity with 3.4× dashboard or 5.3× training FLOP. CoWoS reticle sizes ≠ kwpm. Not used as warrant: journalism; 205 TWh (2018) as 2026 world electricity; the smuggling band as a census.

  • Established (layer 1): inference energy (Luccioni); BLOOM; Masanet 2010–2018; LBNL US DC; Epoch chips overview ~20 million / 3.3× since 2022 (E2); H100/DGX B200 spec; TSMC/ASML; CoWoS 5.5-reticle now (E2, source 26); USGS.
  • Claimed (layer 2): IEA central projections to 2030; IEA Electricity 2026 US DCs about 50% of electricity growth; HBM until 2027; capex 400 billion; TSMC/ASML guidance; CoWoS 14-reticle 2028 roadmap (E2 vendor); DGX B200 3×/15× vs H100; unread industry splits.
  • Constrained (layer 3): Jevons net; local vs. global; 2026 training/inference split; CoWoS kwpm (E0; reticle sizes known); 2026 actual vs. IEA estimate; Masanet 2020 surge warning ≠ 2026 measurement.

Not opened (not a warrant)

  • Epoch AI Topic Overview AI Chips — opened 2026-09-01 (source 24); chip-production data insight source 25. SK hynix/Samsung/Micron annual reports still unopened.
  • NVIDIA H100 datasheet PDF — download returned HTML; spec via product page.
  • TSMC Form 20-F / CoWoS kwpm — reticle sizes via 2026 Symposium PR (source 26); kwpm still not in the letter/PR.
  • SK hynix / Samsung / Micron HBM annual reports — not opened. HBM bottleneck only via IEA.
  • USGS OFR 2024-1057 — the China-99% claim comes from the IEA PDF, not this OFR.
  • Wu et al. 2021 (Meta) arXiv:2111.00364 — citation inside Luccioni only. E3.
  • Barr 2019 AWS Inf1 — not independent. E3.
  • IEA Energy and AI methods annex / supply chapter — not complete.
  • IEA Key Questions full PDF (138 pp) — not opened.
  • JRC/ENTSO-E 2025/26 EU DC update — not opened.
  • Kaack et al. 2022 Nature Climate Change — not opened.