{"slug": "yes-but", "title": "Yes, But", "summary": "US data centers consumed 192 TWh in 2024 and are projected to reach roughly 300 TWh by 2026, with AI accounting for about 75 TWh a year, according to Berkeley Lab, EPRI and IEA figures cited in an analysis of AI's electricity demand. The analysis notes that inference represents about 65% of AI-server energy in 2024 and is heading to 80%, while Nvidia claims its Blackwell platform delivers up to 25× less energy per inference than Hopper and Vera Rubin up to 10× more tokens per megawatt than GB200. Single sites are now nuclear-plant sized, with xAI's Colossus 2 drawing 946 MW and Microsoft's Fairwater Wisconsin planned at 2.3 GW.", "body_md": "# Yes, but…\n\nElectrical use of AI\n\nI was on a fishing boat for the last 4 days, which brings in a mixed crowd. One of the discussions that came up was energy usage of AI, and I found myself saying \"yes, but...\" quite a bit. There's a lot of nuance and factors that go into the future energy usage of AI, and I want to talk through them.\n\n## AI is already a grid-scale load\n\nUS data centers used [176 TWh in 2023](https://www.energy.gov/articles/doe-releases-new-report-evaluating-increase-electricity-demand-data-centers) (4.4% of US power) and [192 TWh in 2024](https://escholarship.org/uc/item/33m6w3x0). Berkeley Lab's reference case then has them growing 22% in 2025 and 29% in 2026, to roughly 300 TWh. EPRI estimates that [15–25% of that is AI](https://restservice.epri.com/publicattachment/96784). The IEA says AI-focused data centers grew [50% in 2025 alone](https://www.iea.org/news/data-centre-electricity-use-surged-in-2025-even-with-tightening-bottlenecks-driving-a-scramble-for-solutions). Put together, that is **about 75 TWh a year** for AI in 2026.\n\nTraining dominates the headlines (despite inference using more), since the single up-front cost raises eyebrows. GPT-3 took [1,287 MWh](https://arxiv.org/abs/2104.10350), and Grok 4 an estimated [310 GWh](https://epoch.ai/data-insights/grok-4-training-resources), 240× more.\n\n## More numbers\n\n- 75 TWh is about 1.8% of the [4,195 TWh](https://www.eia.gov/outlooks/steo/report/elec_coal_renew.php) the US consumed in 2025, or the electricity of roughly 7 million homes.\n- Single sites are now nuclear-plant sized: xAI's Colossus 2 draws [946 MW](https://epoch.ai/graphs/largest-ai-data-centers-by-power-capacity) , and Microsoft's Fairwater Wisconsin is planned at 2.3 GW.\n- The power drawn by frontier training runs grows [2.2× a year](https://epoch.ai/blog/power-demands-of-frontier-ai-training) , and the largest single runs could need 4–16 GW by 2030.\n- Inference is most of the bill: about [65% of AI-server energy in 2024, heading to 80%](https://escholarship.org/uc/item/33m6w3x0) .\n- A single prompt is tiny: [0.24 Wh](https://arxiv.org/abs/2508.15734) for a median Gemini text prompt and[0.34 Wh](https://blog.samaltman.com/the-gentle-singularity) for an average ChatGPT query. But ChatGPT alone handled[2.5 billion prompts a day](https://techcrunch.com/2025/07/21/chatgpt-users-send-2-5-billion-prompts-a-day/) in mid-2025, and reasoning and agent tasks can use[hundreds to thousands of times](https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary) more per query.\n\n## Yes, but GPUs are getting more efficient\n\nNvidia pitched Blackwell as [up to 25× less energy](https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing) per inference than Hopper. Vera Rubin, in full production since [CES 2026](https://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer), claims [up to 10× more tokens per megawatt](https://www.nvidia.com/en-us/data-center/vera-rubin-nvl72/) than GB200, and CoreWeave measured [10× on DeepSeek-R1](https://blogs.nvidia.com/blog/vera-rubin/). On Nvidia's own chart, energy per token on a 1.8T-parameter MoE fell from [17,000 J on Pascal to 0.4 J on Blackwell](https://mcomputers.cz/wp-content/uploads/2025/11/08_Koen_van_den_Berg_NVIDIA-Blackwell_-powering-the-next-generation-AI-Factories-EN-1.pdf), about 45,000× in eight years.\n\nThe overall hardware trend is 1.28–1.40× more efficient per year ([Epoch](https://epoch.ai/blog/can-ai-scaling-continue-through-2030), [Epoch](https://epoch.ai/data-insights/ml-hardware-energy-efficiency)), and the IEA says it is [gradually slowing](https://iea.blob.core.windows.net/assets/3179f7f8-01f6-4dd6-bffa-c9f7b73f1dc9/KeyQuestionsonEnergyandAI.pdf).\n\n## More numbers\n\n- Rubin's racks are fully liquid-cooled, with a [PUE of 1.1 vs ~1.25](https://developer.nvidia.com/blog/scaling-token-factory-revenue-and-ai-efficiency-by-maximizing-performance-per-watt/) .\n- Much of that headline gain is lower precision (FP16 → FP4), bigger NVLink domains and software, not the silicon itself. At fixed precision, Rubin's FLOPs per watt are roughly level with Blackwell's, and dense-model gains are closer to [2–3×](https://blog.barrack.ai/nvidia-rubin-vs-blackwell-rent-now-or-wait/) .\n- Every chip trend before it slowed too. Computations per kWh doubled every 1.6 years until 2000, then [every 2.6](https://www.electronicdesign.com/technologies/microprocessors/article/21802037/energy-efficiency-of-computing-whats-next) . Single-thread speed went from +52% a year to[about +3%](https://dl.acm.org/doi/10.1145/3282307) .\n- Physics also sets a ceiling: CMOS chips top out around [207× an H100's efficiency](https://arxiv.org/abs/2312.08595) , and dropping from FP16 to FP4 has already used most of the number-format gains.\n- So we let the trend fade as it nears that wall, ~130× from Blackwell (range 26–650×). Ten years then yields 6–14× instead of 12–29×. Hold usage at today's level, and AI's electricity still falls every year, just less steeply.\n\n## Yes, but AI adoption is only at 2%\n\na16z's [State of Markets II](https://a16z.com/state-of-markets-ii/) shows only **~2% of US households** paying for an AI subscription as of April 2026 (PNC transaction data). Adoption is going to skyrocket in the next decade. AI is being adopted [about twice as fast](https://www.stlouisfed.org/on-the-economy/2024/sep/rapid-adoption-generative-ai) as the internet was in the '90s, and the internet went from [14% of US adults in 1995](https://www.pewresearch.org/internet/2014/02/27/part-1-how-the-internet-has-woven-itself-into-american-life/) to [68% in 2005](https://www.pewresearch.org/internet/fact-sheet/internet-broadband/). At twice the speed, AI's next ten years look like the internet's twenty, which reached [86% by 2015](https://www.pewresearch.org/internet/fact-sheet/internet-broadband/). If paid AI follows the same kind of S-curve, from ~2% of households to 68–86%, usage rises **34–43×**, more than cancelling the hardware gains.\n\n## Yes, but models are getting more efficient\n\nThe [densing law](https://www.nature.com/articles/s42256-025-01137-0) (Xiao et al., *Nature Machine Intelligence*, 2025) finds that the capability per parameter of open LLMs doubles about every 3.5 months: the same score from half the model, every season. The [preprint](https://arxiv.org/abs/2412.04315) clocks a 266.7× price drop for equal performance, from GPT-3.5 (Dec 2022) to Gemini 1.5 Flash (Aug 2024). These improvements had a direct impact on energy usage. Google cut energy per Gemini prompt [33× in a single year](https://arxiv.org/abs/2508.15734).\n\nObviously these densing gains won't last forever. A decade of it gives an obviously absurd result: at the low end, **all of America's AI running on less power than one house.** The reality is requirements for a given capability [slow sharply as that capability ages](https://epoch.ai/publications/the-plunging-price-of-thought), but there are still tremendous gains to be made.\n\n## More numbers\n\nAndrej Karpathy's GPT-2 reruns are a great way to view the progression of this. OpenAI's 2019 training run cost roughly [$43,000](https://github.com/karpathy/nanochat/discussions/481). llm.c reproduced the 1.5B model in 24 hours for [$672](https://github.com/karpathy/llm.c/discussions/677) in 2024. By March 2026, nanochat beat it in [1.65 hours, for about $48](https://github.com/karpathy/nanochat): roughly 900× cheaper in seven years, or 2.6× a year. On fixed hardware, the community speedrun to GPT-2-small loss fell from [45 minutes to 1.13](https://github.com/KellerJordan/modded-nanogpt). For the estimate here, we took the 2.6× a year (Karpathy) to 10.8× a year (densing law) and let it fade, with the yearly gain halving every 3–10 years.\n\n## Yes, but TAM is larger than the population\n\nAgents, not people, are now the #1 user of tokens. On OpenRouter, agent API keys out-used humans for the first time on [February 6, 2026](https://openrouter.ai/data/newsletter/cache-is-crucial), and by late September they were using **~7× the tokens** humans did. OpenClaw wakes itself on a [30-minute heartbeat](https://github.com/openclaw/openclaw/blob/main/docs/gateway/heartbeat.md) by default, and Jensen Huang predicts a future with [100 billion agents](https://thenewstack.io/huang-semiconductor-tenfold-ai-agents/). Agents are going proactive and are growing in use massively. Some of that 7× is cheaper than it looks: [~90% of agent tokens](https://openrouter.ai/data/newsletter/cache-is-crucial) are cached re-reads of the same prompt. Weighted by compute, agents are about 1.5–2× human usage today.\n\nWe assume agent compute per adopter grows **2–20×** by 2036, or about 1.07–1.35× a year.\n\n## More numbers\n\n- The top public app by tokens on OpenRouter is [Hermes Agent](https://openrouter.ai/apps) , a persistent agent that runs scheduled automations. OpenClaw has[391k GitHub stars](https://github.com/openclaw/openclaw) , and its default heartbeat means 48 unprompted sessions a day.\n- [ChatGPT Pulse](https://mobilesyrup.com/2025/09/26/chatgpt-launches-new-briefing-feature-called-pulse/) researches overnight before you ask, Claude Code's scheduled tasks fire[\"even while the session is idle\"](https://code.claude.com/docs/en/costs) , and one Cursor customer runs[2,000+ agent runs a week](https://releasebot.io/updates/cursor) with no manual prompting.\n- Microsoft already has [~40 million agents](https://www.microsoft.com/en-us/investor/events/fy-2026/earnings-fy-2026-q4) registered.\n- Each agent task is heavy. Agents use [4× the tokens of chat](https://www.anthropic.com/engineering/multi-agent-research-system) and multi-agent systems 15×, Claude Code agent teams use[~7×](https://code.claude.com/docs/en/costs) , and reasoning output is growing[5× a year](https://epoch.ai/data-insights/output-length) .\n- Meta employees burned [60 trillion tokens in 30 days](https://the-decoder.com/meta-employees-compete-for-token-consumption-on-an-internal-ai-leaderboard/) , ~700M each.\n- The 2–20× assumption is a small fraction of 2026's pace, and it stacks on top of human adoption.\n\n## Yes, but hardware is getting more optimized\n\nThere's a ladder of ways to serve a model, each more efficient and less flexible than the last: CPUs, then GPUs, then TPUs, then fixed-function ASICs. Most AI still runs on GPUs: Nvidia shipped [67% of new AI compute](https://epoch.ai/graphs/ai-chip-market-share-by-designer) in Q4 2025. The top rung is [Taalas's HC1](https://taalas.com/?p=251), which hard-wires Llama 3.1 8B into the transistors themselves and gets roughly 3× a fully loaded GPU's tokens per joule.\n\nWe move ASICs from ~25% of AI power today to 40–70% by 2036, at 1.5–20× a GPU's tokens per watt: **up to ~13× more AI from the same power**.\n\n## More numbers\n\n- GPUs are about [42× more efficient on inference](https://blogs.nvidia.com/blog/2023/05/21/gpu-energy-efficiency-nersc/) than CPUs (Nvidia's figure).\n- Google's first TPU managed [30–80× the TOPS/W](https://research.google/pubs/pub46078/) of the CPUs and GPUs of its day, and Ironwood is[~30× the first Cloud TPU](https://blog.google/products/google-cloud/ironwood-tpu-age-of-inference/) .\n- AWS rates Trainium3 at [4× the efficiency of Trainium2](https://press.aboutamazon.com/2025/12/trainium3-ultraservers-now-available-enabling-customers-to-train-and-deploy-ai-models-faster-at-lower-cost) , and transformer-only Etched just raised[$700M at a $21B valuation](https://thenextweb.com/news/etched-700m-series-d-21-billion-jane-street) . TrendForce expects ASIC unit shipments to[pass GPUs in 2026](https://www.trendforce.com/presscenter/news/20251030-12762.html) .\n- The HC1 is TSMC 6 nm, [815 mm², ~200–250 W per card](https://www.nextplatform.com/2026/02/19/taalas-etches-ai-models-onto-transistors-to-rocket-boost-inference/) . It streams ~17,000 tokens/s to a single user, where an H200 manages[~230](https://heise.de/-11185112) .\n- Per joule the gap is smaller than that 74× suggests, because GPUs serve hundreds of users at once: a fully loaded H200 pushes [~17,700 tokens/s](https://nvidia.github.io/TensorRT-LLM/1.2.0rc0/developer-guide/perf-overview.html) on the same model. Taalas itself claims 10×.\n- The cost is flexibility: the model is frozen in metal with 3/6-bit quantization, and a new one takes ~2 months. AMD agreed to [buy Taalas](https://ir.amd.com/news-events/press-releases/detail/1296/amd-acquires-taalas-to-advance-compute-solutions-for-rapidly-growing-ai-inference-market) in August.\n\n## Yes, but Jevons paradox\n\nIn 1865 William Jevons noticed that efficiency doesn't save fuel: when Scottish ironworks cut coal per ton of iron to under a third, [Scotland's coal use rose tenfold](https://www.econlib.org/library/YPDBooks/Jevons/jvnCQ7.html). AI is following the same pattern. Google cut energy per prompt 33×, yet its monthly tokens went from [9.7 trillion to 3.2 quadrillion](https://blog.google/innovation-and-ai/sundar-pichai-io-2026/) in two years.\n\nAdoption and agents are already counted above, so the question here is how much *more* gets re-spent on top. AI's own record in 2025–26 implies **75–85%**: most of every efficiency gain the earlier sections banked comes back as load.\n\n## More numbers\n\n- Jevons' line: \"it is wholly a confusion of ideas to suppose that the economical use of fuel…\" means using less of it.\n- Between 1800 and 2000, the price of light in the UK fell to [under 1/3,000th](https://orca.cardiff.ac.uk/id/eprint/27209/) , while light used per person rose 6,500× and total use 25,000×. Spending on light has held near[0.72% of GDP for 300 years](https://iopscience.iop.org/0022-3727/43/35/354001) , which amounts to roughly 100% rebound.\n- The one real counterexample: from 2010 to 2018, data-center compute grew 6.5× while energy rose [just 6%](https://eta.lbl.gov/publications/recalibrating-global-data-center) . That ended once GPUs arrived.\n- \"Jevons paradox strikes again!\" [posted Satya Nadella](https://fortune.com/2025/01/27/microsoft-ceo-satya-nadella-deepseek-optimism-jevons-paradox) the week DeepSeek R1 landed. Frontier models now cost[3–18× more a year](https://arxiv.org/abs/2511.23455) to run as savings get spent on bigger ones.\n- Where 75–85% comes from: in 2025–26 tokens grew 7× and energy per task fell [≥10×](https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary) , yet AI data-center load still rose[50%](https://www.iea.org/news/data-centre-electricity-use-surged-in-2025-even-with-tightening-bottlenecks-driving-a-scramble-for-solutions) .\n- The evidence is thin, and one study found demand [inelastic across models](https://arxiv.org/html/2601.10088) .\n\n## Yes, but the grid can't be built that fast\n\nRight now AI is limited by supply, not demand. Satya Nadella says Microsoft has [\"a bunch of chips sitting in inventory that I can't plug in\"](https://www.datacenterdynamics.com/en/news/microsoft-has-ai-gpus-sitting-in-inventory-because-it-lacks-the-power-necessary-to-install-them). ERCOT alone has [474 GW of large loads](https://www.ercot.com/files/docs/2026/07/29/ERCOT-Senate-July-29-Panel-1-Assessing-The-Grid.pdf) waiting, yet the most new data-center load the US grid has connected in one year is [~10 GW](https://about.bnef.com/insights/data-centers/six-things-to-know-about-bnefs-new-us-data-center-capacity-outlook/). Every part of the supply chain is backed up: gas turbines take [~6 years](https://www.woodmac.com/press-releases/gas-turbine-prices-soar-195-as-market-faces-supply-demand-crisis/), big transformers [2½–4](https://pv-magazine-usa.com/2026/05/11/u-s-transformer-market-faces-severe-supply-constraints-as-lead-times-extend-to-four-years/).\n\nWe cap AI at **10–25 GW of new capacity a year**, which works out to **650–1,700 TWh by 2036**. Past ~1,700 TWh, it stops being a question of demand. The turbines, transformers and wires don't exist.\n\n## More numbers\n\n- Amazon's Andy Jassy expects [not enough capacity to meet 2026 demand](https://datacenterknowledge.com/infrastructure/amazon-lifts-ai-infrastructure-spending-to-220b-as-demand-outpaces-capacity) , and Alphabet's CFO calls it[\"a supply-constrained environment\"](https://www.marketbeat.com/instant-alerts/alphabet-q2-earnings-call-highlights-2026-07-22/) .\n- Northern Virginia's data-center vacancy is [0.2%](https://www.cbre.com/insights/books/north-america-data-center-trends-h1-2026) , and 80% of what's under construction is already leased.\n- About 90% of ERCOT's waiting loads are data centers. Dominion has [54 GW under contract](https://www.pjm.com/-/media/DotCom/committees-groups/subcommittees/las/2026/20260929/20260929-item-5ai---dominion_energy.pdf) , yet its data-center load grows ~0.5 GW a year.\n- PJM's capacity auction hit its price cap for the third year running and still came up [6.8 GW short](https://www.pjm.com/-/media/DotCom/about-pjm/newsroom/2026-releases/20260714-pjm-capacity-auction-procures-138318-mw-of-generation-resources.pdf) .\n- GE Vernova has [116 GW](https://www.sec.gov/Archives/edgar/data/0001996810/000199681026000147/gevpressrelease2q26.htm) of gas-turbine orders and reservations against ~20 GW a year of output, and turbine prices are up 195% since 2019. A new power plant now waits[over 5 years](https://www.publicpower.org/periodical/article/backlog-power-plants-seeking-transmission-grid-connection-eased-somewhat-2025-lbnl) from grid request to operation.\n- Builders are routing around the grid with on-site gas ([124 GW announced](https://about.bnef.com/insights/data-centers/six-things-to-know-about-bnefs-new-us-data-center-capacity-outlook/) , half of it speculative), but that hits the same turbine queue.\n- 10–25 GW a year runs from today's pace up to roughly double the entire US generation build. Even the high case falls short of the [50 GW by 2028](https://anthropic.com/news/build-ai-in-america) Anthropic says the industry needs.\n\n## So, how much?\n\nWith all seven factors taken into account, the range for 2036 is .  The high end . Published forecasts sit inside this range (Berkeley pegs it at [~290–460 TWh](https://escholarship.org/uc/item/33m6w3x0), and the IEA's estimate works out to [~230 TWh](https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary) for the US).\n\n## Methodology & assumptions\n\nFor each year *t* (0 = 2026), `E(t) = E₀ · A(t) · G(t) · η(t)^(R−1)`. *A* is the adoption multiplier, a logistic curve from 2% to the target share reached in 2036. *G* is agent compute per adopter, following an S-curve from 1× today to its 2036 value. *η* is the combined efficiency gain: GPU × model × the mix of ASIC and GPU compute. *R* is the rebound, meaning the share of efficiency gains spent on more usage (0 = none, 1 = all). The GPU and model trends fade instead of compounding forever: each one's yearly gain halves every *h* years, so its total gain levels off at `rate^(h/ln 2)`. For GPUs, *h* is chosen so that total gain levels off at the physical ceiling. Finally, load can never exceed a supply ceiling that rises roughly linearly from today's 75 TWh to *C* in 2036. The band is the minimum and maximum across every combination of the range endpoints below, and the midpoint uses the middle of each range.\n\n| Input | Range | Basis | \n|---|---|---|\n| E₀, US AI electricity 2026 | 75 TWh | ~300 TWh data-center load ( [LBNL 2025 update](https://escholarship.org/uc/item/33m6w3x0) : 192 TWh in 2024, +22%, +29%) × AI's 15–25% share ([EPRI](https://restservice.epri.com/publicattachment/96784) ), upper end because AI-focused load grew 50% in 2025 ([IEA](https://www.iea.org/news/data-centre-electricity-use-surged-in-2025-even-with-tightening-bottlenecks-driving-a-scramble-for-solutions) ). History (2018–2025) uses the same data-center totals × an AI share rising from ~4% to ~20%. These are estimates. | \n| GPU efficiency | 1.28–1.40× / yr, fading toward a 26–650× ceiling (half-life ≈ 7–18 yrs) | [Epoch](https://epoch.ai/blog/can-ai-scaling-continue-through-2030) FLOP/s per W trend;[Epoch](https://epoch.ai/data-insights/ml-hardware-energy-efficiency) leading hardware +40%/yr;[IEA](https://iea.blob.core.windows.net/assets/3179f7f8-01f6-4dd6-bffa-c9f7b73f1dc9/KeyQuestionsonEnergyandAI.pdf) 30–40%/yr, slowing. Ceiling:[Ho et al.](https://arxiv.org/abs/2312.08595) CMOS limit ≈ 207× H100 (±1 SD: 41–1,037×), divided by Blackwell's ~1.6× over H100. Historical decay:[Koomey](https://www.electronicdesign.com/technologies/microprocessors/article/21802037/energy-efficiency-of-computing-whats-next) 1.6 → 2.6-yr doubling;[Hennessy & Patterson](https://dl.acm.org/doi/10.1145/3282307) . | \n| Adoption | 2% → 68–86% of households (34–43×) | [a16z / PNC](https://a16z.com/state-of-markets-ii/) paid-AI households, April 2026. Bounds: US internet adoption in 2005 and 2015 ([Pew](https://www.pewresearch.org/internet/fact-sheet/internet-broadband/) ), since AI is spreading ~2× as fast ([St. Louis Fed](https://www.stlouisfed.org/on-the-economy/2024/sep/rapid-adoption-generative-ai) ) | \n| Model efficiency | 2.6–10.8× / yr, halving every 3–10 yrs | GPT-2 cost curve via [nanochat](https://github.com/karpathy/nanochat) (~900× in 7 years);[densing law](https://www.nature.com/articles/s42256-025-01137-0) (density doubles every 3.5 months). The half-life is our judgment, informed by price declines slowing as a capability ages ([Epoch](https://epoch.ai/publications/the-plunging-price-of-thought) ), the[data wall](https://epoch.ai/blog/will-we-run-out-of-data-limits-of-llm-scaling-based-on-human-generated-data) , and gains tied to compute scale ([Gundlach et al.](https://arxiv.org/abs/2511.21622) ). This is the least certain input. | \n| Agents G | 2–20× by 2036 | Compute-weighted agent:human ratio ~0.6 → 1.8 on OpenRouter in 2026 (raw tokens 1 → 7×, ~90% cached: [OpenRouter](https://openrouter.ai/data/newsletter/cache-is-crucial) ). Global ratio assumed ~0.3–1 today, growing 10–45%/yr to ~2–30 (an overall multiplier of ~3–30×, or 2–20× relative to today). | \n| Rebound R | 0.75–0.85 | Rebound beyond the adoption and agent growth already modeled, calibrated to 2025–26: energy grew ~1.5× = users ~2× × agents ~1.3× × efficiency ~10–33×^(R−1). Lighting's ~100% ( [Tsao et al.](https://iopscience.iop.org/0022-3727/43/35/354001) ) includes new uses, which are counted separately here. | \n| Supply ceiling C | 650–1,700 TWh in 2036 | 10 GW a year (today's pace) to 25 GW a year (about double the whole US generation build) of new AI capacity, at 70–80% utilization. Calibration: data-center capacity added was 6.4 GW (2024), 8.5 GW (2025) and ~14–16 GW in 2026 ( [Goldman](https://www.publicpower.org/node/30450) ,[SynMax](https://blog.synmax.com/vulcan/2026-wont-deliver-21-gw-of-data-centers) ); the grid's best single year connected ~10 GW ([BNEF](https://about.bnef.com/insights/data-centers/six-things-to-know-about-bnefs-new-us-data-center-capacity-outlook/) );[EIA](https://www.eia.gov/todayinenergy/detail.php?id=67205) 2026 planned additions (86 GW nameplate, ~18 GW average) include only 6.3 GW of gas. | \n| ASIC share / advantage | 25% → 40–70% of AI power; 1.5× → 1.5–20× tokens/W | Today: [Epoch](https://epoch.ai/graphs/ai-chip-market-share-by-designer) Q4 2025 share (TPU 20%, AWS 5%), assumed ~1.5× GPU efficiency (Trainium3's \"4×\" is vs Trainium2, not GPUs). 2036: the ASIC edge ramps linearly; the upper end matches Etched/Groq-class claims, above Taalas's own 10×. The mix is weighted by power: tokens per watt = (1−s) + s·k. | \n\nWhat this leaves out: on-device inference, AI load outside the US that serves US users, grid and supply-chain limits on how fast data centers can actually be built, and any electricity AI might save elsewhere. Every input is an extrapolation. Treat the band as a map of the arguments, not a forecast.", "url": "https://wpnews.pro/news/yes-but", "canonical_source": "https://html.non.io/Yes-but/", "published_at": "2026-10-12 16:30:08+00:00", "updated_at": "2026-10-12 16:49:25.441596+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-chips", "artificial-intelligence"], "entities": ["Nvidia", "Berkeley Lab", "EPRI", "IEA", "xAI", "Microsoft", "OpenAI", "ChatGPT"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/yes-but", "markdown": "https://wpnews.pro/news/yes-but.md", "text": "https://wpnews.pro/news/yes-but.txt", "jsonld": "https://wpnews.pro/news/yes-but.jsonld"}}