Skip to content

The Thermodynamic and Financial Reality of AI Power Starvation: Rider's Bremsstrahlung Limit in Pulsed D-3He Fusion, Tokamak REBCO Supply-Chain Scaling, PJM/FERC Queue Bottlenecks, and Hyperscale Stranded-CapEx Risk

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

By 2026, generative artificial intelligence (AI) infrastructure expansion has collided with hard physical and grid-regulatory walls. International Energy Agency (IEA) projections indicate global data center electricity consumption surging from 460 TWh (2024) to > 1,000 TWh by 2030 (~320 Mt CO2/yr) and 1,300 TWh by 2035—exceeding Japan's entire national electricity demand—while single campuses such as Microsoft's Fairwater facility (3.3 GW) and the $500 billion Stargate initiative (10 GW) strain regional transmission limits amid a projected $740 billion global 2026 AI capital expenditure (CapEx) cycle. To assuage investor and ESG anxieties, hyperscalers have promoted a narrative of imminent baseload salvation via commercial nuclear fusion (Helion Energy, Commonwealth Fusion Systems [CFS]) and Generation-IV Small Modular Reactors (SMRs; Kairos Power). Building upon our prior CERN Zenodo monographs on the CMOS CapEx cliff and Willow quantum transition (Record 23353509), 1.8 fJ/spike room-temperature neuromorphic quantum computing (Record 23334854), and Delaware Caremark fiduciary governance (Record 23360530), this monograph conducts a rigorous plasma-physical, grid-regulatory, and balance-sheet audit of Big Tech's next-generation nuclear and fusion scenarios. We establish four core findings: (1) Rider's Bremsstrahlung Limit & Inductive Recovery Infeasibility in Pulsed D-3He FRC: While Helion Energy secured Washington DOH RML/RAEL licenses in June 2026 for its Orion plant under a 50 MW (2028) PPA with Microsoft, D-3He ignition at T_i ≈ 50 keV suffers catastrophic Bremsstrahlung losses (P_Brem > P_fus in thermal equilibrium). Sustaining T_i >> T_e against rapid Spitzer Coulomb equilibration forces ultra-short pulses requiring an engineeringly implausible magnetic energy recovery efficiency η_rec in [0.999, 0.9999], alongside unmitigated 2.45 MeV D-D side-reaction neutrons and tritium; (2) Tokamak REBCO Supply Chains vs. Gen-IV SMR Pragmatism: CFS's 20 T SPARC/ARC tokamak (paired with DeepMind RL magnetic sweeping and TORAX) and Google's 500 MW Kairos Power FHR-TRISO PPA (accelerated by the 2024 ADVANCE Act and April 2026 NRC 10 CFR Part 53 licensing) represent scientifically grounded 2030s pathways, yet face acute first-of-a-kind (FOAK) cost inflation exemplified by NuScale's $9.3 billion (> $20,000/kW) cancellation; (3) LBNL Queue Paralysis, PJM Capacity Explosion, and FERC Co-Location Vetoes: With 2,061 GW trapped in 61-month U.S. interconnection queues (13% completion rate), PJM capacity prices spiking 833% to $269.92/MW-day (and hitting the $333.44 cap for 2027/2028 with a 6,623 MW shortfall), and FERC rejecting AWS/Talen's Susquehanna nuclear co-location expansion, hyperscalers are forced onto 116 GW backlogged GE Vernova gas turbines, $110-$115/MWh Three Mile Island restarts, and $210-$292/MWh tariff-inflated LFP BESS; and (4) The Stranded-CapEx Impairment Theorem: Because GPU economic half-lives (τ_1/2 ≈ 1 yr) are an order of magnitude shorter than power delivery lead times (τ_power in [5, 15] yr), unenergized CMOS CapEx faces catastrophic balance-sheet write-downs (98.0% impairment at median 5.08-year queue lag) unless replaced by room-temperature quantum-phononic architectures. License & Integrity Covenant: Creative Commons Attribution 4.0 International (CC BY 4.0) with Strict No-Learn Covenant (NLL-v1.0-2026).

View source

Similar papers

#artificial intelligence Conference Open access Jun 2018

Gibson Env: Real-World Perception for Embodied Agents

This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.

F. Xia, Amir Zamir, Zhi-Yang He et al. · 1,069 citations · ⚡112
#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.