The Recursive Grid: Epistemic Weaknesses in Energy Modeling and the Adaptive Trajectories of Artificial Intelligence
再帰的グリッド：エネルギー・モデリングにおける認識論的弱点と人工知能の適応軌跡 (AI 翻訳)
Alfredo De Joannon
🤖 gxceed AI 要約
日本語
この論文は、AIのエネルギー需要予測におけるモデリングの限界を指摘し、エッジコンピューティングやフォトニック処理などの適応的フィードバックが非線形な変動をもたらすことを示す。Jevonsのパラドックスを軸に、効率向上が需要を拡大する可能性を分析し、三つの勘定フレームワークを提案する。
English
This paper critiques current AI energy demand forecasts for treating AI as an exogenous load and ignoring adaptive feedback loops. It examines three under-modeled vectors: edge computing decentralization, photonic processing transitions, and macroeconomic demand destruction, which introduce nonlinear volatility. The analysis centers on the Jevons paradox and proposes a decision map for monitoring emerging AI-energy regimes.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではデータセンターの急拡大と地域グリッドの制約が顕在化している。本論文の適応軌跡と分散型コンピューティングに関するフレームワークは、日本のGX政策や再生可能エネルギー統合の計画に示唆を与える。
In the global GX context
This paper challenges the IEA-style consensus on AI energy demand and introduces adaptive variables that are critical for global energy transition planning. It offers a decision-oriented framework that can inform infrastructure investments and regulatory approaches amid AI growth.
👥 読者別の含意
🔬研究者:Provides a novel analytical framework for modeling AI-energy dynamics with nonlinear feedback loops and a decision map.
🏢実務担当者:Data center operators and energy planners can use the three-ledger framework and leading indicators for capacity planning and risk management.
🏛政策担当者:Highlights the need to incorporate adaptive responses and non-linear volatility into energy infrastructure policy and grid planning.
📄 Abstract（原文）
Current institutional consensus postulates that the proliferation of artificial intelligence will precipitate a compounding strain on global energy infrastructure. The most sophisticated of these forecasts are not naive linear extrapolations: agencies such as the IEA construct multiple demand scenarios spanning hardware efficiency, adoption rates, and supply-chain bottlenecks. Yet across that scenario range they share two structural commitments. They treat AI as an energy load whose magnitude is uncertain but whose relationship to the surrounding economy is exogenous, held ceteris paribus even as the scenarios vary the load itself; and they aggregate that load to national or global totals, holding the spatial topology of where it physically lands outside the frame. This paper argues that the adaptive responses most likely to bend the AI energy trajectory act precisely on those two excluded dimensions. Examining three under-modeled feedback loops — topological decentralization (edge computing), material-science transition (photonic processing), and macroeconomic demand destruction — it shows that these vectors introduce severe, non-linear volatility into infrastructural planning, and that the institutional baseline is best understood not as wrong but as a corner solution in which the adaptive variables are switched off. The central analytical pivot is the Jevons paradox: whether physical constraints can cap the rebound effect before it converts efficiency gains into new, geographically dispersed baseloads.This version reframes the paper as a decision-oriented synthesis rather than a rival forecast. It introduces a three-ledger framework connecting data-center electricity demand, distributed grid stress, hardware-efficiency rebound, and macroeconomic demand displacement, and adds a decision map of leading indicators for monitoring which AI-energy regime is emerging.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.20774415first seen 2026-07-13 04:58:49
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