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| Главные авторы: | , , |
|---|---|
| Формат: | Recurso digital |
| Язык: | китайский |
| Опубликовано: |
Zenodo
2026
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| Online-ссылка: | https://doi.org/10.5281/zenodo.20281721 |
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Оглавление:
- <p class="MsoNormal"><span>This paper argues that chip energy efficiency is not a background condition for AI development — it is a power distribution mechanism. As consumer-grade devices approach the capability threshold required to run frontier AI models locally, the governance structures built on centralized cloud computation will face structural stress. We introduce the concept of the <strong>Local Compute Threshold</strong> — the critical point at which AI's cognitive influence field begins to decentralize — and examine its implications for AI safety frameworks. We argue that current AI safety research has systematically underweighted hardware-layer dynamics, and that the transition to local AI constitutes not merely a technical milestone but a redistribution of cognitive power at civilizational scale.</span></p> <p class="MsoNormal"><span>本文论证:芯片能效不是<span>AI</span>发展的背景条件,而是权力分配机制。随着消费级设备逼近本地运行前沿<span>AI</span>模型所需的能力阈值,建立在中心化云端算力之上的治理结构将面临结构性压力。本文引入<strong>本地算力阈值</strong>概念<span>——</span>即<span>AI</span>认知影响场开始去中心化的临界点<span>——</span>并考察其对<span>AI</span>安全框架的含义。本文论证,当前<span>AI</span>安全研究系统性地低估了硬件层动态,本地<span>AI</span>的到来不只是技术里程碑,而是文明规模上认知权力的重新分配。</span></p>