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Construction Physics 探讨机器人 AI 面临的核心瓶颈：语言模型可依赖海量现成文本数据，但机器人缺乏等价的公开训练数据，文章梳理了数据来源、数据量需求及模拟仿真等潜在解决方案。
Toby Ord 的文章探讨 AI 模型通过更多 token 扩展的效果是否与人类通过更多时间扩展的效果相当，分析了 METR 的对比数据，并讨论了这一问题对 AI 自我改进与智能爆炸前景的影响。
Cerebras Systems CEO Andrew Feldman will headline TechCrunch Disrupt 2026 with a talk titled 'Can AI Keep Scaling?', covering compute demand, energy constraints, and Cerebras' wafer-scale approach to AI hardware.
The article critiques the current focus on individual AI agents and proposes a shift towards viewing them as part of a larger, persistent operating system for managing work goals and workflows.
This paper analyzes the end of AI exponentiation, examining technological, economic, and societal challenges such as peak data limitations and computational demands, and the instability within and outside the AI bubble.
Jerry Tworek discusses the technical insights and challenges in applying reinforcement learning to scale AI models like o1, highlighting the importance of simplicity and techniques such as multiple rollouts.
The article discusses Test Time Training as a potential new scaling axis in AI development, analyzing a paper that frames it as a form of linear attention and exploring its implications for model training and continual learning.
Toyota's enterprise AI team uses LangSmith and Deep Agents to scale AI agent development, reducing production time from 6 months to 4 days and deploying over 50 agents.
Zhipu Founder Tang Jie discusses how AI scaling is evolving beyond parameter count to include factors like training data, compute per forward pass, and post-training, with GLM-5.3 as an example.
Local deployment of large models is limited by hardware throughput. Even with GPUs at full capacity, only about 8.64M Tokens can be processed per day, which is insufficient to support Agent automation clusters or large-scale data analysis. Therefore, scaled applications still rely on cloud APIs.
Elon Musk suggests that orbital computing may become necessary to scale AI by 2029 due to land-based power and permitting issues. Owen Lewis highlights this as an overlooked but crucial point.
Enterprise AI often underestimates the work behind data operations, from labeling to governance, which is critical for scaling.
This article discusses the potential benefits and implications of AI models with over two trillion parameters.
The article highlights that AI adoption is accelerating three times faster than the internet boom, with no signs of slowing down.
The article argues that AI scaling is hitting data limits, requiring a civilizational-scale data effort similar to compute projects, and predicts over $100B/year in data spending by 2030.
The author announces a book exploring how the race to scale AI is becoming a geopolitical competition.
A tweet highlights the massive scale of China's solar electricity projects, noting their importance for AI scaling due to energy and supply chain advantages.
Faruk Guney introduces DeepAdapt and its runtime intelligence ACI, which scales AI by retaining experience rather than increasing computation.
This research compares AI coding agents (like Claude-Code and Codex) with human expert coders on long-horizon tasks, showing that humans scale super-linearly due to continual learning while agents plateau, highlighting a key limitation of current AI in extended problem-solving.
Anyscale on Azure is now in public preview. Daniel Arrizza and Paul Yu will host a working session on building and deploying production AI workloads within an Azure tenant, integrating with existing Azure services.